diff --git a/.ai/skills/check-upstream/SKILL.md b/.ai/skills/check-upstream/SKILL.md new file mode 100644 index 000000000..ac4835a4e --- /dev/null +++ b/.ai/skills/check-upstream/SKILL.md @@ -0,0 +1,383 @@ + + +--- +name: check-upstream +description: Check if upstream Apache DataFusion features (functions, DataFrame ops, SessionContext methods, FFI types) are exposed in this Python project. Use when adding missing functions, auditing API coverage, or ensuring parity with upstream. +argument-hint: [area] (e.g., "scalar functions", "aggregate functions", "window functions", "dataframe", "session context", "ffi types", "all") +--- + +# Check Upstream DataFusion Feature Coverage + +You are auditing the datafusion-python project to find features from the upstream Apache DataFusion Rust library that are **not yet exposed** in this Python binding project. Your goal is to identify gaps and, if asked, implement the missing bindings. + +**IMPORTANT: The Python API is the source of truth for coverage.** A function or method is considered "exposed" if it exists in the Python API (e.g., `python/datafusion/functions.py`), even if there is no corresponding entry in the Rust bindings. Many upstream functions are aliases of other functions — the Python layer can expose these aliases by calling a different underlying Rust binding. Do NOT report a function as missing if it appears in the Python `__all__` list and has a working implementation, regardless of whether a matching `#[pyfunction]` exists in Rust. + +## Areas to Check + +The user may specify an area via `$ARGUMENTS`. If no area is specified or "all" is given, check all areas. + +### 1. Scalar Functions + +**Upstream source of truth:** +- Rust docs: https://docs.rs/datafusion/latest/datafusion/functions/index.html +- User docs: https://datafusion.apache.org/user-guide/sql/scalar_functions.html + +**Where they are exposed in this project:** +- Python API: `python/datafusion/functions.py` — each function wraps a call to `datafusion._internal.functions` +- Rust bindings: `crates/core/src/functions.rs` — `#[pyfunction]` definitions registered via `init_module()` + +**How to check:** +1. Fetch the upstream scalar function documentation page +2. Compare against functions listed in `python/datafusion/functions.py` (check the `__all__` list and function definitions) +3. A function is covered if it exists in the Python API — it does NOT need a dedicated Rust `#[pyfunction]`. Many functions are aliases that reuse another function's Rust binding. +4. Only report functions that are missing from the Python `__all__` list / function definitions + +### 2. Aggregate Functions + +**Upstream source of truth:** +- Rust docs: https://docs.rs/datafusion/latest/datafusion/functions_aggregate/index.html +- User docs: https://datafusion.apache.org/user-guide/sql/aggregate_functions.html + +**Where they are exposed in this project:** +- Python API: `python/datafusion/functions.py` (aggregate functions are mixed in with scalar functions) +- Rust bindings: `crates/core/src/functions.rs` + +**How to check:** +1. Fetch the upstream aggregate function documentation page +2. Compare against aggregate functions in `python/datafusion/functions.py` (check `__all__` list and function definitions) +3. A function is covered if it exists in the Python API, even if it aliases another function's Rust binding +4. Report only functions missing from the Python API + +### 3. Window Functions + +**Upstream source of truth:** +- Rust docs: https://docs.rs/datafusion/latest/datafusion/functions_window/index.html +- User docs: https://datafusion.apache.org/user-guide/sql/window_functions.html + +**Where they are exposed in this project:** +- Python API: `python/datafusion/functions.py` (window functions like `rank`, `dense_rank`, `lag`, `lead`, etc.) +- Rust bindings: `crates/core/src/functions.rs` + +**How to check:** +1. Fetch the upstream window function documentation page +2. Compare against window functions in `python/datafusion/functions.py` (check `__all__` list and function definitions) +3. A function is covered if it exists in the Python API, even if it aliases another function's Rust binding +4. Report only functions missing from the Python API + +### 4. Table Functions + +**Upstream source of truth:** +- Rust docs: https://docs.rs/datafusion/latest/datafusion/functions_table/index.html +- User docs: https://datafusion.apache.org/user-guide/sql/table_functions.html (if available) + +**Where they are exposed in this project:** +- Python API: `python/datafusion/functions.py` and `python/datafusion/user_defined.py` (TableFunction/udtf) +- Rust bindings: `crates/core/src/functions.rs` and `crates/core/src/udtf.rs` + +**How to check:** +1. Fetch the upstream table function documentation +2. Compare against what's available in the Python API +3. A function is covered if it exists in the Python API, even if it aliases another function's Rust binding +4. Report only functions missing from the Python API + +### 5. DataFrame Operations + +**Upstream source of truth:** +- Rust docs: https://docs.rs/datafusion/latest/datafusion/dataframe/struct.DataFrame.html + +**Where they are exposed in this project:** +- Python API: `python/datafusion/dataframe.py` — the `DataFrame` class +- Rust bindings: `crates/core/src/dataframe.rs` — `PyDataFrame` with `#[pymethods]` + +**Evaluated and not requiring separate Python exposure:** +- `show_limit` — already covered by `DataFrame.show()`, which provides the same functionality with a simpler API +- `with_param_values` — already covered by the `param_values` argument on `SessionContext.sql()`, which accomplishes the same thing more robustly +- `union_by_name_distinct` — already covered by `DataFrame.union_by_name(distinct=True)`, which provides a more Pythonic API + +**How to check:** +1. Fetch the upstream DataFrame documentation page listing all methods +2. Compare against methods in `python/datafusion/dataframe.py` — this is the source of truth for coverage +3. The Rust bindings (`crates/core/src/dataframe.rs`) may be consulted for context, but a method is covered if it exists in the Python API +4. Check against the "evaluated and not requiring exposure" list before flagging as a gap +5. Report only methods missing from the Python API + +### 6. SessionContext Methods + +**Upstream source of truth:** +- Rust docs: https://docs.rs/datafusion/latest/datafusion/execution/context/struct.SessionContext.html + +**Where they are exposed in this project:** +- Python API: `python/datafusion/context.py` — the `SessionContext` class +- Rust bindings: `crates/core/src/context.rs` — `PySessionContext` with `#[pymethods]` + +**How to check:** +1. Fetch the upstream SessionContext documentation page listing all methods +2. Compare against methods in `python/datafusion/context.py` — this is the source of truth for coverage +3. The Rust bindings (`crates/core/src/context.rs`) may be consulted for context, but a method is covered if it exists in the Python API +4. Report only methods missing from the Python API + +### 7. FFI Types (datafusion-ffi) + +**Upstream source of truth:** +- Crate source: https://github.com/apache/datafusion/tree/main/datafusion/ffi/src +- Rust docs: https://docs.rs/datafusion-ffi/latest/datafusion_ffi/ + +**Where they are exposed in this project:** +- Rust bindings: various files under `crates/core/src/` and `crates/util/src/` +- FFI example: `examples/datafusion-ffi-example/src/` +- Dependency declared in root `Cargo.toml` and `crates/core/Cargo.toml` + +**Discovering currently supported FFI types:** +Grep for `use datafusion_ffi::` in `crates/core/src/` and `crates/util/src/` to find all FFI types currently imported and used. + +**Evaluated and not requiring direct Python exposure:** +These upstream FFI types have been reviewed and do not need to be independently exposed to end users: +- `FFI_ExecutionPlan` — already used indirectly through table providers; no need for direct exposure +- `FFI_PhysicalExpr` / `FFI_PhysicalSortExpr` — internal physical planning types not expected to be needed by end users +- `FFI_RecordBatchStream` — one level deeper than FFI_ExecutionPlan, used internally when execution plans stream results +- `FFI_SessionRef` / `ForeignSession` — session sharing across FFI; Python manages sessions natively via SessionContext +- `FFI_SessionConfig` — Python can configure sessions natively without FFI +- `FFI_ConfigOptions` / `FFI_TableOptions` — internal configuration plumbing +- `FFI_PlanProperties` / `FFI_Boundedness` / `FFI_EmissionType` — read from existing plans, not user-facing +- `FFI_Partitioning` — supporting type for physical planning +- Supporting/utility types (`FFI_Option`, `FFI_Result`, `WrappedSchema`, `WrappedArray`, `FFI_ColumnarValue`, `FFI_Volatility`, `FFI_InsertOp`, `FFI_AccumulatorArgs`, `FFI_Accumulator`, `FFI_GroupsAccumulator`, `FFI_EmitTo`, `FFI_AggregateOrderSensitivity`, `FFI_PartitionEvaluator`, `FFI_PartitionEvaluatorArgs`, `FFI_Range`, `FFI_SortOptions`, `FFI_Distribution`, `FFI_ExprProperties`, `FFI_SortProperties`, `FFI_Interval`, `FFI_TableProviderFilterPushDown`, `FFI_TableType`) — used as building blocks within the types above, not independently exposed + +**How to check:** +1. Discover currently supported types by grepping for `use datafusion_ffi::` in `crates/core/src/` and `crates/util/src/`, then compare against the upstream `datafusion-ffi` crate's `lib.rs` exports +2. If new FFI types appear upstream, evaluate whether they represent a user-facing capability +3. Check against the "evaluated and not requiring exposure" list before flagging as a gap +4. Report any genuinely new types that enable user-facing functionality +5. For each currently supported FFI type, verify the full pipeline is present using the checklist from "Adding a New FFI Type": + - Rust PyO3 wrapper with `from_pycapsule()` method + - Python Protocol type (e.g., `ScalarUDFExportable`) for FFI objects + - Python wrapper class with full type hints on all public methods + - ABC base class (if the type can be user-implemented) + - Registered in Rust `init_module()` and Python `__init__.py` + - FFI example in `examples/datafusion-ffi-example/` + - Type appears in union type hints where accepted + +## Checking for Existing GitHub Issues + +After identifying missing APIs, search the open issues at https://github.com/apache/datafusion-python/issues for each gap to see if an issue already exists requesting that API be exposed. Search using the function or method name as the query. + +- If an existing issue is found, include a link to it in the report. Do NOT create a new issue. +- If no existing issue is found, note that no issue exists yet. If the user asks to create issues for missing APIs, each issue should specify that Python test coverage is required as part of the implementation. + +## Output Format + +For each area checked, produce a report like: + +``` +## [Area Name] Coverage Report + +### Currently Exposed (X functions/methods) +- list of what's already available + +### Missing from Upstream (Y functions/methods) +- function_name — brief description of what it does (existing issue: #123) +- function_name — brief description of what it does (no existing issue) + +### Notes +- Any relevant observations about partial implementations, naming differences, etc. +``` + +## Implementation Pattern + +If the user asks you to implement missing features, follow these patterns: + +### Adding a New Function (Scalar/Aggregate/Window) + +**Step 1: Rust binding** in `crates/core/src/functions.rs`: +```rust +#[pyfunction] +#[pyo3(signature = (arg1, arg2))] +fn new_function_name(arg1: PyExpr, arg2: PyExpr) -> PyResult { + Ok(datafusion::functions::module::expr_fn::new_function_name(arg1.expr, arg2.expr).into()) +} +``` +Then register in `init_module()`: +```rust +m.add_wrapped(wrap_pyfunction!(new_function_name))?; +``` + +**Step 2: Python wrapper** in `python/datafusion/functions.py`: +```python +def new_function_name(arg1: Expr, arg2: Expr) -> Expr: + """Description of what the function does. + + Args: + arg1: Description of first argument. + arg2: Description of second argument. + + Returns: + Description of return value. + """ + return Expr(f.new_function_name(arg1.expr, arg2.expr)) +``` +Add to `__all__` list. + +### Adding a New DataFrame Method + +**Step 1: Rust binding** in `crates/core/src/dataframe.rs`: +```rust +#[pymethods] +impl PyDataFrame { + fn new_method(&self, py: Python, param: PyExpr) -> PyDataFusionResult { + let df = self.df.as_ref().clone().new_method(param.into())?; + Ok(Self::new(df)) + } +} +``` + +**Step 2: Python wrapper** in `python/datafusion/dataframe.py`: +```python +def new_method(self, param: Expr) -> DataFrame: + """Description of the method.""" + return DataFrame(self.df.new_method(param.expr)) +``` + +### Adding a New SessionContext Method + +**Step 1: Rust binding** in `crates/core/src/context.rs`: +```rust +#[pymethods] +impl PySessionContext { + pub fn new_method(&self, py: Python, param: String) -> PyDataFusionResult { + let df = wait_for_future(py, self.ctx.new_method(¶m))?; + Ok(PyDataFrame::new(df)) + } +} +``` + +**Step 2: Python wrapper** in `python/datafusion/context.py`: +```python +def new_method(self, param: str) -> DataFrame: + """Description of the method.""" + return DataFrame(self.ctx.new_method(param)) +``` + +### Adding a New FFI Type + +FFI types require a full pipeline from C struct through to a typed Python wrapper. Each layer must be present. + +**Step 1: Rust PyO3 wrapper class** in a new or existing file under `crates/core/src/`: +```rust +use datafusion_ffi::new_type::FFI_NewType; + +#[pyclass(from_py_object, frozen, name = "RawNewType", module = "datafusion.module_name", subclass)] +pub struct PyNewType { + pub inner: Arc, +} + +#[pymethods] +impl PyNewType { + #[staticmethod] + fn from_pycapsule(obj: &Bound<'_, PyAny>) -> PyDataFusionResult { + let capsule = obj + .getattr("__datafusion_new_type__")? + .call0()? + .downcast::()?; + let ffi_ptr = unsafe { capsule.reference::() }; + let provider: Arc = ffi_ptr.into(); + Ok(Self { inner: provider }) + } + + fn some_method(&self) -> PyResult<...> { + // wrap inner trait method + } +} +``` +Register in the appropriate `init_module()`: +```rust +m.add_class::()?; +``` + +**Step 2: Python Protocol type** in the appropriate Python module (e.g., `python/datafusion/catalog.py`): +```python +class NewTypeExportable(Protocol): + """Type hint for objects providing a __datafusion_new_type__ PyCapsule.""" + + def __datafusion_new_type__(self) -> object: ... +``` + +**Step 3: Python wrapper class** in the same module: +```python +class NewType: + """Description of the type. + + This class wraps a DataFusion NewType, which can be created from a native + Python implementation or imported from an FFI-compatible library. + """ + + def __init__( + self, + new_type: df_internal.module_name.RawNewType | NewTypeExportable, + ) -> None: + if isinstance(new_type, df_internal.module_name.RawNewType): + self._raw = new_type + else: + self._raw = df_internal.module_name.RawNewType.from_pycapsule(new_type) + + def some_method(self) -> ReturnType: + """Description of the method.""" + return self._raw.some_method() +``` + +**Step 4: ABC base class** (if users should be able to subclass and provide custom implementations in Python): +```python +from abc import ABC, abstractmethod + +class NewTypeProvider(ABC): + """Abstract base class for implementing a custom NewType in Python.""" + + @abstractmethod + def some_method(self) -> ReturnType: + """Description of the method.""" + ... +``` + +**Step 5: Module exports** — add to the appropriate `__init__.py`: +- Add the wrapper class (`NewType`) to `python/datafusion/__init__.py` +- Add the ABC (`NewTypeProvider`) if applicable +- Add the Protocol type (`NewTypeExportable`) if it should be public + +**Step 6: FFI example** — add an example implementation under `examples/datafusion-ffi-example/src/`: +```rust +// examples/datafusion-ffi-example/src/new_type.rs +use datafusion_ffi::new_type::FFI_NewType; +// ... example showing how an external Rust library exposes this type via PyCapsule +``` + +**Checklist for each FFI type:** +- [ ] Rust PyO3 wrapper with `from_pycapsule()` method +- [ ] Python Protocol type (e.g., `NewTypeExportable`) for FFI objects +- [ ] Python wrapper class with full type hints on all public methods +- [ ] ABC base class (if the type can be user-implemented) +- [ ] Registered in Rust `init_module()` and Python `__init__.py` +- [ ] FFI example in `examples/datafusion-ffi-example/` +- [ ] Type appears in union type hints where accepted (e.g., `Table | TableProviderExportable`) + +## Important Notes + +- The upstream DataFusion version used by this project is specified in `crates/core/Cargo.toml` — check the `datafusion` dependency version to ensure you're comparing against the right upstream version. +- Some upstream features may intentionally not be exposed (e.g., internal-only APIs). Use judgment about what's user-facing. +- When fetching upstream docs, prefer the published docs.rs documentation as it matches the crate version. +- Function aliases (e.g., `array_append` / `list_append`) should both be exposed if upstream supports them. +- Check the `__all__` list in `functions.py` to see what's publicly exported vs just defined. diff --git a/.ai/skills/make-pythonic/SKILL.md b/.ai/skills/make-pythonic/SKILL.md new file mode 100644 index 000000000..57145ac6c --- /dev/null +++ b/.ai/skills/make-pythonic/SKILL.md @@ -0,0 +1,430 @@ + + +--- +name: make-pythonic +description: Audit and improve datafusion-python functions to accept native Python types (int, float, str, bool) instead of requiring explicit lit() or col() wrapping. Analyzes function signatures, checks upstream Rust implementations for type constraints, and applies the appropriate coercion pattern. +argument-hint: [scope] (e.g., "string functions", "datetime functions", "array functions", "math functions", "all", or a specific function name like "split_part") +--- + +# Make Python API Functions More Pythonic + +You are improving the datafusion-python API to feel more natural to Python users. The goal is to allow functions to accept native Python types (int, float, str, bool, etc.) for arguments that are contextually always or typically literal values, instead of requiring users to manually wrap them in `lit()`. + +**Core principle:** A Python user should be able to write `split_part(col("a"), ",", 2)` instead of `split_part(col("a"), lit(","), lit(2))` when the arguments are contextually obvious literals. + +## How to Identify Candidates + +The user may specify a scope via `$ARGUMENTS`. If no scope is given or "all" is specified, audit all functions in `python/datafusion/functions.py`. + +For each function, determine if any parameter can accept native Python types by evaluating **two complementary signals**: + +### Signal 1: Contextual Understanding + +Some arguments are contextually always or almost always literal values based on what the function does: + +| Context | Typical Arguments | Examples | +|---------|------------------|----------| +| **String position/count** | Character counts, indices, repetition counts | `left(str, n)`, `right(str, n)`, `repeat(str, n)`, `lpad(str, count, ...)` | +| **Delimiters/separators** | Fixed separator characters | `split_part(str, delim, idx)`, `concat_ws(sep, ...)` | +| **Search/replace patterns** | Literal search strings, replacements | `replace(str, from, to)`, `regexp_replace(str, pattern, replacement, flags)` | +| **Date/time parts** | Part names from a fixed set | `date_part(part, date)`, `date_trunc(part, date)` | +| **Rounding precision** | Decimal place counts | `round(val, places)`, `trunc(val, places)` | +| **Fill characters** | Padding characters | `lpad(str, count, fill)`, `rpad(str, count, fill)` | + +### Signal 2: Upstream Rust Implementation + +Check the Rust binding in `crates/core/src/functions.rs` and the upstream DataFusion function implementation to determine type constraints. The upstream source is cached locally at: + +``` +~/.cargo/registry/src/index.crates.io-*/datafusion-functions-/src/ +``` + +Check the DataFusion version in `crates/core/Cargo.toml` to find the right directory. Key subdirectories: `string/`, `datetime/`, `math/`, `regex/`. + +For **aggregate functions**, the upstream source is in a separate crate: + +``` +~/.cargo/registry/src/index.crates.io-*/datafusion-functions-aggregate-/src/ +``` + +There are five concrete techniques to check, in order of signal strength: + +#### Technique 1: Check `invoke_with_args()` for literal-only enforcement (strongest signal) + +Some functions pattern-match on `ColumnarValue::Scalar` in their `invoke_with_args()` method and **return an error** if the argument is a column/array. This means the argument **must** be a literal — passing a column expression will fail at runtime. + +Example from `date_trunc.rs`: +```rust +let granularity_str = if let ColumnarValue::Scalar(ScalarValue::Utf8(Some(v))) = granularity { + v.to_lowercase() +} else { + return exec_err!("Granularity of `date_trunc` must be non-null scalar Utf8"); +}; +``` + +**If you find this pattern:** The argument is **Category B** — accept only the corresponding native Python type (e.g., `str`), not `Expr`. The function will error at runtime with a column expression anyway. + +#### Technique 1a: Check `accumulator()` for literal-only enforcement (aggregate functions) + +Technique 1 applies to scalar UDFs. Aggregate functions do not have `invoke_with_args()` — instead, they enforce literal-only arguments in their `accumulator()` (or `create_accumulator()`) method, which runs at planning time before any data is processed. + +Look for these patterns inside `accumulator()`: + +- `get_scalar_value(expr)` — evaluates the expression against an empty batch and errors if it's not a scalar +- `validate_percentile_expr(expr)` — specific helper used by percentile functions +- `downcast_ref::()` — checks that the physical expression is a literal constant + +Example from `approx_percentile_cont.rs`: +```rust +fn accumulator(&self, args: AccumulatorArgs) -> Result { + let percentile = + validate_percentile_expr(&args.exprs[1], "APPROX_PERCENTILE_CONT")?; + // ... +} +``` + +Where `validate_percentile_expr` calls `get_scalar_value` and errors with `"must be a literal"`. + +Example from `string_agg.rs`: +```rust +fn accumulator(&self, acc_args: AccumulatorArgs) -> Result> { + let Some(lit) = acc_args.exprs[1].as_any().downcast_ref::() else { + return not_impl_err!( + "The second argument of the string_agg function must be a string literal" + ); + }; + // ... +} +``` + +**If you find this pattern:** The argument is **Category B** — accept only the corresponding native Python type, not `Expr`. The function will error at planning time with a non-literal expression. + +To discover which aggregate functions have literal-only arguments, search the upstream aggregate crate for `get_scalar_value`, `validate_percentile_expr`, and `downcast_ref::()` inside `accumulator()` methods. For example, you should expect to find `approx_percentile_cont` (percentile) and `string_agg` (delimiter) among the results. + +#### Technique 1b: Check `partition_evaluator()` for literal-only enforcement (window functions) + +Window functions do not have `invoke_with_args()` or `accumulator()`. Instead, they enforce literal-only arguments in their `partition_evaluator()` method, which constructs the evaluator that processes each partition. + +The upstream source is in a separate crate: + +``` +~/.cargo/registry/src/index.crates.io-*/datafusion-functions-window-/src/ +``` + +Look for `get_scalar_value_from_args()` calls inside `partition_evaluator()`. This helper (defined in the window crate's `utils.rs`) calls `downcast_ref::()` and errors with `"There is only support Literal types for field at idx: {index} in Window Function"`. + +Example from `ntile.rs`: +```rust +fn partition_evaluator( + &self, + partition_evaluator_args: PartitionEvaluatorArgs, +) -> Result> { + let scalar_n = + get_scalar_value_from_args(partition_evaluator_args.input_exprs(), 0)? + .ok_or_else(|| { + exec_datafusion_err!("NTILE requires a positive integer") + })?; + // ... +} +``` + +**If you find this pattern:** The argument is **Category B** — accept only the corresponding native Python type, not `Expr`. The function will error at planning time with a non-literal expression. + +To discover which window functions have literal-only arguments, search the upstream window crate for `get_scalar_value_from_args` inside `partition_evaluator()` methods. For example, you should expect to find `ntile` (n) and `lead`/`lag` (offset, default_value) among the results. + +#### Technique 2: Check the `Signature` for data type constraints + +Each function defines a `Signature::coercible(...)` that specifies what data types each argument accepts, using `Coercion` entries. This tells you the expected **data type** even if it doesn't enforce literal-only. + +Example from `repeat.rs`: +```rust +signature: Signature::coercible( + vec![ + Coercion::new_exact(TypeSignatureClass::Native(logical_string())), + Coercion::new_implicit( + TypeSignatureClass::Native(logical_int64()), + vec![TypeSignatureClass::Integer], + NativeType::Int64, + ), + ], + Volatility::Immutable, +), +``` + +This tells you arg 2 (`n`) must be an integer type coerced to Int64. Use this to choose the correct Python type (e.g., `int` not `str` or `float`). + +Common mappings: +| Rust Type Constraint | Python Type | +|---------------------|-------------| +| `logical_int64()` / `TypeSignatureClass::Integer` | `int` | +| `logical_float64()` / `TypeSignatureClass::Numeric` | `int \| float` | +| `logical_string()` / `TypeSignatureClass::String` | `str` | +| `LogicalType::Boolean` | `bool` | + +**Important:** In Python's type system (PEP 484), `float` already accepts `int` values, so `int | float` is redundant and will fail the `ruff` linter (rule PYI041). Use `float` alone when the Rust side accepts a float/numeric type — Python users can still pass integer literals like `log(10, col("a"))` or `power(col("a"), 3)` without issue. Only use `int` when the Rust side strictly requires an integer (e.g., `logical_int64()`). + +#### Technique 3: Check `return_field_from_args()` for `scalar_arguments` usage + +Functions that inspect literal values at query planning time use `args.scalar_arguments.get(n)` in their `return_field_from_args()` method. This indicates the argument is **expected to be a literal** for optimal behavior (e.g., to determine output type precision), but may still work as a column. + +Example from `round.rs`: +```rust +let decimal_places: Option = match args.scalar_arguments.get(1) { + None => Some(0), + Some(None) => None, // argument is not a literal (column) + Some(Some(scalar)) if scalar.is_null() => Some(0), + Some(Some(scalar)) => Some(decimal_places_from_scalar(scalar)?), +}; +``` + +**If you find this pattern:** The argument is **Category A** — accept native types AND `Expr`. It works as a column but is primarily used as a literal. + +#### Decision flow + +``` +What kind of function is this? + Scalar UDF: + Is argument rejected at runtime if not a literal? + (check invoke_with_args for ColumnarValue::Scalar-only match + exec_err!) + → YES: Category B — accept only native type, no Expr + → NO: continue below + Aggregate: + Is argument rejected at planning time if not a literal? + (check accumulator() for get_scalar_value / validate_percentile_expr / + downcast_ref::() + error) + → YES: Category B — accept only native type, no Expr + → NO: continue below + Window: + Is argument rejected at planning time if not a literal? + (check partition_evaluator() for get_scalar_value_from_args / + downcast_ref::() + error) + → YES: Category B — accept only native type, no Expr + → NO: continue below + +Does the Signature constrain it to a specific data type? + → YES: Category A — accept Expr | + → NO: Leave as Expr only +``` + +## Coercion Categories + +When making a function more pythonic, apply the correct coercion pattern based on **what the argument represents**: + +### Category A: Arguments That Should Accept Native Types AND Expr + +These are arguments that are *typically* literals but *could* be column references in advanced use cases. For these, accept a union type and coerce native types to `Expr.literal()`. + +**Type hint pattern:** `Expr | int`, `Expr | str`, `Expr | int | str`, etc. + +**When to use:** When the argument could plausibly come from a column in some use case (e.g., the repeat count might come from a column in a data-driven scenario). + +```python +def repeat(string: Expr, n: Expr | int) -> Expr: + """Repeats the ``string`` to ``n`` times. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["ha"]}) + >>> result = df.select( + ... dfn.functions.repeat(dfn.col("a"), 3).alias("r")) + >>> result.collect_column("r")[0].as_py() + 'hahaha' + """ + if not isinstance(n, Expr): + n = Expr.literal(n) + return Expr(f.repeat(string.expr, n.expr)) +``` + +### Category B: Arguments That Should ONLY Accept Specific Native Types + +These are arguments where an `Expr` never makes sense because the value must be a fixed literal known at query-planning time (not a per-row value). For these, accept only the native type(s) and wrap internally. + +**Type hint pattern:** `str`, `int`, `list[str]`, etc. (no `Expr` in the union) + +**When to use:** When the argument is from a fixed enumeration or is always a compile-time constant, **AND** the parameter was not previously typed as `Expr`: +- Separator in `concat_ws` (already typed as `str` in the Rust binding) +- Index in `array_position` (already typed as `int` in the Rust binding) +- Values that the Rust implementation already accepts as native types + +**Backward compatibility rule:** If a parameter was previously typed as `Expr`, you **must** keep `Expr` in the union even if the Rust side requires a literal. Removing `Expr` would break existing user code like `date_part(lit("year"), col("a"))`. Use **Category A** instead — accept `Expr | str` — and let users who pass column expressions discover the runtime error from the Rust side. Never silently break backward compatibility. + +```python +def concat_ws(separator: str, *args: Expr) -> Expr: + """Concatenates the list ``args`` with the separator. + + ``separator`` is already typed as ``str`` in the Rust binding, so + there is no backward-compatibility concern. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"], "b": ["world"]}) + >>> result = df.select( + ... dfn.functions.concat_ws("-", dfn.col("a"), dfn.col("b")).alias("c")) + >>> result.collect_column("c")[0].as_py() + 'hello-world' + """ + args = [arg.expr for arg in args] + return Expr(f.concat_ws(separator, args)) +``` + +### Category C: Arguments That Should Accept str as Column Name + +In some contexts a string argument naturally refers to a column name rather than a literal. This is the pattern used by DataFrame methods. + +**Type hint pattern:** `Expr | str` + +**When to use:** Only when the string contextually means a column name (rare in `functions.py`, more common in DataFrame methods). + +```python +# Use _to_raw_expr() from expr.py for this pattern +from datafusion.expr import _to_raw_expr + +def some_function(column: Expr | str) -> Expr: + raw = _to_raw_expr(column) # str -> col(str) + return Expr(f.some_function(raw)) +``` + +**IMPORTANT:** In `functions.py`, string arguments almost never mean column names. Functions operate on expressions, and column references should use `col()`. Category C applies mainly to DataFrame methods and context APIs, not to scalar/aggregate/window functions. Do NOT convert string arguments to column expressions in `functions.py` unless there is a very clear reason to do so. + +## Implementation Steps + +For each function being updated: + +### Step 1: Analyze the Function + +1. Read the current Python function signature in `python/datafusion/functions.py` +2. Read the Rust binding in `crates/core/src/functions.rs` +3. Optionally check the upstream DataFusion docs for the function +4. Determine which category (A, B, or C) applies to each parameter + +### Step 2: Update the Python Function + +1. **Change the type hints** to accept native types (e.g., `Expr` -> `Expr | int`) +2. **Add coercion logic** at the top of the function body +3. **Update the docstring** examples to use the simpler calling convention +4. **Preserve backward compatibility** — existing code using `Expr` must still work + +### Step 3: Update Alias Type Hints + +After updating a primary function, find all alias functions that delegate to it (e.g., `instr` and `position` delegate to `strpos`). Update each alias's **parameter type hints** to match the primary function's new signature. Do not add coercion logic to aliases — the primary function handles that. + +### Step 4: Update Docstring Examples (primary functions only) + +Per the project's CLAUDE.md rules: +- Every function must have doctest-style examples +- Optional parameters need examples both without and with the optional args, using keyword argument syntax +- Reuse the same input data across examples where possible + +**Update examples to demonstrate the pythonic calling convention:** + +```python +# BEFORE (old style - still works but verbose) +dfn.functions.left(dfn.col("a"), dfn.lit(3)) + +# AFTER (new style - shown in examples) +dfn.functions.left(dfn.col("a"), 3) +``` + +### Step 5: Run Tests + +After making changes, run the doctests to verify: +```bash +python -m pytest --doctest-modules python/datafusion/functions.py -v +``` + +## Coercion Helper Pattern + +Use the coercion helpers from `datafusion.expr` to convert native Python values to `Expr`. These are the complement of `ensure_expr()` — where `ensure_expr` *rejects* non-`Expr` values, the coercion helpers *wrap* them via `Expr.literal()`. + +**For required parameters** use `coerce_to_expr`: + +```python +from datafusion.expr import coerce_to_expr + +def left(string: Expr, n: Expr | int) -> Expr: + n = coerce_to_expr(n) + return Expr(f.left(string.expr, n.expr)) +``` + +**For optional nullable parameters** use `coerce_to_expr_or_none`: + +```python +from datafusion.expr import coerce_to_expr, coerce_to_expr_or_none + +def regexp_count( + string: Expr, + pattern: Expr | str, + start: Expr | int | None = None, + flags: Expr | str | None = None, +) -> Expr: + pattern = coerce_to_expr(pattern) + start = coerce_to_expr_or_none(start) + flags = coerce_to_expr_or_none(flags) + return Expr( + f.regexp_count( + string.expr, + pattern.expr, + start.expr if start is not None else None, + flags.expr if flags is not None else None, + ) + ) +``` + +Both helpers are defined in `python/datafusion/expr.py` alongside `ensure_expr`. Import them in `functions.py` via: + +```python +from datafusion.expr import coerce_to_expr, coerce_to_expr_or_none +``` + +## What NOT to Change + +- **Do not change arguments that represent data columns.** If an argument is the primary data being operated on (e.g., the `string` in `left(string, n)` or the `array` in `array_sort(array)`), it should remain `Expr` only. Users should use `col()` for column references. +- **Do not change variadic `*args: Expr` parameters.** These represent multiple expressions and should stay as `Expr`. +- **Do not change arguments where the coercion is ambiguous.** If it is unclear whether a string should be a column name or a literal, leave it as `Expr` and let the user be explicit. +- **Do not add coercion logic to simple aliases.** If a function is just `return other_function(...)`, the primary function handles coercion. However, you **must update the alias's type hints** to match the primary function's signature so that type checkers and documentation accurately reflect what the alias accepts. +- **Do not change the Rust bindings.** All coercion happens in the Python layer. The Rust functions continue to accept `PyExpr`. + +## Priority Order + +When auditing functions, process them in this order: + +1. **Date/time functions** — `date_part`, `date_trunc`, `date_bin` — these have the clearest literal arguments +2. **String functions** — `left`, `right`, `repeat`, `lpad`, `rpad`, `split_part`, `substring`, `replace`, `regexp_replace`, `regexp_match`, `regexp_count` — common and verbose without coercion +3. **Math functions** — `round`, `trunc`, `power` — numeric literal arguments +4. **Array functions** — `array_slice`, `array_position`, `array_remove_n`, `array_replace_n`, `array_resize`, `array_element` — index and count arguments +5. **Other functions** — any remaining functions with literal arguments + +## Output Format + +For each function analyzed, report: + +``` +## [Function Name] + +**Current signature:** `function(arg1: Expr, arg2: Expr) -> Expr` +**Proposed signature:** `function(arg1: Expr, arg2: Expr | int) -> Expr` +**Category:** A (accepts native + Expr) +**Arguments changed:** +- `arg2`: Expr -> Expr | int (always a literal count) +**Rust binding:** Takes PyExpr, wraps to literal internally +**Status:** [Changed / Skipped / Needs Discussion] +``` + +If asked to implement (not just audit), make the changes directly and show a summary of what was updated. diff --git a/.cargo/config.toml b/.cargo/config.toml deleted file mode 100644 index 91a099a61..000000000 --- a/.cargo/config.toml +++ /dev/null @@ -1,12 +0,0 @@ -[target.x86_64-apple-darwin] -rustflags = [ - "-C", "link-arg=-undefined", - "-C", "link-arg=dynamic_lookup", -] - -[target.aarch64-apple-darwin] -rustflags = [ - "-C", "link-arg=-undefined", - "-C", "link-arg=dynamic_lookup", -] - diff --git a/.claude/skills b/.claude/skills new file mode 120000 index 000000000..6838a1160 --- /dev/null +++ b/.claude/skills @@ -0,0 +1 @@ +../.ai/skills \ No newline at end of file diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 2dc8b96fe..7682d6cb0 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -15,34 +15,67 @@ # specific language governing permissions and limitations # under the License. -name: Python Release Build +# Reusable workflow for running building +# This ensures the same tests run for both debug (PRs) and release (main/tags) builds + +name: Build + on: - pull_request: - branches: ["main"] - push: - tags: ["*-rc*"] - branches: ["branch-*"] + workflow_call: + inputs: + build_mode: + description: 'Build mode: debug or release' + required: true + type: string + run_wheels: + description: 'Whether to build distribution wheels' + required: false + type: boolean + default: false + +env: + CARGO_TERM_COLOR: always + RUST_BACKTRACE: 1 + UV_LOCKED: true jobs: - build: + # ============================================ + # Linting Jobs + # ============================================ + lint-rust: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + + - name: Setup Rust + uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 + with: + toolchain: "nightly" + components: rustfmt + + - name: Cache Cargo + uses: Swatinem/rust-cache@v2 + + - name: Check formatting + run: cargo +nightly fmt --all -- --check + + lint-python: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v6 + - name: Install Python - uses: actions/setup-python@v6 + uses: actions/setup-python@v5 with: python-version: "3.12" - - uses: astral-sh/setup-uv@v7 + - uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 with: - enable-cache: true + enable-cache: true - # Use the --no-install-package to only install the dependencies - # but do not yet build the rust library - name: Install dependencies run: uv sync --dev --no-install-package datafusion - # Update output format to enable automatic inline annotations. - name: Run Ruff run: | uv run --no-project ruff check --output-format=github python/ @@ -50,26 +83,207 @@ jobs: - name: Run codespell run: | - uv run --no-project codespell --toml pyproject.toml + uv run --no-project codespell --toml pyproject.toml + + lint-toml: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + + - name: Install taplo + uses: taiki-e/install-action@v2 + with: + tool: taplo-cli + + # if you encounter an error, try running 'taplo format' to fix the formatting automatically. + - name: Check Cargo.toml formatting + run: taplo format --check + + check-crates-patch: + if: inputs.build_mode == 'release' && startsWith(github.ref, 'refs/tags/') + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + + - name: Ensure [patch.crates-io] is empty + run: python3 dev/check_crates_patch.py generate-license: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v5 - - uses: astral-sh/setup-uv@v7 + - uses: actions/checkout@v6 + + - uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 + with: + enable-cache: true + + - name: Install cargo-license + uses: taiki-e/install-action@v2 with: - enable-cache: true + tool: cargo-license - name: Generate license file run: uv run --no-project python ./dev/create_license.py - - uses: actions/upload-artifact@v4 + + - uses: actions/upload-artifact@v7 with: name: python-wheel-license path: LICENSE.txt + # ============================================ + # Build - Linux x86_64 + # ============================================ + build-manylinux-x86_64: + needs: [generate-license, lint-rust, lint-python] + name: ManyLinux x86_64 + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + + - run: rm LICENSE.txt + - name: Download LICENSE.txt + uses: actions/download-artifact@v8 + with: + name: python-wheel-license + path: . + + - name: Setup Rust + uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 + + - name: Cache Cargo + uses: Swatinem/rust-cache@v2 + with: + key: ${{ inputs.build_mode }} + + - uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 + with: + enable-cache: true + + - name: Add extra swap for release build + if: inputs.build_mode == 'release' + run: | + set -euxo pipefail + sudo swapoff -a || true + sudo rm -f /swapfile + sudo fallocate -l 8G /swapfile || sudo dd if=/dev/zero of=/swapfile bs=1M count=8192 + sudo chmod 600 /swapfile + sudo mkswap /swapfile + sudo swapon /swapfile + free -h + swapon --show + + - name: Build (release mode) + uses: PyO3/maturin-action@v1 + if: inputs.build_mode == 'release' + with: + target: x86_64-unknown-linux-gnu + manylinux: "2_28" + args: --release --strip --features protoc,substrait --out dist + rustup-components: rust-std + + - name: Build (debug mode) + uses: PyO3/maturin-action@v1 + if: inputs.build_mode == 'debug' + with: + target: x86_64-unknown-linux-gnu + manylinux: "2_28" + args: --features protoc,substrait --out dist + rustup-components: rust-std + + - name: Build FFI test library + uses: PyO3/maturin-action@v1 + with: + target: x86_64-unknown-linux-gnu + manylinux: "2_28" + working-directory: examples/datafusion-ffi-example + args: --out dist + rustup-components: rust-std + + - name: Archive wheels + uses: actions/upload-artifact@v7 + with: + name: dist-manylinux-x86_64 + path: dist/* + + - name: Archive FFI test wheel + uses: actions/upload-artifact@v7 + with: + name: test-ffi-manylinux-x86_64 + path: examples/datafusion-ffi-example/dist/* + + # ============================================ + # Build - Linux ARM64 + # ============================================ + build-manylinux-aarch64: + needs: [generate-license, lint-rust, lint-python] + name: ManyLinux arm64 + runs-on: ubuntu-24.04-arm + steps: + - uses: actions/checkout@v6 + + - run: rm LICENSE.txt + - name: Download LICENSE.txt + uses: actions/download-artifact@v8 + with: + name: python-wheel-license + path: . + + - name: Setup Rust + uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 + + - name: Cache Cargo + uses: Swatinem/rust-cache@v2 + with: + key: ${{ inputs.build_mode }} + + - uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 + with: + enable-cache: true + + - name: Add extra swap for release build + if: inputs.build_mode == 'release' + run: | + set -euxo pipefail + sudo swapoff -a || true + sudo rm -f /swapfile + sudo fallocate -l 8G /swapfile || sudo dd if=/dev/zero of=/swapfile bs=1M count=8192 + sudo chmod 600 /swapfile + sudo mkswap /swapfile + sudo swapon /swapfile + free -h + swapon --show + + - name: Build (release mode) + uses: PyO3/maturin-action@v1 + if: inputs.build_mode == 'release' + with: + target: aarch64-unknown-linux-gnu + manylinux: "2_28" + args: --release --strip --features protoc,substrait --out dist + rustup-components: rust-std + + - name: Build (debug mode) + uses: PyO3/maturin-action@v1 + if: inputs.build_mode == 'debug' + with: + target: aarch64-unknown-linux-gnu + manylinux: "2_28" + args: --features protoc,substrait --out dist + rustup-components: rust-std + + - name: Archive wheels + uses: actions/upload-artifact@v7 + if: inputs.build_mode == 'release' + with: + name: dist-manylinux-aarch64 + path: dist/* + + # ============================================ + # Build - macOS arm64 / Windows + # ============================================ build-python-mac-win: - needs: [generate-license] - name: Mac/Win + needs: [generate-license, lint-rust, lint-python] + name: macOS arm64 & Windows runs-on: ${{ matrix.os }} strategy: fail-fast: false @@ -77,35 +291,49 @@ jobs: python-version: ["3.10"] os: [macos-latest, windows-latest] steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v6 - - uses: actions/setup-python@v6 - with: - python-version: ${{ matrix.python-version }} - - - uses: dtolnay/rust-toolchain@stable + - uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 - run: rm LICENSE.txt - name: Download LICENSE.txt - uses: actions/download-artifact@v5 + uses: actions/download-artifact@v8 with: name: python-wheel-license path: . + - name: Cache Cargo + uses: Swatinem/rust-cache@v2 + with: + key: ${{ inputs.build_mode }} + + - uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 + with: + enable-cache: true + - name: Install Protoc uses: arduino/setup-protoc@v3 with: version: "27.4" repo-token: ${{ secrets.GITHUB_TOKEN }} - - uses: astral-sh/setup-uv@v7 - with: - enable-cache: true + - name: Install dependencies + run: uv sync --dev --no-install-package datafusion - - name: Build Python package - run: | - uv sync --dev --no-install-package datafusion - uv run --no-project maturin build --release --strip --features substrait + # Run clippy BEFORE maturin so we can avoid rebuilding. The features must match + # exactly the features used by maturin. Linux maturin builds need to happen in a + # container so only run this for our mac runner. + - name: Run Clippy + if: matrix.os != 'windows-latest' + run: cargo clippy --no-deps --all-targets --features substrait -- -D warnings + + - name: Build Python package (release mode) + if: inputs.build_mode == 'release' + run: uv run --no-project maturin build --release --strip --features substrait + + - name: Build Python package (debug mode) + if: inputs.build_mode != 'release' + run: uv run --no-project maturin build --features substrait - name: List Windows wheels if: matrix.os == 'windows-latest' @@ -119,127 +347,80 @@ jobs: run: find target/wheels/ - name: Archive wheels - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@v7 + if: inputs.build_mode == 'release' with: name: dist-${{ matrix.os }} path: target/wheels/* + # ============================================ + # Build - macOS x86_64 (release only) + # ============================================ build-macos-x86_64: - needs: [generate-license] - name: Mac x86_64 + if: inputs.build_mode == 'release' + needs: [generate-license, lint-rust, lint-python] runs-on: macos-15-intel strategy: fail-fast: false matrix: python-version: ["3.10"] steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v6 - - uses: actions/setup-python@v6 - with: - python-version: ${{ matrix.python-version }} - - - uses: dtolnay/rust-toolchain@stable + - uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 - run: rm LICENSE.txt - name: Download LICENSE.txt - uses: actions/download-artifact@v5 + uses: actions/download-artifact@v8 with: name: python-wheel-license path: . + - name: Cache Cargo + uses: Swatinem/rust-cache@v2 + with: + key: ${{ inputs.build_mode }} + + - uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 + with: + enable-cache: true + - name: Install Protoc uses: arduino/setup-protoc@v3 with: version: "27.4" repo-token: ${{ secrets.GITHUB_TOKEN }} - - uses: astral-sh/setup-uv@v7 - with: - enable-cache: true + - name: Install dependencies + run: uv sync --dev --no-install-package datafusion - - name: Build Python package + - name: Build (release mode) run: | - uv sync --dev --no-install-package datafusion uv run --no-project maturin build --release --strip --features substrait - name: List Mac wheels run: find target/wheels/ - name: Archive wheels - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@v7 with: name: dist-macos-aarch64 path: target/wheels/* - build-manylinux-x86_64: - needs: [generate-license] - name: Manylinux x86_64 - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v5 - - run: rm LICENSE.txt - - name: Download LICENSE.txt - uses: actions/download-artifact@v5 - with: - name: python-wheel-license - path: . - - run: cat LICENSE.txt - - name: Build wheels - uses: PyO3/maturin-action@v1 - env: - RUST_BACKTRACE: 1 - with: - rust-toolchain: nightly - target: x86_64 - manylinux: auto - rustup-components: rust-std rustfmt # Keep them in one line due to https://github.com/PyO3/maturin-action/issues/153 - args: --release --manylinux 2014 --features protoc,substrait - - name: Archive wheels - uses: actions/upload-artifact@v4 - with: - name: dist-manylinux-x86_64 - path: target/wheels/* - - build-manylinux-aarch64: - needs: [generate-license] - name: Manylinux arm64 - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v5 - - run: rm LICENSE.txt - - name: Download LICENSE.txt - uses: actions/download-artifact@v5 - with: - name: python-wheel-license - path: . - - run: cat LICENSE.txt - - name: Build wheels - uses: PyO3/maturin-action@v1 - env: - RUST_BACKTRACE: 1 - with: - rust-toolchain: nightly - target: aarch64 - # Use manylinux_2_28-cross because the manylinux2014-cross has GCC 4.8.5, which causes the build to fail - manylinux: 2_28 - rustup-components: rust-std rustfmt # Keep them in one line due to https://github.com/PyO3/maturin-action/issues/153 - args: --release --features protoc,substrait - - name: Archive wheels - uses: actions/upload-artifact@v4 - with: - name: dist-manylinux-aarch64 - path: target/wheels/* + # ============================================ + # Build - Source Distribution + # ============================================ build-sdist: needs: [generate-license] name: Source distribution + if: inputs.build_mode == 'release' runs-on: ubuntu-latest steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v6 - run: rm LICENSE.txt - name: Download LICENSE.txt - uses: actions/download-artifact@v5 + uses: actions/download-artifact@v8 with: name: python-wheel-license path: . @@ -253,16 +434,22 @@ jobs: args: --release --sdist --out dist --features protoc,substrait - name: Assert sdist build does not generate wheels run: | - if [ "$(ls -A target/wheels)" ]; then - echo "Error: Sdist build generated wheels" - exit 1 - else - echo "Directory is clean" - fi + if [ "$(ls -A target/wheels)" ]; then + echo "Error: Sdist build generated wheels" + exit 1 + else + echo "Directory is clean" + fi shell: bash - + + # ============================================ + # Build - Source Distribution + # ============================================ + merge-build-artifacts: runs-on: ubuntu-latest + name: Merge build artifacts + if: inputs.build_mode == 'release' needs: - build-python-mac-win - build-macos-x86_64 @@ -271,11 +458,14 @@ jobs: - build-sdist steps: - name: Merge Build Artifacts - uses: actions/upload-artifact/merge@v4 + uses: actions/upload-artifact/merge@v7 with: name: dist pattern: dist-* + # ============================================ + # Build - Documentation + # ============================================ # Documentation build job that runs after wheels are built build-docs: name: Build docs @@ -299,11 +489,11 @@ jobs: fi - name: Checkout docs sources - uses: actions/checkout@v5 + uses: actions/checkout@v6 - name: Checkout docs target branch if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref_type == 'tag') - uses: actions/checkout@v5 + uses: actions/checkout@v6 with: fetch-depth: 0 ref: ${{ steps.target-branch.outputs.value }} @@ -312,34 +502,35 @@ jobs: - name: Setup Python uses: actions/setup-python@v6 with: - python-version: "3.11" + python-version: "3.10" - name: Install dependencies - uses: astral-sh/setup-uv@v7 + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 with: enable-cache: true # Download the Linux wheel built in the previous job - name: Download pre-built Linux wheel - uses: actions/download-artifact@v5 + uses: actions/download-artifact@v8 with: name: dist-manylinux-x86_64 path: wheels/ - # Install from the pre-built wheel - - name: Install from pre-built wheel + # Install from the pre-built wheels + - name: Install from pre-built wheels run: | set -x uv venv # Install documentation dependencies uv sync --dev --no-install-package datafusion --group docs - # Install the pre-built wheel - WHEEL=$(find wheels/ -name "*.whl" | head -1) - if [ -n "$WHEEL" ]; then - echo "Installing wheel: $WHEEL" - uv pip install "$WHEEL" + # Install all pre-built wheels + WHEELS=$(find wheels/ -name "*.whl") + if [ -n "$WHEELS" ]; then + echo "Installing wheels:" + echo "$WHEELS" + uv pip install wheels/*.whl else - echo "ERROR: No wheel found!" + echo "ERROR: No wheels found!" exit 1 fi @@ -368,16 +559,3 @@ jobs: git commit -m 'Publish built docs triggered by ${{ github.sha }}' git push || git push --force fi - - # NOTE: PyPI publish needs to be done manually for now after release passed the vote - # release: - # name: Publish in PyPI - # needs: [build-manylinux, build-python-mac-win] - # runs-on: ubuntu-latest - # steps: - # - uses: actions/download-artifact@v5 - # - name: Publish to PyPI - # uses: pypa/gh-action-pypi-publish@master - # with: - # user: __token__ - # password: ${{ secrets.pypi_password }} diff --git a/python/datafusion/udf.py b/.github/workflows/ci.yml similarity index 61% rename from python/datafusion/udf.py rename to .github/workflows/ci.yml index c7265fa09..ab284b522 100644 --- a/python/datafusion/udf.py +++ b/.github/workflows/ci.yml @@ -15,15 +15,27 @@ # specific language governing permissions and limitations # under the License. -"""Deprecated module for user defined functions.""" +# CI workflow for pull requests - runs tests in DEBUG mode for faster feedback -import warnings +name: CI -from datafusion.user_defined import * # noqa: F403 +on: + pull_request: + branches: ["main"] -warnings.warn( - "The module 'udf' is deprecated and will be removed in the next release. " - "Please use 'user_defined' instead.", - DeprecationWarning, - stacklevel=2, -) +concurrency: + group: ${{ github.repository }}-${{ github.head_ref || github.sha }}-${{ github.workflow }} + cancel-in-progress: true + +jobs: + build: + uses: ./.github/workflows/build.yml + with: + build_mode: debug + run_wheels: false + secrets: inherit + + test: + needs: build + uses: ./.github/workflows/test.yml + secrets: inherit diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 000000000..a9855cf48 --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,54 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +name: "CodeQL" + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + schedule: + - cron: '16 4 * * 1' + +permissions: + contents: read + +jobs: + analyze: + name: Analyze Actions + runs-on: ubuntu-latest + permissions: + contents: read + security-events: write + + steps: + - name: Checkout repository + uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 + with: + persist-credentials: false + + - name: Initialize CodeQL + uses: github/codeql-action/init@c793b717bc78562f491db7b0e93a3a178b099162 # v4 + with: + languages: actions + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@c793b717bc78562f491db7b0e93a3a178b099162 # v4 + with: + category: "/language:actions" diff --git a/.github/workflows/dev.yml b/.github/workflows/dev.yml index ac45e9fdf..2c8ecbc5e 100644 --- a/.github/workflows/dev.yml +++ b/.github/workflows/dev.yml @@ -25,10 +25,10 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v5 + uses: actions/checkout@v6 - name: Setup Python uses: actions/setup-python@v6 with: - python-version: "3.10" + python-version: "3.14" - name: Audit licenses run: ./dev/release/run-rat.sh . diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 000000000..bddc89eac --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,49 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# Release workflow - runs tests in RELEASE mode and builds distribution wheels +# Triggered on: +# - Merges to main +# - Release candidate tags (*-rc*) +# - Release tags (e.g., 45.0.0) + +name: Release Build + +on: + push: + branches: + - "main" + tags: + - "*-rc*" # Release candidates (e.g., 45.0.0-rc1) + - "[0-9]+.*" # Release tags (e.g., 45.0.0) + +concurrency: + group: ${{ github.repository }}-${{ github.head_ref || github.sha }}-${{ github.workflow }} + cancel-in-progress: true + +jobs: + build: + uses: ./.github/workflows/build.yml + with: + build_mode: release + run_wheels: true + secrets: inherit + + test: + needs: build + uses: ./.github/workflows/test.yml + secrets: inherit diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml deleted file mode 100644 index 99457b872..000000000 --- a/.github/workflows/test.yaml +++ /dev/null @@ -1,139 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. - -name: Python test -on: - push: - branches: [main] - pull_request: - branches: [main] - -concurrency: - group: ${{ github.repository }}-${{ github.head_ref || github.sha }}-${{ github.workflow }} - cancel-in-progress: true - -jobs: - test-matrix: - runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: - - "3.10" - - "3.11" - - "3.12" - - "3.13" - - "3.14" - toolchain: - - "stable" - - steps: - - uses: actions/checkout@v5 - - - name: Verify example datafusion version - run: | - MAIN_VERSION=$(grep -A 1 "name = \"datafusion-common\"" Cargo.lock | grep "version = " | head -1 | sed 's/.*version = "\(.*\)"/\1/') - EXAMPLE_VERSION=$(grep -A 1 "name = \"datafusion-common\"" examples/datafusion-ffi-example/Cargo.lock | grep "version = " | head -1 | sed 's/.*version = "\(.*\)"/\1/') - echo "Main crate datafusion version: $MAIN_VERSION" - echo "FFI example datafusion version: $EXAMPLE_VERSION" - - if [ "$MAIN_VERSION" != "$EXAMPLE_VERSION" ]; then - echo "❌ Error: FFI example datafusion versions don't match!" - exit 1 - fi - - - name: Setup Rust Toolchain - uses: dtolnay/rust-toolchain@stable - id: rust-toolchain - with: - components: clippy,rustfmt - - - name: Install Protoc - uses: arduino/setup-protoc@v3 - with: - version: '27.4' - repo-token: ${{ secrets.GITHUB_TOKEN }} - - - name: Setup Python - uses: actions/setup-python@v6 - with: - python-version: ${{ matrix.python-version }} - - - name: Cache Cargo - uses: actions/cache@v4 - with: - path: ~/.cargo - key: cargo-cache-${{ steps.rust-toolchain.outputs.cachekey }}-${{ hashFiles('Cargo.lock') }} - - - name: Run Clippy - if: ${{ matrix.python-version == '3.10' && matrix.toolchain == 'stable' }} - run: cargo clippy --all-targets --all-features -- -D clippy::all -D warnings -A clippy::redundant_closure - - - name: Install dependencies and build - uses: astral-sh/setup-uv@v7 - with: - enable-cache: true - - - name: Run tests - env: - RUST_BACKTRACE: 1 - run: | - git submodule update --init - uv sync --dev --no-install-package datafusion - uv run --no-project maturin develop --uv - uv run --no-project pytest -v . - - - name: FFI unit tests - run: | - cd examples/datafusion-ffi-example - uv run --no-project maturin develop --uv - uv run --no-project pytest python/tests/_test*.py - - - name: Cache the generated dataset - id: cache-tpch-dataset - uses: actions/cache@v4 - with: - path: benchmarks/tpch/data - key: tpch-data-2.18.0 - - - name: Run dbgen to create 1 Gb dataset - if: ${{ steps.cache-tpch-dataset.outputs.cache-hit != 'true' }} - run: | - cd benchmarks/tpch - RUN_IN_CI=TRUE ./tpch-gen.sh 1 - - - name: Run TPC-H examples - run: | - cd examples/tpch - uv run --no-project python convert_data_to_parquet.py - uv run --no-project pytest _tests.py - - nightly-fmt: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v5 - - - name: Setup Rust Toolchain - uses: dtolnay/rust-toolchain@stable - id: rust-toolchain - with: - toolchain: "nightly" - components: clippy,rustfmt - - - name: Check Formatting - run: cargo +nightly fmt -- --check diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 000000000..706ccbc55 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,117 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# Reusable workflow for running tests +# This ensures the same tests run for both debug (PRs) and release (main/tags) builds + +name: Test + +on: + workflow_call: + +env: + UV_LOCKED: true + +jobs: + test-matrix: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: + - "3.10" + - "3.11" + - "3.12" + - "3.13" + - "3.14" + toolchain: + - "stable" + + steps: + - uses: actions/checkout@v6 + + - name: Setup Python + uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python-version }} + + - name: Cache Cargo + uses: actions/cache@v5 + with: + path: ~/.cargo + key: cargo-cache-${{ matrix.toolchain }}-${{ hashFiles('Cargo.lock') }} + + - name: Install dependencies + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 + with: + enable-cache: true + + # Download the Linux wheel built in the build workflow + - name: Download pre-built Linux wheel + uses: actions/download-artifact@v8 + with: + name: dist-manylinux-x86_64 + path: wheels/ + + # Download the FFI test wheel + - name: Download pre-built FFI test wheel + uses: actions/download-artifact@v8 + with: + name: test-ffi-manylinux-x86_64 + path: wheels/ + + # Install from the pre-built wheels + - name: Install from pre-built wheels + run: | + set -x + uv venv + # Install development dependencies + uv sync --dev --no-install-package datafusion + # Install all pre-built wheels + WHEELS=$(find wheels/ -name "*.whl") + if [ -n "$WHEELS" ]; then + echo "Installing wheels:" + echo "$WHEELS" + uv pip install wheels/*.whl + else + echo "ERROR: No wheels found!" + exit 1 + fi + + - name: Run tests + env: + RUST_BACKTRACE: 1 + run: | + git submodule update --init + uv run --no-project pytest -v --import-mode=importlib + + - name: FFI unit tests + run: | + cd examples/datafusion-ffi-example + uv run --no-project pytest python/tests/_test*.py + + - name: Run tpchgen-cli to create 1 Gb dataset + run: | + mkdir examples/tpch/data + cd examples/tpch/data + uv pip install tpchgen-cli + uv run --no-project tpchgen-cli -s 1 --format=parquet + + - name: Run TPC-H examples + run: | + cd examples/tpch + uv run --no-project pytest _tests.py diff --git a/.github/workflows/verify-release-candidate.yml b/.github/workflows/verify-release-candidate.yml new file mode 100644 index 000000000..6ecb547b5 --- /dev/null +++ b/.github/workflows/verify-release-candidate.yml @@ -0,0 +1,83 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +name: Verify Release Candidate + +# NOTE: This workflow is intended to be run manually via workflow_dispatch. + +on: + workflow_dispatch: + inputs: + version: + description: Version number (e.g., 52.0.0) + required: true + type: string + rc_number: + description: Release candidate number (e.g., 1) + required: true + type: string + +concurrency: + group: ${{ github.repository }}-${{ github.ref }}-${{ github.workflow }} + cancel-in-progress: true + +jobs: + verify: + name: Verify RC (${{ matrix.os }}-${{ matrix.arch }}) + strategy: + fail-fast: false + matrix: + include: + # Linux + - os: linux + arch: x64 + runner: ubuntu-latest + - os: linux + arch: arm64 + runner: ubuntu-24.04-arm + + # macOS + - os: macos + arch: arm64 + runner: macos-latest + - os: macos + arch: x64 + runner: macos-15-intel + + # Windows + - os: windows + arch: x64 + runner: windows-latest + runs-on: ${{ matrix.runner }} + steps: + - name: Checkout repository + uses: actions/checkout@v6 + + - name: Set up protoc + uses: arduino/setup-protoc@v3 + with: + version: "27.4" + repo-token: ${{ secrets.GITHUB_TOKEN }} + + - name: Set RUSTFLAGS for Windows GNU linker + if: matrix.os == 'windows' + shell: bash + run: echo "RUSTFLAGS=-C link-arg=-Wl,--exclude-libs=ALL" >> "$GITHUB_ENV" + + - name: Run release candidate verification + shell: bash + run: ./dev/release/verify-release-candidate.sh "${{ inputs.version }}" "${{ inputs.rc_number }}" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bcefa405d..2d3c2bc59 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,7 +22,7 @@ repos: - id: actionlint-docker - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. - rev: v0.9.10 + rev: v0.15.1 hooks: # Run the linter. - id: ruff @@ -53,5 +53,12 @@ repos: additional_dependencies: - tomli + - repo: https://github.com/astral-sh/uv-pre-commit + # uv version. + rev: 0.10.7 + hooks: + # Update the uv lockfile + - id: uv-lock + default_language_version: python: python3 diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 000000000..7d3262710 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,88 @@ + + +# Agent Instructions for Contributors + +This file is for agents working **on** the datafusion-python project (developing, +testing, reviewing). If you need to **use** the DataFusion DataFrame API (write +queries, build expressions, understand available functions), see the user-facing +skill at [`SKILL.md`](SKILL.md). + +## Skills + +This project uses AI agent skills stored in `.ai/skills/`. Each skill is a directory containing a `SKILL.md` file with instructions for performing a specific task. + +Skills follow the [Agent Skills](https://agentskills.io) open standard. Each skill directory contains: + +- `SKILL.md` — The skill definition with YAML frontmatter (name, description, argument-hint) and detailed instructions. +- Additional supporting files as needed. + +## Pull Requests + +Every pull request must follow the template in +`.github/pull_request_template.md`. The description must include these sections: + +1. **Which issue does this PR close?** — Link the issue with `Closes #NNN`. +2. **Rationale for this change** — Why the change is needed (skip if the issue + already explains it clearly). +3. **What changes are included in this PR?** — Summarize the individual changes. +4. **Are there any user-facing changes?** — Note any changes visible to users + (new APIs, changed behavior, new files shipped in the package, etc.). If + there are breaking changes to public APIs, add the `api change` label. + +## Pre-commit Checks + +Always run pre-commit checks **before** committing. The hooks are defined in +`.pre-commit-config.yaml` and run automatically on `git commit` if pre-commit +is installed as a git hook. To run all hooks manually: + +```bash +pre-commit run --all-files +``` + +Fix any failures before committing. + +## Python Function Docstrings + +Every Python function must include a docstring with usage examples. + +- **Examples are required**: Each function needs at least one doctest-style example + demonstrating basic usage. +- **Optional parameters**: If a function has optional parameters, include separate + examples that show usage both without and with the optional arguments. Pass + optional arguments using their keyword name (e.g., `step=dfn.lit(3)`) so readers + can immediately see which parameter is being demonstrated. +- **Reuse input data**: Use the same input data across examples wherever possible. + The examples should demonstrate how different optional arguments change the output + for the same input, making the effect of each option easy to understand. +- **Alias functions**: Functions that are simple aliases (e.g., `list_sort` aliasing + `array_sort`) only need a one-line description and a `See Also` reference to the + primary function. They do not need their own examples. + +## Aggregate and Window Function Documentation + +When adding or updating an aggregate or window function, ensure the corresponding +site documentation is kept in sync: + +- **Aggregations**: `docs/source/user-guide/common-operations/aggregations.rst` — + add new aggregate functions to the "Aggregate Functions" list and include usage + examples if appropriate. +- **Window functions**: `docs/source/user-guide/common-operations/windows.rst` — + add new window functions to the "Available Functions" list and include usage + examples if appropriate. diff --git a/CLAUDE.md b/CLAUDE.md new file mode 120000 index 000000000..47dc3e3d8 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1 @@ +AGENTS.md \ No newline at end of file diff --git a/Cargo.lock b/Cargo.lock index ac691ca9d..4efca3eb6 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -1,6 +1,6 @@ # This file is automatically @generated by Cargo. # It is not intended for manual editing. -version = 3 +version = 4 [[package]] name = "abi_stable" @@ -111,26 +111,27 @@ dependencies = [ [[package]] name = "anyhow" -version = "1.0.100" +version = "1.0.102" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a23eb6b1614318a8071c9b2521f36b424b2c83db5eb3a0fead4a6c0809af6e61" +checksum = "7f202df86484c868dbad7eaa557ef785d5c66295e41b460ef922eca0723b842c" [[package]] name = "apache-avro" -version = "0.20.0" +version = "0.21.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3a033b4ced7c585199fb78ef50fca7fe2f444369ec48080c5fd072efa1a03cc7" +checksum = "36fa98bc79671c7981272d91a8753a928ff6a1cd8e4f20a44c45bd5d313840bf" dependencies = [ "bigdecimal", "bon", - "bzip2 0.6.1", + "bzip2", "crc32fast", "digest", + "liblzma", "log", "miniz_oxide", "num-bigint", "quad-rand", - "rand", + "rand 0.9.2", "regex-lite", "serde", "serde_bytes", @@ -140,24 +141,23 @@ dependencies = [ "strum_macros", "thiserror", "uuid", - "xz2", "zstd", ] [[package]] name = "ar_archive_writer" -version = "0.2.0" +version = "0.5.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f0c269894b6fe5e9d7ada0cf69b5bf847ff35bc25fc271f08e1d080fce80339a" +checksum = "7eb93bbb63b9c227414f6eb3a0adfddca591a8ce1e9b60661bb08969b87e340b" dependencies = [ "object", ] [[package]] name = "arc-swap" -version = "1.8.0" +version = "1.9.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "51d03449bb8ca2cc2ef70869af31463d1ae5ccc8fa3e334b307203fbf815207e" +checksum = "a07d1f37ff60921c83bdfc7407723bdefe89b44b98a9b772f225c8f9d67141a6" dependencies = [ "rustversion", ] @@ -176,9 +176,9 @@ checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50" [[package]] name = "arrow" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cb372a7cbcac02a35d3fb7b3fc1f969ec078e871f9bb899bf00a2e1809bec8a3" +checksum = "d441fdda254b65f3e9025910eb2c2066b6295d9c8ed409522b8d2ace1ff8574c" dependencies = [ "arrow-arith", "arrow-array", @@ -198,9 +198,9 @@ dependencies = [ [[package]] name = "arrow-arith" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0f377dcd19e440174596d83deb49cd724886d91060c07fec4f67014ef9d54049" +checksum = "ced5406f8b720cc0bc3aa9cf5758f93e8593cda5490677aa194e4b4b383f9a59" dependencies = [ "arrow-array", "arrow-buffer", @@ -212,9 +212,9 @@ dependencies = [ [[package]] name = "arrow-array" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a23eaff85a44e9fa914660fb0d0bb00b79c4a3d888b5334adb3ea4330c84f002" +checksum = "772bd34cacdda8baec9418d80d23d0fb4d50ef0735685bd45158b83dfeb6e62d" dependencies = [ "ahash", "arrow-buffer", @@ -231,9 +231,9 @@ dependencies = [ [[package]] name = "arrow-buffer" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a2819d893750cb3380ab31ebdc8c68874dd4429f90fd09180f3c93538bd21626" +checksum = "898f4cf1e9598fdb77f356fdf2134feedfd0ee8d5a4e0a5f573e7d0aec16baa4" dependencies = [ "bytes", "half", @@ -243,9 +243,9 @@ dependencies = [ [[package]] name = "arrow-cast" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e3d131abb183f80c450d4591dc784f8d7750c50c6e2bc3fcaad148afc8361271" +checksum = "b0127816c96533d20fc938729f48c52d3e48f99717e7a0b5ade77d742510736d" dependencies = [ "arrow-array", "arrow-buffer", @@ -265,9 +265,9 @@ dependencies = [ [[package]] name = "arrow-csv" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2275877a0e5e7e7c76954669366c2aa1a829e340ab1f612e647507860906fb6b" +checksum = "ca025bd0f38eeecb57c2153c0123b960494138e6a957bbda10da2b25415209fe" dependencies = [ "arrow-array", "arrow-cast", @@ -280,9 +280,9 @@ dependencies = [ [[package]] name = "arrow-data" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "05738f3d42cb922b9096f7786f606fcb8669260c2640df8490533bb2fa38c9d3" +checksum = "42d10beeab2b1c3bb0b53a00f7c944a178b622173a5c7bcabc3cb45d90238df4" dependencies = [ "arrow-buffer", "arrow-schema", @@ -293,9 +293,9 @@ dependencies = [ [[package]] name = "arrow-ipc" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3d09446e8076c4b3f235603d9ea7c5494e73d441b01cd61fb33d7254c11964b3" +checksum = "609a441080e338147a84e8e6904b6da482cefb957c5cdc0f3398872f69a315d0" dependencies = [ "arrow-array", "arrow-buffer", @@ -309,9 +309,9 @@ dependencies = [ [[package]] name = "arrow-json" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = 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- "zstd-safe", ] [[package]] @@ -457,7 +452,7 @@ checksum = "3b43422f69d8ff38f95f1b2bb76517c91589a924d1559a0e935d7c8ce0274c11" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -468,7 +463,7 @@ checksum = "9035ad2d096bed7955a320ee7e2230574d28fd3c3a0f186cbea1ff3c7eed5dbb" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -514,9 +509,9 @@ dependencies = [ [[package]] name = "bitflags" -version = "2.10.0" +version = "2.11.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "812e12b5285cc515a9c72a5c1d3b6d46a19dac5acfef5265968c166106e31dd3" +checksum = "843867be96c8daad0d758b57df9392b6d8d271134fce549de6ce169ff98a92af" [[package]] name = "blake2" @@ -529,15 +524,16 @@ dependencies = [ [[package]] name = "blake3" -version = "1.8.2" +version = "1.8.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = 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"ident_case", @@ -571,7 +567,7 @@ dependencies = [ "proc-macro2", "quote", "rustversion", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -597,9 +593,9 @@ dependencies = [ [[package]] name = "bumpalo" -version = "3.19.1" +version = "3.20.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5dd9dc738b7a8311c7ade152424974d8115f2cdad61e8dab8dac9f2362298510" +checksum = "5d20789868f4b01b2f2caec9f5c4e0213b41e3e5702a50157d699ae31ced2fcb" [[package]] name = "byteorder" @@ -609,18 +605,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b" [[package]] name = "bytes" -version = "1.11.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b35204fbdc0b3f4446b89fc1ac2cf84a8a68971995d0bf2e925ec7cd960f9cb3" - -[[package]] -name = "bzip2" -version = "0.5.2" +version = "1.11.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = 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"1.5.0" @@ -854,9 +872,9 @@ dependencies = [ [[package]] name = "darling" -version = "0.21.3" +version = "0.23.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9cdf337090841a411e2a7f3deb9187445851f91b309c0c0a29e05f74a00a48c0" +checksum = "25ae13da2f202d56bd7f91c25fba009e7717a1e4a1cc98a76d844b65ae912e9d" dependencies = [ "darling_core", "darling_macro", @@ -864,27 +882,26 @@ dependencies = [ [[package]] name = "darling_core" -version = "0.21.3" +version = "0.23.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1247195ecd7e3c85f83c8d2a366e4210d588e802133e1e355180a9870b517ea4" +checksum = "9865a50f7c335f53564bb694ef660825eb8610e0a53d3e11bf1b0d3df31e03b0" dependencies = [ - "fnv", "ident_case", "proc-macro2", "quote", "strsim", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] name = "darling_macro" -version = "0.21.3" +version = "0.23.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d38308df82d1080de0afee5d069fa14b0326a88c14f15c5ccda35b4a6c414c81" +checksum = "ac3984ec7bd6cfa798e62b4a642426a5be0e68f9401cfc2a01e3fa9ea2fcdb8d" dependencies = [ "darling_core", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -903,15 +920,15 @@ dependencies = [ [[package]] name = "datafusion" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8ba7cb113e9c0bedf9e9765926031e132fa05a1b09ba6e93a6d1a4d7044457b8" +checksum = "de9f8117889ba9503440f1dd79ebab32ba52ccf1720bb83cd718a29d4edc0d16" dependencies = [ "arrow", "arrow-schema", "async-trait", "bytes", - "bzip2 0.6.1", + "bzip2", "chrono", "datafusion-catalog", "datafusion-catalog-listing", @@ -942,27 +959,26 @@ dependencies = [ "flate2", "futures", "itertools", + "liblzma", "log", "object_store", "parking_lot", "parquet", - "rand", + "rand 0.9.2", "regex", - "rstest", "sqlparser", "tempfile", "tokio", "url", "uuid", - "xz2", "zstd", ] [[package]] name = "datafusion-catalog" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "66a3a799f914a59b1ea343906a0486f17061f39509af74e874a866428951130d" +checksum = "be893b73a13671f310ffcc8da2c546b81efcc54c22e0382c0a28aa3537017137" dependencies = [ "arrow", "async-trait", @@ -985,9 +1001,9 @@ dependencies = [ [[package]] name = "datafusion-catalog-listing" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6db1b113c80d7a0febcd901476a57aef378e717c54517a163ed51417d87621b0" +checksum = "830487b51ed83807d6b32d6325f349c3144ae0c9bf772cf2a712db180c31d5e6" dependencies = [ "arrow", "async-trait", @@ -1004,14 +1020,13 @@ dependencies = [ "itertools", "log", "object_store", - "tokio", ] [[package]] name = "datafusion-common" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7c10f7659e96127d25e8366be7c8be4109595d6a2c3eac70421f380a7006a1b0" +checksum = "0d7663f3af955292f8004e74bcaf8f7ea3d66cc38438749615bb84815b61a293" dependencies = [ "ahash", "apache-avro", @@ -1019,8 +1034,9 @@ dependencies = [ "arrow-ipc", "chrono", "half", - "hashbrown 0.14.5", + "hashbrown 0.16.1", "indexmap", + "itertools", "libc", "log", "object_store", @@ -1034,9 +1050,9 @@ dependencies = [ [[package]] name = "datafusion-common-runtime" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b92065bbc6532c6651e2f7dd30b55cba0c7a14f860c7e1d15f165c41a1868d95" +checksum = "5f590205c7e32fe1fea48dd53ffb406e56ae0e7a062213a3ac848db8771641bd" dependencies = [ "futures", "log", @@ -1045,15 +1061,15 @@ dependencies = [ [[package]] name = "datafusion-datasource" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fde13794244bc7581cd82f6fff217068ed79cdc344cafe4ab2c3a1c3510b38d6" +checksum = "fde1e030a9dc87b743c806fbd631f5ecfa2ccaa4ffb61fa19144a07fea406b79" dependencies = [ "arrow", "async-compression", "async-trait", "bytes", - "bzip2 0.6.1", + "bzip2", "chrono", "datafusion-common", "datafusion-common-runtime", @@ -1068,21 +1084,21 @@ dependencies = [ "futures", "glob", "itertools", + "liblzma", "log", "object_store", - "rand", + "rand 0.9.2", "tokio", "tokio-util", "url", - "xz2", "zstd", ] [[package]] name = "datafusion-datasource-arrow" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "804fa9b4ecf3157982021770617200ef7c1b2979d57bec9044748314775a9aea" +checksum = "331ebae7055dc108f9b54994b93dff91f3a17445539efe5b74e89264f7b36e15" dependencies = [ "arrow", "arrow-ipc", @@ -1104,9 +1120,9 @@ dependencies = [ [[package]] name = "datafusion-datasource-avro" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "388ed8be535f562cc655b9c3d22edbfb0f1a50a25c242647a98b6d92a75b55a1" +checksum = "49dda81c79b6ba57b1853a9158abc66eb85a3aa1cede0c517dabec6d8a4ed3aa" dependencies = [ "apache-avro", "arrow", @@ -1124,9 +1140,9 @@ dependencies = [ [[package]] name = "datafusion-datasource-csv" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "61a1641a40b259bab38131c5e6f48fac0717bedb7dc93690e604142a849e0568" +checksum = "9e0d475088325e2986876aa27bb30d0574f72a22955a527d202f454681d55c5c" dependencies = [ "arrow", "async-trait", @@ -1147,9 +1163,9 @@ dependencies = [ [[package]] name = "datafusion-datasource-json" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "adeacdb00c1d37271176f8fb6a1d8ce096baba16ea7a4b2671840c5c9c64fe85" +checksum = "ea1520d81f31770f3ad6ee98b391e75e87a68a5bb90de70064ace5e0a7182fe8" dependencies = [ "arrow", "async-trait", @@ -1164,14 +1180,16 @@ dependencies = [ "datafusion-session", "futures", "object_store", + "serde_json", "tokio", + "tokio-stream", ] [[package]] name = "datafusion-datasource-parquet" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "43d0b60ffd66f28bfb026565d62b0a6cbc416da09814766a3797bba7d85a3cd9" +checksum = "95be805d0742ab129720f4c51ad9242cd872599cdb076098b03f061fcdc7f946" dependencies = [ "arrow", "async-trait", @@ -1199,35 +1217,38 @@ dependencies = [ [[package]] name = "datafusion-doc" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2b99e13947667b36ad713549237362afb054b2d8f8cc447751e23ec61202db07" +checksum = "5c93ad9e37730d2c7196e68616f3f2dd3b04c892e03acd3a8eeca6e177f3c06a" [[package]] name = "datafusion-execution" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "63695643190679037bc946ad46a263b62016931547bf119859c511f7ff2f5178" +checksum = "9437d3cd5d363f9319f8122182d4d233427de79c7eb748f23054c9aaa0fdd8df" dependencies = [ "arrow", + "arrow-buffer", "async-trait", + "chrono", "dashmap", "datafusion-common", "datafusion-expr", + "datafusion-physical-expr-common", "futures", "log", "object_store", "parking_lot", - "rand", + "rand 0.9.2", "tempfile", "url", ] [[package]] name = "datafusion-expr" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f9a4787cbf5feb1ab351f789063398f67654a6df75c4d37d7f637dc96f951a91" +checksum = "67164333342b86521d6d93fa54081ee39839894fb10f7a700c099af96d7552cf" dependencies = [ "arrow", "async-trait", @@ -1248,9 +1269,9 @@ dependencies = [ [[package]] name = "datafusion-expr-common" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5ce2fb1b8c15c9ac45b0863c30b268c69dc9ee7a1ee13ecf5d067738338173dc" +checksum = "ab05fdd00e05d5a6ee362882546d29d6d3df43a6c55355164a7fbee12d163bc9" dependencies = [ "arrow", "datafusion-common", @@ -1261,20 +1282,27 @@ dependencies = [ [[package]] name = "datafusion-ffi" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ec510e7787641279b0336e8b79e4b7bd1385d5976875ff9b97f4269ce5231a67" +checksum = "4b8250f7cdf463a0ad145f41d7508bcfa54c9b9f027317e599f0331097e3cc38" dependencies = [ "abi_stable", "arrow", "arrow-schema", "async-ffi", "async-trait", - "datafusion", + "datafusion-catalog", "datafusion-common", + "datafusion-datasource", + "datafusion-execution", + "datafusion-expr", "datafusion-functions-aggregate-common", + "datafusion-physical-expr", + "datafusion-physical-expr-common", + "datafusion-physical-plan", "datafusion-proto", "datafusion-proto-common", + "datafusion-session", "futures", "log", "prost", @@ -1282,11 +1310,31 @@ dependencies = [ "tokio", ] +[[package]] +name = "datafusion-ffi-example" +version = "53.0.0" +dependencies = [ + "arrow", + "arrow-array", + "arrow-schema", + "async-trait", + "datafusion-catalog", + "datafusion-common", + "datafusion-expr", + "datafusion-ffi", + "datafusion-functions-aggregate", + "datafusion-functions-window", + "datafusion-python-util", + "pyo3", + "pyo3-build-config", + "pyo3-log", +] + [[package]] name = "datafusion-functions" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "794a9db7f7b96b3346fc007ff25e994f09b8f0511b4cf7dff651fadfe3ebb28f" +checksum = "04fb863482d987cf938db2079e07ab0d3bb64595f28907a6c2f8671ad71cca7e" dependencies = [ "arrow", "arrow-buffer", @@ -1294,6 +1342,7 @@ dependencies = [ "blake2", "blake3", "chrono", + "chrono-tz", "datafusion-common", "datafusion-doc", "datafusion-execution", @@ -1304,8 +1353,9 @@ dependencies = [ "itertools", "log", "md-5", + "memchr", "num-traits", - "rand", + "rand 0.9.2", "regex", "sha2", "unicode-segmentation", @@ -1314,9 +1364,9 @@ dependencies = [ [[package]] name = "datafusion-functions-aggregate" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1c25210520a9dcf9c2b2cbbce31ebd4131ef5af7fc60ee92b266dc7d159cb305" +checksum = "829856f4e14275fb376c104f27cbf3c3b57a9cfe24885d98677525f5e43ce8d6" dependencies = [ "ahash", "arrow", @@ -1330,14 +1380,15 @@ dependencies = [ "datafusion-physical-expr-common", "half", "log", + "num-traits", "paste", ] [[package]] name = "datafusion-functions-aggregate-common" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "62f4a66f3b87300bb70f4124b55434d2ae3fe80455f3574701d0348da040b55d" +checksum = "08af79cc3d2aa874a362fb97decfcbd73d687190cb096f16a6c85a7780cce311" dependencies = [ "ahash", "arrow", @@ -1348,9 +1399,9 @@ dependencies = [ [[package]] name = "datafusion-functions-nested" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ae5c06eed03918dc7fe7a9f082a284050f0e9ecf95d72f57712d1496da03b8c4" +checksum = "465ae3368146d49c2eda3e2c0ef114424c87e8a6b509ab34c1026ace6497e790" dependencies = [ "arrow", "arrow-ord", @@ -1364,16 +1415,18 @@ dependencies = [ "datafusion-functions-aggregate-common", "datafusion-macros", "datafusion-physical-expr-common", + "hashbrown 0.16.1", "itertools", + "itoa", "log", "paste", ] [[package]] name = "datafusion-functions-table" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "db4fed1d71738fbe22e2712d71396db04c25de4111f1ec252b8f4c6d3b25d7f5" +checksum = "6156e6b22fcf1784112fc0173f3ae6e78c8fdb4d3ed0eace9543873b437e2af6" dependencies = [ "arrow", "async-trait", @@ -1387,9 +1440,9 @@ dependencies = [ [[package]] name = "datafusion-functions-window" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1d92206aa5ae21892f1552b4d61758a862a70956e6fd7a95cb85db1de74bc6d1" +checksum = "ca7baec14f866729012efb89011a6973f3a346dc8090c567bfcd328deff551c1" dependencies = [ "arrow", "datafusion-common", @@ -1405,9 +1458,9 @@ dependencies = [ [[package]] name = "datafusion-functions-window-common" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "53ae9bcc39800820d53a22d758b3b8726ff84a5a3e24cecef04ef4e5fdf1c7cc" +checksum = "159228c3280d342658466bb556dc24de30047fe1d7e559dc5d16ccc5324166f9" dependencies = [ "datafusion-common", "datafusion-physical-expr-common", @@ -1415,20 +1468,20 @@ dependencies = [ [[package]] name = "datafusion-macros" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1063ad4c9e094b3f798acee16d9a47bd7372d9699be2de21b05c3bd3f34ab848" +checksum = "e5427e5da5edca4d21ea1c7f50e1c9421775fe33d7d5726e5641a833566e7578" dependencies = [ "datafusion-doc", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] name = "datafusion-optimizer" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9f35f9ec5d08b87fd1893a30c2929f2559c2f9806ca072d8fefca5009dc0f06a" +checksum = "89099eefcd5b223ec685c36a41d35c69239236310d71d339f2af0fa4383f3f46" dependencies = [ "arrow", "chrono", @@ -1446,9 +1499,9 @@ dependencies = [ [[package]] name = "datafusion-physical-expr" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c30cc8012e9eedcb48bbe112c6eff4ae5ed19cf3003cb0f505662e88b7014c5d" +checksum = "0f222df5195d605d79098ef37bdd5323bff0131c9d877a24da6ec98dfca9fe36" dependencies = [ "ahash", "arrow", @@ -1458,19 +1511,21 @@ dependencies = [ "datafusion-functions-aggregate-common", "datafusion-physical-expr-common", "half", - "hashbrown 0.14.5", + "hashbrown 0.16.1", "indexmap", "itertools", "parking_lot", "paste", - "petgraph 0.8.3", + "petgraph", + "recursive", + "tokio", ] [[package]] name = "datafusion-physical-expr-adapter" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7f9ff2dbd476221b1f67337699eff432781c4e6e1713d2aefdaa517dfbf79768" +checksum = "40838625d63d9c12549d81979db3dd675d159055eb9135009ba272ab0e8d0f64" dependencies = [ "arrow", "datafusion-common", @@ -1483,23 +1538,26 @@ dependencies = [ [[package]] name = "datafusion-physical-expr-common" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "90da43e1ec550b172f34c87ec68161986ced70fd05c8d2a2add66eef9c276f03" +checksum = "eacbcc4cfd502558184ed58fa3c72e775ec65bf077eef5fd2b3453db676f893c" dependencies = [ "ahash", "arrow", + "chrono", "datafusion-common", "datafusion-expr-common", - "hashbrown 0.14.5", + "hashbrown 0.16.1", + "indexmap", "itertools", + "parking_lot", ] [[package]] name = "datafusion-physical-optimizer" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ce9804f799acd7daef3be7aaffe77c0033768ed8fdbf5fb82fc4c5f2e6bc14e6" +checksum = "d501d0e1d0910f015677121601ac177ec59272ef5c9324d1147b394988f40941" dependencies = [ "arrow", "datafusion-common", @@ -1516,30 +1574,31 @@ dependencies = [ [[package]] name = "datafusion-physical-plan" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0acf0ad6b6924c6b1aa7d213b181e012e2d3ec0a64ff5b10ee6282ab0f8532ac" +checksum = "463c88ad6f1ecab1810f4c9f046898bee035b370137eb79b2b2db925e270631d" dependencies = [ "ahash", "arrow", "arrow-ord", "arrow-schema", "async-trait", - "chrono", "datafusion-common", "datafusion-common-runtime", "datafusion-execution", "datafusion-expr", + "datafusion-functions", "datafusion-functions-aggregate-common", "datafusion-functions-window-common", "datafusion-physical-expr", "datafusion-physical-expr-common", "futures", "half", - "hashbrown 0.14.5", + "hashbrown 0.16.1", "indexmap", "itertools", "log", + "num-traits", "parking_lot", "pin-project-lite", "tokio", @@ -1547,9 +1606,9 @@ dependencies = [ [[package]] name = "datafusion-proto" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d368093a98a17d1449b1083ac22ed16b7128e4c67789991869480d8c4a40ecb9" +checksum = "677ee4448a010ed5faeff8d73ff78972c2ace59eff3cd7bd15833a1dafa00492" dependencies = [ "arrow", "chrono", @@ -1570,13 +1629,14 @@ dependencies = [ "datafusion-proto-common", "object_store", "prost", + "rand 0.9.2", ] [[package]] name = "datafusion-proto-common" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3b6aef3d5e5c1d2bc3114c4876730cb76a9bdc5a8df31ef1b6db48f0c1671895" +checksum = "965eca01edc8259edbbd95883a00b6d81e329fd44a019cfac3a03b026a83eade" dependencies = [ "arrow", "datafusion-common", @@ -1585,9 +1645,9 @@ dependencies = [ [[package]] name = "datafusion-pruning" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ac2c2498a1f134a9e11a9f5ed202a2a7d7e9774bd9249295593053ea3be999db" +checksum = "2857618a0ecbd8cd0cf29826889edd3a25774ec26b2995fc3862095c95d88fc6" dependencies = [ "arrow", "datafusion-common", @@ -1602,15 +1662,17 @@ dependencies = [ [[package]] name = "datafusion-python" -version = "51.0.0" +version = "53.0.0" dependencies = [ "arrow", "arrow-select", "async-trait", + "chrono", "cstr", "datafusion", "datafusion-ffi", "datafusion-proto", + "datafusion-python-util", "datafusion-substrait", "futures", "log", @@ -1623,16 +1685,29 @@ dependencies = [ "pyo3-async-runtimes", "pyo3-build-config", "pyo3-log", + "serde_json", "tokio", "url", "uuid", ] +[[package]] +name = "datafusion-python-util" +version = "53.0.0" +dependencies = [ + "arrow", + "datafusion", + "datafusion-ffi", + "prost", + "pyo3", + "tokio", +] + [[package]] name = "datafusion-session" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8f96eebd17555386f459037c65ab73aae8df09f464524c709d6a3134ad4f4776" +checksum = "ef8637e35022c5c775003b3ab1debc6b4a8f0eb41b069bdd5475dd3aa93f6eba" dependencies = [ "async-trait", "datafusion-common", @@ -1644,15 +1719,16 @@ dependencies = [ [[package]] name = "datafusion-sql" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3fc195fe60634b2c6ccfd131b487de46dc30eccae8a3c35a13f136e7f440414f" +checksum = "12d9e9f16a1692a11c94bcc418191fa15fd2b4d72a0c1a0c607db93c0b84dd81" dependencies = [ "arrow", "bigdecimal", "chrono", "datafusion-common", "datafusion-expr", + "datafusion-functions-nested", "indexmap", "log", "recursive", @@ -1662,9 +1738,9 @@ dependencies = [ [[package]] name = "datafusion-substrait" -version = "51.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2505af06d103a55b4e8ded0c6aeb6c72a771948da939c0bd3f8eee67af475a9c" +checksum = "d5e5656a7e63d51dd3e5af3dbd347ea83bbe993a77c66b854b74961570d16490" dependencies = [ "async-recursion", "async-trait", @@ -1678,7 +1754,6 @@ dependencies = [ "substrait", "tokio", "url", - "uuid", ] [[package]] @@ -1700,7 +1775,7 @@ checksum = "97369cbbc041bc366949bc74d34658d6cda5621039731c6310521892a3a20ae0" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -1739,9 +1814,9 @@ checksum = "37909eebbb50d72f9059c3b6d82c0463f2ff062c9e95845c43a6c9c0355411be" [[package]] name = "find-msvc-tools" -version = "0.1.6" +version = "0.1.9" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "645cbb3a84e60b7531617d5ae4e57f7e27308f6445f5abf653209ea76dec8dff" +checksum = "5baebc0774151f905a1a2cc41989300b1e6fbb29aff0ceffa1064fdd3088d582" [[package]] name = "fixedbitset" @@ -1761,13 +1836,13 @@ dependencies = [ [[package]] name = "flate2" -version = "1.1.5" +version = "1.1.9" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bfe33edd8e85a12a67454e37f8c75e730830d83e313556ab9ebf9ee7fbeb3bfb" +checksum = "843fba2746e448b37e26a819579957415c8cef339bf08564fe8b7ddbd959573c" dependencies = [ "crc32fast", - "libz-rs-sys", "miniz_oxide", + "zlib-rs", ] [[package]] @@ -1799,9 +1874,9 @@ dependencies = [ [[package]] name = "futures" -version = "0.3.31" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "65bc07b1a8bc7c85c5f2e110c476c7389b4554ba72af57d8445ea63a576b0876" +checksum = "8b147ee9d1f6d097cef9ce628cd2ee62288d963e16fb287bd9286455b241382d" dependencies = [ "futures-channel", "futures-core", @@ -1814,9 +1889,9 @@ dependencies = [ [[package]] name = "futures-channel" -version = "0.3.31" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2dff15bf788c671c1934e366d07e30c1814a8ef514e1af724a602e8a2fbe1b10" +checksum = "07bbe89c50d7a535e539b8c17bc0b49bdb77747034daa8087407d655f3f7cc1d" dependencies = [ "futures-core", "futures-sink", @@ -1824,15 +1899,15 @@ dependencies = [ [[package]] name = "futures-core" -version = "0.3.31" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "05f29059c0c2090612e8d742178b0580d2dc940c837851ad723096f87af6663e" +checksum = "7e3450815272ef58cec6d564423f6e755e25379b217b0bc688e295ba24df6b1d" [[package]] name = "futures-executor" -version = "0.3.31" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1e28d1d997f585e54aebc3f97d39e72338912123a67330d723fdbb564d646c9f" +checksum = "baf29c38818342a3b26b5b923639e7b1f4a61fc5e76102d4b1981c6dc7a7579d" dependencies = [ "futures-core", "futures-task", @@ -1841,44 +1916,38 @@ dependencies = [ [[package]] name = "futures-io" -version = "0.3.31" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9e5c1b78ca4aae1ac06c48a526a655760685149f0d465d21f37abfe57ce075c6" +checksum = "cecba35d7ad927e23624b22ad55235f2239cfa44fd10428eecbeba6d6a717718" [[package]] name = "futures-macro" -version = "0.3.31" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "162ee34ebcb7c64a8abebc059ce0fee27c2262618d7b60ed8faf72fef13c3650" +checksum = "e835b70203e41293343137df5c0664546da5745f82ec9b84d40be8336958447b" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] name = "futures-sink" -version = "0.3.31" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e575fab7d1e0dcb8d0c7bcf9a63ee213816ab51902e6d244a95819acacf1d4f7" +checksum = "c39754e157331b013978ec91992bde1ac089843443c49cbc7f46150b0fad0893" [[package]] name = "futures-task" -version = "0.3.31" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f90f7dce0722e95104fcb095585910c0977252f286e354b5e3bd38902cd99988" - -[[package]] -name = "futures-timer" -version = "3.0.3" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f288b0a4f20f9a56b5d1da57e2227c661b7b16168e2f72365f57b63326e29b24" +checksum = "037711b3d59c33004d3856fbdc83b99d4ff37a24768fa1be9ce3538a1cde4393" [[package]] name = "futures-util" -version = "0.3.31" +version = "0.3.32" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9fa08315bb612088cc391249efdc3bc77536f16c91f6cf495e6fbe85b20a4a81" +checksum = "389ca41296e6190b48053de0321d02a77f32f8a5d2461dd38762c0593805c6d6" dependencies = [ "futures-channel", "futures-core", @@ -1888,7 +1957,6 @@ dependencies = [ "futures-task", "memchr", "pin-project-lite", - "pin-utils", "slab", ] @@ -1913,9 +1981,9 @@ dependencies = [ [[package]] name = "getrandom" -version = "0.2.16" +version = "0.2.17" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "335ff9f135e4384c8150d6f27c6daed433577f86b4750418338c01a1a2528592" +checksum = "ff2abc00be7fca6ebc474524697ae276ad847ad0a6b3faa4bcb027e9a4614ad0" dependencies = [ "cfg-if", "js-sys", @@ -1933,11 +2001,25 @@ dependencies = [ "cfg-if", "js-sys", "libc", - "r-efi", + "r-efi 5.3.0", "wasip2", "wasm-bindgen", ] +[[package]] +name = "getrandom" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0de51e6874e94e7bf76d726fc5d13ba782deca734ff60d5bb2fb2607c7406555" +dependencies = [ + "cfg-if", + "libc", + "r-efi 6.0.0", + "rand_core 0.10.0", + "wasip2", + "wasip3", +] + [[package]] name = "glob" version = "0.3.3" @@ -1946,9 +2028,9 @@ checksum = "0cc23270f6e1808e30a928bdc84dea0b9b4136a8bc82338574f23baf47bbd280" [[package]] name = "h2" -version = "0.4.12" +version = "0.4.13" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f3c0b69cfcb4e1b9f1bf2f53f95f766e4661169728ec61cd3fe5a0166f2d1386" +checksum = "2f44da3a8150a6703ed5d34e164b875fd14c2cdab9af1252a9a1020bde2bdc54" dependencies = [ "atomic-waker", "bytes", @@ -1980,10 +2062,6 @@ name = "hashbrown" version = "0.14.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1" -dependencies = [ - "ahash", - "allocator-api2", -] [[package]] name = "hashbrown" @@ -2103,14 +2181,13 @@ dependencies = [ [[package]] name = "hyper-util" -version = "0.1.19" +version = "0.1.20" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "727805d60e7938b76b826a6ef209eb70eaa1812794f9424d4a4e2d740662df5f" +checksum = "96547c2556ec9d12fb1578c4eaf448b04993e7fb79cbaad930a656880a6bdfa0" dependencies = [ "base64", "bytes", "futures-channel", - "futures-core", "futures-util", "http", "http-body", @@ -2127,9 +2204,9 @@ dependencies = [ [[package]] name = "iana-time-zone" -version = "0.1.64" +version = "0.1.65" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "33e57f83510bb73707521ebaffa789ec8caf86f9657cad665b092b581d40e9fb" +checksum = "e31bc9ad994ba00e440a8aa5c9ef0ec67d5cb5e5cb0cc7f8b744a35b389cc470" dependencies = [ "android_system_properties", "core-foundation-sys", @@ -2230,6 +2307,12 @@ dependencies = [ "zerovec", ] +[[package]] +name = "id-arena" +version = "2.3.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3d3067d79b975e8844ca9eb072e16b31c3c1c36928edf9c6789548c524d0d954" + [[package]] name = "ident_case" version = "1.0.1" @@ -2259,21 +2342,14 @@ dependencies = [ [[package]] name = "indexmap" -version = "2.12.1" +version = "2.13.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0ad4bb2b565bca0645f4d68c5c9af97fba094e9791da685bf83cb5f3ce74acf2" +checksum = "7714e70437a7dc3ac8eb7e6f8df75fd8eb422675fc7678aff7364301092b1017" dependencies = [ "equivalent", "hashbrown 0.16.1", -] - -[[package]] -name = "indoc" -version = "2.0.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "79cf5c93f93228cf8efb3ba362535fb11199ac548a09ce117c9b1adc3030d706" -dependencies = [ - "rustversion", + "serde", + "serde_core", ] [[package]] @@ -2284,15 +2360,15 @@ checksum = "8bb03732005da905c88227371639bf1ad885cc712789c011c31c5fb3ab3ccf02" [[package]] name = "ipnet" -version = "2.11.0" +version = "2.12.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "469fb0b9cefa57e3ef31275ee7cacb78f2fdca44e4765491884a2b119d4eb130" +checksum = "d98f6fed1fde3f8c21bc40a1abb88dd75e67924f9cffc3ef95607bad8017f8e2" [[package]] name = "iri-string" -version = "0.7.10" +version = "0.7.11" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c91338f0783edbd6195decb37bae672fd3b165faffb89bf7b9e6942f8b1a731a" +checksum = "d8e7418f59cc01c88316161279a7f665217ae316b388e58a0d10e29f54f1e5eb" dependencies = [ "memchr", "serde", @@ -2309,9 +2385,9 @@ dependencies = [ [[package]] name = "itoa" -version = "1.0.17" +version = "1.0.18" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "92ecc6618181def0457392ccd0ee51198e065e016d1d527a7ac1b6dc7c1f09d2" +checksum = "8f42a60cbdf9a97f5d2305f08a87dc4e09308d1276d28c869c684d7777685682" [[package]] name = "jobserver" @@ -2325,14 +2401,20 @@ dependencies = [ [[package]] name = "js-sys" -version = "0.3.83" +version = "0.3.91" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "464a3709c7f55f1f721e5389aa6ea4e3bc6aba669353300af094b29ffbdde1d8" +checksum = "b49715b7073f385ba4bc528e5747d02e66cb39c6146efb66b781f131f0fb399c" dependencies = [ "once_cell", "wasm-bindgen", ] +[[package]] +name = "leb128fmt" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "09edd9e8b54e49e587e4f6295a7d29c3ea94d469cb40ab8ca70b288248a81db2" + [[package]] name = "lexical-core" version = "1.0.6" @@ -2398,9 +2480,9 @@ checksum = "2c4a545a15244c7d945065b5d392b2d2d7f21526fba56ce51467b06ed445e8f7" [[package]] name = "libc" -version = "0.2.179" +version = "0.2.183" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c5a2d376baa530d1238d133232d15e239abad80d05838b4b59354e5268af431f" +checksum = "b5b646652bf6661599e1da8901b3b9522896f01e736bad5f723fe7a3a27f899d" [[package]] name = "libloading" @@ -2413,35 +2495,46 @@ dependencies = [ ] [[package]] -name = "libm" -version = "0.2.15" +name = "liblzma" +version = "0.4.6" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f9fbbcab51052fe104eb5e5d351cf728d30a5be1fe14d9be8a3b097481fb97de" +checksum = "b6033b77c21d1f56deeae8014eb9fbe7bdf1765185a6c508b5ca82eeaed7f899" +dependencies = [ + "liblzma-sys", +] [[package]] -name = "libmimalloc-sys" -version = "0.1.44" +name = "liblzma-sys" +version = "0.4.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "667f4fec20f29dfc6bc7357c582d91796c169ad7e2fce709468aefeb2c099870" +checksum = "9f2db66f3268487b5033077f266da6777d057949b8f93c8ad82e441df25e6186" dependencies = [ "cc", "libc", + "pkg-config", ] [[package]] -name = "libz-rs-sys" -version = "0.5.5" +name = "libm" +version = "0.2.16" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c10501e7805cee23da17c7790e59df2870c0d4043ec6d03f67d31e2b53e77415" +checksum = "b6d2cec3eae94f9f509c767b45932f1ada8350c4bdb85af2fcab4a3c14807981" + +[[package]] +name = "libmimalloc-sys" +version = "0.1.44" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "667f4fec20f29dfc6bc7357c582d91796c169ad7e2fce709468aefeb2c099870" dependencies = [ - "zlib-rs", + "cc", + "libc", ] [[package]] name = "linux-raw-sys" -version = "0.11.0" +version = "0.12.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "df1d3c3b53da64cf5760482273a98e575c651a67eec7f77df96b5b642de8f039" +checksum = "32a66949e030da00e8c7d4434b251670a91556f4144941d37452769c25d58a53" [[package]] name = "litemap" @@ -2472,24 +2565,13 @@ checksum = "112b39cec0b298b6c1999fee3e31427f74f676e4cb9879ed1a121b43661a4154" [[package]] name = "lz4_flex" -version = "0.12.0" +version = "0.13.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ab6473172471198271ff72e9379150e9dfd70d8e533e0752a27e515b48dd375e" +checksum = "db9a0d582c2874f68138a16ce1867e0ffde6c0bb0a0df85e1f36d04146db488a" dependencies = [ "twox-hash", ] -[[package]] -name = "lzma-sys" -version = "0.1.20" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5fda04ab3764e6cde78b9974eec4f779acaba7c4e84b36eca3cf77c581b85d27" -dependencies = [ - "cc", - "libc", - "pkg-config", -] - [[package]] name = "md-5" version = "0.10.6" @@ -2502,18 +2584,9 @@ dependencies = [ [[package]] name = "memchr" -version = "2.7.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f52b00d39961fc5b2736ea853c9cc86238e165017a493d1d5c8eac6bdc4cc273" - -[[package]] -name = "memoffset" -version = "0.9.1" +version = "2.8.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "488016bfae457b036d996092f6cb448677611ce4449e970ceaf42695203f218a" -dependencies = [ - "autocfg", -] +checksum = "f8ca58f447f06ed17d5fc4043ce1b10dd205e060fb3ce5b979b8ed8e59ff3f79" [[package]] name = "mimalloc" @@ -2536,9 +2609,9 @@ dependencies = [ [[package]] name = "mio" -version = "1.1.1" +version = "1.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a69bcab0ad47271a0234d9422b131806bf3968021e5dc9328caf2d4cd58557fc" +checksum = "50b7e5b27aa02a74bac8c3f23f448f8d87ff11f92d3aac1a6ed369ee08cc56c1" dependencies = [ "libc", "wasi", @@ -2592,25 +2665,27 @@ dependencies = [ [[package]] name = "object" -version = "0.32.2" +version = "0.37.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a6a622008b6e321afc04970976f62ee297fdbaa6f95318ca343e3eebb9648441" +checksum = "ff76201f031d8863c38aa7f905eca4f53abbfa15f609db4277d44cd8938f33fe" dependencies = [ "memchr", ] [[package]] name = "object_store" -version = "0.12.4" +version = "0.13.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4c1be0c6c22ec0817cdc77d3842f721a17fd30ab6965001415b5402a74e6b740" +checksum = "622acbc9100d3c10e2ee15804b0caa40e55c933d5aa53814cd520805b7958a49" dependencies = [ "async-trait", "base64", "bytes", "chrono", "form_urlencoded", - "futures", + "futures-channel", + "futures-core", + "futures-util", "http", "http-body-util", "httparse", @@ -2621,10 +2696,10 @@ dependencies = [ "parking_lot", "percent-encoding", "quick-xml", - "rand", + "rand 0.10.0", "reqwest", "ring", - "rustls-pemfile", + "rustls-pki-types", "serde", "serde_json", "serde_urlencoded", @@ -2639,15 +2714,15 @@ dependencies = [ [[package]] name = "once_cell" -version = "1.21.3" +version = "1.21.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "42f5e15c9953c5e4ccceeb2e7382a716482c34515315f7b03532b8b4e8393d2d" +checksum = "9f7c3e4beb33f85d45ae3e3a1792185706c8e16d043238c593331cc7cd313b50" [[package]] name = "openssl-probe" -version = "0.2.0" +version = "0.2.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9f50d9b3dabb09ecd771ad0aa242ca6894994c130308ca3d7684634df8037391" +checksum = "7c87def4c32ab89d880effc9e097653c8da5d6ef28e6b539d313baaacfbafcbe" [[package]] name = "ordered-float" @@ -2683,14 +2758,13 @@ dependencies = [ [[package]] name = "parquet" -version = "57.1.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "be3e4f6d320dd92bfa7d612e265d7d08bba0a240bab86af3425e1d255a511d89" +checksum = "7d3f9f2205199603564127932b89695f52b62322f541d0fc7179d57c2e1c9877" dependencies = [ "ahash", "arrow-array", "arrow-buffer", - "arrow-cast", "arrow-data", "arrow-ipc", "arrow-schema", @@ -2767,16 +2841,6 @@ version = "2.3.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9b4f627cb1b25917193a259e49bdad08f671f8d9708acfd5fe0a8c1455d87220" -[[package]] -name = "petgraph" -version = "0.7.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3672b37090dbd86368a4145bc067582552b29c27377cad4e0a306c97f9bd7772" -dependencies = [ - "fixedbitset", - "indexmap", -] - [[package]] name = "petgraph" version = "0.8.3" @@ -2809,9 +2873,9 @@ dependencies = [ [[package]] name = "pin-project-lite" -version = "0.2.16" +version = "0.2.17" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3b3cff922bd51709b605d9ead9aa71031d81447142d828eb4a6eba76fe619f9b" +checksum = "a89322df9ebe1c1578d689c92318e070967d1042b512afbe49518723f4e6d5cd" [[package]] name = "pin-utils" @@ -2827,9 +2891,9 @@ checksum = "7edddbd0b52d732b21ad9a5fab5c704c14cd949e5e9a1ec5929a24fded1b904c" [[package]] name = "portable-atomic" -version = "1.13.0" +version = "1.13.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f89776e4d69bb58bc6993e99ffa1d11f228b839984854c7daeb5d37f87cbe950" +checksum = "c33a9471896f1c69cecef8d20cbe2f7accd12527ce60845ff44c153bb2a21b49" [[package]] name = "potential_utf" @@ -2856,32 +2920,23 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "479ca8adacdd7ce8f1fb39ce9ecccbfe93a3f1344b3d0d97f20bc0196208f62b" dependencies = [ "proc-macro2", - "syn 2.0.113", -] - -[[package]] -name = "proc-macro-crate" -version = "3.4.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "219cb19e96be00ab2e37d6e299658a0cfa83e52429179969b0f0121b4ac46983" -dependencies = [ - "toml_edit", + "syn 2.0.117", ] [[package]] name = "proc-macro2" -version = "1.0.104" +version = "1.0.106" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9695f8df41bb4f3d222c95a67532365f569318332d03d5f3f67f37b20e6ebdf0" +checksum = "8fd00f0bb2e90d81d1044c2b32617f68fcb9fa3bb7640c23e9c748e53fb30934" dependencies = [ "unicode-ident", ] [[package]] name = "prost" -version = "0.14.1" +version = "0.14.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7231bd9b3d3d33c86b58adbac74b5ec0ad9f496b19d22801d773636feaa95f3d" +checksum = "d2ea70524a2f82d518bce41317d0fae74151505651af45faf1ffbd6fd33f0568" dependencies = [ "bytes", "prost-derive", @@ -2889,42 +2944,41 @@ dependencies = [ [[package]] name = "prost-build" -version = "0.14.1" +version = "0.14.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ac6c3320f9abac597dcbc668774ef006702672474aad53c6d596b62e487b40b1" +checksum = "343d3bd7056eda839b03204e68deff7d1b13aba7af2b2fd16890697274262ee7" dependencies = [ "heck", "itertools", "log", "multimap", - "once_cell", - "petgraph 0.7.1", + "petgraph", "prettyplease", "prost", "prost-types", "regex", - "syn 2.0.113", + "syn 2.0.117", "tempfile", ] [[package]] name = "prost-derive" -version = "0.14.1" +version = "0.14.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9120690fafc389a67ba3803df527d0ec9cbbc9cc45e4cc20b332996dfb672425" +checksum = "27c6023962132f4b30eb4c172c91ce92d933da334c59c23cddee82358ddafb0b" dependencies = [ "anyhow", "itertools", "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] name = "prost-types" -version = "0.14.1" +version = "0.14.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b9b4db3d6da204ed77bb26ba83b6122a73aeb2e87e25fbf7ad2e84c4ccbf8f72" +checksum = "8991c4cbdb8bc5b11f0b074ffe286c30e523de90fee5ba8132f1399f23cb3dd7" dependencies = [ "prost", ] @@ -2940,9 +2994,9 @@ dependencies = [ [[package]] name = "psm" -version = "0.1.28" +version = "0.1.30" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d11f2fedc3b7dafdc2851bc52f277377c5473d378859be234bc7ebb593144d01" +checksum = "3852766467df634d74f0b2d7819bf8dc483a0eb2e3b0f50f756f9cfe8b0d18d8" dependencies = [ "ar_archive_writer", "cc", @@ -2950,28 +3004,26 @@ dependencies = [ [[package]] name = "pyo3" -version = "0.26.0" +version = "0.28.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7ba0117f4212101ee6544044dae45abe1083d30ce7b29c4b5cbdfa2354e07383" +checksum = "cf85e27e86080aafd5a22eae58a162e133a589551542b3e5cee4beb27e54f8e1" dependencies = [ - "indoc", "libc", - "memoffset", "once_cell", "portable-atomic", "pyo3-build-config", "pyo3-ffi", "pyo3-macros", - "unindent", ] [[package]] name = "pyo3-async-runtimes" -version = "0.26.0" +version = "0.28.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e6ee6d4cb3e8d5b925f5cdb38da183e0ff18122eb2048d4041c9e7034d026e23" +checksum = "9e7364a95bf00e8377bbf9b0f09d7ff9715a29d8fcf93b47d1a967363b973178" dependencies = [ - "futures", + "futures-channel", + "futures-util", "once_cell", "pin-project-lite", "pyo3", @@ -2980,18 +3032,18 @@ dependencies = [ [[package]] name = "pyo3-build-config" -version = "0.26.0" +version = "0.28.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4fc6ddaf24947d12a9aa31ac65431fb1b851b8f4365426e182901eabfb87df5f" +checksum = "8bf94ee265674bf76c09fa430b0e99c26e319c945d96ca0d5a8215f31bf81cf7" dependencies = [ "target-lexicon", ] [[package]] name = "pyo3-ffi" -version = "0.26.0" +version = "0.28.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "025474d3928738efb38ac36d4744a74a400c901c7596199e20e45d98eb194105" +checksum = "491aa5fc66d8059dd44a75f4580a2962c1862a1c2945359db36f6c2818b748dc" dependencies = [ "libc", "pyo3-build-config", @@ -2999,9 +3051,9 @@ dependencies = [ [[package]] name = "pyo3-log" -version = "0.13.2" +version = "0.13.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2f8bae9ad5ba08b0b0ed2bb9c2bdbaeccc69cafca96d78cf0fbcea0d45d122bb" +checksum = "26c2ec80932c5c3b2d4fbc578c9b56b2d4502098587edb8bef5b6bfcad43682e" dependencies = [ "arc-swap", "log", @@ -3010,27 +3062,27 @@ dependencies = [ [[package]] name = "pyo3-macros" -version = "0.26.0" +version = "0.28.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2e64eb489f22fe1c95911b77c44cc41e7c19f3082fc81cce90f657cdc42ffded" +checksum = "f5d671734e9d7a43449f8480f8b38115df67bef8d21f76837fa75ee7aaa5e52e" dependencies = [ "proc-macro2", "pyo3-macros-backend", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] name = "pyo3-macros-backend" -version = "0.26.0" +version = "0.28.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "100246c0ecf400b475341b8455a9213344569af29a3c841d29270e53102e0fcf" +checksum = "22faaa1ce6c430a1f71658760497291065e6450d7b5dc2bcf254d49f66ee700a" dependencies = [ "heck", "proc-macro2", "pyo3-build-config", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -3041,9 +3093,9 @@ checksum = "5a651516ddc9168ebd67b24afd085a718be02f8858fe406591b013d101ce2f40" [[package]] name = "quick-xml" -version = "0.38.4" +version = "0.39.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b66c2058c55a409d601666cffe35f04333cf1013010882cec174a7467cd4e21c" +checksum = "958f21e8e7ceb5a1aa7fa87fab28e7c75976e0bfe7e23ff069e0a260f894067d" dependencies = [ "memchr", "serde", @@ -3071,14 +3123,14 @@ dependencies = [ [[package]] name = "quinn-proto" -version = "0.11.13" +version = "0.11.14" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f1906b49b0c3bc04b5fe5d86a77925ae6524a19b816ae38ce1e426255f1d8a31" +checksum = "434b42fec591c96ef50e21e886936e66d3cc3f737104fdb9b737c40ffb94c098" dependencies = [ "bytes", "getrandom 0.3.4", "lru-slab", - "rand", + "rand 0.9.2", "ring", "rustc-hash", "rustls", @@ -3106,9 +3158,9 @@ dependencies = [ [[package]] name = "quote" -version = "1.0.42" +version = "1.0.45" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a338cc41d27e6cc6dce6cefc13a0729dfbb81c262b1f519331575dd80ef3067f" +checksum = "41f2619966050689382d2b44f664f4bc593e129785a36d6ee376ddf37259b924" dependencies = [ "proc-macro2", ] @@ -3119,6 +3171,12 @@ version = "5.3.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "69cdb34c158ceb288df11e18b4bd39de994f6657d83847bdffdbd7f346754b0f" +[[package]] +name = "r-efi" +version = "6.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f8dcc9c7d52a811697d2151c701e0d08956f92b0e24136cf4cf27b57a6a0d9bf" + [[package]] name = "rand" version = "0.9.2" @@ -3126,7 +3184,18 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "6db2770f06117d490610c7488547d543617b21bfa07796d7a12f6f1bd53850d1" dependencies = [ "rand_chacha", - "rand_core", + "rand_core 0.9.5", +] + +[[package]] +name = "rand" +version = "0.10.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bc266eb313df6c5c09c1c7b1fbe2510961e5bcd3add930c1e31f7ed9da0feff8" +dependencies = [ + "chacha20", + "getrandom 0.4.2", + "rand_core 0.10.0", ] [[package]] @@ -3136,18 +3205,24 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d3022b5f1df60f26e1ffddd6c66e8aa15de382ae63b3a0c1bfc0e4d3e3f325cb" dependencies = [ "ppv-lite86", - "rand_core", + "rand_core 0.9.5", ] [[package]] name = "rand_core" -version = "0.9.3" +version = "0.9.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "99d9a13982dcf210057a8a78572b2217b667c3beacbf3a0d8b454f6f82837d38" +checksum = "76afc826de14238e6e8c374ddcc1fa19e374fd8dd986b0d2af0d02377261d83c" dependencies = [ "getrandom 0.3.4", ] +[[package]] +name = "rand_core" +version = "0.10.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0c8d0fd677905edcbeedbf2edb6494d676f0e98d54d5cf9bda0b061cb8fb8aba" + [[package]] name = "recursive" version = "0.1.1" @@ -3165,7 +3240,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "76009fbe0614077fc1a2ce255e3a1881a2e3a3527097d5dc6d8212c585e7e38b" dependencies = [ "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -3179,9 +3254,9 @@ dependencies = [ [[package]] name = "regex" -version = "1.12.2" +version = "1.12.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "843bc0191f75f3e22651ae5f1e72939ab2f72a4bc30fa80a066bd66edefc24d4" +checksum = "e10754a14b9137dd7b1e3e5b0493cc9171fdd105e0ab477f51b72e7f3ac0e276" dependencies = [ "aho-corasick", "memchr", @@ -3191,9 +3266,9 @@ dependencies = [ [[package]] name = "regex-automata" -version = "0.4.13" +version = "0.4.14" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5276caf25ac86c8d810222b3dbb938e512c55c6831a10f3e6ed1c93b84041f1c" +checksum = "6e1dd4122fc1595e8162618945476892eefca7b88c52820e74af6262213cae8f" dependencies = [ "aho-corasick", "memchr", @@ -3202,15 +3277,15 @@ dependencies = [ [[package]] name = "regex-lite" -version = "0.1.8" +version = "0.1.9" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8d942b98df5e658f56f20d592c7f868833fe38115e65c33003d8cd224b0155da" +checksum = "cab834c73d247e67f4fae452806d17d3c7501756d98c8808d7c9c7aa7d18f973" [[package]] name = "regex-syntax" -version = "0.8.8" +version = "0.8.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7a2d987857b319362043e95f5353c0535c1f58eec5336fdfcf626430af7def58" +checksum = "dc897dd8d9e8bd1ed8cdad82b5966c3e0ecae09fb1907d58efaa013543185d0a" [[package]] name = "regress" @@ -3222,12 +3297,6 @@ dependencies = [ "memchr", ] -[[package]] -name = "relative-path" -version = "1.9.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ba39f3699c378cd8970968dcbff9c43159ea4cfbd88d43c00b22f2ef10a435d2" - [[package]] name = "repr_offset" version = "0.2.2" @@ -3287,41 +3356,12 @@ checksum = "a4689e6c2294d81e88dc6261c768b63bc4fcdb852be6d1352498b114f61383b7" dependencies = [ "cc", "cfg-if", - "getrandom 0.2.16", + "getrandom 0.2.17", "libc", "untrusted", "windows-sys 0.52.0", ] -[[package]] -name = "rstest" -version = "0.26.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f5a3193c063baaa2a95a33f03035c8a72b83d97a54916055ba22d35ed3839d49" -dependencies = [ - "futures-timer", - "futures-util", - "rstest_macros", -] - -[[package]] -name = "rstest_macros" -version = "0.26.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9c845311f0ff7951c5506121a9ad75aec44d083c31583b2ea5a30bcb0b0abba0" -dependencies = [ - "cfg-if", - "glob", - "proc-macro-crate", - "proc-macro2", - "quote", - "regex", - "relative-path", - "rustc_version", - "syn 2.0.113", - "unicode-ident", -] - [[package]] name = "rustc-hash" version = "2.1.1" @@ -3339,9 +3379,9 @@ dependencies = [ [[package]] name = "rustix" -version = "1.1.3" +version = "1.1.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "146c9e247ccc180c1f61615433868c99f3de3ae256a30a43b49f67c2d9171f34" +checksum = "b6fe4565b9518b83ef4f91bb47ce29620ca828bd32cb7e408f0062e9930ba190" dependencies = [ "bitflags", "errno", @@ -3352,9 +3392,9 @@ dependencies = [ [[package]] name = "rustls" -version = "0.23.35" +version = "0.23.37" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "533f54bc6a7d4f647e46ad909549eda97bf5afc1585190ef692b4286b198bd8f" +checksum = "758025cb5fccfd3bc2fd74708fd4682be41d99e5dff73c377c0646c6012c73a4" dependencies = [ "once_cell", "ring", @@ -3376,20 +3416,11 @@ dependencies = [ "security-framework", ] -[[package]] -name = "rustls-pemfile" -version = "2.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dce314e5fee3f39953d46bb63bb8a46d40c2f8fb7cc5a3b6cab2bde9721d6e50" -dependencies = [ - "rustls-pki-types", -] - [[package]] name = "rustls-pki-types" -version = "1.13.2" +version = "1.14.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "21e6f2ab2928ca4291b86736a8bd920a277a399bba1589409d72154ff87c1282" +checksum = "be040f8b0a225e40375822a563fa9524378b9d63112f53e19ffff34df5d33fdd" dependencies = [ "web-time", "zeroize", @@ -3397,9 +3428,9 @@ dependencies = [ [[package]] name = "rustls-webpki" -version = "0.103.8" +version = "0.103.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2ffdfa2f5286e2247234e03f680868ac2815974dc39e00ea15adc445d0aafe52" +checksum = "df33b2b81ac578cabaf06b89b0631153a3f416b0a886e8a7a1707fb51abbd1ef" dependencies = [ "ring", "rustls-pki-types", @@ -3414,9 +3445,9 @@ checksum = "b39cdef0fa800fc44525c84ccb54a029961a8215f9619753635a9c0d2538d46d" [[package]] name = "ryu" -version = "1.0.22" +version = "1.0.23" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a50f4cf475b65d88e057964e0e9bb1f0aa9bbb2036dc65c64596b42932536984" +checksum = "9774ba4a74de5f7b1c1451ed6cd5285a32eddb5cccb8cc655a4e50009e06477f" [[package]] name = "same-file" @@ -3429,9 +3460,9 @@ dependencies = [ [[package]] name = "schannel" -version = "0.1.28" +version = "0.1.29" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "891d81b926048e76efe18581bf793546b4c0eaf8448d72be8de2bbee5fd166e1" +checksum = "91c1b7e4904c873ef0710c1f407dde2e6287de2bebc1bbbf7d430bb7cbffd939" dependencies = [ "windows-sys 0.61.2", ] @@ -3457,7 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"windows-sys 0.61.2", ] [[package]] name = "sqlparser" -version = "0.59.0" +version = "0.61.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4591acadbcf52f0af60eafbb2c003232b2b4cd8de5f0e9437cb8b1b59046cc0f" +checksum = "dbf5ea8d4d7c808e1af1cbabebca9a2abe603bcefc22294c5b95018d53200cb7" dependencies = [ "log", "recursive", @@ -3682,13 +3713,13 @@ dependencies = [ [[package]] name = "sqlparser_derive" -version = "0.3.0" +version = "0.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "da5fc6819faabb412da764b99d3b713bb55083c11e7e0c00144d386cd6a1939c" +checksum = "a6dd45d8fc1c79299bfbb7190e42ccbbdf6a5f52e4a6ad98d92357ea965bd289" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -3699,9 +3730,9 @@ checksum = "6ce2be8dc25455e1f91df71bfa12ad37d7af1092ae736f3a6cd0e37bc7810596" [[package]] name = "stacker" -version = "0.1.22" +version = "0.1.23" source = 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"proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] name = "target-lexicon" -version = "0.13.4" +version = "0.13.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b1dd07eb858a2067e2f3c7155d54e929265c264e6f37efe3ee7a8d1b5a1dd0ba" +checksum = "adb6935a6f5c20170eeceb1a3835a49e12e19d792f6dd344ccc76a985ca5a6ca" [[package]] name = "tempfile" -version = "3.24.0" +version = "3.27.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "655da9c7eb6305c55742045d5a8d2037996d61d8de95806335c7c86ce0f82e9c" +checksum = "32497e9a4c7b38532efcdebeef879707aa9f794296a4f0244f6f69e9bc8574bd" dependencies = [ "fastrand", - "getrandom 0.3.4", + "getrandom 0.4.2", "once_cell", "rustix", "windows-sys 0.61.2", @@ -3829,22 +3860,22 @@ dependencies = [ [[package]] name = "thiserror" -version = "2.0.17" +version = "2.0.18" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = 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+4148,21 @@ dependencies = [ "serde", "serde_json", "serde_tokenstream", - "syn 2.0.113", + "syn 2.0.117", "typify-impl", ] [[package]] name = "unicode-ident" -version = "1.0.22" +version = "1.0.24" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9312f7c4f6ff9069b165498234ce8be658059c6728633667c526e27dc2cf1df5" +checksum = "e6e4313cd5fcd3dad5cafa179702e2b244f760991f45397d14d4ebf38247da75" [[package]] name = "unicode-segmentation" -version = "1.12.0" +version = "1.13.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f6ccf251212114b54433ec949fd6a7841275f9ada20dddd2f29e9ceea4501493" +checksum = "9629274872b2bfaf8d66f5f15725007f635594914870f65218920345aa11aa8c" [[package]] name = "unicode-width" @@ -4158,10 +4171,10 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b4ac048d71ede7ee76d585517add45da530660ef4390e49b098733c6e897f254" [[package]] -name = "unindent" -version = "0.2.4" +name = "unicode-xid" 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-checksum = "9b32828d774c412041098d182a8b38b16ea816958e07cf40eec2bc080ae137ac" +checksum = "854ba17bb104abfb26ba36da9729addc7ce7f06f5c0f90f3c391f8461cca21f9" dependencies = [ "js-sys", "wasm-bindgen", @@ -4388,7 +4445,7 @@ checksum = "053e2e040ab57b9dc951b72c264860db7eb3b0200ba345b4e4c3b14f67855ddf" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -4399,7 +4456,7 @@ checksum = "3f316c4a2570ba26bbec722032c4099d8c8bc095efccdc15688708623367e358" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -4592,35 +4649,99 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d6bbff5f0aada427a1e5a6da5f1f98158182f26556f345ac9e04d36d0ebed650" [[package]] -name = "winnow" -version = "0.7.14" +name = "wit-bindgen" +version = "0.51.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5a5364e9d77fcdeeaa6062ced926ee3381faa2ee02d3eb83a5c27a8825540829" +checksum = "d7249219f66ced02969388cf2bb044a09756a083d0fab1e566056b04d9fbcaa5" dependencies = [ - "memchr", + "wit-bindgen-rust-macro", ] [[package]] -name = "wit-bindgen" -version = "0.46.0" +name = "wit-bindgen-core" +version = "0.51.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f17a85883d4e6d00e8a97c586de764dabcc06133f7f1d55dce5cdc070ad7fe59" +checksum = "ea61de684c3ea68cb082b7a88508a8b27fcc8b797d738bfc99a82facf1d752dc" +dependencies = [ + "anyhow", + "heck", + "wit-parser", +] [[package]] -name = "writeable" -version = "0.6.2" +name = "wit-bindgen-rust" +version = "0.51.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9edde0db4769d2dc68579893f2306b26c6ecfbe0ef499b013d731b7b9247e0b9" +checksum = "b7c566e0f4b284dd6561c786d9cb0142da491f46a9fbed79ea69cdad5db17f21" +dependencies = [ + "anyhow", + "heck", + "indexmap", + "prettyplease", + "syn 2.0.117", + "wasm-metadata", + "wit-bindgen-core", + "wit-component", +] [[package]] -name = "xz2" -version = "0.1.7" +name = "wit-bindgen-rust-macro" +version = "0.51.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "388c44dc09d76f1536602ead6d325eb532f5c122f17782bd57fb47baeeb767e2" +checksum = "0c0f9bfd77e6a48eccf51359e3ae77140a7f50b1e2ebfe62422d8afdaffab17a" dependencies = [ - "lzma-sys", + "anyhow", + "prettyplease", + "proc-macro2", + "quote", + "syn 2.0.117", + "wit-bindgen-core", + "wit-bindgen-rust", ] +[[package]] +name = "wit-component" +version = "0.244.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9d66ea20e9553b30172b5e831994e35fbde2d165325bec84fc43dbf6f4eb9cb2" +dependencies = [ + "anyhow", + "bitflags", + "indexmap", + "log", + "serde", + "serde_derive", + "serde_json", + "wasm-encoder", + "wasm-metadata", + "wasmparser", + "wit-parser", +] + +[[package]] +name = "wit-parser" +version = "0.244.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ecc8ac4bc1dc3381b7f59c34f00b67e18f910c2c0f50015669dde7def656a736" +dependencies = [ + "anyhow", + "id-arena", + "indexmap", + "log", + "semver", + "serde", + "serde_derive", + "serde_json", + "unicode-xid", + "wasmparser", +] + +[[package]] +name = "writeable" +version = "0.6.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9edde0db4769d2dc68579893f2306b26c6ecfbe0ef499b013d731b7b9247e0b9" + [[package]] name = "yoke" version = "0.8.1" @@ -4640,28 +4761,28 @@ checksum = "b659052874eb698efe5b9e8cf382204678a0086ebf46982b79d6ca3182927e5d" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", "synstructure", ] [[package]] name = "zerocopy" -version = "0.8.31" +version = "0.8.47" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fd74ec98b9250adb3ca554bdde269adf631549f51d8a8f8f0a10b50f1cb298c3" +checksum = "efbb2a062be311f2ba113ce66f697a4dc589f85e78a4aea276200804cea0ed87" dependencies = [ "zerocopy-derive", ] [[package]] name = "zerocopy-derive" -version = "0.8.31" +version = "0.8.47" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d8a8d209fdf45cf5138cbb5a506f6b52522a25afccc534d1475dad8e31105c6a" +checksum = "0e8bc7269b54418e7aeeef514aa68f8690b8c0489a06b0136e5f57c4c5ccab89" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] @@ -4681,7 +4802,7 @@ checksum = "d71e5d6e06ab090c67b5e44993ec16b72dcbaabc526db883a360057678b48502" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", "synstructure", ] @@ -4721,20 +4842,20 @@ checksum = "eadce39539ca5cb3985590102671f2567e659fca9666581ad3411d59207951f3" dependencies = [ "proc-macro2", "quote", - "syn 2.0.113", + "syn 2.0.117", ] [[package]] name = "zlib-rs" -version = "0.5.5" +version = "0.6.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "40990edd51aae2c2b6907af74ffb635029d5788228222c4bb811e9351c0caad3" +checksum = "3be3d40e40a133f9c916ee3f9f4fa2d9d63435b5fbe1bfc6d9dae0aa0ada1513" [[package]] name = "zmij" -version = "1.0.10" +version = "1.0.21" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "30e0d8dffbae3d840f64bda38e28391faef673a7b5a6017840f2a106c8145868" +checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" [[package]] name = "zstd" diff --git a/Cargo.toml b/Cargo.toml index 364713964..d0e87a9a4 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -15,78 +15,59 @@ # specific language governing permissions and limitations # under the License. -[package] -name = "datafusion-python" -version = "51.0.0" +[workspace.package] +version = "53.0.0" homepage = "https://datafusion.apache.org/python" repository = "https://github.com/apache/datafusion-python" authors = ["Apache DataFusion "] description = "Apache DataFusion DataFrame and SQL Query Engine" readme = "README.md" license = "Apache-2.0" -edition = "2021" -rust-version = "1.78" -include = [ - "/src", - "/datafusion", - "/LICENSE.txt", - "build.rs", - "pyproject.toml", - "Cargo.toml", - "Cargo.lock", -] +edition = "2024" +rust-version = "1.88" -[features] -default = ["mimalloc"] -protoc = ["datafusion-substrait/protoc"] -substrait = ["dep:datafusion-substrait"] +[workspace] +members = ["crates/core", "crates/util", "examples/datafusion-ffi-example"] +resolver = "3" -[dependencies] -tokio = { version = "1.47", features = [ - "macros", - "rt", - "rt-multi-thread", - "sync", -] } -pyo3 = { version = "0.26", features = [ - "extension-module", - "abi3", - "abi3-py310", -] } -pyo3-async-runtimes = { version = "0.26", features = ["tokio-runtime"] } -pyo3-log = "0.13.2" -arrow = { version = "57", features = ["pyarrow"] } -arrow-select = { version = "57" } -datafusion = { version = "51", features = ["avro", "unicode_expressions"] } -datafusion-substrait = { version = "51", optional = true } -datafusion-proto = { version = "51" } -datafusion-ffi = { version = "51" } -prost = "0.14.1" # keep in line with `datafusion-substrait` -uuid = { version = "1.18", features = ["v4"] } -mimalloc = { version = "0.1", optional = true, default-features = false, features = [ - "local_dynamic_tls", -] } +[workspace.dependencies] +tokio = { version = "1.50" } +pyo3 = { version = "0.28" } +pyo3-async-runtimes = { version = "0.28" } +pyo3-log = "0.13.3" +chrono = { version = "0.4", default-features = false } +arrow = { version = "58" } +arrow-array = { version = "58" } +arrow-schema = { version = "58" } +arrow-select = { version = "58" } +datafusion = { version = "53" } +datafusion-substrait = { version = "53" } +datafusion-proto = { version = "53" } +datafusion-ffi = { version = "53" } +datafusion-catalog = { version = "53", default-features = false } +datafusion-common = { version = "53", default-features = false } +datafusion-functions-aggregate = { version = "53" } +datafusion-functions-window = { version = "53" } +datafusion-expr = { version = "53" } +prost = "0.14.3" +serde_json = "1" +uuid = { version = "1.23" } +mimalloc = { version = "0.1", default-features = false } async-trait = "0.1.89" futures = "0.3" cstr = "0.2" -object_store = { version = "0.12.4", features = [ - "aws", - "gcp", - "azure", - "http", -] } +object_store = { version = "0.13.1" } url = "2" -log = "0.4.27" +log = "0.4.29" parking_lot = "0.12" - -[build-dependencies] -prost-types = "0.14.1" # keep in line with `datafusion-substrait` -pyo3-build-config = "0.26" - -[lib] -name = "datafusion_python" -crate-type = ["cdylib", "rlib"] +prost-types = "0.14.3" # keep in line with `datafusion-substrait` +pyo3-build-config = "0.28" +datafusion-python-util = { path = "crates/util", version = "53.0.0" } [profile.release] -lto = true -codegen-units = 1 +lto = "thin" +codegen-units = 2 + +# We cannot publish to crates.io with any patches in the below section. Developers +# must remove any entries in this section before creating a release candidate. +[patch.crates-io] diff --git a/README.md b/README.md index 0cdf17ab8..4baed7d1d 100644 --- a/README.md +++ b/README.md @@ -215,6 +215,22 @@ You can verify the installation by running: '0.6.0' ``` +## Using DataFusion with AI coding assistants + +This project ships a [`SKILL.md`](SKILL.md) at the repo root that teaches AI +coding assistants how to write idiomatic DataFusion Python. It follows the +[Agent Skills](https://agentskills.io) open standard. + +**Preferred:** `npx skills add apache/datafusion-python` — installs the skill in +Claude Code, Cursor, Windsurf, Cline, Codex, Copilot, Gemini CLI, and other +supported agents. + +**Manual:** paste this line into your project's `AGENTS.md` / `CLAUDE.md`: + +``` +For DataFusion Python code, see https://github.com/apache/datafusion-python/blob/main/SKILL.md +``` + ## How to develop This assumes that you have rust and cargo installed. We use the workflow recommended by [pyo3](https://github.com/PyO3/pyo3) and [maturin](https://github.com/PyO3/maturin). The Maturin tools used in this workflow can be installed either via `uv` or `pip`. Both approaches should offer the same experience. It is recommended to use `uv` since it has significant performance improvements @@ -275,7 +291,16 @@ needing to activate the virtual environment: ```bash uv run --no-project maturin develop --uv -uv --no-project pytest . +uv run --no-project pytest +``` + +To run the FFI tests within the examples folder, after you have built +`datafusion-python` with the previous commands: + +```bash +cd examples/datafusion-ffi-example +uv run --no-project maturin develop --uv +uv run --no-project pytest python/tests/_test_*py ``` ### Running & Installing pre-commit hooks @@ -303,6 +328,33 @@ There are scripts in `ci/scripts` for running Rust and Python linters. ./ci/scripts/rust_toml_fmt.sh ``` +## Checking Upstream DataFusion Coverage + +This project includes an [AI agent skill](.ai/skills/check-upstream/SKILL.md) for auditing which +features from the upstream Apache DataFusion Rust library are not yet exposed in these Python +bindings. This is useful when adding missing functions, auditing API coverage, or ensuring parity +with upstream. + +The skill accepts an optional area argument: + +``` +scalar functions +aggregate functions +window functions +dataframe +session context +ffi types +all +``` + +If no argument is provided, it defaults to checking all areas. The skill will fetch the upstream +DataFusion documentation, compare it against the functions and methods exposed in this project, and +produce a coverage report listing what is currently exposed and what is missing. + +The skill definition lives in `.ai/skills/check-upstream/SKILL.md` and follows the +[Agent Skills](https://agentskills.io) open standard. It can be used by any AI coding agent that +supports skill discovery, or followed manually. + ## How to update dependencies To change test dependencies, change the `pyproject.toml` and run diff --git a/SKILL.md b/SKILL.md new file mode 100644 index 000000000..7b07b430f --- /dev/null +++ b/SKILL.md @@ -0,0 +1,722 @@ +--- +name: datafusion-python +description: Use when the user is writing datafusion-python (Apache DataFusion Python bindings) DataFrame or SQL code. Covers imports, data loading, DataFrame operations, expression building, SQL-to-DataFrame mappings, idiomatic patterns, and common pitfalls. +--- + +# DataFusion Python DataFrame API Guide + +## What Is DataFusion? + +DataFusion is an **in-process query engine** built on Apache Arrow. It is not a +database -- there is no server, no connection string, and no external +dependencies. You create a `SessionContext`, point it at data (Parquet, CSV, +JSON, Arrow IPC, Pandas, Polars, or raw Python dicts/lists), and run queries +using either SQL or the DataFrame API described below. + +All data flows through **Apache Arrow**. The canonical Python implementation is +PyArrow (`pyarrow.RecordBatch` / `pyarrow.Table`), but any library that +conforms to the [Arrow C Data Interface](https://arrow.apache.org/docs/format/CDataInterface.html) +can interoperate with DataFusion. + +## Core Abstractions + +| Abstraction | Role | Key import | +|---|---|---| +| `SessionContext` | Entry point. Loads data, runs SQL, produces DataFrames. | `from datafusion import SessionContext` | +| `DataFrame` | Lazy query builder. Each method returns a new DataFrame. | Returned by context methods | +| `Expr` | Expression tree node (column ref, literal, function call, ...). | `from datafusion import col, lit` | +| `functions` | 290+ built-in scalar, aggregate, and window functions. | `from datafusion import functions as F` | + +## Import Conventions + +```python +from datafusion import SessionContext, col, lit +from datafusion import functions as F +``` + +## Data Loading + +```python +ctx = SessionContext() + +# From files +df = ctx.read_parquet("path/to/data.parquet") +df = ctx.read_csv("path/to/data.csv") +df = ctx.read_json("path/to/data.json") + +# From Python objects +df = ctx.from_pydict({"a": [1, 2, 3], "b": ["x", "y", "z"]}) +df = ctx.from_pylist([{"a": 1, "b": "x"}, {"a": 2, "b": "y"}]) +df = ctx.from_pandas(pandas_df) +df = ctx.from_polars(polars_df) +df = ctx.from_arrow(arrow_table) + +# From SQL +df = ctx.sql("SELECT a, b FROM my_table WHERE a > 1") +``` + +To make a DataFrame queryable by name in SQL, register it first: + +```python +ctx.register_parquet("my_table", "path/to/data.parquet") +ctx.register_csv("my_table", "path/to/data.csv") +``` + +## DataFrame Operations Quick Reference + +Every method returns a **new** DataFrame (immutable/lazy). Chain them fluently. + +### Projection + +```python +df.select("a", "b") # preferred: plain names as strings +df.select(col("a"), (col("b") + 1).alias("b_plus_1")) # use col()/Expr only when you need an expression + +df.with_column("new_col", col("a") + lit(10)) # add one column +df.with_columns( + col("a").alias("x"), + y=col("b") + lit(1), # named keyword form +) + +df.drop("unwanted_col") +df.with_column_renamed("old_name", "new_name") +``` + +When a column is referenced by name alone, pass the name as a string rather +than wrapping it in `col()`. Reach for `col()` only when the projection needs +arithmetic, aliasing, casting, or another expression operation. + +**Case sensitivity**: both `select("Name")` and `col("Name")` lowercase the +identifier. For a column whose real name has uppercase letters, embed double +quotes inside the string: `select('"MyCol"')` or `col('"MyCol"')`. Without the +inner quotes the lookup will fail with `No field named mycol`. + +### Filtering + +```python +df.filter(col("a") > 10) +df.filter(col("a") > 10, col("b") == "x") # multiple = AND +df.filter("a > 10") # SQL expression string +``` + +Raw Python values on the right-hand side of a comparison are auto-wrapped +into literals by the `Expr` operators, so prefer `col("a") > 10` over +`col("a") > lit(10)`. See the Comparisons section and pitfall #2 for the +full rule. + +### Aggregation + +```python +# GROUP BY a, compute sum(b) and count(*) +df.aggregate(["a"], [F.sum(col("b")), F.count(col("a"))]) + +# HAVING equivalent: use the filter keyword on the aggregate function +df.aggregate( + ["region"], + [F.sum(col("sales"), filter=col("sales") > 1000).alias("large_sales")], +) +``` + +As with `select()`, group keys can be passed as plain name strings. Reach for +`col(...)` only when the grouping expression needs arithmetic, aliasing, +casting, or another expression operation. + +Most aggregate functions accept an optional `filter` keyword argument. When +provided, only rows where the filter expression is true contribute to the +aggregate. + +### Sorting + +```python +df.sort("a") # ascending (plain name, preferred) +df.sort(col("a")) # ascending via col() +df.sort(col("a").sort(ascending=False)) # descending +df.sort(col("a").sort(nulls_first=False)) # override null placement + +df.sort_by("a", "b") # ascending-only shortcut +``` + +As with `select()` and `aggregate()`, bare column references can be passed as +plain name strings. A plain expression passed to `sort()` is already treated +as ascending, so reach for `col(...).sort(...)` only when you need to override +a default (descending order or null placement). Writing +`col("a").sort(ascending=True)` is redundant. + +For ascending-only sorts with no null-placement override, `df.sort_by(...)` is +a shorter alias for `df.sort(...)`. + +### Joining + +```python +# Equi-join on shared column name +df1.join(df2, on="key") +df1.join(df2, on="key", how="left") + +# Different column names +df1.join(df2, left_on="id", right_on="fk_id", how="inner") + +# Expression-based join (supports inequality predicates) +df1.join_on(df2, col("a") == col("b"), how="inner") + +# Semi join: keep rows from left where a match exists in right (like EXISTS) +df1.join(df2, on="key", how="semi") + +# Anti join: keep rows from left where NO match exists in right (like NOT EXISTS) +df1.join(df2, on="key", how="anti") +``` + +Join types: `"inner"`, `"left"`, `"right"`, `"full"`, `"semi"`, `"anti"`. + +Inner is the default `how`. Prefer `df1.join(df2, on="key")` over +`df1.join(df2, on="key", how="inner")` — drop `how=` unless you need a +non-inner join type. + +When the two sides' join columns have different native names, use +`left_on=`/`right_on=` with the original names rather than aliasing one side +to match the other — see pitfall #7. + +### Window Functions + +```python +from datafusion import WindowFrame + +# Row number partitioned by group, ordered by value +df.window( + F.row_number( + partition_by=[col("group")], + order_by=[col("value")], + ).alias("rn") +) + +# Using a Window object for reuse +from datafusion.expr import Window + +win = Window( + partition_by=[col("group")], + order_by=[col("value").sort(ascending=True)], +) +df.select( + col("group"), + col("value"), + F.sum(col("value")).over(win).alias("running_total"), +) + +# With explicit frame bounds +win = Window( + partition_by=[col("group")], + order_by=[col("value").sort(ascending=True)], + window_frame=WindowFrame("rows", 0, None), # current row to unbounded following +) +``` + +### Set Operations + +```python +df1.union(df2) # UNION ALL (by position) +df1.union(df2, distinct=True) # UNION DISTINCT +df1.union_by_name(df2) # match columns by name, not position +df1.intersect(df2) # INTERSECT ALL +df1.intersect(df2, distinct=True) # INTERSECT (distinct) +df1.except_all(df2) # EXCEPT ALL +df1.except_all(df2, distinct=True) # EXCEPT (distinct) +``` + +### Limit and Offset + +```python +df.limit(10) # first 10 rows +df.limit(10, offset=20) # skip 20, then take 10 +``` + +### Deduplication + +```python +df.distinct() # remove duplicate rows +df.distinct_on( # keep first row per group (like DISTINCT ON in Postgres) + [col("a")], # uniqueness columns + [col("a"), col("b")], # output columns + [col("b").sort(ascending=True)], # which row to keep +) +``` + +## Executing and Collecting Results + +DataFrames are lazy until you collect. + +```python +df.show() # print formatted table to stdout +batches = df.collect() # list[pa.RecordBatch] +arr = df.collect_column("col_name") # pa.Array | pa.ChunkedArray (single column) +table = df.to_arrow_table() # pa.Table +pandas_df = df.to_pandas() # pd.DataFrame +polars_df = df.to_polars() # pl.DataFrame +py_dict = df.to_pydict() # dict[str, list] +py_list = df.to_pylist() # list[dict] +count = df.count() # int +``` + +### Date and Timestamp Type Conversion + +The Python type returned by `to_pydict()` / `to_pylist()` depends on the Arrow +column type, and the mapping is inherited from PyArrow: + +| Arrow type | Python type returned | +|---|---| +| `timestamp(s)` / `(ms)` / `(us)` | `datetime.datetime` | +| `timestamp(ns)` | `pandas.Timestamp` | +| `date32` / `date64` | `datetime.date` | +| `duration(s)` / `(ms)` / `(us)` | `datetime.timedelta` | +| `duration(ns)` | `pandas.Timedelta` | + +The nanosecond-precision fallback to pandas types is the main surprise: +pandas is not a hard dependency of `datafusion`, but PyArrow reaches for it +when `datetime.datetime` / `datetime.timedelta` would lose precision (stdlib +types only go to microseconds). If you need plain stdlib types, cast to a +coarser unit before collecting, e.g. +`df.select(col("ts").cast(pa.timestamp("us")))`. + +`df.to_pandas()` has its own footgun for dates: pandas has no pure-date dtype, +so a `date32`/`date64` column comes back as an `object` column of +`datetime.date` values rather than `datetime64[ns]`. If downstream code +expects a datetime column, cast on the DataFusion side first: +`col("ship_date").cast(pa.timestamp("ns"))`. + +### Streaming Results + +Prefer streaming over `collect()` when the result is too large to materialize +in memory, when you want to start processing before the query finishes, or +when you may break out of the loop early. `execute_stream()` pulls one +`RecordBatch` at a time from the execution plan rather than buffering the +whole result up front. + +```python +# Single-partition stream; batch is a datafusion.RecordBatch +stream = df.execute_stream() +for batch in stream: + process(batch.to_pyarrow()) # convert to pa.RecordBatch if needed + +# DataFrame is iterable directly (delegates to execute_stream) +for batch in df: + process(batch.to_pyarrow()) + +# One stream per partition, for parallel consumption +for stream in df.execute_stream_partitioned(): + for batch in stream: + process(batch.to_pyarrow()) +``` + +Async iteration is also supported via `async for batch in df: ...` (or +`df.execute_stream()`), which is useful when batches are interleaved with +other I/O. + +### Writing Results + +```python +df.write_parquet("output.parquet") +df.write_csv("output.csv") +df.write_json("output.json") +``` + +You can also pass a directory path (e.g., `"output/"`) to write a multi-file +partitioned output. + +## Expression Building + +### Column References and Literals + +```python +col("column_name") # reference a column +lit(42) # integer literal +lit("hello") # string literal +lit(3.14) # float literal +lit(pa.scalar(value)) # PyArrow scalar (preserves Arrow type) +``` + +`lit()` accepts PyArrow scalars directly -- prefer this over converting Arrow +data to Python and back when working with values extracted from query results. + +### Arithmetic + +```python +col("price") * col("quantity") # multiplication +col("a") + lit(1) # addition +col("a") - col("b") # subtraction +col("a") / lit(2) # division +col("a") % lit(3) # modulo +``` + +### Date Arithmetic + +`Date32` and `Date64` columns both require `Interval` types for arithmetic, +not `Duration`. Use PyArrow's `month_day_nano_interval` type, which takes a +`(months, days, nanos)` tuple: + +```python +import pyarrow as pa + +# Subtract 90 days from a date column +col("ship_date") - lit(pa.scalar((0, 90, 0), type=pa.month_day_nano_interval())) + +# Subtract 3 months +col("ship_date") - lit(pa.scalar((3, 0, 0), type=pa.month_day_nano_interval())) +``` + +**Important**: `lit(datetime.timedelta(days=90))` creates a `Duration(µs)` +literal, which is **not** compatible with `Date32`/`Date64` arithmetic +(`Duration(ms)` and `Duration(ns)` are rejected too). Always use +`pa.month_day_nano_interval()` for date operations. + +**Timestamps behave differently**: `Timestamp` columns *do* accept `Duration`, +so `col("ts") - lit(datetime.timedelta(days=1))` works. The interval-only +rule applies specifically to date columns. + +### Comparisons + +```python +col("a") > 10 +col("a") >= 10 +col("a") < 10 +col("a") <= 10 +col("a") == "x" +col("a") != "x" +col("a") == None # same as col("a").is_null() +col("a") != None # same as col("a").is_not_null() +``` + +Comparison operators auto-wrap the right-hand Python value into a literal, +so writing `col("a") > lit(10)` is redundant. Drop the `lit()` in +comparisons. Reach for `lit()` only when auto-wrapping does not apply — see +pitfall #2. + +### Boolean Logic + +**Important**: Python's `and`, `or`, `not` keywords do NOT work with Expr +objects. You must use the bitwise operators: + +```python +(col("a") > 1) & (col("b") < 10) # AND +(col("a") > 1) | (col("b") < 10) # OR +~(col("a") > 1) # NOT +``` + +Always wrap each comparison in parentheses when combining with `&`, `|`, `~` +because Python's operator precedence for bitwise operators is different from +logical operators. + +### Null Handling + +```python +col("a").is_null() +col("a").is_not_null() +col("a").fill_null(lit(0)) # replace NULL with a value +F.coalesce(col("a"), col("b")) # first non-null value +F.nullif(col("a"), lit(0)) # return NULL if a == 0 +``` + +### CASE / WHEN + +```python +# Simple CASE (matching on a single expression) +status_label = ( + F.case(col("status")) + .when(lit("A"), lit("Active")) + .when(lit("I"), lit("Inactive")) + .otherwise(lit("Unknown")) +) + +# Searched CASE (each branch has its own predicate) +severity = ( + F.when(col("value") > 100, lit("high")) + .when(col("value") > 50, lit("medium")) + .otherwise(lit("low")) +) +``` + +### Casting + +```python +import pyarrow as pa + +col("a").cast(pa.float64()) +col("a").cast(pa.utf8()) +col("a").cast(pa.date32()) +``` + +### Aliasing + +```python +(col("a") + col("b")).alias("total") +``` + +### BETWEEN and IN + +```python +col("a").between(lit(1), lit(10)) # 1 <= a <= 10 +F.in_list(col("a"), [lit(1), lit(2), lit(3)]) # a IN (1, 2, 3) +F.in_list(col("a"), [lit(1), lit(2)], negated=True) # a NOT IN (1, 2) +``` + +### Struct and Array Access + +```python +col("struct_col")["field_name"] # access struct field +col("array_col")[0] # access array element (0-indexed) +col("array_col")[1:3] # array slice (0-indexed) +``` + +## SQL-to-DataFrame Reference + +| SQL | DataFrame API | +|---|---| +| `SELECT a, b` | `df.select("a", "b")` | +| `SELECT a, b + 1 AS c` | `df.select(col("a"), (col("b") + lit(1)).alias("c"))` | +| `SELECT *, a + 1 AS c` | `df.with_column("c", col("a") + lit(1))` | +| `WHERE a > 10` | `df.filter(col("a") > 10)` | +| `GROUP BY a` with `SUM(b)` | `df.aggregate(["a"], [F.sum(col("b"))])` | +| `SUM(b) FILTER (WHERE b > 100)` | `F.sum(col("b"), filter=col("b") > 100)` | +| `ORDER BY a DESC` | `df.sort(col("a").sort(ascending=False))` | +| `LIMIT 10 OFFSET 5` | `df.limit(10, offset=5)` | +| `DISTINCT` | `df.distinct()` | +| `a INNER JOIN b ON a.id = b.id` | `a.join(b, on="id")` | +| `a LEFT JOIN b ON a.id = b.fk` | `a.join(b, left_on="id", right_on="fk", how="left")` | +| `WHERE EXISTS (SELECT ...)` | `a.join(b, on="key", how="semi")` | +| `WHERE NOT EXISTS (SELECT ...)` | `a.join(b, on="key", how="anti")` | +| `UNION ALL` | `df1.union(df2)` | +| `UNION` (distinct) | `df1.union(df2, distinct=True)` | +| `INTERSECT ALL` | `df1.intersect(df2)` | +| `INTERSECT` (distinct) | `df1.intersect(df2, distinct=True)` | +| `EXCEPT ALL` | `df1.except_all(df2)` | +| `EXCEPT` (distinct) | `df1.except_all(df2, distinct=True)` | +| `CASE x WHEN 1 THEN 'a' END` | `F.case(col("x")).when(lit(1), lit("a")).end()` | +| `CASE WHEN x > 1 THEN 'a' END` | `F.when(col("x") > 1, lit("a")).end()` | +| `x IN (1, 2, 3)` | `F.in_list(col("x"), [lit(1), lit(2), lit(3)])` | +| `x BETWEEN 1 AND 10` | `col("x").between(lit(1), lit(10))` | +| `CAST(x AS DOUBLE)` | `col("x").cast(pa.float64())` | +| `ROW_NUMBER() OVER (...)` | `F.row_number(partition_by=[...], order_by=[...])` | +| `SUM(x) OVER (...)` | `F.sum(col("x")).over(window)` | +| `x IS NULL` | `col("x").is_null()` | +| `COALESCE(a, b)` | `F.coalesce(col("a"), col("b"))` | + +## Common Pitfalls + +1. **Boolean operators**: Use `&`, `|`, `~` -- not Python's `and`, `or`, `not`. + Always parenthesize: `(col("a") > 1) & (col("b") < 2)`. + +2. **Wrapping scalars with `lit()`**: Prefer raw Python values on the + right-hand side of comparisons — `col("a") > 10`, `col("name") == "Alice"` + — because the Expr comparison operators auto-wrap them. Writing + `col("a") > lit(10)` is redundant. Reserve `lit()` for places where + auto-wrapping does *not* apply: + - standalone scalars passed into function calls: + `F.coalesce(col("a"), lit(0))`, not `F.coalesce(col("a"), 0)` + - arithmetic between two literals with no column involved: + `lit(1) - col("discount")` is fine, but `lit(1) - lit(2)` needs both + - values that must carry a specific Arrow type, via `lit(pa.scalar(...))` + - `.when(...)`, `.otherwise(...)`, `F.nullif(...)`, `.between(...)`, + `F.in_list(...)` and similar method/function arguments + +3. **Column name quoting**: Column names are normalized to lowercase by default + in both `select("...")` and `col("...")`. To reference a column with + uppercase letters, use double quotes inside the string: + `select('"MyColumn"')` or `col('"MyColumn"')`. + +4. **DataFrames are immutable**: Every method returns a **new** DataFrame. You + must capture the return value: + ```python + df = df.filter(col("a") > 1) # correct + df.filter(col("a") > 1) # WRONG -- result is discarded + ``` + +5. **Window frame defaults**: When using `order_by` in a window, the default + frame is `RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW`. For a full + partition frame, set `window_frame=WindowFrame("rows", None, None)`. + +6. **Arithmetic on aggregates belongs in a later `select`, not inside + `aggregate`**: Each item in the aggregate list must be a single aggregate + call (optionally aliased). Combining aggregates with arithmetic inside + `aggregate(...)` fails with `Internal error: Invalid aggregate expression`. + Alias the aggregates, then compute the combination downstream: + ```python + # WRONG -- arithmetic wraps two aggregates + df.aggregate([], [(lit(100) * F.sum(col("a")) / F.sum(col("b"))).alias("ratio")]) + + # CORRECT -- aggregate first, then combine + (df.aggregate([], [F.sum(col("a")).alias("num"), F.sum(col("b")).alias("den")]) + .select((lit(100) * col("num") / col("den")).alias("ratio"))) + ``` + +7. **Don't alias a join column to match the other side**: When equi-joining + with `on="key"`, renaming the join column on one side via `.alias("key")` + in a fresh projection creates a schema where one side's `key` is + qualified (`?table?.key`) and the other is unqualified. The join then + fails with `Schema contains qualified field name ... and unqualified + field name ... which would be ambiguous`. Use `left_on=`/`right_on=` with + the native names, or use `join_on(...)` with an explicit equality. + ```python + # WRONG -- alias on one side produces ambiguous schema after join + failed = orders.select(col("o_orderkey").alias("l_orderkey")) + li.join(failed, on="l_orderkey") # ambiguous l_orderkey error + + # CORRECT -- keep native names, use left_on/right_on + failed = orders.select("o_orderkey") + li.join(failed, left_on="l_orderkey", right_on="o_orderkey") + + # ALSO CORRECT -- explicit predicate via join_on + # (note: join_on keeps both key columns in the output, unlike on="key") + li.join_on(failed, col("l_orderkey") == col("o_orderkey")) + ``` + +## Idiomatic Patterns + +### Fluent Chaining + +```python +result = ( + ctx.read_parquet("data.parquet") + .filter(col("year") >= 2020) + .select(col("region"), col("sales")) + .aggregate(["region"], [F.sum(col("sales")).alias("total")]) + .sort(col("total").sort(ascending=False)) + .limit(10) +) +result.show() +``` + +### Using Variables as CTEs + +Instead of SQL CTEs (`WITH ... AS`), assign intermediate DataFrames to +variables: + +```python +base = ctx.read_parquet("orders.parquet").filter(col("status") == "shipped") +by_region = base.aggregate(["region"], [F.sum(col("amount")).alias("total")]) +top_regions = by_region.filter(col("total") > 10000) +``` + +### Reusing Expressions as Variables + +Just like DataFrames, expressions (`Expr`) can be stored in variables and used +anywhere an `Expr` is expected. This is useful for building up complex +expressions or reusing a computed value across multiple operations: + +```python +# Build an expression and reuse it +disc_price = col("price") * (lit(1) - col("discount")) +df = df.select( + col("id"), + disc_price.alias("disc_price"), + (disc_price * (lit(1) + col("tax"))).alias("total"), +) + +# Use a collected scalar as an expression +max_val = result_df.collect_column("max_price")[0] # PyArrow scalar +cutoff = lit(max_val) - lit(pa.scalar((0, 90, 0), type=pa.month_day_nano_interval())) +df = df.filter(col("ship_date") <= cutoff) # cutoff is already an Expr +``` + +**Important**: Do not wrap an `Expr` in `lit()`. `lit()` is for converting +Python/PyArrow values into expressions. If a value is already an `Expr`, use it +directly. + +### Window Functions for Scalar Subqueries + +Where SQL uses a correlated scalar subquery, the idiomatic DataFrame approach +is a window function: + +```sql +-- SQL scalar subquery +SELECT *, (SELECT SUM(b) FROM t WHERE t.group = s.group) AS group_total FROM s +``` + +```python +# DataFrame: window function +win = Window(partition_by=[col("group")]) +df = df.with_column("group_total", F.sum(col("b")).over(win)) +``` + +### Semi/Anti Joins for EXISTS / NOT EXISTS + +```sql +-- SQL: WHERE EXISTS (SELECT 1 FROM other WHERE other.key = main.key) +-- DataFrame: +result = main.join(other, on="key", how="semi") + +-- SQL: WHERE NOT EXISTS (SELECT 1 FROM other WHERE other.key = main.key) +-- DataFrame: +result = main.join(other, on="key", how="anti") +``` + +### Computed Columns + +```python +# Add computed columns while keeping all originals +df = df.with_column("full_name", F.concat(col("first"), lit(" "), col("last"))) +df = df.with_column("discounted", col("price") * lit(0.9)) +``` + +## Available Functions (Categorized) + +The `functions` module (imported as `F`) provides 290+ functions. Key categories: + +**Aggregate**: `sum`, `avg`, `min`, `max`, `count`, `count_star`, `median`, +`stddev`, `stddev_pop`, `var_samp`, `var_pop`, `corr`, `covar`, `approx_distinct`, +`approx_median`, `approx_percentile_cont`, `array_agg`, `string_agg`, +`first_value`, `last_value`, `bit_and`, `bit_or`, `bit_xor`, `bool_and`, +`bool_or`, `grouping`, `regr_*` (9 regression functions) + +**Window**: `row_number`, `rank`, `dense_rank`, `percent_rank`, `cume_dist`, +`ntile`, `lag`, `lead`, `first_value`, `last_value`, `nth_value` + +**String**: `length`, `lower`, `upper`, `trim`, `ltrim`, `rtrim`, `lpad`, +`rpad`, `starts_with`, `ends_with`, `contains`, `substr`, `substring`, +`replace`, `reverse`, `repeat`, `split_part`, `concat`, `concat_ws`, +`initcap`, `ascii`, `chr`, `left`, `right`, `strpos`, `translate`, `overlay`, +`levenshtein` + +`F.substr(str, start)` takes **only two arguments** and returns the tail of +the string from `start` onward — passing a third length argument raises +`TypeError: substr() takes 2 positional arguments but 3 were given`. For the +SQL-style 3-arg form (`SUBSTRING(str FROM start FOR length)`), use +`F.substring(col("s"), lit(start), lit(length))`. For a fixed-length prefix, +`F.left(col("s"), lit(n))` is cleanest. + +```python +# WRONG — substr does not accept a length argument +F.substr(col("c_phone"), lit(1), lit(2)) +# CORRECT +F.substring(col("c_phone"), lit(1), lit(2)) # explicit length +F.left(col("c_phone"), lit(2)) # prefix shortcut +``` + +**Math**: `abs`, `ceil`, `floor`, `round`, `trunc`, `sqrt`, `cbrt`, `exp`, +`ln`, `log`, `log2`, `log10`, `pow`, `signum`, `pi`, `random`, `factorial`, +`gcd`, `lcm`, `greatest`, `least`, sin/cos/tan and inverse/hyperbolic variants + +**Date/Time**: `now`, `today`, `current_date`, `current_time`, +`current_timestamp`, `date_part`, `date_trunc`, `date_bin`, `extract`, +`to_timestamp`, `to_timestamp_millis`, `to_timestamp_micros`, +`to_timestamp_nanos`, `to_timestamp_seconds`, `to_unixtime`, `from_unixtime`, +`make_date`, `make_time`, `to_date`, `to_time`, `to_local_time`, `date_format` + +**Conditional**: `case`, `when`, `coalesce`, `nullif`, `ifnull`, `nvl`, `nvl2` + +**Array/List**: `array`, `make_array`, `array_agg`, `array_length`, +`array_element`, `array_slice`, `array_append`, `array_prepend`, +`array_concat`, `array_has`, `array_has_all`, `array_has_any`, `array_position`, +`array_remove`, `array_distinct`, `array_sort`, `array_reverse`, `flatten`, +`array_to_string`, `array_intersect`, `array_union`, `array_except`, +`generate_series` +(Most `array_*` functions also have `list_*` aliases.) + +**Struct/Map**: `struct`, `named_struct`, `get_field`, `make_map`, `map_keys`, +`map_values`, `map_entries`, `map_extract` + +**Regex**: `regexp_like`, `regexp_match`, `regexp_replace`, `regexp_count`, +`regexp_instr` + +**Hash**: `md5`, `sha224`, `sha256`, `sha384`, `sha512`, `digest` + +**Type**: `arrow_typeof`, `arrow_cast`, `arrow_metadata` + +**Other**: `in_list`, `order_by`, `alias`, `col`, `encode`, `decode`, +`to_hex`, `to_char`, `uuid`, `version`, `bit_length`, `octet_length` diff --git a/benchmarks/tpch/tpch.py b/benchmarks/tpch/tpch.py index 9cc897e76..ffee5554c 100644 --- a/benchmarks/tpch/tpch.py +++ b/benchmarks/tpch/tpch.py @@ -23,7 +23,7 @@ def bench(data_path, query_path) -> None: - with Path.open("results.csv", "w") as results: + with Path("results.csv").open("w") as results: # register tables start = time.time() total_time_millis = 0 @@ -46,7 +46,7 @@ def bench(data_path, query_path) -> None: print("Configuration:\n", ctx) # register tables - with Path.open("create_tables.sql") as f: + with Path("create_tables.sql").open() as f: sql = "" for line in f.readlines(): if line.startswith("--"): @@ -66,7 +66,7 @@ def bench(data_path, query_path) -> None: # run queries for query in range(1, 23): - with Path.open(f"{query_path}/q{query}.sql") as f: + with Path(f"{query_path}/q{query}.sql").open() as f: text = f.read() tmp = text.split(";") queries = [s.strip() for s in tmp if len(s.strip()) > 0] diff --git a/conftest.py b/conftest.py new file mode 100644 index 000000000..0c9410636 --- /dev/null +++ b/conftest.py @@ -0,0 +1,36 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Pytest configuration for doctest namespace injection.""" + +import datafusion as dfn +import numpy as np +import pyarrow as pa +import pytest +from datafusion import col, lit +from datafusion import functions as F + + +@pytest.fixture(autouse=True) +def _doctest_namespace(doctest_namespace: dict) -> None: + """Add common imports to the doctest namespace.""" + doctest_namespace["dfn"] = dfn + doctest_namespace["np"] = np + doctest_namespace["pa"] = pa + doctest_namespace["col"] = col + doctest_namespace["lit"] = lit + doctest_namespace["F"] = F diff --git a/crates/core/Cargo.toml b/crates/core/Cargo.toml new file mode 100644 index 000000000..d714dc978 --- /dev/null +++ b/crates/core/Cargo.toml @@ -0,0 +1,83 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +[package] +name = "datafusion-python" +version.workspace = true +edition.workspace = true +rust-version.workspace = true +license.workspace = true +description.workspace = true +homepage.workspace = true +repository.workspace = true +include = [ + "src", + "../LICENSE.txt", + "build.rs", + "../pyproject.toml", + "Cargo.toml", + "../Cargo.lock", +] + +[dependencies] +tokio = { workspace = true, features = [ + "macros", + "rt", + "rt-multi-thread", + "sync", +] } +pyo3 = { workspace = true, features = [ + "extension-module", + "abi3", + "abi3-py310", +] } +pyo3-async-runtimes = { workspace = true, features = ["tokio-runtime"] } +pyo3-log = { workspace = true } +chrono = { workspace = true } +arrow = { workspace = true, features = ["pyarrow"] } +arrow-select = { workspace = true } +datafusion = { workspace = true, features = ["avro", "unicode_expressions"] } +datafusion-substrait = { workspace = true, optional = true } +datafusion-proto = { workspace = true } +datafusion-ffi = { workspace = true } +prost = { workspace = true } # keep in line with `datafusion-substrait` +serde_json = { workspace = true } +uuid = { workspace = true, features = ["v4"] } +mimalloc = { workspace = true, optional = true, features = [ + "local_dynamic_tls", +] } +async-trait = { workspace = true } +futures = { workspace = true } +cstr = { workspace = true } +object_store = { workspace = true, features = ["aws", "gcp", "azure", "http"] } +url = { workspace = true } +log = { workspace = true } +parking_lot = { workspace = true } +datafusion-python-util = { workspace = true } + +[build-dependencies] +prost-types = { workspace = true } +pyo3-build-config = { workspace = true } + +[features] +default = ["mimalloc"] +protoc = ["datafusion-substrait/protoc"] +substrait = ["dep:datafusion-substrait"] + +[lib] +name = "datafusion_python" +crate-type = ["cdylib", "rlib"] diff --git a/build.rs b/crates/core/build.rs similarity index 100% rename from build.rs rename to crates/core/build.rs diff --git a/crates/core/src/array.rs b/crates/core/src/array.rs new file mode 100644 index 000000000..f284fa9de --- /dev/null +++ b/crates/core/src/array.rs @@ -0,0 +1,88 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::ptr::NonNull; +use std::sync::Arc; + +use arrow::array::{Array, ArrayRef}; +use arrow::datatypes::{Field, FieldRef}; +use arrow::ffi::{FFI_ArrowArray, FFI_ArrowSchema}; +use arrow::pyarrow::ToPyArrow; +use pyo3::prelude::{PyAnyMethods, PyCapsuleMethods}; +use pyo3::types::PyCapsule; +use pyo3::{Bound, PyAny, PyResult, Python, pyclass, pymethods}; + +use crate::errors::PyDataFusionResult; + +/// A Python object which implements the Arrow PyCapsule for importing +/// into other libraries. +#[pyclass( + from_py_object, + name = "ArrowArrayExportable", + module = "datafusion", + frozen +)] +#[derive(Clone)] +pub struct PyArrowArrayExportable { + array: ArrayRef, + field: FieldRef, +} + +#[pymethods] +impl PyArrowArrayExportable { + #[pyo3(signature = (requested_schema=None))] + fn __arrow_c_array__<'py>( + &'py self, + py: Python<'py>, + requested_schema: Option>, + ) -> PyDataFusionResult<(Bound<'py, PyCapsule>, Bound<'py, PyCapsule>)> { + let field = if let Some(schema_capsule) = requested_schema { + let data: NonNull = schema_capsule + .pointer_checked(Some(c"arrow_schema"))? + .cast(); + let schema_ptr = unsafe { data.as_ref() }; + let desired_field = Field::try_from(schema_ptr)?; + + Arc::new(desired_field) + } else { + Arc::clone(&self.field) + }; + + let ffi_schema = FFI_ArrowSchema::try_from(&field)?; + let schema_capsule = PyCapsule::new(py, ffi_schema, Some(cr"arrow_schema".into()))?; + + let ffi_array = FFI_ArrowArray::new(&self.array.to_data()); + let array_capsule = PyCapsule::new(py, ffi_array, Some(cr"arrow_array".into()))?; + + Ok((schema_capsule, array_capsule)) + } +} + +impl ToPyArrow for PyArrowArrayExportable { + fn to_pyarrow<'py>(&self, py: Python<'py>) -> PyResult> { + let module = py.import("pyarrow")?; + let method = module.getattr("array")?; + let array = method.call((self.clone(),), None)?; + Ok(array) + } +} + +impl PyArrowArrayExportable { + pub fn new(array: ArrayRef, field: FieldRef) -> Self { + Self { array, field } + } +} diff --git a/crates/core/src/catalog.rs b/crates/core/src/catalog.rs new file mode 100644 index 000000000..30ec4744c --- /dev/null +++ b/crates/core/src/catalog.rs @@ -0,0 +1,726 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::any::Any; +use std::collections::HashSet; +use std::ptr::NonNull; +use std::sync::Arc; + +use async_trait::async_trait; +use datafusion::catalog::{ + CatalogProvider, CatalogProviderList, MemoryCatalogProvider, MemoryCatalogProviderList, + MemorySchemaProvider, SchemaProvider, +}; +use datafusion::common::DataFusionError; +use datafusion::datasource::TableProvider; +use datafusion_ffi::catalog_provider::FFI_CatalogProvider; +use datafusion_ffi::proto::logical_extension_codec::FFI_LogicalExtensionCodec; +use datafusion_ffi::schema_provider::FFI_SchemaProvider; +use datafusion_python_util::{ + create_logical_extension_capsule, ffi_logical_codec_from_pycapsule, wait_for_future, +}; +use pyo3::IntoPyObjectExt; +use pyo3::exceptions::PyKeyError; +use pyo3::prelude::*; +use pyo3::types::PyCapsule; + +use crate::context::PySessionContext; +use crate::dataset::Dataset; +use crate::errors::{PyDataFusionError, PyDataFusionResult, py_datafusion_err, to_datafusion_err}; +use crate::table::PyTable; + +#[pyclass( + from_py_object, + frozen, + name = "RawCatalogList", + module = "datafusion.catalog", + subclass +)] +#[derive(Clone)] +pub struct PyCatalogList { + pub catalog_list: Arc, + codec: Arc, +} + +#[pyclass( + from_py_object, + frozen, + name = "RawCatalog", + module = "datafusion.catalog", + subclass +)] +#[derive(Clone)] +pub struct PyCatalog { + pub catalog: Arc, + codec: Arc, +} + +#[pyclass( + from_py_object, + frozen, + name = "RawSchema", + module = "datafusion.catalog", + subclass +)] +#[derive(Clone)] +pub struct PySchema { + pub schema: Arc, + codec: Arc, +} + +impl PyCatalog { + pub(crate) fn new_from_parts( + catalog: Arc, + codec: Arc, + ) -> Self { + Self { catalog, codec } + } +} + +impl PySchema { + pub(crate) fn new_from_parts( + schema: Arc, + codec: Arc, + ) -> Self { + Self { schema, codec } + } +} + +#[pymethods] +impl PyCatalogList { + #[new] + pub fn new( + py: Python, + catalog_list: Py, + session: Option>, + ) -> PyResult { + let codec = extract_logical_extension_codec(py, session)?; + let catalog_list = Arc::new(RustWrappedPyCatalogProviderList::new( + catalog_list, + codec.clone(), + )) as Arc; + Ok(Self { + catalog_list, + codec, + }) + } + + #[staticmethod] + pub fn memory_catalog_list(py: Python, session: Option>) -> PyResult { + let codec = extract_logical_extension_codec(py, session)?; + let catalog_list = + Arc::new(MemoryCatalogProviderList::default()) as Arc; + Ok(Self { + catalog_list, + codec, + }) + } + + pub fn catalog_names(&self) -> HashSet { + self.catalog_list.catalog_names().into_iter().collect() + } + + #[pyo3(signature = (name="public"))] + pub fn catalog(&self, name: &str) -> PyResult> { + let catalog = self + .catalog_list + .catalog(name) + .ok_or(PyKeyError::new_err(format!( + "Schema with name {name} doesn't exist." + )))?; + + Python::attach(|py| { + match catalog + .as_any() + .downcast_ref::() + { + Some(wrapped_catalog) => Ok(wrapped_catalog.catalog_provider.clone_ref(py)), + None => PyCatalog::new_from_parts(catalog, self.codec.clone()).into_py_any(py), + } + }) + } + + pub fn register_catalog(&self, name: &str, catalog_provider: Bound<'_, PyAny>) -> PyResult<()> { + let provider = extract_catalog_provider_from_pyobj(catalog_provider, self.codec.as_ref())?; + + let _ = self + .catalog_list + .register_catalog(name.to_owned(), provider); + + Ok(()) + } + + pub fn __repr__(&self) -> PyResult { + let mut names: Vec = self.catalog_names().into_iter().collect(); + names.sort(); + Ok(format!("CatalogList(catalog_names=[{}])", names.join(", "))) + } +} + +#[pymethods] +impl PyCatalog { + #[new] + pub fn new(py: Python, catalog: Py, session: Option>) -> PyResult { + let codec = extract_logical_extension_codec(py, session)?; + let catalog = Arc::new(RustWrappedPyCatalogProvider::new(catalog, codec.clone())) + as Arc; + Ok(Self { catalog, codec }) + } + + #[staticmethod] + pub fn memory_catalog(py: Python, session: Option>) -> PyResult { + let codec = extract_logical_extension_codec(py, session)?; + let catalog = Arc::new(MemoryCatalogProvider::default()) as Arc; + Ok(Self { catalog, codec }) + } + + pub fn schema_names(&self) -> HashSet { + self.catalog.schema_names().into_iter().collect() + } + + #[pyo3(signature = (name="public"))] + pub fn schema(&self, name: &str) -> PyResult> { + let schema = self + .catalog + .schema(name) + .ok_or(PyKeyError::new_err(format!( + "Schema with name {name} doesn't exist." + )))?; + + Python::attach(|py| { + match schema + .as_any() + .downcast_ref::() + { + Some(wrapped_schema) => Ok(wrapped_schema.schema_provider.clone_ref(py)), + None => PySchema::new_from_parts(schema, self.codec.clone()).into_py_any(py), + } + }) + } + + pub fn register_schema(&self, name: &str, schema_provider: Bound<'_, PyAny>) -> PyResult<()> { + let provider = extract_schema_provider_from_pyobj(schema_provider, self.codec.as_ref())?; + + let _ = self + .catalog + .register_schema(name, provider) + .map_err(py_datafusion_err)?; + + Ok(()) + } + + pub fn deregister_schema(&self, name: &str, cascade: bool) -> PyResult<()> { + let _ = self + .catalog + .deregister_schema(name, cascade) + .map_err(py_datafusion_err)?; + + Ok(()) + } + + pub fn __repr__(&self) -> PyResult { + let mut names: Vec = self.schema_names().into_iter().collect(); + names.sort(); + Ok(format!("Catalog(schema_names=[{}])", names.join(", "))) + } +} + +#[pymethods] +impl PySchema { + #[new] + pub fn new( + py: Python, + schema_provider: Py, + session: Option>, + ) -> PyResult { + let codec = extract_logical_extension_codec(py, session)?; + let schema = + Arc::new(RustWrappedPySchemaProvider::new(schema_provider)) as Arc; + Ok(Self { schema, codec }) + } + + #[staticmethod] + fn memory_schema(py: Python, session: Option>) -> PyResult { + let codec = extract_logical_extension_codec(py, session)?; + let schema = Arc::new(MemorySchemaProvider::default()) as Arc; + Ok(Self { schema, codec }) + } + + #[getter] + fn table_names(&self) -> HashSet { + self.schema.table_names().into_iter().collect() + } + + fn table(&self, name: &str, py: Python) -> PyDataFusionResult { + if let Some(table) = wait_for_future(py, self.schema.table(name))?? { + Ok(PyTable::from(table)) + } else { + Err(PyDataFusionError::Common(format!( + "Table not found: {name}" + ))) + } + } + + fn __repr__(&self) -> PyResult { + let mut names: Vec = self.table_names().into_iter().collect(); + names.sort(); + Ok(format!("Schema(table_names=[{}])", names.join(";"))) + } + + fn register_table(&self, name: &str, table_provider: Bound<'_, PyAny>) -> PyResult<()> { + let py = table_provider.py(); + let codec_capsule = create_logical_extension_capsule(py, self.codec.as_ref())? + .as_any() + .clone(); + + let table = PyTable::new(table_provider, Some(codec_capsule))?; + + let _ = self + .schema + .register_table(name.to_string(), table.table) + .map_err(py_datafusion_err)?; + + Ok(()) + } + + fn deregister_table(&self, name: &str) -> PyResult<()> { + let _ = self + .schema + .deregister_table(name) + .map_err(py_datafusion_err)?; + + Ok(()) + } + + fn table_exist(&self, name: &str) -> bool { + self.schema.table_exist(name) + } +} + +#[derive(Debug)] +pub(crate) struct RustWrappedPySchemaProvider { + schema_provider: Py, + owner_name: Option, +} + +impl RustWrappedPySchemaProvider { + pub fn new(schema_provider: Py) -> Self { + let owner_name = Python::attach(|py| { + schema_provider + .bind(py) + .getattr("owner_name") + .ok() + .map(|name| name.to_string()) + }); + + Self { + schema_provider, + owner_name, + } + } + + fn table_inner(&self, name: &str) -> PyResult>> { + Python::attach(|py| { + let provider = self.schema_provider.bind(py); + let py_table_method = provider.getattr("table")?; + + let py_table = py_table_method.call((name,), None)?; + if py_table.is_none() { + return Ok(None); + } + + let table = PyTable::new(py_table, None)?; + + Ok(Some(table.table)) + }) + } +} + +#[async_trait] +impl SchemaProvider for RustWrappedPySchemaProvider { + fn owner_name(&self) -> Option<&str> { + self.owner_name.as_deref() + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn table_names(&self) -> Vec { + Python::attach(|py| { + let provider = self.schema_provider.bind(py); + + provider + .getattr("table_names") + .and_then(|names| names.extract::>()) + .unwrap_or_else(|err| { + log::error!("Unable to get table_names: {err}"); + Vec::default() + }) + }) + } + + async fn table( + &self, + name: &str, + ) -> datafusion::common::Result>, DataFusionError> { + self.table_inner(name) + .map_err(|e| DataFusionError::External(Box::new(e))) + } + + fn register_table( + &self, + name: String, + table: Arc, + ) -> datafusion::common::Result>> { + let py_table = PyTable::from(table); + Python::attach(|py| { + let provider = self.schema_provider.bind(py); + let _ = provider + .call_method1("register_table", (name, py_table)) + .map_err(to_datafusion_err)?; + // Since the definition of `register_table` says that an error + // will be returned if the table already exists, there is no + // case where we want to return a table provider as output. + Ok(None) + }) + } + + fn deregister_table( + &self, + name: &str, + ) -> datafusion::common::Result>> { + Python::attach(|py| { + let provider = self.schema_provider.bind(py); + let table = provider + .call_method1("deregister_table", (name,)) + .map_err(to_datafusion_err)?; + if table.is_none() { + return Ok(None); + } + + // If we can turn this table provider into a `Dataset`, return it. + // Otherwise, return None. + let dataset = match Dataset::new(&table, py) { + Ok(dataset) => Some(Arc::new(dataset) as Arc), + Err(_) => None, + }; + + Ok(dataset) + }) + } + + fn table_exist(&self, name: &str) -> bool { + Python::attach(|py| { + let provider = self.schema_provider.bind(py); + provider + .call_method1("table_exist", (name,)) + .and_then(|pyobj| pyobj.extract()) + .unwrap_or(false) + }) + } +} + +#[derive(Debug)] +pub(crate) struct RustWrappedPyCatalogProvider { + pub(crate) catalog_provider: Py, + codec: Arc, +} + +impl RustWrappedPyCatalogProvider { + pub fn new(catalog_provider: Py, codec: Arc) -> Self { + Self { + catalog_provider, + codec, + } + } + + fn schema_inner(&self, name: &str) -> PyResult>> { + Python::attach(|py| { + let provider = self.catalog_provider.bind(py); + + let py_schema = provider.call_method1("schema", (name,))?; + if py_schema.is_none() { + return Ok(None); + } + + extract_schema_provider_from_pyobj(py_schema, self.codec.as_ref()).map(Some) + }) + } +} + +#[async_trait] +impl CatalogProvider for RustWrappedPyCatalogProvider { + fn as_any(&self) -> &dyn Any { + self + } + + fn schema_names(&self) -> Vec { + Python::attach(|py| { + let provider = self.catalog_provider.bind(py); + provider + .call_method0("schema_names") + .and_then(|names| names.extract::>()) + .map(|names| names.into_iter().collect()) + .unwrap_or_else(|err| { + log::error!("Unable to get schema_names: {err}"); + Vec::default() + }) + }) + } + + fn schema(&self, name: &str) -> Option> { + self.schema_inner(name).unwrap_or_else(|err| { + log::error!("CatalogProvider schema returned error: {err}"); + None + }) + } + + fn register_schema( + &self, + name: &str, + schema: Arc, + ) -> datafusion::common::Result>> { + Python::attach(|py| { + let py_schema = match schema + .as_any() + .downcast_ref::() + { + Some(wrapped_schema) => wrapped_schema.schema_provider.as_any(), + None => &PySchema::new_from_parts(schema, self.codec.clone()) + .into_py_any(py) + .map_err(to_datafusion_err)?, + }; + + let provider = self.catalog_provider.bind(py); + let schema = provider + .call_method1("register_schema", (name, py_schema)) + .map_err(to_datafusion_err)?; + if schema.is_none() { + return Ok(None); + } + + let schema = Arc::new(RustWrappedPySchemaProvider::new(schema.into())) + as Arc; + + Ok(Some(schema)) + }) + } + + fn deregister_schema( + &self, + name: &str, + cascade: bool, + ) -> datafusion::common::Result>> { + Python::attach(|py| { + let provider = self.catalog_provider.bind(py); + let schema = provider + .call_method1("deregister_schema", (name, cascade)) + .map_err(to_datafusion_err)?; + if schema.is_none() { + return Ok(None); + } + + let schema = Arc::new(RustWrappedPySchemaProvider::new(schema.into())) + as Arc; + + Ok(Some(schema)) + }) + } +} + +#[derive(Debug)] +pub(crate) struct RustWrappedPyCatalogProviderList { + pub(crate) catalog_provider_list: Py, + codec: Arc, +} + +impl RustWrappedPyCatalogProviderList { + pub fn new(catalog_provider_list: Py, codec: Arc) -> Self { + Self { + catalog_provider_list, + codec, + } + } + + fn catalog_inner(&self, name: &str) -> PyResult>> { + Python::attach(|py| { + let provider = self.catalog_provider_list.bind(py); + + let py_schema = provider.call_method1("catalog", (name,))?; + if py_schema.is_none() { + return Ok(None); + } + + extract_catalog_provider_from_pyobj(py_schema, self.codec.as_ref()).map(Some) + }) + } +} + +#[async_trait] +impl CatalogProviderList for RustWrappedPyCatalogProviderList { + fn as_any(&self) -> &dyn Any { + self + } + + fn catalog_names(&self) -> Vec { + Python::attach(|py| { + let provider = self.catalog_provider_list.bind(py); + provider + .call_method0("catalog_names") + .and_then(|names| names.extract::>()) + .map(|names| names.into_iter().collect()) + .unwrap_or_else(|err| { + log::error!("Unable to get catalog_names: {err}"); + Vec::default() + }) + }) + } + + fn catalog(&self, name: &str) -> Option> { + self.catalog_inner(name).unwrap_or_else(|err| { + log::error!("CatalogProvider catalog returned error: {err}"); + None + }) + } + + fn register_catalog( + &self, + name: String, + catalog: Arc, + ) -> Option> { + Python::attach(|py| { + let py_catalog = match catalog + .as_any() + .downcast_ref::() + { + Some(wrapped_schema) => wrapped_schema.catalog_provider.as_any().clone_ref(py), + None => { + match PyCatalog::new_from_parts(catalog, self.codec.clone()).into_py_any(py) { + Ok(c) => c, + Err(err) => { + log::error!( + "register_catalog returned error during conversion to PyAny: {err}" + ); + return None; + } + } + } + }; + + let provider = self.catalog_provider_list.bind(py); + let catalog = match provider.call_method1("register_catalog", (name, py_catalog)) { + Ok(c) => c, + Err(err) => { + log::error!("register_catalog returned error: {err}"); + return None; + } + }; + if catalog.is_none() { + return None; + } + + let catalog = Arc::new(RustWrappedPyCatalogProvider::new( + catalog.into(), + self.codec.clone(), + )) as Arc; + + Some(catalog) + }) + } +} + +fn extract_catalog_provider_from_pyobj( + mut catalog_provider: Bound, + codec: &FFI_LogicalExtensionCodec, +) -> PyResult> { + if catalog_provider.hasattr("__datafusion_catalog_provider__")? { + let py = catalog_provider.py(); + let codec_capsule = create_logical_extension_capsule(py, codec)?; + catalog_provider = catalog_provider + .getattr("__datafusion_catalog_provider__")? + .call1((codec_capsule,))?; + } + + let provider = if let Ok(capsule) = catalog_provider.cast::() { + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_catalog_provider"))? + .cast(); + let provider = unsafe { data.as_ref() }; + let provider: Arc = provider.into(); + provider as Arc + } else { + match catalog_provider.extract::() { + Ok(py_catalog) => py_catalog.catalog, + Err(_) => Arc::new(RustWrappedPyCatalogProvider::new( + catalog_provider.into(), + Arc::new(codec.clone()), + )) as Arc, + } + }; + + Ok(provider) +} + +fn extract_schema_provider_from_pyobj( + mut schema_provider: Bound, + codec: &FFI_LogicalExtensionCodec, +) -> PyResult> { + if schema_provider.hasattr("__datafusion_schema_provider__")? { + let py = schema_provider.py(); + let codec_capsule = create_logical_extension_capsule(py, codec)?; + schema_provider = schema_provider + .getattr("__datafusion_schema_provider__")? + .call1((codec_capsule,))?; + } + + let provider = if let Ok(capsule) = schema_provider.cast::() { + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_schema_provider"))? + .cast(); + let provider = unsafe { data.as_ref() }; + let provider: Arc = provider.into(); + provider as Arc + } else { + match schema_provider.extract::() { + Ok(py_schema) => py_schema.schema, + Err(_) => Arc::new(RustWrappedPySchemaProvider::new(schema_provider.into())) + as Arc, + } + }; + + Ok(provider) +} + +fn extract_logical_extension_codec( + py: Python, + obj: Option>, +) -> PyResult> { + let obj = match obj { + Some(obj) => obj, + None => PySessionContext::global_ctx()?.into_bound_py_any(py)?, + }; + ffi_logical_codec_from_pycapsule(obj).map(Arc::new) +} + +pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + + Ok(()) +} diff --git a/src/common.rs b/crates/core/src/common.rs similarity index 100% rename from src/common.rs rename to crates/core/src/common.rs diff --git a/src/common/data_type.rs b/crates/core/src/common/data_type.rs similarity index 96% rename from src/common/data_type.rs rename to crates/core/src/common/data_type.rs index 55848da5c..af4179806 100644 --- a/src/common/data_type.rs +++ b/crates/core/src/common/data_type.rs @@ -15,6 +15,8 @@ // specific language governing permissions and limitations // under the License. +use std::sync::Arc; + use datafusion::arrow::array::Array; use datafusion::arrow::datatypes::{DataType, IntervalUnit, TimeUnit}; use datafusion::common::ScalarValue; @@ -22,6 +24,9 @@ use datafusion::logical_expr::expr::NullTreatment as DFNullTreatment; use pyo3::exceptions::{PyNotImplementedError, PyValueError}; use pyo3::prelude::*; +/// A [`ScalarValue`] wrapped in a Python object. This struct allows for conversion +/// from a variety of Python objects into a [`ScalarValue`]. See +/// ``FromPyArrow::from_pyarrow_bound`` conversion details. #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd)] pub struct PyScalarValue(pub ScalarValue); @@ -37,7 +42,14 @@ impl From for ScalarValue { } #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] -#[pyclass(frozen, eq, eq_int, name = "RexType", module = "datafusion.common")] +#[pyclass( + from_py_object, + frozen, + eq, + eq_int, + name = "RexType", + module = "datafusion.common" +)] pub enum RexType { Alias, Literal, @@ -58,7 +70,12 @@ pub enum RexType { /// to map types from one system to another. // TODO: This looks like this needs pyo3 tracking so leaving unfrozen for now #[derive(Debug, Clone)] -#[pyclass(name = "DataTypeMap", module = "datafusion.common", subclass)] +#[pyclass( + from_py_object, + name = "DataTypeMap", + module = "datafusion.common", + subclass +)] pub struct DataTypeMap { #[pyo3(get, set)] pub arrow_type: PyDataType, @@ -344,6 +361,10 @@ impl DataTypeMap { ScalarValue::Map(_) => Err(PyNotImplementedError::new_err( "ScalarValue::Map".to_string(), )), + ScalarValue::RunEndEncoded(field1, field2, _) => Ok(DataType::RunEndEncoded( + Arc::clone(field1), + Arc::clone(field2), + )), } } } @@ -584,7 +605,12 @@ impl DataTypeMap { /// Since `DataType` exists in another package we cannot make that happen here so we wrap /// `DataType` as `PyDataType` This exists solely to satisfy those constraints. #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] -#[pyclass(frozen, name = "DataType", module = "datafusion.common")] +#[pyclass( + from_py_object, + frozen, + name = "DataType", + module = "datafusion.common" +)] pub struct PyDataType { pub data_type: DataType, } @@ -642,7 +668,14 @@ impl From for PyDataType { /// Represents the possible Python types that can be mapped to the SQL types #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] -#[pyclass(frozen, eq, eq_int, name = "PythonType", module = "datafusion.common")] +#[pyclass( + from_py_object, + frozen, + eq, + eq_int, + name = "PythonType", + module = "datafusion.common" +)] pub enum PythonType { Array, Bool, @@ -662,7 +695,14 @@ pub enum PythonType { #[allow(non_camel_case_types)] #[allow(clippy::upper_case_acronyms)] #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] -#[pyclass(frozen, eq, eq_int, name = "SqlType", module = "datafusion.common")] +#[pyclass( + from_py_object, + frozen, + eq, + eq_int, + name = "SqlType", + module = "datafusion.common" +)] pub enum SqlType { ANY, ARRAY, @@ -721,6 +761,7 @@ pub enum SqlType { #[allow(clippy::upper_case_acronyms)] #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] #[pyclass( + from_py_object, frozen, eq, eq_int, diff --git a/src/common/df_schema.rs b/crates/core/src/common/df_schema.rs similarity index 93% rename from src/common/df_schema.rs rename to crates/core/src/common/df_schema.rs index eb62469cf..9167e772e 100644 --- a/src/common/df_schema.rs +++ b/crates/core/src/common/df_schema.rs @@ -21,7 +21,13 @@ use datafusion::common::DFSchema; use pyo3::prelude::*; #[derive(Debug, Clone)] -#[pyclass(frozen, name = "DFSchema", module = "datafusion.common", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "DFSchema", + module = "datafusion.common", + subclass +)] pub struct PyDFSchema { schema: Arc, } diff --git a/src/common/function.rs b/crates/core/src/common/function.rs similarity index 93% rename from src/common/function.rs rename to crates/core/src/common/function.rs index bc6f23160..41cab515f 100644 --- a/src/common/function.rs +++ b/crates/core/src/common/function.rs @@ -22,7 +22,13 @@ use pyo3::prelude::*; use super::data_type::PyDataType; -#[pyclass(frozen, name = "SqlFunction", module = "datafusion.common", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "SqlFunction", + module = "datafusion.common", + subclass +)] #[derive(Debug, Clone)] pub struct SqlFunction { pub name: String, diff --git a/src/common/schema.rs b/crates/core/src/common/schema.rs similarity index 92% rename from src/common/schema.rs rename to crates/core/src/common/schema.rs index 4e46592aa..29a27b204 100644 --- a/src/common/schema.rs +++ b/crates/core/src/common/schema.rs @@ -34,7 +34,13 @@ use super::data_type::DataTypeMap; use super::function::SqlFunction; use crate::sql::logical::PyLogicalPlan; -#[pyclass(name = "SqlSchema", module = "datafusion.common", subclass, frozen)] +#[pyclass( + from_py_object, + name = "SqlSchema", + module = "datafusion.common", + subclass, + frozen +)] #[derive(Debug, Clone)] pub struct SqlSchema { name: Arc>, @@ -43,7 +49,12 @@ pub struct SqlSchema { functions: Arc>>, } -#[pyclass(name = "SqlTable", module = "datafusion.common", subclass)] +#[pyclass( + from_py_object, + name = "SqlTable", + module = "datafusion.common", + subclass +)] #[derive(Debug, Clone)] pub struct SqlTable { #[pyo3(get, set)] @@ -87,7 +98,12 @@ impl SqlTable { } } -#[pyclass(name = "SqlView", module = "datafusion.common", subclass)] +#[pyclass( + from_py_object, + name = "SqlView", + module = "datafusion.common", + subclass +)] #[derive(Debug, Clone)] pub struct SqlView { #[pyo3(get, set)] @@ -247,7 +263,13 @@ fn is_supported_push_down_expr(_expr: &Expr) -> bool { true } -#[pyclass(frozen, name = "SqlStatistics", module = "datafusion.common", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "SqlStatistics", + module = "datafusion.common", + subclass +)] #[derive(Debug, Clone)] pub struct SqlStatistics { row_count: f64, @@ -266,7 +288,13 @@ impl SqlStatistics { } } -#[pyclass(frozen, name = "Constraints", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Constraints", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyConstraints { pub constraints: Constraints, @@ -291,7 +319,14 @@ impl Display for PyConstraints { } #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] -#[pyclass(frozen, eq, eq_int, name = "TableType", module = "datafusion.common")] +#[pyclass( + from_py_object, + frozen, + eq, + eq_int, + name = "TableType", + module = "datafusion.common" +)] pub enum PyTableType { Base, View, @@ -318,7 +353,13 @@ impl From for PyTableType { } } -#[pyclass(frozen, name = "TableSource", module = "datafusion.common", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "TableSource", + module = "datafusion.common", + subclass +)] #[derive(Clone)] pub struct PyTableSource { pub table_source: Arc, diff --git a/src/config.rs b/crates/core/src/config.rs similarity index 91% rename from src/config.rs rename to crates/core/src/config.rs index 583dea7ef..fdb693a12 100644 --- a/src/config.rs +++ b/crates/core/src/config.rs @@ -22,9 +22,15 @@ use parking_lot::RwLock; use pyo3::prelude::*; use pyo3::types::*; +use crate::common::data_type::PyScalarValue; use crate::errors::PyDataFusionResult; -use crate::utils::py_obj_to_scalar_value; -#[pyclass(name = "Config", module = "datafusion", subclass, frozen)] +#[pyclass( + from_py_object, + name = "Config", + module = "datafusion", + subclass, + frozen +)] #[derive(Clone)] pub(crate) struct PyConfig { config: Arc>, @@ -65,9 +71,9 @@ impl PyConfig { /// Set a configuration option pub fn set(&self, key: &str, value: Py, py: Python) -> PyDataFusionResult<()> { - let scalar_value = py_obj_to_scalar_value(py, value)?; + let scalar_value: PyScalarValue = value.extract(py)?; let mut options = self.config.write(); - options.set(key, scalar_value.to_string().as_str())?; + options.set(key, scalar_value.0.to_string().as_str())?; Ok(()) } diff --git a/src/context.rs b/crates/core/src/context.rs similarity index 72% rename from src/context.rs rename to crates/core/src/context.rs index ad4fc36b1..e46d359d6 100644 --- a/src/context.rs +++ b/crates/core/src/context.rs @@ -17,6 +17,7 @@ use std::collections::{HashMap, HashSet}; use std::path::PathBuf; +use std::ptr::NonNull; use std::str::FromStr; use std::sync::Arc; @@ -26,55 +27,77 @@ use arrow::pyarrow::FromPyArrow; use datafusion::arrow::datatypes::{DataType, Schema, SchemaRef}; use datafusion::arrow::pyarrow::PyArrowType; use datafusion::arrow::record_batch::RecordBatch; -use datafusion::catalog::CatalogProvider; -use datafusion::common::{exec_err, ScalarValue, TableReference}; +use datafusion::catalog::{CatalogProvider, CatalogProviderList, TableProviderFactory}; +use datafusion::common::{DFSchema, ScalarValue, TableReference, exec_err}; use datafusion::datasource::file_format::file_compression_type::FileCompressionType; use datafusion::datasource::file_format::parquet::ParquetFormat; use datafusion::datasource::listing::{ ListingOptions, ListingTable, ListingTableConfig, ListingTableUrl, }; use datafusion::datasource::{MemTable, TableProvider}; +use datafusion::execution::TaskContextProvider; use datafusion::execution::context::{ DataFilePaths, SQLOptions, SessionConfig, SessionContext, TaskContext, }; use datafusion::execution::disk_manager::DiskManagerMode; use datafusion::execution::memory_pool::{FairSpillPool, GreedyMemoryPool, UnboundedMemoryPool}; -use datafusion::execution::options::ReadOptions; +use datafusion::execution::options::{ArrowReadOptions, ReadOptions}; use datafusion::execution::runtime_env::RuntimeEnvBuilder; use datafusion::execution::session_state::SessionStateBuilder; use datafusion::prelude::{ - AvroReadOptions, CsvReadOptions, DataFrame, NdJsonReadOptions, ParquetReadOptions, + AvroReadOptions, CsvReadOptions, DataFrame, JsonReadOptions, ParquetReadOptions, +}; +use datafusion_ffi::catalog_provider::FFI_CatalogProvider; +use datafusion_ffi::catalog_provider_list::FFI_CatalogProviderList; +use datafusion_ffi::config::extension_options::FFI_ExtensionOptions; +use datafusion_ffi::execution::FFI_TaskContextProvider; +use datafusion_ffi::proto::logical_extension_codec::FFI_LogicalExtensionCodec; +use datafusion_ffi::table_provider_factory::FFI_TableProviderFactory; +use datafusion_proto::logical_plan::DefaultLogicalExtensionCodec; +use datafusion_python_util::{ + create_logical_extension_capsule, ffi_logical_codec_from_pycapsule, get_global_ctx, + get_tokio_runtime, spawn_future, wait_for_future, }; -use datafusion_ffi::catalog_provider::{FFI_CatalogProvider, ForeignCatalogProvider}; use object_store::ObjectStore; -use pyo3::exceptions::{PyKeyError, PyValueError}; -use pyo3::prelude::*; -use pyo3::types::{PyCapsule, PyDict, PyList, PyTuple, PyType}; use pyo3::IntoPyObjectExt; +use pyo3::exceptions::{PyKeyError, PyRuntimeError, PyValueError}; +use pyo3::prelude::*; +use pyo3::types::{PyCapsule, PyDict, PyList, PyTuple}; use url::Url; use uuid::Uuid; -use crate::catalog::{PyCatalog, RustWrappedPyCatalogProvider}; +use crate::catalog::{ + PyCatalog, PyCatalogList, RustWrappedPyCatalogProvider, RustWrappedPyCatalogProviderList, +}; use crate::common::data_type::PyScalarValue; +use crate::common::df_schema::PyDFSchema; use crate::dataframe::PyDataFrame; use crate::dataset::Dataset; -use crate::errors::{py_datafusion_err, PyDataFusionError, PyDataFusionResult}; +use crate::errors::{ + PyDataFusionError, PyDataFusionResult, from_datafusion_error, py_datafusion_err, +}; +use crate::expr::PyExpr; use crate::expr::sort_expr::PySortExpr; +use crate::options::PyCsvReadOptions; use crate::physical_plan::PyExecutionPlan; use crate::record_batch::PyRecordBatchStream; -use crate::sql::exceptions::py_value_err; use crate::sql::logical::PyLogicalPlan; use crate::sql::util::replace_placeholders_with_strings; use crate::store::StorageContexts; -use crate::table::PyTable; +use crate::table::{PyTable, RustWrappedPyTableProviderFactory}; use crate::udaf::PyAggregateUDF; use crate::udf::PyScalarUDF; use crate::udtf::PyTableFunction; use crate::udwf::PyWindowUDF; -use crate::utils::{get_global_ctx, spawn_future, validate_pycapsule, wait_for_future}; /// Configuration options for a SessionContext -#[pyclass(frozen, name = "SessionConfig", module = "datafusion", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "SessionConfig", + module = "datafusion", + subclass +)] #[derive(Clone, Default)] pub struct PySessionConfig { pub config: SessionConfig, @@ -164,10 +187,43 @@ impl PySessionConfig { fn set(&self, key: &str, value: &str) -> Self { Self::from(self.config.clone().set_str(key, value)) } + + pub fn with_extension(&self, extension: Bound) -> PyResult { + if !extension.hasattr("__datafusion_extension_options__")? { + return Err(pyo3::exceptions::PyAttributeError::new_err( + "Expected extension object to define __datafusion_extension_options__()", + )); + } + let capsule = extension.call_method0("__datafusion_extension_options__")?; + let capsule = capsule.cast::()?; + + let extension: NonNull = capsule + .pointer_checked(Some(c"datafusion_extension_options"))? + .cast(); + let mut extension = unsafe { extension.as_ref() }.clone(); + + let mut config = self.config.clone(); + let options = config.options_mut(); + if let Some(prior_extension) = options.extensions.get::() { + extension + .merge(prior_extension) + .map_err(py_datafusion_err)?; + } + + options.extensions.insert(extension); + + Ok(Self::from(config)) + } } /// Runtime options for a SessionContext -#[pyclass(frozen, name = "RuntimeEnvBuilder", module = "datafusion", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "RuntimeEnvBuilder", + module = "datafusion", + subclass +)] #[derive(Clone)] pub struct PyRuntimeEnvBuilder { pub builder: RuntimeEnvBuilder, @@ -254,7 +310,13 @@ impl PyRuntimeEnvBuilder { } /// `PySQLOptions` allows you to specify options to the sql execution. -#[pyclass(frozen, name = "SQLOptions", module = "datafusion", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "SQLOptions", + module = "datafusion", + subclass +)] #[derive(Clone)] pub struct PySQLOptions { pub options: SQLOptions, @@ -293,10 +355,17 @@ impl PySQLOptions { /// `PySessionContext` is able to plan and execute DataFusion plans. /// It has a powerful optimizer, a physical planner for local execution, and a /// multi-threaded execution engine to perform the execution. -#[pyclass(frozen, name = "SessionContext", module = "datafusion", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "SessionContext", + module = "datafusion", + subclass +)] #[derive(Clone)] pub struct PySessionContext { - pub ctx: SessionContext, + pub ctx: Arc, + logical_codec: Arc, } #[pymethods] @@ -323,23 +392,24 @@ impl PySessionContext { .with_runtime_env(runtime) .with_default_features() .build(); - Ok(PySessionContext { - ctx: SessionContext::new_with_state(session_state), - }) + let ctx = Arc::new(SessionContext::new_with_state(session_state)); + let logical_codec = Self::default_logical_codec(&ctx); + Ok(PySessionContext { ctx, logical_codec }) } pub fn enable_url_table(&self) -> PyResult { Ok(PySessionContext { - ctx: self.ctx.clone().enable_url_table(), + ctx: Arc::new(self.ctx.as_ref().clone().enable_url_table()), + logical_codec: Arc::clone(&self.logical_codec), }) } - #[classmethod] + #[staticmethod] #[pyo3(signature = ())] - fn global_ctx(_cls: &Bound<'_, PyType>) -> PyResult { - Ok(Self { - ctx: get_global_ctx().clone(), - }) + pub fn global_ctx() -> PyResult { + let ctx = get_global_ctx().clone(); + let logical_codec = Self::default_logical_codec(&ctx); + Ok(Self { ctx, logical_codec }) } /// Register an object store with the given name @@ -366,11 +436,25 @@ impl PySessionContext { &upstream_host }; let url_string = format!("{scheme}{derived_host}"); - let url = Url::parse(&url_string).unwrap(); + let url = Url::parse(&url_string).map_err(|e| PyValueError::new_err(e.to_string()))?; self.ctx.runtime_env().register_object_store(&url, store); Ok(()) } + /// Deregister an object store with the given url + #[pyo3(signature = (scheme, host=None))] + pub fn deregister_object_store( + &self, + scheme: &str, + host: Option<&str>, + ) -> PyDataFusionResult<()> { + let host = host.unwrap_or(""); + let url_string = format!("{scheme}{host}"); + let url = Url::parse(&url_string).map_err(|e| PyDataFusionError::Common(e.to_string()))?; + self.ctx.runtime_env().deregister_object_store(&url)?; + Ok(()) + } + #[allow(clippy::too_many_arguments)] #[pyo3(signature = (name, path, table_partition_cols=vec![], file_extension=".parquet", @@ -424,6 +508,10 @@ impl PySessionContext { self.ctx.register_udtf(&name, func); } + pub fn deregister_udtf(&self, name: &str) { + self.ctx.deregister_udtf(name); + } + #[pyo3(signature = (query, options=None, param_values=HashMap::default(), param_strings=HashMap::default()))] pub fn sql_with_options( &self, @@ -453,7 +541,8 @@ impl PySessionContext { let mut df = wait_for_future(py, async { self.ctx.sql_with_options(&query, options).await - })??; + })? + .map_err(from_datafusion_error)?; if !param_values.is_empty() { df = df.with_param_values(param_values)?; @@ -606,7 +695,8 @@ impl PySessionContext { } pub fn register_table(&self, name: &str, table: Bound<'_, PyAny>) -> PyDataFusionResult<()> { - let table = PyTable::new(&table)?; + let session = self.clone().into_bound_py_any(table.py())?; + let table = PyTable::new(table, Some(session))?; self.ctx.register_table(name, table.table)?; Ok(()) @@ -617,26 +707,102 @@ impl PySessionContext { Ok(()) } + pub fn register_table_factory( + &self, + format: &str, + mut factory: Bound<'_, PyAny>, + ) -> PyDataFusionResult<()> { + if factory.hasattr("__datafusion_table_provider_factory__")? { + let py = factory.py(); + let codec_capsule = create_logical_extension_capsule(py, self.logical_codec.as_ref())?; + factory = factory + .getattr("__datafusion_table_provider_factory__")? + .call1((codec_capsule,))?; + } + + let factory: Arc = + if let Ok(capsule) = factory.cast::().map_err(py_datafusion_err) { + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_table_provider_factory"))? + .cast(); + let factory = unsafe { data.as_ref() }; + factory.into() + } else { + Arc::new(RustWrappedPyTableProviderFactory::new( + factory.into(), + self.logical_codec.clone(), + )) + }; + + let st = self.ctx.state_ref(); + let mut lock = st.write(); + lock.table_factories_mut() + .insert(format.to_owned(), factory); + + Ok(()) + } + + pub fn register_catalog_provider_list( + &self, + mut provider: Bound, + ) -> PyDataFusionResult<()> { + if provider.hasattr("__datafusion_catalog_provider_list__")? { + let py = provider.py(); + let codec_capsule = create_logical_extension_capsule(py, self.logical_codec.as_ref())?; + provider = provider + .getattr("__datafusion_catalog_provider_list__")? + .call1((codec_capsule,))?; + } + + let provider = if let Ok(capsule) = provider.cast::() { + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_catalog_provider_list"))? + .cast(); + let provider = unsafe { data.as_ref() }; + let provider: Arc = provider.into(); + provider as Arc + } else { + match provider.extract::() { + Ok(py_catalog_list) => py_catalog_list.catalog_list, + Err(_) => Arc::new(RustWrappedPyCatalogProviderList::new( + provider.into(), + Arc::clone(&self.logical_codec), + )) as Arc, + } + }; + + self.ctx.register_catalog_list(provider); + + Ok(()) + } + pub fn register_catalog_provider( &self, name: &str, - provider: Bound<'_, PyAny>, + mut provider: Bound<'_, PyAny>, ) -> PyDataFusionResult<()> { - let provider = if provider.hasattr("__datafusion_catalog_provider__")? { - let capsule = provider + if provider.hasattr("__datafusion_catalog_provider__")? { + let py = provider.py(); + let codec_capsule = create_logical_extension_capsule(py, self.logical_codec.as_ref())?; + provider = provider .getattr("__datafusion_catalog_provider__")? - .call0()?; - let capsule = capsule.downcast::().map_err(py_datafusion_err)?; - validate_pycapsule(capsule, "datafusion_catalog_provider")?; + .call1((codec_capsule,))?; + } - let provider = unsafe { capsule.reference::() }; - let provider: ForeignCatalogProvider = provider.into(); - Arc::new(provider) as Arc + let provider = if let Ok(capsule) = provider.cast::() { + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_catalog_provider"))? + .cast(); + let provider = unsafe { data.as_ref() }; + let provider: Arc = provider.into(); + provider as Arc } else { match provider.extract::() { Ok(py_catalog) => py_catalog.catalog, - Err(_) => Arc::new(RustWrappedPyCatalogProvider::new(provider.into())) - as Arc, + Err(_) => Arc::new(RustWrappedPyCatalogProvider::new( + provider.into(), + Arc::clone(&self.logical_codec), + )) as Arc, } }; @@ -707,41 +873,20 @@ impl PySessionContext { Ok(()) } - #[allow(clippy::too_many_arguments)] #[pyo3(signature = (name, path, - schema=None, - has_header=true, - delimiter=",", - schema_infer_max_records=1000, - file_extension=".csv", - file_compression_type=None))] + options=None))] pub fn register_csv( &self, name: &str, path: &Bound<'_, PyAny>, - schema: Option>, - has_header: bool, - delimiter: &str, - schema_infer_max_records: usize, - file_extension: &str, - file_compression_type: Option, + options: Option<&PyCsvReadOptions>, py: Python, ) -> PyDataFusionResult<()> { - let delimiter = delimiter.as_bytes(); - if delimiter.len() != 1 { - return Err(PyDataFusionError::PythonError(py_value_err( - "Delimiter must be a single character", - ))); - } - - let mut options = CsvReadOptions::new() - .has_header(has_header) - .delimiter(delimiter[0]) - .schema_infer_max_records(schema_infer_max_records) - .file_extension(file_extension) - .file_compression_type(parse_file_compression_type(file_compression_type)?); - options.schema = schema.as_ref().map(|x| &x.0); + let options = options + .map(|opts| opts.try_into()) + .transpose()? + .unwrap_or_default(); if path.is_instance_of::() { let paths = path.extract::>()?; @@ -779,7 +924,7 @@ impl PySessionContext { .to_str() .ok_or_else(|| PyValueError::new_err("Unable to convert path to a string"))?; - let mut options = NdJsonReadOptions::default() + let mut options = JsonReadOptions::default() .file_compression_type(parse_file_compression_type(file_compression_type)?) .table_partition_cols( table_partition_cols @@ -831,6 +976,39 @@ impl PySessionContext { Ok(()) } + #[pyo3(signature = (name, path, schema=None, file_extension=".arrow", table_partition_cols=vec![]))] + pub fn register_arrow( + &self, + name: &str, + path: &str, + schema: Option>, + file_extension: &str, + table_partition_cols: Vec<(String, PyArrowType)>, + py: Python, + ) -> PyDataFusionResult<()> { + let mut options = ArrowReadOptions::default().table_partition_cols( + table_partition_cols + .into_iter() + .map(|(name, ty)| (name, ty.0)) + .collect::>(), + ); + options.file_extension = file_extension; + options.schema = schema.as_ref().map(|x| &x.0); + + let result = self.ctx.register_arrow(name, path, options); + wait_for_future(py, result)??; + Ok(()) + } + + pub fn register_batch( + &self, + name: &str, + batch: PyArrowType, + ) -> PyDataFusionResult<()> { + self.ctx.register_batch(name, batch.0)?; + Ok(()) + } + // Registers a PyArrow.Dataset pub fn register_dataset( &self, @@ -850,62 +1028,60 @@ impl PySessionContext { Ok(()) } + pub fn deregister_udf(&self, name: &str) { + self.ctx.deregister_udf(name); + } + pub fn register_udaf(&self, udaf: PyAggregateUDF) -> PyResult<()> { self.ctx.register_udaf(udaf.function); Ok(()) } + pub fn deregister_udaf(&self, name: &str) { + self.ctx.deregister_udaf(name); + } + pub fn register_udwf(&self, udwf: PyWindowUDF) -> PyResult<()> { self.ctx.register_udwf(udwf.function); Ok(()) } + pub fn deregister_udwf(&self, name: &str) { + self.ctx.deregister_udwf(name); + } + #[pyo3(signature = (name="datafusion"))] - pub fn catalog(&self, name: &str) -> PyResult> { + pub fn catalog(&self, py: Python, name: &str) -> PyResult> { let catalog = self.ctx.catalog(name).ok_or(PyKeyError::new_err(format!( "Catalog with name {name} doesn't exist." )))?; - Python::attach(|py| { - match catalog - .as_any() - .downcast_ref::() - { - Some(wrapped_schema) => Ok(wrapped_schema.catalog_provider.clone_ref(py)), - None => PyCatalog::from(catalog).into_py_any(py), - } - }) + match catalog + .as_any() + .downcast_ref::() + { + Some(wrapped_schema) => Ok(wrapped_schema.catalog_provider.clone_ref(py)), + None => Ok( + PyCatalog::new_from_parts(catalog, Arc::clone(&self.logical_codec)) + .into_py_any(py)?, + ), + } } pub fn catalog_names(&self) -> HashSet { self.ctx.catalog_names().into_iter().collect() } - pub fn tables(&self) -> HashSet { - self.ctx - .catalog_names() - .into_iter() - .filter_map(|name| self.ctx.catalog(&name)) - .flat_map(move |catalog| { - catalog - .schema_names() - .into_iter() - .filter_map(move |name| catalog.schema(&name)) - }) - .flat_map(|schema| schema.table_names()) - .collect() - } - pub fn table(&self, name: &str, py: Python) -> PyResult { let res = wait_for_future(py, self.ctx.table(name)) .map_err(|e| PyKeyError::new_err(e.to_string()))?; match res { Ok(df) => Ok(PyDataFrame::new(df)), Err(e) => { - if let datafusion::error::DataFusionError::Plan(msg) = &e { - if msg.contains("No table named") { - return Err(PyKeyError::new_err(msg.to_string())); - } + if let datafusion::error::DataFusionError::Plan(msg) = &e + && msg.contains("No table named") + { + return Err(PyKeyError::new_err(msg.to_string())); } Err(py_datafusion_err(e)) } @@ -924,6 +1100,49 @@ impl PySessionContext { self.ctx.session_id() } + pub fn session_start_time(&self) -> String { + self.ctx.session_start_time().to_rfc3339() + } + + pub fn enable_ident_normalization(&self) -> bool { + self.ctx.enable_ident_normalization() + } + + pub fn parse_sql_expr(&self, sql: &str, schema: PyDFSchema) -> PyDataFusionResult { + let df_schema: DFSchema = schema.into(); + Ok(self.ctx.parse_sql_expr(sql, &df_schema)?.into()) + } + + pub fn execute_logical_plan( + &self, + plan: PyLogicalPlan, + py: Python, + ) -> PyDataFusionResult { + let df = wait_for_future( + py, + self.ctx.execute_logical_plan(plan.plan.as_ref().clone()), + )??; + Ok(PyDataFrame::new(df)) + } + + pub fn refresh_catalogs(&self, py: Python) -> PyDataFusionResult<()> { + wait_for_future(py, self.ctx.refresh_catalogs())??; + Ok(()) + } + + pub fn remove_optimizer_rule(&self, name: &str) -> bool { + self.ctx.remove_optimizer_rule(name) + } + + pub fn table_provider(&self, name: &str, py: Python) -> PyResult { + let provider = wait_for_future(py, self.ctx.table_provider(name)) + // Outer error: runtime/async failure + .map_err(|e| PyRuntimeError::new_err(e.to_string()))? + // Inner error: table not found + .map_err(|e| PyKeyError::new_err(e.to_string()))?; + Ok(PyTable { table: provider }) + } + #[allow(clippy::too_many_arguments)] #[pyo3(signature = (path, schema=None, schema_infer_max_records=1000, file_extension=".json", table_partition_cols=vec![], file_compression_type=None))] pub fn read_json( @@ -939,7 +1158,7 @@ impl PySessionContext { let path = path .to_str() .ok_or_else(|| PyValueError::new_err("Unable to convert path to a string"))?; - let mut options = NdJsonReadOptions::default() + let mut options = JsonReadOptions::default() .table_partition_cols( table_partition_cols .into_iter() @@ -960,48 +1179,19 @@ impl PySessionContext { Ok(PyDataFrame::new(df)) } - #[allow(clippy::too_many_arguments)] #[pyo3(signature = ( path, - schema=None, - has_header=true, - delimiter=",", - schema_infer_max_records=1000, - file_extension=".csv", - table_partition_cols=vec![], - file_compression_type=None))] + options=None))] pub fn read_csv( &self, path: &Bound<'_, PyAny>, - schema: Option>, - has_header: bool, - delimiter: &str, - schema_infer_max_records: usize, - file_extension: &str, - table_partition_cols: Vec<(String, PyArrowType)>, - file_compression_type: Option, + options: Option<&PyCsvReadOptions>, py: Python, ) -> PyDataFusionResult { - let delimiter = delimiter.as_bytes(); - if delimiter.len() != 1 { - return Err(PyDataFusionError::PythonError(py_value_err( - "Delimiter must be a single character", - ))); - }; - - let mut options = CsvReadOptions::new() - .has_header(has_header) - .delimiter(delimiter[0]) - .schema_infer_max_records(schema_infer_max_records) - .file_extension(file_extension) - .table_partition_cols( - table_partition_cols - .into_iter() - .map(|(name, ty)| (name, ty.0)) - .collect::>(), - ) - .file_compression_type(parse_file_compression_type(file_compression_type)?); - options.schema = schema.as_ref().map(|x| &x.0); + let options = options + .map(|opts| opts.try_into()) + .transpose()? + .unwrap_or_default(); if path.is_instance_of::() { let paths = path.extract::>()?; @@ -1087,8 +1277,32 @@ impl PySessionContext { Ok(PyDataFrame::new(df)) } + #[pyo3(signature = (path, schema=None, file_extension=".arrow", table_partition_cols=vec![]))] + pub fn read_arrow( + &self, + path: &str, + schema: Option>, + file_extension: &str, + table_partition_cols: Vec<(String, PyArrowType)>, + py: Python, + ) -> PyDataFusionResult { + let mut options = ArrowReadOptions::default().table_partition_cols( + table_partition_cols + .into_iter() + .map(|(name, ty)| (name, ty.0)) + .collect::>(), + ); + options.file_extension = file_extension; + options.schema = schema.as_ref().map(|x| &x.0); + + let result = self.ctx.read_arrow(path, options); + let df = wait_for_future(py, result)??; + Ok(PyDataFrame::new(df)) + } + pub fn read_table(&self, table: Bound<'_, PyAny>) -> PyDataFusionResult { - let table = PyTable::new(&table)?; + let session = self.clone().into_bound_py_any(table.py())?; + let table = PyTable::new(table, Some(session))?; let df = self.ctx.read_table(table.table())?; Ok(PyDataFrame::new(df)) } @@ -1122,6 +1336,39 @@ impl PySessionContext { let stream = spawn_future(py, async move { plan.execute(part, Arc::new(ctx)) })?; Ok(PyRecordBatchStream::new(stream)) } + + pub fn __datafusion_task_context_provider__<'py>( + &self, + py: Python<'py>, + ) -> PyResult> { + let name = cr"datafusion_task_context_provider".into(); + + let ctx_provider = Arc::clone(&self.ctx) as Arc; + let ffi_ctx_provider = FFI_TaskContextProvider::from(&ctx_provider); + + PyCapsule::new(py, ffi_ctx_provider, Some(name)) + } + + pub fn __datafusion_logical_extension_codec__<'py>( + &self, + py: Python<'py>, + ) -> PyResult> { + create_logical_extension_capsule(py, self.logical_codec.as_ref()) + } + + pub fn with_logical_extension_codec<'py>( + &self, + codec: Bound<'py, PyAny>, + ) -> PyDataFusionResult { + let logical_codec = Arc::new(ffi_logical_codec_from_pycapsule(codec)?); + + Ok({ + Self { + ctx: Arc::clone(&self.ctx), + logical_codec, + } + }) + } } impl PySessionContext { @@ -1168,6 +1415,17 @@ impl PySessionContext { .register_table(TableReference::Bare { table: name.into() }, Arc::new(table))?; Ok(()) } + + fn default_logical_codec(ctx: &Arc) -> Arc { + let codec = Arc::new(DefaultLogicalExtensionCodec {}); + let runtime = get_tokio_runtime().handle().clone(); + let ctx_provider = Arc::clone(ctx) as Arc; + Arc::new(FFI_LogicalExtensionCodec::new( + codec, + Some(runtime), + &ctx_provider, + )) + } } pub fn parse_file_compression_type( @@ -1181,12 +1439,15 @@ pub fn parse_file_compression_type( impl From for SessionContext { fn from(ctx: PySessionContext) -> SessionContext { - ctx.ctx + ctx.ctx.as_ref().clone() } } impl From for PySessionContext { fn from(ctx: SessionContext) -> PySessionContext { - PySessionContext { ctx } + let ctx = Arc::new(ctx); + let logical_codec = Self::default_logical_codec(&ctx); + + PySessionContext { ctx, logical_codec } } } diff --git a/src/dataframe.rs b/crates/core/src/dataframe.rs similarity index 80% rename from src/dataframe.rs rename to crates/core/src/dataframe.rs index d920df71e..2e74991b8 100644 --- a/src/dataframe.rs +++ b/crates/core/src/dataframe.rs @@ -17,9 +17,11 @@ use std::collections::HashMap; use std::ffi::{CStr, CString}; +use std::ptr::NonNull; +use std::str::FromStr; use std::sync::Arc; -use arrow::array::{new_null_array, Array, ArrayRef, RecordBatch, RecordBatchReader}; +use arrow::array::{Array, ArrayRef, RecordBatch, RecordBatchReader, new_null_array}; use arrow::compute::can_cast_types; use arrow::error::ArrowError; use arrow::ffi::FFI_ArrowSchema; @@ -35,28 +37,33 @@ use datafusion::config::{CsvOptions, ParquetColumnOptions, ParquetOptions, Table use datafusion::dataframe::{DataFrame, DataFrameWriteOptions}; use datafusion::error::DataFusionError; use datafusion::execution::SendableRecordBatchStream; +use datafusion::execution::context::TaskContext; use datafusion::logical_expr::dml::InsertOp; -use datafusion::logical_expr::SortExpr; +use datafusion::logical_expr::{LogicalPlan, SortExpr}; use datafusion::parquet::basic::{BrotliLevel, Compression, GzipLevel, ZstdLevel}; +use datafusion::physical_plan::{ + ExecutionPlan as DFExecutionPlan, collect as df_collect, + collect_partitioned as df_collect_partitioned, execute_stream as df_execute_stream, + execute_stream_partitioned as df_execute_stream_partitioned, +}; use datafusion::prelude::*; +use datafusion_python_util::{is_ipython_env, spawn_future, wait_for_future}; use futures::{StreamExt, TryStreamExt}; use parking_lot::Mutex; +use pyo3::PyErr; use pyo3::exceptions::PyValueError; use pyo3::prelude::*; use pyo3::pybacked::PyBackedStr; use pyo3::types::{PyCapsule, PyList, PyTuple, PyTupleMethods}; -use pyo3::PyErr; -use crate::errors::{py_datafusion_err, PyDataFusionError, PyDataFusionResult}; -use crate::expr::sort_expr::{to_sort_expressions, PySortExpr}; +use crate::common::data_type::PyScalarValue; +use crate::errors::{PyDataFusionError, PyDataFusionResult, py_datafusion_err}; use crate::expr::PyExpr; +use crate::expr::sort_expr::{PySortExpr, to_sort_expressions}; use crate::physical_plan::PyExecutionPlan; -use crate::record_batch::{poll_next_batch, PyRecordBatchStream}; +use crate::record_batch::{PyRecordBatchStream, poll_next_batch}; use crate::sql::logical::PyLogicalPlan; use crate::table::{PyTable, TempViewTable}; -use crate::utils::{ - is_ipython_env, py_obj_to_scalar_value, spawn_future, validate_pycapsule, wait_for_future, -}; /// File-level static CStr for the Arrow array stream capsule name. static ARROW_ARRAY_STREAM_NAME: &CStr = cstr!("arrow_array_stream"); @@ -71,18 +78,18 @@ type SharedCachedBatches = Arc>; pub struct FormatterConfig { /// Maximum memory in bytes to use for display (default: 2MB) pub max_bytes: usize, - /// Minimum number of rows to display (default: 20) + /// Minimum number of rows to display (default: 10) pub min_rows: usize, - /// Number of rows to include in __repr__ output (default: 10) - pub repr_rows: usize, + /// Maximum number of rows to include in __repr__ output (default: 10) + pub max_rows: usize, } impl Default for FormatterConfig { fn default() -> Self { Self { max_bytes: 2 * 1024 * 1024, // 2MB - min_rows: 20, - repr_rows: 10, + min_rows: 10, + max_rows: 10, } } } @@ -102,8 +109,12 @@ impl FormatterConfig { return Err("min_rows must be a positive integer".to_string()); } - if self.repr_rows == 0 { - return Err("repr_rows must be a positive integer".to_string()); + if self.max_rows == 0 { + return Err("max_rows must be a positive integer".to_string()); + } + + if self.min_rows > self.max_rows { + return Err("min_rows must be less than or equal to max_rows".to_string()); } Ok(()) @@ -135,11 +146,11 @@ fn import_python_formatter(py: Python<'_>) -> PyResult> { // Helper function to extract attributes with fallback to default fn get_attr<'a, T>(py_object: &'a Bound<'a, PyAny>, attr_name: &str, default_value: T) -> T where - T: for<'py> pyo3::FromPyObject<'py> + Clone, + T: for<'py> pyo3::FromPyObject<'py, 'py> + Clone, { py_object .getattr(attr_name) - .and_then(|v| v.extract::()) + .and_then(|v| v.extract::().map_err(Into::::into)) .unwrap_or_else(|_| default_value.clone()) } @@ -147,13 +158,30 @@ where fn build_formatter_config_from_python(formatter: &Bound<'_, PyAny>) -> PyResult { let default_config = FormatterConfig::default(); let max_bytes = get_attr(formatter, "max_memory_bytes", default_config.max_bytes); - let min_rows = get_attr(formatter, "min_rows_display", default_config.min_rows); - let repr_rows = get_attr(formatter, "repr_rows", default_config.repr_rows); + let min_rows = get_attr(formatter, "min_rows", default_config.min_rows); + + // Backward compatibility: Try max_rows first (new name), fall back to repr_rows (deprecated), + // then use default. This ensures backward compatibility with custom formatter implementations + // during the deprecation period. + let max_rows = get_attr(formatter, "max_rows", 0usize); + let max_rows = if max_rows > 0 { + // max_rows attribute exists and has a value + max_rows + } else { + // Try the deprecated repr_rows attribute + let repr_rows = get_attr(formatter, "repr_rows", 0usize); + if repr_rows > 0 { + repr_rows + } else { + // Use default + default_config.max_rows + } + }; let config = FormatterConfig { max_bytes, min_rows, - repr_rows, + max_rows, }; // Return the validated config, converting String error to PyErr @@ -162,7 +190,13 @@ fn build_formatter_config_from_python(formatter: &Bound<'_, PyAny>) -> PyResult< } /// Python mapping of `ParquetOptions` (includes just the writer-related options). -#[pyclass(frozen, name = "ParquetWriterOptions", module = "datafusion", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "ParquetWriterOptions", + module = "datafusion", + subclass +)] #[derive(Clone, Default)] pub struct PyParquetWriterOptions { options: ParquetOptions, @@ -175,7 +209,7 @@ impl PyParquetWriterOptions { pub fn new( data_pagesize_limit: usize, write_batch_size: usize, - writer_version: String, + writer_version: &str, skip_arrow_metadata: bool, compression: Option, dictionary_enabled: Option, @@ -193,8 +227,11 @@ impl PyParquetWriterOptions { allow_single_file_parallelism: bool, maximum_parallel_row_group_writers: usize, maximum_buffered_record_batches_per_stream: usize, - ) -> Self { - Self { + ) -> PyResult { + let writer_version = + datafusion::common::parquet_config::DFParquetWriterVersion::from_str(writer_version) + .map_err(py_datafusion_err)?; + Ok(Self { options: ParquetOptions { data_pagesize_limit, write_batch_size, @@ -218,12 +255,18 @@ impl PyParquetWriterOptions { maximum_buffered_record_batches_per_stream, ..Default::default() }, - } + }) } } /// Python mapping of `ParquetColumnOptions`. -#[pyclass(frozen, name = "ParquetColumnOptions", module = "datafusion", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "ParquetColumnOptions", + module = "datafusion", + subclass +)] #[derive(Clone, Default)] pub struct PyParquetColumnOptions { options: ParquetColumnOptions, @@ -258,13 +301,22 @@ impl PyParquetColumnOptions { /// A PyDataFrame is a representation of a logical plan and an API to compose statements. /// Use it to build a plan and `.collect()` to execute the plan and collect the result. /// The actual execution of a plan runs natively on Rust and Arrow on a multi-threaded environment. -#[pyclass(name = "DataFrame", module = "datafusion", subclass, frozen)] +#[pyclass( + from_py_object, + name = "DataFrame", + module = "datafusion", + subclass, + frozen +)] #[derive(Clone)] pub struct PyDataFrame { df: Arc, // In IPython environment cache batches between __repr__ and _repr_html_ calls. batches: SharedCachedBatches, + + // Cache the last physical plan so that metrics are available after execution. + last_plan: Arc>>>, } impl PyDataFrame { @@ -273,6 +325,7 @@ impl PyDataFrame { Self { df: Arc::new(df), batches: Arc::new(Mutex::new(None)), + last_plan: Arc::new(Mutex::new(None)), } } @@ -344,6 +397,20 @@ impl PyDataFrame { Ok(html_str) } + /// Create the physical plan, cache it in `last_plan`, and return the plan together + /// with a task context. Centralises the repeated three-line pattern that appears in + /// `collect`, `collect_partitioned`, `execute_stream`, and `execute_stream_partitioned`. + fn create_and_cache_plan( + &self, + py: Python, + ) -> PyDataFusionResult<(Arc, Arc)> { + let df = self.df.as_ref().clone(); + let new_plan = wait_for_future(py, df.create_physical_plan())??; + *self.last_plan.lock() = Some(Arc::clone(&new_plan)); + let task_ctx = Arc::new(self.df.as_ref().task_ctx()); + Ok((new_plan, task_ctx)) + } + async fn collect_column_inner(&self, column: &str) -> Result { let batches = self .df @@ -425,17 +492,17 @@ impl PyDataFrame { fn __getitem__(&self, key: Bound<'_, PyAny>) -> PyDataFusionResult { if let Ok(key) = key.extract::() { // df[col] - self.select_columns(vec![key]) - } else if let Ok(tuple) = key.downcast::() { + self.select_exprs(vec![key]) + } else if let Ok(tuple) = key.cast::() { // df[col1, col2, col3] let keys = tuple .iter() .map(|item| item.extract::()) .collect::>>()?; - self.select_columns(keys) + self.select_exprs(keys) } else if let Ok(keys) = key.extract::>() { // df[[col1, col2, col3]] - self.select_columns(keys) + self.select_exprs(keys) } else { let message = "DataFrame can only be indexed by string index or indices".to_string(); Err(PyDataFusionError::Common(message)) @@ -511,13 +578,6 @@ impl PyDataFrame { Ok(PyTable::from(table_provider)) } - #[pyo3(signature = (*args))] - fn select_columns(&self, args: Vec) -> PyDataFusionResult { - let args = args.iter().map(|s| s.as_ref()).collect::>(); - let df = self.df.as_ref().clone().select_columns(&args)?; - Ok(Self::new(df)) - } - #[pyo3(signature = (*args))] fn select_exprs(&self, args: Vec) -> PyDataFusionResult { let args = args.iter().map(|s| s.as_ref()).collect::>(); @@ -539,6 +599,14 @@ impl PyDataFrame { Ok(Self::new(df)) } + /// Apply window function expressions to the DataFrame + #[pyo3(signature = (*exprs))] + fn window(&self, exprs: Vec) -> PyDataFusionResult { + let window_exprs = exprs.into_iter().map(|e| e.into()).collect(); + let df = self.df.as_ref().clone().window(window_exprs)?; + Ok(Self::new(df)) + } + fn filter(&self, predicate: PyExpr) -> PyDataFusionResult { let df = self.df.as_ref().clone().filter(predicate.into())?; Ok(Self::new(df)) @@ -602,8 +670,9 @@ impl PyDataFrame { /// Unless some order is specified in the plan, there is no /// guarantee of the order of the result. fn collect<'py>(&self, py: Python<'py>) -> PyResult>> { - let batches = wait_for_future(py, self.df.as_ref().clone().collect())? - .map_err(PyDataFusionError::from)?; + let (plan, task_ctx) = self.create_and_cache_plan(py)?; + let batches = + wait_for_future(py, df_collect(plan, task_ctx))?.map_err(PyDataFusionError::from)?; // cannot use PyResult> return type due to // https://github.com/PyO3/pyo3/issues/1813 batches.into_iter().map(|rb| rb.to_pyarrow(py)).collect() @@ -618,7 +687,8 @@ impl PyDataFrame { /// Executes this DataFrame and collects all results into a vector of vector of RecordBatch /// maintaining the input partitioning. fn collect_partitioned<'py>(&self, py: Python<'py>) -> PyResult>>> { - let batches = wait_for_future(py, self.df.as_ref().clone().collect_partitioned())? + let (plan, task_ctx) = self.create_and_cache_plan(py)?; + let batches = wait_for_future(py, df_collect_partitioned(plan, task_ctx))? .map_err(PyDataFusionError::from)?; batches @@ -637,7 +707,15 @@ impl PyDataFrame { /// Print the result, 20 lines by default #[pyo3(signature = (num=20))] fn show(&self, py: Python, num: usize) -> PyDataFusionResult<()> { - let df = self.df.as_ref().clone().limit(0, Some(num))?; + let mut df = self.df.as_ref().clone(); + df = match self.df.logical_plan() { + LogicalPlan::Explain(_) | LogicalPlan::Analyze(_) => { + // Explain and Analyzer require they are at the top + // of the plan, so do not add a limit. + df + } + _ => df.limit(0, Some(num))?, + }; print_dataframe(py, df) } @@ -761,9 +839,27 @@ impl PyDataFrame { } /// Print the query plan - #[pyo3(signature = (verbose=false, analyze=false))] - fn explain(&self, py: Python, verbose: bool, analyze: bool) -> PyDataFusionResult<()> { - let df = self.df.as_ref().clone().explain(verbose, analyze)?; + #[pyo3(signature = (verbose=false, analyze=false, format=None))] + fn explain( + &self, + py: Python, + verbose: bool, + analyze: bool, + format: Option<&str>, + ) -> PyDataFusionResult<()> { + let explain_format = match format { + Some(f) => f + .parse::() + .map_err(|e| { + PyDataFusionError::Common(format!("Invalid explain format '{}': {}", f, e)) + })?, + None => datafusion::common::format::ExplainFormat::Indent, + }; + let opts = datafusion::logical_expr::ExplainOption::default() + .with_verbose(verbose) + .with_analyze(analyze) + .with_format(explain_format); + let df = self.df.as_ref().clone().explain_with_options(opts)?; print_dataframe(py, df) } @@ -778,7 +874,13 @@ impl PyDataFrame { } /// Get the execution plan for this `DataFrame` + /// + /// If the DataFrame has already been executed (e.g. via `collect()`), + /// returns the cached plan which includes populated metrics. fn execution_plan(&self, py: Python) -> PyDataFusionResult { + if let Some(plan) = self.last_plan.lock().as_ref() { + return Ok(PyExecutionPlan::new(Arc::clone(plan))); + } let plan = wait_for_future(py, self.df.as_ref().clone().create_physical_plan())??; Ok(plan.into()) } @@ -821,39 +923,14 @@ impl PyDataFrame { Ok(Self::new(new_df)) } - /// Calculate the distinct union of two `DataFrame`s. The - /// two `DataFrame`s must have exactly the same schema - fn union_distinct(&self, py_df: PyDataFrame) -> PyDataFusionResult { - let new_df = self - .df - .as_ref() - .clone() - .union_distinct(py_df.df.as_ref().clone())?; - Ok(Self::new(new_df)) - } - - #[pyo3(signature = (column, preserve_nulls=true))] - fn unnest_column(&self, column: &str, preserve_nulls: bool) -> PyDataFusionResult { - // TODO: expose RecursionUnnestOptions - // REF: https://github.com/apache/datafusion/pull/11577 - let unnest_options = UnnestOptions::default().with_preserve_nulls(preserve_nulls); - let df = self - .df - .as_ref() - .clone() - .unnest_columns_with_options(&[column], unnest_options)?; - Ok(Self::new(df)) - } - - #[pyo3(signature = (columns, preserve_nulls=true))] + #[pyo3(signature = (columns, preserve_nulls=true, recursions=None))] fn unnest_columns( &self, columns: Vec, preserve_nulls: bool, + recursions: Option>, ) -> PyDataFusionResult { - // TODO: expose RecursionUnnestOptions - // REF: https://github.com/apache/datafusion/pull/11577 - let unnest_options = UnnestOptions::default().with_preserve_nulls(preserve_nulls); + let unnest_options = build_unnest_options(preserve_nulls, recursions); let cols = columns.iter().map(|s| s.as_ref()).collect::>(); let df = self .df @@ -864,21 +941,79 @@ impl PyDataFrame { } /// Calculate the intersection of two `DataFrame`s. The two `DataFrame`s must have exactly the same schema - fn intersect(&self, py_df: PyDataFrame) -> PyDataFusionResult { - let new_df = self - .df - .as_ref() - .clone() - .intersect(py_df.df.as_ref().clone())?; + #[pyo3(signature = (py_df, distinct=false))] + fn intersect(&self, py_df: PyDataFrame, distinct: bool) -> PyDataFusionResult { + let base = self.df.as_ref().clone(); + let other = py_df.df.as_ref().clone(); + let new_df = if distinct { + base.intersect_distinct(other)? + } else { + base.intersect(other)? + }; Ok(Self::new(new_df)) } /// Calculate the exception of two `DataFrame`s. The two `DataFrame`s must have exactly the same schema - fn except_all(&self, py_df: PyDataFrame) -> PyDataFusionResult { - let new_df = self.df.as_ref().clone().except(py_df.df.as_ref().clone())?; + #[pyo3(signature = (py_df, distinct=false))] + fn except_all(&self, py_df: PyDataFrame, distinct: bool) -> PyDataFusionResult { + let base = self.df.as_ref().clone(); + let other = py_df.df.as_ref().clone(); + let new_df = if distinct { + base.except_distinct(other)? + } else { + base.except(other)? + }; + Ok(Self::new(new_df)) + } + + /// Union two DataFrames matching columns by name + #[pyo3(signature = (py_df, distinct=false))] + fn union_by_name(&self, py_df: PyDataFrame, distinct: bool) -> PyDataFusionResult { + let base = self.df.as_ref().clone(); + let other = py_df.df.as_ref().clone(); + let new_df = if distinct { + base.union_by_name_distinct(other)? + } else { + base.union_by_name(other)? + }; Ok(Self::new(new_df)) } + /// Deduplicate rows based on specific columns, keeping the first row per group + fn distinct_on( + &self, + on_expr: Vec, + select_expr: Vec, + sort_expr: Option>, + ) -> PyDataFusionResult { + let on_expr = on_expr.into_iter().map(|e| e.into()).collect(); + let select_expr = select_expr.into_iter().map(|e| e.into()).collect(); + let sort_expr = sort_expr.map(to_sort_expressions); + let df = self + .df + .as_ref() + .clone() + .distinct_on(on_expr, select_expr, sort_expr)?; + Ok(Self::new(df)) + } + + /// Sort by column expressions with ascending order and nulls last + fn sort_by(&self, exprs: Vec) -> PyDataFusionResult { + let exprs = exprs.into_iter().map(|e| e.into()).collect(); + let df = self.df.as_ref().clone().sort_by(exprs)?; + Ok(Self::new(df)) + } + + /// Return fully qualified column expressions for the given column names + fn find_qualified_columns(&self, names: Vec) -> PyDataFusionResult> { + let name_refs: Vec<&str> = names.iter().map(|s| s.as_str()).collect(); + let qualified = self.df.find_qualified_columns(&name_refs)?; + Ok(qualified + .into_iter() + .map(|q| Expr::Column(Column::from(q)).into()) + .collect()) + } + /// Write a `DataFrame` to a CSV file. fn write_csv( &self, @@ -1073,9 +1208,10 @@ impl PyDataFrame { let mut projection: Option = None; if let Some(schema_capsule) = requested_schema { - validate_pycapsule(&schema_capsule, "arrow_schema")?; - - let schema_ptr = unsafe { schema_capsule.reference::() }; + let data: NonNull = schema_capsule + .pointer_checked(Some(c"arrow_schema"))? + .cast(); + let schema_ptr = unsafe { data.as_ref() }; let desired_schema = Schema::try_from(schema_ptr)?; schema = project_schema(schema, desired_schema)?; @@ -1102,14 +1238,17 @@ impl PyDataFrame { } fn execute_stream(&self, py: Python) -> PyDataFusionResult { - let df = self.df.as_ref().clone(); - let stream = spawn_future(py, async move { df.execute_stream().await })?; + let (plan, task_ctx) = self.create_and_cache_plan(py)?; + let stream = spawn_future(py, async move { df_execute_stream(plan, task_ctx) })?; Ok(PyRecordBatchStream::new(stream)) } fn execute_stream_partitioned(&self, py: Python) -> PyResult> { - let df = self.df.as_ref().clone(); - let streams = spawn_future(py, async move { df.execute_stream_partitioned().await })?; + let (plan, task_ctx) = self.create_and_cache_plan(py)?; + let streams = spawn_future( + py, + async move { df_execute_stream_partitioned(plan, task_ctx) }, + )?; Ok(streams.into_iter().map(PyRecordBatchStream::new).collect()) } @@ -1166,20 +1305,27 @@ impl PyDataFrame { columns: Option>, py: Python, ) -> PyDataFusionResult { - let scalar_value = py_obj_to_scalar_value(py, value)?; + let scalar_value: PyScalarValue = value.extract(py)?; let cols = match columns { Some(col_names) => col_names.iter().map(|c| c.to_string()).collect(), None => Vec::new(), // Empty vector means fill null for all columns }; - let df = self.df.as_ref().clone().fill_null(scalar_value, cols)?; + let df = self.df.as_ref().clone().fill_null(scalar_value.0, cols)?; Ok(Self::new(df)) } } #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] -#[pyclass(frozen, eq, eq_int, name = "InsertOp", module = "datafusion")] +#[pyclass( + from_py_object, + frozen, + eq, + eq_int, + name = "InsertOp", + module = "datafusion" +)] pub enum PyInsertOp { APPEND, REPLACE, @@ -1197,7 +1343,12 @@ impl From for InsertOp { } #[derive(Debug, Clone)] -#[pyclass(frozen, name = "DataFrameWriteOptions", module = "datafusion")] +#[pyclass( + from_py_object, + frozen, + name = "DataFrameWriteOptions", + module = "datafusion" +)] pub struct PyDataFrameWriteOptions { insert_operation: InsertOp, single_file_output: bool, @@ -1239,6 +1390,26 @@ impl PyDataFrameWriteOptions { } } +fn build_unnest_options( + preserve_nulls: bool, + recursions: Option>, +) -> UnnestOptions { + let mut opts = UnnestOptions::default().with_preserve_nulls(preserve_nulls); + if let Some(recs) = recursions { + opts.recursions = recs + .into_iter() + .map( + |(input, output, depth)| datafusion::common::RecursionUnnestOption { + input_column: datafusion::common::Column::from(input.as_str()), + output_column: datafusion::common::Column::from(output.as_str()), + depth, + }, + ) + .collect(); + } + opts +} + /// Print DataFrame fn print_dataframe(py: Python, df: DataFrame) -> PyDataFusionResult<()> { // Get string representation of record batches @@ -1303,7 +1474,10 @@ fn record_batch_into_schema( } else if field.is_nullable() { data_arrays.push(new_null_array(desired_data_type, array_size)); } else { - return Err(ArrowError::CastError(format!("Attempting to cast to non-nullable and non-castable field {} during schema projection.", field.name()))); + return Err(ArrowError::CastError(format!( + "Attempting to cast to non-nullable and non-castable field {} during schema projection.", + field.name() + ))); } } else { if !field.is_nullable() { @@ -1336,7 +1510,7 @@ async fn collect_record_batches_to_display( let FormatterConfig { max_bytes, min_rows, - repr_rows, + max_rows, } = config; let partitioned_stream = df.execute_stream_partitioned().await?; @@ -1346,8 +1520,11 @@ async fn collect_record_batches_to_display( let mut record_batches = Vec::default(); let mut has_more = false; - // ensure minimum rows even if memory/row limits are hit - while (size_estimate_so_far < max_bytes && rows_so_far < repr_rows) || rows_so_far < min_rows { + // Collect rows until we hit a limit (memory or max_rows) OR reach the guaranteed minimum. + // The minimum rows constraint overrides both memory and row limits to ensure a baseline + // of data is always displayed, even if it temporarily exceeds those limits. + // This provides better UX by guaranteeing users see at least min_rows rows. + while (size_estimate_so_far < max_bytes && rows_so_far < max_rows) || rows_so_far < min_rows { let mut rb = match stream.next().await { None => { break; @@ -1360,11 +1537,14 @@ async fn collect_record_batches_to_display( if rows_in_rb > 0 { size_estimate_so_far += rb.get_array_memory_size(); + // When memory limit is exceeded, scale back row count proportionally to stay within budget if size_estimate_so_far > max_bytes { let ratio = max_bytes as f32 / size_estimate_so_far as f32; let total_rows = rows_in_rb + rows_so_far; + // Calculate reduced rows maintaining the memory/data proportion let mut reduced_row_num = (total_rows as f32 * ratio).round() as usize; + // Ensure we always respect the minimum rows guarantee if reduced_row_num < min_rows { reduced_row_num = min_rows.min(total_rows); } @@ -1377,8 +1557,8 @@ async fn collect_record_batches_to_display( } } - if rows_in_rb + rows_so_far > repr_rows { - rb = rb.slice(0, repr_rows - rows_so_far); + if rows_in_rb + rows_so_far > max_rows { + rb = rb.slice(0, max_rows - rows_so_far); has_more = true; } diff --git a/src/dataset.rs b/crates/core/src/dataset.rs similarity index 98% rename from src/dataset.rs rename to crates/core/src/dataset.rs index 6a4fdb1fa..dbeafcd9f 100644 --- a/src/dataset.rs +++ b/crates/core/src/dataset.rs @@ -47,7 +47,7 @@ impl Dataset { // Ensure that we were passed an instance of pyarrow.dataset.Dataset let ds = PyModule::import(py, "pyarrow.dataset")?; let ds_attr = ds.getattr("Dataset")?; - let ds_type = ds_attr.downcast::()?; + let ds_type = ds_attr.cast::()?; if dataset.is_instance(ds_type)? { Ok(Dataset { dataset: dataset.clone().unbind(), diff --git a/src/dataset_exec.rs b/crates/core/src/dataset_exec.rs similarity index 95% rename from src/dataset_exec.rs rename to crates/core/src/dataset_exec.rs index a83b10941..a7dd1500d 100644 --- a/src/dataset_exec.rs +++ b/crates/core/src/dataset_exec.rs @@ -24,16 +24,16 @@ use datafusion::arrow::pyarrow::PyArrowType; use datafusion::arrow::record_batch::RecordBatch; use datafusion::error::{DataFusionError as InnerDataFusionError, Result as DFResult}; use datafusion::execution::context::TaskContext; -use datafusion::logical_expr::utils::conjunction; use datafusion::logical_expr::Expr; +use datafusion::logical_expr::utils::conjunction; use datafusion::physical_expr::{EquivalenceProperties, LexOrdering}; use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType}; use datafusion::physical_plan::stream::RecordBatchStreamAdapter; use datafusion::physical_plan::{ DisplayAs, DisplayFormatType, ExecutionPlan, ExecutionPlanProperties, Partitioning, - SendableRecordBatchStream, Statistics, + PlanProperties, SendableRecordBatchStream, Statistics, }; -use futures::{stream, TryStreamExt}; +use futures::{TryStreamExt, stream}; /// Implements a Datafusion physical ExecutionPlan that delegates to a PyArrow Dataset /// This actually performs the projection, filtering and scanning of a Dataset use pyo3::prelude::*; @@ -71,7 +71,7 @@ pub(crate) struct DatasetExec { columns: Option>, filter_expr: Option>, projected_statistics: Statistics, - plan_properties: datafusion::physical_plan::PlanProperties, + plan_properties: Arc, } impl DatasetExec { @@ -111,7 +111,7 @@ impl DatasetExec { let scanner = dataset.call_method("scanner", (), Some(&kwargs))?; - let schema = Arc::new( + let schema: SchemaRef = Arc::new( scanner .getattr("projected_schema")? .extract::>()? @@ -128,15 +128,15 @@ impl DatasetExec { )?; let fragments_iter = pylist.call1((fragments_iterator,))?; - let fragments = fragments_iter.downcast::().map_err(PyErr::from)?; + let fragments = fragments_iter.cast::().map_err(PyErr::from)?; let projected_statistics = Statistics::new_unknown(&schema); - let plan_properties = datafusion::physical_plan::PlanProperties::new( + let plan_properties = Arc::new(PlanProperties::new( EquivalenceProperties::new(schema.clone()), Partitioning::UnknownPartitioning(fragments.len()), EmissionType::Final, Boundedness::Bounded, - ); + )); Ok(DatasetExec { dataset: dataset.clone().unbind(), @@ -235,11 +235,11 @@ impl ExecutionPlan for DatasetExec { }) } - fn statistics(&self) -> DFResult { + fn partition_statistics(&self, _partition: Option) -> DFResult { Ok(self.projected_statistics.clone()) } - fn properties(&self) -> &datafusion::physical_plan::PlanProperties { + fn properties(&self) -> &Arc { &self.plan_properties } } diff --git a/src/expr/grouping_set.rs b/crates/core/src/errors.rs similarity index 61% rename from src/expr/grouping_set.rs rename to crates/core/src/errors.rs index 107dd9370..8babc5a56 100644 --- a/src/expr/grouping_set.rs +++ b/crates/core/src/errors.rs @@ -15,23 +15,4 @@ // specific language governing permissions and limitations // under the License. -use datafusion::logical_expr::GroupingSet; -use pyo3::prelude::*; - -#[pyclass(frozen, name = "GroupingSet", module = "datafusion.expr", subclass)] -#[derive(Clone)] -pub struct PyGroupingSet { - grouping_set: GroupingSet, -} - -impl From for GroupingSet { - fn from(grouping_set: PyGroupingSet) -> Self { - grouping_set.grouping_set - } -} - -impl From for PyGroupingSet { - fn from(grouping_set: GroupingSet) -> PyGroupingSet { - PyGroupingSet { grouping_set } - } -} +pub use datafusion_python_util::errors::*; diff --git a/src/expr.rs b/crates/core/src/expr.rs similarity index 93% rename from src/expr.rs rename to crates/core/src/expr.rs index fc8023b20..c4f2a12da 100644 --- a/src/expr.rs +++ b/crates/core/src/expr.rs @@ -24,16 +24,16 @@ use datafusion::arrow::pyarrow::PyArrowType; use datafusion::functions::core::expr_ext::FieldAccessor; use datafusion::logical_expr::expr::{ AggregateFunction, AggregateFunctionParams, FieldMetadata, InList, InSubquery, ScalarFunction, - WindowFunction, + SetComparison, WindowFunction, }; use datafusion::logical_expr::utils::exprlist_to_fields; use datafusion::logical_expr::{ - col, lit, lit_with_metadata, Between, BinaryExpr, Case, Cast, Expr, ExprFuncBuilder, - ExprFunctionExt, Like, LogicalPlan, Operator, TryCast, WindowFunctionDefinition, + Between, BinaryExpr, Case, Cast, Expr, ExprFuncBuilder, ExprFunctionExt, Like, LogicalPlan, + Operator, TryCast, WindowFunctionDefinition, col, lit, lit_with_metadata, }; +use pyo3::IntoPyObjectExt; use pyo3::basic::CompareOp; use pyo3::prelude::*; -use pyo3::IntoPyObjectExt; use window::PyWindowFrame; use self::alias::PyAlias; @@ -44,7 +44,7 @@ use self::bool_expr::{ use self::like::{PyILike, PyLike, PySimilarTo}; use self::scalar_variable::PyScalarVariable; use crate::common::data_type::{DataTypeMap, NullTreatment, PyScalarValue, RexType}; -use crate::errors::{py_runtime_err, py_type_err, py_unsupported_variant_err, PyDataFusionResult}; +use crate::errors::{PyDataFusionResult, py_runtime_err, py_type_err, py_unsupported_variant_err}; use crate::expr::aggregate_expr::PyAggregateFunction; use crate::expr::binary_expr::PyBinaryExpr; use crate::expr::column::PyColumn; @@ -98,6 +98,7 @@ pub mod recursive_query; pub mod repartition; pub mod scalar_subquery; pub mod scalar_variable; +pub mod set_comparison; pub mod signature; pub mod sort; pub mod sort_expr; @@ -111,10 +112,16 @@ pub mod unnest_expr; pub mod values; pub mod window; -use sort_expr::{to_sort_expressions, PySortExpr}; +use sort_expr::{PySortExpr, to_sort_expressions}; /// A PyExpr that can be used on a DataFrame -#[pyclass(frozen, name = "RawExpr", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "RawExpr", + module = "datafusion.expr", + subclass +)] #[derive(Debug, Clone)] pub struct PyExpr { pub expr: Expr, @@ -141,15 +148,18 @@ pub fn py_expr_list(expr: &[Expr]) -> PyResult> { impl PyExpr { /// Return the specific expression fn to_variant<'py>(&self, py: Python<'py>) -> PyResult> { - Python::attach(|_| { - match &self.expr { + Python::attach(|_| match &self.expr { Expr::Alias(alias) => Ok(PyAlias::from(alias.clone()).into_bound_py_any(py)?), Expr::Column(col) => Ok(PyColumn::from(col.clone()).into_bound_py_any(py)?), - Expr::ScalarVariable(data_type, variables) => { - Ok(PyScalarVariable::new(data_type, variables).into_bound_py_any(py)?) + Expr::ScalarVariable(field, variables) => { + Ok(PyScalarVariable::new(field, variables).into_bound_py_any(py)?) } Expr::Like(value) => Ok(PyLike::from(value.clone()).into_bound_py_any(py)?), - Expr::Literal(value, metadata) => Ok(PyLiteral::new_with_metadata(value.clone(), metadata.clone()).into_bound_py_any(py)?), + Expr::Literal(value, metadata) => Ok(PyLiteral::new_with_metadata( + value.clone(), + metadata.clone(), + ) + .into_bound_py_any(py)?), Expr::BinaryExpr(expr) => Ok(PyBinaryExpr::from(expr.clone()).into_bound_py_any(py)?), Expr::Not(expr) => Ok(PyNot::new(*expr.clone()).into_bound_py_any(py)?), Expr::IsNotNull(expr) => Ok(PyIsNotNull::new(*expr.clone()).into_bound_py_any(py)?), @@ -159,13 +169,17 @@ impl PyExpr { Expr::IsUnknown(expr) => Ok(PyIsUnknown::new(*expr.clone()).into_bound_py_any(py)?), Expr::IsNotTrue(expr) => Ok(PyIsNotTrue::new(*expr.clone()).into_bound_py_any(py)?), Expr::IsNotFalse(expr) => Ok(PyIsNotFalse::new(*expr.clone()).into_bound_py_any(py)?), - Expr::IsNotUnknown(expr) => Ok(PyIsNotUnknown::new(*expr.clone()).into_bound_py_any(py)?), + Expr::IsNotUnknown(expr) => { + Ok(PyIsNotUnknown::new(*expr.clone()).into_bound_py_any(py)?) + } Expr::Negative(expr) => Ok(PyNegative::new(*expr.clone()).into_bound_py_any(py)?), Expr::AggregateFunction(expr) => { Ok(PyAggregateFunction::from(expr.clone()).into_bound_py_any(py)?) } Expr::SimilarTo(value) => Ok(PySimilarTo::from(value.clone()).into_bound_py_any(py)?), - Expr::Between(value) => Ok(between::PyBetween::from(value.clone()).into_bound_py_any(py)?), + Expr::Between(value) => { + Ok(between::PyBetween::from(value.clone()).into_bound_py_any(py)?) + } Expr::Case(value) => Ok(case::PyCase::from(value.clone()).into_bound_py_any(py)?), Expr::Cast(value) => Ok(cast::PyCast::from(value.clone()).into_bound_py_any(py)?), Expr::TryCast(value) => Ok(cast::PyTryCast::from(value.clone()).into_bound_py_any(py)?), @@ -175,7 +189,9 @@ impl PyExpr { Expr::WindowFunction(value) => Err(py_unsupported_variant_err(format!( "Converting Expr::WindowFunction to a Python object is not implemented: {value:?}" ))), - Expr::InList(value) => Ok(in_list::PyInList::from(value.clone()).into_bound_py_any(py)?), + Expr::InList(value) => { + Ok(in_list::PyInList::from(value.clone()).into_bound_py_any(py)?) + } Expr::Exists(value) => Ok(exists::PyExists::from(value.clone()).into_bound_py_any(py)?), Expr::InSubquery(value) => { Ok(in_subquery::PyInSubquery::from(value.clone()).into_bound_py_any(py)?) @@ -193,11 +209,17 @@ impl PyExpr { Expr::Placeholder(value) => { Ok(placeholder::PyPlaceholder::from(value.clone()).into_bound_py_any(py)?) } - Expr::OuterReferenceColumn(data_type, column) => Err(py_unsupported_variant_err(format!( - "Converting Expr::OuterReferenceColumn to a Python object is not implemented: {data_type:?} - {column:?}" - ))), - Expr::Unnest(value) => Ok(unnest_expr::PyUnnestExpr::from(value.clone()).into_bound_py_any(py)?), - } + Expr::OuterReferenceColumn(data_type, column) => { + Err(py_unsupported_variant_err(format!( + "Converting Expr::OuterReferenceColumn to a Python object is not implemented: {data_type:?} - {column:?}" + ))) + } + Expr::Unnest(value) => { + Ok(unnest_expr::PyUnnestExpr::from(value.clone()).into_bound_py_any(py)?) + } + Expr::SetComparison(value) => { + Ok(set_comparison::PySetComparison::from(value.clone()).into_bound_py_any(py)?) + } }) } @@ -364,11 +386,12 @@ impl PyExpr { | Expr::Placeholder { .. } | Expr::OuterReferenceColumn(_, _) | Expr::Unnest(_) - | Expr::IsNotUnknown(_) => RexType::Call, + | Expr::IsNotUnknown(_) + | Expr::SetComparison(_) => RexType::Call, Expr::ScalarSubquery(..) => RexType::ScalarSubquery, #[allow(deprecated)] Expr::Wildcard { .. } => { - return Err(py_unsupported_variant_err("Expr::Wildcard is unsupported")) + return Err(py_unsupported_variant_err("Expr::Wildcard is unsupported")); } }) } @@ -415,7 +438,10 @@ impl PyExpr { | Expr::Negative(expr) | Expr::Cast(Cast { expr, .. }) | Expr::TryCast(TryCast { expr, .. }) - | Expr::InSubquery(InSubquery { expr, .. }) => Ok(vec![PyExpr::from(*expr.clone())]), + | Expr::InSubquery(InSubquery { expr, .. }) + | Expr::SetComparison(SetComparison { expr, .. }) => { + Ok(vec![PyExpr::from(*expr.clone())]) + } // Expr variants containing a collection of Expr(s) for operands Expr::AggregateFunction(AggregateFunction { @@ -555,7 +581,7 @@ impl PyExpr { return Err(py_type_err(format!( "Catch all triggered in get_operator_name: {:?}", &self.expr - ))) + ))); } }) } @@ -636,7 +662,13 @@ impl PyExpr { } } -#[pyclass(frozen, name = "ExprFuncBuilder", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "ExprFuncBuilder", + module = "datafusion.expr", + subclass +)] #[derive(Debug, Clone)] pub struct PyExprFuncBuilder { pub builder: ExprFuncBuilder, @@ -747,7 +779,8 @@ impl PyExpr { | Operator::AtQuestion | Operator::Question | Operator::QuestionAnd - | Operator::QuestionPipe => Err(py_type_err(format!("Unsupported expr: ${op}"))), + | Operator::QuestionPipe + | Operator::Colon => Err(py_type_err(format!("Unsupported expr: ${op}"))), }, Expr::Cast(Cast { expr: _, data_type }) => DataTypeMap::map_from_arrow_type(data_type), Expr::Literal(scalar_value, _) => DataTypeMap::map_from_scalar_value(scalar_value), diff --git a/src/expr/aggregate.rs b/crates/core/src/expr/aggregate.rs similarity index 97% rename from src/expr/aggregate.rs rename to crates/core/src/expr/aggregate.rs index 4cb41b26a..5a6a771a7 100644 --- a/src/expr/aggregate.rs +++ b/crates/core/src/expr/aggregate.rs @@ -18,11 +18,11 @@ use std::fmt::{self, Display, Formatter}; use datafusion::common::DataFusionError; +use datafusion::logical_expr::Expr; use datafusion::logical_expr::expr::{AggregateFunction, AggregateFunctionParams, Alias}; use datafusion::logical_expr::logical_plan::Aggregate; -use datafusion::logical_expr::Expr; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; @@ -30,7 +30,13 @@ use crate::errors::py_type_err; use crate::expr::PyExpr; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Aggregate", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Aggregate", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyAggregate { aggregate: Aggregate, diff --git a/src/expr/aggregate_expr.rs b/crates/core/src/expr/aggregate_expr.rs similarity index 99% rename from src/expr/aggregate_expr.rs rename to crates/core/src/expr/aggregate_expr.rs index d3b695a27..88e47999f 100644 --- a/src/expr/aggregate_expr.rs +++ b/crates/core/src/expr/aggregate_expr.rs @@ -23,6 +23,7 @@ use pyo3::prelude::*; use crate::expr::PyExpr; #[pyclass( + from_py_object, frozen, name = "AggregateFunction", module = "datafusion.expr", diff --git a/src/expr/alias.rs b/crates/core/src/expr/alias.rs similarity index 94% rename from src/expr/alias.rs rename to crates/core/src/expr/alias.rs index c6d486284..b76e82e22 100644 --- a/src/expr/alias.rs +++ b/crates/core/src/expr/alias.rs @@ -22,7 +22,13 @@ use pyo3::prelude::*; use crate::expr::PyExpr; -#[pyclass(frozen, name = "Alias", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Alias", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyAlias { alias: Alias, diff --git a/src/expr/analyze.rs b/crates/core/src/expr/analyze.rs similarity index 95% rename from src/expr/analyze.rs rename to crates/core/src/expr/analyze.rs index 05ec8dc22..137765fe1 100644 --- a/src/expr/analyze.rs +++ b/crates/core/src/expr/analyze.rs @@ -18,14 +18,20 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::logical_plan::Analyze; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Analyze", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Analyze", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyAnalyze { analyze: Analyze, diff --git a/src/expr/between.rs b/crates/core/src/expr/between.rs similarity index 94% rename from src/expr/between.rs rename to crates/core/src/expr/between.rs index 4f0b34add..6943b6c3b 100644 --- a/src/expr/between.rs +++ b/crates/core/src/expr/between.rs @@ -22,7 +22,13 @@ use pyo3::prelude::*; use crate::expr::PyExpr; -#[pyclass(frozen, name = "Between", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Between", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyBetween { between: Between, diff --git a/src/expr/binary_expr.rs b/crates/core/src/expr/binary_expr.rs similarity index 93% rename from src/expr/binary_expr.rs rename to crates/core/src/expr/binary_expr.rs index f67a08c7c..2326ba705 100644 --- a/src/expr/binary_expr.rs +++ b/crates/core/src/expr/binary_expr.rs @@ -20,7 +20,13 @@ use pyo3::prelude::*; use crate::expr::PyExpr; -#[pyclass(frozen, name = "BinaryExpr", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "BinaryExpr", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyBinaryExpr { expr: BinaryExpr, diff --git a/src/expr/bool_expr.rs b/crates/core/src/expr/bool_expr.rs similarity index 83% rename from src/expr/bool_expr.rs rename to crates/core/src/expr/bool_expr.rs index abd259409..9e374c7e2 100644 --- a/src/expr/bool_expr.rs +++ b/crates/core/src/expr/bool_expr.rs @@ -22,7 +22,13 @@ use pyo3::prelude::*; use super::PyExpr; -#[pyclass(frozen, name = "Not", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Not", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyNot { expr: Expr, @@ -52,7 +58,13 @@ impl PyNot { } } -#[pyclass(frozen, name = "IsNotNull", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "IsNotNull", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyIsNotNull { expr: Expr, @@ -82,7 +94,13 @@ impl PyIsNotNull { } } -#[pyclass(frozen, name = "IsNull", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "IsNull", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyIsNull { expr: Expr, @@ -112,7 +130,13 @@ impl PyIsNull { } } -#[pyclass(frozen, name = "IsTrue", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "IsTrue", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyIsTrue { expr: Expr, @@ -142,7 +166,13 @@ impl PyIsTrue { } } -#[pyclass(frozen, name = "IsFalse", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "IsFalse", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyIsFalse { expr: Expr, @@ -172,7 +202,13 @@ impl PyIsFalse { } } -#[pyclass(frozen, name = "IsUnknown", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "IsUnknown", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyIsUnknown { expr: Expr, @@ -202,7 +238,13 @@ impl PyIsUnknown { } } -#[pyclass(frozen, name = "IsNotTrue", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "IsNotTrue", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyIsNotTrue { expr: Expr, @@ -232,7 +274,13 @@ impl PyIsNotTrue { } } -#[pyclass(frozen, name = "IsNotFalse", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "IsNotFalse", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyIsNotFalse { expr: Expr, @@ -262,7 +310,13 @@ impl PyIsNotFalse { } } -#[pyclass(frozen, name = "IsNotUnknown", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "IsNotUnknown", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyIsNotUnknown { expr: Expr, @@ -292,7 +346,13 @@ impl PyIsNotUnknown { } } -#[pyclass(frozen, name = "Negative", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Negative", + module = "datafusion.expr", + subclass +)] #[derive(Clone, Debug)] pub struct PyNegative { expr: Expr, diff --git a/src/expr/case.rs b/crates/core/src/expr/case.rs similarity index 93% rename from src/expr/case.rs rename to crates/core/src/expr/case.rs index b49c19081..4f00449d8 100644 --- a/src/expr/case.rs +++ b/crates/core/src/expr/case.rs @@ -20,7 +20,13 @@ use pyo3::prelude::*; use crate::expr::PyExpr; -#[pyclass(frozen, name = "Case", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Case", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyCase { case: Case, diff --git a/src/expr/cast.rs b/crates/core/src/expr/cast.rs similarity index 91% rename from src/expr/cast.rs rename to crates/core/src/expr/cast.rs index 1aca9ea95..37d603538 100644 --- a/src/expr/cast.rs +++ b/crates/core/src/expr/cast.rs @@ -21,7 +21,13 @@ use pyo3::prelude::*; use crate::common::data_type::PyDataType; use crate::expr::PyExpr; -#[pyclass(frozen, name = "Cast", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Cast", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyCast { cast: Cast, @@ -50,7 +56,7 @@ impl PyCast { } } -#[pyclass(name = "TryCast", module = "datafusion.expr", subclass)] +#[pyclass(from_py_object, name = "TryCast", module = "datafusion.expr", subclass)] #[derive(Clone)] pub struct PyTryCast { try_cast: TryCast, diff --git a/src/expr/column.rs b/crates/core/src/expr/column.rs similarity index 93% rename from src/expr/column.rs rename to crates/core/src/expr/column.rs index 300079481..c1238f98a 100644 --- a/src/expr/column.rs +++ b/crates/core/src/expr/column.rs @@ -18,7 +18,13 @@ use datafusion::common::Column; use pyo3::prelude::*; -#[pyclass(frozen, name = "Column", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Column", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyColumn { pub col: Column, diff --git a/src/expr/conditional_expr.rs b/crates/core/src/expr/conditional_expr.rs similarity index 95% rename from src/expr/conditional_expr.rs rename to crates/core/src/expr/conditional_expr.rs index da6102dbf..ea21fdb20 100644 --- a/src/expr/conditional_expr.rs +++ b/crates/core/src/expr/conditional_expr.rs @@ -24,7 +24,13 @@ use crate::expr::PyExpr; // TODO(tsaucer) replace this all with CaseBuilder after it implements Clone #[derive(Clone, Debug)] -#[pyclass(name = "CaseBuilder", module = "datafusion.expr", subclass, frozen)] +#[pyclass( + from_py_object, + name = "CaseBuilder", + module = "datafusion.expr", + subclass, + frozen +)] pub struct PyCaseBuilder { expr: Option, when: Vec, diff --git a/src/expr/copy_to.rs b/crates/core/src/expr/copy_to.rs similarity index 93% rename from src/expr/copy_to.rs rename to crates/core/src/expr/copy_to.rs index 0b874e37d..78e53cdff 100644 --- a/src/expr/copy_to.rs +++ b/crates/core/src/expr/copy_to.rs @@ -21,13 +21,19 @@ use std::sync::Arc; use datafusion::common::file_options::file_type::FileType; use datafusion::logical_expr::dml::CopyTo; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "CopyTo", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "CopyTo", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyCopyTo { copy: CopyTo, @@ -113,7 +119,13 @@ impl PyCopyTo { } } -#[pyclass(frozen, name = "FileType", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "FileType", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyFileType { file_type: Arc, diff --git a/src/expr/create_catalog.rs b/crates/core/src/expr/create_catalog.rs similarity index 95% rename from src/expr/create_catalog.rs rename to crates/core/src/expr/create_catalog.rs index 400246a82..fa95980c0 100644 --- a/src/expr/create_catalog.rs +++ b/crates/core/src/expr/create_catalog.rs @@ -19,14 +19,20 @@ use std::fmt::{self, Display, Formatter}; use std::sync::Arc; use datafusion::logical_expr::CreateCatalog; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "CreateCatalog", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "CreateCatalog", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyCreateCatalog { create: CreateCatalog, diff --git a/src/expr/create_catalog_schema.rs b/crates/core/src/expr/create_catalog_schema.rs similarity index 99% rename from src/expr/create_catalog_schema.rs rename to crates/core/src/expr/create_catalog_schema.rs index 641e2116d..d836284a0 100644 --- a/src/expr/create_catalog_schema.rs +++ b/crates/core/src/expr/create_catalog_schema.rs @@ -19,14 +19,15 @@ use std::fmt::{self, Display, Formatter}; use std::sync::Arc; use datafusion::logical_expr::CreateCatalogSchema; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; #[pyclass( + from_py_object, frozen, name = "CreateCatalogSchema", module = "datafusion.expr", diff --git a/src/expr/create_external_table.rs b/crates/core/src/expr/create_external_table.rs similarity index 99% rename from src/expr/create_external_table.rs rename to crates/core/src/expr/create_external_table.rs index 05f9249b0..980eea131 100644 --- a/src/expr/create_external_table.rs +++ b/crates/core/src/expr/create_external_table.rs @@ -20,8 +20,8 @@ use std::fmt::{self, Display, Formatter}; use std::sync::Arc; use datafusion::logical_expr::CreateExternalTable; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use super::sort_expr::PySortExpr; @@ -31,6 +31,7 @@ use crate::expr::PyExpr; use crate::sql::logical::PyLogicalPlan; #[pyclass( + from_py_object, frozen, name = "CreateExternalTable", module = "datafusion.expr", diff --git a/src/expr/create_function.rs b/crates/core/src/expr/create_function.rs similarity index 94% rename from src/expr/create_function.rs rename to crates/core/src/expr/create_function.rs index 2a35635c2..622858913 100644 --- a/src/expr/create_function.rs +++ b/crates/core/src/expr/create_function.rs @@ -21,16 +21,22 @@ use std::sync::Arc; use datafusion::logical_expr::{ CreateFunction, CreateFunctionBody, OperateFunctionArg, Volatility, }; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; -use super::logical_node::LogicalNode; use super::PyExpr; +use super::logical_node::LogicalNode; use crate::common::data_type::PyDataType; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "CreateFunction", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "CreateFunction", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyCreateFunction { create: CreateFunction, @@ -55,6 +61,7 @@ impl Display for PyCreateFunction { } #[pyclass( + from_py_object, frozen, name = "OperateFunctionArg", module = "datafusion.expr", @@ -66,7 +73,14 @@ pub struct PyOperateFunctionArg { } #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] -#[pyclass(frozen, eq, eq_int, name = "Volatility", module = "datafusion.expr")] +#[pyclass( + from_py_object, + frozen, + eq, + eq_int, + name = "Volatility", + module = "datafusion.expr" +)] pub enum PyVolatility { Immutable, Stable, @@ -74,6 +88,7 @@ pub enum PyVolatility { } #[pyclass( + from_py_object, frozen, name = "CreateFunctionBody", module = "datafusion.expr", diff --git a/src/expr/create_index.rs b/crates/core/src/expr/create_index.rs similarity index 96% rename from src/expr/create_index.rs rename to crates/core/src/expr/create_index.rs index 5c378332c..5f9bd11e8 100644 --- a/src/expr/create_index.rs +++ b/crates/core/src/expr/create_index.rs @@ -19,15 +19,21 @@ use std::fmt::{self, Display, Formatter}; use std::sync::Arc; use datafusion::logical_expr::CreateIndex; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use super::sort_expr::PySortExpr; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "CreateIndex", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "CreateIndex", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyCreateIndex { create: CreateIndex, diff --git a/src/expr/create_memory_table.rs b/crates/core/src/expr/create_memory_table.rs similarity index 99% rename from src/expr/create_memory_table.rs rename to crates/core/src/expr/create_memory_table.rs index 7759eb420..3214dab0e 100644 --- a/src/expr/create_memory_table.rs +++ b/crates/core/src/expr/create_memory_table.rs @@ -18,13 +18,14 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::CreateMemoryTable; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; #[pyclass( + from_py_object, frozen, name = "CreateMemoryTable", module = "datafusion.expr", diff --git a/src/expr/create_view.rs b/crates/core/src/expr/create_view.rs similarity index 96% rename from src/expr/create_view.rs rename to crates/core/src/expr/create_view.rs index 16faaf9d5..6941ef769 100644 --- a/src/expr/create_view.rs +++ b/crates/core/src/expr/create_view.rs @@ -18,14 +18,20 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::{CreateView, DdlStatement, LogicalPlan}; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::errors::py_type_err; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "CreateView", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "CreateView", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyCreateView { create: CreateView, diff --git a/src/expr/describe_table.rs b/crates/core/src/expr/describe_table.rs similarity index 95% rename from src/expr/describe_table.rs rename to crates/core/src/expr/describe_table.rs index 9b139ed3b..73955bb34 100644 --- a/src/expr/describe_table.rs +++ b/crates/core/src/expr/describe_table.rs @@ -21,14 +21,20 @@ use std::sync::Arc; use arrow::datatypes::Schema; use arrow::pyarrow::PyArrowType; use datafusion::logical_expr::DescribeTable; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "DescribeTable", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "DescribeTable", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyDescribeTable { describe: DescribeTable, diff --git a/src/expr/distinct.rs b/crates/core/src/expr/distinct.rs similarity index 95% rename from src/expr/distinct.rs rename to crates/core/src/expr/distinct.rs index 1505ec3e6..68c2a17fe 100644 --- a/src/expr/distinct.rs +++ b/crates/core/src/expr/distinct.rs @@ -18,13 +18,19 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::Distinct; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Distinct", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Distinct", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyDistinct { distinct: Distinct, diff --git a/src/expr/dml.rs b/crates/core/src/expr/dml.rs similarity index 91% rename from src/expr/dml.rs rename to crates/core/src/expr/dml.rs index 091dcbc18..26f975820 100644 --- a/src/expr/dml.rs +++ b/crates/core/src/expr/dml.rs @@ -17,15 +17,21 @@ use datafusion::logical_expr::dml::InsertOp; use datafusion::logical_expr::{DmlStatement, WriteOp}; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::common::schema::PyTableSource; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "DmlStatement", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "DmlStatement", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyDmlStatement { dml: DmlStatement, @@ -89,15 +95,21 @@ impl PyDmlStatement { } #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] -#[pyclass(eq, eq_int, name = "WriteOp", module = "datafusion.expr")] +#[pyclass( + from_py_object, + eq, + eq_int, + name = "WriteOp", + module = "datafusion.expr" +)] pub enum PyWriteOp { Append, Overwrite, Replace, - Update, Delete, Ctas, + Truncate, } impl From for PyWriteOp { @@ -106,10 +118,10 @@ impl From for PyWriteOp { WriteOp::Insert(InsertOp::Append) => PyWriteOp::Append, WriteOp::Insert(InsertOp::Overwrite) => PyWriteOp::Overwrite, WriteOp::Insert(InsertOp::Replace) => PyWriteOp::Replace, - WriteOp::Update => PyWriteOp::Update, WriteOp::Delete => PyWriteOp::Delete, WriteOp::Ctas => PyWriteOp::Ctas, + WriteOp::Truncate => PyWriteOp::Truncate, } } } @@ -120,10 +132,10 @@ impl From for WriteOp { PyWriteOp::Append => WriteOp::Insert(InsertOp::Append), PyWriteOp::Overwrite => WriteOp::Insert(InsertOp::Overwrite), PyWriteOp::Replace => WriteOp::Insert(InsertOp::Replace), - PyWriteOp::Update => WriteOp::Update, PyWriteOp::Delete => WriteOp::Delete, PyWriteOp::Ctas => WriteOp::Ctas, + PyWriteOp::Truncate => WriteOp::Truncate, } } } diff --git a/src/expr/drop_catalog_schema.rs b/crates/core/src/expr/drop_catalog_schema.rs similarity index 99% rename from src/expr/drop_catalog_schema.rs rename to crates/core/src/expr/drop_catalog_schema.rs index db6041a1b..fd5105332 100644 --- a/src/expr/drop_catalog_schema.rs +++ b/crates/core/src/expr/drop_catalog_schema.rs @@ -21,15 +21,16 @@ use std::sync::Arc; use datafusion::common::SchemaReference; use datafusion::logical_expr::DropCatalogSchema; use datafusion::sql::TableReference; +use pyo3::IntoPyObjectExt; use pyo3::exceptions::PyValueError; use pyo3::prelude::*; -use pyo3::IntoPyObjectExt; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; #[pyclass( + from_py_object, frozen, name = "DropCatalogSchema", module = "datafusion.expr", diff --git a/src/expr/drop_function.rs b/crates/core/src/expr/drop_function.rs similarity index 95% rename from src/expr/drop_function.rs rename to crates/core/src/expr/drop_function.rs index 070d15783..0599dd49e 100644 --- a/src/expr/drop_function.rs +++ b/crates/core/src/expr/drop_function.rs @@ -19,14 +19,20 @@ use std::fmt::{self, Display, Formatter}; use std::sync::Arc; use datafusion::logical_expr::DropFunction; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "DropFunction", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "DropFunction", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyDropFunction { drop: DropFunction, diff --git a/src/expr/drop_table.rs b/crates/core/src/expr/drop_table.rs similarity index 95% rename from src/expr/drop_table.rs rename to crates/core/src/expr/drop_table.rs index ffb56e4ed..46fe67465 100644 --- a/src/expr/drop_table.rs +++ b/crates/core/src/expr/drop_table.rs @@ -18,13 +18,19 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::logical_plan::DropTable; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "DropTable", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "DropTable", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyDropTable { drop: DropTable, diff --git a/src/expr/drop_view.rs b/crates/core/src/expr/drop_view.rs similarity index 95% rename from src/expr/drop_view.rs rename to crates/core/src/expr/drop_view.rs index 9d72f2077..0d0c51f13 100644 --- a/src/expr/drop_view.rs +++ b/crates/core/src/expr/drop_view.rs @@ -19,14 +19,20 @@ use std::fmt::{self, Display, Formatter}; use std::sync::Arc; use datafusion::logical_expr::DropView; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "DropView", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "DropView", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyDropView { drop: DropView, diff --git a/src/expr/empty_relation.rs b/crates/core/src/expr/empty_relation.rs similarity index 95% rename from src/expr/empty_relation.rs rename to crates/core/src/expr/empty_relation.rs index 35c3fa79b..f3c237731 100644 --- a/src/expr/empty_relation.rs +++ b/crates/core/src/expr/empty_relation.rs @@ -18,14 +18,20 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::EmptyRelation; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "EmptyRelation", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "EmptyRelation", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyEmptyRelation { empty: EmptyRelation, diff --git a/src/expr/exists.rs b/crates/core/src/expr/exists.rs similarity index 91% rename from src/expr/exists.rs rename to crates/core/src/expr/exists.rs index 392bfcb9e..d2e816127 100644 --- a/src/expr/exists.rs +++ b/crates/core/src/expr/exists.rs @@ -20,7 +20,13 @@ use pyo3::prelude::*; use super::subquery::PySubquery; -#[pyclass(frozen, name = "Exists", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Exists", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyExists { exists: Exists, diff --git a/src/expr/explain.rs b/crates/core/src/expr/explain.rs similarity index 96% rename from src/expr/explain.rs rename to crates/core/src/expr/explain.rs index c6884e98a..6259951de 100644 --- a/src/expr/explain.rs +++ b/crates/core/src/expr/explain.rs @@ -17,17 +17,23 @@ use std::fmt::{self, Display, Formatter}; -use datafusion::logical_expr::logical_plan::Explain; use datafusion::logical_expr::LogicalPlan; -use pyo3::prelude::*; +use datafusion::logical_expr::logical_plan::Explain; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::errors::py_type_err; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Explain", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Explain", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyExplain { explain: Explain, diff --git a/src/expr/extension.rs b/crates/core/src/expr/extension.rs similarity index 92% rename from src/expr/extension.rs rename to crates/core/src/expr/extension.rs index b4c688bd0..a0b617565 100644 --- a/src/expr/extension.rs +++ b/crates/core/src/expr/extension.rs @@ -16,13 +16,19 @@ // under the License. use datafusion::logical_expr::Extension; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Extension", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Extension", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyExtension { pub node: Extension, diff --git a/src/expr/filter.rs b/crates/core/src/expr/filter.rs similarity index 95% rename from src/expr/filter.rs rename to crates/core/src/expr/filter.rs index 25a1e76b3..67426806d 100644 --- a/src/expr/filter.rs +++ b/crates/core/src/expr/filter.rs @@ -18,15 +18,21 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::logical_plan::Filter; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::common::df_schema::PyDFSchema; -use crate::expr::logical_node::LogicalNode; use crate::expr::PyExpr; +use crate::expr::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Filter", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Filter", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyFilter { filter: Filter, diff --git a/crates/core/src/expr/grouping_set.rs b/crates/core/src/expr/grouping_set.rs new file mode 100644 index 000000000..11d8f4fcd --- /dev/null +++ b/crates/core/src/expr/grouping_set.rs @@ -0,0 +1,78 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use datafusion::logical_expr::{Expr, GroupingSet}; +use pyo3::prelude::*; + +use crate::expr::PyExpr; + +#[pyclass( + from_py_object, + frozen, + name = "GroupingSet", + module = "datafusion.expr", + subclass +)] +#[derive(Clone)] +pub struct PyGroupingSet { + grouping_set: GroupingSet, +} + +#[pymethods] +impl PyGroupingSet { + #[staticmethod] + #[pyo3(signature = (*exprs))] + fn rollup(exprs: Vec) -> PyExpr { + Expr::GroupingSet(GroupingSet::Rollup( + exprs.into_iter().map(|e| e.expr).collect(), + )) + .into() + } + + #[staticmethod] + #[pyo3(signature = (*exprs))] + fn cube(exprs: Vec) -> PyExpr { + Expr::GroupingSet(GroupingSet::Cube( + exprs.into_iter().map(|e| e.expr).collect(), + )) + .into() + } + + #[staticmethod] + #[pyo3(signature = (*expr_lists))] + fn grouping_sets(expr_lists: Vec>) -> PyExpr { + Expr::GroupingSet(GroupingSet::GroupingSets( + expr_lists + .into_iter() + .map(|list| list.into_iter().map(|e| e.expr).collect()) + .collect(), + )) + .into() + } +} + +impl From for GroupingSet { + fn from(grouping_set: PyGroupingSet) -> Self { + grouping_set.grouping_set + } +} + +impl From for PyGroupingSet { + fn from(grouping_set: GroupingSet) -> PyGroupingSet { + PyGroupingSet { grouping_set } + } +} diff --git a/src/expr/in_list.rs b/crates/core/src/expr/in_list.rs similarity index 92% rename from src/expr/in_list.rs rename to crates/core/src/expr/in_list.rs index 128c3f4c2..0612cc21e 100644 --- a/src/expr/in_list.rs +++ b/crates/core/src/expr/in_list.rs @@ -20,7 +20,13 @@ use pyo3::prelude::*; use crate::expr::PyExpr; -#[pyclass(frozen, name = "InList", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "InList", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyInList { in_list: InList, diff --git a/src/expr/in_subquery.rs b/crates/core/src/expr/in_subquery.rs similarity index 92% rename from src/expr/in_subquery.rs rename to crates/core/src/expr/in_subquery.rs index 5cff86c06..81a2c5794 100644 --- a/src/expr/in_subquery.rs +++ b/crates/core/src/expr/in_subquery.rs @@ -18,10 +18,16 @@ use datafusion::logical_expr::expr::InSubquery; use pyo3::prelude::*; -use super::subquery::PySubquery; use super::PyExpr; +use super::subquery::PySubquery; -#[pyclass(frozen, name = "InSubquery", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "InSubquery", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyInSubquery { in_subquery: InSubquery, diff --git a/src/expr/indexed_field.rs b/crates/core/src/expr/indexed_field.rs similarity index 94% rename from src/expr/indexed_field.rs rename to crates/core/src/expr/indexed_field.rs index 1dfa0ed2f..98a90d8d4 100644 --- a/src/expr/indexed_field.rs +++ b/crates/core/src/expr/indexed_field.rs @@ -15,14 +15,21 @@ // specific language governing permissions and limitations // under the License. -use crate::expr::PyExpr; +use std::fmt::{Display, Formatter}; + use datafusion::logical_expr::expr::{GetFieldAccess, GetIndexedField}; use pyo3::prelude::*; -use std::fmt::{Display, Formatter}; use super::literal::PyLiteral; +use crate::expr::PyExpr; -#[pyclass(frozen, name = "GetIndexedField", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "GetIndexedField", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyGetIndexedField { indexed_field: GetIndexedField, diff --git a/src/expr/join.rs b/crates/core/src/expr/join.rs similarity index 94% rename from src/expr/join.rs rename to crates/core/src/expr/join.rs index 82cc2a607..b90f2f57d 100644 --- a/src/expr/join.rs +++ b/crates/core/src/expr/join.rs @@ -19,16 +19,16 @@ use std::fmt::{self, Display, Formatter}; use datafusion::common::NullEquality; use datafusion::logical_expr::logical_plan::{Join, JoinConstraint, JoinType}; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::common::df_schema::PyDFSchema; -use crate::expr::logical_node::LogicalNode; use crate::expr::PyExpr; +use crate::expr::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; #[derive(Debug, Clone, PartialEq, Eq, Hash)] -#[pyclass(frozen, name = "JoinType", module = "datafusion.expr")] +#[pyclass(from_py_object, frozen, name = "JoinType", module = "datafusion.expr")] pub struct PyJoinType { join_type: JoinType, } @@ -63,7 +63,12 @@ impl Display for PyJoinType { } #[derive(Debug, Clone, Copy)] -#[pyclass(frozen, name = "JoinConstraint", module = "datafusion.expr")] +#[pyclass( + from_py_object, + frozen, + name = "JoinConstraint", + module = "datafusion.expr" +)] pub struct PyJoinConstraint { join_constraint: JoinConstraint, } @@ -90,7 +95,13 @@ impl PyJoinConstraint { } } -#[pyclass(frozen, name = "Join", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Join", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyJoin { join: Join, diff --git a/src/expr/like.rs b/crates/core/src/expr/like.rs similarity index 92% rename from src/expr/like.rs rename to crates/core/src/expr/like.rs index 94860bd6c..417dc9182 100644 --- a/src/expr/like.rs +++ b/crates/core/src/expr/like.rs @@ -22,7 +22,13 @@ use pyo3::prelude::*; use crate::expr::PyExpr; -#[pyclass(frozen, name = "Like", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Like", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyLike { like: Like, @@ -80,7 +86,13 @@ impl PyLike { } } -#[pyclass(frozen, name = "ILike", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "ILike", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyILike { like: Like, @@ -138,7 +150,13 @@ impl PyILike { } } -#[pyclass(frozen, name = "SimilarTo", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "SimilarTo", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PySimilarTo { like: Like, diff --git a/src/expr/limit.rs b/crates/core/src/expr/limit.rs similarity index 96% rename from src/expr/limit.rs rename to crates/core/src/expr/limit.rs index 9318eff97..c04b8bfa8 100644 --- a/src/expr/limit.rs +++ b/crates/core/src/expr/limit.rs @@ -18,14 +18,20 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::logical_plan::Limit; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::common::df_schema::PyDFSchema; use crate::expr::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Limit", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Limit", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyLimit { limit: Limit, diff --git a/src/expr/literal.rs b/crates/core/src/expr/literal.rs similarity index 97% rename from src/expr/literal.rs rename to crates/core/src/expr/literal.rs index 3e8e229f9..9db0f594b 100644 --- a/src/expr/literal.rs +++ b/crates/core/src/expr/literal.rs @@ -17,12 +17,18 @@ use datafusion::common::ScalarValue; use datafusion::logical_expr::expr::FieldMetadata; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::errors::PyDataFusionError; -#[pyclass(name = "Literal", module = "datafusion.expr", subclass, frozen)] +#[pyclass( + from_py_object, + name = "Literal", + module = "datafusion.expr", + subclass, + frozen +)] #[derive(Clone)] pub struct PyLiteral { pub value: ScalarValue, diff --git a/src/expr/logical_node.rs b/crates/core/src/expr/logical_node.rs similarity index 100% rename from src/expr/logical_node.rs rename to crates/core/src/expr/logical_node.rs diff --git a/src/expr/placeholder.rs b/crates/core/src/expr/placeholder.rs similarity index 93% rename from src/expr/placeholder.rs rename to crates/core/src/expr/placeholder.rs index f1e8694a9..6bd88321c 100644 --- a/src/expr/placeholder.rs +++ b/crates/core/src/expr/placeholder.rs @@ -22,7 +22,13 @@ use pyo3::prelude::*; use crate::common::data_type::PyDataType; -#[pyclass(frozen, name = "Placeholder", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Placeholder", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyPlaceholder { placeholder: Placeholder, diff --git a/src/expr/projection.rs b/crates/core/src/expr/projection.rs similarity index 96% rename from src/expr/projection.rs rename to crates/core/src/expr/projection.rs index bd21418a2..456e06412 100644 --- a/src/expr/projection.rs +++ b/crates/core/src/expr/projection.rs @@ -17,17 +17,23 @@ use std::fmt::{self, Display, Formatter}; -use datafusion::logical_expr::logical_plan::Projection; use datafusion::logical_expr::Expr; -use pyo3::prelude::*; +use datafusion::logical_expr::logical_plan::Projection; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::common::df_schema::PyDFSchema; -use crate::expr::logical_node::LogicalNode; use crate::expr::PyExpr; +use crate::expr::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Projection", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Projection", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyProjection { pub projection: Projection, diff --git a/src/expr/recursive_query.rs b/crates/core/src/expr/recursive_query.rs similarity index 96% rename from src/expr/recursive_query.rs rename to crates/core/src/expr/recursive_query.rs index 0e1171ea9..e03137b80 100644 --- a/src/expr/recursive_query.rs +++ b/crates/core/src/expr/recursive_query.rs @@ -18,13 +18,19 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::RecursiveQuery; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "RecursiveQuery", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "RecursiveQuery", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyRecursiveQuery { query: RecursiveQuery, diff --git a/src/expr/repartition.rs b/crates/core/src/expr/repartition.rs similarity index 94% rename from src/expr/repartition.rs rename to crates/core/src/expr/repartition.rs index 0b3cc4b2b..be39b9978 100644 --- a/src/expr/repartition.rs +++ b/crates/core/src/expr/repartition.rs @@ -19,21 +19,33 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::logical_plan::Repartition; use datafusion::logical_expr::{Expr, Partitioning}; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; -use super::logical_node::LogicalNode; use super::PyExpr; +use super::logical_node::LogicalNode; use crate::errors::py_type_err; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Repartition", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Repartition", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyRepartition { repartition: Repartition, } -#[pyclass(frozen, name = "Partitioning", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Partitioning", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyPartitioning { partitioning: Partitioning, diff --git a/src/expr/scalar_subquery.rs b/crates/core/src/expr/scalar_subquery.rs similarity index 91% rename from src/expr/scalar_subquery.rs rename to crates/core/src/expr/scalar_subquery.rs index e58d66e19..c7852a4c4 100644 --- a/src/expr/scalar_subquery.rs +++ b/crates/core/src/expr/scalar_subquery.rs @@ -20,7 +20,13 @@ use pyo3::prelude::*; use super::subquery::PySubquery; -#[pyclass(frozen, name = "ScalarSubquery", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "ScalarSubquery", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyScalarSubquery { subquery: Subquery, diff --git a/src/expr/scalar_variable.rs b/crates/core/src/expr/scalar_variable.rs similarity index 76% rename from src/expr/scalar_variable.rs rename to crates/core/src/expr/scalar_variable.rs index f3c128a4c..2d3bc4b76 100644 --- a/src/expr/scalar_variable.rs +++ b/crates/core/src/expr/scalar_variable.rs @@ -15,22 +15,28 @@ // specific language governing permissions and limitations // under the License. -use datafusion::arrow::datatypes::DataType; +use arrow::datatypes::FieldRef; use pyo3::prelude::*; use crate::common::data_type::PyDataType; -#[pyclass(frozen, name = "ScalarVariable", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "ScalarVariable", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyScalarVariable { - data_type: DataType, + field: FieldRef, variables: Vec, } impl PyScalarVariable { - pub fn new(data_type: &DataType, variables: &[String]) -> Self { + pub fn new(field: &FieldRef, variables: &[String]) -> Self { Self { - data_type: data_type.to_owned(), + field: field.to_owned(), variables: variables.to_vec(), } } @@ -40,7 +46,7 @@ impl PyScalarVariable { impl PyScalarVariable { /// Get the data type fn data_type(&self) -> PyResult { - Ok(self.data_type.clone().into()) + Ok(self.field.data_type().clone().into()) } fn variables(&self) -> PyResult> { @@ -48,6 +54,6 @@ impl PyScalarVariable { } fn __repr__(&self) -> PyResult { - Ok(format!("{}{:?}", self.data_type, self.variables)) + Ok(format!("{}{:?}", self.field.data_type(), self.variables)) } } diff --git a/crates/core/src/expr/set_comparison.rs b/crates/core/src/expr/set_comparison.rs new file mode 100644 index 000000000..9f0c077e1 --- /dev/null +++ b/crates/core/src/expr/set_comparison.rs @@ -0,0 +1,59 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use datafusion::logical_expr::expr::SetComparison; +use pyo3::prelude::*; + +use super::subquery::PySubquery; +use crate::expr::PyExpr; + +#[pyclass( + from_py_object, + frozen, + name = "SetComparison", + module = "datafusion.set_comparison", + subclass +)] +#[derive(Clone)] +pub struct PySetComparison { + set_comparison: SetComparison, +} + +impl From for PySetComparison { + fn from(set_comparison: SetComparison) -> Self { + PySetComparison { set_comparison } + } +} + +#[pymethods] +impl PySetComparison { + fn expr(&self) -> PyExpr { + (*self.set_comparison.expr).clone().into() + } + + fn subquery(&self) -> PySubquery { + self.set_comparison.subquery.clone().into() + } + + fn op(&self) -> String { + format!("{}", self.set_comparison.op) + } + + fn quantifier(&self) -> String { + format!("{}", self.set_comparison.quantifier) + } +} diff --git a/src/expr/signature.rs b/crates/core/src/expr/signature.rs similarity index 91% rename from src/expr/signature.rs rename to crates/core/src/expr/signature.rs index e2c23dce9..35268e3a9 100644 --- a/src/expr/signature.rs +++ b/crates/core/src/expr/signature.rs @@ -19,7 +19,13 @@ use datafusion::logical_expr::{TypeSignature, Volatility}; use pyo3::prelude::*; #[allow(dead_code)] -#[pyclass(frozen, name = "Signature", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Signature", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PySignature { type_signature: TypeSignature, diff --git a/src/expr/sort.rs b/crates/core/src/expr/sort.rs similarity index 96% rename from src/expr/sort.rs rename to crates/core/src/expr/sort.rs index 8914c8f93..7c1e654c5 100644 --- a/src/expr/sort.rs +++ b/crates/core/src/expr/sort.rs @@ -19,15 +19,21 @@ use std::fmt::{self, Display, Formatter}; use datafusion::common::DataFusionError; use datafusion::logical_expr::logical_plan::Sort; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::common::df_schema::PyDFSchema; use crate::expr::logical_node::LogicalNode; use crate::expr::sort_expr::PySortExpr; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Sort", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Sort", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PySort { sort: Sort, diff --git a/src/expr/sort_expr.rs b/crates/core/src/expr/sort_expr.rs similarity index 95% rename from src/expr/sort_expr.rs rename to crates/core/src/expr/sort_expr.rs index 23c066156..3c3c86bc1 100644 --- a/src/expr/sort_expr.rs +++ b/crates/core/src/expr/sort_expr.rs @@ -22,7 +22,13 @@ use pyo3::prelude::*; use crate::expr::PyExpr; -#[pyclass(frozen, name = "SortExpr", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "SortExpr", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PySortExpr { pub(crate) sort: SortExpr, diff --git a/src/expr/statement.rs b/crates/core/src/expr/statement.rs similarity index 86% rename from src/expr/statement.rs rename to crates/core/src/expr/statement.rs index 40666dd8b..5aa1e4e9c 100644 --- a/src/expr/statement.rs +++ b/crates/core/src/expr/statement.rs @@ -20,17 +20,18 @@ use std::sync::Arc; use arrow::datatypes::Field; use arrow::pyarrow::PyArrowType; use datafusion::logical_expr::{ - Deallocate, Execute, Prepare, SetVariable, TransactionAccessMode, TransactionConclusion, - TransactionEnd, TransactionIsolationLevel, TransactionStart, + Deallocate, Execute, Prepare, ResetVariable, SetVariable, TransactionAccessMode, + TransactionConclusion, TransactionEnd, TransactionIsolationLevel, TransactionStart, }; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; -use super::logical_node::LogicalNode; use super::PyExpr; +use super::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; #[pyclass( + from_py_object, frozen, name = "TransactionStart", module = "datafusion.expr", @@ -67,6 +68,7 @@ impl LogicalNode for PyTransactionStart { #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] #[pyclass( + from_py_object, frozen, eq, eq_int, @@ -100,6 +102,7 @@ impl TryFrom for TransactionAccessMode { #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] #[pyclass( + from_py_object, frozen, eq, eq_int, @@ -178,7 +181,13 @@ impl PyTransactionStart { } } -#[pyclass(frozen, name = "TransactionEnd", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "TransactionEnd", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyTransactionEnd { transaction_end: TransactionEnd, @@ -210,6 +219,7 @@ impl LogicalNode for PyTransactionEnd { #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] #[pyclass( + from_py_object, frozen, eq, eq_int, @@ -259,7 +269,63 @@ impl PyTransactionEnd { } } -#[pyclass(frozen, name = "SetVariable", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "ResetVariable", + module = "datafusion.expr", + subclass +)] +#[derive(Clone)] +pub struct PyResetVariable { + reset_variable: ResetVariable, +} + +impl From for PyResetVariable { + fn from(reset_variable: ResetVariable) -> PyResetVariable { + PyResetVariable { reset_variable } + } +} + +impl TryFrom for ResetVariable { + type Error = PyErr; + + fn try_from(py: PyResetVariable) -> Result { + Ok(py.reset_variable) + } +} + +impl LogicalNode for PyResetVariable { + fn inputs(&self) -> Vec { + vec![] + } + + fn to_variant<'py>(&self, py: Python<'py>) -> PyResult> { + self.clone().into_bound_py_any(py) + } +} + +#[pymethods] +impl PyResetVariable { + #[new] + pub fn new(variable: String) -> Self { + PyResetVariable { + reset_variable: ResetVariable { variable }, + } + } + + pub fn variable(&self) -> String { + self.reset_variable.variable.clone() + } +} + +#[pyclass( + from_py_object, + frozen, + name = "SetVariable", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PySetVariable { set_variable: SetVariable, @@ -307,7 +373,13 @@ impl PySetVariable { } } -#[pyclass(frozen, name = "Prepare", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Prepare", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyPrepare { prepare: Prepare, @@ -372,7 +444,13 @@ impl PyPrepare { } } -#[pyclass(frozen, name = "Execute", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Execute", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyExecute { execute: Execute, @@ -429,7 +507,13 @@ impl PyExecute { } } -#[pyclass(frozen, name = "Deallocate", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Deallocate", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyDeallocate { deallocate: Deallocate, diff --git a/src/expr/subquery.rs b/crates/core/src/expr/subquery.rs similarity index 95% rename from src/expr/subquery.rs rename to crates/core/src/expr/subquery.rs index 94c2583ba..c6fa83db8 100644 --- a/src/expr/subquery.rs +++ b/crates/core/src/expr/subquery.rs @@ -18,13 +18,19 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::Subquery; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Subquery", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Subquery", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PySubquery { subquery: Subquery, diff --git a/src/expr/subquery_alias.rs b/crates/core/src/expr/subquery_alias.rs similarity index 95% rename from src/expr/subquery_alias.rs rename to crates/core/src/expr/subquery_alias.rs index 9bf1c9c51..a6b09e842 100644 --- a/src/expr/subquery_alias.rs +++ b/crates/core/src/expr/subquery_alias.rs @@ -18,14 +18,20 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::SubqueryAlias; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "SubqueryAlias", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "SubqueryAlias", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PySubqueryAlias { subquery_alias: SubqueryAlias, diff --git a/src/expr/table_scan.rs b/crates/core/src/expr/table_scan.rs similarity index 97% rename from src/expr/table_scan.rs rename to crates/core/src/expr/table_scan.rs index bbf225f4c..8ba7e4a69 100644 --- a/src/expr/table_scan.rs +++ b/crates/core/src/expr/table_scan.rs @@ -19,15 +19,21 @@ use std::fmt::{self, Display, Formatter}; use datafusion::common::TableReference; use datafusion::logical_expr::logical_plan::TableScan; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::common::df_schema::PyDFSchema; -use crate::expr::logical_node::LogicalNode; use crate::expr::PyExpr; +use crate::expr::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "TableScan", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "TableScan", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyTableScan { table_scan: TableScan, diff --git a/src/expr/union.rs b/crates/core/src/expr/union.rs similarity index 95% rename from src/expr/union.rs rename to crates/core/src/expr/union.rs index c74d170aa..a3b9efe91 100644 --- a/src/expr/union.rs +++ b/crates/core/src/expr/union.rs @@ -18,14 +18,20 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::logical_plan::Union; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::common::df_schema::PyDFSchema; use crate::expr::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Union", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Union", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyUnion { union_: Union, diff --git a/src/expr/unnest.rs b/crates/core/src/expr/unnest.rs similarity index 95% rename from src/expr/unnest.rs rename to crates/core/src/expr/unnest.rs index 7e68c15f4..880d0a279 100644 --- a/src/expr/unnest.rs +++ b/crates/core/src/expr/unnest.rs @@ -18,14 +18,20 @@ use std::fmt::{self, Display, Formatter}; use datafusion::logical_expr::logical_plan::Unnest; -use pyo3::prelude::*; use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; use crate::common::df_schema::PyDFSchema; use crate::expr::logical_node::LogicalNode; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Unnest", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Unnest", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyUnnest { unnest_: Unnest, diff --git a/src/expr/unnest_expr.rs b/crates/core/src/expr/unnest_expr.rs similarity index 93% rename from src/expr/unnest_expr.rs rename to crates/core/src/expr/unnest_expr.rs index dc6c4cb50..97feef1d1 100644 --- a/src/expr/unnest_expr.rs +++ b/crates/core/src/expr/unnest_expr.rs @@ -22,7 +22,13 @@ use pyo3::prelude::*; use super::PyExpr; -#[pyclass(frozen, name = "UnnestExpr", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "UnnestExpr", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyUnnestExpr { unnest: Unnest, diff --git a/src/expr/values.rs b/crates/core/src/expr/values.rs similarity index 93% rename from src/expr/values.rs rename to crates/core/src/expr/values.rs index 7ae7350fc..d40b0e7cf 100644 --- a/src/expr/values.rs +++ b/crates/core/src/expr/values.rs @@ -19,14 +19,20 @@ use std::sync::Arc; use datafusion::logical_expr::Values; use pyo3::prelude::*; -use pyo3::{pyclass, IntoPyObjectExt, PyErr, PyResult, Python}; +use pyo3::{IntoPyObjectExt, PyErr, PyResult, Python, pyclass}; -use super::logical_node::LogicalNode; use super::PyExpr; +use super::logical_node::LogicalNode; use crate::common::df_schema::PyDFSchema; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Values", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Values", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyValues { values: Values, diff --git a/src/expr/window.rs b/crates/core/src/expr/window.rs similarity index 96% rename from src/expr/window.rs rename to crates/core/src/expr/window.rs index b93e813c4..92d909bfc 100644 --- a/src/expr/window.rs +++ b/crates/core/src/expr/window.rs @@ -19,26 +19,38 @@ use std::fmt::{self, Display, Formatter}; use datafusion::common::{DataFusionError, ScalarValue}; use datafusion::logical_expr::{Expr, Window, WindowFrame, WindowFrameBound, WindowFrameUnits}; +use pyo3::IntoPyObjectExt; use pyo3::exceptions::PyNotImplementedError; use pyo3::prelude::*; -use pyo3::IntoPyObjectExt; use super::py_expr_list; use crate::common::data_type::PyScalarValue; use crate::common::df_schema::PyDFSchema; -use crate::errors::{py_type_err, PyDataFusionResult}; -use crate::expr::logical_node::LogicalNode; -use crate::expr::sort_expr::{py_sort_expr_list, PySortExpr}; +use crate::errors::{PyDataFusionResult, py_type_err}; use crate::expr::PyExpr; +use crate::expr::logical_node::LogicalNode; +use crate::expr::sort_expr::{PySortExpr, py_sort_expr_list}; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "WindowExpr", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "WindowExpr", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyWindowExpr { window: Window, } -#[pyclass(frozen, name = "WindowFrame", module = "datafusion.expr", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "WindowFrame", + module = "datafusion.expr", + subclass +)] #[derive(Clone)] pub struct PyWindowFrame { window_frame: WindowFrame, @@ -57,6 +69,7 @@ impl From for PyWindowFrame { } #[pyclass( + from_py_object, frozen, name = "WindowFrameBound", module = "datafusion.expr", diff --git a/src/functions.rs b/crates/core/src/functions.rs similarity index 81% rename from src/functions.rs rename to crates/core/src/functions.rs index e67781ccd..7feb62d79 100644 --- a/src/functions.rs +++ b/crates/core/src/functions.rs @@ -18,24 +18,18 @@ use std::collections::HashMap; use datafusion::common::{Column, ScalarValue, TableReference}; -use datafusion::execution::FunctionRegistry; -use datafusion::functions_aggregate::all_default_aggregate_functions; -use datafusion::functions_window::all_default_window_functions; -use datafusion::logical_expr::expr::{ - Alias, FieldMetadata, NullTreatment as DFNullTreatment, WindowFunction, WindowFunctionParams, -}; -use datafusion::logical_expr::{lit, Expr, ExprFunctionExt, WindowFrame, WindowFunctionDefinition}; +use datafusion::logical_expr::expr::{Alias, FieldMetadata, NullTreatment as DFNullTreatment}; +use datafusion::logical_expr::{Expr, ExprFunctionExt, lit}; use datafusion::{functions, functions_aggregate, functions_window}; use pyo3::prelude::*; use pyo3::wrap_pyfunction; use crate::common::data_type::{NullTreatment, PyScalarValue}; -use crate::context::PySessionContext; -use crate::errors::{PyDataFusionError, PyDataFusionResult}; +use crate::errors::PyDataFusionResult; +use crate::expr::PyExpr; use crate::expr::conditional_expr::PyCaseBuilder; -use crate::expr::sort_expr::{to_sort_expressions, PySortExpr}; +use crate::expr::sort_expr::{PySortExpr, to_sort_expressions}; use crate::expr::window::PyWindowFrame; -use crate::expr::PyExpr; fn add_builder_fns_to_aggregate( agg_fn: Expr, @@ -93,6 +87,57 @@ fn array_cat(exprs: Vec) -> PyExpr { array_concat(exprs) } +#[pyfunction] +fn array_distance(array1: PyExpr, array2: PyExpr) -> PyExpr { + let args = vec![array1.into(), array2.into()]; + Expr::ScalarFunction(datafusion::logical_expr::expr::ScalarFunction::new_udf( + datafusion::functions_nested::distance::array_distance_udf(), + args, + )) + .into() +} + +#[pyfunction] +fn arrays_zip(exprs: Vec) -> PyExpr { + let exprs = exprs.into_iter().map(|x| x.into()).collect(); + datafusion::functions_nested::expr_fn::arrays_zip(exprs).into() +} + +#[pyfunction] +#[pyo3(signature = (string, delimiter, null_string=None))] +fn string_to_array(string: PyExpr, delimiter: PyExpr, null_string: Option) -> PyExpr { + let mut args = vec![string.into(), delimiter.into()]; + if let Some(null_string) = null_string { + args.push(null_string.into()); + } + Expr::ScalarFunction(datafusion::logical_expr::expr::ScalarFunction::new_udf( + datafusion::functions_nested::string::string_to_array_udf(), + args, + )) + .into() +} + +#[pyfunction] +#[pyo3(signature = (start, stop, step=None))] +fn gen_series(start: PyExpr, stop: PyExpr, step: Option) -> PyExpr { + let mut args = vec![start.into(), stop.into()]; + if let Some(step) = step { + args.push(step.into()); + } + Expr::ScalarFunction(datafusion::logical_expr::expr::ScalarFunction::new_udf( + datafusion::functions_nested::range::gen_series_udf(), + args, + )) + .into() +} + +#[pyfunction] +fn make_map(keys: Vec, values: Vec) -> PyExpr { + let keys = keys.into_iter().map(|x| x.into()).collect(); + let values = values.into_iter().map(|x| x.into()).collect(); + datafusion::functions_nested::map::map(keys, values).into() +} + #[pyfunction] #[pyo3(signature = (array, element, index=None))] fn array_position(array: PyExpr, element: PyExpr, index: Option) -> PyExpr { @@ -189,6 +234,29 @@ fn regexp_count( .into()) } +#[pyfunction] +#[pyo3(signature = (values, regex, start=None, n=None, flags=None, subexpr=None))] +/// Returns the position in a string where the specified occurrence of a regular expression is located +fn regexp_instr( + values: PyExpr, + regex: PyExpr, + start: Option, + n: Option, + flags: Option, + subexpr: Option, +) -> PyResult { + Ok(functions::expr_fn::regexp_instr( + values.into(), + regex.into(), + start.map(|x| x.expr).or(Some(lit(1))), + n.map(|x| x.expr).or(Some(lit(1))), + None, + flags.map(|x| x.expr).or(Some(lit(""))), + subexpr.map(|x| x.expr).or(Some(lit(0))), + ) + .into()) +} + /// Creates a new Sort Expr #[pyfunction] fn order_by(expr: PyExpr, asc: bool, nulls_first: bool) -> PyResult { @@ -232,126 +300,6 @@ fn when(when: PyExpr, then: PyExpr) -> PyResult { Ok(PyCaseBuilder::new(None).when(when, then)) } -/// Helper function to find the appropriate window function. -/// -/// Search procedure: -/// 1) Search built in window functions, which are being deprecated. -/// 1) If a session context is provided: -/// 1) search User Defined Aggregate Functions (UDAFs) -/// 1) search registered window functions -/// 1) search registered aggregate functions -/// 1) If no function has been found, search default aggregate functions. -/// -/// NOTE: we search the built-ins first because the `UDAF` versions currently do not have the same behavior. -fn find_window_fn( - name: &str, - ctx: Option, -) -> PyDataFusionResult { - if let Some(ctx) = ctx { - // search UDAFs - let udaf = ctx - .ctx - .udaf(name) - .map(WindowFunctionDefinition::AggregateUDF) - .ok(); - - if let Some(udaf) = udaf { - return Ok(udaf); - } - - let session_state = ctx.ctx.state(); - - // search registered window functions - let window_fn = session_state - .window_functions() - .get(name) - .map(|f| WindowFunctionDefinition::WindowUDF(f.clone())); - - if let Some(window_fn) = window_fn { - return Ok(window_fn); - } - - // search registered aggregate functions - let agg_fn = session_state - .aggregate_functions() - .get(name) - .map(|f| WindowFunctionDefinition::AggregateUDF(f.clone())); - - if let Some(agg_fn) = agg_fn { - return Ok(agg_fn); - } - } - - // search default aggregate functions - let agg_fn = all_default_aggregate_functions() - .iter() - .find(|v| v.name() == name || v.aliases().contains(&name.to_string())) - .map(|f| WindowFunctionDefinition::AggregateUDF(f.clone())); - - if let Some(agg_fn) = agg_fn { - return Ok(agg_fn); - } - - // search default window functions - let window_fn = all_default_window_functions() - .iter() - .find(|v| v.name() == name || v.aliases().contains(&name.to_string())) - .map(|f| WindowFunctionDefinition::WindowUDF(f.clone())); - - if let Some(window_fn) = window_fn { - return Ok(window_fn); - } - - Err(PyDataFusionError::Common(format!( - "window function `{name}` not found" - ))) -} - -/// Creates a new Window function expression -#[allow(clippy::too_many_arguments)] -#[pyfunction] -#[pyo3(signature = (name, args, partition_by=None, order_by=None, window_frame=None, filter=None, distinct=false, ctx=None))] -fn window( - name: &str, - args: Vec, - partition_by: Option>, - order_by: Option>, - window_frame: Option, - filter: Option, - distinct: bool, - ctx: Option, -) -> PyResult { - let fun = find_window_fn(name, ctx)?; - - let window_frame = window_frame - .map(|w| w.into()) - .unwrap_or(WindowFrame::new(order_by.as_ref().map(|v| !v.is_empty()))); - let filter = filter.map(|f| f.expr.into()); - - Ok(PyExpr { - expr: datafusion::logical_expr::Expr::WindowFunction(Box::new(WindowFunction { - fun, - params: WindowFunctionParams { - args: args.into_iter().map(|x| x.expr).collect::>(), - partition_by: partition_by - .unwrap_or_default() - .into_iter() - .map(|x| x.expr) - .collect::>(), - order_by: order_by - .unwrap_or_default() - .into_iter() - .map(|x| x.into()) - .collect::>(), - window_frame, - filter, - distinct, - null_treatment: None, - }, - })), - }) -} - // Generates a [pyo3] wrapper for associated aggregate functions. // All of the builder options are exposed to the python internal // function and we rely on the wrappers to only use those that @@ -441,7 +389,11 @@ macro_rules! array_fn { expr_fn!(abs, num); expr_fn!(acos, num); expr_fn!(acosh, num); -expr_fn!(ascii, arg1, "Returns the numeric code of the first character of the argument. In UTF8 encoding, returns the Unicode code point of the character. In other multibyte encodings, the argument must be an ASCII character."); +expr_fn!( + ascii, + arg1, + "Returns the numeric code of the first character of the argument. In UTF8 encoding, returns the Unicode code point of the character. In other multibyte encodings, the argument must be an ASCII character." +); expr_fn!(asin, num); expr_fn!(asinh, num); expr_fn!(atan, num); @@ -452,7 +404,10 @@ expr_fn!( arg, "Returns number of bits in the string (8 times the octet_length)." ); -expr_fn_vec!(btrim, "Removes the longest string containing only characters in characters (a space by default) from the start and end of string."); +expr_fn_vec!( + btrim, + "Removes the longest string containing only characters in characters (a space by default) from the start and end of string." +); expr_fn!(cbrt, num); expr_fn!(ceil, num); expr_fn!( @@ -464,6 +419,13 @@ expr_fn!(length, string); expr_fn!(char_length, string); expr_fn!(chr, arg, "Returns the character with the given code."); expr_fn_vec!(coalesce); +expr_fn_vec!(greatest); +expr_fn_vec!(least); +expr_fn!( + contains, + string search_str, + "Return true if search_str is found within string (case-sensitive)." +); expr_fn!(cos, num); expr_fn!(cosh, num); expr_fn!(cot, num); @@ -475,7 +437,11 @@ expr_fn!(exp, num); expr_fn!(factorial, num); expr_fn!(floor, num); expr_fn!(gcd, x y); -expr_fn!(initcap, string, "Converts the first letter of each word to upper case and the rest to lower case. Words are sequences of alphanumeric characters separated by non-alphanumeric characters."); +expr_fn!( + initcap, + string, + "Converts the first letter of each word to upper case and the rest to lower case. Words are sequences of alphanumeric characters separated by non-alphanumeric characters." +); expr_fn!(isnan, num); expr_fn!(iszero, num); expr_fn!(levenshtein, string1 string2); @@ -486,8 +452,14 @@ expr_fn!(log, base num); expr_fn!(log10, num); expr_fn!(log2, num); expr_fn!(lower, arg1, "Converts the string to all lower case"); -expr_fn_vec!(lpad, "Extends the string to length length by prepending the characters fill (a space by default). If the string is already longer than length then it is truncated (on the right)."); -expr_fn_vec!(ltrim, "Removes the longest string containing only characters in characters (a space by default) from the start of string."); +expr_fn_vec!( + lpad, + "Extends the string to length length by prepending the characters fill (a space by default). If the string is already longer than length then it is truncated (on the right)." +); +expr_fn_vec!( + ltrim, + "Removes the longest string containing only characters in characters (a space by default) from the start of string." +); expr_fn!( md5, input_arg, @@ -503,8 +475,17 @@ expr_fn!( x y, "Returns x if x is not NULL otherwise returns y." ); +expr_fn!( + nvl2, + x y z, + "Returns y if x is not NULL; otherwise returns z." +); expr_fn!(nullif, arg_1 arg_2); -expr_fn!(octet_length, args, "Returns number of bytes in the string. Since this version of the function accepts type character directly, it will not strip trailing spaces."); +expr_fn!( + octet_length, + args, + "Returns number of bytes in the string. Since this version of the function accepts type character directly, it will not strip trailing spaces." +); expr_fn_vec!(overlay); expr_fn!(pi); expr_fn!(power, base exponent); @@ -522,8 +503,14 @@ expr_fn!( ); expr_fn!(right, string n, "Returns last n characters in the string, or when n is negative, returns all but first |n| characters."); expr_fn_vec!(round); -expr_fn_vec!(rpad, "Extends the string to length length by appending the characters fill (a space by default). If the string is already longer than length then it is truncated."); -expr_fn_vec!(rtrim, "Removes the longest string containing only characters in characters (a space by default) from the end of string."); +expr_fn_vec!( + rpad, + "Extends the string to length length by appending the characters fill (a space by default). If the string is already longer than length then it is truncated." +); +expr_fn_vec!( + rtrim, + "Removes the longest string containing only characters in characters (a space by default) from the end of string." +); expr_fn!(sha224, input_arg1); expr_fn!(sha256, input_arg1); expr_fn!(sha384, input_arg1); @@ -551,6 +538,9 @@ expr_fn!( "Converts the number to its equivalent hexadecimal representation." ); expr_fn!(now); +expr_fn_vec!(to_date); +expr_fn_vec!(to_local_time); +expr_fn_vec!(to_time); expr_fn_vec!(to_timestamp); expr_fn_vec!(to_timestamp_millis); expr_fn_vec!(to_timestamp_nanos); @@ -563,9 +553,14 @@ expr_fn!(date_part, part date); expr_fn!(date_trunc, part date); expr_fn!(date_bin, stride source origin); expr_fn!(make_date, year month day); +expr_fn!(make_time, hour minute second); +expr_fn!(to_char, datetime format); expr_fn!(translate, string from to, "Replaces each character in string that matches a character in the from set with the corresponding character in the to set. If from is longer than to, occurrences of the extra characters in from are deleted."); -expr_fn_vec!(trim, "Removes the longest string containing only characters in characters (a space by default) from the start, end, or both ends (BOTH is the default) of string."); +expr_fn_vec!( + trim, + "Removes the longest string containing only characters in characters (a space by default) from the start, end, or both ends (BOTH is the default) of string." +); expr_fn_vec!(trunc); expr_fn!(upper, arg1, "Converts the string to all upper case."); expr_fn!(uuid); @@ -574,8 +569,29 @@ expr_fn_vec!(named_struct); expr_fn!(from_unixtime, unixtime); expr_fn!(arrow_typeof, arg_1); expr_fn!(arrow_cast, arg_1 datatype); +expr_fn_vec!(arrow_metadata); +expr_fn!(union_tag, arg1); expr_fn!(random); +#[pyfunction] +fn get_field(expr: PyExpr, name: PyExpr) -> PyExpr { + functions::core::get_field() + .call(vec![expr.into(), name.into()]) + .into() +} + +#[pyfunction] +fn union_extract(union_expr: PyExpr, field_name: PyExpr) -> PyExpr { + functions::core::union_extract() + .call(vec![union_expr.into(), field_name.into()]) + .into() +} + +#[pyfunction] +fn version() -> PyExpr { + functions::core::version().call(vec![]).into() +} + // Array Functions array_fn!(array_append, array element); array_fn!(array_to_string, array delimiter); @@ -604,10 +620,20 @@ array_fn!(array_intersect, first_array second_array); array_fn!(array_union, array1 array2); array_fn!(array_except, first_array second_array); array_fn!(array_resize, array size value); +array_fn!(array_any_value, array); +array_fn!(array_max, array); +array_fn!(array_min, array); +array_fn!(array_reverse, array); array_fn!(cardinality, array); array_fn!(flatten, array); array_fn!(range, start stop step); +// Map Functions +array_fn!(map_keys, map); +array_fn!(map_values, map); +array_fn!(map_extract, map key); +array_fn!(map_entries, map); + aggregate_function!(array_agg); aggregate_function!(max); aggregate_function!(min); @@ -639,9 +665,10 @@ aggregate_function!(var_pop); aggregate_function!(approx_distinct); aggregate_function!(approx_median); -// Code is commented out since grouping is not yet implemented -// https://github.com/apache/datafusion-python/issues/861 -// aggregate_function!(grouping); +// The grouping function's physical plan is not implemented, but the +// ResolveGroupingFunction analyzer rule rewrites it before the physical +// planner sees it, so it works correctly at runtime. +aggregate_function!(grouping); #[pyfunction] #[pyo3(signature = (sort_expression, percentile, num_centroids=None, filter=None))] @@ -679,6 +706,19 @@ pub fn approx_percentile_cont_with_weight( add_builder_fns_to_aggregate(agg_fn, None, filter, None, None) } +#[pyfunction] +#[pyo3(signature = (sort_expression, percentile, filter=None))] +pub fn percentile_cont( + sort_expression: PySortExpr, + percentile: f64, + filter: Option, +) -> PyDataFusionResult { + let agg_fn = + functions_aggregate::expr_fn::percentile_cont(sort_expression.sort, lit(percentile)); + + add_builder_fns_to_aggregate(agg_fn, None, filter, None, None) +} + // We handle last_value explicitly because the signature expects an order_by // https://github.com/apache/datafusion/issues/12376 #[pyfunction] @@ -879,10 +919,12 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(approx_median))?; m.add_wrapped(wrap_pyfunction!(approx_percentile_cont))?; m.add_wrapped(wrap_pyfunction!(approx_percentile_cont_with_weight))?; + m.add_wrapped(wrap_pyfunction!(percentile_cont))?; m.add_wrapped(wrap_pyfunction!(range))?; m.add_wrapped(wrap_pyfunction!(array_agg))?; m.add_wrapped(wrap_pyfunction!(arrow_typeof))?; m.add_wrapped(wrap_pyfunction!(arrow_cast))?; + m.add_wrapped(wrap_pyfunction!(arrow_metadata))?; m.add_wrapped(wrap_pyfunction!(ascii))?; m.add_wrapped(wrap_pyfunction!(asin))?; m.add_wrapped(wrap_pyfunction!(asinh))?; @@ -903,6 +945,7 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(col))?; m.add_wrapped(wrap_pyfunction!(concat_ws))?; m.add_wrapped(wrap_pyfunction!(concat))?; + m.add_wrapped(wrap_pyfunction!(contains))?; m.add_wrapped(wrap_pyfunction!(corr))?; m.add_wrapped(wrap_pyfunction!(cos))?; m.add_wrapped(wrap_pyfunction!(cosh))?; @@ -917,6 +960,7 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(date_part))?; m.add_wrapped(wrap_pyfunction!(date_trunc))?; m.add_wrapped(wrap_pyfunction!(make_date))?; + m.add_wrapped(wrap_pyfunction!(make_time))?; m.add_wrapped(wrap_pyfunction!(digest))?; m.add_wrapped(wrap_pyfunction!(ends_with))?; m.add_wrapped(wrap_pyfunction!(exp))?; @@ -924,13 +968,15 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(floor))?; m.add_wrapped(wrap_pyfunction!(from_unixtime))?; m.add_wrapped(wrap_pyfunction!(gcd))?; - // m.add_wrapped(wrap_pyfunction!(grouping))?; + m.add_wrapped(wrap_pyfunction!(greatest))?; + m.add_wrapped(wrap_pyfunction!(grouping))?; m.add_wrapped(wrap_pyfunction!(in_list))?; m.add_wrapped(wrap_pyfunction!(initcap))?; m.add_wrapped(wrap_pyfunction!(isnan))?; m.add_wrapped(wrap_pyfunction!(iszero))?; m.add_wrapped(wrap_pyfunction!(levenshtein))?; m.add_wrapped(wrap_pyfunction!(lcm))?; + m.add_wrapped(wrap_pyfunction!(least))?; m.add_wrapped(wrap_pyfunction!(left))?; m.add_wrapped(wrap_pyfunction!(length))?; m.add_wrapped(wrap_pyfunction!(ln))?; @@ -948,6 +994,7 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(named_struct))?; m.add_wrapped(wrap_pyfunction!(nanvl))?; m.add_wrapped(wrap_pyfunction!(nvl))?; + m.add_wrapped(wrap_pyfunction!(nvl2))?; m.add_wrapped(wrap_pyfunction!(now))?; m.add_wrapped(wrap_pyfunction!(nullif))?; m.add_wrapped(wrap_pyfunction!(octet_length))?; @@ -958,6 +1005,7 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(radians))?; m.add_wrapped(wrap_pyfunction!(random))?; m.add_wrapped(wrap_pyfunction!(regexp_count))?; + m.add_wrapped(wrap_pyfunction!(regexp_instr))?; m.add_wrapped(wrap_pyfunction!(regexp_like))?; m.add_wrapped(wrap_pyfunction!(regexp_match))?; m.add_wrapped(wrap_pyfunction!(regexp_replace))?; @@ -991,6 +1039,10 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(tan))?; m.add_wrapped(wrap_pyfunction!(tanh))?; m.add_wrapped(wrap_pyfunction!(to_hex))?; + m.add_wrapped(wrap_pyfunction!(to_char))?; + m.add_wrapped(wrap_pyfunction!(to_date))?; + m.add_wrapped(wrap_pyfunction!(to_local_time))?; + m.add_wrapped(wrap_pyfunction!(to_time))?; m.add_wrapped(wrap_pyfunction!(to_timestamp))?; m.add_wrapped(wrap_pyfunction!(to_timestamp_millis))?; m.add_wrapped(wrap_pyfunction!(to_timestamp_nanos))?; @@ -1001,10 +1053,13 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(trim))?; m.add_wrapped(wrap_pyfunction!(trunc))?; m.add_wrapped(wrap_pyfunction!(upper))?; + m.add_wrapped(wrap_pyfunction!(get_field))?; + m.add_wrapped(wrap_pyfunction!(union_extract))?; + m.add_wrapped(wrap_pyfunction!(union_tag))?; + m.add_wrapped(wrap_pyfunction!(version))?; m.add_wrapped(wrap_pyfunction!(self::uuid))?; // Use self to avoid name collision m.add_wrapped(wrap_pyfunction!(var_pop))?; m.add_wrapped(wrap_pyfunction!(var_sample))?; - m.add_wrapped(wrap_pyfunction!(window))?; m.add_wrapped(wrap_pyfunction!(regr_avgx))?; m.add_wrapped(wrap_pyfunction!(regr_avgy))?; m.add_wrapped(wrap_pyfunction!(regr_count))?; @@ -1059,9 +1114,24 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(array_replace_all))?; m.add_wrapped(wrap_pyfunction!(array_sort))?; m.add_wrapped(wrap_pyfunction!(array_slice))?; + m.add_wrapped(wrap_pyfunction!(array_any_value))?; + m.add_wrapped(wrap_pyfunction!(array_distance))?; + m.add_wrapped(wrap_pyfunction!(array_max))?; + m.add_wrapped(wrap_pyfunction!(array_min))?; + m.add_wrapped(wrap_pyfunction!(array_reverse))?; + m.add_wrapped(wrap_pyfunction!(arrays_zip))?; + m.add_wrapped(wrap_pyfunction!(string_to_array))?; + m.add_wrapped(wrap_pyfunction!(gen_series))?; m.add_wrapped(wrap_pyfunction!(flatten))?; m.add_wrapped(wrap_pyfunction!(cardinality))?; + // Map Functions + m.add_wrapped(wrap_pyfunction!(make_map))?; + m.add_wrapped(wrap_pyfunction!(map_keys))?; + m.add_wrapped(wrap_pyfunction!(map_values))?; + m.add_wrapped(wrap_pyfunction!(map_extract))?; + m.add_wrapped(wrap_pyfunction!(map_entries))?; + // Window Functions m.add_wrapped(wrap_pyfunction!(lead))?; m.add_wrapped(wrap_pyfunction!(lag))?; diff --git a/src/lib.rs b/crates/core/src/lib.rs similarity index 92% rename from src/lib.rs rename to crates/core/src/lib.rs index 9483a5252..77d69911a 100644 --- a/src/lib.rs +++ b/crates/core/src/lib.rs @@ -16,9 +16,9 @@ // under the License. // Re-export Apache Arrow DataFusion dependencies -pub use datafusion; pub use datafusion::{ - common as datafusion_common, logical_expr as datafusion_expr, optimizer, sql as datafusion_sql, + self, common as datafusion_common, logical_expr as datafusion_expr, optimizer, + sql as datafusion_sql, }; #[cfg(feature = "substrait")] pub use datafusion_substrait; @@ -43,6 +43,8 @@ pub mod errors; pub mod expr; #[allow(clippy::borrow_deref_ref)] mod functions; +pub mod metrics; +mod options; pub mod physical_plan; mod pyarrow_filter_expression; pub mod pyarrow_util; @@ -52,6 +54,7 @@ pub mod store; pub mod table; pub mod unparser; +mod array; #[cfg(feature = "substrait")] pub mod substrait; #[allow(clippy::borrow_deref_ref)] @@ -60,15 +63,11 @@ mod udaf; mod udf; pub mod udtf; mod udwf; -pub mod utils; #[cfg(feature = "mimalloc")] #[global_allocator] static GLOBAL: MiMalloc = MiMalloc; -// Used to define Tokio Runtime as a Python module attribute -pub(crate) struct TokioRuntime(tokio::runtime::Runtime); - /// Low-level DataFusion internal package. /// /// The higher-level public API is defined in pure python files under the @@ -94,6 +93,8 @@ fn _internal(py: Python, m: Bound<'_, PyModule>) -> PyResult<()> { m.add_class::()?; m.add_class::()?; m.add_class::()?; + m.add_class::()?; + m.add_class::()?; m.add_class::()?; m.add_class::()?; m.add_class::()?; @@ -125,6 +126,10 @@ fn _internal(py: Python, m: Bound<'_, PyModule>) -> PyResult<()> { store::init_module(&store)?; m.add_submodule(&store)?; + let options = PyModule::new(py, "options")?; + options::init_module(&options)?; + m.add_submodule(&options)?; + // Register substrait as a submodule #[cfg(feature = "substrait")] setup_substrait_module(py, &m)?; diff --git a/crates/core/src/metrics.rs b/crates/core/src/metrics.rs new file mode 100644 index 000000000..ee0937e25 --- /dev/null +++ b/crates/core/src/metrics.rs @@ -0,0 +1,169 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::collections::HashMap; +use std::sync::Arc; + +use chrono::{Datelike, Timelike}; +use datafusion::physical_plan::metrics::{Metric, MetricValue, MetricsSet, Timestamp}; +use pyo3::prelude::*; + +#[pyclass(from_py_object, frozen, name = "MetricsSet", module = "datafusion")] +#[derive(Debug, Clone)] +pub struct PyMetricsSet { + metrics: MetricsSet, +} + +impl PyMetricsSet { + pub fn new(metrics: MetricsSet) -> Self { + Self { metrics } + } +} + +#[pymethods] +impl PyMetricsSet { + fn metrics(&self) -> Vec { + self.metrics + .iter() + .map(|m| PyMetric::new(Arc::clone(m))) + .collect() + } + + fn output_rows(&self) -> Option { + self.metrics.output_rows() + } + + fn elapsed_compute(&self) -> Option { + self.metrics.elapsed_compute() + } + + fn spill_count(&self) -> Option { + self.metrics.spill_count() + } + + fn spilled_bytes(&self) -> Option { + self.metrics.spilled_bytes() + } + + fn spilled_rows(&self) -> Option { + self.metrics.spilled_rows() + } + + fn sum_by_name(&self, name: &str) -> Option { + self.metrics.sum_by_name(name).map(|v| v.as_usize()) + } + + fn __repr__(&self) -> String { + format!("{}", self.metrics) + } +} + +#[pyclass(from_py_object, frozen, name = "Metric", module = "datafusion")] +#[derive(Debug, Clone)] +pub struct PyMetric { + metric: Arc, +} + +impl PyMetric { + pub fn new(metric: Arc) -> Self { + Self { metric } + } + + fn timestamp_to_pyobject<'py>( + py: Python<'py>, + ts: &Timestamp, + ) -> PyResult>> { + match ts.value() { + Some(dt) => { + let datetime_mod = py.import("datetime")?; + let datetime_cls = datetime_mod.getattr("datetime")?; + let tz_utc = datetime_mod.getattr("timezone")?.getattr("utc")?; + let result = datetime_cls.call1(( + dt.year(), + dt.month(), + dt.day(), + dt.hour(), + dt.minute(), + dt.second(), + dt.timestamp_subsec_micros(), + tz_utc, + ))?; + Ok(Some(result)) + } + None => Ok(None), + } + } +} + +#[pymethods] +impl PyMetric { + #[getter] + fn name(&self) -> String { + self.metric.value().name().to_string() + } + + #[getter] + fn value<'py>(&self, py: Python<'py>) -> PyResult>> { + match self.metric.value() { + MetricValue::OutputRows(c) => Ok(Some(c.value().into_pyobject(py)?.into_any())), + MetricValue::OutputBytes(c) => Ok(Some(c.value().into_pyobject(py)?.into_any())), + MetricValue::ElapsedCompute(t) => Ok(Some(t.value().into_pyobject(py)?.into_any())), + MetricValue::SpillCount(c) => Ok(Some(c.value().into_pyobject(py)?.into_any())), + MetricValue::SpilledBytes(c) => Ok(Some(c.value().into_pyobject(py)?.into_any())), + MetricValue::SpilledRows(c) => Ok(Some(c.value().into_pyobject(py)?.into_any())), + MetricValue::CurrentMemoryUsage(g) => Ok(Some(g.value().into_pyobject(py)?.into_any())), + MetricValue::Count { count, .. } => { + Ok(Some(count.value().into_pyobject(py)?.into_any())) + } + MetricValue::Gauge { gauge, .. } => { + Ok(Some(gauge.value().into_pyobject(py)?.into_any())) + } + MetricValue::Time { time, .. } => Ok(Some(time.value().into_pyobject(py)?.into_any())), + MetricValue::StartTimestamp(ts) | MetricValue::EndTimestamp(ts) => { + Self::timestamp_to_pyobject(py, ts) + } + _ => Ok(None), + } + } + + #[getter] + fn value_as_datetime<'py>(&self, py: Python<'py>) -> PyResult>> { + match self.metric.value() { + MetricValue::StartTimestamp(ts) | MetricValue::EndTimestamp(ts) => { + Self::timestamp_to_pyobject(py, ts) + } + _ => Ok(None), + } + } + + #[getter] + fn partition(&self) -> Option { + self.metric.partition() + } + + fn labels(&self) -> HashMap { + self.metric + .labels() + .iter() + .map(|l| (l.name().to_string(), l.value().to_string())) + .collect() + } + + fn __repr__(&self) -> String { + format!("{}", self.metric.value()) + } +} diff --git a/crates/core/src/options.rs b/crates/core/src/options.rs new file mode 100644 index 000000000..6b6037695 --- /dev/null +++ b/crates/core/src/options.rs @@ -0,0 +1,159 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use arrow::datatypes::{DataType, Schema}; +use arrow::pyarrow::PyArrowType; +use datafusion::prelude::CsvReadOptions; +use pyo3::prelude::{PyModule, PyModuleMethods}; +use pyo3::{Bound, PyResult, pyclass, pymethods}; + +use crate::context::parse_file_compression_type; +use crate::errors::PyDataFusionError; +use crate::expr::sort_expr::PySortExpr; + +/// Options for reading CSV files +#[pyclass(name = "CsvReadOptions", module = "datafusion.options", frozen)] +pub struct PyCsvReadOptions { + pub has_header: bool, + pub delimiter: u8, + pub quote: u8, + pub terminator: Option, + pub escape: Option, + pub comment: Option, + pub newlines_in_values: bool, + pub schema: Option>, + pub schema_infer_max_records: usize, + pub file_extension: String, + pub table_partition_cols: Vec<(String, PyArrowType)>, + pub file_compression_type: String, + pub file_sort_order: Vec>, + pub null_regex: Option, + pub truncated_rows: bool, +} + +#[pymethods] +impl PyCsvReadOptions { + #[allow(clippy::too_many_arguments)] + #[pyo3(signature = ( + has_header=true, + delimiter=b',', + quote=b'"', + terminator=None, + escape=None, + comment=None, + newlines_in_values=false, + schema=None, + schema_infer_max_records=1000, + file_extension=".csv".to_string(), + table_partition_cols=vec![], + file_compression_type="".to_string(), + file_sort_order=vec![], + null_regex=None, + truncated_rows=false + ))] + #[new] + fn new( + has_header: bool, + delimiter: u8, + quote: u8, + terminator: Option, + escape: Option, + comment: Option, + newlines_in_values: bool, + schema: Option>, + schema_infer_max_records: usize, + file_extension: String, + table_partition_cols: Vec<(String, PyArrowType)>, + file_compression_type: String, + file_sort_order: Vec>, + null_regex: Option, + truncated_rows: bool, + ) -> Self { + Self { + has_header, + delimiter, + quote, + terminator, + escape, + comment, + newlines_in_values, + schema, + schema_infer_max_records, + file_extension, + table_partition_cols, + file_compression_type, + file_sort_order, + null_regex, + truncated_rows, + } + } +} + +impl<'a> TryFrom<&'a PyCsvReadOptions> for CsvReadOptions<'a> { + type Error = PyDataFusionError; + + fn try_from(value: &'a PyCsvReadOptions) -> Result, Self::Error> { + let partition_cols: Vec<(String, DataType)> = value + .table_partition_cols + .iter() + .map(|(name, dtype)| (name.clone(), dtype.0.clone())) + .collect(); + + let compression = parse_file_compression_type(Some(value.file_compression_type.clone()))?; + + let sort_order: Vec> = value + .file_sort_order + .iter() + .map(|inner| { + inner + .iter() + .map(|sort_expr| sort_expr.sort.clone()) + .collect() + }) + .collect(); + + // Explicit struct initialization to catch upstream changes + let mut options = CsvReadOptions { + has_header: value.has_header, + delimiter: value.delimiter, + quote: value.quote, + terminator: value.terminator, + escape: value.escape, + comment: value.comment, + newlines_in_values: value.newlines_in_values, + schema: None, // Will be set separately due to lifetime constraints + schema_infer_max_records: value.schema_infer_max_records, + file_extension: value.file_extension.as_str(), + table_partition_cols: partition_cols, + file_compression_type: compression, + file_sort_order: sort_order, + null_regex: value.null_regex.clone(), + truncated_rows: value.truncated_rows, + }; + + // Set schema separately to handle the lifetime + options.schema = value.schema.as_ref().map(|s| &s.0); + + Ok(options) + } +} + +pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + + Ok(()) +} diff --git a/src/physical_plan.rs b/crates/core/src/physical_plan.rs similarity index 88% rename from src/physical_plan.rs rename to crates/core/src/physical_plan.rs index 645649e2c..fac973884 100644 --- a/src/physical_plan.rs +++ b/crates/core/src/physical_plan.rs @@ -17,7 +17,7 @@ use std::sync::Arc; -use datafusion::physical_plan::{displayable, ExecutionPlan, ExecutionPlanProperties}; +use datafusion::physical_plan::{ExecutionPlan, ExecutionPlanProperties, displayable}; use datafusion_proto::physical_plan::{AsExecutionPlan, DefaultPhysicalExtensionCodec}; use prost::Message; use pyo3::exceptions::PyRuntimeError; @@ -26,8 +26,15 @@ use pyo3::types::PyBytes; use crate::context::PySessionContext; use crate::errors::PyDataFusionResult; - -#[pyclass(frozen, name = "ExecutionPlan", module = "datafusion", subclass)] +use crate::metrics::PyMetricsSet; + +#[pyclass( + from_py_object, + frozen, + name = "ExecutionPlan", + module = "datafusion", + subclass +)] #[derive(Debug, Clone)] pub struct PyExecutionPlan { pub plan: Arc, @@ -77,7 +84,7 @@ impl PyExecutionPlan { ctx: PySessionContext, proto_msg: Bound<'_, PyBytes>, ) -> PyDataFusionResult { - let bytes: &[u8] = proto_msg.extract()?; + let bytes: &[u8] = proto_msg.extract().map_err(Into::::into)?; let proto_plan = datafusion_proto::protobuf::PhysicalPlanNode::decode(bytes).map_err(|e| { PyRuntimeError::new_err(format!( @@ -90,6 +97,10 @@ impl PyExecutionPlan { Ok(Self::new(plan)) } + pub fn metrics(&self) -> Option { + self.plan.metrics().map(PyMetricsSet::new) + } + fn __repr__(&self) -> String { self.display_indent() } diff --git a/src/pyarrow_filter_expression.rs b/crates/core/src/pyarrow_filter_expression.rs similarity index 98% rename from src/pyarrow_filter_expression.rs rename to crates/core/src/pyarrow_filter_expression.rs index c9d3df32d..e3b4b6009 100644 --- a/src/pyarrow_filter_expression.rs +++ b/crates/core/src/pyarrow_filter_expression.rs @@ -22,7 +22,7 @@ use datafusion::common::{Column, ScalarValue}; use datafusion::logical_expr::expr::InList; use datafusion::logical_expr::{Between, BinaryExpr, Expr, Operator}; /// Converts a Datafusion logical plan expression (Expr) into a PyArrow compute expression -use pyo3::{prelude::*, IntoPyObjectExt}; +use pyo3::{IntoPyObjectExt, prelude::*}; use crate::errors::{PyDataFusionError, PyDataFusionResult}; use crate::pyarrow_util::scalar_to_pyarrow; @@ -47,7 +47,7 @@ fn operator_to_py<'py>( _ => { return Err(PyDataFusionError::Common(format!( "Unsupported operator {operator:?}" - ))) + ))); } }; Ok(py_op) @@ -57,7 +57,7 @@ fn extract_scalar_list<'py>( exprs: &[Expr], py: Python<'py>, ) -> PyDataFusionResult>> { - let ret = exprs + exprs .iter() .map(|expr| match expr { // TODO: should we also leverage `ScalarValue::to_pyarrow` here? @@ -83,8 +83,7 @@ fn extract_scalar_list<'py>( "Only a list of Literals are supported got {expr:?}" ))), }) - .collect(); - ret + .collect() } impl PyArrowFilterExpression { diff --git a/crates/core/src/pyarrow_util.rs b/crates/core/src/pyarrow_util.rs new file mode 100644 index 000000000..1401a4938 --- /dev/null +++ b/crates/core/src/pyarrow_util.rs @@ -0,0 +1,163 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Conversions between PyArrow and DataFusion types + +use std::sync::Arc; + +use arrow::array::{Array, ArrayData, ArrayRef, ListArray, make_array}; +use arrow::buffer::OffsetBuffer; +use arrow::datatypes::Field; +use arrow::pyarrow::{FromPyArrow, ToPyArrow}; +use datafusion::common::exec_err; +use datafusion::scalar::ScalarValue; +use pyo3::types::{PyAnyMethods, PyList}; +use pyo3::{Borrowed, Bound, FromPyObject, PyAny, PyErr, PyResult, Python}; + +use crate::common::data_type::PyScalarValue; +use crate::errors::PyDataFusionError; + +/// Helper function to turn an Array into a ScalarValue. If ``as_list_array`` is true, +/// the array will be turned into a ``ListArray``. Otherwise, we extract the first value +/// from the array. +fn array_to_scalar_value(array: ArrayRef, as_list_array: bool) -> PyResult { + if as_list_array { + let field = Arc::new(Field::new_list_field( + array.data_type().clone(), + array.nulls().is_some(), + )); + let offsets = OffsetBuffer::from_lengths(vec![array.len()]); + let list_array = ListArray::new(field, offsets, array, None); + Ok(PyScalarValue(ScalarValue::List(Arc::new(list_array)))) + } else { + let scalar = ScalarValue::try_from_array(&array, 0).map_err(PyDataFusionError::from)?; + Ok(PyScalarValue(scalar)) + } +} + +/// Helper function to take any Python object that contains an Arrow PyCapsule +/// interface and attempt to extract a scalar value from it. If `as_list_array` +/// is true, the array will be turned into a ``ListArray``. Otherwise, we extract +/// the first value from the array. +fn pyobj_extract_scalar_via_capsule( + value: &Bound<'_, PyAny>, + as_list_array: bool, +) -> PyResult { + let array_data = ArrayData::from_pyarrow_bound(value)?; + let array = make_array(array_data); + + array_to_scalar_value(array, as_list_array) +} + +impl FromPyArrow for PyScalarValue { + fn from_pyarrow_bound(value: &Bound<'_, PyAny>) -> PyResult { + let py = value.py(); + let pyarrow_mod = py.import("pyarrow"); + + // Is it a PyArrow object? + if let Ok(pa) = pyarrow_mod.as_ref() { + let scalar_type = pa.getattr("Scalar")?; + if value.is_instance(&scalar_type)? { + let typ = value.getattr("type")?; + + // construct pyarrow array from the python value and pyarrow type + let factory = py.import("pyarrow")?.getattr("array")?; + let args = PyList::new(py, [value])?; + let array = factory.call1((args, typ))?; + + return pyobj_extract_scalar_via_capsule(&array, false); + } + + let array_type = pa.getattr("Array")?; + if value.is_instance(&array_type)? { + return pyobj_extract_scalar_via_capsule(value, true); + } + } + + // Is it a NanoArrow scalar? + if let Ok(na) = py.import("nanoarrow") { + let scalar_type = py.import("nanoarrow.array")?.getattr("Scalar")?; + if value.is_instance(&scalar_type)? { + return pyobj_extract_scalar_via_capsule(value, false); + } + let array_type = na.getattr("Array")?; + if value.is_instance(&array_type)? { + return pyobj_extract_scalar_via_capsule(value, true); + } + } + + // Is it a arro3 scalar? + if let Ok(arro3) = py.import("arro3").and_then(|arro3| arro3.getattr("core")) { + let scalar_type = arro3.getattr("Scalar")?; + if value.is_instance(&scalar_type)? { + return pyobj_extract_scalar_via_capsule(value, false); + } + let array_type = arro3.getattr("Array")?; + if value.is_instance(&array_type)? { + return pyobj_extract_scalar_via_capsule(value, true); + } + } + + // Does it have a PyCapsule interface but isn't one of our known libraries? + // If so do our "best guess". Try checking type name, and if that fails + // return a single value if the length is 1 and return a List value otherwise + if value.hasattr("__arrow_c_array__")? { + let type_name = value.get_type().repr()?; + if type_name.contains("Scalar")? { + return pyobj_extract_scalar_via_capsule(value, false); + } + if type_name.contains("Array")? { + return pyobj_extract_scalar_via_capsule(value, true); + } + + let array_data = ArrayData::from_pyarrow_bound(value)?; + let array = make_array(array_data); + + let as_array_list = array.len() != 1; + return array_to_scalar_value(array, as_array_list); + } + + // Last attempt - try to create a PyArrow scalar from a plain Python object + if let Ok(pa) = pyarrow_mod.as_ref() { + let scalar = pa.call_method1("scalar", (value,))?; + + PyScalarValue::from_pyarrow_bound(&scalar) + } else { + exec_err!("Unable to import scalar value").map_err(PyDataFusionError::from)? + } + } +} + +impl<'source> FromPyObject<'_, 'source> for PyScalarValue { + type Error = PyErr; + + fn extract(value: Borrowed<'_, 'source, PyAny>) -> Result { + Self::from_pyarrow_bound(&value) + } +} + +pub fn scalar_to_pyarrow<'py>( + scalar: &ScalarValue, + py: Python<'py>, +) -> PyResult> { + let array = scalar.to_array().map_err(PyDataFusionError::from)?; + // convert to pyarrow array using C data interface + let pyarray = array.to_data().to_pyarrow(py)?; + let pyscalar = pyarray.call_method1("__getitem__", (0,))?; + + Ok(pyscalar) +} diff --git a/src/record_batch.rs b/crates/core/src/record_batch.rs similarity index 97% rename from src/record_batch.rs rename to crates/core/src/record_batch.rs index 2e50ba75e..0492c6c76 100644 --- a/src/record_batch.rs +++ b/crates/core/src/record_batch.rs @@ -20,14 +20,14 @@ use std::sync::Arc; use datafusion::arrow::pyarrow::ToPyArrow; use datafusion::arrow::record_batch::RecordBatch; use datafusion::physical_plan::SendableRecordBatchStream; +use datafusion_python_util::wait_for_future; use futures::StreamExt; use pyo3::exceptions::{PyStopAsyncIteration, PyStopIteration}; use pyo3::prelude::*; -use pyo3::{pyclass, pymethods, PyAny, PyResult, Python}; +use pyo3::{PyAny, PyResult, Python, pyclass, pymethods}; use tokio::sync::Mutex; use crate::errors::PyDataFusionError; -use crate::utils::wait_for_future; #[pyclass(name = "RecordBatch", module = "datafusion", subclass, frozen)] pub struct PyRecordBatch { diff --git a/src/sql.rs b/crates/core/src/sql.rs similarity index 100% rename from src/sql.rs rename to crates/core/src/sql.rs diff --git a/src/sql/exceptions.rs b/crates/core/src/sql/exceptions.rs similarity index 100% rename from src/sql/exceptions.rs rename to crates/core/src/sql/exceptions.rs diff --git a/src/sql/logical.rs b/crates/core/src/sql/logical.rs similarity index 95% rename from src/sql/logical.rs rename to crates/core/src/sql/logical.rs index 37f20d287..631aa9b09 100644 --- a/src/sql/logical.rs +++ b/crates/core/src/sql/logical.rs @@ -55,7 +55,8 @@ use crate::expr::recursive_query::PyRecursiveQuery; use crate::expr::repartition::PyRepartition; use crate::expr::sort::PySort; use crate::expr::statement::{ - PyDeallocate, PyExecute, PyPrepare, PySetVariable, PyTransactionEnd, PyTransactionStart, + PyDeallocate, PyExecute, PyPrepare, PyResetVariable, PySetVariable, PyTransactionEnd, + PyTransactionStart, }; use crate::expr::subquery::PySubquery; use crate::expr::subquery_alias::PySubqueryAlias; @@ -65,8 +66,15 @@ use crate::expr::unnest::PyUnnest; use crate::expr::values::PyValues; use crate::expr::window::PyWindowExpr; -#[pyclass(frozen, name = "LogicalPlan", module = "datafusion", subclass)] -#[derive(Debug, Clone)] +#[pyclass( + from_py_object, + frozen, + name = "LogicalPlan", + module = "datafusion", + subclass, + eq +)] +#[derive(Debug, Clone, PartialEq, Eq)] pub struct PyLogicalPlan { pub(crate) plan: Arc, } @@ -115,6 +123,9 @@ impl PyLogicalPlan { PyTransactionEnd::from(plan.clone()).to_variant(py) } Statement::SetVariable(plan) => PySetVariable::from(plan.clone()).to_variant(py), + Statement::ResetVariable(plan) => { + PyResetVariable::from(plan.clone()).to_variant(py) + } Statement::Prepare(plan) => PyPrepare::from(plan.clone()).to_variant(py), Statement::Execute(plan) => PyExecute::from(plan.clone()).to_variant(py), Statement::Deallocate(plan) => PyDeallocate::from(plan.clone()).to_variant(py), @@ -199,7 +210,7 @@ impl PyLogicalPlan { ctx: PySessionContext, proto_msg: Bound<'_, PyBytes>, ) -> PyDataFusionResult { - let bytes: &[u8] = proto_msg.extract()?; + let bytes: &[u8] = proto_msg.extract().map_err(Into::::into)?; let proto_plan = datafusion_proto::protobuf::LogicalPlanNode::decode(bytes).map_err(|e| { PyRuntimeError::new_err(format!( diff --git a/src/sql/util.rs b/crates/core/src/sql/util.rs similarity index 97% rename from src/sql/util.rs rename to crates/core/src/sql/util.rs index 5edff006f..d1e8964f8 100644 --- a/src/sql/util.rs +++ b/crates/core/src/sql/util.rs @@ -17,7 +17,7 @@ use std::collections::HashMap; -use datafusion::common::{exec_err, plan_datafusion_err, DataFusionError}; +use datafusion::common::{DataFusionError, exec_err, plan_datafusion_err}; use datafusion::logical_expr::sqlparser::dialect::dialect_from_str; use datafusion::sql::sqlparser::dialect::Dialect; use datafusion::sql::sqlparser::parser::Parser; diff --git a/src/store.rs b/crates/core/src/store.rs similarity index 91% rename from src/store.rs rename to crates/core/src/store.rs index dcbcbd325..8535e83b7 100644 --- a/src/store.rs +++ b/crates/core/src/store.rs @@ -36,6 +36,7 @@ pub enum StorageContexts { } #[pyclass( + from_py_object, frozen, name = "LocalFileSystem", module = "datafusion.store", @@ -66,7 +67,13 @@ impl PyLocalFileSystemContext { } } -#[pyclass(frozen, name = "MicrosoftAzure", module = "datafusion.store", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "MicrosoftAzure", + module = "datafusion.store", + subclass +)] #[derive(Debug, Clone)] pub struct PyMicrosoftAzureContext { pub inner: Arc, @@ -76,7 +83,7 @@ pub struct PyMicrosoftAzureContext { #[pymethods] impl PyMicrosoftAzureContext { #[allow(clippy::too_many_arguments)] - #[pyo3(signature = (container_name, account=None, access_key=None, bearer_token=None, client_id=None, client_secret=None, tenant_id=None, sas_query_pairs=None, use_emulator=None, allow_http=None))] + #[pyo3(signature = (container_name, account=None, access_key=None, bearer_token=None, client_id=None, client_secret=None, tenant_id=None, sas_query_pairs=None, use_emulator=None, allow_http=None, use_fabric_endpoint=None))] #[new] fn new( container_name: String, @@ -89,6 +96,7 @@ impl PyMicrosoftAzureContext { sas_query_pairs: Option>, use_emulator: Option, allow_http: Option, + use_fabric_endpoint: Option, ) -> Self { let mut builder = MicrosoftAzureBuilder::from_env().with_container_name(&container_name); @@ -127,6 +135,10 @@ impl PyMicrosoftAzureContext { builder = builder.with_allow_http(allow_http); } + if let Some(use_fabric_endpoint) = use_fabric_endpoint { + builder = builder.with_use_fabric_endpoint(use_fabric_endpoint); + } + Self { inner: Arc::new( builder @@ -138,7 +150,13 @@ impl PyMicrosoftAzureContext { } } -#[pyclass(frozen, name = "GoogleCloud", module = "datafusion.store", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "GoogleCloud", + module = "datafusion.store", + subclass +)] #[derive(Debug, Clone)] pub struct PyGoogleCloudContext { pub inner: Arc, @@ -168,7 +186,13 @@ impl PyGoogleCloudContext { } } -#[pyclass(frozen, name = "AmazonS3", module = "datafusion.store", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "AmazonS3", + module = "datafusion.store", + subclass +)] #[derive(Debug, Clone)] pub struct PyAmazonS3Context { pub inner: Arc, @@ -232,7 +256,13 @@ impl PyAmazonS3Context { } } -#[pyclass(frozen, name = "Http", module = "datafusion.store", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Http", + module = "datafusion.store", + subclass +)] #[derive(Debug, Clone)] pub struct PyHttpContext { pub url: String, diff --git a/src/substrait.rs b/crates/core/src/substrait.rs similarity index 80% rename from src/substrait.rs rename to crates/core/src/substrait.rs index 7b06aff74..27e446f48 100644 --- a/src/substrait.rs +++ b/crates/core/src/substrait.rs @@ -15,6 +15,7 @@ // specific language governing permissions and limitations // under the License. +use datafusion_python_util::wait_for_future; use datafusion_substrait::logical_plan::{consumer, producer}; use datafusion_substrait::serializer; use datafusion_substrait::substrait::proto::Plan; @@ -23,11 +24,16 @@ use pyo3::prelude::*; use pyo3::types::PyBytes; use crate::context::PySessionContext; -use crate::errors::{py_datafusion_err, PyDataFusionError, PyDataFusionResult}; +use crate::errors::{PyDataFusionError, PyDataFusionResult, py_datafusion_err, to_datafusion_err}; use crate::sql::logical::PyLogicalPlan; -use crate::utils::wait_for_future; -#[pyclass(frozen, name = "Plan", module = "datafusion.substrait", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Plan", + module = "datafusion.substrait", + subclass +)] #[derive(Debug, Clone)] pub struct PyPlan { pub plan: Plan, @@ -42,6 +48,19 @@ impl PyPlan { .map_err(PyDataFusionError::EncodeError)?; Ok(PyBytes::new(py, &proto_bytes).into()) } + + /// Get the JSON representation of the substrait plan + fn to_json(&self) -> PyDataFusionResult { + let json = serde_json::to_string_pretty(&self.plan).map_err(to_datafusion_err)?; + Ok(json) + } + + /// Parse a Substrait Plan from its JSON representation + #[staticmethod] + fn from_json(json: &str) -> PyDataFusionResult { + let plan: Plan = serde_json::from_str(json).map_err(to_datafusion_err)?; + Ok(PyPlan { plan }) + } } impl From for Plan { @@ -59,7 +78,13 @@ impl From for PyPlan { /// A PySubstraitSerializer is a representation of a Serializer that is capable of both serializing /// a `LogicalPlan` instance to Substrait Protobuf bytes and also deserialize Substrait Protobuf bytes /// to a valid `LogicalPlan` instance. -#[pyclass(frozen, name = "Serde", module = "datafusion.substrait", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Serde", + module = "datafusion.substrait", + subclass +)] #[derive(Debug, Clone)] pub struct PySubstraitSerializer; @@ -83,7 +108,7 @@ impl PySubstraitSerializer { py: Python, ) -> PyDataFusionResult { PySubstraitSerializer::serialize_bytes(sql, ctx, py).and_then(|proto_bytes| { - let proto_bytes = proto_bytes.bind(py).downcast::().unwrap(); + let proto_bytes = proto_bytes.bind(py).cast::().unwrap(); PySubstraitSerializer::deserialize_bytes(proto_bytes.as_bytes().to_vec(), py) }) } @@ -112,7 +137,13 @@ impl PySubstraitSerializer { } } -#[pyclass(frozen, name = "Producer", module = "datafusion.substrait", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Producer", + module = "datafusion.substrait", + subclass +)] #[derive(Debug, Clone)] pub struct PySubstraitProducer; @@ -129,7 +160,13 @@ impl PySubstraitProducer { } } -#[pyclass(frozen, name = "Consumer", module = "datafusion.substrait", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Consumer", + module = "datafusion.substrait", + subclass +)] #[derive(Debug, Clone)] pub struct PySubstraitConsumer; diff --git a/src/table.rs b/crates/core/src/table.rs similarity index 67% rename from src/table.rs rename to crates/core/src/table.rs index 0eec57f75..623349771 100644 --- a/src/table.rs +++ b/crates/core/src/table.rs @@ -21,22 +21,35 @@ use std::sync::Arc; use arrow::datatypes::SchemaRef; use arrow::pyarrow::ToPyArrow; use async_trait::async_trait; -use datafusion::catalog::Session; +use datafusion::catalog::{Session, TableProviderFactory}; use datafusion::common::Column; use datafusion::datasource::{TableProvider, TableType}; -use datafusion::logical_expr::{Expr, LogicalPlanBuilder, TableProviderFilterPushDown}; +use datafusion::logical_expr::{ + CreateExternalTable, Expr, LogicalPlanBuilder, TableProviderFilterPushDown, +}; use datafusion::physical_plan::ExecutionPlan; use datafusion::prelude::DataFrame; +use datafusion_ffi::proto::logical_extension_codec::FFI_LogicalExtensionCodec; +use datafusion_python_util::{create_logical_extension_capsule, table_provider_from_pycapsule}; +use pyo3::IntoPyObjectExt; use pyo3::prelude::*; +use crate::context::PySessionContext; use crate::dataframe::PyDataFrame; use crate::dataset::Dataset; -use crate::utils::table_provider_from_pycapsule; +use crate::errors; +use crate::expr::create_external_table::PyCreateExternalTable; /// This struct is used as a common method for all TableProviders, /// whether they refer to an FFI provider, an internally known /// implementation, a dataset, or a dataframe view. -#[pyclass(frozen, name = "RawTable", module = "datafusion.catalog", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "RawTable", + module = "datafusion.catalog", + subclass +)] #[derive(Clone)] pub struct PyTable { pub table: Arc, @@ -60,12 +73,13 @@ impl PyTable { /// - FFI Table Providers via PyCapsule /// - PyArrow Dataset objects #[new] - pub fn new(obj: &Bound<'_, PyAny>) -> PyResult { + pub fn new(obj: Bound<'_, PyAny>, session: Option>) -> PyResult { + let py = obj.py(); if let Ok(py_table) = obj.extract::() { Ok(py_table) } else if let Ok(py_table) = obj .getattr("_inner") - .and_then(|inner| inner.extract::()) + .and_then(|inner| inner.extract::().map_err(Into::::into)) { Ok(py_table) } else if let Ok(py_df) = obj.extract::() { @@ -73,15 +87,20 @@ impl PyTable { Ok(PyTable::from(provider)) } else if let Ok(py_df) = obj .getattr("df") - .and_then(|inner| inner.extract::()) + .and_then(|inner| inner.extract::().map_err(Into::::into)) { let provider = py_df.inner_df().as_ref().clone().into_view(); Ok(PyTable::from(provider)) - } else if let Some(provider) = table_provider_from_pycapsule(obj)? { + } else if let Some(provider) = { + let session = match session { + Some(session) => session, + None => PySessionContext::global_ctx()?.into_bound_py_any(obj.py())?, + }; + table_provider_from_pycapsule(obj.clone(), session)? + } { Ok(PyTable::from(provider)) } else { - let py = obj.py(); - let provider = Arc::new(Dataset::new(obj, py)?) as Arc; + let provider = Arc::new(Dataset::new(&obj, py)?) as Arc; Ok(PyTable::from(provider)) } } @@ -192,3 +211,51 @@ impl TableProvider for TempViewTable { Ok(vec![TableProviderFilterPushDown::Exact; filters.len()]) } } + +#[derive(Debug)] +pub(crate) struct RustWrappedPyTableProviderFactory { + pub(crate) table_provider_factory: Py, + pub(crate) codec: Arc, +} + +impl RustWrappedPyTableProviderFactory { + pub fn new(table_provider_factory: Py, codec: Arc) -> Self { + Self { + table_provider_factory, + codec, + } + } + + fn create_inner( + &self, + cmd: CreateExternalTable, + codec: Bound, + ) -> PyResult> { + Python::attach(|py| { + let provider = self.table_provider_factory.bind(py); + let cmd = PyCreateExternalTable::from(cmd); + + provider + .call_method1("create", (cmd,)) + .and_then(|t| PyTable::new(t, Some(codec))) + .map(|t| t.table()) + }) + } +} + +#[async_trait] +impl TableProviderFactory for RustWrappedPyTableProviderFactory { + async fn create( + &self, + _: &dyn Session, + cmd: &CreateExternalTable, + ) -> datafusion::common::Result> { + Python::attach(|py| { + let codec = create_logical_extension_capsule(py, self.codec.as_ref()) + .map_err(errors::to_datafusion_err)?; + + self.create_inner(cmd.clone(), codec.into_any()) + .map_err(errors::to_datafusion_err) + }) + } +} diff --git a/src/udaf.rs b/crates/core/src/udaf.rs similarity index 78% rename from src/udaf.rs rename to crates/core/src/udaf.rs index 92857f9f7..80ef51716 100644 --- a/src/udaf.rs +++ b/crates/core/src/udaf.rs @@ -15,24 +15,25 @@ // specific language governing permissions and limitations // under the License. +use std::ptr::NonNull; use std::sync::Arc; -use datafusion::arrow::array::{Array, ArrayRef}; +use datafusion::arrow::array::ArrayRef; use datafusion::arrow::datatypes::DataType; use datafusion::arrow::pyarrow::{PyArrowType, ToPyArrow}; use datafusion::common::ScalarValue; use datafusion::error::{DataFusionError, Result}; use datafusion::logical_expr::{ - create_udaf, Accumulator, AccumulatorFactoryFunction, AggregateUDF, + Accumulator, AccumulatorFactoryFunction, AggregateUDF, AggregateUDFImpl, create_udaf, }; -use datafusion_ffi::udaf::{FFI_AggregateUDF, ForeignAggregateUDF}; +use datafusion_ffi::udaf::FFI_AggregateUDF; +use datafusion_python_util::parse_volatility; use pyo3::prelude::*; use pyo3::types::{PyCapsule, PyTuple}; use crate::common::data_type::PyScalarValue; -use crate::errors::{py_datafusion_err, to_datafusion_err, PyDataFusionResult}; +use crate::errors::{PyDataFusionResult, py_datafusion_err, to_datafusion_err}; use crate::expr::PyExpr; -use crate::utils::{parse_volatility, validate_pycapsule}; #[derive(Debug)] struct RustAccumulator { @@ -47,24 +48,24 @@ impl RustAccumulator { impl Accumulator for RustAccumulator { fn state(&mut self) -> Result> { - Python::attach(|py| { - self.accum - .bind(py) - .call_method0("state")? - .extract::>() + Python::attach(|py| -> PyResult> { + let values = self.accum.bind(py).call_method0("state")?; + let mut scalars = Vec::new(); + for item in values.try_iter()? { + let item: Bound<'_, PyAny> = item?; + let scalar = item.extract::()?.0; + scalars.push(scalar); + } + Ok(scalars) }) - .map(|v| v.into_iter().map(|x| x.0).collect()) .map_err(|e| DataFusionError::Execution(format!("{e}"))) } fn evaluate(&mut self) -> Result { - Python::attach(|py| { - self.accum - .bind(py) - .call_method0("evaluate")? - .extract::() + Python::attach(|py| -> PyResult { + let value = self.accum.bind(py).call_method0("evaluate")?; + value.extract::().map(|v| v.0) }) - .map(|v| v.0) .map_err(|e| DataFusionError::Execution(format!("{e}"))) } @@ -73,7 +74,7 @@ impl Accumulator for RustAccumulator { // 1. cast args to Pyarrow array let py_args = values .iter() - .map(|arg| arg.into_data().to_pyarrow(py).unwrap()) + .map(|arg| arg.to_data().to_pyarrow(py).unwrap()) .collect::>(); let py_args = PyTuple::new(py, py_args).map_err(to_datafusion_err)?; @@ -94,7 +95,7 @@ impl Accumulator for RustAccumulator { .iter() .map(|state| { state - .into_data() + .to_data() .to_pyarrow(py) .map_err(|e| DataFusionError::Execution(format!("{e}"))) }) @@ -119,7 +120,7 @@ impl Accumulator for RustAccumulator { // 1. cast args to Pyarrow array let py_args = values .iter() - .map(|arg| arg.into_data().to_pyarrow(py).unwrap()) + .map(|arg| arg.to_data().to_pyarrow(py).unwrap()) .collect::>(); let py_args = PyTuple::new(py, py_args).map_err(to_datafusion_err)?; @@ -144,7 +145,7 @@ impl Accumulator for RustAccumulator { } pub fn to_rust_accumulator(accum: Py) -> AccumulatorFactoryFunction { - Arc::new(move |_| -> Result> { + Arc::new(move |_args| -> Result> { let accum = Python::attach(|py| { accum .call0(py) @@ -155,16 +156,23 @@ pub fn to_rust_accumulator(accum: Py) -> AccumulatorFactoryFunction { } fn aggregate_udf_from_capsule(capsule: &Bound<'_, PyCapsule>) -> PyDataFusionResult { - validate_pycapsule(capsule, "datafusion_aggregate_udf")?; - - let udaf = unsafe { capsule.reference::() }; - let udaf: ForeignAggregateUDF = udaf.try_into()?; + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_aggregate_udf"))? + .cast(); + let udaf = unsafe { data.as_ref() }; + let udaf: Arc = udaf.into(); - Ok(udaf.into()) + Ok(AggregateUDF::new_from_shared_impl(udaf)) } /// Represents an AggregateUDF -#[pyclass(frozen, name = "AggregateUDF", module = "datafusion", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "AggregateUDF", + module = "datafusion", + subclass +)] #[derive(Debug, Clone)] pub struct PyAggregateUDF { pub(crate) function: AggregateUDF, @@ -196,14 +204,14 @@ impl PyAggregateUDF { #[staticmethod] pub fn from_pycapsule(func: Bound<'_, PyAny>) -> PyDataFusionResult { if func.is_instance_of::() { - let capsule = func.downcast::().map_err(py_datafusion_err)?; + let capsule = func.cast::().map_err(py_datafusion_err)?; let function = aggregate_udf_from_capsule(capsule)?; return Ok(Self { function }); } if func.hasattr("__datafusion_aggregate_udf__")? { let capsule = func.getattr("__datafusion_aggregate_udf__")?.call0()?; - let capsule = capsule.downcast::().map_err(py_datafusion_err)?; + let capsule = capsule.cast::().map_err(py_datafusion_err)?; let function = aggregate_udf_from_capsule(capsule)?; return Ok(Self { function }); } diff --git a/crates/core/src/udf.rs b/crates/core/src/udf.rs new file mode 100644 index 000000000..c0a39cb47 --- /dev/null +++ b/crates/core/src/udf.rs @@ -0,0 +1,223 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::any::Any; +use std::hash::{Hash, Hasher}; +use std::ptr::NonNull; +use std::sync::Arc; + +use arrow::datatypes::{Field, FieldRef}; +use arrow::pyarrow::ToPyArrow; +use datafusion::arrow::array::{ArrayData, make_array}; +use datafusion::arrow::datatypes::DataType; +use datafusion::arrow::pyarrow::{FromPyArrow, PyArrowType}; +use datafusion::common::internal_err; +use datafusion::error::DataFusionError; +use datafusion::logical_expr::{ + ColumnarValue, ReturnFieldArgs, ScalarFunctionArgs, ScalarUDF, ScalarUDFImpl, Signature, + Volatility, +}; +use datafusion_ffi::udf::FFI_ScalarUDF; +use datafusion_python_util::parse_volatility; +use pyo3::prelude::*; +use pyo3::types::{PyCapsule, PyTuple}; + +use crate::array::PyArrowArrayExportable; +use crate::errors::{PyDataFusionResult, to_datafusion_err}; +use crate::expr::PyExpr; + +/// This struct holds the Python written function that is a +/// ScalarUDF. +#[derive(Debug)] +struct PythonFunctionScalarUDF { + name: String, + func: Py, + signature: Signature, + return_field: FieldRef, +} + +impl PythonFunctionScalarUDF { + fn new( + name: String, + func: Py, + input_fields: Vec, + return_field: Field, + volatility: Volatility, + ) -> Self { + let input_types = input_fields.iter().map(|f| f.data_type().clone()).collect(); + let signature = Signature::exact(input_types, volatility); + Self { + name, + func, + signature, + return_field: Arc::new(return_field), + } + } +} + +impl Eq for PythonFunctionScalarUDF {} +impl PartialEq for PythonFunctionScalarUDF { + fn eq(&self, other: &Self) -> bool { + self.name == other.name + && self.signature == other.signature + && self.return_field == other.return_field + && Python::attach(|py| self.func.bind(py).eq(other.func.bind(py)).unwrap_or(false)) + } +} + +impl Hash for PythonFunctionScalarUDF { + fn hash(&self, state: &mut H) { + self.name.hash(state); + self.signature.hash(state); + self.return_field.hash(state); + + Python::attach(|py| { + let py_hash = self.func.bind(py).hash().unwrap_or(0); // Handle unhashable objects + + state.write_isize(py_hash); + }); + } +} + +impl ScalarUDFImpl for PythonFunctionScalarUDF { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + &self.name + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, _arg_types: &[DataType]) -> datafusion::common::Result { + internal_err!( + "return_field should not be called when return_field_from_args is implemented." + ) + } + + fn return_field_from_args( + &self, + _args: ReturnFieldArgs, + ) -> datafusion::common::Result { + Ok(Arc::clone(&self.return_field)) + } + + fn invoke_with_args( + &self, + args: ScalarFunctionArgs, + ) -> datafusion::common::Result { + let num_rows = args.number_rows; + Python::attach(|py| { + // 1. cast args to Pyarrow arrays + let py_args = args + .args + .into_iter() + .zip(args.arg_fields) + .map(|(arg, field)| { + let array = arg.to_array(num_rows)?; + PyArrowArrayExportable::new(array, field) + .to_pyarrow(py) + .map_err(to_datafusion_err) + }) + .collect::, _>>()?; + let py_args = PyTuple::new(py, py_args).map_err(to_datafusion_err)?; + + // 2. call function + let value = self + .func + .call(py, py_args, None) + .map_err(|e| DataFusionError::Execution(format!("{e:?}")))?; + + // 3. cast to arrow::array::Array + let array_data = ArrayData::from_pyarrow_bound(value.bind(py)) + .map_err(|e| DataFusionError::Execution(format!("{e:?}")))?; + Ok(ColumnarValue::Array(make_array(array_data))) + }) + } +} + +/// Represents a PyScalarUDF +#[pyclass( + from_py_object, + frozen, + name = "ScalarUDF", + module = "datafusion", + subclass +)] +#[derive(Debug, Clone)] +pub struct PyScalarUDF { + pub(crate) function: ScalarUDF, +} + +#[pymethods] +impl PyScalarUDF { + #[new] + #[pyo3(signature=(name, func, input_types, return_type, volatility))] + fn new( + name: String, + func: Py, + input_types: PyArrowType>, + return_type: PyArrowType, + volatility: &str, + ) -> PyResult { + let py_function = PythonFunctionScalarUDF::new( + name, + func, + input_types.0, + return_type.0, + parse_volatility(volatility)?, + ); + let function = ScalarUDF::new_from_impl(py_function); + + Ok(Self { function }) + } + + #[staticmethod] + pub fn from_pycapsule(func: Bound<'_, PyAny>) -> PyDataFusionResult { + if func.hasattr("__datafusion_scalar_udf__")? { + let capsule = func.getattr("__datafusion_scalar_udf__")?.call0()?; + let capsule = capsule.cast::().map_err(to_datafusion_err)?; + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_scalar_udf"))? + .cast(); + let udf = unsafe { data.as_ref() }; + let udf: Arc = udf.into(); + + Ok(Self { + function: ScalarUDF::new_from_shared_impl(udf), + }) + } else { + Err(crate::errors::PyDataFusionError::Common( + "__datafusion_scalar_udf__ does not exist on ScalarUDF object.".to_string(), + )) + } + } + + /// creates a new PyExpr with the call of the udf + #[pyo3(signature = (*args))] + fn __call__(&self, args: Vec) -> PyResult { + let args = args.iter().map(|e| e.expr.clone()).collect(); + Ok(self.function.call(args).into()) + } + + fn __repr__(&self) -> PyResult { + Ok(format!("ScalarUDF({})", self.function.name())) + } +} diff --git a/src/udtf.rs b/crates/core/src/udtf.rs similarity index 65% rename from src/udtf.rs rename to crates/core/src/udtf.rs index 7226dbe92..9371732dc 100644 --- a/src/udtf.rs +++ b/crates/core/src/udtf.rs @@ -15,22 +15,25 @@ // specific language governing permissions and limitations // under the License. +use std::ptr::NonNull; use std::sync::Arc; use datafusion::catalog::{TableFunctionImpl, TableProvider}; use datafusion::error::Result as DataFusionResult; use datafusion::logical_expr::Expr; -use datafusion_ffi::udtf::{FFI_TableFunction, ForeignTableFunction}; +use datafusion_ffi::udtf::FFI_TableFunction; +use pyo3::IntoPyObjectExt; +use pyo3::exceptions::{PyImportError, PyTypeError}; use pyo3::prelude::*; -use pyo3::types::{PyCapsule, PyTuple}; +use pyo3::types::{PyCapsule, PyTuple, PyType}; +use crate::context::PySessionContext; use crate::errors::{py_datafusion_err, to_datafusion_err}; use crate::expr::PyExpr; use crate::table::PyTable; -use crate::utils::validate_pycapsule; /// Represents a user defined table function -#[pyclass(frozen, name = "TableFunction", module = "datafusion")] +#[pyclass(from_py_object, frozen, name = "TableFunction", module = "datafusion")] #[derive(Debug, Clone)] pub struct PyTableFunction { pub(crate) name: String, @@ -47,17 +50,35 @@ pub(crate) enum PyTableFunctionInner { #[pymethods] impl PyTableFunction { #[new] - #[pyo3(signature=(name, func))] - pub fn new(name: &str, func: Bound<'_, PyAny>) -> PyResult { + #[pyo3(signature=(name, func, session))] + pub fn new( + name: &str, + func: Bound<'_, PyAny>, + session: Option>, + ) -> PyResult { let inner = if func.hasattr("__datafusion_table_function__")? { - let capsule = func.getattr("__datafusion_table_function__")?.call0()?; - let capsule = capsule.downcast::().map_err(py_datafusion_err)?; - validate_pycapsule(capsule, "datafusion_table_function")?; - - let ffi_func = unsafe { capsule.reference::() }; - let foreign_func: ForeignTableFunction = ffi_func.to_owned().into(); - - PyTableFunctionInner::FFIFunction(Arc::new(foreign_func)) + let py = func.py(); + let session = match session { + Some(session) => session, + None => PySessionContext::global_ctx()?.into_bound_py_any(py)?, + }; + let capsule = func + .getattr("__datafusion_table_function__")? + .call1((session,)).map_err(|err| { + if err.get_type(py).is(PyType::new::(py)) { + PyImportError::new_err("Incompatible libraries. DataFusion 52.0.0 introduced an incompatible signature change for table functions. Either downgrade DataFusion or upgrade your function library.") + } else { + err + } + })?; + let capsule = capsule.cast::()?; + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_table_function"))? + .cast(); + let ffi_func = unsafe { data.as_ref() }; + let foreign_func: Arc = ffi_func.to_owned().into(); + + PyTableFunctionInner::FFIFunction(foreign_func) } else { let py_obj = Arc::new(func.unbind()); PyTableFunctionInner::PythonFunction(py_obj) @@ -96,9 +117,9 @@ fn call_python_table_function( Python::attach(|py| { let py_args = PyTuple::new(py, args)?; let provider_obj = func.call1(py, py_args)?; - let provider = provider_obj.bind(py); + let provider = provider_obj.bind(py).clone(); - Ok::, PyErr>(PyTable::new(provider)?.table) + Ok::, PyErr>(PyTable::new(provider, None)?.table) }) .map_err(to_datafusion_err) } diff --git a/src/udwf.rs b/crates/core/src/udwf.rs similarity index 90% rename from src/udwf.rs rename to crates/core/src/udwf.rs index d347ec0f1..1d3608ada 100644 --- a/src/udwf.rs +++ b/crates/core/src/udwf.rs @@ -17,9 +17,10 @@ use std::any::Any; use std::ops::Range; +use std::ptr::NonNull; use std::sync::Arc; -use arrow::array::{make_array, Array, ArrayData, ArrayRef}; +use arrow::array::{Array, ArrayData, ArrayRef, make_array}; use datafusion::arrow::datatypes::DataType; use datafusion::arrow::pyarrow::{FromPyArrow, PyArrowType, ToPyArrow}; use datafusion::error::{DataFusionError, Result}; @@ -30,15 +31,15 @@ use datafusion::logical_expr::{ PartitionEvaluator, PartitionEvaluatorFactory, Signature, Volatility, WindowUDF, WindowUDFImpl, }; use datafusion::scalar::ScalarValue; -use datafusion_ffi::udwf::{FFI_WindowUDF, ForeignWindowUDF}; +use datafusion_ffi::udwf::FFI_WindowUDF; +use datafusion_python_util::parse_volatility; use pyo3::exceptions::PyValueError; use pyo3::prelude::*; use pyo3::types::{PyCapsule, PyList, PyTuple}; use crate::common::data_type::PyScalarValue; -use crate::errors::{py_datafusion_err, to_datafusion_err, PyDataFusionResult}; +use crate::errors::{PyDataFusionResult, to_datafusion_err}; use crate::expr::PyExpr; -use crate::utils::{parse_volatility, validate_pycapsule}; #[derive(Debug)] struct RustPartitionEvaluator { @@ -94,7 +95,6 @@ impl PartitionEvaluator for RustPartitionEvaluator { } fn evaluate_all(&mut self, values: &[ArrayRef], num_rows: usize) -> Result { - println!("evaluate all called with number of values {}", values.len()); Python::attach(|py| { let py_values = PyList::new( py, @@ -210,7 +210,13 @@ pub fn to_rust_partition_evaluator(evaluator: Py) -> PartitionEvaluatorFa } /// Represents an WindowUDF -#[pyclass(frozen, name = "WindowUDF", module = "datafusion", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "WindowUDF", + module = "datafusion", + subclass +)] #[derive(Debug, Clone)] pub struct PyWindowUDF { pub(crate) function: WindowUDF, @@ -249,22 +255,22 @@ impl PyWindowUDF { #[staticmethod] pub fn from_pycapsule(func: Bound<'_, PyAny>) -> PyDataFusionResult { - if func.hasattr("__datafusion_window_udf__")? { - let capsule = func.getattr("__datafusion_window_udf__")?.call0()?; - let capsule = capsule.downcast::().map_err(py_datafusion_err)?; - validate_pycapsule(capsule, "datafusion_window_udf")?; - - let udwf = unsafe { capsule.reference::() }; - let udwf: ForeignWindowUDF = udwf.try_into()?; - - Ok(Self { - function: udwf.into(), - }) + let capsule = if func.hasattr("__datafusion_window_udf__")? { + func.getattr("__datafusion_window_udf__")?.call0()? } else { - Err(crate::errors::PyDataFusionError::Common( - "__datafusion_window_udf__ does not exist on WindowUDF object.".to_string(), - )) - } + func + }; + + let capsule = capsule.cast::().map_err(to_datafusion_err)?; + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_window_udf"))? + .cast(); + let udwf = unsafe { data.as_ref() }; + let udwf: Arc = udwf.into(); + + Ok(Self { + function: WindowUDF::new_from_shared_impl(udwf), + }) } fn __repr__(&self) -> PyResult { diff --git a/src/unparser/dialect.rs b/crates/core/src/unparser/dialect.rs similarity index 93% rename from src/unparser/dialect.rs rename to crates/core/src/unparser/dialect.rs index 5df0a0c2e..52a2da00b 100644 --- a/src/unparser/dialect.rs +++ b/crates/core/src/unparser/dialect.rs @@ -22,7 +22,13 @@ use datafusion::sql::unparser::dialect::{ }; use pyo3::prelude::*; -#[pyclass(frozen, name = "Dialect", module = "datafusion.unparser", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Dialect", + module = "datafusion.unparser", + subclass +)] #[derive(Clone)] pub struct PyDialect { pub dialect: Arc, diff --git a/src/unparser/mod.rs b/crates/core/src/unparser/mod.rs similarity index 94% rename from src/unparser/mod.rs rename to crates/core/src/unparser/mod.rs index 908b59d3b..5142b918e 100644 --- a/src/unparser/mod.rs +++ b/crates/core/src/unparser/mod.rs @@ -19,15 +19,21 @@ mod dialect; use std::sync::Arc; -use datafusion::sql::unparser::dialect::Dialect; use datafusion::sql::unparser::Unparser; +use datafusion::sql::unparser::dialect::Dialect; use dialect::PyDialect; use pyo3::exceptions::PyValueError; use pyo3::prelude::*; use crate::sql::logical::PyLogicalPlan; -#[pyclass(frozen, name = "Unparser", module = "datafusion.unparser", subclass)] +#[pyclass( + from_py_object, + frozen, + name = "Unparser", + module = "datafusion.unparser", + subclass +)] #[derive(Clone)] pub struct PyUnparser { dialect: Arc, diff --git a/python/datafusion/html_formatter.py b/crates/util/Cargo.toml similarity index 62% rename from python/datafusion/html_formatter.py rename to crates/util/Cargo.toml index 65eb1f042..00d5946a5 100644 --- a/python/datafusion/html_formatter.py +++ b/crates/util/Cargo.toml @@ -15,15 +15,20 @@ # specific language governing permissions and limitations # under the License. -"""Deprecated module for dataframe formatting.""" +[package] +name = "datafusion-python-util" +version.workspace = true +edition.workspace = true +rust-version.workspace = true +license.workspace = true +description.workspace = true +homepage.workspace = true +repository.workspace = true -import warnings - -from datafusion.dataframe_formatter import * # noqa: F403 - -warnings.warn( - "The module 'html_formatter' is deprecated and will be removed in the next release." - "Please use 'dataframe_formatter' instead.", - DeprecationWarning, - stacklevel=3, -) +[dependencies] +tokio = { workspace = true, features = ["macros", "rt", "rt-multi-thread"] } +pyo3 = { workspace = true } +datafusion = { workspace = true } +datafusion-ffi = { workspace = true } +arrow = { workspace = true } +prost = { workspace = true } diff --git a/src/errors.rs b/crates/util/src/errors.rs similarity index 87% rename from src/errors.rs rename to crates/util/src/errors.rs index fc079eb6c..0d25c8847 100644 --- a/src/errors.rs +++ b/crates/util/src/errors.rs @@ -22,8 +22,8 @@ use std::fmt::Debug; use datafusion::arrow::error::ArrowError; use datafusion::error::DataFusionError as InnerDataFusionError; use prost::EncodeError; -use pyo3::exceptions::PyException; use pyo3::PyErr; +use pyo3::exceptions::{PyException, PyValueError}; pub type PyDataFusionResult = std::result::Result; @@ -39,7 +39,7 @@ pub enum PyDataFusionError { impl fmt::Display for PyDataFusionError { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { match self { - PyDataFusionError::ExecutionError(e) => write!(f, "DataFusion error: {e:?}"), + PyDataFusionError::ExecutionError(e) => write!(f, "DataFusion error: {e}"), PyDataFusionError::ArrowError(e) => write!(f, "Arrow error: {e:?}"), PyDataFusionError::PythonError(e) => write!(f, "Python error {e:?}"), PyDataFusionError::Common(e) => write!(f, "{e}"), @@ -96,3 +96,13 @@ pub fn py_unsupported_variant_err(e: impl Debug) -> PyErr { pub fn to_datafusion_err(e: impl Debug) -> InnerDataFusionError { InnerDataFusionError::Execution(format!("{e:?}")) } + +pub fn from_datafusion_error(err: InnerDataFusionError) -> PyErr { + match err { + InnerDataFusionError::External(boxed) => match boxed.downcast::() { + Ok(py_err) => *py_err, + Err(original_boxed) => PyValueError::new_err(format!("{original_boxed}")), + }, + _ => PyValueError::new_err(format!("{err}")), + } +} diff --git a/src/utils.rs b/crates/util/src/lib.rs similarity index 66% rename from src/utils.rs rename to crates/util/src/lib.rs index 6038c77b1..5b1c89936 100644 --- a/src/utils.rs +++ b/crates/util/src/lib.rs @@ -16,39 +16,40 @@ // under the License. use std::future::Future; +use std::ptr::NonNull; use std::sync::{Arc, OnceLock}; use std::time::Duration; -use datafusion::common::ScalarValue; use datafusion::datasource::TableProvider; use datafusion::execution::context::SessionContext; use datafusion::logical_expr::Volatility; -use datafusion_ffi::table_provider::{FFI_TableProvider, ForeignTableProvider}; -use pyo3::exceptions::PyValueError; +use datafusion_ffi::proto::logical_extension_codec::FFI_LogicalExtensionCodec; +use datafusion_ffi::table_provider::FFI_TableProvider; +use pyo3::exceptions::{PyImportError, PyTypeError, PyValueError}; use pyo3::prelude::*; -use pyo3::types::PyCapsule; +use pyo3::types::{PyCapsule, PyType}; use tokio::runtime::Runtime; use tokio::task::JoinHandle; use tokio::time::sleep; -use crate::common::data_type::PyScalarValue; -use crate::errors::{py_datafusion_err, to_datafusion_err, PyDataFusionError, PyDataFusionResult}; -use crate::TokioRuntime; +use crate::errors::{PyDataFusionError, PyDataFusionResult, to_datafusion_err}; + +pub mod errors; /// Utility to get the Tokio Runtime from Python #[inline] -pub(crate) fn get_tokio_runtime() -> &'static TokioRuntime { +pub fn get_tokio_runtime() -> &'static Runtime { // NOTE: Other pyo3 python libraries have had issues with using tokio // behind a forking app-server like `gunicorn` // If we run into that problem, in the future we can look to `delta-rs` // which adds a check in that disallows calls from a forked process // https://github.com/delta-io/delta-rs/blob/87010461cfe01563d91a4b9cd6fa468e2ad5f283/python/src/utils.rs#L10-L31 - static RUNTIME: OnceLock = OnceLock::new(); - RUNTIME.get_or_init(|| TokioRuntime(tokio::runtime::Runtime::new().unwrap())) + static RUNTIME: OnceLock = OnceLock::new(); + RUNTIME.get_or_init(|| Runtime::new().unwrap()) } #[inline] -pub(crate) fn is_ipython_env(py: Python) -> &'static bool { +pub fn is_ipython_env(py: Python) -> &'static bool { static IS_IPYTHON_ENV: OnceLock = OnceLock::new(); IS_IPYTHON_ENV.get_or_init(|| { py.import("IPython") @@ -60,9 +61,9 @@ pub(crate) fn is_ipython_env(py: Python) -> &'static bool { /// Utility to get the Global Datafussion CTX #[inline] -pub(crate) fn get_global_ctx() -> &'static SessionContext { - static CTX: OnceLock = OnceLock::new(); - CTX.get_or_init(SessionContext::new) +pub fn get_global_ctx() -> &'static Arc { + static CTX: OnceLock> = OnceLock::new(); + CTX.get_or_init(|| Arc::new(SessionContext::new())) } /// Utility to collect rust futures with GIL released and respond to @@ -74,7 +75,7 @@ where F: Future + Send, F::Output: Send, { - let runtime: &Runtime = &get_tokio_runtime().0; + let runtime: &Runtime = get_tokio_runtime(); const INTERVAL_CHECK_SIGNALS: Duration = Duration::from_millis(1_000); // Some fast running processes that generate many `wait_for_future` calls like @@ -108,12 +109,12 @@ where /// Spawn a [`Future`] on the Tokio runtime and wait for completion /// while respecting Python signal handling. -pub(crate) fn spawn_future(py: Python, fut: F) -> PyDataFusionResult +pub fn spawn_future(py: Python, fut: F) -> PyDataFusionResult where F: Future> + Send + 'static, T: Send + 'static, { - let rt = &get_tokio_runtime().0; + let rt = get_tokio_runtime(); let handle: JoinHandle> = rt.spawn(fut); // Wait for the join handle while respecting Python signal handling. // We handle errors in two steps so `?` maps the error types correctly: @@ -135,7 +136,7 @@ where Ok(inner_result?) } -pub(crate) fn parse_volatility(value: &str) -> PyDataFusionResult { +pub fn parse_volatility(value: &str) -> PyDataFusionResult { Ok(match value { "immutable" => Volatility::Immutable, "stable" => Volatility::Stable, @@ -144,12 +145,12 @@ pub(crate) fn parse_volatility(value: &str) -> PyDataFusionResult { return Err(PyDataFusionError::Common(format!( "Unsupported volatility type: `{value}`, supported \ values are: immutable, stable and volatile." - ))) + ))); } }) } -pub(crate) fn validate_pycapsule(capsule: &Bound, name: &str) -> PyResult<()> { +pub fn validate_pycapsule(capsule: &Bound, name: &str) -> PyResult<()> { let capsule_name = capsule.name()?; if capsule_name.is_none() { return Err(PyValueError::new_err(format!( @@ -157,7 +158,7 @@ pub(crate) fn validate_pycapsule(capsule: &Bound, name: &str) -> PyRe ))); } - let capsule_name = capsule_name.unwrap().to_str()?; + let capsule_name = unsafe { capsule_name.unwrap().as_cstr().to_str()? }; if capsule_name != name { return Err(PyValueError::new_err(format!( "Expected name '{name}' in PyCapsule, instead got '{capsule_name}'" @@ -167,35 +168,59 @@ pub(crate) fn validate_pycapsule(capsule: &Bound, name: &str) -> PyRe Ok(()) } -pub(crate) fn table_provider_from_pycapsule( - obj: &Bound, +pub fn table_provider_from_pycapsule<'py>( + mut obj: Bound<'py, PyAny>, + session: Bound<'py, PyAny>, ) -> PyResult>> { if obj.hasattr("__datafusion_table_provider__")? { - let capsule = obj.getattr("__datafusion_table_provider__")?.call0()?; - let capsule = capsule.downcast::().map_err(py_datafusion_err)?; - validate_pycapsule(capsule, "datafusion_table_provider")?; + obj = obj + .getattr("__datafusion_table_provider__")? + .call1((session,)).map_err(|err| { + let py = obj.py(); + if err.get_type(py).is(PyType::new::(py)) { + PyImportError::new_err("Incompatible libraries. DataFusion 52.0.0 introduced an incompatible signature change for table providers. Either downgrade DataFusion or upgrade your function library.") + } else { + err + } + })?; + } - let provider = unsafe { capsule.reference::() }; - let provider: ForeignTableProvider = provider.into(); + if let Ok(capsule) = obj.cast::() { + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_table_provider"))? + .cast(); + let provider = unsafe { data.as_ref() }; + let provider: Arc = provider.into(); - Ok(Some(Arc::new(provider))) + Ok(Some(provider)) } else { Ok(None) } } -pub(crate) fn py_obj_to_scalar_value(py: Python, obj: Py) -> PyResult { - // convert Python object to PyScalarValue to ScalarValue +pub fn create_logical_extension_capsule<'py>( + py: Python<'py>, + codec: &FFI_LogicalExtensionCodec, +) -> PyResult> { + let name = cr"datafusion_logical_extension_codec".into(); + let codec = codec.clone(); - let pa = py.import("pyarrow")?; + PyCapsule::new(py, codec, Some(name)) +} - // Convert Python object to PyArrow scalar - let scalar = pa.call_method1("scalar", (obj,))?; +pub fn ffi_logical_codec_from_pycapsule(obj: Bound) -> PyResult { + let attr_name = "__datafusion_logical_extension_codec__"; + let capsule = if obj.hasattr(attr_name)? { + obj.getattr(attr_name)?.call0()? + } else { + obj + }; - // Convert PyArrow scalar to PyScalarValue - let py_scalar = PyScalarValue::extract_bound(scalar.as_ref()) - .map_err(|e| PyValueError::new_err(format!("Failed to extract PyScalarValue: {e}")))?; + let capsule = capsule.cast::()?; + let data: NonNull = capsule + .pointer_checked(Some(c"datafusion_logical_extension_codec"))? + .cast(); + let codec = unsafe { data.as_ref() }; - // Convert PyScalarValue to ScalarValue - Ok(py_scalar.into()) + Ok(codec.clone()) } diff --git a/dev/changelog/52.0.0.md b/dev/changelog/52.0.0.md new file mode 100644 index 000000000..3f848bb47 --- /dev/null +++ b/dev/changelog/52.0.0.md @@ -0,0 +1,78 @@ + + +# Apache DataFusion Python 52.0.0 Changelog + +This release consists of 26 commits from 9 contributors. See credits at the end of this changelog for more information. + +**Implemented enhancements:** + +- feat: add CatalogProviderList support [#1363](https://github.com/apache/datafusion-python/pull/1363) (timsaucer) +- feat: add support for generating JSON formatted substrait plan [#1376](https://github.com/apache/datafusion-python/pull/1376) (Prathamesh9284) +- feat: add regexp_instr function [#1382](https://github.com/apache/datafusion-python/pull/1382) (mesejo) + +**Fixed bugs:** + +- fix: mangled errors [#1377](https://github.com/apache/datafusion-python/pull/1377) (mesejo) + +**Documentation updates:** + +- docs: Clarify first_value usage in select vs aggregate [#1348](https://github.com/apache/datafusion-python/pull/1348) (AdMub) + +**Other:** + +- Release 51.0.0 [#1333](https://github.com/apache/datafusion-python/pull/1333) (timsaucer) +- Use explicit timer in unit test [#1338](https://github.com/apache/datafusion-python/pull/1338) (timsaucer) +- Add use_fabric_endpoint parameter to MicrosoftAzure class [#1357](https://github.com/apache/datafusion-python/pull/1357) (djouallah) +- Prepare for DF52 release [#1337](https://github.com/apache/datafusion-python/pull/1337) (timsaucer) +- build(deps): bump actions/checkout from 5 to 6 [#1310](https://github.com/apache/datafusion-python/pull/1310) (dependabot[bot]) +- build(deps): bump actions/download-artifact from 5 to 7 [#1321](https://github.com/apache/datafusion-python/pull/1321) (dependabot[bot]) +- build(deps): bump actions/upload-artifact from 4 to 6 [#1322](https://github.com/apache/datafusion-python/pull/1322) (dependabot[bot]) +- build(deps): bump actions/cache from 4 to 5 [#1323](https://github.com/apache/datafusion-python/pull/1323) (dependabot[bot]) +- Pass Field information back and forth when using scalar UDFs [#1299](https://github.com/apache/datafusion-python/pull/1299) (timsaucer) +- Update dependency minor versions to prepare for DF52 release [#1368](https://github.com/apache/datafusion-python/pull/1368) (timsaucer) +- Improve displayed error by using `DataFusionError`'s `Display` trait [#1370](https://github.com/apache/datafusion-python/pull/1370) (abey79) +- Enforce DataFrame display memory limits with `max_rows` + `min_rows` constraint (deprecate `repr_rows`) [#1367](https://github.com/apache/datafusion-python/pull/1367) (kosiew) +- Implement all CSV reader options [#1361](https://github.com/apache/datafusion-python/pull/1361) (timsaucer) +- chore: add confirmation before tarball is released [#1372](https://github.com/apache/datafusion-python/pull/1372) (milenkovicm) +- Build in debug mode for PRs [#1375](https://github.com/apache/datafusion-python/pull/1375) (timsaucer) +- minor: remove ffi test wheel from distribution artifact [#1378](https://github.com/apache/datafusion-python/pull/1378) (timsaucer) +- chore: update rust 2024 edition [#1371](https://github.com/apache/datafusion-python/pull/1371) (timsaucer) +- Fix Python UDAF list-of-timestamps return by enforcing list-valued scalars and caching PyArrow types [#1347](https://github.com/apache/datafusion-python/pull/1347) (kosiew) +- minor: update cargo dependencies [#1383](https://github.com/apache/datafusion-python/pull/1383) (timsaucer) +- chore: bump Python version for RAT checking [#1386](https://github.com/apache/datafusion-python/pull/1386) (timsaucer) + +## Credits + +Thank you to everyone who contributed to this release. Here is a breakdown of commits (PRs merged) per contributor. + +``` + 13 Tim Saucer + 4 dependabot[bot] + 2 Daniel Mesejo + 2 kosiew + 1 Adisa Mubarak (AdMub) + 1 Antoine Beyeler + 1 Dhanashri Prathamesh Iranna + 1 Marko Milenković + 1 Mimoune +``` + +Thank you also to everyone who contributed in other ways such as filing issues, reviewing PRs, and providing feedback on this release. + diff --git a/dev/changelog/53.0.0.md b/dev/changelog/53.0.0.md new file mode 100644 index 000000000..3e27a852d --- /dev/null +++ b/dev/changelog/53.0.0.md @@ -0,0 +1,107 @@ + + +# Apache DataFusion Python 53.0.0 Changelog + +This release consists of 52 commits from 9 contributors. See credits at the end of this changelog for more information. + +**Breaking changes:** + +- minor: remove deprecated interfaces [#1481](https://github.com/apache/datafusion-python/pull/1481) (timsaucer) + +**Implemented enhancements:** + +- feat: feat: add to_time, to_local_time, to_date functions [#1387](https://github.com/apache/datafusion-python/pull/1387) (mesejo) +- feat: Add FFI_TableProviderFactory support [#1396](https://github.com/apache/datafusion-python/pull/1396) (davisp) + +**Fixed bugs:** + +- fix: satisfy rustfmt check in lib.rs re-exports [#1406](https://github.com/apache/datafusion-python/pull/1406) (kevinjqliu) + +**Documentation updates:** + +- docs: clarify DataFusion 52 FFI session-parameter requirement for provider hooks [#1439](https://github.com/apache/datafusion-python/pull/1439) (kevinjqliu) + +**Other:** + +- Merge release 52.0.0 into main [#1389](https://github.com/apache/datafusion-python/pull/1389) (timsaucer) +- Add workflow to verify release candidate on multiple systems [#1388](https://github.com/apache/datafusion-python/pull/1388) (timsaucer) +- Allow running "verify release candidate" github workflow on Windows [#1392](https://github.com/apache/datafusion-python/pull/1392) (kevinjqliu) +- ci: update pre-commit hooks, fix linting, and refresh dependencies [#1385](https://github.com/apache/datafusion-python/pull/1385) (dariocurr) +- Add CI check for crates.io patches [#1407](https://github.com/apache/datafusion-python/pull/1407) (timsaucer) +- Enable doc tests in local and CI testing [#1409](https://github.com/apache/datafusion-python/pull/1409) (ntjohnson1) +- Upgrade to DataFusion 53 [#1402](https://github.com/apache/datafusion-python/pull/1402) (nuno-faria) +- Catch warnings in FFI unit tests [#1410](https://github.com/apache/datafusion-python/pull/1410) (timsaucer) +- Add docstring examples for Scalar trigonometric functions [#1411](https://github.com/apache/datafusion-python/pull/1411) (ntjohnson1) +- Create workspace with core and util crates [#1414](https://github.com/apache/datafusion-python/pull/1414) (timsaucer) +- Add docstring examples for Scalar regex, crypto, struct and other [#1422](https://github.com/apache/datafusion-python/pull/1422) (ntjohnson1) +- Add docstring examples for Scalar math functions [#1421](https://github.com/apache/datafusion-python/pull/1421) (ntjohnson1) +- Add docstring examples for Common utility functions [#1419](https://github.com/apache/datafusion-python/pull/1419) (ntjohnson1) +- Add docstring examples for Aggregate basic and bitwise/boolean functions [#1416](https://github.com/apache/datafusion-python/pull/1416) (ntjohnson1) +- Fix CI errors on main [#1432](https://github.com/apache/datafusion-python/pull/1432) (timsaucer) +- Add docstring examples for Scalar temporal functions [#1424](https://github.com/apache/datafusion-python/pull/1424) (ntjohnson1) +- Add docstring examples for Aggregate statistical and regression functions [#1417](https://github.com/apache/datafusion-python/pull/1417) (ntjohnson1) +- Add docstring examples for Scalar array/list functions [#1420](https://github.com/apache/datafusion-python/pull/1420) (ntjohnson1) +- Add docstring examples for Scalar string functions [#1423](https://github.com/apache/datafusion-python/pull/1423) (ntjohnson1) +- Add docstring examples for Aggregate window functions [#1418](https://github.com/apache/datafusion-python/pull/1418) (ntjohnson1) +- ci: pin third-party actions to Apache-approved SHAs [#1438](https://github.com/apache/datafusion-python/pull/1438) (kevinjqliu) +- minor: bump datafusion to release version [#1441](https://github.com/apache/datafusion-python/pull/1441) (timsaucer) +- ci: add swap during build, use tpchgen-cli [#1443](https://github.com/apache/datafusion-python/pull/1443) (timsaucer) +- Update remaining existing examples to make testable/standalone executable [#1437](https://github.com/apache/datafusion-python/pull/1437) (ntjohnson1) +- Do not run validate_pycapsule if pointer_checked is used [#1426](https://github.com/apache/datafusion-python/pull/1426) (Tpt) +- Implement configuration extension support [#1391](https://github.com/apache/datafusion-python/pull/1391) (timsaucer) +- Add a working, more complete example of using a catalog (docs) [#1427](https://github.com/apache/datafusion-python/pull/1427) (toppyy) +- chore: update dependencies [#1447](https://github.com/apache/datafusion-python/pull/1447) (timsaucer) +- Complete doc string examples for functions.py [#1435](https://github.com/apache/datafusion-python/pull/1435) (ntjohnson1) +- chore: enforce uv lockfile consistency in CI and pre-commit [#1398](https://github.com/apache/datafusion-python/pull/1398) (mesejo) +- CI: Add CodeQL workflow for GitHub Actions security scanning [#1408](https://github.com/apache/datafusion-python/pull/1408) (kevinjqliu) +- ci: update codespell paths [#1469](https://github.com/apache/datafusion-python/pull/1469) (timsaucer) +- Add missing datetime functions [#1467](https://github.com/apache/datafusion-python/pull/1467) (timsaucer) +- Add AI skill to check current repository against upstream APIs [#1460](https://github.com/apache/datafusion-python/pull/1460) (timsaucer) +- Add missing string function `contains` [#1465](https://github.com/apache/datafusion-python/pull/1465) (timsaucer) +- Add missing conditional functions [#1464](https://github.com/apache/datafusion-python/pull/1464) (timsaucer) +- Reduce peak memory usage during release builds to fix OOM on manylinux runners [#1445](https://github.com/apache/datafusion-python/pull/1445) (kevinjqliu) +- Add missing map functions [#1461](https://github.com/apache/datafusion-python/pull/1461) (timsaucer) +- minor: Fix pytest instructions in the README [#1477](https://github.com/apache/datafusion-python/pull/1477) (nuno-faria) +- Add missing array functions [#1468](https://github.com/apache/datafusion-python/pull/1468) (timsaucer) +- Add missing scalar functions [#1470](https://github.com/apache/datafusion-python/pull/1470) (timsaucer) +- Add missing aggregate functions [#1471](https://github.com/apache/datafusion-python/pull/1471) (timsaucer) +- Add missing Dataframe functions [#1472](https://github.com/apache/datafusion-python/pull/1472) (timsaucer) +- Add missing deregister methods to SessionContext [#1473](https://github.com/apache/datafusion-python/pull/1473) (timsaucer) +- Add missing registration methods [#1474](https://github.com/apache/datafusion-python/pull/1474) (timsaucer) +- Add missing SessionContext utility methods [#1475](https://github.com/apache/datafusion-python/pull/1475) (timsaucer) + +## Credits + +Thank you to everyone who contributed to this release. Here is a breakdown of commits (PRs merged) per contributor. + +``` + 25 Tim Saucer + 13 Nick + 6 Kevin Liu + 2 Daniel Mesejo + 2 Nuno Faria + 1 Paul J. Davis + 1 Thomas Tanon + 1 Topias Pyykkönen + 1 dario curreri +``` + +Thank you also to everyone who contributed in other ways such as filing issues, reviewing PRs, and providing feedback on this release. + diff --git a/dev/check_crates_patch.py b/dev/check_crates_patch.py new file mode 100644 index 000000000..74e489e1f --- /dev/null +++ b/dev/check_crates_patch.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Check that no Cargo.toml files contain [patch.crates-io] entries. + +Release builds must not depend on patched crates. During development it is +common to temporarily patch crates-io dependencies, but those patches must +be removed before creating a release. + +An empty [patch.crates-io] section is allowed. +""" + +import sys +from pathlib import Path + +import tomllib + + +def main() -> int: + errors: list[str] = [] + for cargo_toml in sorted(Path().rglob("Cargo.toml")): + if "target" in cargo_toml.parts: + continue + with Path.open(cargo_toml, "rb") as f: + data = tomllib.load(f) + patch = data.get("patch", {}).get("crates-io", {}) + if patch: + errors.append(str(cargo_toml)) + for name, spec in patch.items(): + errors.append(f" {name} = {spec}") + + if errors: + print("ERROR: Release builds must not contain [patch.crates-io] entries.") + print() + for line in errors: + print(line) + print() + print("Remove all [patch.crates-io] entries before creating a release.") + return 1 + + print("OK: No [patch.crates-io] entries found.") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/dev/create_license.py b/dev/create_license.py index a28a0abec..acbf8587c 100644 --- a/dev/create_license.py +++ b/dev/create_license.py @@ -22,11 +22,9 @@ import subprocess from pathlib import Path -subprocess.check_output(["cargo", "install", "cargo-license"]) data = subprocess.check_output( [ - "cargo", - "license", + "cargo-license", "--avoid-build-deps", "--avoid-dev-deps", "--do-not-bundle", diff --git a/dev/release/README.md b/dev/release/README.md index 5d2fae5a7..4833be55a 100644 --- a/dev/release/README.md +++ b/dev/release/README.md @@ -26,11 +26,11 @@ required due to changes in DataFusion rather than having a large amount of work is available. When there is a new official release of DataFusion, we update the `main` branch to point to that, update the version -number, and create a new release branch, such as `branch-0.8`. Once this branch is created, we switch the `main` branch +number, and create a new release branch, such as `branch-53`. Once this branch is created, we switch the `main` branch back to using GitHub dependencies. The release activity (such as generating the changelog) can then happen on the release branch without blocking ongoing development in the `main` branch. -We can cherry-pick commits from the `main` branch into `branch-0.8` as needed and then create new patch releases +We can cherry-pick commits from the `main` branch into `branch-53` as needed and then create new patch releases from that branch. ## Detailed Guide @@ -54,7 +54,8 @@ Before creating a new release: - We need to ensure that the main branch does not have any GitHub dependencies - a PR should be created and merged to update the major version number of the project -- A new release branch should be created, such as `branch-0.8` +- A new release branch should be created, such as `branch-53` +- It is best to push this branch to the apache repository rather than a personal fork in case patch releases are required. ## Preparing a Release Candidate @@ -65,14 +66,14 @@ We maintain a `CHANGELOG.md` so our users know what has been changed between rel The changelog is generated using a Python script: ```bash -$ GITHUB_TOKEN= ./dev/release/generate-changelog.py 24.0.0 HEAD 25.0.0 > dev/changelog/25.0.0.md +$ GITHUB_TOKEN= ./dev/release/generate-changelog.py 52.0.0 HEAD 53.0.0 > dev/changelog/53.0.0.md ``` This script creates a changelog from GitHub PRs based on the labels associated with them as well as looking for titles starting with `feat:`, `fix:`, or `docs:` . The script will produce output similar to: ``` -Fetching list of commits between 24.0.0 and HEAD +Fetching list of commits between 52.0.0 and HEAD Fetching pull requests Categorizing pull requests Generating changelog content @@ -81,6 +82,7 @@ Generating changelog content ### Update the version number The only place you should need to update the version is in the root `Cargo.toml`. +You will need to update this both in the workspace section and also in the dependencies. After updating the toml file, run `cargo update` to update the cargo lock file. If you do not want to update all the dependencies, you can instead run `cargo build` which should only update the version number for `datafusion-python`. @@ -94,14 +96,14 @@ you need to push a tag to start the CI process for release candidates. The follo the upstream repository is called `apache`. ```bash -git tag 0.8.0-rc1 -git push apache 0.8.0-rc1 +git tag 53.0.0-rc1 +git push apache 53.0.0-rc1 ``` ### Create a source release ```bash -./dev/release/create-tarball.sh 0.8.0 1 +./dev/release/create-tarball.sh 53.0.0 1 ``` This will also create the email template to send to the mailing list. @@ -124,10 +126,10 @@ Click on the action and scroll down to the bottom of the page titled "Artifacts" contain files such as: ```text -datafusion-22.0.0-cp37-abi3-macosx_10_7_x86_64.whl -datafusion-22.0.0-cp37-abi3-macosx_11_0_arm64.whl -datafusion-22.0.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl -datafusion-22.0.0-cp37-abi3-win_amd64.whl +datafusion-53.0.0-cp37-abi3-macosx_10_7_x86_64.whl +datafusion-53.0.0-cp37-abi3-macosx_11_0_arm64.whl +datafusion-53.0.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl +datafusion-53.0.0-cp37-abi3-win_amd64.whl ``` Upload the wheels to testpypi. @@ -135,23 +137,42 @@ Upload the wheels to testpypi. ```bash unzip dist.zip python3 -m pip install --upgrade setuptools twine build -python3 -m twine upload --repository testpypi datafusion-22.0.0-cp37-abi3-*.whl +python3 -m twine upload --repository testpypi datafusion-53.0.0-cp37-abi3-*.whl ``` When prompted for username, enter `__token__`. When prompted for a password, enter a valid GitHub Personal Access Token #### Publish Python Source Distribution to testpypi -Download the source tarball created in the previous step, untar it, and run: +Download the source tarball from the Apache server created in the previous step, untar it, and run: ```bash maturin sdist ``` -This will create a file named `dist/datafusion-0.7.0.tar.gz`. Upload this to testpypi: +This will create a file named `dist/datafusion-53.0.0.tar.gz`. Upload this to testpypi: ```bash -python3 -m twine upload --repository testpypi dist/datafusion-0.7.0.tar.gz +python3 -m twine upload --repository testpypi dist/datafusion-53.0.0.tar.gz +``` + +### Run Verify Release Candidate Workflow + +Before sending the vote email, run the manually triggered GitHub Actions workflow +"Verify Release Candidate" and confirm all matrix jobs pass across the OS/architecture matrix +(for example, Linux, macOS, and Windows runners): + +1. Go to https://github.com/apache/datafusion-python/actions/workflows/verify-release-candidate.yml +2. Click "Run workflow" +3. Set `version` to the release version (for example, `53.0.0`) +4. Set `rc_number` to the RC number (for example, `1`) +5. Wait for all jobs to complete successfully + +Include a short note in the vote email template that this workflow was run across all OS/architecture +matrix entries and that all jobs passed. + +```text +Verification note: The manually triggered "Verify Release Candidate" workflow was run for version and rc_number across all configured OS/architecture matrix entries, and all matrix jobs completed successfully. ``` ### Send the Email @@ -164,7 +185,7 @@ Releases may be verified using `verify-release-candidate.sh`: ```bash git clone https://github.com/apache/datafusion-python.git -dev/release/verify-release-candidate.sh 48.0.0 1 +dev/release/verify-release-candidate.sh 53.0.0 1 ``` Alternatively, one can run unit tests against a testpypi release candidate: @@ -176,7 +197,7 @@ cd datafusion-python # checkout the release commit git fetch --tags -git checkout 40.0.0-rc1 +git checkout 53.0.0-rc1 git submodule update --init --recursive # create the env @@ -184,7 +205,7 @@ python3 -m venv .venv source .venv/bin/activate # install release candidate -pip install --extra-index-url https://test.pypi.org/simple/ datafusion==40.0.0 +pip install --extra-index-url https://test.pypi.org/simple/ datafusion==53.0.0 # install test dependencies pip install pytest numpy pytest-asyncio @@ -205,7 +226,7 @@ Once the vote passes, we can publish the release. Create the source release tarball: ```bash -./dev/release/release-tarball.sh 0.8.0 1 +./dev/release/release-tarball.sh 53.0.0 1 ``` ### Publishing Rust Crate to crates.io @@ -213,7 +234,7 @@ Create the source release tarball: Some projects depend on the Rust crate directly, so we publish this to crates.io ```shell -cargo publish +cargo publish --workspace ``` ### Publishing Python Artifacts to PyPi @@ -233,15 +254,15 @@ Pypi packages auto upload to conda-forge via [datafusion feedstock](https://gith ### Push the Release Tag ```bash -git checkout 0.8.0-rc1 -git tag 0.8.0 -git push apache 0.8.0 +git checkout 53.0.0-rc1 +git tag 53.0.0 +git push apache 53.0.0 ``` ### Add the release to Apache Reporter Add the release to https://reporter.apache.org/addrelease.html?datafusion with a version name prefixed with `DATAFUSION-PYTHON`, -for example `DATAFUSION-PYTHON-31.0.0`. +for example `DATAFUSION-PYTHON-53.0.0`. The release information is used to generate a template for a board report (see example from Apache Arrow [here](https://github.com/apache/arrow/pull/14357)). @@ -264,7 +285,7 @@ svn ls https://dist.apache.org/repos/dist/dev/datafusion | grep datafusion-pytho Delete a release candidate: ```bash -svn delete -m "delete old DataFusion RC" https://dist.apache.org/repos/dist/dev/datafusion/apache-datafusion-python-7.1.0-rc1/ +svn delete -m "delete old DataFusion RC" https://dist.apache.org/repos/dist/dev/datafusion/apache-datafusion-python-53.0.0-rc1/ ``` #### Deleting old releases from `release` svn @@ -280,5 +301,5 @@ svn ls https://dist.apache.org/repos/dist/release/datafusion | grep datafusion-p Delete a release: ```bash -svn delete -m "delete old DataFusion release" https://dist.apache.org/repos/dist/release/datafusion/datafusion-python-7.0.0 +svn delete -m "delete old DataFusion release" https://dist.apache.org/repos/dist/release/datafusion/datafusion-python-52.0.0 ``` diff --git a/dev/release/rat_exclude_files.txt b/dev/release/rat_exclude_files.txt index dcd5d9aac..a7a497dab 100644 --- a/dev/release/rat_exclude_files.txt +++ b/dev/release/rat_exclude_files.txt @@ -47,4 +47,6 @@ benchmarks/tpch/queries/q*.sql benchmarks/tpch/create_tables.sql .cargo/config.toml **/.cargo/config.toml -uv.lock \ No newline at end of file +uv.lock +examples/tpch/answers_sf1/*.tbl +SKILL.md \ No newline at end of file diff --git a/dev/release/release-tarball.sh b/dev/release/release-tarball.sh index 8c305a676..2b82d1bac 100755 --- a/dev/release/release-tarball.sh +++ b/dev/release/release-tarball.sh @@ -43,6 +43,13 @@ fi version=$1 rc=$2 +read -r -p "Proceed to release tarball for ${version}-rc${rc}? [y/N]: " answer +answer=${answer:-no} +if [ "${answer}" != "y" ]; then + echo "Cancelled tarball release!" + exit 1 +fi + tmp_dir=tmp-apache-datafusion-python-dist echo "Recreate temporary directory: ${tmp_dir}" diff --git a/dev/release/verify-release-candidate.sh b/dev/release/verify-release-candidate.sh index 2bfce0e2d..9591e0335 100755 --- a/dev/release/verify-release-candidate.sh +++ b/dev/release/verify-release-candidate.sh @@ -112,8 +112,17 @@ test_source_distribution() { curl https://sh.rustup.rs -sSf | sh -s -- -y --no-modify-path - export PATH=$RUSTUP_HOME/bin:$PATH - source $RUSTUP_HOME/env + # On Unix, rustup creates an env file. On Windows GitHub runners (MSYS bash), + # that file may not exist, so fall back to adding Cargo bin directly. + if [ -f "$CARGO_HOME/env" ]; then + # shellcheck disable=SC1090 + source "$CARGO_HOME/env" + elif [ -f "$RUSTUP_HOME/env" ]; then + # shellcheck disable=SC1090 + source "$RUSTUP_HOME/env" + else + export PATH="$CARGO_HOME/bin:$PATH" + fi # build and test rust @@ -126,10 +135,20 @@ test_source_distribution() { git clone https://github.com/apache/parquet-testing.git parquet-testing python3 -m venv .venv - source .venv/bin/activate - python3 -m pip install -U pip - python3 -m pip install -U maturin - maturin develop + if [ -x ".venv/bin/python" ]; then + VENV_PYTHON=".venv/bin/python" + elif [ -x ".venv/Scripts/python.exe" ]; then + VENV_PYTHON=".venv/Scripts/python.exe" + elif [ -x ".venv/Scripts/python" ]; then + VENV_PYTHON=".venv/Scripts/python" + else + echo "Unable to find python executable in virtual environment" + exit 1 + fi + + "$VENV_PYTHON" -m pip install -U pip + "$VENV_PYTHON" -m pip install -U maturin + "$VENV_PYTHON" -m maturin develop #TODO: we should really run tests here as well #python3 -m pytest diff --git a/docs/source/contributor-guide/ffi.rst b/docs/source/contributor-guide/ffi.rst index 64413866f..e0158e0a2 100644 --- a/docs/source/contributor-guide/ffi.rst +++ b/docs/source/contributor-guide/ffi.rst @@ -15,6 +15,8 @@ .. specific language governing permissions and limitations .. under the License. +.. _ffi: + Python Extensions ================= @@ -154,7 +156,7 @@ instead of mutating the container directly: .. code-block:: rust - #[pyclass(name = "Config", module = "datafusion", subclass, frozen)] + #[pyclass(from_py_object, name = "Config", module = "datafusion", subclass, frozen)] #[derive(Clone)] pub(crate) struct PyConfig { config: Arc>, @@ -168,7 +170,7 @@ existing instance in place: .. code-block:: rust - #[pyclass(frozen, name = "SessionContext", module = "datafusion", subclass)] + #[pyclass(from_py_object, frozen, name = "SessionContext", module = "datafusion", subclass)] #[derive(Clone)] pub struct PySessionContext { pub ctx: SessionContext, @@ -184,7 +186,7 @@ field updates: // TODO: This looks like this needs pyo3 tracking so leaving unfrozen for now #[derive(Debug, Clone)] - #[pyclass(name = "DataTypeMap", module = "datafusion.common", subclass)] + #[pyclass(from_py_object, name = "DataTypeMap", module = "datafusion.common", subclass)] pub struct DataTypeMap { #[pyo3(get, set)] pub arrow_type: PyDataType, @@ -230,8 +232,11 @@ can then be turned into a ``ForeignTableProvider`` the associated code is: .. code-block:: rust - let capsule = capsule.downcast::()?; - let provider = unsafe { capsule.reference::() }; + let capsule = capsule.cast::()?; + let data: NonNull = capsule + .pointer_checked(Some(name))? + .cast(); + let codec = unsafe { data.as_ref() }; By convention the ``datafusion-python`` library expects a Python object that has a ``TableProvider`` PyCapsule to have this capsule accessible by calling a function named diff --git a/docs/source/index.rst b/docs/source/index.rst index adec60f48..134d41cb6 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -77,6 +77,7 @@ Example user-guide/io/index user-guide/configuration user-guide/sql + user-guide/upgrade-guides .. _toc.contributor_guide: diff --git a/docs/source/user-guide/common-operations/aggregations.rst b/docs/source/user-guide/common-operations/aggregations.rst index e458e5fcb..de24a2ba5 100644 --- a/docs/source/user-guide/common-operations/aggregations.rst +++ b/docs/source/user-guide/common-operations/aggregations.rst @@ -163,6 +163,168 @@ Suppose we want to find the speed values for only Pokemon that have low Attack v f.avg(col_speed, filter=col_attack < lit(50)).alias("Avg Speed Low Attack")]) +Grouping Sets +------------- + +The default style of aggregation produces one row per group. Sometimes you want a single query to +produce rows at multiple levels of detail — for example, totals per type *and* an overall grand +total, or subtotals for every combination of two columns plus the individual column totals. Writing +separate queries and concatenating them is tedious and runs the data multiple times. Grouping sets +solve this by letting you specify several grouping levels in one pass. + +DataFusion supports three grouping set styles through the +:py:class:`~datafusion.expr.GroupingSet` class: + +- :py:meth:`~datafusion.expr.GroupingSet.rollup` — hierarchical subtotals, like a drill-down report +- :py:meth:`~datafusion.expr.GroupingSet.cube` — every possible subtotal combination, like a pivot table +- :py:meth:`~datafusion.expr.GroupingSet.grouping_sets` — explicitly list exactly which grouping levels you want + +Because result rows come from different grouping levels, a column that is *not* part of a +particular level will be ``null`` in that row. Use :py:func:`~datafusion.functions.grouping` to +distinguish a real ``null`` in the data from one that means "this column was aggregated across." +It returns ``0`` when the column is a grouping key for that row, and ``1`` when it is not. + +Rollup +^^^^^^ + +:py:meth:`~datafusion.expr.GroupingSet.rollup` creates a hierarchy. ``rollup(a, b)`` produces +grouping sets ``(a, b)``, ``(a)``, and ``()`` — like nested subtotals in a report. This is useful +when your columns have a natural hierarchy, such as region → city or type → subtype. + +Suppose we want to summarize Pokemon stats by ``Type 1`` with subtotals and a grand total. With +the default aggregation style we would need two separate queries. With ``rollup`` we get it all at +once: + +.. ipython:: python + + from datafusion.expr import GroupingSet + + df.aggregate( + [GroupingSet.rollup(col_type_1)], + [f.count(col_speed).alias("Count"), + f.avg(col_speed).alias("Avg Speed"), + f.max(col_speed).alias("Max Speed")] + ).sort(col_type_1.sort(ascending=True, nulls_first=True)) + +The first row — where ``Type 1`` is ``null`` — is the grand total across all types. But how do you +tell a grand-total ``null`` apart from a Pokemon that genuinely has no type? The +:py:func:`~datafusion.functions.grouping` function returns ``0`` when the column is a grouping key +for that row and ``1`` when it is aggregated across. + +.. note:: + + Due to an upstream DataFusion limitation + (`apache/datafusion#21411 `_), + ``.alias()`` cannot be applied directly to a ``grouping()`` expression — it will raise an + error at execution time. Instead, use + :py:meth:`~datafusion.dataframe.DataFrame.with_column_renamed` on the result DataFrame to + give the column a readable name. Once the upstream issue is resolved, you will be able to + use ``.alias()`` directly and the workaround below will no longer be necessary. + +The raw column name generated by ``grouping()`` contains internal identifiers, so we use +:py:meth:`~datafusion.dataframe.DataFrame.with_column_renamed` to clean it up: + +.. ipython:: python + + result = df.aggregate( + [GroupingSet.rollup(col_type_1)], + [f.count(col_speed).alias("Count"), + f.avg(col_speed).alias("Avg Speed"), + f.grouping(col_type_1)] + ) + for field in result.schema(): + if field.name.startswith("grouping("): + result = result.with_column_renamed(field.name, "Is Total") + result.sort(col_type_1.sort(ascending=True, nulls_first=True)) + +With two columns the hierarchy becomes more apparent. ``rollup(Type 1, Type 2)`` produces: + +- one row per ``(Type 1, Type 2)`` pair — the most detailed level +- one row per ``Type 1`` — subtotals +- one grand total row + +.. ipython:: python + + df.aggregate( + [GroupingSet.rollup(col_type_1, col_type_2)], + [f.count(col_speed).alias("Count"), + f.avg(col_speed).alias("Avg Speed")] + ).sort( + col_type_1.sort(ascending=True, nulls_first=True), + col_type_2.sort(ascending=True, nulls_first=True) + ) + +Cube +^^^^ + +:py:meth:`~datafusion.expr.GroupingSet.cube` produces every possible subset. ``cube(a, b)`` +produces grouping sets ``(a, b)``, ``(a)``, ``(b)``, and ``()`` — one more than ``rollup`` because +it also includes ``(b)`` alone. This is useful when neither column is "above" the other in a +hierarchy and you want all cross-tabulations. + +For our Pokemon data, ``cube(Type 1, Type 2)`` gives us stats broken down by the type pair, +by ``Type 1`` alone, by ``Type 2`` alone, and a grand total — all in one query: + +.. ipython:: python + + df.aggregate( + [GroupingSet.cube(col_type_1, col_type_2)], + [f.count(col_speed).alias("Count"), + f.avg(col_speed).alias("Avg Speed")] + ).sort( + col_type_1.sort(ascending=True, nulls_first=True), + col_type_2.sort(ascending=True, nulls_first=True) + ) + +Compared to the ``rollup`` example above, notice the extra rows where ``Type 1`` is ``null`` but +``Type 2`` has a value — those are the per-``Type 2`` subtotals that ``rollup`` does not include. + +Explicit Grouping Sets +^^^^^^^^^^^^^^^^^^^^^^ + +:py:meth:`~datafusion.expr.GroupingSet.grouping_sets` lets you list exactly which grouping levels +you need when ``rollup`` or ``cube`` would produce too many or too few. Each argument is a list of +columns forming one grouping set. + +For example, if we want only the per-``Type 1`` totals and per-``Type 2`` totals — but *not* the +full ``(Type 1, Type 2)`` detail rows or the grand total — we can ask for exactly that: + +.. ipython:: python + + df.aggregate( + [GroupingSet.grouping_sets([col_type_1], [col_type_2])], + [f.count(col_speed).alias("Count"), + f.avg(col_speed).alias("Avg Speed")] + ).sort( + col_type_1.sort(ascending=True, nulls_first=True), + col_type_2.sort(ascending=True, nulls_first=True) + ) + +Each row belongs to exactly one grouping level. The :py:func:`~datafusion.functions.grouping` +function tells you which level each row comes from: + +.. ipython:: python + + result = df.aggregate( + [GroupingSet.grouping_sets([col_type_1], [col_type_2])], + [f.count(col_speed).alias("Count"), + f.avg(col_speed).alias("Avg Speed"), + f.grouping(col_type_1), + f.grouping(col_type_2)] + ) + for field in result.schema(): + if field.name.startswith("grouping("): + clean = field.name.split(".")[-1].rstrip(")") + result = result.with_column_renamed(field.name, f"grouping({clean})") + result.sort( + col_type_1.sort(ascending=True, nulls_first=True), + col_type_2.sort(ascending=True, nulls_first=True) + ) + +Where ``grouping(Type 1)`` is ``0`` the row is a per-``Type 1`` total (and ``Type 2`` is ``null``). +Where ``grouping(Type 2)`` is ``0`` the row is a per-``Type 2`` total (and ``Type 1`` is ``null``). + + Aggregate Functions ------------------- @@ -192,6 +354,7 @@ The available aggregate functions are: - :py:func:`datafusion.functions.stddev_pop` - :py:func:`datafusion.functions.var_samp` - :py:func:`datafusion.functions.var_pop` + - :py:func:`datafusion.functions.var_population` 6. Linear Regression Functions - :py:func:`datafusion.functions.regr_count` - :py:func:`datafusion.functions.regr_slope` @@ -208,9 +371,16 @@ The available aggregate functions are: - :py:func:`datafusion.functions.nth_value` 8. String Functions - :py:func:`datafusion.functions.string_agg` -9. Approximation Functions +9. Percentile Functions + - :py:func:`datafusion.functions.percentile_cont` + - :py:func:`datafusion.functions.quantile_cont` - :py:func:`datafusion.functions.approx_distinct` - :py:func:`datafusion.functions.approx_median` - :py:func:`datafusion.functions.approx_percentile_cont` - :py:func:`datafusion.functions.approx_percentile_cont_with_weight` +10. Grouping Set Functions + - :py:func:`datafusion.functions.grouping` + - :py:meth:`datafusion.expr.GroupingSet.rollup` + - :py:meth:`datafusion.expr.GroupingSet.cube` + - :py:meth:`datafusion.expr.GroupingSet.grouping_sets` diff --git a/docs/source/user-guide/common-operations/joins.rst b/docs/source/user-guide/common-operations/joins.rst index 1d9d70385..a289c9377 100644 --- a/docs/source/user-guide/common-operations/joins.rst +++ b/docs/source/user-guide/common-operations/joins.rst @@ -134,3 +134,36 @@ In contrast to the above example, if we wish to get both columns: .. ipython:: python left.join(right, "id", how="inner", coalesce_duplicate_keys=False) + +Disambiguating Columns with ``DataFrame.col()`` +------------------------------------------------ + +When both DataFrames contain non-key columns with the same name, you can use +:py:meth:`~datafusion.dataframe.DataFrame.col` on each DataFrame **before** the +join to create fully qualified column references. These references can then be +used in the join predicate and when selecting from the result. + +This is especially useful with :py:meth:`~datafusion.dataframe.DataFrame.join_on`, +which accepts expression-based predicates. + +.. ipython:: python + + left = ctx.from_pydict( + { + "id": [1, 2, 3], + "val": [10, 20, 30], + } + ) + + right = ctx.from_pydict( + { + "id": [1, 2, 3], + "val": [40, 50, 60], + } + ) + + joined = left.join_on( + right, left.col("id") == right.col("id"), how="inner" + ) + + joined.select(left.col("id"), left.col("val"), right.col("val")) diff --git a/docs/source/user-guide/common-operations/udf-and-udfa.rst b/docs/source/user-guide/common-operations/udf-and-udfa.rst index 0830fa81c..f669721a3 100644 --- a/docs/source/user-guide/common-operations/udf-and-udfa.rst +++ b/docs/source/user-guide/common-operations/udf-and-udfa.rst @@ -90,6 +90,17 @@ converting to Python objects to do the evaluation. df.select(col("a"), is_null_arr(col("a")).alias("is_null")).show() +In this example we passed the PyArrow ``DataType`` when we defined the function +by calling ``udf()``. If you need additional control, such as specifying +metadata or nullability of the input or output, you can instead specify a +PyArrow ``Field``. + +If you need to write a custom function but do not want to incur the performance +cost of converting to Python objects and back, a more advanced approach is to +write Rust based UDFs and to expose them to Python. There is an example in the +`DataFusion blog `_ +describing how to do this. + Aggregate Functions ------------------- @@ -112,7 +123,7 @@ also see how the inputs to ``update`` and ``merge`` differ. .. code-block:: python - import pyarrow + import pyarrow as pa import pyarrow.compute import datafusion from datafusion import col, udaf, Accumulator @@ -125,16 +136,16 @@ also see how the inputs to ``update`` and ``merge`` differ. def __init__(self): self._sum = 0.0 - def update(self, values_a: pyarrow.Array, values_b: pyarrow.Array) -> None: + def update(self, values_a: pa.Array, values_b: pa.Array) -> None: self._sum = self._sum + pyarrow.compute.sum(values_a).as_py() - pyarrow.compute.sum(values_b).as_py() - def merge(self, states: List[pyarrow.Array]) -> None: + def merge(self, states: list[pa.Array]) -> None: self._sum = self._sum + pyarrow.compute.sum(states[0]).as_py() - def state(self) -> pyarrow.Array: - return pyarrow.array([self._sum]) + def state(self) -> list[pa.Scalar]: + return [pyarrow.scalar(self._sum)] - def evaluate(self) -> pyarrow.Scalar: + def evaluate(self) -> pa.Scalar: return pyarrow.scalar(self._sum) ctx = datafusion.SessionContext() @@ -145,10 +156,29 @@ also see how the inputs to ``update`` and ``merge`` differ. } ) - my_udaf = udaf(MyAccumulator, [pyarrow.float64(), pyarrow.float64()], pyarrow.float64(), [pyarrow.float64()], 'stable') + my_udaf = udaf(MyAccumulator, [pa.float64(), pa.float64()], pa.float64(), [pa.float64()], 'stable') df.aggregate([], [my_udaf(col("a"), col("b")).alias("col_diff")]) +FAQ +^^^ + +**How do I return a list from a UDAF?** + +Both the ``evaluate`` and the ``state`` functions expect to return scalar values. +If you wish to return a list array as a scalar value, the best practice is to +wrap the values in a ``pyarrow.Scalar`` object. For example, you can return a +timestamp list with ``pa.scalar([...], type=pa.list_(pa.timestamp("ms")))`` and +register the appropriate return or state types as +``return_type=pa.list_(pa.timestamp("ms"))`` and +``state_type=[pa.list_(pa.timestamp("ms"))]``, respectively. + +As of DataFusion 52.0.0 , you can pass return any Python object, including a +PyArrow array, as the return value(s) for these functions and DataFusion will +attempt to create a scalar type from the value. DataFusion has been tested to +convert PyArrow, nanoarrow, and arro3 objects as well as primitive data types +like integers, strings, and so on. + Window Functions ---------------- diff --git a/docs/source/user-guide/common-operations/windows.rst b/docs/source/user-guide/common-operations/windows.rst index c8fdea8f4..d77881bcf 100644 --- a/docs/source/user-guide/common-operations/windows.rst +++ b/docs/source/user-guide/common-operations/windows.rst @@ -175,10 +175,7 @@ it's ``Type 2`` column that are null. Aggregate Functions ------------------- -You can use any :ref:`Aggregation Function` as a window function. Currently -aggregate functions must use the deprecated -:py:func:`datafusion.functions.window` API but this should be resolved in -DataFusion 42.0 (`Issue Link `_). Here +You can use any :ref:`Aggregation Function` as a window function. Here is an example that shows how to compare each pokemons’s attack power with the average attack power in its ``"Type 1"`` using the :py:func:`datafusion.functions.avg` function. @@ -189,10 +186,12 @@ power in its ``"Type 1"`` using the :py:func:`datafusion.functions.avg` function col('"Name"'), col('"Attack"'), col('"Type 1"'), - f.window("avg", [col('"Attack"')]) - .partition_by(col('"Type 1"')) - .build() - .alias("Average Attack"), + f.avg(col('"Attack"')).over( + Window( + window_frame=WindowFrame("rows", None, None), + partition_by=[col('"Type 1"')], + ) + ).alias("Average Attack"), ) Available Functions diff --git a/docs/source/user-guide/data-sources.rst b/docs/source/user-guide/data-sources.rst index 26f1303c4..48ff4c014 100644 --- a/docs/source/user-guide/data-sources.rst +++ b/docs/source/user-guide/data-sources.rst @@ -224,25 +224,37 @@ A common technique for organizing tables is using a three level hierarchical app supports this form of organizing using the :py:class:`~datafusion.catalog.Catalog`, :py:class:`~datafusion.catalog.Schema`, and :py:class:`~datafusion.catalog.Table`. By default, a :py:class:`~datafusion.context.SessionContext` comes with a single Catalog and a single Schema -with the names ``datafusion`` and ``default``, respectively. +with the names ``datafusion`` and ``public``, respectively. The default implementation uses an in-memory approach to the catalog and schema. We have support -for adding additional in-memory catalogs and schemas. This can be done like in the following +for adding additional in-memory catalogs and schemas. You can access tables registered in a schema +either through the Dataframe API or via sql commands. This can be done like in the following example: .. code-block:: python + import pyarrow as pa from datafusion.catalog import Catalog, Schema + from datafusion import SessionContext + + ctx = SessionContext() my_catalog = Catalog.memory_catalog() - my_schema = Schema.memory_schema() + my_schema = Schema.memory_schema() + my_catalog.register_schema('my_schema_name', my_schema) + ctx.register_catalog_provider('my_catalog_name', my_catalog) - my_catalog.register_schema("my_schema_name", my_schema) + # Create an in-memory table + table = pa.table({ + 'name': ['Bulbasaur', 'Charmander', 'Squirtle'], + 'type': ['Grass', 'Fire', 'Water'], + 'hp': [45, 39, 44], + }) + df = ctx.create_dataframe([table.to_batches()], name='pokemon') - ctx.register_catalog("my_catalog_name", my_catalog) + my_schema.register_table('pokemon', df) -You could then register tables in ``my_schema`` and access them either through the DataFrame -API or via sql commands such as ``"SELECT * from my_catalog_name.my_schema_name.my_table"``. + ctx.sql('SELECT * FROM my_catalog_name.my_schema_name.pokemon').show() User Defined Catalog and Schema ------------------------------- diff --git a/docs/source/user-guide/dataframe/execution-metrics.rst b/docs/source/user-guide/dataframe/execution-metrics.rst new file mode 100644 index 000000000..764fa76ef --- /dev/null +++ b/docs/source/user-guide/dataframe/execution-metrics.rst @@ -0,0 +1,215 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _execution_metrics: + +Execution Metrics +================= + +Overview +-------- + +When DataFusion executes a query it compiles the logical plan into a tree of +*physical plan operators* (e.g. ``FilterExec``, ``ProjectionExec``, +``HashAggregateExec``). Each operator can record runtime statistics while it +runs. These statistics are called **execution metrics**. + +Typical metrics include: + +- **output_rows** – number of rows produced by the operator +- **elapsed_compute** – total CPU time (nanoseconds) spent inside the operator +- **spill_count** – number of times the operator spilled data to disk +- **spilled_bytes** – total bytes written to disk during spills +- **spilled_rows** – total rows written to disk during spills + +Metrics are collected *per-partition*: DataFusion may execute each operator +in parallel across several partitions. The convenience properties on +:py:class:`~datafusion.MetricsSet` (e.g. ``output_rows``, ``elapsed_compute``) +automatically sum the named metric across **all** partitions, giving a single +aggregate value for the operator as a whole. You can also access the raw +per-partition :py:class:`~datafusion.Metric` objects via +:py:meth:`~datafusion.MetricsSet.metrics`. + +When Are Metrics Available? +--------------------------- + +Some operators (for example ``DataSourceExec``) eagerly create a +:py:class:`~datafusion.MetricsSet` when the physical plan is built, so +:py:meth:`~datafusion.ExecutionPlan.metrics` may return a set even before any +rows have been processed. However, metric **values** such as ``output_rows`` +are only meaningful **after** the DataFrame has been executed via one of the +terminal operations: + +- :py:meth:`~datafusion.DataFrame.collect` +- :py:meth:`~datafusion.DataFrame.collect_partitioned` +- :py:meth:`~datafusion.DataFrame.execute_stream` + (metrics are available once the stream has been fully consumed) +- :py:meth:`~datafusion.DataFrame.execute_stream_partitioned` + (metrics are available once all partition streams have been fully consumed) + +Before execution, metric values will be ``0`` or ``None``. + +.. note:: + + **display() does not populate metrics.** + When a DataFrame is displayed in a notebook (e.g. via ``display(df)`` or + automatic ``repr`` output), DataFusion runs a *limited* internal execution + to fetch preview rows. This internal execution does **not** cache the + physical plan used, so :py:meth:`~datafusion.ExecutionPlan.collect_metrics` + will not reflect the display execution. To access metrics you must call + one of the terminal operations listed above. + +If you call :py:meth:`~datafusion.DataFrame.collect` (or another terminal +operation) multiple times on the same DataFrame, each call creates a fresh +physical plan. Metrics from :py:meth:`~datafusion.DataFrame.execution_plan` +always reflect the **most recent** execution. + +Reading the Physical Plan Tree +-------------------------------- + +:py:meth:`~datafusion.DataFrame.execution_plan` returns the root +:py:class:`~datafusion.ExecutionPlan` node of the physical plan tree. The tree +mirrors the operator pipeline: the root is typically a projection or +coalescing node; its children are filters, aggregates, scans, etc. + +The ``operator_name`` string returned by +:py:meth:`~datafusion.ExecutionPlan.collect_metrics` is the *display* name of +the node, for example ``"FilterExec: column1@0 > 1"``. This is the same string +you would see when calling ``plan.display()``. + +Aggregated vs Per-Partition Metrics +------------------------------------ + +DataFusion executes each operator across one or more **partitions** in +parallel. The :py:class:`~datafusion.MetricsSet` convenience properties +(``output_rows``, ``elapsed_compute``, etc.) automatically **sum** the named +metric across all partitions, giving a single aggregate value. + +To inspect individual partitions — for example to detect data skew where one +partition processes far more rows than others — iterate over the raw +:py:class:`~datafusion.Metric` objects: + +.. code-block:: python + + for metric in metrics_set.metrics(): + print(f" partition={metric.partition} {metric.name}={metric.value}") + +The ``partition`` property is a 0-based index (``0``, ``1``, …) identifying +which parallel slot processed this metric. It is ``None`` for metrics that +apply globally (not tied to a specific partition). + +Available Metrics +----------------- + +The following metrics are directly accessible as properties on +:py:class:`~datafusion.MetricsSet`: + +.. list-table:: + :header-rows: 1 + :widths: 25 75 + + * - Property + - Description + * - ``output_rows`` + - Number of rows emitted by the operator (summed across partitions). + * - ``elapsed_compute`` + - Wall-clock CPU time **in nanoseconds** spent inside the operator's + compute loop, excluding I/O wait. Useful for identifying which + operators are most expensive (summed across partitions). + * - ``spill_count`` + - Number of spill-to-disk events triggered by memory pressure. This is + a unitless count of events, not a measure of data volume (summed across + partitions). + * - ``spilled_bytes`` + - Total bytes written to disk during spill events (summed across + partitions). + * - ``spilled_rows`` + - Total rows written to disk during spill events (summed across + partitions). + +Any metric not listed above can be accessed via +:py:meth:`~datafusion.MetricsSet.sum_by_name`, or by iterating over the raw +:py:class:`~datafusion.Metric` objects returned by +:py:meth:`~datafusion.MetricsSet.metrics`. + +Labels +------ + +A :py:class:`~datafusion.Metric` may carry *labels*: key/value pairs that +provide additional context. Labels are operator-specific; most metrics have +an empty label dict. + +Some operators tag their metrics with labels to distinguish variants. For +example, a ``HashAggregateExec`` may record separate ``output_rows`` metrics +for intermediate and final output: + +.. code-block:: python + + for metric in metrics_set.metrics(): + print(metric.name, metric.labels()) + # output_rows {'output_type': 'final'} + # output_rows {'output_type': 'intermediate'} + +When summing by name (via :py:attr:`~datafusion.MetricsSet.output_rows` or +:py:meth:`~datafusion.MetricsSet.sum_by_name`), **all** metrics with that +name are summed regardless of labels. To filter by label, iterate over the +raw :py:class:`~datafusion.Metric` objects directly. + +End-to-End Example +------------------ + +.. code-block:: python + + from datafusion import SessionContext + + ctx = SessionContext() + ctx.sql("CREATE TABLE sales AS VALUES (1, 100), (2, 200), (3, 50)") + + df = ctx.sql("SELECT * FROM sales WHERE column1 > 1") + + # Execute the query — this populates the metrics + results = df.collect() + + # Retrieve the physical plan with metrics + plan = df.execution_plan() + + # Walk every operator and print its metrics + for operator_name, ms in plan.collect_metrics(): + if ms.output_rows is not None: + print(f"{operator_name}") + print(f" output_rows = {ms.output_rows}") + print(f" elapsed_compute = {ms.elapsed_compute} ns") + + # Access raw per-partition metrics + for operator_name, ms in plan.collect_metrics(): + for metric in ms.metrics(): + print( + f" partition={metric.partition} " + f"{metric.name}={metric.value} " + f"labels={metric.labels()}" + ) + +API Reference +------------- + +- :py:class:`datafusion.ExecutionPlan` — physical plan node +- :py:meth:`datafusion.ExecutionPlan.collect_metrics` — walk the tree and + return ``(operator_name, MetricsSet)`` pairs +- :py:meth:`datafusion.ExecutionPlan.metrics` — return the + :py:class:`~datafusion.MetricsSet` for a single node +- :py:class:`datafusion.MetricsSet` — aggregated metrics for one operator +- :py:class:`datafusion.Metric` — a single per-partition metric value diff --git a/docs/source/user-guide/dataframe/index.rst b/docs/source/user-guide/dataframe/index.rst index 510bcbc68..8475a7bd7 100644 --- a/docs/source/user-guide/dataframe/index.rst +++ b/docs/source/user-guide/dataframe/index.rst @@ -365,7 +365,16 @@ DataFusion provides many built-in functions for data manipulation: For a complete list of available functions, see the :py:mod:`datafusion.functions` module documentation. +Execution Metrics +----------------- + +After executing a DataFrame (via ``collect()``, ``execute_stream()``, etc.), +DataFusion populates per-operator runtime statistics such as row counts and +compute time. See :doc:`execution-metrics` for a full explanation and +worked example. + .. toctree:: :maxdepth: 1 rendering + execution-metrics diff --git a/docs/source/user-guide/dataframe/rendering.rst b/docs/source/user-guide/dataframe/rendering.rst index 4c37c7471..dc61a422f 100644 --- a/docs/source/user-guide/dataframe/rendering.rst +++ b/docs/source/user-guide/dataframe/rendering.rst @@ -15,18 +15,18 @@ .. specific language governing permissions and limitations .. under the License. -HTML Rendering in Jupyter -========================= +DataFrame Rendering +=================== -When working in Jupyter notebooks or other environments that support rich HTML display, -DataFusion DataFrames automatically render as nicely formatted HTML tables. This functionality -is provided by the ``_repr_html_`` method, which is automatically called by Jupyter to provide -a richer visualization than plain text output. +DataFusion provides configurable rendering for DataFrames in both plain text and HTML +formats. The ``datafusion.dataframe_formatter`` module controls how DataFrames are +displayed in Jupyter notebooks (via ``_repr_html_``), in the terminal (via ``__repr__``), +and anywhere else a string or HTML representation is needed. -Basic HTML Rendering --------------------- +Basic Rendering +--------------- -In a Jupyter environment, simply displaying a DataFrame object will trigger HTML rendering: +In a Jupyter environment, displaying a DataFrame triggers HTML rendering: .. code-block:: python @@ -36,74 +36,117 @@ In a Jupyter environment, simply displaying a DataFrame object will trigger HTML # Explicit display also uses HTML rendering display(df) -Customizing HTML Rendering ---------------------------- +In a terminal or when converting to string, plain text rendering is used: + +.. code-block:: python -DataFusion provides extensive customization options for HTML table rendering through the -``datafusion.html_formatter`` module. + # Plain text table output + print(df) -Configuring the HTML Formatter -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Configuring the Formatter +------------------------- -You can customize how DataFrames are rendered by configuring the formatter: +You can customize how DataFrames are rendered by configuring the global formatter: .. code-block:: python - from datafusion.html_formatter import configure_formatter - - # Change the default styling + from datafusion.dataframe_formatter import configure_formatter + configure_formatter( - max_cell_length=25, # Maximum characters in a cell before truncation - max_width=1000, # Maximum width in pixels - max_height=300, # Maximum height in pixels - max_memory_bytes=2097152, # Maximum memory for rendering (2MB) - min_rows_display=20, # Minimum number of rows to display - repr_rows=10, # Number of rows to display in __repr__ - enable_cell_expansion=True,# Allow expanding truncated cells - custom_css=None, # Additional custom CSS + max_cell_length=25, # Maximum characters in a cell before truncation + max_width=1000, # Maximum width in pixels (HTML only) + max_height=300, # Maximum height in pixels (HTML only) + max_memory_bytes=2097152, # Maximum memory for rendering (2MB) + min_rows=10, # Minimum number of rows to display + max_rows=10, # Maximum rows to display + enable_cell_expansion=True, # Allow expanding truncated cells (HTML only) + custom_css=None, # Additional custom CSS (HTML only) show_truncation_message=True, # Show message when data is truncated - style_provider=None, # Custom styling provider - use_shared_styles=True # Share styles across tables + style_provider=None, # Custom styling provider (HTML only) + use_shared_styles=True, # Share styles across tables (HTML only) ) The formatter settings affect all DataFrames displayed after configuration. Custom Style Providers ------------------------ +---------------------- -For advanced styling needs, you can create a custom style provider: +For HTML styling, you can create a custom style provider that implements the +``StyleProvider`` protocol: .. code-block:: python - from datafusion.html_formatter import StyleProvider, configure_formatter - - class MyStyleProvider(StyleProvider): - def get_table_styles(self): - return { - "table": "border-collapse: collapse; width: 100%;", - "th": "background-color: #007bff; color: white; padding: 8px; text-align: left;", - "td": "border: 1px solid #ddd; padding: 8px;", - "tr:nth-child(even)": "background-color: #f2f2f2;", - } - - def get_value_styles(self, dtype, value): - """Return custom styles for specific values""" - if dtype == "float" and value < 0: - return "color: red;" - return None - + from datafusion.dataframe_formatter import configure_formatter + + class MyStyleProvider: + def get_cell_style(self): + """Return CSS style string for table data cells.""" + return "border: 1px solid #ddd; padding: 8px; text-align: left;" + + def get_header_style(self): + """Return CSS style string for table header cells.""" + return ( + "background-color: #007bff; color: white; " + "padding: 8px; text-align: left;" + ) + # Apply the custom style provider configure_formatter(style_provider=MyStyleProvider()) +Custom Cell Formatters +---------------------- + +You can register custom formatters for specific Python types. A cell formatter is any +callable that takes a value and returns a string: + +.. code-block:: python + + from datafusion.dataframe_formatter import get_formatter + + formatter = get_formatter() + + # Format floats to 2 decimal places + formatter.register_formatter(float, lambda v: f"{v:.2f}") + + # Format dates in a custom way + from datetime import date + formatter.register_formatter(date, lambda v: v.strftime("%B %d, %Y")) + +Custom Cell and Header Builders +------------------------------- + +For full control over the HTML of individual cells or headers, you can set custom +builder functions: + +.. code-block:: python + + from datafusion.dataframe_formatter import get_formatter + + formatter = get_formatter() + + # Custom cell builder receives (value, row, col, table_id) and returns HTML + def my_cell_builder(value, row, col, table_id): + color = "red" if isinstance(value, (int, float)) and value < 0 else "black" + return f"{value}" + + formatter.set_custom_cell_builder(my_cell_builder) + + # Custom header builder receives a schema field and returns HTML + def my_header_builder(field): + return f"{field.name}" + + formatter.set_custom_header_builder(my_header_builder) + Performance Optimization with Shared Styles -------------------------------------------- -The ``use_shared_styles`` parameter (enabled by default) optimizes performance when displaying -multiple DataFrames in notebook environments: +The ``use_shared_styles`` parameter (enabled by default) optimizes performance when +displaying multiple DataFrames in notebook environments: .. code-block:: python - from datafusion.html_formatter import StyleProvider, configure_formatter + from datafusion.dataframe_formatter import configure_formatter + # Default: Use shared styles (recommended for notebooks) configure_formatter(use_shared_styles=True) @@ -111,76 +154,48 @@ multiple DataFrames in notebook environments: configure_formatter(use_shared_styles=False) When ``use_shared_styles=True``: + - CSS styles and JavaScript are included only once per notebook session - This reduces HTML output size and prevents style duplication - Improves rendering performance with many DataFrames - Applies consistent styling across all DataFrames -Creating a Custom Formatter ----------------------------- +Working with the Formatter Directly +------------------------------------ -For complete control over rendering, you can implement a custom formatter: +You can use ``get_formatter()`` and ``set_formatter()`` for direct access to the global +formatter instance: .. code-block:: python - from datafusion.html_formatter import Formatter, get_formatter - - class MyFormatter(Formatter): - def format_html(self, batches, schema, has_more=False, table_uuid=None): - # Create your custom HTML here - html = "
" - # ... formatting logic ... - html += "
" - return html - - # Set as the global formatter - configure_formatter(formatter_class=MyFormatter) - - # Or use the formatter just for specific operations + from datafusion.dataframe_formatter import ( + DataFrameHtmlFormatter, + get_formatter, + set_formatter, + ) + + # Get and modify the current formatter formatter = get_formatter() - custom_html = formatter.format_html(batches, schema) + print(formatter.max_rows) + print(formatter.max_cell_length) -Managing Formatters -------------------- + # Create and set a fully custom formatter + custom_formatter = DataFrameHtmlFormatter( + max_cell_length=50, + max_rows=20, + enable_cell_expansion=False, + ) + set_formatter(custom_formatter) Reset to default formatting: .. code-block:: python - from datafusion.html_formatter import reset_formatter - + from datafusion.dataframe_formatter import reset_formatter + # Reset to default settings reset_formatter() -Get the current formatter settings: - -.. code-block:: python - - from datafusion.html_formatter import get_formatter - - formatter = get_formatter() - print(formatter.max_rows) - print(formatter.theme) - -Contextual Formatting ----------------------- - -You can also use a context manager to temporarily change formatting settings: - -.. code-block:: python - - from datafusion.html_formatter import formatting_context - - # Default formatting - df.show() - - # Temporarily use different formatting - with formatting_context(max_rows=100, theme="dark"): - df.show() # Will use the temporary settings - - # Back to default formatting - df.show() - Memory and Display Controls --------------------------- @@ -188,10 +203,12 @@ You can control how much data is displayed and how much memory is used for rende .. code-block:: python + from datafusion.dataframe_formatter import configure_formatter + configure_formatter( max_memory_bytes=4 * 1024 * 1024, # 4MB maximum memory for display - min_rows_display=50, # Always show at least 50 rows - repr_rows=20 # Show 20 rows in __repr__ output + min_rows=20, # Always show at least 20 rows + max_rows=50, # Show up to 50 rows in output ) These parameters help balance comprehensive data display against performance considerations. @@ -216,7 +233,7 @@ Additional Resources * :doc:`../io/index` - I/O Guide for reading data from various sources * :doc:`../data-sources` - Comprehensive data sources guide * :ref:`io_csv` - CSV file reading -* :ref:`io_parquet` - Parquet file reading +* :ref:`io_parquet` - Parquet file reading * :ref:`io_json` - JSON file reading * :ref:`io_avro` - Avro file reading * :ref:`io_custom_table_provider` - Custom table providers diff --git a/docs/source/user-guide/io/csv.rst b/docs/source/user-guide/io/csv.rst index 144b6615c..9c23c291b 100644 --- a/docs/source/user-guide/io/csv.rst +++ b/docs/source/user-guide/io/csv.rst @@ -36,3 +36,25 @@ An alternative is to use :py:func:`~datafusion.context.SessionContext.register_c ctx.register_csv("file", "file.csv") df = ctx.table("file") + +If you require additional control over how to read the CSV file, you can use +:py:class:`~datafusion.options.CsvReadOptions` to set a variety of options. + +.. code-block:: python + + from datafusion import CsvReadOptions + options = ( + CsvReadOptions() + .with_has_header(True) # File contains a header row + .with_delimiter(";") # Use ; as the delimiter instead of , + .with_comment("#") # Skip lines starting with # + .with_escape("\\") # Escape character + .with_null_regex(r"^(null|NULL|N/A)$") # Treat these as NULL + .with_truncated_rows(True) # Allow rows to have incomplete columns + .with_file_compression_type("gzip") # Read gzipped CSV + .with_file_extension(".gz") # File extension other than .csv + ) + df = ctx.read_csv("data.csv.gz", options=options) + +Details for all CSV reading options can be found on the +`DataFusion documentation site `_. diff --git a/docs/source/user-guide/upgrade-guides.rst b/docs/source/user-guide/upgrade-guides.rst new file mode 100644 index 000000000..2ac7f7703 --- /dev/null +++ b/docs/source/user-guide/upgrade-guides.rst @@ -0,0 +1,119 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +Upgrade Guides +============== + +DataFusion 53.0.0 +----------------- + +This version includes an upgraded version of ``pyo3``, which changed the way to extract an FFI +object. Example: + +Before: + +.. code-block:: rust + + let codec = unsafe { capsule.reference::() }; + +Now: + +.. code-block:: rust + + let data: NonNull = capsule + .pointer_checked(Some(c_str!("datafusion_logical_extension_codec")))? + .cast(); + let codec = unsafe { data.as_ref() }; + +DataFusion 52.0.0 +----------------- + +This version includes a major update to the :ref:`ffi` due to upgrades +to the `Foreign Function Interface `_. +Users who contribute their own ``CatalogProvider``, ``SchemaProvider``, +``TableProvider`` or ``TableFunction`` via FFI must now provide access to a +``LogicalExtensionCodec`` and a ``TaskContextProvider``. The function signatures +for the methods to get these ``PyCapsule`` objects now requires an additional +parameter, which is a Python object that can be used to extract the +``FFI_LogicalExtensionCodec`` that is necessary. + +A complete example can be found in the `FFI example `_. +Your FFI hook methods — ``__datafusion_catalog_provider__``, +``__datafusion_schema_provider__``, ``__datafusion_table_provider__``, and +``__datafusion_table_function__`` — need to be updated to accept an additional +``session: Bound`` parameter, as shown in this example. + +.. code-block:: rust + + #[pymethods] + impl MyCatalogProvider { + pub fn __datafusion_catalog_provider__<'py>( + &self, + py: Python<'py>, + session: Bound, + ) -> PyResult> { + let name = cr"datafusion_catalog_provider".into(); + + let provider = Arc::clone(&self.inner) as Arc; + + let codec = ffi_logical_codec_from_pycapsule(session)?; + let provider = FFI_CatalogProvider::new_with_ffi_codec(provider, None, codec); + + PyCapsule::new(py, provider, Some(name)) + } + } + +To extract the logical extension codec FFI object from the provided object you +can implement a helper method such as: + +.. code-block:: rust + + pub(crate) fn ffi_logical_codec_from_pycapsule( + obj: Bound, + ) -> PyResult { + let attr_name = "__datafusion_logical_extension_codec__"; + let capsule = if obj.hasattr(attr_name)? { + obj.getattr(attr_name)?.call0()? + } else { + obj + }; + + let capsule = capsule.downcast::()?; + validate_pycapsule(capsule, "datafusion_logical_extension_codec")?; + + let codec = unsafe { capsule.reference::() }; + + Ok(codec.clone()) + } + + +The DataFusion FFI interface updates no longer depend directly on the +``datafusion`` core crate. You can improve your build times and potentially +reduce your library binary size by removing this dependency and instead +using the specific datafusion project crates. + +For example, instead of including expressions like: + +.. code-block:: rust + + use datafusion::catalog::MemTable; + +Instead you can now write: + +.. code-block:: rust + + use datafusion_catalog::MemTable; diff --git a/examples/csv-read-options.py b/examples/csv-read-options.py new file mode 100644 index 000000000..a5952d950 --- /dev/null +++ b/examples/csv-read-options.py @@ -0,0 +1,96 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Example demonstrating CsvReadOptions usage.""" + +from datafusion import CsvReadOptions, SessionContext + +# Create a SessionContext +ctx = SessionContext() + +# Example 1: Using CsvReadOptions with default values +print("Example 1: Default CsvReadOptions") +options = CsvReadOptions() +df = ctx.read_csv("data.csv", options=options) + +# Example 2: Using CsvReadOptions with custom parameters +print("\nExample 2: Custom CsvReadOptions") +options = CsvReadOptions( + has_header=True, + delimiter=",", + quote='"', + schema_infer_max_records=1000, + file_extension=".csv", +) +df = ctx.read_csv("data.csv", options=options) + +# Example 3: Using the builder pattern (recommended for readability) +print("\nExample 3: Builder pattern") +options = ( + CsvReadOptions() + .with_has_header(True) # noqa: FBT003 + .with_delimiter("|") + .with_quote("'") + .with_schema_infer_max_records(500) + .with_truncated_rows(False) # noqa: FBT003 + .with_newlines_in_values(True) # noqa: FBT003 +) +df = ctx.read_csv("data.csv", options=options) + +# Example 4: Advanced options +print("\nExample 4: Advanced options") +options = ( + CsvReadOptions() + .with_has_header(True) # noqa: FBT003 + .with_delimiter(",") + .with_comment("#") # Skip lines starting with # + .with_escape("\\") # Escape character + .with_null_regex(r"^(null|NULL|N/A)$") # Treat these as NULL + .with_truncated_rows(True) # noqa: FBT003 + .with_file_compression_type("gzip") # Read gzipped CSV + .with_file_extension(".gz") +) +df = ctx.read_csv("data.csv.gz", options=options) + +# Example 5: Register CSV table with options +print("\nExample 5: Register CSV table") +options = CsvReadOptions().with_has_header(True).with_delimiter(",") # noqa: FBT003 +ctx.register_csv("my_table", "data.csv", options=options) +df = ctx.sql("SELECT * FROM my_table") + +# Example 6: Backward compatibility (without options) +print("\nExample 6: Backward compatibility") +# Still works the old way! +df = ctx.read_csv("data.csv", has_header=True, delimiter=",") + +print("\nAll examples completed!") +print("\nFor all available options, see the CsvReadOptions documentation:") +print(" - has_header: bool") +print(" - delimiter: str") +print(" - quote: str") +print(" - terminator: str | None") +print(" - escape: str | None") +print(" - comment: str | None") +print(" - newlines_in_values: bool") +print(" - schema: pa.Schema | None") +print(" - schema_infer_max_records: int") +print(" - file_extension: str") +print(" - table_partition_cols: list[tuple[str, pa.DataType]]") +print(" - file_compression_type: str") +print(" - file_sort_order: list[list[SortExpr]]") +print(" - null_regex: str | None") +print(" - truncated_rows: bool") diff --git a/examples/datafusion-ffi-example/.cargo/config.toml b/examples/datafusion-ffi-example/.cargo/config.toml deleted file mode 100644 index 91a099a61..000000000 --- a/examples/datafusion-ffi-example/.cargo/config.toml +++ /dev/null @@ -1,12 +0,0 @@ -[target.x86_64-apple-darwin] -rustflags = [ - "-C", "link-arg=-undefined", - "-C", "link-arg=dynamic_lookup", -] - -[target.aarch64-apple-darwin] -rustflags = [ - "-C", "link-arg=-undefined", - 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"Programming Language :: Rust", - "Programming Language :: Python :: Implementation :: CPython", - "Programming Language :: Python :: Implementation :: PyPy", + "Programming Language :: Rust", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", ] dynamic = ["version"] diff --git a/examples/datafusion-ffi-example/python/tests/_test_catalog_provider.py b/examples/datafusion-ffi-example/python/tests/_test_catalog_provider.py index 1bf1bf136..a862b23ba 100644 --- a/examples/datafusion-ffi-example/python/tests/_test_catalog_provider.py +++ b/examples/datafusion-ffi-example/python/tests/_test_catalog_provider.py @@ -18,43 +18,119 @@ from __future__ import annotations import pyarrow as pa -from datafusion import SessionContext -from datafusion_ffi_example import MyCatalogProvider +import pyarrow.dataset as ds +import pytest +from datafusion import SessionContext, Table +from datafusion.catalog import Schema +from datafusion_ffi_example import MyCatalogProvider, MyCatalogProviderList -def test_catalog_provider(): +def create_test_dataset() -> Table: + """Create a simple test dataset.""" + batch = pa.RecordBatch.from_arrays( + [pa.array([100, 200, 300]), pa.array([1.1, 2.2, 3.3])], + names=["id", "value"], + ) + dataset = ds.dataset([batch]) + return Table(dataset) + + +@pytest.mark.parametrize("inner_capsule", [True, False]) +def test_ffi_catalog_provider_list(inner_capsule: bool) -> None: + """Test basic FFI CatalogProviderList functionality.""" ctx = SessionContext() - my_catalog_name = "my_catalog" - expected_schema_name = "my_schema" - expected_table_name = "my_table" - expected_table_columns = ["units", "price"] + # Register FFI catalog + catalog_provider_list = MyCatalogProviderList() + if inner_capsule: + catalog_provider_list = ( + catalog_provider_list.__datafusion_catalog_provider_list__(ctx) + ) + + ctx.register_catalog_provider_list(catalog_provider_list) + + # Verify the catalog exists + catalog = ctx.catalog("auto_ffi_catalog") + schema_names = catalog.names() + assert "my_schema" in schema_names + + ctx.register_catalog_provider("second", MyCatalogProvider()) + + assert ctx.catalog_names() == {"auto_ffi_catalog", "second"} + + +@pytest.mark.parametrize("inner_capsule", [True, False]) +def test_ffi_catalog_provider_basic(inner_capsule: bool) -> None: + """Test basic FFI CatalogProvider functionality.""" + ctx = SessionContext() + # Register FFI catalog catalog_provider = MyCatalogProvider() - ctx.register_catalog_provider(my_catalog_name, catalog_provider) - my_catalog = ctx.catalog(my_catalog_name) - - my_catalog_schemas = my_catalog.names() - assert expected_schema_name in my_catalog_schemas - my_schema = my_catalog.schema(expected_schema_name) - assert expected_table_name in my_schema.names() - my_table = my_schema.table(expected_table_name) - assert expected_table_columns == my_table.schema.names - - result = ctx.table( - f"{my_catalog_name}.{expected_schema_name}.{expected_table_name}" - ).collect() + if inner_capsule: + catalog_provider = catalog_provider.__datafusion_catalog_provider__(ctx) + + ctx.register_catalog_provider("ffi_catalog", catalog_provider) + + # Verify the catalog exists + catalog = ctx.catalog("ffi_catalog") + schema_names = catalog.names() + assert "my_schema" in schema_names + + # Query the pre-populated table + result = ctx.sql("SELECT * FROM ffi_catalog.my_schema.my_table").collect() assert len(result) == 2 + assert result[0].num_columns == 2 + + +def test_ffi_catalog_provider_register_schema(): + """Test registering additional schemas to FFI CatalogProvider.""" + ctx = SessionContext() + + catalog_provider = MyCatalogProvider() + ctx.register_catalog_provider("ffi_catalog", catalog_provider) + + catalog = ctx.catalog("ffi_catalog") + + # Register a new memory schema + new_schema = Schema.memory_schema() + catalog.register_schema("additional_schema", new_schema) + + # Verify the schema was registered + assert "additional_schema" in catalog.names() + + # Add a table to the new schema + new_schema.register_table("new_table", create_test_dataset()) + + # Query the new table + result = ctx.sql("SELECT * FROM ffi_catalog.additional_schema.new_table").collect() + assert len(result) == 1 + assert result[0].column(0) == pa.array([100, 200, 300]) + + +def test_ffi_catalog_provider_deregister_schema(): + """Test deregistering schemas from FFI CatalogProvider.""" + ctx = SessionContext() + + catalog_provider = MyCatalogProvider() + ctx.register_catalog_provider("ffi_catalog", catalog_provider) + + catalog = ctx.catalog("ffi_catalog") + + # Register two schemas + schema1 = Schema.memory_schema() + schema2 = Schema.memory_schema() + catalog.register_schema("temp_schema1", schema1) + catalog.register_schema("temp_schema2", schema2) + + # Verify both exist + names = catalog.names() + assert "temp_schema1" in names + assert "temp_schema2" in names + + # Deregister one schema + catalog.deregister_schema("temp_schema1") - col0_result = [r.column(0) for r in result] - col1_result = [r.column(1) for r in result] - expected_col0 = [ - pa.array([10, 20, 30], type=pa.int32()), - pa.array([5, 7], type=pa.int32()), - ] - expected_col1 = [ - pa.array([1, 2, 5], type=pa.float64()), - pa.array([1.5, 2.5], type=pa.float64()), - ] - assert col0_result == expected_col0 - assert col1_result == expected_col1 + # Verify it's gone + names = catalog.names() + assert "temp_schema1" not in names + assert "temp_schema2" in names diff --git a/examples/datafusion-ffi-example/python/tests/_test_config.py b/examples/datafusion-ffi-example/python/tests/_test_config.py new file mode 100644 index 000000000..24d527921 --- /dev/null +++ b/examples/datafusion-ffi-example/python/tests/_test_config.py @@ -0,0 +1,35 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from datafusion import SessionConfig, SessionContext +from datafusion_ffi_example import MyConfig + + +def test_config_extension_show_set(): + config = MyConfig() + config = SessionConfig( + {"datafusion.catalog.information_schema": "true"} + ).with_extension(config) + config.set("my_config.baz_count", "42") + ctx = SessionContext(config) + + result = ctx.sql("SHOW my_config.baz_count;").collect() + assert result[0][1][0].as_py() == "42" + + ctx.sql("SET my_config.baz_count=1;") + result = ctx.sql("SHOW my_config.baz_count;").collect() + assert result[0][1][0].as_py() == "1" diff --git a/examples/datafusion-ffi-example/python/tests/_test_schema_provider.py b/examples/datafusion-ffi-example/python/tests/_test_schema_provider.py new file mode 100644 index 000000000..93449c660 --- /dev/null +++ b/examples/datafusion-ffi-example/python/tests/_test_schema_provider.py @@ -0,0 +1,232 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from __future__ import annotations + +import pyarrow as pa +import pyarrow.dataset as ds +import pytest +from datafusion import SessionContext, Table +from datafusion.catalog import Schema +from datafusion_ffi_example import FixedSchemaProvider, MyCatalogProvider + + +def create_test_dataset() -> Table: + """Create a simple test dataset.""" + batch = pa.RecordBatch.from_arrays( + [pa.array([100, 200, 300]), pa.array([1.1, 2.2, 3.3])], + names=["id", "value"], + ) + dataset = ds.dataset([batch]) + return Table(dataset) + + +@pytest.mark.parametrize("inner_capsule", [True, False]) +def test_schema_provider_extract_values(inner_capsule: bool) -> None: + ctx = SessionContext() + + my_schema_name = "my_schema" + + schema_provider = FixedSchemaProvider() + if inner_capsule: + schema_provider = schema_provider.__datafusion_schema_provider__(ctx) + + ctx.catalog().register_schema(my_schema_name, schema_provider) + + expected_schema_name = "my_schema" + expected_table_name = "my_table" + expected_table_columns = ["units", "price"] + + default_catalog = ctx.catalog() + + catalog_schemas = default_catalog.names() + assert expected_schema_name in catalog_schemas + my_schema = default_catalog.schema(expected_schema_name) + assert expected_table_name in my_schema.names() + my_table = my_schema.table(expected_table_name) + assert expected_table_columns == my_table.schema.names + + result = ctx.table(f"{expected_schema_name}.{expected_table_name}").collect() + assert len(result) == 2 + + col0_result = [r.column(0) for r in result] + col1_result = [r.column(1) for r in result] + expected_col0 = [ + pa.array([10, 20, 30], type=pa.int32()), + pa.array([5, 7], type=pa.int32()), + ] + expected_col1 = [ + pa.array([1, 2, 5], type=pa.float64()), + pa.array([1.5, 2.5], type=pa.float64()), + ] + assert col0_result == expected_col0 + assert col1_result == expected_col1 + + +def test_ffi_schema_provider_basic(): + """Test basic FFI SchemaProvider functionality.""" + ctx = SessionContext() + + # Register FFI schema + schema_provider = FixedSchemaProvider() + ctx.catalog().register_schema("ffi_schema", schema_provider) + + # Verify the schema exists + schema = ctx.catalog().schema("ffi_schema") + table_names = schema.names() + assert "my_table" in table_names + + # Query the pre-populated table + result = ctx.sql("SELECT * FROM ffi_schema.my_table").collect() + assert len(result) == 2 + assert result[0].num_columns == 2 + + +def test_ffi_schema_provider_register_table(): + """Test registering additional tables to FFI SchemaProvider.""" + ctx = SessionContext() + + schema_provider = FixedSchemaProvider() + ctx.catalog().register_schema("ffi_schema", schema_provider) + + schema = ctx.catalog().schema("ffi_schema") + + # Register a new table + schema.register_table("additional_table", create_test_dataset()) + + # Verify the table was registered + assert "additional_table" in schema.names() + + # Query the new table + result = ctx.sql("SELECT * FROM ffi_schema.additional_table").collect() + assert len(result) == 1 + assert result[0].column(0) == pa.array([100, 200, 300]) + assert result[0].column(1) == pa.array([1.1, 2.2, 3.3]) + + +def test_ffi_schema_provider_deregister_table(): + """Test deregistering tables from FFI SchemaProvider.""" + ctx = SessionContext() + + schema_provider = FixedSchemaProvider() + ctx.catalog().register_schema("ffi_schema", schema_provider) + + schema = ctx.catalog().schema("ffi_schema") + + # Register two tables + schema.register_table("temp_table1", create_test_dataset()) + schema.register_table("temp_table2", create_test_dataset()) + + # Verify both exist + names = schema.names() + assert "temp_table1" in names + assert "temp_table2" in names + + # Deregister one table + schema.deregister_table("temp_table1") + + # Verify it's gone + names = schema.names() + assert "temp_table1" not in names + assert "temp_table2" in names + + +def test_mixed_ffi_and_python_providers(): + """Test mixing FFI and Python providers in the same catalog/schema.""" + ctx = SessionContext() + + # Register FFI catalog + ffi_catalog = MyCatalogProvider() + ctx.register_catalog_provider("ffi_catalog", ffi_catalog) + + # Register Python memory schema to FFI catalog + python_schema = Schema.memory_schema() + ctx.catalog("ffi_catalog").register_schema("python_schema", python_schema) + + # Add table to Python schema + python_schema.register_table("python_table", create_test_dataset()) + + # Query both FFI table and Python table + result_ffi = ctx.sql("SELECT * FROM ffi_catalog.my_schema.my_table").collect() + assert len(result_ffi) == 2 + + result_python = ctx.sql( + "SELECT * FROM ffi_catalog.python_schema.python_table" + ).collect() + assert len(result_python) == 1 + assert result_python[0].column(0) == pa.array([100, 200, 300]) + + +def test_ffi_catalog_with_multiple_schemas(): + """Test FFI catalog with multiple schemas of different types.""" + ctx = SessionContext() + + catalog_provider = MyCatalogProvider() + ctx.register_catalog_provider("multi_catalog", catalog_provider) + + catalog = ctx.catalog("multi_catalog") + + # Register different types of schemas + ffi_schema = FixedSchemaProvider() + memory_schema = Schema.memory_schema() + + catalog.register_schema("ffi_schema", ffi_schema) + catalog.register_schema("memory_schema", memory_schema) + + # Add tables to memory schema + memory_schema.register_table("mem_table", create_test_dataset()) + + # Verify all schemas exist + names = catalog.names() + assert "my_schema" in names # Pre-populated + assert "ffi_schema" in names + assert "memory_schema" in names + + # Query tables from each schema + result = ctx.sql("SELECT * FROM multi_catalog.my_schema.my_table").collect() + assert len(result) == 2 + + result = ctx.sql("SELECT * FROM multi_catalog.ffi_schema.my_table").collect() + assert len(result) == 2 + + result = ctx.sql("SELECT * FROM multi_catalog.memory_schema.mem_table").collect() + assert len(result) == 1 + assert result[0].column(0) == pa.array([100, 200, 300]) + + +def test_ffi_schema_table_exist(): + """Test table_exist method on FFI SchemaProvider.""" + ctx = SessionContext() + + schema_provider = FixedSchemaProvider() + ctx.catalog().register_schema("ffi_schema", schema_provider) + + schema = ctx.catalog().schema("ffi_schema") + + # Check pre-populated table + assert schema.table_exist("my_table") + + # Check non-existent table + assert not schema.table_exist("nonexistent_table") + + # Register a new table and check + schema.register_table("new_table", create_test_dataset()) + assert schema.table_exist("new_table") + + # Deregister and check + schema.deregister_table("new_table") + assert not schema.table_exist("new_table") diff --git a/examples/datafusion-ffi-example/python/tests/_test_table_function.py b/examples/datafusion-ffi-example/python/tests/_test_table_function.py index 4b8b21454..bf5aae3bd 100644 --- a/examples/datafusion-ffi-example/python/tests/_test_table_function.py +++ b/examples/datafusion-ffi-example/python/tests/_test_table_function.py @@ -27,9 +27,10 @@ from datafusion.context import TableProviderExportable -def test_ffi_table_function_register(): +def test_ffi_table_function_register() -> None: ctx = SessionContext() table_func = MyTableFunction() + table_udtf = udtf(table_func, "my_table_func") ctx.register_udtf(table_udtf) result = ctx.sql("select * from my_table_func()").collect() diff --git a/examples/datafusion-ffi-example/python/tests/_test_table_provider.py b/examples/datafusion-ffi-example/python/tests/_test_table_provider.py index 48feaff64..fc77d2d3b 100644 --- a/examples/datafusion-ffi-example/python/tests/_test_table_provider.py +++ b/examples/datafusion-ffi-example/python/tests/_test_table_provider.py @@ -18,13 +18,18 @@ from __future__ import annotations import pyarrow as pa +import pytest from datafusion import SessionContext from datafusion_ffi_example import MyTableProvider -def test_table_loading(): +@pytest.mark.parametrize("inner_capsule", [True, False]) +def test_table_provider_ffi(inner_capsule: bool) -> None: ctx = SessionContext() table = MyTableProvider(3, 2, 4) + if inner_capsule: + table = table.__datafusion_table_provider__(ctx) + ctx.register_table("t", table) result = ctx.table("t").collect() diff --git a/examples/datafusion-ffi-example/python/tests/_test_table_provider_factory.py b/examples/datafusion-ffi-example/python/tests/_test_table_provider_factory.py new file mode 100644 index 000000000..b1e94ec73 --- /dev/null +++ b/examples/datafusion-ffi-example/python/tests/_test_table_provider_factory.py @@ -0,0 +1,41 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from __future__ import annotations + +from datafusion import SessionContext +from datafusion_ffi_example import MyTableProviderFactory + + +def test_table_provider_factory_ffi() -> None: + ctx = SessionContext() + table = MyTableProviderFactory() + + ctx.register_table_factory("MY_FORMAT", table) + + # Create a new external table + ctx.sql(""" + CREATE EXTERNAL TABLE + foo + STORED AS my_format + LOCATION ''; + """).collect() + + # Query the pre-populated table + result = ctx.sql("SELECT * FROM foo;").collect() + assert len(result) == 2 + assert result[0].num_columns == 2 diff --git a/examples/datafusion-ffi-example/python/tests/conftest.py b/examples/datafusion-ffi-example/python/tests/conftest.py new file mode 100644 index 000000000..68f8057af --- /dev/null +++ b/examples/datafusion-ffi-example/python/tests/conftest.py @@ -0,0 +1,42 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +import pytest + +if TYPE_CHECKING: + from collections.abc import Generator + from typing import Any + + +class _FailOnWarning(logging.Handler): + def emit(self, record: logging.LogRecord) -> None: + if record.levelno >= logging.WARNING: + err = f"Unexpected log warning from '{record.name}': {self.format(record)}" + raise AssertionError(err) + + +@pytest.fixture(autouse=True) +def fail_on_log_warnings() -> Generator[None, Any, None]: + handler = _FailOnWarning() + logging.root.addHandler(handler) + yield + logging.root.removeHandler(handler) diff --git a/examples/datafusion-ffi-example/src/aggregate_udf.rs b/examples/datafusion-ffi-example/src/aggregate_udf.rs index bb7505f7f..d5343ff91 100644 --- a/examples/datafusion-ffi-example/src/aggregate_udf.rs +++ b/examples/datafusion-ffi-example/src/aggregate_udf.rs @@ -15,19 +15,25 @@ // specific language governing permissions and limitations // under the License. +use std::any::Any; +use std::sync::Arc; + use arrow_schema::DataType; -use datafusion::error::Result as DataFusionResult; -use datafusion::functions_aggregate::sum::Sum; -use datafusion::logical_expr::function::AccumulatorArgs; -use datafusion::logical_expr::{Accumulator, AggregateUDF, AggregateUDFImpl, Signature}; +use datafusion_common::error::Result as DataFusionResult; +use datafusion_expr::function::AccumulatorArgs; +use datafusion_expr::{Accumulator, AggregateUDF, AggregateUDFImpl, Signature}; use datafusion_ffi::udaf::FFI_AggregateUDF; +use datafusion_functions_aggregate::sum::Sum; use pyo3::types::PyCapsule; -use pyo3::{pyclass, pymethods, Bound, PyResult, Python}; -use std::any::Any; -use std::sync::Arc; +use pyo3::{Bound, PyResult, Python, pyclass, pymethods}; -#[pyclass(name = "MySumUDF", module = "datafusion_ffi_example", subclass)] -#[derive(Debug, Clone, PartialEq, Eq, Hash)] +#[pyclass( + from_py_object, + name = "MySumUDF", + module = "datafusion_ffi_example", + subclass +)] +#[derive(Debug, Clone, Eq, PartialEq, Hash)] pub(crate) struct MySumUDF { inner: Arc, } @@ -35,10 +41,10 @@ pub(crate) struct MySumUDF { #[pymethods] impl MySumUDF { #[new] - fn new() -> Self { - Self { + fn new() -> PyResult { + Ok(Self { inner: Arc::new(Sum::new()), - } + }) } fn __datafusion_aggregate_udf__<'py>( diff --git a/examples/datafusion-ffi-example/src/catalog_provider.rs b/examples/datafusion-ffi-example/src/catalog_provider.rs index cd2616916..bd5da1e4d 100644 --- a/examples/datafusion-ffi-example/src/catalog_provider.rs +++ b/examples/datafusion-ffi-example/src/catalog_provider.rs @@ -15,24 +15,27 @@ // specific language governing permissions and limitations // under the License. -use pyo3::{pyclass, pymethods, Bound, PyResult, Python}; -use std::{any::Any, fmt::Debug, sync::Arc}; +use std::any::Any; +use std::fmt::Debug; +use std::sync::Arc; use arrow::datatypes::Schema; use async_trait::async_trait; -use datafusion::{ - catalog::{ - CatalogProvider, MemoryCatalogProvider, MemorySchemaProvider, SchemaProvider, TableProvider, - }, - datasource::MemTable, - error::{DataFusionError, Result}, +use datafusion_catalog::{ + CatalogProvider, CatalogProviderList, MemTable, MemoryCatalogProvider, + MemoryCatalogProviderList, MemorySchemaProvider, SchemaProvider, TableProvider, }; +use datafusion_common::error::{DataFusionError, Result}; use datafusion_ffi::catalog_provider::FFI_CatalogProvider; +use datafusion_ffi::catalog_provider_list::FFI_CatalogProviderList; +use datafusion_ffi::schema_provider::FFI_SchemaProvider; +use datafusion_python_util::ffi_logical_codec_from_pycapsule; use pyo3::types::PyCapsule; +use pyo3::{Bound, PyAny, PyResult, Python, pyclass, pymethods}; pub fn my_table() -> Arc { use arrow::datatypes::{DataType, Field}; - use datafusion::common::record_batch; + use datafusion_common::record_batch; let schema = Arc::new(Schema::new(vec![ Field::new("units", DataType::Int32, true), @@ -55,14 +58,20 @@ pub fn my_table() -> Arc { Arc::new(MemTable::try_new(schema, vec![partitions]).unwrap()) } +#[pyclass( + skip_from_py_object, + name = "FixedSchemaProvider", + module = "datafusion_ffi_example", + subclass +)] #[derive(Debug)] pub struct FixedSchemaProvider { - inner: MemorySchemaProvider, + inner: Arc, } impl Default for FixedSchemaProvider { fn default() -> Self { - let inner = MemorySchemaProvider::new(); + let inner = Arc::new(MemorySchemaProvider::new()); let table = my_table(); @@ -72,6 +81,29 @@ impl Default for FixedSchemaProvider { } } +#[pymethods] +impl FixedSchemaProvider { + #[new] + pub fn new() -> Self { + Self::default() + } + + pub fn __datafusion_schema_provider__<'py>( + &self, + py: Python<'py>, + session: Bound, + ) -> PyResult> { + let name = cr"datafusion_schema_provider".into(); + + let provider = Arc::clone(&self.inner) as Arc; + + let codec = ffi_logical_codec_from_pycapsule(session)?; + let provider = FFI_SchemaProvider::new_with_ffi_codec(provider, None, codec); + + PyCapsule::new(py, provider, Some(name)) + } +} + #[async_trait] impl SchemaProvider for FixedSchemaProvider { fn as_any(&self) -> &dyn Any { @@ -106,24 +138,14 @@ impl SchemaProvider for FixedSchemaProvider { /// This catalog provider is intended only for unit tests. It prepopulates with one /// schema and only allows for schemas named after four types of fruit. #[pyclass( + skip_from_py_object, name = "MyCatalogProvider", module = "datafusion_ffi_example", subclass )] -#[derive(Debug)] +#[derive(Debug, Clone)] pub(crate) struct MyCatalogProvider { - inner: MemoryCatalogProvider, -} - -impl Default for MyCatalogProvider { - fn default() -> Self { - let inner = MemoryCatalogProvider::new(); - - let schema_name: &str = "my_schema"; - let _ = inner.register_schema(schema_name, Arc::new(FixedSchemaProvider::default())); - - Self { inner } - } + inner: Arc, } impl CatalogProvider for MyCatalogProvider { @@ -159,20 +181,92 @@ impl CatalogProvider for MyCatalogProvider { #[pymethods] impl MyCatalogProvider { #[new] - pub fn new() -> Self { - Self { - inner: Default::default(), - } + pub fn new() -> PyResult { + let inner = Arc::new(MemoryCatalogProvider::new()); + + let schema_name: &str = "my_schema"; + let _ = inner.register_schema(schema_name, Arc::new(FixedSchemaProvider::default())); + + Ok(Self { inner }) } pub fn __datafusion_catalog_provider__<'py>( &self, py: Python<'py>, + session: Bound, ) -> PyResult> { let name = cr"datafusion_catalog_provider".into(); - let catalog_provider = - FFI_CatalogProvider::new(Arc::new(MyCatalogProvider::default()), None); - PyCapsule::new(py, catalog_provider, Some(name)) + let provider = Arc::clone(&self.inner) as Arc; + + let codec = ffi_logical_codec_from_pycapsule(session)?; + let provider = FFI_CatalogProvider::new_with_ffi_codec(provider, None, codec); + + PyCapsule::new(py, provider, Some(name)) + } +} + +/// This catalog provider list is intended only for unit tests. +/// It pre-populates with a single catalog. +#[pyclass( + skip_from_py_object, + name = "MyCatalogProviderList", + module = "datafusion_ffi_example", + subclass +)] +#[derive(Debug, Clone)] +pub(crate) struct MyCatalogProviderList { + inner: Arc, +} + +impl CatalogProviderList for MyCatalogProviderList { + fn as_any(&self) -> &dyn Any { + self + } + + fn catalog_names(&self) -> Vec { + self.inner.catalog_names() + } + + fn catalog(&self, name: &str) -> Option> { + self.inner.catalog(name) + } + + fn register_catalog( + &self, + name: String, + catalog: Arc, + ) -> Option> { + self.inner.register_catalog(name, catalog) + } +} + +#[pymethods] +impl MyCatalogProviderList { + #[new] + pub fn new() -> PyResult { + let inner = Arc::new(MemoryCatalogProviderList::new()); + + inner.register_catalog( + "auto_ffi_catalog".to_owned(), + Arc::new(MyCatalogProvider::new()?), + ); + + Ok(Self { inner }) + } + + pub fn __datafusion_catalog_provider_list__<'py>( + &self, + py: Python<'py>, + session: Bound, + ) -> PyResult> { + let name = cr"datafusion_catalog_provider_list".into(); + + let provider = Arc::clone(&self.inner) as Arc; + + let codec = ffi_logical_codec_from_pycapsule(session)?; + let provider = FFI_CatalogProviderList::new_with_ffi_codec(provider, None, codec); + + PyCapsule::new(py, provider, Some(name)) } } diff --git a/examples/datafusion-ffi-example/src/config.rs b/examples/datafusion-ffi-example/src/config.rs new file mode 100644 index 000000000..6cdb8aa83 --- /dev/null +++ b/examples/datafusion-ffi-example/src/config.rs @@ -0,0 +1,133 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::any::Any; + +use datafusion_common::config::{ + ConfigEntry, ConfigExtension, ConfigField, ExtensionOptions, Visit, +}; +use datafusion_common::{DataFusionError, config_err}; +use datafusion_ffi::config::extension_options::FFI_ExtensionOptions; +use pyo3::exceptions::PyRuntimeError; +use pyo3::types::PyCapsule; +use pyo3::{Bound, PyResult, Python, pyclass, pymethods}; + +/// My own config options. +#[pyclass( + from_py_object, + name = "MyConfig", + module = "datafusion_ffi_example", + subclass +)] +#[derive(Clone, Debug)] +pub struct MyConfig { + /// Should "foo" be replaced by "bar"? + pub foo_to_bar: bool, + + /// How many "baz" should be created? + pub baz_count: usize, +} + +#[pymethods] +impl MyConfig { + #[new] + fn new() -> Self { + Self::default() + } + + fn __datafusion_extension_options__<'py>( + &self, + py: Python<'py>, + ) -> PyResult> { + let name = cr"datafusion_extension_options".into(); + + let mut config = FFI_ExtensionOptions::default(); + config + .add_config(self) + .map_err(|e| PyRuntimeError::new_err(e.to_string()))?; + + PyCapsule::new(py, config, Some(name)) + } +} + +impl Default for MyConfig { + fn default() -> Self { + Self { + foo_to_bar: true, + baz_count: 1337, + } + } +} + +impl ConfigExtension for MyConfig { + const PREFIX: &'static str = "my_config"; +} + +impl ExtensionOptions for MyConfig { + fn as_any(&self) -> &dyn Any { + self + } + + fn as_any_mut(&mut self) -> &mut dyn Any { + self + } + + fn cloned(&self) -> Box { + Box::new(self.clone()) + } + + fn set(&mut self, key: &str, value: &str) -> datafusion_common::Result<()> { + datafusion_common::config::ConfigField::set(self, key, value) + } + + fn entries(&self) -> Vec { + vec![ + ConfigEntry { + key: "foo_to_bar".to_owned(), + value: Some(format!("{}", self.foo_to_bar)), + description: "foo to bar", + }, + ConfigEntry { + key: "baz_count".to_owned(), + value: Some(format!("{}", self.baz_count)), + description: "baz count", + }, + ] + } +} + +impl ConfigField for MyConfig { + fn visit(&self, v: &mut V, _key: &str, _description: &'static str) { + let key = "foo_to_bar"; + let desc = "foo to bar"; + self.foo_to_bar.visit(v, key, desc); + + let key = "baz_count"; + let desc = "baz count"; + self.baz_count.visit(v, key, desc); + } + + fn set(&mut self, key: &str, value: &str) -> Result<(), DataFusionError> { + let (key, rem) = key.split_once('.').unwrap_or((key, "")); + match key { + "foo_to_bar" => self.foo_to_bar.set(rem, value.as_ref()), + "baz_count" => self.baz_count.set(rem, value.as_ref()), + + _ => config_err!("Config value \"{}\" not found on MyConfig", key), + } + } +} diff --git a/examples/datafusion-ffi-example/src/lib.rs b/examples/datafusion-ffi-example/src/lib.rs index f5f96cd49..e708c49cc 100644 --- a/examples/datafusion-ffi-example/src/lib.rs +++ b/examples/datafusion-ffi-example/src/lib.rs @@ -15,28 +15,39 @@ // specific language governing permissions and limitations // under the License. +use pyo3::prelude::*; + use crate::aggregate_udf::MySumUDF; -use crate::catalog_provider::MyCatalogProvider; +use crate::catalog_provider::{FixedSchemaProvider, MyCatalogProvider, MyCatalogProviderList}; +use crate::config::MyConfig; use crate::scalar_udf::IsNullUDF; use crate::table_function::MyTableFunction; use crate::table_provider::MyTableProvider; +use crate::table_provider_factory::MyTableProviderFactory; use crate::window_udf::MyRankUDF; -use pyo3::prelude::*; pub(crate) mod aggregate_udf; pub(crate) mod catalog_provider; +pub(crate) mod config; pub(crate) mod scalar_udf; pub(crate) mod table_function; pub(crate) mod table_provider; +pub(crate) mod table_provider_factory; pub(crate) mod window_udf; #[pymodule] fn datafusion_ffi_example(m: &Bound<'_, PyModule>) -> PyResult<()> { + pyo3_log::init(); + m.add_class::()?; + m.add_class::()?; m.add_class::()?; m.add_class::()?; + m.add_class::()?; + m.add_class::()?; m.add_class::()?; m.add_class::()?; m.add_class::()?; + m.add_class::()?; Ok(()) } diff --git a/examples/datafusion-ffi-example/src/scalar_udf.rs b/examples/datafusion-ffi-example/src/scalar_udf.rs index 19b4e8b91..374924781 100644 --- a/examples/datafusion-ffi-example/src/scalar_udf.rs +++ b/examples/datafusion-ffi-example/src/scalar_udf.rs @@ -15,21 +15,27 @@ // specific language governing permissions and limitations // under the License. +use std::any::Any; +use std::sync::Arc; + use arrow_array::{Array, BooleanArray}; use arrow_schema::DataType; -use datafusion::common::ScalarValue; -use datafusion::error::Result as DataFusionResult; -use datafusion::logical_expr::{ +use datafusion_common::ScalarValue; +use datafusion_common::error::Result as DataFusionResult; +use datafusion_expr::{ ColumnarValue, ScalarFunctionArgs, ScalarUDF, ScalarUDFImpl, Signature, TypeSignature, Volatility, }; use datafusion_ffi::udf::FFI_ScalarUDF; use pyo3::types::PyCapsule; -use pyo3::{pyclass, pymethods, Bound, PyResult, Python}; -use std::any::Any; -use std::sync::Arc; +use pyo3::{Bound, PyResult, Python, pyclass, pymethods}; -#[pyclass(name = "IsNullUDF", module = "datafusion_ffi_example", subclass)] +#[pyclass( + from_py_object, + name = "IsNullUDF", + module = "datafusion_ffi_example", + subclass +)] #[derive(Debug, Clone, PartialEq, Eq, Hash)] pub(crate) struct IsNullUDF { signature: Signature, diff --git a/examples/datafusion-ffi-example/src/table_function.rs b/examples/datafusion-ffi-example/src/table_function.rs index 2d7b356e3..79c13f64d 100644 --- a/examples/datafusion-ffi-example/src/table_function.rs +++ b/examples/datafusion-ffi-example/src/table_function.rs @@ -15,16 +15,24 @@ // specific language governing permissions and limitations // under the License. -use crate::table_provider::MyTableProvider; -use datafusion::catalog::{TableFunctionImpl, TableProvider}; -use datafusion::error::Result as DataFusionResult; -use datafusion::prelude::Expr; +use std::sync::Arc; + +use datafusion_catalog::{TableFunctionImpl, TableProvider}; +use datafusion_common::error::Result as DataFusionResult; +use datafusion_expr::Expr; use datafusion_ffi::udtf::FFI_TableFunction; +use datafusion_python_util::ffi_logical_codec_from_pycapsule; use pyo3::types::PyCapsule; -use pyo3::{pyclass, pymethods, Bound, PyResult, Python}; -use std::sync::Arc; +use pyo3::{Bound, PyAny, PyResult, Python, pyclass, pymethods}; + +use crate::table_provider::MyTableProvider; -#[pyclass(name = "MyTableFunction", module = "datafusion_ffi_example", subclass)] +#[pyclass( + from_py_object, + name = "MyTableFunction", + module = "datafusion_ffi_example", + subclass +)] #[derive(Debug, Clone)] pub(crate) struct MyTableFunction {} @@ -38,11 +46,13 @@ impl MyTableFunction { fn __datafusion_table_function__<'py>( &self, py: Python<'py>, + session: Bound, ) -> PyResult> { let name = cr"datafusion_table_function".into(); let func = self.clone(); - let provider = FFI_TableFunction::new(Arc::new(func), None); + let codec = ffi_logical_codec_from_pycapsule(session)?; + let provider = FFI_TableFunction::new_with_ffi_codec(Arc::new(func), None, codec); PyCapsule::new(py, provider, Some(name)) } diff --git a/examples/datafusion-ffi-example/src/table_provider.rs b/examples/datafusion-ffi-example/src/table_provider.rs index e884585b5..358ef7402 100644 --- a/examples/datafusion-ffi-example/src/table_provider.rs +++ b/examples/datafusion-ffi-example/src/table_provider.rs @@ -15,19 +15,26 @@ // specific language governing permissions and limitations // under the License. +use std::sync::Arc; + use arrow_array::{ArrayRef, RecordBatch}; use arrow_schema::{DataType, Field, Schema}; -use datafusion::catalog::MemTable; -use datafusion::error::{DataFusionError, Result as DataFusionResult}; +use datafusion_catalog::MemTable; +use datafusion_common::error::{DataFusionError, Result as DataFusionResult}; use datafusion_ffi::table_provider::FFI_TableProvider; +use datafusion_python_util::ffi_logical_codec_from_pycapsule; use pyo3::exceptions::PyRuntimeError; use pyo3::types::PyCapsule; -use pyo3::{pyclass, pymethods, Bound, PyResult, Python}; -use std::sync::Arc; +use pyo3::{Bound, PyAny, PyResult, Python, pyclass, pymethods}; /// In order to provide a test that demonstrates different sized record batches, /// the first batch will have num_rows, the second batch num_rows+1, and so on. -#[pyclass(name = "MyTableProvider", module = "datafusion_ffi_example", subclass)] +#[pyclass( + from_py_object, + name = "MyTableProvider", + module = "datafusion_ffi_example", + subclass +)] #[derive(Clone)] pub(crate) struct MyTableProvider { num_cols: usize, @@ -90,13 +97,17 @@ impl MyTableProvider { pub fn __datafusion_table_provider__<'py>( &self, py: Python<'py>, + session: Bound, ) -> PyResult> { let name = cr"datafusion_table_provider".into(); let provider = self .create_table() - .map_err(|e| PyRuntimeError::new_err(e.to_string()))?; - let provider = FFI_TableProvider::new(Arc::new(provider), false, None); + .map_err(|e: DataFusionError| PyRuntimeError::new_err(e.to_string()))?; + + let codec = ffi_logical_codec_from_pycapsule(session)?; + let provider = + FFI_TableProvider::new_with_ffi_codec(Arc::new(provider), false, None, codec); PyCapsule::new(py, provider, Some(name)) } diff --git a/examples/datafusion-ffi-example/src/table_provider_factory.rs b/examples/datafusion-ffi-example/src/table_provider_factory.rs new file mode 100644 index 000000000..53248a905 --- /dev/null +++ b/examples/datafusion-ffi-example/src/table_provider_factory.rs @@ -0,0 +1,87 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::sync::Arc; + +use async_trait::async_trait; +use datafusion_catalog::{Session, TableProvider, TableProviderFactory}; +use datafusion_common::error::Result as DataFusionResult; +use datafusion_expr::CreateExternalTable; +use datafusion_ffi::table_provider_factory::FFI_TableProviderFactory; +use datafusion_python_util::ffi_logical_codec_from_pycapsule; +use pyo3::types::PyCapsule; +use pyo3::{Bound, PyAny, PyResult, Python, pyclass, pymethods}; + +use crate::catalog_provider; + +#[derive(Debug)] +pub(crate) struct ExampleTableProviderFactory {} + +impl ExampleTableProviderFactory { + fn new() -> Self { + Self {} + } +} + +#[async_trait] +impl TableProviderFactory for ExampleTableProviderFactory { + async fn create( + &self, + _state: &dyn Session, + _cmd: &CreateExternalTable, + ) -> DataFusionResult> { + Ok(catalog_provider::my_table()) + } +} + +#[pyclass( + name = "MyTableProviderFactory", + module = "datafusion_ffi_example", + subclass +)] +#[derive(Debug)] +pub struct MyTableProviderFactory { + inner: Arc, +} + +impl Default for MyTableProviderFactory { + fn default() -> Self { + let inner = Arc::new(ExampleTableProviderFactory::new()); + Self { inner } + } +} + +#[pymethods] +impl MyTableProviderFactory { + #[new] + pub fn new() -> Self { + Self::default() + } + + pub fn __datafusion_table_provider_factory__<'py>( + &self, + py: Python<'py>, + codec: Bound, + ) -> PyResult> { + let name = cr"datafusion_table_provider_factory".into(); + let codec = ffi_logical_codec_from_pycapsule(codec)?; + let factory = Arc::clone(&self.inner) as Arc; + let factory = FFI_TableProviderFactory::new_with_ffi_codec(factory, None, codec); + + PyCapsule::new(py, factory, Some(name)) + } +} diff --git a/examples/datafusion-ffi-example/src/window_udf.rs b/examples/datafusion-ffi-example/src/window_udf.rs index 0ec8c7c2d..cbf179a86 100644 --- a/examples/datafusion-ffi-example/src/window_udf.rs +++ b/examples/datafusion-ffi-example/src/window_udf.rs @@ -15,19 +15,25 @@ // specific language governing permissions and limitations // under the License. +use std::any::Any; +use std::sync::Arc; + use arrow_schema::{DataType, FieldRef}; -use datafusion::error::Result as DataFusionResult; -use datafusion::functions_window::rank::rank_udwf; -use datafusion::logical_expr::function::{PartitionEvaluatorArgs, WindowUDFFieldArgs}; -use datafusion::logical_expr::{PartitionEvaluator, Signature, WindowUDF, WindowUDFImpl}; +use datafusion_common::error::Result as DataFusionResult; +use datafusion_expr::function::{PartitionEvaluatorArgs, WindowUDFFieldArgs}; +use datafusion_expr::{PartitionEvaluator, Signature, WindowUDF, WindowUDFImpl}; use datafusion_ffi::udwf::FFI_WindowUDF; +use datafusion_functions_window::rank::rank_udwf; use pyo3::types::PyCapsule; -use pyo3::{pyclass, pymethods, Bound, PyResult, Python}; -use std::any::Any; -use std::sync::Arc; +use pyo3::{Bound, PyResult, Python, pyclass, pymethods}; -#[pyclass(name = "MyRankUDF", module = "datafusion_ffi_example", subclass)] -#[derive(Debug, Clone, PartialEq, Eq, Hash)] +#[pyclass( + from_py_object, + name = "MyRankUDF", + module = "datafusion_ffi_example", + subclass +)] +#[derive(Debug, Clone, Eq, PartialEq, Hash)] pub(crate) struct MyRankUDF { inner: Arc, } @@ -35,8 +41,8 @@ pub(crate) struct MyRankUDF { #[pymethods] impl MyRankUDF { #[new] - fn new() -> Self { - Self { inner: rank_udwf() } + fn new() -> PyResult { + Ok(Self { inner: rank_udwf() }) } fn __datafusion_window_udf__<'py>(&self, py: Python<'py>) -> PyResult> { diff --git a/examples/tpch/_tests.py b/examples/tpch/_tests.py index 80ff80244..82dab2974 100644 --- a/examples/tpch/_tests.py +++ b/examples/tpch/_tests.py @@ -25,8 +25,10 @@ def df_selection(col_name, col_type): - if col_type == pa.float64() or isinstance(col_type, pa.Decimal128Type): + if col_type == pa.float64(): return F.round(col(col_name), lit(2)).alias(col_name) + if isinstance(col_type, pa.Decimal128Type): + return F.round(col(col_name).cast(pa.float64()), lit(2)).alias(col_name) if col_type == pa.string() or col_type == pa.string_view(): return F.trim(col(col_name)).alias(col_name) return col(col_name) @@ -113,7 +115,7 @@ def test_tpch_query_vs_answer_file(query_code: str, answer_file: str) -> None: get_answer_file(answer_file), schema=read_schema, delimiter="|", - file_extension=".out", + file_extension=".tbl", ) df_expected = df_expected.select(*expected_selections) diff --git a/examples/tpch/answers_sf1/q1.tbl b/examples/tpch/answers_sf1/q1.tbl new file mode 100644 index 000000000..03ca993e7 --- /dev/null +++ b/examples/tpch/answers_sf1/q1.tbl @@ -0,0 +1,5 @@ +l|l|sum_qty |sum_base_price |sum_disc_price |sum_charge |avg_qty |avg_price |avg_disc |count_order +A|F|37734107.00|56586554400.73|53758257134.87|55909065222.83|25.52|38273.13|0.05| 1478493 +N|F|991417.00|1487504710.38|1413082168.05|1469649223.19|25.52|38284.47|0.05| 38854 +N|O|74476040.00|111701729697.74|106118230307.61|110367043872.50|25.50|38249.12|0.05| 2920374 +R|F|37719753.00|56568041380.90|53741292684.60|55889619119.83|25.51|38250.85|0.05| 1478870 diff --git a/examples/tpch/answers_sf1/q10.tbl b/examples/tpch/answers_sf1/q10.tbl new file mode 100644 index 000000000..95f303907 --- /dev/null +++ b/examples/tpch/answers_sf1/q10.tbl @@ -0,0 +1,21 @@ +c_custkey |c_name |revenue |c_acctbal |n_name |c_address |c_phone |c_comment + 57040|Customer#000057040 |734235.25|632.87|JAPAN |Eioyzjf4pp |22-895-641-3466|sits. slyly regular requests sleep alongside of the regular inst + 143347|Customer#000143347 |721002.69|2557.47|EGYPT |1aReFYv,Kw4 |14-742-935-3718|ggle carefully enticing requests. final deposits use bold, bold pinto beans. ironic, idle re + 60838|Customer#000060838 |679127.31|2454.77|BRAZIL |64EaJ5vMAHWJlBOxJklpNc2RJiWE |12-913-494-9813| need to boost against the slyly regular account + 101998|Customer#000101998 |637029.57|3790.89|UNITED KINGDOM |01c9CILnNtfOQYmZj |33-593-865-6378|ress foxes wake slyly after the bold excuses. ironic platelets are furiously carefully bold theodolites + 125341|Customer#000125341 |633508.09|4983.51|GERMANY |S29ODD6bceU8QSuuEJznkNaK |17-582-695-5962|arefully even depths. blithely even excuses sleep furiously. foxes use except the dependencies. ca + 25501|Customer#000025501 |620269.78|7725.04|ETHIOPIA | W556MXuoiaYCCZamJI,Rn0B4ACUGdkQ8DZ |15-874-808-6793|he pending instructions wake carefully at the pinto beans. regular, final instructions along the slyly fina + 115831|Customer#000115831 |596423.87|5098.10|FRANCE |rFeBbEEyk dl ne7zV5fDrmiq1oK09wV7pxqCgIc|16-715-386-3788|l somas sleep. furiously final deposits wake blithely regular pinto b + 84223|Customer#000084223 |594998.02|528.65|UNITED KINGDOM |nAVZCs6BaWap rrM27N 2qBnzc5WBauxbA |33-442-824-8191| slyly final deposits haggle regular, pending dependencies. pending escapades wake + 54289|Customer#000054289 |585603.39|5583.02|IRAN |vXCxoCsU0Bad5JQI ,oobkZ |20-834-292-4707|ely special foxes are quickly finally ironic p + 39922|Customer#000039922 |584878.11|7321.11|GERMANY |Zgy4s50l2GKN4pLDPBU8m342gIw6R |17-147-757-8036|y final requests. furiously final foxes cajole blithely special platelets. f + 6226|Customer#000006226 |576783.76|2230.09|UNITED KINGDOM |8gPu8,NPGkfyQQ0hcIYUGPIBWc,ybP5g, |33-657-701-3391|ending platelets along the express deposits cajole carefully final + 922|Customer#000000922 |576767.53|3869.25|GERMANY |Az9RFaut7NkPnc5zSD2PwHgVwr4jRzq |17-945-916-9648|luffily fluffy deposits. packages c + 147946|Customer#000147946 |576455.13|2030.13|ALGERIA |iANyZHjqhyy7Ajah0pTrYyhJ |10-886-956-3143|ithely ironic deposits haggle blithely ironic requests. quickly regu + 115640|Customer#000115640 |569341.19|6436.10|ARGENTINA |Vtgfia9qI 7EpHgecU1X |11-411-543-4901|ost slyly along the patterns; pinto be + 73606|Customer#000073606 |568656.86|1785.67|JAPAN |xuR0Tro5yChDfOCrjkd2ol |22-437-653-6966|he furiously regular ideas. slowly + 110246|Customer#000110246 |566842.98|7763.35|VIETNAM |7KzflgX MDOq7sOkI |31-943-426-9837|egular deposits serve blithely above the fl + 142549|Customer#000142549 |563537.24|5085.99|INDONESIA |ChqEoK43OysjdHbtKCp6dKqjNyvvi9 |19-955-562-2398|sleep pending courts. ironic deposits against the carefully unusual platelets cajole carefully express accounts. + 146149|Customer#000146149 |557254.99|1791.55|ROMANIA |s87fvzFQpU |29-744-164-6487| of the slyly silent accounts. quickly final accounts across the + 52528|Customer#000052528 |556397.35|551.79|ARGENTINA |NFztyTOR10UOJ |11-208-192-3205| deposits hinder. blithely pending asymptotes breach slyly regular re + 23431|Customer#000023431 |554269.54|3381.86|ROMANIA |HgiV0phqhaIa9aydNoIlb |29-915-458-2654|nusual, even instructions: furiously stealthy n diff --git a/examples/tpch/answers_sf1/q11.tbl b/examples/tpch/answers_sf1/q11.tbl new file mode 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+l_shipmode|high_line_count |low_line_count +MAIL | 6202| 9324 +SHIP | 6200| 9262 diff --git a/examples/tpch/answers_sf1/q13.tbl b/examples/tpch/answers_sf1/q13.tbl new file mode 100644 index 000000000..deecc0d83 --- /dev/null +++ b/examples/tpch/answers_sf1/q13.tbl @@ -0,0 +1,43 @@ +c_count |custdist + 0| 50005 + 9| 6641 + 10| 6532 + 11| 6014 + 8| 5937 + 12| 5639 + 13| 5024 + 19| 4793 + 7| 4687 + 17| 4587 + 18| 4529 + 20| 4516 + 15| 4505 + 14| 4446 + 16| 4273 + 21| 4190 + 22| 3623 + 6| 3265 + 23| 3225 + 24| 2742 + 25| 2086 + 5| 1948 + 26| 1612 + 27| 1179 + 4| 1007 + 28| 893 + 29| 593 + 3| 415 + 30| 376 + 31| 226 + 32| 148 + 2| 134 + 33| 75 + 34| 50 + 35| 37 + 1| 17 + 36| 14 + 38| 5 + 37| 5 + 40| 4 + 41| 2 + 39| 1 diff --git a/examples/tpch/answers_sf1/q14.tbl b/examples/tpch/answers_sf1/q14.tbl new file mode 100644 index 000000000..0e6571a1d --- /dev/null +++ b/examples/tpch/answers_sf1/q14.tbl @@ -0,0 +1,2 @@ +promo_revenue +16.38 diff --git a/examples/tpch/answers_sf1/q15.tbl 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16 +Brand#13 |SMALL ANODIZED STEEL | 45| 16 +Brand#13 |SMALL BURNISHED STEEL | 19| 16 +Brand#13 |SMALL PLATED BRASS | 36| 16 +Brand#13 |STANDARD ANODIZED BRASS | 23| 16 +Brand#13 |STANDARD ANODIZED STEEL | 23| 16 +Brand#13 |STANDARD BURNISHED BRASS | 9| 16 +Brand#13 |STANDARD PLATED NICKEL | 9| 16 +Brand#13 |STANDARD PLATED TIN | 23| 16 +Brand#14 |ECONOMY BRUSHED STEEL | 3| 16 +Brand#14 |ECONOMY PLATED NICKEL | 9| 16 +Brand#14 |ECONOMY PLATED STEEL | 9| 16 +Brand#14 |ECONOMY POLISHED NICKEL | 19| 16 +Brand#14 |LARGE ANODIZED COPPER | 14| 16 +Brand#14 |LARGE BRUSHED NICKEL | 19| 16 +Brand#14 |LARGE POLISHED STEEL | 3| 16 +Brand#14 |LARGE POLISHED TIN | 23| 16 +Brand#14 |MEDIUM BURNISHED COPPER | 3| 16 +Brand#14 |PROMO ANODIZED STEEL | 36| 16 +Brand#14 |PROMO PLATED BRASS | 9| 16 +Brand#14 |PROMO PLATED NICKEL | 49| 16 +Brand#14 |PROMO POLISHED BRASS | 19| 16 +Brand#14 |PROMO POLISHED STEEL | 19| 16 +Brand#14 |PROMO POLISHED TIN | 45| 16 +Brand#14 |SMALL BRUSHED BRASS | 14| 16 +Brand#14 |SMALL BURNISHED COPPER | 45| 16 +Brand#14 |STANDARD BRUSHED TIN | 19| 16 +Brand#14 |STANDARD PLATED COPPER | 45| 16 +Brand#14 |STANDARD PLATED TIN | 9| 16 +Brand#14 |STANDARD POLISHED TIN | 49| 16 +Brand#15 |ECONOMY BRUSHED STEEL | 19| 16 +Brand#15 |LARGE BRUSHED BRASS | 14| 16 +Brand#15 |LARGE BRUSHED STEEL | 14| 16 +Brand#15 |LARGE BURNISHED NICKEL | 3| 16 +Brand#15 |LARGE PLATED COPPER | 49| 16 +Brand#15 |PROMO ANODIZED NICKEL | 3| 16 +Brand#15 |PROMO BURNISHED TIN | 49| 16 +Brand#15 |PROMO PLATED STEEL | 3| 16 +Brand#15 |PROMO POLISHED STEEL | 49| 16 +Brand#15 |SMALL BRUSHED COPPER | 9| 16 +Brand#15 |SMALL BRUSHED NICKEL | 23| 16 +Brand#15 |SMALL PLATED BRASS | 49| 16 +Brand#15 |STANDARD ANODIZED COPPER | 45| 16 +Brand#15 |STANDARD BRUSHED COPPER | 14| 16 +Brand#15 |STANDARD PLATED TIN | 36| 16 +Brand#21 |ECONOMY ANODIZED STEEL | 45| 16 +Brand#21 |ECONOMY BRUSHED COPPER | 9| 16 +Brand#21 |ECONOMY POLISHED STEEL | 19| 16 +Brand#21 |LARGE ANODIZED STEEL | 14| 16 +Brand#21 |MEDIUM ANODIZED STEEL | 36| 16 +Brand#21 |PROMO POLISHED BRASS | 14| 16 +Brand#21 |PROMO POLISHED TIN | 49| 16 +Brand#21 |SMALL BRUSHED COPPER | 3| 16 +Brand#21 |SMALL PLATED STEEL | 45| 16 +Brand#21 |SMALL PLATED TIN | 45| 16 +Brand#21 |STANDARD POLISHED STEEL | 36| 16 +Brand#22 |ECONOMY BRUSHED BRASS | 9| 16 +Brand#22 |ECONOMY BRUSHED NICKEL | 36| 16 +Brand#22 |ECONOMY POLISHED TIN | 36| 16 +Brand#22 |LARGE BRUSHED COPPER | 19| 16 +Brand#22 |LARGE BRUSHED TIN | 36| 16 +Brand#22 |LARGE POLISHED COPPER | 19| 16 +Brand#22 |MEDIUM ANODIZED BRASS | 23| 16 +Brand#22 |MEDIUM ANODIZED NICKEL | 9| 16 +Brand#22 |MEDIUM BRUSHED NICKEL | 14| 16 +Brand#22 |MEDIUM PLATED NICKEL | 23| 16 +Brand#22 |PROMO ANODIZED TIN | 45| 16 +Brand#22 |PROMO POLISHED STEEL | 49| 16 +Brand#22 |SMALL BRUSHED NICKEL | 45| 16 +Brand#22 |SMALL POLISHED BRASS | 36| 16 +Brand#22 |SMALL POLISHED STEEL | 9| 16 +Brand#22 |STANDARD BURNISHED BRASS | 45| 16 +Brand#22 |STANDARD BURNISHED NICKEL| 3| 16 +Brand#22 |STANDARD PLATED BRASS | 9| 16 +Brand#23 |ECONOMY BRUSHED TIN | 49| 16 +Brand#23 |ECONOMY BURNISHED COPPER | 45| 16 +Brand#23 |ECONOMY BURNISHED NICKEL | 19| 16 +Brand#23 |ECONOMY BURNISHED TIN | 9| 16 +Brand#23 |ECONOMY PLATED BRASS | 9| 16 +Brand#23 |ECONOMY PLATED COPPER | 14| 16 +Brand#23 |LARGE ANODIZED STEEL | 23| 16 +Brand#23 |LARGE ANODIZED STEEL | 49| 16 +Brand#23 |LARGE BURNISHED COPPER | 23| 16 +Brand#23 |LARGE POLISHED NICKEL | 9| 16 +Brand#23 |MEDIUM BRUSHED STEEL | 3| 16 +Brand#23 |PROMO ANODIZED COPPER | 19| 16 +Brand#23 |PROMO ANODIZED TIN | 3| 16 +Brand#23 |PROMO BURNISHED COPPER | 14| 16 +Brand#23 |PROMO PLATED BRASS | 3| 16 +Brand#23 |SMALL ANODIZED BRASS | 23| 16 +Brand#23 |SMALL BRUSHED BRASS | 45| 16 +Brand#23 |SMALL POLISHED TIN | 3| 16 +Brand#23 |STANDARD BURNISHED COPPER| 19| 16 +Brand#23 |STANDARD BURNISHED NICKEL| 49| 16 +Brand#23 |STANDARD PLATED BRASS | 9| 16 +Brand#23 |STANDARD PLATED COPPER | 45| 16 +Brand#23 |STANDARD POLISHED BRASS | 9| 16 +Brand#24 |ECONOMY ANODIZED BRASS | 3| 16 +Brand#24 |ECONOMY BRUSHED COPPER | 36| 16 +Brand#24 |ECONOMY BRUSHED STEEL | 14| 16 +Brand#24 |ECONOMY POLISHED COPPER | 36| 16 +Brand#24 |ECONOMY POLISHED NICKEL | 3| 16 +Brand#24 |LARGE ANODIZED BRASS | 23| 16 +Brand#24 |LARGE BURNISHED BRASS | 45| 16 +Brand#24 |LARGE BURNISHED STEEL | 14| 16 +Brand#24 |LARGE PLATED TIN | 9| 16 +Brand#24 |MEDIUM BRUSHED NICKEL | 49| 16 +Brand#24 |MEDIUM BURNISHED STEEL | 3| 16 +Brand#24 |PROMO BURNISHED COPPER | 49| 16 +Brand#24 |PROMO BURNISHED STEEL | 49| 16 +Brand#24 |PROMO POLISHED STEEL | 23| 16 +Brand#24 |SMALL ANODIZED NICKEL | 19| 16 +Brand#24 |STANDARD BURNISHED COPPER| 19| 16 +Brand#24 |STANDARD BURNISHED STEEL | 36| 16 +Brand#24 |STANDARD PLATED NICKEL | 23| 16 +Brand#24 |STANDARD PLATED TIN | 49| 16 +Brand#25 |ECONOMY ANODIZED COPPER | 14| 16 +Brand#25 |ECONOMY BURNISHED NICKEL | 9| 16 +Brand#25 |ECONOMY PLATED TIN | 14| 16 +Brand#25 |ECONOMY POLISHED TIN | 45| 16 +Brand#25 |LARGE ANODIZED STEEL | 9| 16 +Brand#25 |LARGE ANODIZED TIN | 45| 16 +Brand#25 |LARGE BRUSHED NICKEL | 36| 16 +Brand#25 |LARGE BURNISHED NICKEL | 14| 16 +Brand#25 |LARGE POLISHED STEEL | 19| 16 +Brand#25 |MEDIUM BRUSHED COPPER | 9| 16 +Brand#25 |MEDIUM BURNISHED COPPER | 49| 16 +Brand#25 |MEDIUM BURNISHED TIN | 3| 16 +Brand#25 |MEDIUM PLATED STEEL | 9| 16 +Brand#25 |PROMO ANODIZED BRASS | 49| 16 +Brand#25 |PROMO ANODIZED STEEL | 19| 16 +Brand#25 |PROMO ANODIZED TIN | 23| 16 +Brand#25 |PROMO BURNISHED COPPER | 49| 16 +Brand#25 |PROMO POLISHED COPPER | 14| 16 +Brand#25 |SMALL ANODIZED COPPER | 23| 16 +Brand#25 |SMALL BRUSHED STEEL | 23| 16 +Brand#25 |SMALL POLISHED COPPER | 23| 16 +Brand#25 |STANDARD BURNISHED STEEL | 23| 16 +Brand#25 |STANDARD BURNISHED TIN | 3| 16 +Brand#25 |STANDARD BURNISHED TIN | 36| 16 +Brand#25 |STANDARD PLATED BRASS | 45| 16 +Brand#25 |STANDARD PLATED COPPER | 49| 16 +Brand#31 |ECONOMY ANODIZED BRASS | 45| 16 +Brand#31 |ECONOMY BRUSHED COPPER | 14| 16 +Brand#31 |ECONOMY BRUSHED COPPER | 36| 16 +Brand#31 |LARGE ANODIZED STEEL | 45| 16 +Brand#31 |LARGE BURNISHED NICKEL | 45| 16 +Brand#31 |LARGE PLATED TIN | 14| 16 +Brand#31 |LARGE POLISHED COPPER | 49| 16 +Brand#31 |MEDIUM ANODIZED NICKEL | 49| 16 +Brand#31 |MEDIUM BURNISHED BRASS | 19| 16 +Brand#31 |PROMO ANODIZED NICKEL | 14| 16 +Brand#31 |PROMO BRUSHED TIN | 45| 16 +Brand#31 |PROMO BURNISHED STEEL | 36| 16 +Brand#31 |SMALL ANODIZED NICKEL | 23| 16 +Brand#31 |SMALL BRUSHED NICKEL | 14| 16 +Brand#31 |SMALL BRUSHED TIN | 19| 16 +Brand#31 |SMALL PLATED NICKEL | 23| 16 +Brand#31 |SMALL POLISHED BRASS | 23| 16 +Brand#31 |SMALL POLISHED TIN | 14| 16 +Brand#31 |SMALL POLISHED TIN | 45| 16 +Brand#31 |STANDARD BRUSHED COPPER | 45| 16 +Brand#31 |STANDARD POLISHED STEEL | 36| 16 +Brand#32 |ECONOMY BRUSHED STEEL | 9| 16 +Brand#32 |ECONOMY PLATED STEEL | 14| 16 +Brand#32 |LARGE ANODIZED BRASS | 36| 16 +Brand#32 |LARGE BURNISHED NICKEL | 36| 16 +Brand#32 |LARGE PLATED BRASS | 36| 16 +Brand#32 |LARGE PLATED STEEL | 23| 16 +Brand#32 |MEDIUM BRUSHED BRASS | 49| 16 +Brand#32 |MEDIUM BRUSHED TIN | 9| 16 +Brand#32 |MEDIUM PLATED COPPER | 36| 16 +Brand#32 |PROMO ANODIZED TIN | 36| 16 +Brand#32 |PROMO BRUSHED BRASS | 9| 16 +Brand#32 |PROMO BURNISHED STEEL | 36| 16 +Brand#32 |PROMO PLATED STEEL | 3| 16 +Brand#32 |PROMO PLATED TIN | 45| 16 +Brand#32 |SMALL BURNISHED TIN | 49| 16 +Brand#32 |SMALL PLATED NICKEL | 36| 16 +Brand#32 |SMALL POLISHED NICKEL | 36| 16 +Brand#32 |SMALL POLISHED STEEL | 9| 16 +Brand#32 |SMALL POLISHED TIN | 36| 16 +Brand#32 |STANDARD ANODIZED COPPER | 14| 16 +Brand#32 |STANDARD ANODIZED TIN | 9| 16 +Brand#32 |STANDARD BURNISHED COPPER| 45| 16 +Brand#32 |STANDARD BURNISHED COPPER| 49| 16 +Brand#32 |STANDARD POLISHED BRASS | 14| 16 +Brand#32 |STANDARD POLISHED STEEL | 14| 16 +Brand#33 |ECONOMY ANODIZED STEEL | 49| 16 +Brand#33 |ECONOMY PLATED BRASS | 36| 16 +Brand#33 |ECONOMY PLATED COPPER | 19| 16 +Brand#33 |ECONOMY POLISHED NICKEL | 19| 16 +Brand#33 |LARGE ANODIZED STEEL | 45| 16 +Brand#33 |LARGE ANODIZED TIN | 45| 16 +Brand#33 |LARGE BURNISHED COPPER | 45| 16 +Brand#33 |LARGE POLISHED STEEL | 3| 16 +Brand#33 |MEDIUM ANODIZED BRASS | 23| 16 +Brand#33 |MEDIUM ANODIZED NICKEL | 3| 16 +Brand#33 |MEDIUM ANODIZED TIN | 14| 16 +Brand#33 |MEDIUM BRUSHED COPPER | 49| 16 +Brand#33 |MEDIUM BURNISHED COPPER | 9| 16 +Brand#33 |PROMO BURNISHED BRASS | 9| 16 +Brand#33 |PROMO BURNISHED BRASS | 19| 16 +Brand#33 |PROMO PLATED STEEL | 49| 16 +Brand#33 |SMALL ANODIZED BRASS | 36| 16 +Brand#33 |SMALL BRUSHED BRASS | 3| 16 +Brand#33 |SMALL BRUSHED STEEL | 9| 16 +Brand#33 |SMALL POLISHED BRASS | 14| 16 +Brand#33 |SMALL POLISHED COPPER | 36| 16 +Brand#33 |SMALL POLISHED NICKEL | 19| 16 +Brand#33 |STANDARD ANODIZED BRASS | 9| 16 +Brand#33 |STANDARD ANODIZED TIN | 3| 16 +Brand#33 |STANDARD BURNISHED NICKEL| 49| 16 +Brand#33 |STANDARD PLATED NICKEL | 49| 16 +Brand#33 |STANDARD POLISHED BRASS | 9| 16 +Brand#33 |STANDARD POLISHED BRASS | 14| 16 +Brand#33 |STANDARD POLISHED COPPER | 49| 16 +Brand#33 |STANDARD POLISHED STEEL | 3| 16 +Brand#34 |ECONOMY BURNISHED BRASS | 14| 16 +Brand#34 |ECONOMY POLISHED STEEL | 36| 16 +Brand#34 |LARGE BRUSHED BRASS | 23| 16 +Brand#34 |LARGE PLATED BRASS | 36| 16 +Brand#34 |LARGE PLATED TIN | 3| 16 +Brand#34 |LARGE POLISHED COPPER | 14| 16 +Brand#34 |MEDIUM ANODIZED COPPER | 36| 16 +Brand#34 |MEDIUM BRUSHED STEEL | 23| 16 +Brand#34 |MEDIUM PLATED NICKEL | 23| 16 +Brand#34 |PROMO BRUSHED NICKEL | 45| 16 +Brand#34 |PROMO POLISHED TIN | 3| 16 +Brand#34 |SMALL ANODIZED NICKEL | 14| 16 +Brand#34 |SMALL BURNISHED TIN | 3| 16 +Brand#34 |SMALL POLISHED NICKEL | 36| 16 +Brand#34 |STANDARD ANODIZED STEEL | 9| 16 +Brand#34 |STANDARD BURNISHED NICKEL| 19| 16 +Brand#34 |STANDARD BURNISHED NICKEL| 23| 16 +Brand#34 |STANDARD POLISHED COPPER | 23| 16 +Brand#35 |ECONOMY ANODIZED COPPER | 36| 16 +Brand#35 |ECONOMY BURNISHED NICKEL | 19| 16 +Brand#35 |ECONOMY BURNISHED TIN | 9| 16 +Brand#35 |ECONOMY PLATED STEEL | 14| 16 +Brand#35 |LARGE ANODIZED BRASS | 9| 16 +Brand#35 |LARGE ANODIZED COPPER | 49| 16 +Brand#35 |LARGE ANODIZED NICKEL | 9| 16 +Brand#35 |LARGE BRUSHED TIN | 49| 16 +Brand#35 |LARGE BURNISHED COPPER | 23| 16 +Brand#35 |LARGE BURNISHED NICKEL | 9| 16 +Brand#35 |LARGE BURNISHED STEEL | 3| 16 +Brand#35 |LARGE PLATED COPPER | 19| 16 +Brand#35 |MEDIUM BRUSHED STEEL | 23| 16 +Brand#35 |MEDIUM PLATED NICKEL | 23| 16 +Brand#35 |PROMO BRUSHED NICKEL | 19| 16 +Brand#35 |SMALL ANODIZED BRASS | 45| 16 +Brand#35 |SMALL BRUSHED TIN | 49| 16 +Brand#41 |ECONOMY ANODIZED STEEL | 49| 16 +Brand#41 |ECONOMY PLATED STEEL | 3| 16 +Brand#41 |ECONOMY PLATED TIN | 3| 16 +Brand#41 |ECONOMY POLISHED STEEL | 19| 16 +Brand#41 |ECONOMY POLISHED STEEL | 45| 16 +Brand#41 |LARGE ANODIZED BRASS | 36| 16 +Brand#41 |LARGE BURNISHED BRASS | 23| 16 +Brand#41 |LARGE POLISHED BRASS | 36| 16 +Brand#41 |LARGE POLISHED NICKEL | 3| 16 +Brand#41 |MEDIUM BURNISHED TIN | 3| 16 +Brand#41 |MEDIUM PLATED STEEL | 3| 16 +Brand#41 |PROMO PLATED BRASS | 9| 16 +Brand#41 |PROMO PLATED STEEL | 36| 16 +Brand#41 |PROMO POLISHED STEEL | 36| 16 +Brand#41 |PROMO POLISHED TIN | 19| 16 +Brand#41 |SMALL ANODIZED COPPER | 23| 16 +Brand#41 |SMALL ANODIZED STEEL | 45| 16 +Brand#41 |SMALL BRUSHED NICKEL | 45| 16 +Brand#41 |SMALL BURNISHED NICKEL | 36| 16 +Brand#41 |SMALL POLISHED NICKEL | 9| 16 +Brand#41 |SMALL POLISHED STEEL | 45| 16 +Brand#41 |SMALL POLISHED TIN | 14| 16 +Brand#41 |STANDARD BRUSHED NICKEL | 45| 16 +Brand#42 |ECONOMY BRUSHED STEEL | 14| 16 +Brand#42 |ECONOMY BURNISHED STEEL | 9| 16 +Brand#42 |ECONOMY BURNISHED STEEL | 45| 16 +Brand#42 |LARGE ANODIZED TIN | 23| 16 +Brand#42 |LARGE BRUSHED STEEL | 14| 16 +Brand#42 |LARGE BURNISHED NICKEL | 19| 16 +Brand#42 |LARGE PLATED STEEL | 45| 16 +Brand#42 |LARGE POLISHED STEEL | 14| 16 +Brand#42 |MEDIUM ANODIZED STEEL | 14| 16 +Brand#42 |MEDIUM ANODIZED TIN | 19| 16 +Brand#42 |MEDIUM BRUSHED COPPER | 9| 16 +Brand#42 |MEDIUM BRUSHED STEEL | 14| 16 +Brand#42 |MEDIUM BURNISHED COPPER | 49| 16 +Brand#42 |MEDIUM BURNISHED NICKEL | 23| 16 +Brand#42 |MEDIUM BURNISHED TIN | 49| 16 +Brand#42 |PROMO ANODIZED NICKEL | 49| 16 +Brand#42 |PROMO ANODIZED STEEL | 49| 16 +Brand#42 |PROMO BURNISHED TIN | 49| 16 +Brand#42 |SMALL ANODIZED BRASS | 23| 16 +Brand#42 |SMALL ANODIZED NICKEL | 19| 16 +Brand#42 |SMALL ANODIZED TIN | 49| 16 +Brand#42 |SMALL PLATED COPPER | 23| 16 +Brand#42 |STANDARD ANODIZED BRASS | 9| 16 +Brand#42 |STANDARD ANODIZED NICKEL | 9| 16 +Brand#42 |STANDARD BRUSHED STEEL | 49| 16 +Brand#42 |STANDARD BRUSHED TIN | 45| 16 +Brand#42 |STANDARD PLATED TIN | 23| 16 +Brand#43 |ECONOMY BRUSHED STEEL | 23| 16 +Brand#43 |ECONOMY PLATED TIN | 49| 16 +Brand#43 |ECONOMY POLISHED TIN | 14| 16 +Brand#43 |LARGE BRUSHED COPPER | 9| 16 +Brand#43 |LARGE BURNISHED STEEL | 9| 16 +Brand#43 |LARGE PLATED BRASS | 14| 16 +Brand#43 |LARGE PLATED BRASS | 19| 16 +Brand#43 |LARGE PLATED NICKEL | 45| 16 +Brand#43 |MEDIUM ANODIZED COPPER | 49| 16 +Brand#43 |PROMO BRUSHED BRASS | 36| 16 +Brand#43 |PROMO BRUSHED STEEL | 49| 16 +Brand#43 |PROMO PLATED BRASS | 45| 16 +Brand#43 |SMALL BURNISHED COPPER | 19| 16 +Brand#43 |SMALL BURNISHED TIN | 23| 16 +Brand#43 |SMALL BURNISHED TIN | 45| 16 +Brand#43 |SMALL PLATED COPPER | 23| 16 +Brand#43 |SMALL POLISHED STEEL | 19| 16 +Brand#43 |STANDARD ANODIZED TIN | 45| 16 +Brand#43 |STANDARD PLATED BRASS | 3| 16 +Brand#44 |ECONOMY ANODIZED BRASS | 45| 16 +Brand#44 |ECONOMY BRUSHED TIN | 45| 16 +Brand#44 |ECONOMY PLATED COPPER | 23| 16 +Brand#44 |ECONOMY PLATED STEEL | 3| 16 +Brand#44 |LARGE BRUSHED BRASS | 9| 16 +Brand#44 |LARGE PLATED BRASS | 49| 16 +Brand#44 |LARGE PLATED STEEL | 14| 16 +Brand#44 |LARGE POLISHED TIN | 19| 16 +Brand#44 |MEDIUM ANODIZED NICKEL | 9| 16 +Brand#44 |MEDIUM ANODIZED TIN | 49| 16 +Brand#44 |MEDIUM BRUSHED NICKEL | 36| 16 +Brand#44 |MEDIUM BURNISHED NICKEL | 23| 16 +Brand#44 |MEDIUM BURNISHED NICKEL | 45| 16 +Brand#44 |MEDIUM PLATED BRASS | 9| 16 +Brand#44 |MEDIUM PLATED STEEL | 49| 16 +Brand#44 |PROMO BURNISHED TIN | 3| 16 +Brand#44 |SMALL ANODIZED COPPER | 9| 16 +Brand#44 |SMALL ANODIZED STEEL | 14| 16 +Brand#44 |SMALL BRUSHED STEEL | 19| 16 +Brand#44 |SMALL BRUSHED TIN | 14| 16 +Brand#44 |SMALL BURNISHED STEEL | 23| 16 +Brand#44 |SMALL PLATED STEEL | 19| 16 +Brand#44 |STANDARD ANODIZED NICKEL | 45| 16 +Brand#44 |STANDARD ANODIZED STEEL | 19| 16 +Brand#44 |STANDARD BRUSHED COPPER | 36| 16 +Brand#44 |STANDARD PLATED BRASS | 49| 16 +Brand#44 |STANDARD PLATED NICKEL | 45| 16 +Brand#44 |STANDARD PLATED STEEL | 36| 16 +Brand#51 |ECONOMY ANODIZED STEEL | 9| 16 +Brand#51 |ECONOMY BRUSHED STEEL | 23| 16 +Brand#51 |ECONOMY PLATED STEEL | 9| 16 +Brand#51 |LARGE BURNISHED COPPER | 14| 16 +Brand#51 |LARGE PLATED BRASS | 3| 16 +Brand#51 |LARGE PLATED BRASS | 36| 16 +Brand#51 |LARGE PLATED BRASS | 49| 16 +Brand#51 |LARGE POLISHED BRASS | 3| 16 +Brand#51 |LARGE POLISHED NICKEL | 19| 16 +Brand#51 |MEDIUM ANODIZED BRASS | 9| 16 +Brand#51 |MEDIUM ANODIZED TIN | 9| 16 +Brand#51 |MEDIUM PLATED BRASS | 14| 16 +Brand#51 |PROMO BURNISHED NICKEL | 14| 16 +Brand#51 |PROMO BURNISHED TIN | 9| 16 +Brand#51 |PROMO PLATED NICKEL | 14| 16 +Brand#51 |SMALL ANODIZED COPPER | 45| 16 +Brand#51 |SMALL BURNISHED COPPER | 36| 16 +Brand#51 |SMALL BURNISHED TIN | 9| 16 +Brand#51 |STANDARD BURNISHED STEEL | 45| 16 +Brand#51 |STANDARD BURNISHED TIN | 9| 16 +Brand#51 |STANDARD PLATED BRASS | 36| 16 +Brand#51 |STANDARD PLATED STEEL | 45| 16 +Brand#52 |ECONOMY BRUSHED NICKEL | 3| 16 +Brand#52 |ECONOMY BURNISHED COPPER | 9| 16 +Brand#52 |ECONOMY BURNISHED STEEL | 14| 16 +Brand#52 |LARGE ANODIZED BRASS | 23| 16 +Brand#52 |LARGE BRUSHED BRASS | 14| 16 +Brand#52 |LARGE BURNISHED TIN | 23| 16 +Brand#52 |MEDIUM ANODIZED COPPER | 23| 16 +Brand#52 |PROMO BRUSHED STEEL | 36| 16 +Brand#52 |PROMO PLATED COPPER | 14| 16 +Brand#52 |SMALL PLATED COPPER | 3| 16 +Brand#52 |STANDARD BRUSHED COPPER | 14| 16 +Brand#52 |STANDARD BURNISHED BRASS | 14| 16 +Brand#52 |STANDARD BURNISHED BRASS | 19| 16 +Brand#52 |STANDARD POLISHED NICKEL | 36| 16 +Brand#53 |ECONOMY ANODIZED BRASS | 19| 16 +Brand#53 |LARGE BRUSHED COPPER | 14| 16 +Brand#53 |LARGE BRUSHED NICKEL | 45| 16 +Brand#53 |LARGE BURNISHED COPPER | 36| 16 +Brand#53 |LARGE PLATED COPPER | 36| 16 +Brand#53 |LARGE PLATED STEEL | 36| 16 +Brand#53 |LARGE PLATED TIN | 14| 16 +Brand#53 |LARGE POLISHED BRASS | 14| 16 +Brand#53 |LARGE POLISHED STEEL | 49| 16 +Brand#53 |MEDIUM BRUSHED NICKEL | 49| 16 +Brand#53 |MEDIUM BURNISHED BRASS | 3| 16 +Brand#53 |MEDIUM BURNISHED COPPER | 49| 16 +Brand#53 |PROMO ANODIZED COPPER | 36| 16 +Brand#53 |PROMO ANODIZED NICKEL | 3| 16 +Brand#53 |PROMO BURNISHED STEEL | 9| 16 +Brand#53 |PROMO PLATED COPPER | 3| 16 +Brand#53 |SMALL ANODIZED TIN | 9| 16 +Brand#53 |STANDARD PLATED BRASS | 23| 16 +Brand#54 |ECONOMY BRUSHED BRASS | 45| 16 +Brand#54 |ECONOMY BRUSHED COPPER | 14| 16 +Brand#54 |LARGE ANODIZED NICKEL | 49| 16 +Brand#54 |LARGE BURNISHED BRASS | 49| 16 +Brand#54 |LARGE BURNISHED COPPER | 19| 16 +Brand#54 |LARGE POLISHED NICKEL | 36| 16 +Brand#54 |PROMO BURNISHED TIN | 19| 16 +Brand#54 |PROMO PLATED BRASS | 49| 16 +Brand#54 |PROMO POLISHED TIN | 23| 16 +Brand#54 |SMALL ANODIZED COPPER | 14| 16 +Brand#54 |SMALL BRUSHED COPPER | 9| 16 +Brand#54 |SMALL PLATED NICKEL | 9| 16 +Brand#54 |STANDARD ANODIZED COPPER | 49| 16 +Brand#54 |STANDARD ANODIZED TIN | 14| 16 +Brand#54 |STANDARD BRUSHED COPPER | 45| 16 +Brand#54 |STANDARD PLATED COPPER | 23| 16 +Brand#54 |STANDARD PLATED COPPER | 45| 16 +Brand#54 |STANDARD POLISHED BRASS | 19| 16 +Brand#54 |STANDARD POLISHED STEEL | 14| 16 +Brand#55 |ECONOMY BRUSHED TIN | 36| 16 +Brand#55 |ECONOMY POLISHED TIN | 14| 16 +Brand#55 |LARGE PLATED BRASS | 9| 16 +Brand#55 |LARGE POLISHED STEEL | 9| 16 +Brand#55 |MEDIUM BURNISHED TIN | 36| 16 +Brand#55 |PROMO ANODIZED BRASS | 14| 16 +Brand#55 |PROMO ANODIZED COPPER | 14| 16 +Brand#55 |SMALL BURNISHED STEEL | 9| 16 +Brand#55 |STANDARD POLISHED COPPER | 19| 16 +Brand#23 |PROMO POLISHED COPPER | 36| 15 +Brand#33 |PROMO POLISHED STEEL | 9| 15 +Brand#34 |LARGE BURNISHED BRASS | 23| 15 +Brand#41 |PROMO ANODIZED BRASS | 49| 15 +Brand#11 |ECONOMY ANODIZED NICKEL | 14| 12 +Brand#11 |ECONOMY ANODIZED NICKEL | 23| 12 +Brand#11 |ECONOMY ANODIZED STEEL | 36| 12 +Brand#11 |ECONOMY ANODIZED TIN | 14| 12 +Brand#11 |ECONOMY BRUSHED COPPER | 14| 12 +Brand#11 |ECONOMY BURNISHED BRASS | 36| 12 +Brand#11 |ECONOMY BURNISHED COPPER | 3| 12 +Brand#11 |ECONOMY BURNISHED COPPER | 49| 12 +Brand#11 |ECONOMY PLATED COPPER | 3| 12 +Brand#11 |ECONOMY PLATED COPPER | 19| 12 +Brand#11 |ECONOMY PLATED NICKEL | 14| 12 +Brand#11 |ECONOMY POLISHED COPPER | 14| 12 +Brand#11 |ECONOMY POLISHED TIN | 23| 12 +Brand#11 |LARGE ANODIZED NICKEL | 9| 12 +Brand#11 |LARGE ANODIZED STEEL | 23| 12 +Brand#11 |LARGE ANODIZED TIN | 36| 12 +Brand#11 |LARGE BRUSHED BRASS | 19| 12 +Brand#11 |LARGE BRUSHED STEEL | 19| 12 +Brand#11 |LARGE BRUSHED STEEL | 36| 12 +Brand#11 |LARGE BURNISHED BRASS | 3| 12 +Brand#11 |LARGE PLATED TIN | 19| 12 +Brand#11 |MEDIUM ANODIZED BRASS | 45| 12 +Brand#11 |MEDIUM BRUSHED BRASS | 3| 12 +Brand#11 |MEDIUM BRUSHED BRASS | 23| 12 +Brand#11 |MEDIUM BRUSHED BRASS | 45| 12 +Brand#11 |MEDIUM BRUSHED NICKEL | 36| 12 +Brand#11 |MEDIUM BRUSHED STEEL | 19| 12 +Brand#11 |MEDIUM BRUSHED STEEL | 23| 12 +Brand#11 |MEDIUM BURNISHED NICKEL | 23| 12 +Brand#11 |MEDIUM BURNISHED STEEL | 9| 12 +Brand#11 |MEDIUM PLATED BRASS | 14| 12 +Brand#11 |MEDIUM PLATED COPPER | 3| 12 +Brand#11 |MEDIUM PLATED STEEL | 14| 12 +Brand#11 |PROMO ANODIZED BRASS | 45| 12 +Brand#11 |PROMO BRUSHED NICKEL | 9| 12 +Brand#11 |PROMO BRUSHED STEEL | 45| 12 +Brand#11 |PROMO BURNISHED BRASS | 23| 12 +Brand#11 |PROMO BURNISHED COPPER | 23| 12 +Brand#11 |PROMO BURNISHED NICKEL | 36| 12 +Brand#11 |PROMO PLATED BRASS | 14| 12 +Brand#11 |PROMO PLATED COPPER | 14| 12 +Brand#11 |PROMO PLATED STEEL | 49| 12 +Brand#11 |PROMO PLATED TIN | 3| 12 +Brand#11 |PROMO POLISHED COPPER | 14| 12 +Brand#11 |PROMO POLISHED NICKEL | 3| 12 +Brand#11 |PROMO POLISHED STEEL | 3| 12 +Brand#11 |PROMO POLISHED STEEL | 23| 12 +Brand#11 |PROMO POLISHED TIN | 14| 12 +Brand#11 |SMALL ANODIZED BRASS | 49| 12 +Brand#11 |SMALL ANODIZED COPPER | 49| 12 +Brand#11 |SMALL ANODIZED NICKEL | 9| 12 +Brand#11 |SMALL ANODIZED STEEL | 45| 12 +Brand#11 |SMALL BURNISHED BRASS | 19| 12 +Brand#11 |SMALL BURNISHED BRASS | 49| 12 +Brand#11 |SMALL BURNISHED NICKEL | 9| 12 +Brand#11 |SMALL BURNISHED NICKEL | 49| 12 +Brand#11 |SMALL PLATED COPPER | 45| 12 +Brand#11 |SMALL PLATED NICKEL | 45| 12 +Brand#11 |SMALL PLATED TIN | 36| 12 +Brand#11 |SMALL POLISHED BRASS | 14| 12 +Brand#11 |SMALL POLISHED BRASS | 19| 12 +Brand#11 |SMALL POLISHED STEEL | 3| 12 +Brand#11 |SMALL POLISHED STEEL | 36| 12 +Brand#11 |STANDARD ANODIZED COPPER | 49| 12 +Brand#11 |STANDARD BRUSHED COPPER | 23| 12 +Brand#11 |STANDARD BRUSHED NICKEL | 9| 12 +Brand#11 |STANDARD BURNISHED BRASS | 19| 12 +Brand#11 |STANDARD BURNISHED COPPER| 9| 12 +Brand#11 |STANDARD PLATED STEEL | 19| 12 +Brand#11 |STANDARD PLATED TIN | 45| 12 +Brand#11 |STANDARD POLISHED STEEL | 9| 12 +Brand#11 |STANDARD POLISHED STEEL | 19| 12 +Brand#11 |STANDARD POLISHED TIN | 14| 12 +Brand#12 |ECONOMY ANODIZED BRASS | 49| 12 +Brand#12 |ECONOMY ANODIZED COPPER | 14| 12 +Brand#12 |ECONOMY ANODIZED NICKEL | 19| 12 +Brand#12 |ECONOMY ANODIZED NICKEL | 45| 12 +Brand#12 |ECONOMY BRUSHED BRASS | 23| 12 +Brand#12 |ECONOMY BRUSHED STEEL | 9| 12 +Brand#12 |ECONOMY BRUSHED TIN | 3| 12 +Brand#12 |ECONOMY BRUSHED TIN | 19| 12 +Brand#12 |ECONOMY BURNISHED BRASS | 19| 12 +Brand#12 |ECONOMY BURNISHED COPPER | 49| 12 +Brand#12 |ECONOMY BURNISHED STEEL | 9| 12 +Brand#12 |ECONOMY BURNISHED STEEL | 36| 12 +Brand#12 |ECONOMY PLATED BRASS | 3| 12 +Brand#12 |ECONOMY PLATED NICKEL | 9| 12 +Brand#12 |ECONOMY PLATED TIN | 45| 12 +Brand#12 |ECONOMY POLISHED NICKEL | 45| 12 +Brand#12 |ECONOMY POLISHED STEEL | 9| 12 +Brand#12 |ECONOMY POLISHED STEEL | 19| 12 +Brand#12 |ECONOMY POLISHED TIN | 14| 12 +Brand#12 |LARGE ANODIZED COPPER | 19| 12 +Brand#12 |LARGE ANODIZED NICKEL | 49| 12 +Brand#12 |LARGE ANODIZED TIN | 49| 12 +Brand#12 |LARGE BRUSHED BRASS | 9| 12 +Brand#12 |LARGE BRUSHED BRASS | 23| 12 +Brand#12 |LARGE BRUSHED BRASS | 49| 12 +Brand#12 |LARGE BURNISHED NICKEL | 45| 12 +Brand#12 |LARGE PLATED BRASS | 3| 12 +Brand#12 |LARGE POLISHED BRASS | 23| 12 +Brand#12 |LARGE POLISHED COPPER | 19| 12 +Brand#12 |MEDIUM ANODIZED BRASS | 3| 12 +Brand#12 |MEDIUM ANODIZED COPPER | 9| 12 +Brand#12 |MEDIUM BRUSHED BRASS | 14| 12 +Brand#12 |MEDIUM BRUSHED BRASS | 23| 12 +Brand#12 |MEDIUM BRUSHED BRASS | 45| 12 +Brand#12 |MEDIUM BRUSHED COPPER | 23| 12 +Brand#12 |MEDIUM BRUSHED NICKEL | 14| 12 +Brand#12 |MEDIUM BRUSHED TIN | 14| 12 +Brand#12 |MEDIUM BRUSHED TIN | 36| 12 +Brand#12 |MEDIUM BURNISHED BRASS | 19| 12 +Brand#12 |MEDIUM PLATED BRASS | 23| 12 +Brand#12 |MEDIUM PLATED NICKEL | 45| 12 +Brand#12 |MEDIUM PLATED STEEL | 19| 12 +Brand#12 |MEDIUM PLATED TIN | 23| 12 +Brand#12 |PROMO BRUSHED COPPER | 36| 12 +Brand#12 |PROMO BRUSHED STEEL | 19| 12 +Brand#12 |PROMO BRUSHED STEEL | 45| 12 +Brand#12 |PROMO PLATED COPPER | 14| 12 +Brand#12 |PROMO PLATED STEEL | 19| 12 +Brand#12 |PROMO POLISHED COPPER | 45| 12 +Brand#12 |PROMO POLISHED STEEL | 45| 12 +Brand#12 |PROMO POLISHED TIN | 3| 12 +Brand#12 |PROMO POLISHED TIN | 14| 12 +Brand#12 |SMALL ANODIZED BRASS | 9| 12 +Brand#12 |SMALL ANODIZED STEEL | 14| 12 +Brand#12 |SMALL BRUSHED BRASS | 36| 12 +Brand#12 |SMALL BRUSHED NICKEL | 3| 12 +Brand#12 |SMALL BRUSHED NICKEL | 9| 12 +Brand#12 |SMALL BURNISHED BRASS | 14| 12 +Brand#12 |SMALL BURNISHED BRASS | 23| 12 +Brand#12 |SMALL BURNISHED TIN | 14| 12 +Brand#12 |SMALL POLISHED NICKEL | 23| 12 +Brand#12 |STANDARD ANODIZED COPPER | 45| 12 +Brand#12 |STANDARD BRUSHED COPPER | 3| 12 +Brand#12 |STANDARD BRUSHED NICKEL | 23| 12 +Brand#12 |STANDARD BRUSHED STEEL | 3| 12 +Brand#12 |STANDARD BRUSHED TIN | 45| 12 +Brand#12 |STANDARD BURNISHED BRASS | 14| 12 +Brand#12 |STANDARD BURNISHED COPPER| 3| 12 +Brand#12 |STANDARD BURNISHED COPPER| 45| 12 +Brand#12 |STANDARD BURNISHED STEEL | 9| 12 +Brand#12 |STANDARD BURNISHED TIN | 3| 12 +Brand#12 |STANDARD PLATED COPPER | 49| 12 +Brand#12 |STANDARD PLATED NICKEL | 19| 12 +Brand#12 |STANDARD PLATED NICKEL | 45| 12 +Brand#12 |STANDARD PLATED STEEL | 19| 12 +Brand#12 |STANDARD PLATED STEEL | 36| 12 +Brand#12 |STANDARD POLISHED BRASS | 45| 12 +Brand#13 |ECONOMY ANODIZED BRASS | 36| 12 +Brand#13 |ECONOMY ANODIZED BRASS | 45| 12 +Brand#13 |ECONOMY ANODIZED COPPER | 14| 12 +Brand#13 |ECONOMY ANODIZED NICKEL | 14| 12 +Brand#13 |ECONOMY ANODIZED NICKEL | 19| 12 +Brand#13 |ECONOMY ANODIZED TIN | 23| 12 +Brand#13 |ECONOMY BRUSHED BRASS | 45| 12 +Brand#13 |ECONOMY BRUSHED NICKEL | 45| 12 +Brand#13 |ECONOMY BURNISHED BRASS | 3| 12 +Brand#13 |ECONOMY BURNISHED COPPER | 19| 12 +Brand#13 |ECONOMY BURNISHED NICKEL | 36| 12 +Brand#13 |ECONOMY PLATED COPPER | 49| 12 +Brand#13 |ECONOMY PLATED NICKEL | 3| 12 +Brand#13 |ECONOMY PLATED NICKEL | 19| 12 +Brand#13 |ECONOMY PLATED STEEL | 23| 12 +Brand#13 |ECONOMY POLISHED STEEL | 19| 12 +Brand#13 |ECONOMY POLISHED STEEL | 36| 12 +Brand#13 |LARGE ANODIZED BRASS | 49| 12 +Brand#13 |LARGE ANODIZED TIN | 9| 12 +Brand#13 |LARGE ANODIZED TIN | 19| 12 +Brand#13 |LARGE BRUSHED BRASS | 3| 12 +Brand#13 |LARGE BRUSHED COPPER | 9| 12 +Brand#13 |LARGE BRUSHED NICKEL | 3| 12 +Brand#13 |LARGE BURNISHED COPPER | 45| 12 +Brand#13 |LARGE PLATED COPPER | 23| 12 +Brand#13 |LARGE PLATED COPPER | 36| 12 +Brand#13 |LARGE PLATED NICKEL | 23| 12 +Brand#13 |LARGE PLATED NICKEL | 49| 12 +Brand#13 |LARGE PLATED STEEL | 14| 12 +Brand#13 |LARGE PLATED TIN | 9| 12 +Brand#13 |LARGE POLISHED BRASS | 49| 12 +Brand#13 |LARGE POLISHED STEEL | 9| 12 +Brand#13 |MEDIUM ANODIZED NICKEL | 3| 12 +Brand#13 |MEDIUM ANODIZED NICKEL | 36| 12 +Brand#13 |MEDIUM ANODIZED NICKEL | 45| 12 +Brand#13 |MEDIUM ANODIZED STEEL | 9| 12 +Brand#13 |MEDIUM ANODIZED STEEL | 14| 12 +Brand#13 |MEDIUM BRUSHED BRASS | 9| 12 +Brand#13 |MEDIUM BRUSHED COPPER | 3| 12 +Brand#13 |MEDIUM BRUSHED COPPER | 14| 12 +Brand#13 |MEDIUM BRUSHED STEEL | 19| 12 +Brand#13 |MEDIUM BRUSHED TIN | 19| 12 +Brand#13 |MEDIUM BURNISHED NICKEL | 36| 12 +Brand#13 |MEDIUM PLATED BRASS | 9| 12 +Brand#13 |PROMO ANODIZED COPPER | 45| 12 +Brand#13 |PROMO BRUSHED NICKEL | 23| 12 +Brand#13 |PROMO BRUSHED STEEL | 45| 12 +Brand#13 |PROMO BRUSHED TIN | 3| 12 +Brand#13 |PROMO BURNISHED BRASS | 19| 12 +Brand#13 |PROMO BURNISHED COPPER | 19| 12 +Brand#13 |PROMO BURNISHED NICKEL | 3| 12 +Brand#13 |PROMO BURNISHED NICKEL | 49| 12 +Brand#13 |PROMO PLATED COPPER | 3| 12 +Brand#13 |PROMO PLATED NICKEL | 3| 12 +Brand#13 |PROMO PLATED STEEL | 45| 12 +Brand#13 |PROMO POLISHED NICKEL | 3| 12 +Brand#13 |PROMO POLISHED STEEL | 14| 12 +Brand#13 |SMALL ANODIZED BRASS | 49| 12 +Brand#13 |SMALL ANODIZED COPPER | 36| 12 +Brand#13 |SMALL ANODIZED TIN | 9| 12 +Brand#13 |SMALL ANODIZED TIN | 23| 12 +Brand#13 |SMALL BRUSHED COPPER | 14| 12 +Brand#13 |SMALL BRUSHED COPPER | 45| 12 +Brand#13 |SMALL BURNISHED NICKEL | 3| 12 +Brand#13 |SMALL PLATED BRASS | 45| 12 +Brand#13 |SMALL PLATED NICKEL | 45| 12 +Brand#13 |SMALL PLATED TIN | 14| 12 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BRASS | 23| 12 +Brand#14 |SMALL PLATED COPPER | 9| 12 +Brand#14 |SMALL PLATED STEEL | 23| 12 +Brand#14 |SMALL POLISHED BRASS | 3| 12 +Brand#14 |SMALL POLISHED BRASS | 9| 12 +Brand#14 |SMALL POLISHED COPPER | 36| 12 +Brand#14 |SMALL POLISHED NICKEL | 49| 12 +Brand#14 |SMALL POLISHED STEEL | 14| 12 +Brand#14 |SMALL POLISHED TIN | 49| 12 +Brand#14 |STANDARD ANODIZED STEEL | 49| 12 +Brand#14 |STANDARD BRUSHED BRASS | 3| 12 +Brand#14 |STANDARD BRUSHED STEEL | 49| 12 +Brand#14 |STANDARD BURNISHED BRASS | 23| 12 +Brand#14 |STANDARD PLATED NICKEL | 49| 12 +Brand#14 |STANDARD POLISHED COPPER | 36| 12 +Brand#14 |STANDARD POLISHED COPPER | 45| 12 +Brand#15 |ECONOMY ANODIZED TIN | 19| 12 +Brand#15 |ECONOMY BRUSHED NICKEL | 14| 12 +Brand#15 |ECONOMY BURNISHED STEEL | 19| 12 +Brand#15 |ECONOMY PLATED NICKEL | 9| 12 +Brand#15 |ECONOMY PLATED STEEL | 3| 12 +Brand#15 |ECONOMY PLATED STEEL | 19| 12 +Brand#15 |ECONOMY PLATED TIN | 9| 12 +Brand#15 |ECONOMY POLISHED COPPER | 36| 12 +Brand#15 |ECONOMY POLISHED NICKEL | 45| 12 +Brand#15 |LARGE ANODIZED BRASS | 19| 12 +Brand#15 |LARGE ANODIZED STEEL | 14| 12 +Brand#15 |LARGE ANODIZED TIN | 23| 12 +Brand#15 |LARGE BRUSHED BRASS | 19| 12 +Brand#15 |LARGE BRUSHED BRASS | 49| 12 +Brand#15 |LARGE BURNISHED BRASS | 3| 12 +Brand#15 |LARGE BURNISHED BRASS | 23| 12 +Brand#15 |LARGE BURNISHED COPPER | 9| 12 +Brand#15 |LARGE BURNISHED COPPER | 49| 12 +Brand#15 |LARGE BURNISHED STEEL | 9| 12 +Brand#15 |LARGE PLATED BRASS | 9| 12 +Brand#15 |MEDIUM BRUSHED BRASS | 14| 12 +Brand#15 |MEDIUM BRUSHED NICKEL | 14| 12 +Brand#15 |MEDIUM BRUSHED NICKEL | 19| 12 +Brand#15 |MEDIUM BRUSHED STEEL | 36| 12 +Brand#15 |MEDIUM BRUSHED TIN | 14| 12 +Brand#15 |MEDIUM BURNISHED STEEL | 3| 12 +Brand#15 |MEDIUM PLATED TIN | 9| 12 +Brand#15 |MEDIUM PLATED TIN | 45| 12 +Brand#15 |PROMO BRUSHED BRASS | 36| 12 +Brand#15 |PROMO BRUSHED STEEL | 9| 12 +Brand#15 |PROMO BURNISHED NICKEL | 9| 12 +Brand#15 |PROMO PLATED COPPER | 36| 12 +Brand#15 |PROMO POLISHED BRASS | 14| 12 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PLATED TIN | 19| 12 +Brand#21 |ECONOMY PLATED TIN | 23| 12 +Brand#21 |ECONOMY POLISHED BRASS | 9| 12 +Brand#21 |ECONOMY POLISHED STEEL | 14| 12 +Brand#21 |LARGE ANODIZED COPPER | 3| 12 +Brand#21 |LARGE ANODIZED TIN | 3| 12 +Brand#21 |LARGE ANODIZED TIN | 14| 12 +Brand#21 |LARGE ANODIZED TIN | 45| 12 +Brand#21 |LARGE BRUSHED COPPER | 23| 12 +Brand#21 |LARGE BRUSHED NICKEL | 36| 12 +Brand#21 |LARGE BRUSHED STEEL | 23| 12 +Brand#21 |LARGE BRUSHED TIN | 45| 12 +Brand#21 |LARGE BRUSHED TIN | 49| 12 +Brand#21 |LARGE BURNISHED BRASS | 14| 12 +Brand#21 |LARGE BURNISHED NICKEL | 14| 12 +Brand#21 |LARGE BURNISHED STEEL | 19| 12 +Brand#21 |LARGE PLATED BRASS | 14| 12 +Brand#21 |LARGE PLATED COPPER | 19| 12 +Brand#21 |LARGE PLATED COPPER | 49| 12 +Brand#21 |LARGE POLISHED COPPER | 14| 12 +Brand#21 |LARGE POLISHED STEEL | 45| 12 +Brand#21 |MEDIUM ANODIZED NICKEL | 3| 12 +Brand#21 |MEDIUM ANODIZED STEEL | 14| 12 +Brand#21 |MEDIUM BRUSHED BRASS | 23| 12 +Brand#21 |MEDIUM BURNISHED COPPER | 49| 12 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3| 12 +Brand#21 |PROMO POLISHED STEEL | 9| 12 +Brand#21 |PROMO POLISHED TIN | 9| 12 +Brand#21 |PROMO POLISHED TIN | 14| 12 +Brand#21 |PROMO POLISHED TIN | 19| 12 +Brand#21 |SMALL BRUSHED NICKEL | 9| 12 +Brand#21 |SMALL BRUSHED NICKEL | 45| 12 +Brand#21 |SMALL BRUSHED STEEL | 3| 12 +Brand#21 |SMALL BRUSHED STEEL | 9| 12 +Brand#21 |SMALL BRUSHED TIN | 14| 12 +Brand#21 |SMALL PLATED BRASS | 36| 12 +Brand#21 |SMALL PLATED COPPER | 14| 12 +Brand#21 |SMALL PLATED COPPER | 23| 12 +Brand#21 |SMALL POLISHED NICKEL | 9| 12 +Brand#21 |SMALL POLISHED STEEL | 3| 12 +Brand#21 |STANDARD ANODIZED NICKEL | 3| 12 +Brand#21 |STANDARD ANODIZED NICKEL | 19| 12 +Brand#21 |STANDARD BRUSHED BRASS | 9| 12 +Brand#21 |STANDARD BRUSHED NICKEL | 23| 12 +Brand#21 |STANDARD BRUSHED NICKEL | 45| 12 +Brand#21 |STANDARD BURNISHED BRASS | 49| 12 +Brand#21 |STANDARD PLATED COPPER | 45| 12 +Brand#21 |STANDARD PLATED NICKEL | 49| 12 +Brand#21 |STANDARD PLATED STEEL | 36| 12 +Brand#21 |STANDARD PLATED TIN | 9| 12 +Brand#21 |STANDARD POLISHED COPPER | 49| 12 +Brand#22 |ECONOMY ANODIZED COPPER | 36| 12 +Brand#22 |ECONOMY ANODIZED COPPER | 45| 12 +Brand#22 |ECONOMY ANODIZED NICKEL | 45| 12 +Brand#22 |ECONOMY ANODIZED STEEL | 45| 12 +Brand#22 |ECONOMY ANODIZED TIN | 49| 12 +Brand#22 |ECONOMY BRUSHED STEEL | 45| 12 +Brand#22 |ECONOMY BRUSHED TIN | 49| 12 +Brand#22 |ECONOMY BURNISHED BRASS | 19| 12 +Brand#22 |ECONOMY BURNISHED BRASS | 23| 12 +Brand#22 |ECONOMY BURNISHED BRASS | 45| 12 +Brand#22 |ECONOMY BURNISHED COPPER | 3| 12 +Brand#22 |ECONOMY BURNISHED COPPER | 9| 12 +Brand#22 |ECONOMY BURNISHED COPPER | 49| 12 +Brand#22 |ECONOMY BURNISHED NICKEL | 14| 12 +Brand#22 |ECONOMY BURNISHED NICKEL | 23| 12 +Brand#22 |ECONOMY BURNISHED STEEL | 23| 12 +Brand#22 |ECONOMY BURNISHED STEEL | 45| 12 +Brand#22 |ECONOMY BURNISHED STEEL | 49| 12 +Brand#22 |ECONOMY BURNISHED TIN | 9| 12 +Brand#22 |ECONOMY BURNISHED TIN | 19| 12 +Brand#22 |ECONOMY PLATED BRASS | 36| 12 +Brand#22 |ECONOMY PLATED COPPER | 3| 12 +Brand#22 |ECONOMY PLATED STEEL | 23| 12 +Brand#22 |ECONOMY POLISHED COPPER | 14| 12 +Brand#22 |ECONOMY POLISHED TIN | 49| 12 +Brand#22 |LARGE ANODIZED NICKEL | 14| 12 +Brand#22 |LARGE ANODIZED TIN | 14| 12 +Brand#22 |LARGE BRUSHED BRASS | 9| 12 +Brand#22 |LARGE BRUSHED BRASS | 49| 12 +Brand#22 |LARGE BRUSHED COPPER | 14| 12 +Brand#22 |LARGE BRUSHED STEEL | 19| 12 +Brand#22 |LARGE BRUSHED TIN | 23| 12 +Brand#22 |LARGE BURNISHED BRASS | 14| 12 +Brand#22 |LARGE BURNISHED TIN | 36| 12 +Brand#22 |LARGE PLATED STEEL | 9| 12 +Brand#22 |LARGE PLATED TIN | 49| 12 +Brand#22 |LARGE POLISHED COPPER | 23| 12 +Brand#22 |LARGE POLISHED NICKEL | 19| 12 +Brand#22 |LARGE POLISHED NICKEL | 23| 12 +Brand#22 |LARGE POLISHED STEEL | 3| 12 +Brand#22 |MEDIUM ANODIZED COPPER | 19| 12 +Brand#22 |MEDIUM ANODIZED NICKEL | 45| 12 +Brand#22 |MEDIUM BRUSHED NICKEL | 9| 12 +Brand#22 |MEDIUM BRUSHED STEEL | 3| 12 +Brand#22 |MEDIUM PLATED BRASS | 36| 12 +Brand#22 |MEDIUM PLATED NICKEL | 14| 12 +Brand#22 |PROMO ANODIZED COPPER | 45| 12 +Brand#22 |PROMO ANODIZED STEEL | 36| 12 +Brand#22 |PROMO BURNISHED BRASS | 3| 12 +Brand#22 |PROMO BURNISHED BRASS | 23| 12 +Brand#22 |PROMO BURNISHED STEEL | 3| 12 +Brand#22 |PROMO PLATED BRASS | 14| 12 +Brand#22 |PROMO POLISHED BRASS | 14| 12 +Brand#22 |PROMO POLISHED COPPER | 3| 12 +Brand#22 |PROMO POLISHED COPPER | 23| 12 +Brand#22 |PROMO POLISHED NICKEL | 19| 12 +Brand#22 |PROMO POLISHED NICKEL | 36| 12 +Brand#22 |PROMO POLISHED STEEL | 36| 12 +Brand#22 |SMALL ANODIZED COPPER | 9| 12 +Brand#22 |SMALL ANODIZED STEEL | 19| 12 +Brand#22 |SMALL ANODIZED TIN | 19| 12 +Brand#22 |SMALL ANODIZED TIN | 49| 12 +Brand#22 |SMALL BRUSHED COPPER | 36| 12 +Brand#22 |SMALL BRUSHED TIN | 45| 12 +Brand#22 |SMALL BURNISHED COPPER | 49| 12 +Brand#22 |SMALL BURNISHED NICKEL | 9| 12 +Brand#22 |SMALL PLATED BRASS | 9| 12 +Brand#22 |SMALL PLATED COPPER | 3| 12 +Brand#22 |SMALL POLISHED NICKEL | 9| 12 +Brand#22 |SMALL POLISHED NICKEL | 49| 12 +Brand#22 |SMALL POLISHED STEEL | 49| 12 +Brand#22 |STANDARD ANODIZED BRASS | 23| 12 +Brand#22 |STANDARD ANODIZED STEEL | 49| 12 +Brand#22 |STANDARD BRUSHED BRASS | 36| 12 +Brand#22 |STANDARD BRUSHED TIN | 19| 12 +Brand#22 |STANDARD BRUSHED TIN | 49| 12 +Brand#22 |STANDARD BURNISHED TIN | 14| 12 +Brand#22 |STANDARD PLATED BRASS | 45| 12 +Brand#22 |STANDARD PLATED COPPER | 36| 12 +Brand#22 |STANDARD PLATED NICKEL | 9| 12 +Brand#22 |STANDARD PLATED STEEL | 36| 12 +Brand#22 |STANDARD PLATED STEEL | 49| 12 +Brand#22 |STANDARD PLATED TIN | 3| 12 +Brand#22 |STANDARD PLATED TIN | 36| 12 +Brand#22 |STANDARD PLATED TIN | 49| 12 +Brand#22 |STANDARD POLISHED BRASS | 19| 12 +Brand#22 |STANDARD POLISHED COPPER | 9| 12 +Brand#22 |STANDARD POLISHED NICKEL | 19| 12 +Brand#22 |STANDARD POLISHED STEEL | 9| 12 +Brand#22 |STANDARD POLISHED TIN | 45| 12 +Brand#23 |ECONOMY ANODIZED BRASS | 36| 12 +Brand#23 |ECONOMY ANODIZED NICKEL | 9| 12 +Brand#23 |ECONOMY ANODIZED STEEL | 49| 12 +Brand#23 |ECONOMY BRUSHED COPPER | 3| 12 +Brand#23 |ECONOMY BRUSHED COPPER | 49| 12 +Brand#23 |ECONOMY BRUSHED NICKEL | 23| 12 +Brand#23 |ECONOMY BURNISHED STEEL | 49| 12 +Brand#23 |ECONOMY BURNISHED TIN | 3| 12 +Brand#23 |ECONOMY PLATED STEEL | 14| 12 +Brand#23 |ECONOMY PLATED TIN | 49| 12 +Brand#23 |ECONOMY POLISHED COPPER | 23| 12 +Brand#23 |ECONOMY POLISHED NICKEL | 36| 12 +Brand#23 |ECONOMY POLISHED TIN | 3| 12 +Brand#23 |LARGE ANODIZED TIN | 14| 12 +Brand#23 |LARGE BURNISHED STEEL | 23| 12 +Brand#23 |LARGE BURNISHED TIN | 19| 12 +Brand#23 |LARGE PLATED COPPER | 14| 12 +Brand#23 |LARGE PLATED STEEL | 9| 12 +Brand#23 |LARGE POLISHED BRASS | 19| 12 +Brand#23 |LARGE POLISHED COPPER | 45| 12 +Brand#23 |LARGE POLISHED COPPER | 49| 12 +Brand#23 |LARGE POLISHED TIN | 3| 12 +Brand#23 |MEDIUM BRUSHED BRASS | 9| 12 +Brand#23 |MEDIUM BRUSHED COPPER | 3| 12 +Brand#23 |MEDIUM BRUSHED NICKEL | 23| 12 +Brand#23 |MEDIUM BRUSHED NICKEL | 36| 12 +Brand#23 |MEDIUM BURNISHED COPPER | 9| 12 +Brand#23 |MEDIUM BURNISHED COPPER | 19| 12 +Brand#23 |MEDIUM PLATED COPPER | 19| 12 +Brand#23 |MEDIUM PLATED STEEL | 14| 12 +Brand#23 |PROMO ANODIZED BRASS | 9| 12 +Brand#23 |PROMO ANODIZED BRASS | 19| 12 +Brand#23 |PROMO ANODIZED NICKEL | 3| 12 +Brand#23 |PROMO ANODIZED STEEL | 36| 12 +Brand#23 |PROMO BRUSHED COPPER | 36| 12 +Brand#23 |PROMO BURNISHED BRASS | 9| 12 +Brand#23 |PROMO BURNISHED STEEL | 9| 12 +Brand#23 |PROMO BURNISHED TIN | 3| 12 +Brand#23 |PROMO BURNISHED TIN | 45| 12 +Brand#23 |PROMO PLATED BRASS | 19| 12 +Brand#23 |PROMO PLATED BRASS | 23| 12 +Brand#23 |PROMO PLATED BRASS | 49| 12 +Brand#23 |PROMO PLATED NICKEL | 3| 12 +Brand#23 |PROMO PLATED TIN | 14| 12 +Brand#23 |PROMO POLISHED TIN | 45| 12 +Brand#23 |SMALL ANODIZED STEEL | 3| 12 +Brand#23 |SMALL ANODIZED TIN | 45| 12 +Brand#23 |SMALL BRUSHED BRASS | 19| 12 +Brand#23 |SMALL BRUSHED STEEL | 3| 12 +Brand#23 |SMALL BURNISHED BRASS | 14| 12 +Brand#23 |SMALL BURNISHED COPPER | 36| 12 +Brand#23 |SMALL BURNISHED STEEL | 45| 12 +Brand#23 |SMALL PLATED BRASS | 49| 12 +Brand#23 |SMALL PLATED STEEL | 23| 12 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12 +Brand#24 |ECONOMY BURNISHED COPPER | 3| 12 +Brand#24 |ECONOMY BURNISHED COPPER | 19| 12 +Brand#24 |ECONOMY BURNISHED STEEL | 45| 12 +Brand#24 |ECONOMY PLATED COPPER | 49| 12 +Brand#24 |ECONOMY PLATED STEEL | 45| 12 +Brand#24 |ECONOMY POLISHED BRASS | 23| 12 +Brand#24 |ECONOMY POLISHED STEEL | 14| 12 +Brand#24 |ECONOMY POLISHED TIN | 14| 12 +Brand#24 |ECONOMY POLISHED TIN | 45| 12 +Brand#24 |ECONOMY POLISHED TIN | 49| 12 +Brand#24 |LARGE ANODIZED BRASS | 3| 12 +Brand#24 |LARGE ANODIZED BRASS | 45| 12 +Brand#24 |LARGE BRUSHED BRASS | 14| 12 +Brand#24 |LARGE BRUSHED BRASS | 45| 12 +Brand#24 |LARGE BRUSHED STEEL | 23| 12 +Brand#24 |LARGE BRUSHED STEEL | 45| 12 +Brand#24 |LARGE BURNISHED STEEL | 3| 12 +Brand#24 |LARGE BURNISHED TIN | 23| 12 +Brand#24 |LARGE PLATED COPPER | 23| 12 +Brand#24 |LARGE PLATED STEEL | 3| 12 +Brand#24 |LARGE POLISHED COPPER | 9| 12 +Brand#24 |LARGE POLISHED TIN | 14| 12 +Brand#24 |MEDIUM ANODIZED BRASS | 14| 12 +Brand#24 |MEDIUM BRUSHED NICKEL | 9| 12 +Brand#24 |MEDIUM BRUSHED NICKEL | 36| 12 +Brand#24 |MEDIUM BRUSHED STEEL | 23| 12 +Brand#24 |MEDIUM BRUSHED STEEL | 49| 12 +Brand#24 |MEDIUM BURNISHED BRASS | 36| 12 +Brand#24 |MEDIUM BURNISHED STEEL | 49| 12 +Brand#24 |MEDIUM BURNISHED TIN | 23| 12 +Brand#24 |MEDIUM PLATED BRASS | 3| 12 +Brand#24 |MEDIUM PLATED NICKEL | 36| 12 +Brand#24 |PROMO ANODIZED NICKEL | 19| 12 +Brand#24 |PROMO ANODIZED NICKEL | 45| 12 +Brand#24 |PROMO ANODIZED TIN | 14| 12 +Brand#24 |PROMO BRUSHED COPPER | 23| 12 +Brand#24 |PROMO BRUSHED COPPER | 49| 12 +Brand#24 |PROMO BRUSHED NICKEL | 3| 12 +Brand#24 |PROMO BURNISHED BRASS | 36| 12 +Brand#24 |PROMO BURNISHED STEEL | 14| 12 +Brand#24 |PROMO BURNISHED TIN | 14| 12 +Brand#24 |PROMO PLATED STEEL | 3| 12 +Brand#24 |PROMO POLISHED BRASS | 3| 12 +Brand#24 |PROMO POLISHED BRASS | 14| 12 +Brand#24 |PROMO POLISHED COPPER | 45| 12 +Brand#24 |SMALL ANODIZED COPPER | 3| 12 +Brand#24 |SMALL ANODIZED NICKEL | 23| 12 +Brand#24 |SMALL BRUSHED BRASS | 45| 12 +Brand#24 |SMALL BRUSHED COPPER | 9| 12 +Brand#24 |SMALL BRUSHED NICKEL | 49| 12 +Brand#24 |SMALL BURNISHED BRASS | 3| 12 +Brand#24 |SMALL BURNISHED BRASS | 14| 12 +Brand#24 |SMALL BURNISHED COPPER | 19| 12 +Brand#24 |SMALL BURNISHED NICKEL | 9| 12 +Brand#24 |SMALL PLATED BRASS | 3| 12 +Brand#24 |SMALL PLATED BRASS | 14| 12 +Brand#24 |SMALL PLATED NICKEL | 14| 12 +Brand#24 |SMALL POLISHED BRASS | 3| 12 +Brand#24 |SMALL POLISHED NICKEL | 19| 12 +Brand#24 |SMALL POLISHED TIN | 9| 12 +Brand#24 |STANDARD ANODIZED TIN | 49| 12 +Brand#24 |STANDARD BRUSHED BRASS | 14| 12 +Brand#24 |STANDARD BRUSHED BRASS | 23| 12 +Brand#24 |STANDARD BRUSHED NICKEL | 19| 12 +Brand#24 |STANDARD BRUSHED STEEL | 23| 12 +Brand#24 |STANDARD PLATED BRASS | 36| 12 +Brand#24 |STANDARD PLATED COPPER | 49| 12 +Brand#24 |STANDARD PLATED NICKEL | 36| 12 +Brand#24 |STANDARD POLISHED BRASS | 9| 12 +Brand#24 |STANDARD POLISHED COPPER | 9| 12 +Brand#25 |ECONOMY ANODIZED STEEL | 14| 12 +Brand#25 |ECONOMY ANODIZED STEEL | 45| 12 +Brand#25 |ECONOMY BRUSHED NICKEL | 9| 12 +Brand#25 |ECONOMY BRUSHED STEEL | 3| 12 +Brand#25 |ECONOMY BRUSHED TIN | 14| 12 +Brand#25 |ECONOMY PLATED COPPER | 3| 12 +Brand#25 |ECONOMY PLATED NICKEL | 19| 12 +Brand#25 |ECONOMY PLATED STEEL | 9| 12 +Brand#25 |ECONOMY POLISHED BRASS | 3| 12 +Brand#25 |ECONOMY POLISHED BRASS | 9| 12 +Brand#25 |ECONOMY POLISHED NICKEL | 3| 12 +Brand#25 |LARGE ANODIZED BRASS | 14| 12 +Brand#25 |LARGE ANODIZED BRASS | 23| 12 +Brand#25 |LARGE ANODIZED COPPER | 19| 12 +Brand#25 |LARGE ANODIZED COPPER | 36| 12 +Brand#25 |LARGE BRUSHED BRASS | 19| 12 +Brand#25 |LARGE BRUSHED NICKEL | 49| 12 +Brand#25 |LARGE BRUSHED STEEL | 36| 12 +Brand#25 |LARGE BRUSHED TIN | 3| 12 +Brand#25 |LARGE BRUSHED TIN | 9| 12 +Brand#25 |LARGE BURNISHED BRASS | 23| 12 +Brand#25 |LARGE BURNISHED STEEL | 36| 12 +Brand#25 |LARGE BURNISHED TIN | 14| 12 +Brand#25 |LARGE BURNISHED TIN | 36| 12 +Brand#25 |LARGE PLATED NICKEL | 45| 12 +Brand#25 |LARGE PLATED TIN | 23| 12 +Brand#25 |MEDIUM ANODIZED BRASS | 3| 12 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POLISHED NICKEL | 36| 12 +Brand#25 |PROMO POLISHED STEEL | 45| 12 +Brand#25 |SMALL ANODIZED COPPER | 45| 12 +Brand#25 |SMALL ANODIZED TIN | 14| 12 +Brand#25 |SMALL BRUSHED COPPER | 14| 12 +Brand#25 |SMALL BURNISHED BRASS | 3| 12 +Brand#25 |SMALL BURNISHED NICKEL | 45| 12 +Brand#25 |SMALL BURNISHED STEEL | 14| 12 +Brand#25 |SMALL PLATED BRASS | 19| 12 +Brand#25 |SMALL PLATED BRASS | 49| 12 +Brand#25 |SMALL PLATED COPPER | 23| 12 +Brand#25 |SMALL PLATED TIN | 3| 12 +Brand#25 |SMALL POLISHED COPPER | 9| 12 +Brand#25 |STANDARD BRUSHED TIN | 45| 12 +Brand#25 |STANDARD BURNISHED BRASS | 3| 12 +Brand#25 |STANDARD BURNISHED BRASS | 14| 12 +Brand#25 |STANDARD BURNISHED NICKEL| 36| 12 +Brand#25 |STANDARD PLATED COPPER | 9| 12 +Brand#25 |STANDARD PLATED COPPER | 23| 12 +Brand#25 |STANDARD PLATED NICKEL | 36| 12 +Brand#25 |STANDARD PLATED NICKEL | 49| 12 +Brand#25 |STANDARD PLATED TIN | 36| 12 +Brand#25 |STANDARD POLISHED COPPER | 23| 12 +Brand#25 |STANDARD POLISHED NICKEL | 45| 12 +Brand#25 |STANDARD POLISHED TIN | 3| 12 +Brand#31 |ECONOMY ANODIZED BRASS | 19| 12 +Brand#31 |ECONOMY ANODIZED TIN | 36| 12 +Brand#31 |ECONOMY BRUSHED NICKEL | 14| 12 +Brand#31 |ECONOMY BURNISHED COPPER | 14| 12 +Brand#31 |ECONOMY BURNISHED NICKEL | 19| 12 +Brand#31 |ECONOMY PLATED NICKEL | 9| 12 +Brand#31 |ECONOMY POLISHED COPPER | 3| 12 +Brand#31 |ECONOMY POLISHED TIN | 36| 12 +Brand#31 |LARGE ANODIZED COPPER | 3| 12 +Brand#31 |LARGE ANODIZED COPPER | 14| 12 +Brand#31 |LARGE ANODIZED STEEL | 36| 12 +Brand#31 |LARGE ANODIZED TIN | 3| 12 +Brand#31 |LARGE BRUSHED BRASS | 36| 12 +Brand#31 |LARGE BRUSHED NICKEL | 19| 12 +Brand#31 |LARGE BRUSHED STEEL | 36| 12 +Brand#31 |LARGE BRUSHED TIN | 14| 12 +Brand#31 |LARGE BURNISHED BRASS | 36| 12 +Brand#31 |LARGE BURNISHED NICKEL | 14| 12 +Brand#31 |LARGE PLATED STEEL | 23| 12 +Brand#31 |LARGE POLISHED BRASS | 9| 12 +Brand#31 |LARGE POLISHED STEEL | 45| 12 +Brand#31 |MEDIUM ANODIZED STEEL | 14| 12 +Brand#31 |MEDIUM ANODIZED TIN | 9| 12 +Brand#31 |MEDIUM ANODIZED TIN | 23| 12 +Brand#31 |MEDIUM BRUSHED BRASS | 23| 12 +Brand#31 |MEDIUM BRUSHED STEEL | 3| 12 +Brand#31 |MEDIUM BURNISHED BRASS | 14| 12 +Brand#31 |MEDIUM BURNISHED STEEL | 9| 12 +Brand#31 |PROMO ANODIZED COPPER | 14| 12 +Brand#31 |PROMO ANODIZED TIN | 36| 12 +Brand#31 |PROMO BRUSHED BRASS | 3| 12 +Brand#31 |PROMO BRUSHED COPPER | 23| 12 +Brand#31 |PROMO BRUSHED STEEL | 23| 12 +Brand#31 |PROMO BURNISHED BRASS | 49| 12 +Brand#31 |PROMO BURNISHED STEEL | 3| 12 +Brand#31 |PROMO PLATED BRASS | 36| 12 +Brand#31 |PROMO POLISHED NICKEL | 49| 12 +Brand#31 |SMALL ANODIZED COPPER | 3| 12 +Brand#31 |SMALL ANODIZED NICKEL | 9| 12 +Brand#31 |SMALL ANODIZED TIN | 3| 12 +Brand#31 |SMALL BRUSHED COPPER | 14| 12 +Brand#31 |SMALL BRUSHED COPPER | 19| 12 +Brand#31 |SMALL BRUSHED NICKEL | 3| 12 +Brand#31 |SMALL BRUSHED NICKEL | 23| 12 +Brand#31 |SMALL BRUSHED NICKEL | 36| 12 +Brand#31 |SMALL BURNISHED BRASS | 3| 12 +Brand#31 |SMALL BURNISHED NICKEL | 9| 12 +Brand#31 |SMALL BURNISHED TIN | 23| 12 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3| 12 +Brand#32 |LARGE ANODIZED STEEL | 14| 12 +Brand#32 |LARGE BRUSHED BRASS | 45| 12 +Brand#32 |LARGE BRUSHED NICKEL | 3| 12 +Brand#32 |LARGE BRUSHED STEEL | 45| 12 +Brand#32 |LARGE BRUSHED TIN | 19| 12 +Brand#32 |LARGE PLATED BRASS | 3| 12 +Brand#32 |LARGE PLATED BRASS | 9| 12 +Brand#32 |LARGE POLISHED COPPER | 19| 12 +Brand#32 |LARGE POLISHED NICKEL | 3| 12 +Brand#32 |MEDIUM ANODIZED COPPER | 45| 12 +Brand#32 |MEDIUM ANODIZED STEEL | 19| 12 +Brand#32 |MEDIUM ANODIZED STEEL | 49| 12 +Brand#32 |MEDIUM ANODIZED TIN | 45| 12 +Brand#32 |MEDIUM ANODIZED TIN | 49| 12 +Brand#32 |MEDIUM BURNISHED BRASS | 23| 12 +Brand#32 |MEDIUM BURNISHED NICKEL | 23| 12 +Brand#32 |MEDIUM PLATED BRASS | 49| 12 +Brand#32 |MEDIUM PLATED TIN | 3| 12 +Brand#32 |PROMO ANODIZED NICKEL | 49| 12 +Brand#32 |PROMO BRUSHED COPPER | 45| 12 +Brand#32 |PROMO BRUSHED STEEL | 23| 12 +Brand#32 |PROMO BRUSHED STEEL | 49| 12 +Brand#32 |PROMO BRUSHED TIN | 14| 12 +Brand#32 |PROMO BRUSHED TIN | 36| 12 +Brand#32 |PROMO BURNISHED NICKEL | 45| 12 +Brand#32 |PROMO BURNISHED TIN | 49| 12 +Brand#32 |PROMO PLATED COPPER | 49| 12 +Brand#32 |PROMO PLATED STEEL | 49| 12 +Brand#32 |PROMO POLISHED STEEL | 49| 12 +Brand#32 |PROMO POLISHED TIN | 19| 12 +Brand#32 |PROMO POLISHED TIN | 23| 12 +Brand#32 |PROMO POLISHED TIN | 45| 12 +Brand#32 |SMALL ANODIZED NICKEL | 9| 12 +Brand#32 |SMALL BRUSHED TIN | 3| 12 +Brand#32 |SMALL BRUSHED TIN | 9| 12 +Brand#32 |SMALL BURNISHED TIN | 23| 12 +Brand#32 |SMALL BURNISHED TIN | 36| 12 +Brand#32 |SMALL PLATED BRASS | 36| 12 +Brand#32 |SMALL PLATED COPPER | 14| 12 +Brand#32 |SMALL PLATED COPPER | 45| 12 +Brand#32 |SMALL PLATED STEEL | 36| 12 +Brand#32 |SMALL PLATED TIN | 14| 12 +Brand#32 |SMALL POLISHED NICKEL | 45| 12 +Brand#32 |SMALL POLISHED STEEL | 23| 12 +Brand#32 |SMALL POLISHED STEEL | 36| 12 +Brand#32 |STANDARD ANODIZED NICKEL | 9| 12 +Brand#32 |STANDARD ANODIZED STEEL | 3| 12 +Brand#32 |STANDARD ANODIZED TIN | 14| 12 +Brand#32 |STANDARD ANODIZED TIN | 19| 12 +Brand#32 |STANDARD BRUSHED BRASS | 14| 12 +Brand#32 |STANDARD BRUSHED STEEL | 14| 12 +Brand#32 |STANDARD BRUSHED TIN | 9| 12 +Brand#32 |STANDARD BURNISHED BRASS | 45| 12 +Brand#32 |STANDARD BURNISHED COPPER| 3| 12 +Brand#32 |STANDARD BURNISHED NICKEL| 3| 12 +Brand#32 |STANDARD PLATED STEEL | 9| 12 +Brand#32 |STANDARD PLATED STEEL | 49| 12 +Brand#32 |STANDARD POLISHED COPPER | 36| 12 +Brand#33 |ECONOMY ANODIZED NICKEL | 36| 12 +Brand#33 |ECONOMY ANODIZED STEEL | 23| 12 +Brand#33 |ECONOMY ANODIZED STEEL | 45| 12 +Brand#33 |ECONOMY BURNISHED NICKEL | 14| 12 +Brand#33 |ECONOMY BURNISHED TIN | 45| 12 +Brand#33 |ECONOMY PLATED STEEL | 3| 12 +Brand#33 |ECONOMY PLATED TIN | 3| 12 +Brand#33 |ECONOMY PLATED TIN | 9| 12 +Brand#33 |ECONOMY POLISHED BRASS | 3| 12 +Brand#33 |ECONOMY POLISHED BRASS | 14| 12 +Brand#33 |LARGE ANODIZED BRASS | 3| 12 +Brand#33 |LARGE ANODIZED BRASS | 36| 12 +Brand#33 |LARGE ANODIZED NICKEL | 23| 12 +Brand#33 |LARGE ANODIZED STEEL | 3| 12 +Brand#33 |LARGE ANODIZED TIN | 36| 12 +Brand#33 |LARGE BRUSHED BRASS | 23| 12 +Brand#33 |LARGE BRUSHED STEEL | 3| 12 +Brand#33 |LARGE BRUSHED TIN | 36| 12 +Brand#33 |LARGE BURNISHED BRASS | 19| 12 +Brand#33 |LARGE BURNISHED BRASS | 49| 12 +Brand#33 |LARGE PLATED NICKEL | 9| 12 +Brand#33 |LARGE PLATED NICKEL | 19| 12 +Brand#33 |LARGE POLISHED BRASS | 9| 12 +Brand#33 |LARGE POLISHED NICKEL | 45| 12 +Brand#33 |MEDIUM ANODIZED NICKEL | 19| 12 +Brand#33 |MEDIUM ANODIZED TIN | 49| 12 +Brand#33 |MEDIUM BRUSHED BRASS | 45| 12 +Brand#33 |MEDIUM BRUSHED NICKEL | 14| 12 +Brand#33 |MEDIUM BRUSHED STEEL | 14| 12 +Brand#33 |MEDIUM BRUSHED STEEL | 36| 12 +Brand#33 |MEDIUM BURNISHED BRASS | 49| 12 +Brand#33 |MEDIUM BURNISHED TIN | 3| 12 +Brand#33 |MEDIUM BURNISHED TIN | 49| 12 +Brand#33 |MEDIUM PLATED STEEL | 3| 12 +Brand#33 |MEDIUM PLATED TIN | 23| 12 +Brand#33 |PROMO ANODIZED STEEL | 23| 12 +Brand#33 |PROMO ANODIZED TIN | 9| 12 +Brand#33 |PROMO ANODIZED TIN | 49| 12 +Brand#33 |PROMO BRUSHED BRASS | 3| 12 +Brand#33 |PROMO BRUSHED BRASS | 19| 12 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14| 12 +Brand#33 |STANDARD ANODIZED STEEL | 45| 12 +Brand#33 |STANDARD ANODIZED TIN | 9| 12 +Brand#33 |STANDARD BRUSHED BRASS | 19| 12 +Brand#33 |STANDARD BRUSHED NICKEL | 14| 12 +Brand#33 |STANDARD BURNISHED BRASS | 9| 12 +Brand#33 |STANDARD BURNISHED TIN | 23| 12 +Brand#33 |STANDARD POLISHED STEEL | 45| 12 +Brand#34 |ECONOMY ANODIZED NICKEL | 9| 12 +Brand#34 |ECONOMY ANODIZED NICKEL | 49| 12 +Brand#34 |ECONOMY ANODIZED STEEL | 45| 12 +Brand#34 |ECONOMY BURNISHED COPPER | 9| 12 +Brand#34 |ECONOMY BURNISHED COPPER | 23| 12 +Brand#34 |ECONOMY BURNISHED COPPER | 36| 12 +Brand#34 |ECONOMY BURNISHED NICKEL | 19| 12 +Brand#34 |ECONOMY BURNISHED NICKEL | 49| 12 +Brand#34 |ECONOMY BURNISHED STEEL | 9| 12 +Brand#34 |ECONOMY BURNISHED TIN | 14| 12 +Brand#34 |ECONOMY PLATED BRASS | 3| 12 +Brand#34 |ECONOMY PLATED COPPER | 3| 12 +Brand#34 |ECONOMY PLATED TIN | 3| 12 +Brand#34 |ECONOMY PLATED TIN | 14| 12 +Brand#34 |ECONOMY POLISHED TIN | 36| 12 +Brand#34 |LARGE ANODIZED COPPER | 3| 12 +Brand#34 |LARGE ANODIZED NICKEL | 3| 12 +Brand#34 |LARGE ANODIZED NICKEL | 49| 12 +Brand#34 |LARGE BRUSHED COPPER | 36| 12 +Brand#34 |LARGE BRUSHED NICKEL | 19| 12 +Brand#34 |LARGE BRUSHED NICKEL | 49| 12 +Brand#34 |LARGE BURNISHED COPPER | 23| 12 +Brand#34 |LARGE BURNISHED NICKEL | 23| 12 +Brand#34 |LARGE BURNISHED TIN | 14| 12 +Brand#34 |LARGE BURNISHED TIN | 23| 12 +Brand#34 |LARGE BURNISHED TIN | 49| 12 +Brand#34 |LARGE PLATED COPPER | 9| 12 +Brand#34 |LARGE PLATED TIN | 14| 12 +Brand#34 |LARGE POLISHED BRASS | 3| 12 +Brand#34 |LARGE POLISHED BRASS | 45| 12 +Brand#34 |LARGE POLISHED COPPER | 3| 12 +Brand#34 |LARGE POLISHED NICKEL | 3| 12 +Brand#34 |LARGE POLISHED NICKEL | 49| 12 +Brand#34 |MEDIUM ANODIZED BRASS | 45| 12 +Brand#34 |MEDIUM BRUSHED BRASS | 49| 12 +Brand#34 |MEDIUM BRUSHED COPPER | 9| 12 +Brand#34 |MEDIUM BRUSHED COPPER | 23| 12 +Brand#34 |MEDIUM BRUSHED NICKEL | 9| 12 +Brand#34 |MEDIUM BRUSHED STEEL | 45| 12 +Brand#34 |MEDIUM BRUSHED TIN | 36| 12 +Brand#34 |MEDIUM BURNISHED 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BURNISHED STEEL | 36| 12 +Brand#35 |ECONOMY PLATED TIN | 45| 12 +Brand#35 |ECONOMY PLATED TIN | 49| 12 +Brand#35 |ECONOMY POLISHED COPPER | 9| 12 +Brand#35 |ECONOMY POLISHED NICKEL | 23| 12 +Brand#35 |ECONOMY POLISHED STEEL | 9| 12 +Brand#35 |ECONOMY POLISHED TIN | 23| 12 +Brand#35 |LARGE ANODIZED BRASS | 3| 12 +Brand#35 |LARGE ANODIZED BRASS | 45| 12 +Brand#35 |LARGE ANODIZED COPPER | 19| 12 +Brand#35 |LARGE ANODIZED COPPER | 36| 12 +Brand#35 |LARGE ANODIZED STEEL | 45| 12 +Brand#35 |LARGE ANODIZED TIN | 45| 12 +Brand#35 |LARGE BRUSHED COPPER | 23| 12 +Brand#35 |LARGE BRUSHED NICKEL | 36| 12 +Brand#35 |LARGE BRUSHED STEEL | 3| 12 +Brand#35 |LARGE BRUSHED TIN | 36| 12 +Brand#35 |LARGE BURNISHED BRASS | 45| 12 +Brand#35 |LARGE BURNISHED STEEL | 9| 12 +Brand#35 |LARGE BURNISHED STEEL | 45| 12 +Brand#35 |LARGE BURNISHED TIN | 49| 12 +Brand#35 |LARGE PLATED BRASS | 3| 12 +Brand#35 |LARGE PLATED BRASS | 23| 12 +Brand#35 |LARGE PLATED STEEL | 19| 12 +Brand#35 |LARGE PLATED STEEL | 49| 12 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STEEL | 23| 12 +Brand#35 |STANDARD PLATED TIN | 45| 12 +Brand#35 |STANDARD POLISHED STEEL | 23| 12 +Brand#35 |STANDARD POLISHED TIN | 3| 12 +Brand#41 |ECONOMY ANODIZED BRASS | 45| 12 +Brand#41 |ECONOMY ANODIZED TIN | 14| 12 +Brand#41 |ECONOMY BRUSHED BRASS | 23| 12 +Brand#41 |ECONOMY BRUSHED NICKEL | 49| 12 +Brand#41 |ECONOMY BRUSHED STEEL | 36| 12 +Brand#41 |ECONOMY BRUSHED TIN | 45| 12 +Brand#41 |ECONOMY BURNISHED COPPER | 3| 12 +Brand#41 |ECONOMY BURNISHED COPPER | 45| 12 +Brand#41 |ECONOMY PLATED NICKEL | 23| 12 +Brand#41 |ECONOMY PLATED STEEL | 36| 12 +Brand#41 |ECONOMY PLATED TIN | 23| 12 +Brand#41 |ECONOMY POLISHED BRASS | 36| 12 +Brand#41 |ECONOMY POLISHED COPPER | 49| 12 +Brand#41 |ECONOMY POLISHED NICKEL | 9| 12 +Brand#41 |ECONOMY POLISHED NICKEL | 19| 12 +Brand#41 |ECONOMY POLISHED NICKEL | 23| 12 +Brand#41 |ECONOMY POLISHED STEEL | 49| 12 +Brand#41 |LARGE ANODIZED BRASS | 14| 12 +Brand#41 |LARGE ANODIZED BRASS | 23| 12 +Brand#41 |LARGE ANODIZED COPPER | 36| 12 +Brand#41 |LARGE ANODIZED STEEL | 23| 12 +Brand#41 |LARGE BRUSHED BRASS | 9| 12 +Brand#41 |LARGE BRUSHED COPPER | 23| 12 +Brand#41 |LARGE BURNISHED BRASS | 36| 12 +Brand#41 |LARGE BURNISHED STEEL | 23| 12 +Brand#41 |LARGE PLATED NICKEL | 14| 12 +Brand#41 |LARGE POLISHED BRASS | 45| 12 +Brand#41 |LARGE POLISHED COPPER | 23| 12 +Brand#41 |LARGE POLISHED COPPER | 36| 12 +Brand#41 |LARGE POLISHED STEEL | 3| 12 +Brand#41 |LARGE POLISHED STEEL | 9| 12 +Brand#41 |MEDIUM ANODIZED NICKEL | 3| 12 +Brand#41 |MEDIUM ANODIZED TIN | 3| 12 +Brand#41 |MEDIUM BURNISHED COPPER | 23| 12 +Brand#41 |MEDIUM BURNISHED TIN | 14| 12 +Brand#41 |MEDIUM BURNISHED TIN | 45| 12 +Brand#41 |MEDIUM PLATED BRASS | 19| 12 +Brand#41 |MEDIUM PLATED COPPER | 19| 12 +Brand#41 |MEDIUM PLATED COPPER | 45| 12 +Brand#41 |PROMO ANODIZED BRASS | 14| 12 +Brand#41 |PROMO ANODIZED NICKEL | 49| 12 +Brand#41 |PROMO ANODIZED TIN | 9| 12 +Brand#41 |PROMO BURNISHED COPPER | 49| 12 +Brand#41 |PROMO BURNISHED TIN | 14| 12 +Brand#41 |PROMO PLATED 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STEEL | 19| 12 +Brand#42 |PROMO POLISHED STEEL | 45| 12 +Brand#42 |SMALL ANODIZED BRASS | 36| 12 +Brand#42 |SMALL BRUSHED BRASS | 36| 12 +Brand#42 |SMALL BURNISHED BRASS | 3| 12 +Brand#42 |SMALL BURNISHED BRASS | 36| 12 +Brand#42 |SMALL BURNISHED STEEL | 23| 12 +Brand#42 |SMALL BURNISHED TIN | 9| 12 +Brand#42 |SMALL BURNISHED TIN | 49| 12 +Brand#42 |SMALL PLATED COPPER | 9| 12 +Brand#42 |SMALL PLATED COPPER | 19| 12 +Brand#42 |SMALL POLISHED BRASS | 3| 12 +Brand#42 |SMALL POLISHED COPPER | 36| 12 +Brand#42 |SMALL POLISHED NICKEL | 23| 12 +Brand#42 |STANDARD ANODIZED BRASS | 23| 12 +Brand#42 |STANDARD ANODIZED COPPER | 45| 12 +Brand#42 |STANDARD ANODIZED STEEL | 23| 12 +Brand#42 |STANDARD ANODIZED TIN | 23| 12 +Brand#42 |STANDARD BRUSHED TIN | 3| 12 +Brand#42 |STANDARD BURNISHED COPPER| 36| 12 +Brand#42 |STANDARD BURNISHED TIN | 23| 12 +Brand#42 |STANDARD PLATED COPPER | 9| 12 +Brand#42 |STANDARD PLATED TIN | 3| 12 +Brand#42 |STANDARD POLISHED NICKEL | 9| 12 +Brand#42 |STANDARD POLISHED 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PLATED TIN | 3| 12 +Brand#54 |PROMO PLATED TIN | 49| 12 +Brand#54 |PROMO POLISHED STEEL | 19| 12 +Brand#54 |PROMO POLISHED STEEL | 45| 12 +Brand#54 |PROMO POLISHED TIN | 19| 12 +Brand#54 |SMALL ANODIZED COPPER | 49| 12 +Brand#54 |SMALL BRUSHED BRASS | 23| 12 +Brand#54 |SMALL BRUSHED BRASS | 36| 12 +Brand#54 |SMALL BRUSHED COPPER | 19| 12 +Brand#54 |SMALL BRUSHED TIN | 14| 12 +Brand#54 |SMALL BURNISHED BRASS | 3| 12 +Brand#54 |SMALL BURNISHED COPPER | 49| 12 +Brand#54 |SMALL BURNISHED NICKEL | 14| 12 +Brand#54 |SMALL BURNISHED STEEL | 19| 12 +Brand#54 |SMALL BURNISHED TIN | 9| 12 +Brand#54 |SMALL PLATED BRASS | 23| 12 +Brand#54 |SMALL PLATED COPPER | 36| 12 +Brand#54 |SMALL PLATED NICKEL | 36| 12 +Brand#54 |STANDARD ANODIZED BRASS | 3| 12 +Brand#54 |STANDARD ANODIZED STEEL | 49| 12 +Brand#54 |STANDARD BRUSHED BRASS | 14| 12 +Brand#54 |STANDARD BRUSHED COPPER | 19| 12 +Brand#54 |STANDARD BURNISHED BRASS | 9| 12 +Brand#54 |STANDARD BURNISHED NICKEL| 14| 12 +Brand#54 |STANDARD PLATED BRASS | 45| 12 +Brand#54 |STANDARD PLATED COPPER | 9| 12 +Brand#54 |STANDARD PLATED COPPER | 19| 12 +Brand#54 |STANDARD PLATED NICKEL | 49| 12 +Brand#54 |STANDARD PLATED TIN | 45| 12 +Brand#54 |STANDARD POLISHED STEEL | 49| 12 +Brand#55 |ECONOMY BRUSHED BRASS | 3| 12 +Brand#55 |ECONOMY BRUSHED COPPER | 9| 12 +Brand#55 |ECONOMY BRUSHED COPPER | 14| 12 +Brand#55 |ECONOMY BRUSHED NICKEL | 19| 12 +Brand#55 |ECONOMY BRUSHED STEEL | 3| 12 +Brand#55 |ECONOMY BURNISHED COPPER | 9| 12 +Brand#55 |ECONOMY PLATED STEEL | 9| 12 +Brand#55 |ECONOMY POLISHED STEEL | 3| 12 +Brand#55 |LARGE ANODIZED NICKEL | 9| 12 +Brand#55 |LARGE BRUSHED COPPER | 14| 12 +Brand#55 |LARGE BRUSHED COPPER | 23| 12 +Brand#55 |LARGE BRUSHED COPPER | 49| 12 +Brand#55 |LARGE BURNISHED COPPER | 14| 12 +Brand#55 |LARGE BURNISHED NICKEL | 14| 12 +Brand#55 |LARGE PLATED BRASS | 45| 12 +Brand#55 |LARGE PLATED NICKEL | 14| 12 +Brand#55 |LARGE PLATED STEEL | 23| 12 +Brand#55 |LARGE POLISHED NICKEL | 3| 12 +Brand#55 |LARGE POLISHED STEEL | 45| 12 +Brand#55 |MEDIUM ANODIZED NICKEL | 36| 12 +Brand#55 |MEDIUM ANODIZED TIN | 49| 12 +Brand#55 |MEDIUM BRUSHED BRASS | 19| 12 +Brand#55 |MEDIUM BRUSHED COPPER | 49| 12 +Brand#55 |MEDIUM BRUSHED NICKEL | 23| 12 +Brand#55 |MEDIUM BRUSHED NICKEL | 45| 12 +Brand#55 |MEDIUM BRUSHED STEEL | 45| 12 +Brand#55 |MEDIUM BURNISHED COPPER | 36| 12 +Brand#55 |MEDIUM PLATED NICKEL | 23| 12 +Brand#55 |MEDIUM PLATED STEEL | 3| 12 +Brand#55 |MEDIUM PLATED TIN | 19| 12 +Brand#55 |PROMO ANODIZED TIN | 19| 12 +Brand#55 |PROMO BRUSHED BRASS | 23| 12 +Brand#55 |PROMO BRUSHED BRASS | 45| 12 +Brand#55 |PROMO BRUSHED NICKEL | 23| 12 +Brand#55 |PROMO BRUSHED TIN | 9| 12 +Brand#55 |PROMO BURNISHED STEEL | 23| 12 +Brand#55 |PROMO POLISHED BRASS | 45| 12 +Brand#55 |SMALL ANODIZED STEEL | 23| 12 +Brand#55 |SMALL ANODIZED STEEL | 45| 12 +Brand#55 |SMALL BRUSHED STEEL | 36| 12 +Brand#55 |SMALL BRUSHED TIN | 3| 12 +Brand#55 |SMALL BURNISHED BRASS | 49| 12 +Brand#55 |SMALL BURNISHED TIN | 49| 12 +Brand#55 |SMALL PLATED NICKEL | 36| 12 +Brand#55 |SMALL PLATED NICKEL | 45| 12 +Brand#55 |SMALL PLATED STEEL | 9| 12 +Brand#55 |SMALL PLATED STEEL | 19| 12 +Brand#55 |SMALL POLISHED STEEL | 14| 12 +Brand#55 |STANDARD ANODIZED BRASS | 3| 12 +Brand#55 |STANDARD ANODIZED STEEL | 19| 12 +Brand#55 |STANDARD ANODIZED TIN | 9| 12 +Brand#55 |STANDARD BRUSHED COPPER | 9| 12 +Brand#55 |STANDARD BRUSHED NICKEL | 9| 12 +Brand#55 |STANDARD BRUSHED TIN | 36| 12 +Brand#55 |STANDARD BRUSHED TIN | 45| 12 +Brand#55 |STANDARD BURNISHED BRASS | 3| 12 +Brand#55 |STANDARD BURNISHED COPPER| 49| 12 +Brand#55 |STANDARD BURNISHED TIN | 3| 12 +Brand#55 |STANDARD PLATED BRASS | 3| 12 +Brand#55 |STANDARD PLATED COPPER | 3| 12 +Brand#55 |STANDARD PLATED COPPER | 19| 12 +Brand#55 |STANDARD PLATED NICKEL | 9| 12 +Brand#55 |STANDARD PLATED TIN | 19| 12 +Brand#55 |STANDARD POLISHED NICKEL | 14| 12 +Brand#11 |ECONOMY POLISHED BRASS | 14| 11 +Brand#11 |SMALL PLATED BRASS | 14| 11 +Brand#12 |MEDIUM BURNISHED TIN | 45| 11 +Brand#12 |SMALL BURNISHED COPPER | 23| 11 +Brand#15 |SMALL PLATED NICKEL | 45| 11 +Brand#21 |ECONOMY PLATED COPPER | 3| 11 +Brand#21 |SMALL BRUSHED TIN | 19| 11 +Brand#23 |LARGE BRUSHED NICKEL | 23| 11 +Brand#24 |PROMO BRUSHED NICKEL | 9| 11 +Brand#25 |SMALL PLATED TIN | 23| 11 +Brand#31 |ECONOMY POLISHED COPPER | 14| 11 +Brand#32 |SMALL PLATED NICKEL | 45| 11 +Brand#33 |PROMO ANODIZED TIN | 19| 11 +Brand#43 |PROMO BRUSHED NICKEL | 9| 11 +Brand#44 |LARGE PLATED STEEL | 3| 11 +Brand#52 |ECONOMY ANODIZED COPPER | 36| 11 +Brand#52 |SMALL POLISHED BRASS | 49| 11 +Brand#53 |MEDIUM BRUSHED BRASS | 49| 11 +Brand#53 |PROMO BRUSHED NICKEL | 3| 11 +Brand#54 |LARGE PLATED BRASS | 19| 11 +Brand#54 |LARGE POLISHED NICKEL | 3| 11 +Brand#55 |PROMO ANODIZED STEEL | 45| 11 +Brand#55 |STANDARD POLISHED STEEL | 19| 11 +Brand#11 |ECONOMY ANODIZED BRASS | 19| 8 +Brand#11 |ECONOMY ANODIZED NICKEL | 9| 8 +Brand#11 |ECONOMY ANODIZED NICKEL | 19| 8 +Brand#11 |ECONOMY ANODIZED NICKEL | 36| 8 +Brand#11 |ECONOMY ANODIZED NICKEL | 45| 8 +Brand#11 |ECONOMY ANODIZED TIN | 36| 8 +Brand#11 |ECONOMY BRUSHED COPPER | 9| 8 +Brand#11 |ECONOMY BRUSHED COPPER | 49| 8 +Brand#11 |ECONOMY BRUSHED NICKEL | 49| 8 +Brand#11 |ECONOMY BRUSHED STEEL | 9| 8 +Brand#11 |ECONOMY BRUSHED STEEL | 14| 8 +Brand#11 |ECONOMY BRUSHED STEEL | 23| 8 +Brand#11 |ECONOMY BRUSHED TIN | 19| 8 +Brand#11 |ECONOMY BRUSHED TIN | 36| 8 +Brand#11 |ECONOMY BRUSHED TIN | 49| 8 +Brand#11 |ECONOMY BURNISHED BRASS | 23| 8 +Brand#11 |ECONOMY BURNISHED COPPER | 9| 8 +Brand#11 |ECONOMY BURNISHED NICKEL | 14| 8 +Brand#11 |ECONOMY BURNISHED NICKEL | 19| 8 +Brand#11 |ECONOMY BURNISHED TIN | 9| 8 +Brand#11 |ECONOMY BURNISHED TIN | 14| 8 +Brand#11 |ECONOMY BURNISHED TIN | 49| 8 +Brand#11 |ECONOMY PLATED COPPER | 14| 8 +Brand#11 |ECONOMY PLATED COPPER | 49| 8 +Brand#11 |ECONOMY PLATED NICKEL | 23| 8 +Brand#11 |ECONOMY PLATED NICKEL | 36| 8 +Brand#11 |ECONOMY PLATED NICKEL | 45| 8 +Brand#11 |ECONOMY PLATED STEEL | 23| 8 +Brand#11 |ECONOMY PLATED TIN | 49| 8 +Brand#11 |ECONOMY POLISHED BRASS | 3| 8 +Brand#11 |ECONOMY POLISHED COPPER | 45| 8 +Brand#11 |ECONOMY POLISHED COPPER | 49| 8 +Brand#11 |ECONOMY POLISHED NICKEL | 3| 8 +Brand#11 |ECONOMY POLISHED NICKEL | 9| 8 +Brand#11 |ECONOMY POLISHED NICKEL | 14| 8 +Brand#11 |ECONOMY POLISHED NICKEL | 23| 8 +Brand#11 |ECONOMY POLISHED STEEL | 19| 8 +Brand#11 |ECONOMY POLISHED TIN | 3| 8 +Brand#11 |ECONOMY POLISHED TIN | 14| 8 +Brand#11 |ECONOMY POLISHED TIN | 36| 8 +Brand#11 |LARGE ANODIZED BRASS | 49| 8 +Brand#11 |LARGE ANODIZED COPPER | 23| 8 +Brand#11 |LARGE ANODIZED NICKEL | 36| 8 +Brand#11 |LARGE ANODIZED NICKEL | 45| 8 +Brand#11 |LARGE ANODIZED NICKEL | 49| 8 +Brand#11 |LARGE ANODIZED STEEL | 9| 8 +Brand#11 |LARGE ANODIZED TIN | 23| 8 +Brand#11 |LARGE ANODIZED TIN | 45| 8 +Brand#11 |LARGE BRUSHED BRASS | 14| 8 +Brand#11 |LARGE BRUSHED BRASS | 23| 8 +Brand#11 |LARGE BRUSHED COPPER | 19| 8 +Brand#11 |LARGE BRUSHED COPPER | 23| 8 +Brand#11 |LARGE BRUSHED COPPER | 36| 8 +Brand#11 |LARGE BRUSHED NICKEL | 3| 8 +Brand#11 |LARGE BRUSHED NICKEL | 14| 8 +Brand#11 |LARGE BRUSHED NICKEL | 19| 8 +Brand#11 |LARGE BRUSHED STEEL | 49| 8 +Brand#11 |LARGE BRUSHED TIN | 14| 8 +Brand#11 |LARGE BRUSHED TIN | 23| 8 +Brand#11 |LARGE BURNISHED BRASS | 14| 8 +Brand#11 |LARGE BURNISHED BRASS | 23| 8 +Brand#11 |LARGE BURNISHED BRASS | 45| 8 +Brand#11 |LARGE BURNISHED BRASS | 49| 8 +Brand#11 |LARGE BURNISHED COPPER | 9| 8 +Brand#11 |LARGE BURNISHED COPPER | 36| 8 +Brand#11 |LARGE BURNISHED NICKEL | 45| 8 +Brand#11 |LARGE BURNISHED STEEL | 36| 8 +Brand#11 |LARGE BURNISHED STEEL | 49| 8 +Brand#11 |LARGE BURNISHED TIN | 14| 8 +Brand#11 |LARGE BURNISHED TIN | 23| 8 +Brand#11 |LARGE PLATED BRASS | 14| 8 +Brand#11 |LARGE PLATED BRASS | 23| 8 +Brand#11 |LARGE PLATED NICKEL | 3| 8 +Brand#11 |LARGE PLATED NICKEL | 36| 8 +Brand#11 |LARGE PLATED STEEL | 3| 8 +Brand#11 |LARGE PLATED STEEL | 23| 8 +Brand#11 |LARGE PLATED STEEL | 36| 8 +Brand#11 |LARGE PLATED TIN | 9| 8 +Brand#11 |LARGE PLATED TIN | 14| 8 +Brand#11 |LARGE POLISHED BRASS | 49| 8 +Brand#11 |LARGE POLISHED COPPER | 14| 8 +Brand#11 |LARGE POLISHED NICKEL | 14| 8 +Brand#11 |LARGE POLISHED STEEL | 36| 8 +Brand#11 |LARGE POLISHED TIN | 3| 8 +Brand#11 |MEDIUM ANODIZED BRASS | 14| 8 +Brand#11 |MEDIUM ANODIZED BRASS | 49| 8 +Brand#11 |MEDIUM ANODIZED COPPER | 23| 8 +Brand#11 |MEDIUM ANODIZED NICKEL | 9| 8 +Brand#11 |MEDIUM ANODIZED NICKEL | 14| 8 +Brand#11 |MEDIUM ANODIZED NICKEL | 36| 8 +Brand#11 |MEDIUM ANODIZED NICKEL | 45| 8 +Brand#11 |MEDIUM ANODIZED STEEL | 9| 8 +Brand#11 |MEDIUM ANODIZED TIN | 23| 8 +Brand#11 |MEDIUM ANODIZED TIN | 49| 8 +Brand#11 |MEDIUM BRUSHED COPPER | 23| 8 +Brand#11 |MEDIUM BRUSHED NICKEL | 23| 8 +Brand#11 |MEDIUM BURNISHED BRASS | 3| 8 +Brand#11 |MEDIUM BURNISHED BRASS | 19| 8 +Brand#11 |MEDIUM BURNISHED BRASS | 45| 8 +Brand#11 |MEDIUM BURNISHED COPPER | 9| 8 +Brand#11 |MEDIUM BURNISHED COPPER | 14| 8 +Brand#11 |MEDIUM BURNISHED COPPER | 49| 8 +Brand#11 |MEDIUM BURNISHED STEEL | 19| 8 +Brand#11 |MEDIUM BURNISHED TIN | 19| 8 +Brand#11 |MEDIUM BURNISHED TIN | 36| 8 +Brand#11 |MEDIUM PLATED BRASS | 3| 8 +Brand#11 |MEDIUM PLATED BRASS | 36| 8 +Brand#11 |MEDIUM PLATED NICKEL | 14| 8 +Brand#11 |MEDIUM PLATED NICKEL | 45| 8 +Brand#11 |MEDIUM PLATED STEEL | 3| 8 +Brand#11 |MEDIUM PLATED STEEL | 9| 8 +Brand#11 |MEDIUM PLATED STEEL | 23| 8 +Brand#11 |MEDIUM PLATED STEEL | 36| 8 +Brand#11 |MEDIUM PLATED TIN | 3| 8 +Brand#11 |MEDIUM PLATED TIN | 19| 8 +Brand#11 |MEDIUM PLATED TIN | 23| 8 +Brand#11 |MEDIUM PLATED TIN | 45| 8 +Brand#11 |PROMO ANODIZED COPPER | 14| 8 +Brand#11 |PROMO ANODIZED NICKEL | 3| 8 +Brand#11 |PROMO ANODIZED NICKEL | 45| 8 +Brand#11 |PROMO ANODIZED STEEL | 23| 8 +Brand#11 |PROMO ANODIZED STEEL | 49| 8 +Brand#11 |PROMO ANODIZED TIN | 36| 8 +Brand#11 |PROMO BRUSHED BRASS | 3| 8 +Brand#11 |PROMO BRUSHED BRASS | 36| 8 +Brand#11 |PROMO BRUSHED COPPER | 14| 8 +Brand#11 |PROMO BRUSHED COPPER | 19| 8 +Brand#11 |PROMO BRUSHED NICKEL | 19| 8 +Brand#11 |PROMO BRUSHED STEEL | 49| 8 +Brand#11 |PROMO BRUSHED TIN | 19| 8 +Brand#11 |PROMO BRUSHED TIN | 36| 8 +Brand#11 |PROMO BURNISHED BRASS | 3| 8 +Brand#11 |PROMO BURNISHED BRASS | 19| 8 +Brand#11 |PROMO BURNISHED BRASS | 36| 8 +Brand#11 |PROMO BURNISHED BRASS | 49| 8 +Brand#11 |PROMO BURNISHED COPPER | 14| 8 +Brand#11 |PROMO BURNISHED NICKEL | 3| 8 +Brand#11 |PROMO BURNISHED NICKEL | 14| 8 +Brand#11 |PROMO BURNISHED STEEL | 14| 8 +Brand#11 |PROMO BURNISHED STEEL | 19| 8 +Brand#11 |PROMO BURNISHED STEEL | 36| 8 +Brand#11 |PROMO BURNISHED STEEL | 49| 8 +Brand#11 |PROMO PLATED BRASS | 23| 8 +Brand#11 |PROMO PLATED NICKEL | 14| 8 +Brand#11 |PROMO PLATED NICKEL | 49| 8 +Brand#11 |PROMO PLATED STEEL | 19| 8 +Brand#11 |PROMO PLATED STEEL | 23| 8 +Brand#11 |PROMO POLISHED BRASS | 3| 8 +Brand#11 |PROMO POLISHED BRASS | 19| 8 +Brand#11 |PROMO POLISHED BRASS | 36| 8 +Brand#11 |PROMO POLISHED COPPER | 45| 8 +Brand#11 |PROMO POLISHED TIN | 3| 8 +Brand#11 |PROMO POLISHED TIN | 9| 8 +Brand#11 |PROMO POLISHED TIN | 49| 8 +Brand#11 |SMALL ANODIZED COPPER | 19| 8 +Brand#11 |SMALL ANODIZED NICKEL | 49| 8 +Brand#11 |SMALL ANODIZED STEEL | 3| 8 +Brand#11 |SMALL ANODIZED STEEL | 14| 8 +Brand#11 |SMALL ANODIZED TIN | 9| 8 +Brand#11 |SMALL ANODIZED TIN | 19| 8 +Brand#11 |SMALL BRUSHED BRASS | 45| 8 +Brand#11 |SMALL BRUSHED BRASS | 49| 8 +Brand#11 |SMALL BRUSHED COPPER | 14| 8 +Brand#11 |SMALL BRUSHED COPPER | 19| 8 +Brand#11 |SMALL BRUSHED NICKEL | 3| 8 +Brand#11 |SMALL BRUSHED NICKEL | 45| 8 +Brand#11 |SMALL BRUSHED NICKEL | 49| 8 +Brand#11 |SMALL BRUSHED TIN | 14| 8 +Brand#11 |SMALL BURNISHED COPPER | 23| 8 +Brand#11 |SMALL BURNISHED COPPER | 36| 8 +Brand#11 |SMALL BURNISHED COPPER | 49| 8 +Brand#11 |SMALL BURNISHED STEEL | 3| 8 +Brand#11 |SMALL BURNISHED STEEL | 9| 8 +Brand#11 |SMALL BURNISHED STEEL | 36| 8 +Brand#11 |SMALL BURNISHED STEEL | 45| 8 +Brand#11 |SMALL BURNISHED TIN | 3| 8 +Brand#11 |SMALL BURNISHED TIN | 19| 8 +Brand#11 |SMALL BURNISHED TIN | 45| 8 +Brand#11 |SMALL PLATED BRASS | 3| 8 +Brand#11 |SMALL PLATED BRASS | 19| 8 +Brand#11 |SMALL PLATED BRASS | 36| 8 +Brand#11 |SMALL PLATED COPPER | 49| 8 +Brand#11 |SMALL PLATED NICKEL | 9| 8 +Brand#11 |SMALL PLATED NICKEL | 49| 8 +Brand#11 |SMALL PLATED STEEL | 9| 8 +Brand#11 |SMALL PLATED TIN | 9| 8 +Brand#11 |SMALL PLATED TIN | 19| 8 +Brand#11 |SMALL PLATED TIN | 45| 8 +Brand#11 |SMALL POLISHED BRASS | 23| 8 +Brand#11 |SMALL POLISHED COPPER | 36| 8 +Brand#11 |SMALL POLISHED NICKEL | 45| 8 +Brand#11 |SMALL POLISHED STEEL | 14| 8 +Brand#11 |SMALL POLISHED STEEL | 23| 8 +Brand#11 |STANDARD ANODIZED BRASS | 23| 8 +Brand#11 |STANDARD ANODIZED COPPER | 9| 8 +Brand#11 |STANDARD ANODIZED STEEL | 19| 8 +Brand#11 |STANDARD BRUSHED COPPER | 14| 8 +Brand#11 |STANDARD BRUSHED NICKEL | 14| 8 +Brand#11 |STANDARD BRUSHED NICKEL | 45| 8 +Brand#11 |STANDARD BRUSHED TIN | 19| 8 +Brand#11 |STANDARD BURNISHED BRASS | 49| 8 +Brand#11 |STANDARD BURNISHED COPPER| 14| 8 +Brand#11 |STANDARD BURNISHED COPPER| 23| 8 +Brand#11 |STANDARD BURNISHED STEEL | 23| 8 +Brand#11 |STANDARD BURNISHED TIN | 49| 8 +Brand#11 |STANDARD PLATED BRASS | 19| 8 +Brand#11 |STANDARD PLATED BRASS | 23| 8 +Brand#11 |STANDARD PLATED BRASS | 49| 8 +Brand#11 |STANDARD PLATED NICKEL | 36| 8 +Brand#11 |STANDARD PLATED NICKEL | 45| 8 +Brand#11 |STANDARD PLATED STEEL | 23| 8 +Brand#11 |STANDARD PLATED STEEL | 45| 8 +Brand#11 |STANDARD PLATED TIN | 36| 8 +Brand#11 |STANDARD POLISHED BRASS | 9| 8 +Brand#11 |STANDARD POLISHED NICKEL | 19| 8 +Brand#11 |STANDARD POLISHED STEEL | 49| 8 +Brand#12 |ECONOMY ANODIZED STEEL | 3| 8 +Brand#12 |ECONOMY ANODIZED STEEL | 19| 8 +Brand#12 |ECONOMY ANODIZED STEEL | 23| 8 +Brand#12 |ECONOMY ANODIZED TIN | 23| 8 +Brand#12 |ECONOMY BRUSHED COPPER | 3| 8 +Brand#12 |ECONOMY BRUSHED COPPER | 14| 8 +Brand#12 |ECONOMY BRUSHED COPPER | 19| 8 +Brand#12 |ECONOMY BRUSHED COPPER | 49| 8 +Brand#12 |ECONOMY BRUSHED NICKEL | 3| 8 +Brand#12 |ECONOMY BRUSHED STEEL | 3| 8 +Brand#12 |ECONOMY BRUSHED STEEL | 49| 8 +Brand#12 |ECONOMY BRUSHED TIN | 9| 8 +Brand#12 |ECONOMY BRUSHED TIN | 49| 8 +Brand#12 |ECONOMY BURNISHED BRASS | 49| 8 +Brand#12 |ECONOMY BURNISHED COPPER | 3| 8 +Brand#12 |ECONOMY BURNISHED COPPER | 19| 8 +Brand#12 |ECONOMY BURNISHED NICKEL | 3| 8 +Brand#12 |ECONOMY BURNISHED NICKEL | 23| 8 +Brand#12 |ECONOMY BURNISHED STEEL | 3| 8 +Brand#12 |ECONOMY BURNISHED TIN | 14| 8 +Brand#12 |ECONOMY BURNISHED TIN | 19| 8 +Brand#12 |ECONOMY PLATED BRASS | 19| 8 +Brand#12 |ECONOMY PLATED BRASS | 49| 8 +Brand#12 |ECONOMY PLATED COPPER | 23| 8 +Brand#12 |ECONOMY PLATED STEEL | 23| 8 +Brand#12 |ECONOMY PLATED TIN | 36| 8 +Brand#12 |ECONOMY PLATED TIN | 49| 8 +Brand#12 |ECONOMY POLISHED BRASS | 9| 8 +Brand#12 |ECONOMY POLISHED BRASS | 14| 8 +Brand#12 |ECONOMY POLISHED COPPER | 9| 8 +Brand#12 |ECONOMY POLISHED COPPER | 49| 8 +Brand#12 |ECONOMY POLISHED TIN | 3| 8 +Brand#12 |ECONOMY POLISHED TIN | 19| 8 +Brand#12 |ECONOMY POLISHED TIN | 36| 8 +Brand#12 |LARGE ANODIZED BRASS | 23| 8 +Brand#12 |LARGE ANODIZED BRASS | 36| 8 +Brand#12 |LARGE ANODIZED COPPER | 14| 8 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|LARGE PLATED COPPER | 3| 8 +Brand#12 |LARGE PLATED COPPER | 36| 8 +Brand#12 |LARGE PLATED COPPER | 45| 8 +Brand#12 |LARGE PLATED NICKEL | 49| 8 +Brand#12 |LARGE PLATED STEEL | 3| 8 +Brand#12 |LARGE PLATED STEEL | 36| 8 +Brand#12 |LARGE PLATED TIN | 14| 8 +Brand#12 |LARGE POLISHED BRASS | 9| 8 +Brand#12 |LARGE POLISHED BRASS | 19| 8 +Brand#12 |LARGE POLISHED COPPER | 9| 8 +Brand#12 |LARGE POLISHED COPPER | 36| 8 +Brand#12 |LARGE POLISHED NICKEL | 23| 8 +Brand#12 |LARGE POLISHED NICKEL | 36| 8 +Brand#12 |LARGE POLISHED NICKEL | 49| 8 +Brand#12 |LARGE POLISHED STEEL | 49| 8 +Brand#12 |MEDIUM ANODIZED BRASS | 23| 8 +Brand#12 |MEDIUM ANODIZED NICKEL | 9| 8 +Brand#12 |MEDIUM ANODIZED STEEL | 19| 8 +Brand#12 |MEDIUM ANODIZED TIN | 9| 8 +Brand#12 |MEDIUM BRUSHED COPPER | 3| 8 +Brand#12 |MEDIUM BRUSHED COPPER | 9| 8 +Brand#12 |MEDIUM BRUSHED COPPER | 36| 8 +Brand#12 |MEDIUM BRUSHED NICKEL | 49| 8 +Brand#12 |MEDIUM BRUSHED STEEL | 3| 8 +Brand#12 |MEDIUM BRUSHED STEEL | 36| 8 +Brand#12 |MEDIUM BURNISHED BRASS | 23| 8 +Brand#12 |MEDIUM BURNISHED COPPER | 49| 8 +Brand#12 |MEDIUM BURNISHED NICKEL | 3| 8 +Brand#12 |MEDIUM BURNISHED NICKEL | 9| 8 +Brand#12 |MEDIUM BURNISHED NICKEL | 49| 8 +Brand#12 |MEDIUM BURNISHED STEEL | 3| 8 +Brand#12 |MEDIUM BURNISHED STEEL | 9| 8 +Brand#12 |MEDIUM BURNISHED STEEL | 14| 8 +Brand#12 |MEDIUM BURNISHED STEEL | 19| 8 +Brand#12 |MEDIUM BURNISHED TIN | 14| 8 +Brand#12 |MEDIUM PLATED BRASS | 14| 8 +Brand#12 |MEDIUM PLATED BRASS | 49| 8 +Brand#12 |MEDIUM PLATED NICKEL | 9| 8 +Brand#12 |MEDIUM PLATED NICKEL | 36| 8 +Brand#12 |MEDIUM PLATED NICKEL | 49| 8 +Brand#12 |MEDIUM PLATED STEEL | 14| 8 +Brand#12 |MEDIUM PLATED STEEL | 23| 8 +Brand#12 |MEDIUM PLATED STEEL | 45| 8 +Brand#12 |MEDIUM PLATED TIN | 14| 8 +Brand#12 |MEDIUM PLATED TIN | 19| 8 +Brand#12 |MEDIUM PLATED TIN | 45| 8 +Brand#12 |PROMO ANODIZED BRASS | 49| 8 +Brand#12 |PROMO ANODIZED COPPER | 3| 8 +Brand#12 |PROMO ANODIZED COPPER | 36| 8 +Brand#12 |PROMO ANODIZED COPPER | 45| 8 +Brand#12 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POLISHED BRASS | 36| 8 +Brand#14 |LARGE POLISHED COPPER | 9| 8 +Brand#14 |LARGE POLISHED COPPER | 23| 8 +Brand#14 |LARGE POLISHED NICKEL | 14| 8 +Brand#14 |LARGE POLISHED NICKEL | 36| 8 +Brand#14 |LARGE POLISHED STEEL | 23| 8 +Brand#14 |LARGE POLISHED TIN | 36| 8 +Brand#14 |LARGE POLISHED TIN | 45| 8 +Brand#14 |LARGE POLISHED TIN | 49| 8 +Brand#14 |MEDIUM ANODIZED BRASS | 14| 8 +Brand#14 |MEDIUM ANODIZED COPPER | 9| 8 +Brand#14 |MEDIUM ANODIZED COPPER | 19| 8 +Brand#14 |MEDIUM ANODIZED COPPER | 36| 8 +Brand#14 |MEDIUM ANODIZED COPPER | 49| 8 +Brand#14 |MEDIUM ANODIZED NICKEL | 9| 8 +Brand#14 |MEDIUM ANODIZED NICKEL | 36| 8 +Brand#14 |MEDIUM BRUSHED COPPER | 9| 8 +Brand#14 |MEDIUM BRUSHED COPPER | 23| 8 +Brand#14 |MEDIUM BRUSHED STEEL | 49| 8 +Brand#14 |MEDIUM BRUSHED TIN | 3| 8 +Brand#14 |MEDIUM BRUSHED TIN | 9| 8 +Brand#14 |MEDIUM BURNISHED BRASS | 19| 8 +Brand#14 |MEDIUM BURNISHED BRASS | 23| 8 +Brand#14 |MEDIUM BURNISHED NICKEL | 9| 8 +Brand#14 |MEDIUM BURNISHED NICKEL | 19| 8 +Brand#14 |MEDIUM BURNISHED NICKEL | 23| 8 +Brand#14 |MEDIUM BURNISHED NICKEL | 49| 8 +Brand#14 |MEDIUM BURNISHED STEEL | 36| 8 +Brand#14 |MEDIUM BURNISHED STEEL | 49| 8 +Brand#14 |MEDIUM BURNISHED TIN | 49| 8 +Brand#14 |MEDIUM PLATED BRASS | 9| 8 +Brand#14 |MEDIUM PLATED BRASS | 23| 8 +Brand#14 |MEDIUM PLATED BRASS | 36| 8 +Brand#14 |MEDIUM PLATED BRASS | 45| 8 +Brand#14 |MEDIUM PLATED BRASS | 49| 8 +Brand#14 |MEDIUM PLATED NICKEL | 23| 8 +Brand#14 |MEDIUM PLATED STEEL | 36| 8 +Brand#14 |MEDIUM PLATED STEEL | 49| 8 +Brand#14 |MEDIUM PLATED TIN | 3| 8 +Brand#14 |MEDIUM PLATED TIN | 14| 8 +Brand#14 |MEDIUM PLATED TIN | 45| 8 +Brand#14 |PROMO ANODIZED BRASS | 23| 8 +Brand#14 |PROMO ANODIZED BRASS | 36| 8 +Brand#14 |PROMO ANODIZED COPPER | 19| 8 +Brand#14 |PROMO ANODIZED COPPER | 36| 8 +Brand#14 |PROMO ANODIZED COPPER | 45| 8 +Brand#14 |PROMO ANODIZED STEEL | 45| 8 +Brand#14 |PROMO ANODIZED TIN | 14| 8 +Brand#14 |PROMO ANODIZED TIN | 19| 8 +Brand#14 |PROMO BRUSHED BRASS | 14| 8 +Brand#14 |PROMO BRUSHED BRASS | 19| 8 +Brand#14 |PROMO BRUSHED BRASS | 36| 8 +Brand#14 |PROMO BRUSHED BRASS | 45| 8 +Brand#14 |PROMO BRUSHED COPPER | 23| 8 +Brand#14 |PROMO BRUSHED COPPER | 49| 8 +Brand#14 |PROMO BRUSHED NICKEL | 19| 8 +Brand#14 |PROMO BRUSHED NICKEL | 36| 8 +Brand#14 |PROMO BRUSHED STEEL | 9| 8 +Brand#14 |PROMO BRUSHED STEEL | 36| 8 +Brand#14 |PROMO BRUSHED STEEL | 49| 8 +Brand#14 |PROMO BURNISHED BRASS | 9| 8 +Brand#14 |PROMO BURNISHED BRASS | 23| 8 +Brand#14 |PROMO BURNISHED BRASS | 36| 8 +Brand#14 |PROMO BURNISHED BRASS | 45| 8 +Brand#14 |PROMO BURNISHED NICKEL | 9| 8 +Brand#14 |PROMO BURNISHED STEEL | 36| 8 +Brand#14 |PROMO BURNISHED TIN | 49| 8 +Brand#14 |PROMO PLATED BRASS | 14| 8 +Brand#14 |PROMO PLATED BRASS | 45| 8 +Brand#14 |PROMO PLATED COPPER | 23| 8 +Brand#14 |PROMO PLATED NICKEL | 9| 8 +Brand#14 |PROMO PLATED STEEL | 3| 8 +Brand#14 |PROMO PLATED STEEL | 14| 8 +Brand#14 |PROMO PLATED STEEL | 19| 8 +Brand#14 |PROMO PLATED STEEL | 49| 8 +Brand#14 |PROMO PLATED TIN | 3| 8 +Brand#14 |PROMO PLATED TIN | 9| 8 +Brand#14 |PROMO POLISHED BRASS | 36| 8 +Brand#14 |PROMO POLISHED COPPER | 3| 8 +Brand#14 |PROMO POLISHED NICKEL | 3| 8 +Brand#14 |PROMO POLISHED NICKEL | 45| 8 +Brand#14 |PROMO POLISHED TIN | 9| 8 +Brand#14 |PROMO POLISHED TIN | 49| 8 +Brand#14 |SMALL ANODIZED BRASS | 9| 8 +Brand#14 |SMALL ANODIZED BRASS | 14| 8 +Brand#14 |SMALL ANODIZED COPPER | 14| 8 +Brand#14 |SMALL ANODIZED NICKEL | 36| 8 +Brand#14 |SMALL ANODIZED STEEL | 23| 8 +Brand#14 |SMALL ANODIZED TIN | 19| 8 +Brand#14 |SMALL BRUSHED BRASS | 19| 8 +Brand#14 |SMALL BRUSHED BRASS | 45| 8 +Brand#14 |SMALL BRUSHED COPPER | 36| 8 +Brand#14 |SMALL BRUSHED COPPER | 49| 8 +Brand#14 |SMALL BRUSHED TIN | 9| 8 +Brand#14 |SMALL BRUSHED TIN | 14| 8 +Brand#14 |SMALL BRUSHED TIN | 36| 8 +Brand#14 |SMALL BURNISHED BRASS | 19| 8 +Brand#14 |SMALL BURNISHED BRASS | 45| 8 +Brand#14 |SMALL BURNISHED COPPER | 14| 8 +Brand#14 |SMALL BURNISHED COPPER | 36| 8 +Brand#14 |SMALL BURNISHED NICKEL | 36| 8 +Brand#14 |SMALL BURNISHED NICKEL | 45| 8 +Brand#14 |SMALL BURNISHED STEEL | 14| 8 +Brand#14 |SMALL BURNISHED STEEL | 45| 8 +Brand#14 |SMALL BURNISHED TIN | 19| 8 +Brand#14 |SMALL BURNISHED TIN | 23| 8 +Brand#14 |SMALL PLATED BRASS | 14| 8 +Brand#14 |SMALL PLATED COPPER | 23| 8 +Brand#14 |SMALL PLATED NICKEL | 19| 8 +Brand#14 |SMALL PLATED STEEL | 14| 8 +Brand#14 |SMALL PLATED STEEL | 36| 8 +Brand#14 |SMALL PLATED TIN | 9| 8 +Brand#14 |SMALL PLATED TIN | 49| 8 +Brand#14 |SMALL POLISHED BRASS | 19| 8 +Brand#14 |SMALL POLISHED BRASS | 36| 8 +Brand#14 |SMALL POLISHED BRASS | 45| 8 +Brand#14 |SMALL POLISHED COPPER | 3| 8 +Brand#14 |SMALL POLISHED NICKEL | 9| 8 +Brand#14 |SMALL POLISHED NICKEL | 19| 8 +Brand#14 |SMALL POLISHED STEEL | 49| 8 +Brand#14 |SMALL POLISHED TIN | 3| 8 +Brand#14 |SMALL POLISHED TIN | 36| 8 +Brand#14 |STANDARD ANODIZED BRASS | 3| 8 +Brand#14 |STANDARD ANODIZED COPPER | 3| 8 +Brand#14 |STANDARD ANODIZED COPPER | 23| 8 +Brand#14 |STANDARD ANODIZED STEEL | 9| 8 +Brand#14 |STANDARD BRUSHED BRASS | 19| 8 +Brand#14 |STANDARD BRUSHED COPPER | 3| 8 +Brand#14 |STANDARD BRUSHED NICKEL | 3| 8 +Brand#14 |STANDARD BRUSHED STEEL | 23| 8 +Brand#14 |STANDARD BRUSHED TIN | 9| 8 +Brand#14 |STANDARD BRUSHED TIN | 45| 8 +Brand#14 |STANDARD BRUSHED TIN | 49| 8 +Brand#14 |STANDARD BURNISHED BRASS | 9| 8 +Brand#14 |STANDARD BURNISHED BRASS | 49| 8 +Brand#14 |STANDARD BURNISHED COPPER| 14| 8 +Brand#14 |STANDARD BURNISHED NICKEL| 3| 8 +Brand#14 |STANDARD BURNISHED NICKEL| 19| 8 +Brand#14 |STANDARD BURNISHED NICKEL| 23| 8 +Brand#14 |STANDARD BURNISHED STEEL | 14| 8 +Brand#14 |STANDARD BURNISHED STEEL | 19| 8 +Brand#14 |STANDARD BURNISHED STEEL | 23| 8 +Brand#14 |STANDARD BURNISHED TIN | 3| 8 +Brand#14 |STANDARD BURNISHED TIN | 9| 8 +Brand#14 |STANDARD PLATED BRASS | 9| 8 +Brand#14 |STANDARD PLATED BRASS | 45| 8 +Brand#14 |STANDARD PLATED COPPER | 14| 8 +Brand#14 |STANDARD PLATED NICKEL | 14| 8 +Brand#14 |STANDARD PLATED STEEL | 23| 8 +Brand#14 |STANDARD PLATED TIN | 3| 8 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45| 8 +Brand#15 |ECONOMY BURNISHED NICKEL | 49| 8 +Brand#15 |ECONOMY PLATED BRASS | 14| 8 +Brand#15 |ECONOMY PLATED COPPER | 36| 8 +Brand#15 |ECONOMY PLATED COPPER | 45| 8 +Brand#15 |ECONOMY POLISHED COPPER | 49| 8 +Brand#15 |ECONOMY POLISHED NICKEL | 9| 8 +Brand#15 |ECONOMY POLISHED NICKEL | 14| 8 +Brand#15 |ECONOMY POLISHED STEEL | 49| 8 +Brand#15 |LARGE ANODIZED BRASS | 9| 8 +Brand#15 |LARGE ANODIZED BRASS | 36| 8 +Brand#15 |LARGE ANODIZED COPPER | 23| 8 +Brand#15 |LARGE ANODIZED COPPER | 36| 8 +Brand#15 |LARGE ANODIZED COPPER | 45| 8 +Brand#15 |LARGE ANODIZED NICKEL | 23| 8 +Brand#15 |LARGE ANODIZED NICKEL | 49| 8 +Brand#15 |LARGE ANODIZED TIN | 14| 8 +Brand#15 |LARGE BRUSHED BRASS | 9| 8 +Brand#15 |LARGE BRUSHED COPPER | 3| 8 +Brand#15 |LARGE BRUSHED COPPER | 14| 8 +Brand#15 |LARGE BRUSHED STEEL | 19| 8 +Brand#15 |LARGE BRUSHED STEEL | 23| 8 +Brand#15 |LARGE BRUSHED TIN | 3| 8 +Brand#15 |LARGE BRUSHED TIN | 9| 8 +Brand#15 |LARGE BRUSHED TIN | 19| 8 +Brand#15 |LARGE BRUSHED TIN | 49| 8 +Brand#15 |LARGE BURNISHED BRASS | 9| 8 +Brand#15 |LARGE BURNISHED BRASS | 14| 8 +Brand#15 |LARGE BURNISHED BRASS | 19| 8 +Brand#15 |LARGE BURNISHED COPPER | 23| 8 +Brand#15 |LARGE BURNISHED COPPER | 45| 8 +Brand#15 |LARGE BURNISHED NICKEL | 36| 8 +Brand#15 |LARGE BURNISHED STEEL | 36| 8 +Brand#15 |LARGE BURNISHED STEEL | 49| 8 +Brand#15 |LARGE BURNISHED TIN | 49| 8 +Brand#15 |LARGE PLATED COPPER | 19| 8 +Brand#15 |LARGE PLATED COPPER | 45| 8 +Brand#15 |LARGE PLATED NICKEL | 14| 8 +Brand#15 |LARGE PLATED STEEL | 9| 8 +Brand#15 |LARGE PLATED TIN | 49| 8 +Brand#15 |LARGE POLISHED BRASS | 23| 8 +Brand#15 |LARGE POLISHED STEEL | 36| 8 +Brand#15 |LARGE POLISHED STEEL | 49| 8 +Brand#15 |LARGE POLISHED TIN | 19| 8 +Brand#15 |MEDIUM ANODIZED BRASS | 3| 8 +Brand#15 |MEDIUM ANODIZED BRASS | 9| 8 +Brand#15 |MEDIUM ANODIZED BRASS | 19| 8 +Brand#15 |MEDIUM ANODIZED BRASS | 23| 8 +Brand#15 |MEDIUM ANODIZED COPPER | 36| 8 +Brand#15 |MEDIUM ANODIZED NICKEL | 45| 8 +Brand#15 |MEDIUM ANODIZED 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PLATED TIN | 19| 8 +Brand#15 |MEDIUM PLATED TIN | 49| 8 +Brand#15 |PROMO ANODIZED BRASS | 14| 8 +Brand#15 |PROMO ANODIZED BRASS | 36| 8 +Brand#15 |PROMO ANODIZED COPPER | 45| 8 +Brand#15 |PROMO ANODIZED NICKEL | 36| 8 +Brand#15 |PROMO ANODIZED NICKEL | 49| 8 +Brand#15 |PROMO ANODIZED STEEL | 14| 8 +Brand#15 |PROMO BRUSHED BRASS | 14| 8 +Brand#15 |PROMO BRUSHED COPPER | 9| 8 +Brand#15 |PROMO BRUSHED COPPER | 19| 8 +Brand#15 |PROMO BRUSHED NICKEL | 9| 8 +Brand#15 |PROMO BRUSHED NICKEL | 19| 8 +Brand#15 |PROMO BRUSHED NICKEL | 23| 8 +Brand#15 |PROMO BRUSHED STEEL | 14| 8 +Brand#15 |PROMO BRUSHED STEEL | 23| 8 +Brand#15 |PROMO BRUSHED STEEL | 49| 8 +Brand#15 |PROMO BRUSHED TIN | 3| 8 +Brand#15 |PROMO BRUSHED TIN | 23| 8 +Brand#15 |PROMO BRUSHED TIN | 36| 8 +Brand#15 |PROMO BURNISHED BRASS | 23| 8 +Brand#15 |PROMO BURNISHED BRASS | 36| 8 +Brand#15 |PROMO BURNISHED COPPER | 3| 8 +Brand#15 |PROMO BURNISHED COPPER | 9| 8 +Brand#15 |PROMO BURNISHED COPPER | 19| 8 +Brand#15 |PROMO BURNISHED 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|SMALL ANODIZED BRASS | 14| 8 +Brand#15 |SMALL ANODIZED BRASS | 19| 8 +Brand#15 |SMALL ANODIZED NICKEL | 3| 8 +Brand#15 |SMALL ANODIZED NICKEL | 14| 8 +Brand#15 |SMALL ANODIZED NICKEL | 36| 8 +Brand#15 |SMALL ANODIZED STEEL | 3| 8 +Brand#15 |SMALL ANODIZED TIN | 45| 8 +Brand#15 |SMALL BRUSHED BRASS | 3| 8 +Brand#15 |SMALL BRUSHED BRASS | 9| 8 +Brand#15 |SMALL BRUSHED BRASS | 19| 8 +Brand#15 |SMALL BRUSHED NICKEL | 9| 8 +Brand#15 |SMALL BRUSHED NICKEL | 49| 8 +Brand#15 |SMALL BRUSHED STEEL | 14| 8 +Brand#15 |SMALL BRUSHED STEEL | 23| 8 +Brand#15 |SMALL BRUSHED TIN | 9| 8 +Brand#15 |SMALL BRUSHED TIN | 23| 8 +Brand#15 |SMALL BRUSHED TIN | 36| 8 +Brand#15 |SMALL BRUSHED TIN | 45| 8 +Brand#15 |SMALL BURNISHED BRASS | 19| 8 +Brand#15 |SMALL BURNISHED COPPER | 14| 8 +Brand#15 |SMALL BURNISHED COPPER | 49| 8 +Brand#15 |SMALL BURNISHED NICKEL | 3| 8 +Brand#15 |SMALL BURNISHED NICKEL | 9| 8 +Brand#15 |SMALL BURNISHED NICKEL | 36| 8 +Brand#15 |SMALL BURNISHED STEEL | 9| 8 +Brand#15 |SMALL BURNISHED STEEL | 19| 8 +Brand#15 |SMALL BURNISHED TIN | 14| 8 +Brand#15 |SMALL BURNISHED TIN | 19| 8 +Brand#15 |SMALL BURNISHED TIN | 23| 8 +Brand#15 |SMALL PLATED STEEL | 3| 8 +Brand#15 |SMALL PLATED STEEL | 9| 8 +Brand#15 |SMALL PLATED TIN | 9| 8 +Brand#15 |SMALL POLISHED COPPER | 3| 8 +Brand#15 |SMALL POLISHED COPPER | 9| 8 +Brand#15 |SMALL POLISHED NICKEL | 14| 8 +Brand#15 |SMALL POLISHED STEEL | 3| 8 +Brand#15 |SMALL POLISHED STEEL | 9| 8 +Brand#15 |SMALL POLISHED STEEL | 23| 8 +Brand#15 |SMALL POLISHED STEEL | 36| 8 +Brand#15 |SMALL POLISHED TIN | 9| 8 +Brand#15 |SMALL POLISHED TIN | 19| 8 +Brand#15 |SMALL POLISHED TIN | 45| 8 +Brand#15 |STANDARD ANODIZED BRASS | 19| 8 +Brand#15 |STANDARD ANODIZED BRASS | 23| 8 +Brand#15 |STANDARD ANODIZED COPPER | 3| 8 +Brand#15 |STANDARD ANODIZED COPPER | 23| 8 +Brand#15 |STANDARD ANODIZED COPPER | 36| 8 +Brand#15 |STANDARD BRUSHED COPPER | 23| 8 +Brand#15 |STANDARD BRUSHED NICKEL | 9| 8 +Brand#15 |STANDARD BRUSHED NICKEL | 19| 8 +Brand#15 |STANDARD BRUSHED STEEL | 49| 8 +Brand#15 |STANDARD BRUSHED TIN | 45| 8 +Brand#15 |STANDARD BURNISHED BRASS | 23| 8 +Brand#15 |STANDARD BURNISHED NICKEL| 9| 8 +Brand#15 |STANDARD BURNISHED NICKEL| 14| 8 +Brand#15 |STANDARD BURNISHED NICKEL| 49| 8 +Brand#15 |STANDARD BURNISHED STEEL | 45| 8 +Brand#15 |STANDARD PLATED BRASS | 14| 8 +Brand#15 |STANDARD PLATED BRASS | 36| 8 +Brand#15 |STANDARD PLATED COPPER | 9| 8 +Brand#15 |STANDARD PLATED NICKEL | 9| 8 +Brand#15 |STANDARD PLATED STEEL | 23| 8 +Brand#15 |STANDARD POLISHED BRASS | 3| 8 +Brand#15 |STANDARD POLISHED BRASS | 9| 8 +Brand#15 |STANDARD POLISHED BRASS | 14| 8 +Brand#15 |STANDARD POLISHED COPPER | 3| 8 +Brand#15 |STANDARD POLISHED COPPER | 23| 8 +Brand#15 |STANDARD POLISHED NICKEL | 14| 8 +Brand#15 |STANDARD POLISHED NICKEL | 36| 8 +Brand#15 |STANDARD POLISHED NICKEL | 45| 8 +Brand#15 |STANDARD POLISHED TIN | 3| 8 +Brand#15 |STANDARD POLISHED TIN | 36| 8 +Brand#21 |ECONOMY ANODIZED BRASS | 14| 8 +Brand#21 |ECONOMY ANODIZED COPPER 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| 23| 8 +Brand#21 |ECONOMY POLISHED BRASS | 23| 8 +Brand#21 |ECONOMY POLISHED COPPER | 3| 8 +Brand#21 |ECONOMY POLISHED NICKEL | 3| 8 +Brand#21 |ECONOMY POLISHED NICKEL | 19| 8 +Brand#21 |ECONOMY POLISHED NICKEL | 36| 8 +Brand#21 |ECONOMY POLISHED STEEL | 36| 8 +Brand#21 |ECONOMY POLISHED STEEL | 49| 8 +Brand#21 |ECONOMY POLISHED TIN | 3| 8 +Brand#21 |ECONOMY POLISHED TIN | 45| 8 +Brand#21 |LARGE ANODIZED BRASS | 45| 8 +Brand#21 |LARGE ANODIZED NICKEL | 9| 8 +Brand#21 |LARGE ANODIZED NICKEL | 19| 8 +Brand#21 |LARGE ANODIZED NICKEL | 49| 8 +Brand#21 |LARGE ANODIZED STEEL | 3| 8 +Brand#21 |LARGE ANODIZED STEEL | 36| 8 +Brand#21 |LARGE BRUSHED BRASS | 19| 8 +Brand#21 |LARGE BRUSHED BRASS | 23| 8 +Brand#21 |LARGE BRUSHED BRASS | 45| 8 +Brand#21 |LARGE BRUSHED COPPER | 19| 8 +Brand#21 |LARGE BRUSHED NICKEL | 14| 8 +Brand#21 |LARGE BRUSHED NICKEL | 45| 8 +Brand#21 |LARGE BRUSHED STEEL | 45| 8 +Brand#21 |LARGE BRUSHED TIN | 9| 8 +Brand#21 |LARGE BRUSHED TIN | 19| 8 +Brand#21 |LARGE BRUSHED TIN | 36| 8 +Brand#21 |LARGE BURNISHED COPPER | 3| 8 +Brand#21 |LARGE BURNISHED COPPER | 9| 8 +Brand#21 |LARGE BURNISHED COPPER | 14| 8 +Brand#21 |LARGE BURNISHED COPPER | 19| 8 +Brand#21 |LARGE BURNISHED COPPER | 23| 8 +Brand#21 |LARGE BURNISHED NICKEL | 9| 8 +Brand#21 |LARGE BURNISHED NICKEL | 36| 8 +Brand#21 |LARGE BURNISHED STEEL | 14| 8 +Brand#21 |LARGE BURNISHED STEEL | 45| 8 +Brand#21 |LARGE BURNISHED STEEL | 49| 8 +Brand#21 |LARGE BURNISHED TIN | 14| 8 +Brand#21 |LARGE BURNISHED TIN | 49| 8 +Brand#21 |LARGE PLATED BRASS | 19| 8 +Brand#21 |LARGE PLATED BRASS | 23| 8 +Brand#21 |LARGE PLATED NICKEL | 23| 8 +Brand#21 |LARGE PLATED STEEL | 3| 8 +Brand#21 |LARGE PLATED STEEL | 19| 8 +Brand#21 |LARGE PLATED STEEL | 45| 8 +Brand#21 |LARGE PLATED TIN | 9| 8 +Brand#21 |LARGE PLATED TIN | 23| 8 +Brand#21 |LARGE POLISHED BRASS | 36| 8 +Brand#21 |LARGE POLISHED BRASS | 49| 8 +Brand#21 |LARGE POLISHED COPPER | 23| 8 +Brand#21 |LARGE POLISHED NICKEL | 3| 8 +Brand#21 |LARGE POLISHED NICKEL | 23| 8 +Brand#21 |LARGE POLISHED NICKEL | 45| 8 +Brand#21 |LARGE POLISHED STEEL | 3| 8 +Brand#21 |LARGE POLISHED STEEL | 9| 8 +Brand#21 |LARGE POLISHED STEEL | 23| 8 +Brand#21 |LARGE POLISHED TIN | 3| 8 +Brand#21 |LARGE POLISHED TIN | 19| 8 +Brand#21 |LARGE POLISHED TIN | 45| 8 +Brand#21 |MEDIUM ANODIZED BRASS | 3| 8 +Brand#21 |MEDIUM ANODIZED BRASS | 14| 8 +Brand#21 |MEDIUM ANODIZED BRASS | 23| 8 +Brand#21 |MEDIUM ANODIZED NICKEL | 36| 8 +Brand#21 |MEDIUM ANODIZED TIN | 9| 8 +Brand#21 |MEDIUM ANODIZED TIN | 14| 8 +Brand#21 |MEDIUM ANODIZED TIN | 23| 8 +Brand#21 |MEDIUM ANODIZED TIN | 45| 8 +Brand#21 |MEDIUM BRUSHED BRASS | 45| 8 +Brand#21 |MEDIUM BRUSHED BRASS | 49| 8 +Brand#21 |MEDIUM BRUSHED COPPER | 3| 8 +Brand#21 |MEDIUM BRUSHED COPPER | 14| 8 +Brand#21 |MEDIUM BRUSHED NICKEL | 14| 8 +Brand#21 |MEDIUM BRUSHED STEEL | 23| 8 +Brand#21 |MEDIUM BRUSHED STEEL | 45| 8 +Brand#21 |MEDIUM BURNISHED BRASS | 36| 8 +Brand#21 |MEDIUM BURNISHED NICKEL | 14| 8 +Brand#21 |MEDIUM BURNISHED STEEL | 23| 8 +Brand#21 |MEDIUM PLATED BRASS | 45| 8 +Brand#21 |MEDIUM PLATED COPPER | 23| 8 +Brand#21 |MEDIUM PLATED COPPER | 49| 8 +Brand#21 |MEDIUM PLATED TIN | 36| 8 +Brand#21 |PROMO ANODIZED BRASS | 14| 8 +Brand#21 |PROMO ANODIZED BRASS | 19| 8 +Brand#21 |PROMO ANODIZED COPPER | 14| 8 +Brand#21 |PROMO ANODIZED COPPER | 23| 8 +Brand#21 |PROMO ANODIZED COPPER | 45| 8 +Brand#21 |PROMO ANODIZED NICKEL | 14| 8 +Brand#21 |PROMO ANODIZED NICKEL | 23| 8 +Brand#21 |PROMO ANODIZED STEEL | 3| 8 +Brand#21 |PROMO ANODIZED STEEL | 9| 8 +Brand#21 |PROMO ANODIZED TIN | 23| 8 +Brand#21 |PROMO BRUSHED BRASS | 23| 8 +Brand#21 |PROMO BRUSHED BRASS | 45| 8 +Brand#21 |PROMO BRUSHED BRASS | 49| 8 +Brand#21 |PROMO BRUSHED NICKEL | 45| 8 +Brand#21 |PROMO BRUSHED STEEL | 23| 8 +Brand#21 |PROMO BURNISHED BRASS | 19| 8 +Brand#21 |PROMO BURNISHED BRASS | 23| 8 +Brand#21 |PROMO BURNISHED COPPER | 9| 8 +Brand#21 |PROMO BURNISHED COPPER | 49| 8 +Brand#21 |PROMO BURNISHED NICKEL | 19| 8 +Brand#21 |PROMO BURNISHED NICKEL | 23| 8 +Brand#21 |PROMO BURNISHED STEEL | 23| 8 +Brand#21 |PROMO PLATED BRASS | 14| 8 +Brand#21 |PROMO PLATED BRASS | 23| 8 +Brand#21 |PROMO PLATED COPPER | 3| 8 +Brand#21 |PROMO PLATED NICKEL | 3| 8 +Brand#21 |PROMO PLATED STEEL | 9| 8 +Brand#21 |PROMO PLATED STEEL | 23| 8 +Brand#21 |PROMO PLATED STEEL | 49| 8 +Brand#21 |PROMO PLATED TIN | 3| 8 +Brand#21 |PROMO POLISHED COPPER | 14| 8 +Brand#21 |PROMO POLISHED STEEL | 19| 8 +Brand#21 |PROMO POLISHED STEEL | 23| 8 +Brand#21 |PROMO POLISHED STEEL | 45| 8 +Brand#21 |SMALL ANODIZED BRASS | 45| 8 +Brand#21 |SMALL ANODIZED COPPER | 49| 8 +Brand#21 |SMALL ANODIZED NICKEL | 9| 8 +Brand#21 |SMALL ANODIZED NICKEL | 14| 8 +Brand#21 |SMALL ANODIZED NICKEL | 19| 8 +Brand#21 |SMALL ANODIZED NICKEL | 23| 8 +Brand#21 |SMALL ANODIZED NICKEL | 45| 8 +Brand#21 |SMALL ANODIZED STEEL | 49| 8 +Brand#21 |SMALL ANODIZED TIN | 9| 8 +Brand#21 |SMALL ANODIZED TIN | 14| 8 +Brand#21 |SMALL ANODIZED TIN | 19| 8 +Brand#21 |SMALL ANODIZED TIN | 36| 8 +Brand#21 |SMALL ANODIZED TIN | 49| 8 +Brand#21 |SMALL BRUSHED BRASS | 36| 8 +Brand#21 |SMALL BRUSHED NICKEL | 36| 8 +Brand#21 |SMALL BRUSHED STEEL | 14| 8 +Brand#21 |SMALL BRUSHED TIN | 45| 8 +Brand#21 |SMALL BURNISHED BRASS | 36| 8 +Brand#21 |SMALL BURNISHED COPPER | 3| 8 +Brand#21 |SMALL BURNISHED COPPER | 14| 8 +Brand#21 |SMALL BURNISHED COPPER | 19| 8 +Brand#21 |SMALL BURNISHED COPPER | 45| 8 +Brand#21 |SMALL BURNISHED NICKEL | 14| 8 +Brand#21 |SMALL BURNISHED STEEL | 49| 8 +Brand#21 |SMALL PLATED BRASS | 14| 8 +Brand#21 |SMALL PLATED COPPER | 3| 8 +Brand#21 |SMALL PLATED COPPER | 9| 8 +Brand#21 |SMALL PLATED COPPER | 45| 8 +Brand#21 |SMALL PLATED NICKEL | 3| 8 +Brand#21 |SMALL PLATED STEEL | 3| 8 +Brand#21 |SMALL PLATED STEEL | 9| 8 +Brand#21 |SMALL PLATED TIN | 19| 8 +Brand#21 |SMALL POLISHED STEEL | 36| 8 +Brand#21 |STANDARD ANODIZED BRASS | 3| 8 +Brand#21 |STANDARD ANODIZED BRASS | 36| 8 +Brand#21 |STANDARD ANODIZED BRASS | 45| 8 +Brand#21 |STANDARD ANODIZED COPPER | 23| 8 +Brand#21 |STANDARD ANODIZED STEEL | 36| 8 +Brand#21 |STANDARD ANODIZED STEEL | 49| 8 +Brand#21 |STANDARD ANODIZED TIN | 9| 8 +Brand#21 |STANDARD ANODIZED TIN | 49| 8 +Brand#21 |STANDARD BRUSHED BRASS | 14| 8 +Brand#21 |STANDARD BRUSHED BRASS | 45| 8 +Brand#21 |STANDARD BRUSHED COPPER | 23| 8 +Brand#21 |STANDARD BRUSHED NICKEL | 19| 8 +Brand#21 |STANDARD BRUSHED STEEL | 9| 8 +Brand#21 |STANDARD BRUSHED STEEL | 49| 8 +Brand#21 |STANDARD BRUSHED TIN | 23| 8 +Brand#21 |STANDARD BRUSHED TIN | 36| 8 +Brand#21 |STANDARD BRUSHED TIN | 45| 8 +Brand#21 |STANDARD BURNISHED BRASS | 36| 8 +Brand#21 |STANDARD BURNISHED COPPER| 9| 8 +Brand#21 |STANDARD BURNISHED COPPER| 19| 8 +Brand#21 |STANDARD BURNISHED NICKEL| 9| 8 +Brand#21 |STANDARD BURNISHED NICKEL| 23| 8 +Brand#21 |STANDARD BURNISHED NICKEL| 45| 8 +Brand#21 |STANDARD BURNISHED STEEL | 14| 8 +Brand#21 |STANDARD BURNISHED STEEL | 36| 8 +Brand#21 |STANDARD BURNISHED STEEL | 45| 8 +Brand#21 |STANDARD BURNISHED TIN | 19| 8 +Brand#21 |STANDARD BURNISHED TIN | 36| 8 +Brand#21 |STANDARD PLATED BRASS | 14| 8 +Brand#21 |STANDARD PLATED BRASS | 19| 8 +Brand#21 |STANDARD PLATED BRASS | 49| 8 +Brand#21 |STANDARD PLATED COPPER | 19| 8 +Brand#21 |STANDARD PLATED COPPER | 23| 8 +Brand#21 |STANDARD PLATED COPPER | 49| 8 +Brand#21 |STANDARD PLATED NICKEL | 3| 8 +Brand#21 |STANDARD PLATED NICKEL | 45| 8 +Brand#21 |STANDARD PLATED TIN | 14| 8 +Brand#21 |STANDARD PLATED TIN | 49| 8 +Brand#21 |STANDARD POLISHED BRASS | 9| 8 +Brand#21 |STANDARD POLISHED BRASS | 19| 8 +Brand#21 |STANDARD POLISHED BRASS | 45| 8 +Brand#21 |STANDARD POLISHED COPPER | 23| 8 +Brand#21 |STANDARD POLISHED NICKEL | 14| 8 +Brand#21 |STANDARD POLISHED NICKEL | 23| 8 +Brand#21 |STANDARD POLISHED STEEL | 19| 8 +Brand#21 |STANDARD POLISHED TIN | 36| 8 +Brand#22 |ECONOMY ANODIZED BRASS | 3| 8 +Brand#22 |ECONOMY ANODIZED BRASS | 36| 8 +Brand#22 |ECONOMY ANODIZED COPPER | 14| 8 +Brand#22 |ECONOMY ANODIZED STEEL | 23| 8 +Brand#22 |ECONOMY ANODIZED TIN | 36| 8 +Brand#22 |ECONOMY BRUSHED 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ANODIZED BRASS | 3| 8 +Brand#22 |PROMO ANODIZED BRASS | 9| 8 +Brand#22 |PROMO ANODIZED BRASS | 45| 8 +Brand#22 |PROMO ANODIZED COPPER | 3| 8 +Brand#22 |PROMO ANODIZED COPPER | 9| 8 +Brand#22 |PROMO ANODIZED COPPER | 23| 8 +Brand#22 |PROMO ANODIZED NICKEL | 9| 8 +Brand#22 |PROMO ANODIZED NICKEL | 45| 8 +Brand#22 |PROMO ANODIZED STEEL | 19| 8 +Brand#22 |PROMO ANODIZED TIN | 14| 8 +Brand#22 |PROMO ANODIZED TIN | 23| 8 +Brand#22 |PROMO BRUSHED BRASS | 3| 8 +Brand#22 |PROMO BRUSHED BRASS | 19| 8 +Brand#22 |PROMO BRUSHED BRASS | 23| 8 +Brand#22 |PROMO BRUSHED BRASS | 36| 8 +Brand#22 |PROMO BRUSHED NICKEL | 19| 8 +Brand#22 |PROMO BRUSHED NICKEL | 45| 8 +Brand#22 |PROMO BRUSHED STEEL | 36| 8 +Brand#22 |PROMO BRUSHED TIN | 3| 8 +Brand#22 |PROMO BURNISHED COPPER | 14| 8 +Brand#22 |PROMO BURNISHED COPPER | 36| 8 +Brand#22 |PROMO BURNISHED STEEL | 14| 8 +Brand#22 |PROMO BURNISHED STEEL | 36| 8 +Brand#22 |PROMO BURNISHED TIN | 3| 8 +Brand#22 |PROMO PLATED BRASS | 36| 8 +Brand#22 |PROMO PLATED BRASS | 45| 8 +Brand#22 |PROMO PLATED COPPER | 36| 8 +Brand#22 |PROMO PLATED NICKEL | 9| 8 +Brand#22 |PROMO PLATED NICKEL | 19| 8 +Brand#22 |PROMO PLATED NICKEL | 49| 8 +Brand#22 |PROMO PLATED STEEL | 45| 8 +Brand#22 |PROMO PLATED TIN | 3| 8 +Brand#22 |PROMO PLATED TIN | 23| 8 +Brand#22 |PROMO POLISHED BRASS | 3| 8 +Brand#22 |PROMO POLISHED BRASS | 9| 8 +Brand#22 |PROMO POLISHED NICKEL | 9| 8 +Brand#22 |PROMO POLISHED STEEL | 14| 8 +Brand#22 |PROMO POLISHED TIN | 3| 8 +Brand#22 |PROMO POLISHED TIN | 23| 8 +Brand#22 |PROMO POLISHED TIN | 49| 8 +Brand#22 |SMALL ANODIZED BRASS | 14| 8 +Brand#22 |SMALL ANODIZED COPPER | 3| 8 +Brand#22 |SMALL ANODIZED NICKEL | 3| 8 +Brand#22 |SMALL ANODIZED NICKEL | 36| 8 +Brand#22 |SMALL ANODIZED STEEL | 23| 8 +Brand#22 |SMALL BRUSHED BRASS | 36| 8 +Brand#22 |SMALL BRUSHED TIN | 19| 8 +Brand#22 |SMALL BRUSHED TIN | 23| 8 +Brand#22 |SMALL BURNISHED BRASS | 3| 8 +Brand#22 |SMALL BURNISHED NICKEL | 3| 8 +Brand#22 |SMALL BURNISHED NICKEL | 49| 8 +Brand#22 |SMALL BURNISHED STEEL | 9| 8 +Brand#22 |SMALL BURNISHED STEEL | 23| 8 +Brand#22 |SMALL BURNISHED STEEL | 49| 8 +Brand#22 |SMALL BURNISHED TIN | 45| 8 +Brand#22 |SMALL PLATED BRASS | 23| 8 +Brand#22 |SMALL PLATED COPPER | 14| 8 +Brand#22 |SMALL PLATED COPPER | 36| 8 +Brand#22 |SMALL PLATED NICKEL | 3| 8 +Brand#22 |SMALL PLATED NICKEL | 19| 8 +Brand#22 |SMALL PLATED STEEL | 3| 8 +Brand#22 |SMALL PLATED STEEL | 45| 8 +Brand#22 |SMALL POLISHED COPPER | 9| 8 +Brand#22 |SMALL POLISHED COPPER | 23| 8 +Brand#22 |SMALL POLISHED NICKEL | 14| 8 +Brand#22 |SMALL POLISHED NICKEL | 19| 8 +Brand#22 |SMALL POLISHED STEEL | 14| 8 +Brand#22 |SMALL POLISHED TIN | 14| 8 +Brand#22 |SMALL POLISHED TIN | 19| 8 +Brand#22 |STANDARD ANODIZED BRASS | 14| 8 +Brand#22 |STANDARD ANODIZED BRASS | 19| 8 +Brand#22 |STANDARD ANODIZED COPPER | 9| 8 +Brand#22 |STANDARD ANODIZED COPPER | 23| 8 +Brand#22 |STANDARD ANODIZED COPPER | 36| 8 +Brand#22 |STANDARD ANODIZED NICKEL | 3| 8 +Brand#22 |STANDARD ANODIZED NICKEL | 14| 8 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NICKEL | 36| 8 +Brand#22 |STANDARD PLATED STEEL | 9| 8 +Brand#22 |STANDARD PLATED STEEL | 14| 8 +Brand#22 |STANDARD POLISHED BRASS | 14| 8 +Brand#22 |STANDARD POLISHED NICKEL | 23| 8 +Brand#22 |STANDARD POLISHED NICKEL | 45| 8 +Brand#22 |STANDARD POLISHED STEEL | 36| 8 +Brand#22 |STANDARD POLISHED STEEL | 45| 8 +Brand#22 |STANDARD POLISHED STEEL | 49| 8 +Brand#22 |STANDARD POLISHED TIN | 9| 8 +Brand#22 |STANDARD POLISHED TIN | 19| 8 +Brand#23 |ECONOMY ANODIZED COPPER | 3| 8 +Brand#23 |ECONOMY ANODIZED NICKEL | 3| 8 +Brand#23 |ECONOMY ANODIZED NICKEL | 49| 8 +Brand#23 |ECONOMY ANODIZED STEEL | 14| 8 +Brand#23 |ECONOMY ANODIZED TIN | 14| 8 +Brand#23 |ECONOMY ANODIZED TIN | 19| 8 +Brand#23 |ECONOMY ANODIZED TIN | 45| 8 +Brand#23 |ECONOMY ANODIZED TIN | 49| 8 +Brand#23 |ECONOMY BRUSHED BRASS | 3| 8 +Brand#23 |ECONOMY BRUSHED BRASS | 36| 8 +Brand#23 |ECONOMY BRUSHED COPPER | 9| 8 +Brand#23 |ECONOMY BRUSHED TIN | 9| 8 +Brand#23 |ECONOMY BRUSHED TIN | 19| 8 +Brand#23 |ECONOMY BRUSHED TIN | 23| 8 +Brand#23 |ECONOMY BURNISHED BRASS | 9| 8 +Brand#23 |ECONOMY BURNISHED BRASS | 14| 8 +Brand#23 |ECONOMY BURNISHED COPPER | 14| 8 +Brand#23 |ECONOMY BURNISHED NICKEL | 9| 8 +Brand#23 |ECONOMY BURNISHED NICKEL | 23| 8 +Brand#23 |ECONOMY BURNISHED STEEL | 45| 8 +Brand#23 |ECONOMY PLATED BRASS | 23| 8 +Brand#23 |ECONOMY PLATED COPPER | 23| 8 +Brand#23 |ECONOMY PLATED NICKEL | 3| 8 +Brand#23 |ECONOMY PLATED NICKEL | 23| 8 +Brand#23 |ECONOMY PLATED STEEL | 19| 8 +Brand#23 |ECONOMY PLATED TIN | 3| 8 +Brand#23 |ECONOMY PLATED TIN | 19| 8 +Brand#23 |ECONOMY PLATED TIN | 23| 8 +Brand#23 |ECONOMY PLATED TIN | 36| 8 +Brand#23 |ECONOMY POLISHED BRASS | 36| 8 +Brand#23 |ECONOMY POLISHED COPPER | 3| 8 +Brand#23 |ECONOMY POLISHED COPPER | 14| 8 +Brand#23 |ECONOMY POLISHED COPPER | 49| 8 +Brand#23 |ECONOMY POLISHED STEEL | 3| 8 +Brand#23 |ECONOMY POLISHED STEEL | 23| 8 +Brand#23 |ECONOMY POLISHED STEEL | 36| 8 +Brand#23 |ECONOMY POLISHED TIN | 45| 8 +Brand#23 |LARGE ANODIZED BRASS | 9| 8 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|LARGE PLATED STEEL | 45| 8 +Brand#23 |LARGE POLISHED NICKEL | 3| 8 +Brand#23 |LARGE POLISHED NICKEL | 49| 8 +Brand#23 |LARGE POLISHED TIN | 9| 8 +Brand#23 |LARGE POLISHED TIN | 14| 8 +Brand#23 |LARGE POLISHED TIN | 36| 8 +Brand#23 |LARGE POLISHED TIN | 49| 8 +Brand#23 |MEDIUM ANODIZED COPPER | 45| 8 +Brand#23 |MEDIUM ANODIZED NICKEL | 3| 8 +Brand#23 |MEDIUM ANODIZED NICKEL | 14| 8 +Brand#23 |MEDIUM ANODIZED STEEL | 3| 8 +Brand#23 |MEDIUM ANODIZED STEEL | 19| 8 +Brand#23 |MEDIUM ANODIZED STEEL | 49| 8 +Brand#23 |MEDIUM ANODIZED TIN | 14| 8 +Brand#23 |MEDIUM ANODIZED TIN | 23| 8 +Brand#23 |MEDIUM ANODIZED TIN | 45| 8 +Brand#23 |MEDIUM BRUSHED BRASS | 45| 8 +Brand#23 |MEDIUM BRUSHED COPPER | 19| 8 +Brand#23 |MEDIUM BRUSHED COPPER | 23| 8 +Brand#23 |MEDIUM BRUSHED NICKEL | 3| 8 +Brand#23 |MEDIUM BRUSHED NICKEL | 14| 8 +Brand#23 |MEDIUM BRUSHED TIN | 14| 8 +Brand#23 |MEDIUM BRUSHED TIN | 45| 8 +Brand#23 |MEDIUM BURNISHED BRASS | 3| 8 +Brand#23 |MEDIUM BURNISHED BRASS | 9| 8 +Brand#23 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23| 8 +Brand#23 |STANDARD PLATED COPPER | 36| 8 +Brand#23 |STANDARD PLATED STEEL | 3| 8 +Brand#23 |STANDARD PLATED STEEL | 19| 8 +Brand#23 |STANDARD PLATED STEEL | 36| 8 +Brand#23 |STANDARD PLATED STEEL | 49| 8 +Brand#23 |STANDARD PLATED TIN | 3| 8 +Brand#23 |STANDARD PLATED TIN | 23| 8 +Brand#23 |STANDARD POLISHED BRASS | 36| 8 +Brand#23 |STANDARD POLISHED BRASS | 49| 8 +Brand#23 |STANDARD POLISHED COPPER | 14| 8 +Brand#23 |STANDARD POLISHED COPPER | 45| 8 +Brand#23 |STANDARD POLISHED TIN | 3| 8 +Brand#24 |ECONOMY ANODIZED BRASS | 23| 8 +Brand#24 |ECONOMY ANODIZED COPPER | 9| 8 +Brand#24 |ECONOMY ANODIZED COPPER | 14| 8 +Brand#24 |ECONOMY ANODIZED NICKEL | 9| 8 +Brand#24 |ECONOMY ANODIZED NICKEL | 14| 8 +Brand#24 |ECONOMY ANODIZED NICKEL | 19| 8 +Brand#24 |ECONOMY ANODIZED NICKEL | 36| 8 +Brand#24 |ECONOMY ANODIZED STEEL | 3| 8 +Brand#24 |ECONOMY ANODIZED STEEL | 36| 8 +Brand#24 |ECONOMY ANODIZED TIN | 19| 8 +Brand#24 |ECONOMY ANODIZED TIN | 36| 8 +Brand#24 |ECONOMY BRUSHED BRASS | 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| 23| 8 +Brand#24 |LARGE BURNISHED COPPER | 3| 8 +Brand#24 |LARGE BURNISHED NICKEL | 14| 8 +Brand#24 |LARGE BURNISHED NICKEL | 19| 8 +Brand#24 |LARGE BURNISHED TIN | 45| 8 +Brand#24 |LARGE PLATED BRASS | 9| 8 +Brand#24 |LARGE PLATED BRASS | 23| 8 +Brand#24 |LARGE PLATED COPPER | 45| 8 +Brand#24 |LARGE PLATED COPPER | 49| 8 +Brand#24 |LARGE PLATED NICKEL | 14| 8 +Brand#24 |LARGE PLATED NICKEL | 49| 8 +Brand#24 |LARGE PLATED STEEL | 19| 8 +Brand#24 |LARGE PLATED STEEL | 36| 8 +Brand#24 |LARGE PLATED TIN | 19| 8 +Brand#24 |LARGE POLISHED BRASS | 3| 8 +Brand#24 |LARGE POLISHED BRASS | 14| 8 +Brand#24 |LARGE POLISHED BRASS | 36| 8 +Brand#24 |LARGE POLISHED NICKEL | 9| 8 +Brand#24 |LARGE POLISHED NICKEL | 19| 8 +Brand#24 |LARGE POLISHED NICKEL | 36| 8 +Brand#24 |LARGE POLISHED STEEL | 23| 8 +Brand#24 |LARGE POLISHED STEEL | 49| 8 +Brand#24 |MEDIUM ANODIZED BRASS | 45| 8 +Brand#24 |MEDIUM ANODIZED BRASS | 49| 8 +Brand#24 |MEDIUM ANODIZED COPPER | 45| 8 +Brand#24 |MEDIUM ANODIZED NICKEL | 36| 8 +Brand#24 |MEDIUM ANODIZED STEEL | 9| 8 +Brand#24 |MEDIUM ANODIZED STEEL | 23| 8 +Brand#24 |MEDIUM ANODIZED STEEL | 49| 8 +Brand#24 |MEDIUM BRUSHED BRASS | 3| 8 +Brand#24 |MEDIUM BRUSHED COPPER | 14| 8 +Brand#24 |MEDIUM BRUSHED TIN | 49| 8 +Brand#24 |MEDIUM BURNISHED BRASS | 9| 8 +Brand#24 |MEDIUM BURNISHED COPPER | 3| 8 +Brand#24 |MEDIUM BURNISHED COPPER | 9| 8 +Brand#24 |MEDIUM BURNISHED NICKEL | 36| 8 +Brand#24 |MEDIUM BURNISHED NICKEL | 45| 8 +Brand#24 |MEDIUM BURNISHED STEEL | 19| 8 +Brand#24 |MEDIUM BURNISHED STEEL | 36| 8 +Brand#24 |MEDIUM PLATED BRASS | 19| 8 +Brand#24 |MEDIUM PLATED BRASS | 23| 8 +Brand#24 |MEDIUM PLATED COPPER | 3| 8 +Brand#24 |MEDIUM PLATED COPPER | 9| 8 +Brand#24 |MEDIUM PLATED COPPER | 23| 8 +Brand#24 |MEDIUM PLATED COPPER | 45| 8 +Brand#24 |MEDIUM PLATED NICKEL | 3| 8 +Brand#24 |MEDIUM PLATED NICKEL | 19| 8 +Brand#24 |MEDIUM PLATED STEEL | 14| 8 +Brand#24 |MEDIUM PLATED STEEL | 19| 8 +Brand#24 |PROMO ANODIZED BRASS | 3| 8 +Brand#24 |PROMO ANODIZED NICKEL | 14| 8 +Brand#24 |PROMO ANODIZED STEEL | 9| 8 +Brand#24 |PROMO ANODIZED STEEL | 45| 8 +Brand#24 |PROMO BRUSHED BRASS | 19| 8 +Brand#24 |PROMO BRUSHED COPPER | 3| 8 +Brand#24 |PROMO BRUSHED COPPER | 45| 8 +Brand#24 |PROMO BRUSHED NICKEL | 19| 8 +Brand#24 |PROMO BRUSHED NICKEL | 45| 8 +Brand#24 |PROMO BRUSHED NICKEL | 49| 8 +Brand#24 |PROMO BRUSHED STEEL | 19| 8 +Brand#24 |PROMO BRUSHED TIN | 14| 8 +Brand#24 |PROMO BURNISHED BRASS | 49| 8 +Brand#24 |PROMO BURNISHED STEEL | 3| 8 +Brand#24 |PROMO PLATED BRASS | 3| 8 +Brand#24 |PROMO PLATED BRASS | 9| 8 +Brand#24 |PROMO PLATED BRASS | 19| 8 +Brand#24 |PROMO PLATED BRASS | 49| 8 +Brand#24 |PROMO PLATED COPPER | 9| 8 +Brand#24 |PROMO PLATED NICKEL | 9| 8 +Brand#24 |PROMO PLATED STEEL | 36| 8 +Brand#24 |PROMO PLATED TIN | 23| 8 +Brand#24 |PROMO PLATED TIN | 49| 8 +Brand#24 |PROMO POLISHED BRASS | 45| 8 +Brand#24 |PROMO POLISHED COPPER | 49| 8 +Brand#24 |PROMO POLISHED NICKEL | 45| 8 +Brand#24 |PROMO POLISHED NICKEL | 49| 8 +Brand#24 |PROMO POLISHED STEEL | 14| 8 +Brand#24 |PROMO POLISHED STEEL | 36| 8 +Brand#24 |PROMO POLISHED TIN | 3| 8 +Brand#24 |PROMO POLISHED TIN | 14| 8 +Brand#24 |PROMO POLISHED TIN | 45| 8 +Brand#24 |SMALL ANODIZED BRASS | 19| 8 +Brand#24 |SMALL ANODIZED BRASS | 23| 8 +Brand#24 |SMALL ANODIZED COPPER | 36| 8 +Brand#24 |SMALL ANODIZED NICKEL | 9| 8 +Brand#24 |SMALL ANODIZED NICKEL | 45| 8 +Brand#24 |SMALL ANODIZED NICKEL | 49| 8 +Brand#24 |SMALL ANODIZED STEEL | 45| 8 +Brand#24 |SMALL ANODIZED TIN | 9| 8 +Brand#24 |SMALL ANODIZED TIN | 23| 8 +Brand#24 |SMALL ANODIZED TIN | 36| 8 +Brand#24 |SMALL BRUSHED BRASS | 9| 8 +Brand#24 |SMALL BRUSHED COPPER | 19| 8 +Brand#24 |SMALL BRUSHED NICKEL | 36| 8 +Brand#24 |SMALL BRUSHED STEEL | 9| 8 +Brand#24 |SMALL BRUSHED STEEL | 19| 8 +Brand#24 |SMALL BRUSHED STEEL | 36| 8 +Brand#24 |SMALL BRUSHED TIN | 3| 8 +Brand#24 |SMALL BRUSHED TIN | 14| 8 +Brand#24 |SMALL BRUSHED TIN | 36| 8 +Brand#24 |SMALL BRUSHED TIN | 49| 8 +Brand#24 |SMALL BURNISHED BRASS | 19| 8 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|STANDARD ANODIZED BRASS | 23| 8 +Brand#24 |STANDARD ANODIZED BRASS | 49| 8 +Brand#24 |STANDARD ANODIZED COPPER | 14| 8 +Brand#24 |STANDARD ANODIZED NICKEL | 36| 8 +Brand#24 |STANDARD ANODIZED STEEL | 3| 8 +Brand#24 |STANDARD ANODIZED STEEL | 14| 8 +Brand#24 |STANDARD BRUSHED BRASS | 3| 8 +Brand#24 |STANDARD BRUSHED BRASS | 36| 8 +Brand#24 |STANDARD BRUSHED COPPER | 9| 8 +Brand#24 |STANDARD BRUSHED COPPER | 23| 8 +Brand#24 |STANDARD BRUSHED NICKEL | 45| 8 +Brand#24 |STANDARD BRUSHED STEEL | 19| 8 +Brand#24 |STANDARD BRUSHED TIN | 14| 8 +Brand#24 |STANDARD BURNISHED NICKEL| 9| 8 +Brand#24 |STANDARD BURNISHED NICKEL| 23| 8 +Brand#24 |STANDARD BURNISHED NICKEL| 36| 8 +Brand#24 |STANDARD BURNISHED STEEL | 9| 8 +Brand#24 |STANDARD BURNISHED STEEL | 45| 8 +Brand#24 |STANDARD BURNISHED TIN | 14| 8 +Brand#24 |STANDARD BURNISHED TIN | 23| 8 +Brand#24 |STANDARD PLATED BRASS | 14| 8 +Brand#24 |STANDARD PLATED COPPER | 14| 8 +Brand#24 |STANDARD PLATED NICKEL | 3| 8 +Brand#24 |STANDARD PLATED NICKEL | 14| 8 +Brand#24 |STANDARD PLATED NICKEL | 45| 8 +Brand#24 |STANDARD PLATED STEEL | 19| 8 +Brand#24 |STANDARD PLATED STEEL | 49| 8 +Brand#24 |STANDARD PLATED TIN | 36| 8 +Brand#24 |STANDARD PLATED TIN | 45| 8 +Brand#24 |STANDARD POLISHED BRASS | 49| 8 +Brand#24 |STANDARD POLISHED COPPER | 23| 8 +Brand#24 |STANDARD POLISHED COPPER | 45| 8 +Brand#24 |STANDARD POLISHED NICKEL | 3| 8 +Brand#24 |STANDARD POLISHED NICKEL | 14| 8 +Brand#24 |STANDARD POLISHED STEEL | 3| 8 +Brand#24 |STANDARD POLISHED STEEL | 9| 8 +Brand#24 |STANDARD POLISHED STEEL | 19| 8 +Brand#24 |STANDARD POLISHED STEEL | 23| 8 +Brand#25 |ECONOMY ANODIZED BRASS | 49| 8 +Brand#25 |ECONOMY ANODIZED COPPER | 9| 8 +Brand#25 |ECONOMY ANODIZED COPPER | 23| 8 +Brand#25 |ECONOMY ANODIZED NICKEL | 9| 8 +Brand#25 |ECONOMY ANODIZED NICKEL | 19| 8 +Brand#25 |ECONOMY ANODIZED NICKEL | 45| 8 +Brand#25 |ECONOMY ANODIZED STEEL | 3| 8 +Brand#25 |ECONOMY ANODIZED STEEL | 19| 8 +Brand#25 |ECONOMY ANODIZED TIN | 49| 8 +Brand#25 |ECONOMY BRUSHED BRASS | 36| 8 +Brand#25 |ECONOMY BRUSHED NICKEL | 36| 8 +Brand#25 |ECONOMY BRUSHED STEEL | 49| 8 +Brand#25 |ECONOMY BURNISHED COPPER | 9| 8 +Brand#25 |ECONOMY BURNISHED COPPER | 14| 8 +Brand#25 |ECONOMY BURNISHED COPPER | 45| 8 +Brand#25 |ECONOMY BURNISHED NICKEL | 36| 8 +Brand#25 |ECONOMY BURNISHED STEEL | 9| 8 +Brand#25 |ECONOMY PLATED NICKEL | 45| 8 +Brand#25 |ECONOMY PLATED STEEL | 49| 8 +Brand#25 |ECONOMY PLATED TIN | 3| 8 +Brand#25 |ECONOMY PLATED TIN | 19| 8 +Brand#25 |ECONOMY PLATED TIN | 36| 8 +Brand#25 |ECONOMY POLISHED BRASS | 36| 8 +Brand#25 |ECONOMY POLISHED BRASS | 45| 8 +Brand#25 |ECONOMY POLISHED COPPER | 9| 8 +Brand#25 |ECONOMY POLISHED TIN | 36| 8 +Brand#25 |LARGE ANODIZED BRASS | 45| 8 +Brand#25 |LARGE ANODIZED NICKEL | 36| 8 +Brand#25 |LARGE ANODIZED STEEL | 3| 8 +Brand#25 |LARGE BRUSHED BRASS | 3| 8 +Brand#25 |LARGE BRUSHED NICKEL | 19| 8 +Brand#25 |LARGE BURNISHED BRASS | 9| 8 +Brand#25 |LARGE BURNISHED BRASS | 49| 8 +Brand#25 |LARGE BURNISHED COPPER | 3| 8 +Brand#25 |LARGE BURNISHED COPPER | 14| 8 +Brand#25 |LARGE BURNISHED COPPER | 19| 8 +Brand#25 |LARGE BURNISHED COPPER | 45| 8 +Brand#25 |LARGE BURNISHED TIN | 3| 8 +Brand#25 |LARGE BURNISHED TIN | 9| 8 +Brand#25 |LARGE PLATED COPPER | 36| 8 +Brand#25 |LARGE PLATED NICKEL | 36| 8 +Brand#25 |LARGE PLATED STEEL | 9| 8 +Brand#25 |LARGE PLATED STEEL | 23| 8 +Brand#25 |LARGE PLATED STEEL | 49| 8 +Brand#25 |LARGE PLATED TIN | 3| 8 +Brand#25 |LARGE PLATED TIN | 9| 8 +Brand#25 |LARGE PLATED TIN | 19| 8 +Brand#25 |LARGE PLATED TIN | 45| 8 +Brand#25 |LARGE POLISHED BRASS | 9| 8 +Brand#25 |LARGE POLISHED BRASS | 19| 8 +Brand#25 |LARGE POLISHED COPPER | 14| 8 +Brand#25 |LARGE POLISHED COPPER | 23| 8 +Brand#25 |LARGE POLISHED COPPER | 36| 8 +Brand#25 |LARGE POLISHED NICKEL | 14| 8 +Brand#25 |LARGE POLISHED STEEL | 9| 8 +Brand#25 |LARGE POLISHED STEEL | 36| 8 +Brand#25 |LARGE POLISHED STEEL | 45| 8 +Brand#25 |MEDIUM ANODIZED COPPER | 9| 8 +Brand#25 |MEDIUM ANODIZED COPPER | 36| 8 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|MEDIUM PLATED BRASS | 45| 8 +Brand#25 |MEDIUM PLATED COPPER | 49| 8 +Brand#25 |MEDIUM PLATED NICKEL | 9| 8 +Brand#25 |MEDIUM PLATED NICKEL | 19| 8 +Brand#25 |MEDIUM PLATED NICKEL | 23| 8 +Brand#25 |MEDIUM PLATED NICKEL | 36| 8 +Brand#25 |MEDIUM PLATED NICKEL | 45| 8 +Brand#25 |MEDIUM PLATED TIN | 3| 8 +Brand#25 |PROMO ANODIZED BRASS | 23| 8 +Brand#25 |PROMO ANODIZED BRASS | 45| 8 +Brand#25 |PROMO ANODIZED COPPER | 19| 8 +Brand#25 |PROMO ANODIZED COPPER | 45| 8 +Brand#25 |PROMO ANODIZED TIN | 3| 8 +Brand#25 |PROMO ANODIZED TIN | 14| 8 +Brand#25 |PROMO ANODIZED TIN | 19| 8 +Brand#25 |PROMO BRUSHED COPPER | 49| 8 +Brand#25 |PROMO BRUSHED NICKEL | 3| 8 +Brand#25 |PROMO BRUSHED NICKEL | 19| 8 +Brand#25 |PROMO BRUSHED TIN | 14| 8 +Brand#25 |PROMO BRUSHED TIN | 19| 8 +Brand#25 |PROMO BURNISHED COPPER | 14| 8 +Brand#25 |PROMO BURNISHED NICKEL | 3| 8 +Brand#25 |PROMO BURNISHED NICKEL | 36| 8 +Brand#25 |PROMO BURNISHED STEEL | 19| 8 +Brand#25 |PROMO BURNISHED TIN | 36| 8 +Brand#25 |PROMO PLATED 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POLISHED TIN | 45| 8 +Brand#25 |SMALL POLISHED TIN | 49| 8 +Brand#25 |STANDARD ANODIZED BRASS | 9| 8 +Brand#25 |STANDARD ANODIZED BRASS | 45| 8 +Brand#25 |STANDARD ANODIZED NICKEL | 14| 8 +Brand#25 |STANDARD ANODIZED NICKEL | 23| 8 +Brand#25 |STANDARD ANODIZED NICKEL | 49| 8 +Brand#25 |STANDARD ANODIZED STEEL | 3| 8 +Brand#25 |STANDARD ANODIZED STEEL | 9| 8 +Brand#25 |STANDARD ANODIZED TIN | 9| 8 +Brand#25 |STANDARD ANODIZED TIN | 14| 8 +Brand#25 |STANDARD ANODIZED TIN | 23| 8 +Brand#25 |STANDARD ANODIZED TIN | 49| 8 +Brand#25 |STANDARD BRUSHED COPPER | 3| 8 +Brand#25 |STANDARD BRUSHED COPPER | 36| 8 +Brand#25 |STANDARD BRUSHED NICKEL | 14| 8 +Brand#25 |STANDARD BRUSHED NICKEL | 19| 8 +Brand#25 |STANDARD BRUSHED TIN | 36| 8 +Brand#25 |STANDARD BURNISHED NICKEL| 9| 8 +Brand#25 |STANDARD BURNISHED NICKEL| 19| 8 +Brand#25 |STANDARD BURNISHED STEEL | 14| 8 +Brand#25 |STANDARD BURNISHED STEEL | 36| 8 +Brand#25 |STANDARD BURNISHED STEEL | 45| 8 +Brand#25 |STANDARD BURNISHED TIN | 14| 8 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ANODIZED BRASS | 14| 8 +Brand#31 |LARGE ANODIZED BRASS | 36| 8 +Brand#31 |LARGE ANODIZED COPPER | 23| 8 +Brand#31 |LARGE ANODIZED COPPER | 45| 8 +Brand#31 |LARGE ANODIZED NICKEL | 49| 8 +Brand#31 |LARGE ANODIZED STEEL | 3| 8 +Brand#31 |LARGE ANODIZED STEEL | 9| 8 +Brand#31 |LARGE ANODIZED TIN | 9| 8 +Brand#31 |LARGE BRUSHED BRASS | 45| 8 +Brand#31 |LARGE BRUSHED BRASS | 49| 8 +Brand#31 |LARGE BRUSHED COPPER | 19| 8 +Brand#31 |LARGE BRUSHED NICKEL | 14| 8 +Brand#31 |LARGE BRUSHED NICKEL | 23| 8 +Brand#31 |LARGE BRUSHED STEEL | 14| 8 +Brand#31 |LARGE BRUSHED TIN | 45| 8 +Brand#31 |LARGE BURNISHED BRASS | 19| 8 +Brand#31 |LARGE BURNISHED BRASS | 23| 8 +Brand#31 |LARGE BURNISHED COPPER | 23| 8 +Brand#31 |LARGE BURNISHED COPPER | 45| 8 +Brand#31 |LARGE BURNISHED NICKEL | 9| 8 +Brand#31 |LARGE BURNISHED NICKEL | 19| 8 +Brand#31 |LARGE BURNISHED STEEL | 9| 8 +Brand#31 |LARGE BURNISHED STEEL | 36| 8 +Brand#31 |LARGE BURNISHED TIN | 14| 8 +Brand#31 |LARGE BURNISHED TIN | 23| 8 +Brand#31 |LARGE BURNISHED TIN | 49| 8 +Brand#31 |LARGE PLATED BRASS | 14| 8 +Brand#31 |LARGE PLATED COPPER | 19| 8 +Brand#31 |LARGE PLATED TIN | 9| 8 +Brand#31 |LARGE PLATED TIN | 36| 8 +Brand#31 |LARGE POLISHED BRASS | 45| 8 +Brand#31 |LARGE POLISHED COPPER | 3| 8 +Brand#31 |LARGE POLISHED COPPER | 9| 8 +Brand#31 |LARGE POLISHED COPPER | 19| 8 +Brand#31 |LARGE POLISHED NICKEL | 14| 8 +Brand#31 |LARGE POLISHED NICKEL | 19| 8 +Brand#31 |LARGE POLISHED TIN | 14| 8 +Brand#31 |LARGE POLISHED TIN | 19| 8 +Brand#31 |LARGE POLISHED TIN | 23| 8 +Brand#31 |MEDIUM ANODIZED BRASS | 19| 8 +Brand#31 |MEDIUM ANODIZED BRASS | 23| 8 +Brand#31 |MEDIUM ANODIZED COPPER | 14| 8 +Brand#31 |MEDIUM ANODIZED COPPER | 19| 8 +Brand#31 |MEDIUM ANODIZED STEEL | 49| 8 +Brand#31 |MEDIUM ANODIZED TIN | 19| 8 +Brand#31 |MEDIUM BRUSHED BRASS | 19| 8 +Brand#31 |MEDIUM BRUSHED BRASS | 36| 8 +Brand#31 |MEDIUM BRUSHED COPPER | 9| 8 +Brand#31 |MEDIUM BRUSHED COPPER | 23| 8 +Brand#31 |MEDIUM BRUSHED COPPER | 49| 8 +Brand#31 |MEDIUM BRUSHED 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BRUSHED BRASS | 23| 8 +Brand#31 |PROMO BRUSHED BRASS | 36| 8 +Brand#31 |PROMO BRUSHED COPPER | 45| 8 +Brand#31 |PROMO BRUSHED NICKEL | 23| 8 +Brand#31 |PROMO BRUSHED NICKEL | 49| 8 +Brand#31 |PROMO BRUSHED STEEL | 49| 8 +Brand#31 |PROMO BRUSHED TIN | 9| 8 +Brand#31 |PROMO BRUSHED TIN | 36| 8 +Brand#31 |PROMO BURNISHED COPPER | 3| 8 +Brand#31 |PROMO BURNISHED COPPER | 14| 8 +Brand#31 |PROMO BURNISHED COPPER | 19| 8 +Brand#31 |PROMO BURNISHED COPPER | 36| 8 +Brand#31 |PROMO BURNISHED NICKEL | 45| 8 +Brand#31 |PROMO BURNISHED STEEL | 19| 8 +Brand#31 |PROMO PLATED COPPER | 19| 8 +Brand#31 |PROMO PLATED COPPER | 36| 8 +Brand#31 |PROMO PLATED COPPER | 49| 8 +Brand#31 |PROMO PLATED NICKEL | 36| 8 +Brand#31 |PROMO PLATED STEEL | 19| 8 +Brand#31 |PROMO PLATED STEEL | 23| 8 +Brand#31 |PROMO PLATED TIN | 3| 8 +Brand#31 |PROMO PLATED TIN | 49| 8 +Brand#31 |PROMO POLISHED BRASS | 3| 8 +Brand#31 |PROMO POLISHED BRASS | 49| 8 +Brand#31 |PROMO POLISHED NICKEL | 3| 8 +Brand#31 |PROMO POLISHED TIN | 9| 8 +Brand#31 |PROMO POLISHED TIN | 45| 8 +Brand#31 |SMALL ANODIZED BRASS | 9| 8 +Brand#31 |SMALL ANODIZED BRASS | 36| 8 +Brand#31 |SMALL ANODIZED COPPER | 36| 8 +Brand#31 |SMALL ANODIZED COPPER | 45| 8 +Brand#31 |SMALL ANODIZED NICKEL | 14| 8 +Brand#31 |SMALL ANODIZED NICKEL | 49| 8 +Brand#31 |SMALL ANODIZED STEEL | 9| 8 +Brand#31 |SMALL ANODIZED STEEL | 45| 8 +Brand#31 |SMALL ANODIZED TIN | 23| 8 +Brand#31 |SMALL BRUSHED BRASS | 23| 8 +Brand#31 |SMALL BRUSHED NICKEL | 19| 8 +Brand#31 |SMALL BRUSHED NICKEL | 49| 8 +Brand#31 |SMALL BRUSHED STEEL | 36| 8 +Brand#31 |SMALL BRUSHED STEEL | 49| 8 +Brand#31 |SMALL BRUSHED TIN | 9| 8 +Brand#31 |SMALL BRUSHED TIN | 45| 8 +Brand#31 |SMALL BURNISHED NICKEL | 23| 8 +Brand#31 |SMALL BURNISHED STEEL | 3| 8 +Brand#31 |SMALL BURNISHED STEEL | 9| 8 +Brand#31 |SMALL BURNISHED STEEL | 19| 8 +Brand#31 |SMALL BURNISHED STEEL | 23| 8 +Brand#31 |SMALL BURNISHED STEEL | 36| 8 +Brand#31 |SMALL BURNISHED STEEL | 49| 8 +Brand#31 |SMALL BURNISHED TIN | 36| 8 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PLATED NICKEL | 23| 8 +Brand#34 |ECONOMY PLATED NICKEL | 36| 8 +Brand#34 |ECONOMY PLATED NICKEL | 45| 8 +Brand#34 |ECONOMY PLATED STEEL | 3| 8 +Brand#34 |ECONOMY PLATED STEEL | 9| 8 +Brand#34 |ECONOMY PLATED TIN | 45| 8 +Brand#34 |ECONOMY POLISHED BRASS | 14| 8 +Brand#34 |ECONOMY POLISHED BRASS | 19| 8 +Brand#34 |ECONOMY POLISHED BRASS | 36| 8 +Brand#34 |ECONOMY POLISHED COPPER | 14| 8 +Brand#34 |ECONOMY POLISHED COPPER | 19| 8 +Brand#34 |ECONOMY POLISHED COPPER | 45| 8 +Brand#34 |ECONOMY POLISHED STEEL | 14| 8 +Brand#34 |ECONOMY POLISHED STEEL | 23| 8 +Brand#34 |ECONOMY POLISHED STEEL | 45| 8 +Brand#34 |LARGE ANODIZED TIN | 36| 8 +Brand#34 |LARGE BRUSHED BRASS | 14| 8 +Brand#34 |LARGE BRUSHED BRASS | 49| 8 +Brand#34 |LARGE BRUSHED STEEL | 19| 8 +Brand#34 |LARGE BRUSHED STEEL | 49| 8 +Brand#34 |LARGE BRUSHED TIN | 9| 8 +Brand#34 |LARGE BURNISHED BRASS | 36| 8 +Brand#34 |LARGE BURNISHED BRASS | 45| 8 +Brand#34 |LARGE BURNISHED COPPER | 3| 8 +Brand#34 |LARGE BURNISHED COPPER | 14| 8 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8 +Brand#34 |SMALL BURNISHED TIN | 36| 8 +Brand#34 |SMALL BURNISHED TIN | 49| 8 +Brand#34 |SMALL PLATED BRASS | 23| 8 +Brand#34 |SMALL PLATED COPPER | 19| 8 +Brand#34 |SMALL PLATED COPPER | 23| 8 +Brand#34 |SMALL PLATED COPPER | 49| 8 +Brand#34 |SMALL PLATED NICKEL | 3| 8 +Brand#34 |SMALL PLATED NICKEL | 9| 8 +Brand#34 |SMALL PLATED NICKEL | 23| 8 +Brand#34 |SMALL PLATED STEEL | 9| 8 +Brand#34 |SMALL PLATED STEEL | 45| 8 +Brand#34 |SMALL PLATED TIN | 14| 8 +Brand#34 |SMALL PLATED TIN | 19| 8 +Brand#34 |SMALL POLISHED BRASS | 14| 8 +Brand#34 |SMALL POLISHED COPPER | 9| 8 +Brand#34 |SMALL POLISHED COPPER | 45| 8 +Brand#34 |STANDARD ANODIZED BRASS | 14| 8 +Brand#34 |STANDARD ANODIZED BRASS | 23| 8 +Brand#34 |STANDARD ANODIZED COPPER | 3| 8 +Brand#34 |STANDARD ANODIZED COPPER | 45| 8 +Brand#34 |STANDARD ANODIZED NICKEL | 3| 8 +Brand#34 |STANDARD ANODIZED NICKEL | 9| 8 +Brand#34 |STANDARD ANODIZED NICKEL | 23| 8 +Brand#34 |STANDARD ANODIZED NICKEL | 36| 8 +Brand#34 |STANDARD ANODIZED STEEL 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8 +Brand#42 |LARGE BRUSHED COPPER | 9| 8 +Brand#42 |LARGE BRUSHED NICKEL | 19| 8 +Brand#42 |LARGE BRUSHED NICKEL | 36| 8 +Brand#42 |LARGE BRUSHED NICKEL | 49| 8 +Brand#42 |LARGE BRUSHED TIN | 23| 8 +Brand#42 |LARGE BRUSHED TIN | 49| 8 +Brand#42 |LARGE BURNISHED BRASS | 3| 8 +Brand#42 |LARGE BURNISHED BRASS | 49| 8 +Brand#42 |LARGE BURNISHED TIN | 45| 8 +Brand#42 |LARGE PLATED COPPER | 9| 8 +Brand#42 |LARGE PLATED COPPER | 45| 8 +Brand#42 |LARGE PLATED NICKEL | 45| 8 +Brand#42 |LARGE PLATED TIN | 3| 8 +Brand#42 |LARGE PLATED TIN | 45| 8 +Brand#42 |LARGE POLISHED COPPER | 49| 8 +Brand#42 |LARGE POLISHED NICKEL | 23| 8 +Brand#42 |LARGE POLISHED NICKEL | 36| 8 +Brand#42 |LARGE POLISHED STEEL | 3| 8 +Brand#42 |LARGE POLISHED TIN | 3| 8 +Brand#42 |LARGE POLISHED TIN | 19| 8 +Brand#42 |LARGE POLISHED TIN | 45| 8 +Brand#42 |MEDIUM ANODIZED BRASS | 9| 8 +Brand#42 |MEDIUM ANODIZED BRASS | 49| 8 +Brand#42 |MEDIUM ANODIZED COPPER | 3| 8 +Brand#42 |MEDIUM ANODIZED COPPER | 19| 8 +Brand#42 |MEDIUM ANODIZED COPPER | 49| 8 +Brand#42 |MEDIUM ANODIZED NICKEL | 36| 8 +Brand#42 |MEDIUM ANODIZED STEEL | 3| 8 +Brand#42 |MEDIUM ANODIZED TIN | 14| 8 +Brand#42 |MEDIUM ANODIZED TIN | 36| 8 +Brand#42 |MEDIUM ANODIZED TIN | 45| 8 +Brand#42 |MEDIUM BRUSHED COPPER | 14| 8 +Brand#42 |MEDIUM BRUSHED COPPER | 49| 8 +Brand#42 |MEDIUM BRUSHED NICKEL | 14| 8 +Brand#42 |MEDIUM BRUSHED STEEL | 36| 8 +Brand#42 |MEDIUM BRUSHED STEEL | 49| 8 +Brand#42 |MEDIUM BURNISHED BRASS | 45| 8 +Brand#42 |MEDIUM BURNISHED COPPER | 3| 8 +Brand#42 |MEDIUM BURNISHED NICKEL | 14| 8 +Brand#42 |MEDIUM BURNISHED STEEL | 9| 8 +Brand#42 |MEDIUM BURNISHED STEEL | 14| 8 +Brand#42 |MEDIUM BURNISHED STEEL | 36| 8 +Brand#42 |MEDIUM BURNISHED TIN | 3| 8 +Brand#42 |MEDIUM PLATED BRASS | 49| 8 +Brand#42 |MEDIUM PLATED COPPER | 3| 8 +Brand#42 |MEDIUM PLATED COPPER | 49| 8 +Brand#42 |MEDIUM PLATED NICKEL | 9| 8 +Brand#42 |MEDIUM PLATED STEEL | 9| 8 +Brand#42 |MEDIUM PLATED STEEL | 14| 8 +Brand#42 |MEDIUM PLATED STEEL | 36| 8 +Brand#42 |MEDIUM PLATED TIN | 9| 8 +Brand#42 |MEDIUM PLATED TIN | 14| 8 +Brand#42 |PROMO ANODIZED BRASS | 9| 8 +Brand#42 |PROMO ANODIZED BRASS | 36| 8 +Brand#42 |PROMO ANODIZED BRASS | 45| 8 +Brand#42 |PROMO ANODIZED COPPER | 3| 8 +Brand#42 |PROMO ANODIZED COPPER | 23| 8 +Brand#42 |PROMO ANODIZED COPPER | 45| 8 +Brand#42 |PROMO ANODIZED NICKEL | 9| 8 +Brand#42 |PROMO ANODIZED TIN | 3| 8 +Brand#42 |PROMO BRUSHED COPPER | 14| 8 +Brand#42 |PROMO BRUSHED STEEL | 19| 8 +Brand#42 |PROMO BRUSHED STEEL | 23| 8 +Brand#42 |PROMO BRUSHED STEEL | 45| 8 +Brand#42 |PROMO BURNISHED BRASS | 14| 8 +Brand#42 |PROMO BURNISHED BRASS | 45| 8 +Brand#42 |PROMO BURNISHED BRASS | 49| 8 +Brand#42 |PROMO BURNISHED COPPER | 45| 8 +Brand#42 |PROMO BURNISHED NICKEL | 36| 8 +Brand#42 |PROMO PLATED NICKEL | 23| 8 +Brand#42 |PROMO PLATED STEEL | 45| 8 +Brand#42 |PROMO PLATED TIN | 9| 8 +Brand#42 |PROMO PLATED TIN | 19| 8 +Brand#42 |PROMO PLATED TIN | 23| 8 +Brand#42 |PROMO PLATED TIN | 36| 8 +Brand#42 |PROMO PLATED TIN | 45| 8 +Brand#42 |PROMO POLISHED BRASS | 19| 8 +Brand#42 |PROMO POLISHED BRASS | 23| 8 +Brand#42 |PROMO POLISHED BRASS | 45| 8 +Brand#42 |PROMO POLISHED COPPER | 36| 8 +Brand#42 |PROMO POLISHED NICKEL | 3| 8 +Brand#42 |PROMO POLISHED NICKEL | 9| 8 +Brand#42 |PROMO POLISHED STEEL | 9| 8 +Brand#42 |PROMO POLISHED STEEL | 23| 8 +Brand#42 |PROMO POLISHED TIN | 3| 8 +Brand#42 |PROMO POLISHED TIN | 9| 8 +Brand#42 |SMALL ANODIZED BRASS | 19| 8 +Brand#42 |SMALL ANODIZED COPPER | 14| 8 +Brand#42 |SMALL ANODIZED COPPER | 19| 8 +Brand#42 |SMALL ANODIZED COPPER | 36| 8 +Brand#42 |SMALL ANODIZED NICKEL | 14| 8 +Brand#42 |SMALL ANODIZED NICKEL | 23| 8 +Brand#42 |SMALL ANODIZED NICKEL | 45| 8 +Brand#42 |SMALL ANODIZED STEEL | 3| 8 +Brand#42 |SMALL ANODIZED STEEL | 9| 8 +Brand#42 |SMALL ANODIZED STEEL | 36| 8 +Brand#42 |SMALL ANODIZED TIN | 3| 8 +Brand#42 |SMALL ANODIZED TIN | 19| 8 +Brand#42 |SMALL BRUSHED COPPER | 9| 8 +Brand#42 |SMALL BRUSHED COPPER | 36| 8 +Brand#42 |SMALL BRUSHED NICKEL | 23| 8 +Brand#42 |SMALL BRUSHED STEEL | 3| 8 +Brand#42 |SMALL BRUSHED STEEL | 9| 8 +Brand#42 |SMALL BRUSHED STEEL | 14| 8 +Brand#42 |SMALL BRUSHED STEEL | 36| 8 +Brand#42 |SMALL BRUSHED STEEL | 45| 8 +Brand#42 |SMALL BRUSHED TIN | 9| 8 +Brand#42 |SMALL BRUSHED TIN | 14| 8 +Brand#42 |SMALL BRUSHED TIN | 45| 8 +Brand#42 |SMALL BRUSHED TIN | 49| 8 +Brand#42 |SMALL BURNISHED BRASS | 23| 8 +Brand#42 |SMALL BURNISHED NICKEL | 19| 8 +Brand#42 |SMALL BURNISHED STEEL | 14| 8 +Brand#42 |SMALL PLATED BRASS | 19| 8 +Brand#42 |SMALL PLATED COPPER | 36| 8 +Brand#42 |SMALL PLATED STEEL | 3| 8 +Brand#42 |SMALL PLATED STEEL | 23| 8 +Brand#42 |SMALL PLATED STEEL | 36| 8 +Brand#42 |SMALL PLATED TIN | 14| 8 +Brand#42 |SMALL PLATED TIN | 19| 8 +Brand#42 |SMALL PLATED TIN | 36| 8 +Brand#42 |SMALL POLISHED BRASS | 23| 8 +Brand#42 |SMALL POLISHED BRASS | 45| 8 +Brand#42 |SMALL POLISHED COPPER | 23| 8 +Brand#42 |SMALL POLISHED COPPER | 45| 8 +Brand#42 |SMALL POLISHED NICKEL | 14| 8 +Brand#42 |SMALL POLISHED NICKEL | 19| 8 +Brand#42 |SMALL POLISHED NICKEL | 45| 8 +Brand#42 |SMALL POLISHED STEEL | 49| 8 +Brand#42 |SMALL POLISHED TIN | 14| 8 +Brand#42 |SMALL POLISHED TIN | 36| 8 +Brand#42 |SMALL POLISHED TIN | 49| 8 +Brand#42 |STANDARD ANODIZED BRASS | 36| 8 +Brand#42 |STANDARD ANODIZED COPPER | 14| 8 +Brand#42 |STANDARD ANODIZED STEEL | 3| 8 +Brand#42 |STANDARD ANODIZED STEEL | 9| 8 +Brand#42 |STANDARD ANODIZED STEEL | 45| 8 +Brand#42 |STANDARD ANODIZED TIN | 3| 8 +Brand#42 |STANDARD BRUSHED BRASS | 3| 8 +Brand#42 |STANDARD BRUSHED BRASS | 9| 8 +Brand#42 |STANDARD BRUSHED BRASS | 23| 8 +Brand#42 |STANDARD BRUSHED COPPER | 36| 8 +Brand#42 |STANDARD BRUSHED COPPER | 49| 8 +Brand#42 |STANDARD BRUSHED NICKEL | 23| 8 +Brand#42 |STANDARD BRUSHED NICKEL | 49| 8 +Brand#42 |STANDARD BRUSHED STEEL | 23| 8 +Brand#42 |STANDARD BRUSHED TIN | 49| 8 +Brand#42 |STANDARD BURNISHED BRASS | 9| 8 +Brand#42 |STANDARD BURNISHED BRASS | 14| 8 +Brand#42 |STANDARD BURNISHED BRASS | 49| 8 +Brand#42 |STANDARD BURNISHED NICKEL| 14| 8 +Brand#42 |STANDARD BURNISHED NICKEL| 49| 8 +Brand#42 |STANDARD BURNISHED STEEL | 36| 8 +Brand#42 |STANDARD BURNISHED TIN | 9| 8 +Brand#42 |STANDARD PLATED COPPER | 49| 8 +Brand#42 |STANDARD PLATED NICKEL | 14| 8 +Brand#42 |STANDARD PLATED NICKEL | 45| 8 +Brand#42 |STANDARD PLATED STEEL | 14| 8 +Brand#42 |STANDARD PLATED STEEL | 19| 8 +Brand#42 |STANDARD PLATED STEEL | 36| 8 +Brand#42 |STANDARD PLATED STEEL | 45| 8 +Brand#42 |STANDARD PLATED TIN | 9| 8 +Brand#42 |STANDARD PLATED TIN | 14| 8 +Brand#42 |STANDARD POLISHED BRASS | 19| 8 +Brand#42 |STANDARD POLISHED BRASS | 36| 8 +Brand#42 |STANDARD POLISHED COPPER | 14| 8 +Brand#42 |STANDARD POLISHED COPPER | 19| 8 +Brand#42 |STANDARD POLISHED COPPER | 49| 8 +Brand#42 |STANDARD POLISHED NICKEL | 14| 8 +Brand#42 |STANDARD POLISHED NICKEL | 23| 8 +Brand#42 |STANDARD POLISHED STEEL | 23| 8 +Brand#42 |STANDARD POLISHED TIN | 14| 8 +Brand#42 |STANDARD POLISHED TIN | 23| 8 +Brand#42 |STANDARD POLISHED TIN | 36| 8 +Brand#43 |ECONOMY ANODIZED BRASS | 3| 8 +Brand#43 |ECONOMY ANODIZED NICKEL | 3| 8 +Brand#43 |ECONOMY ANODIZED NICKEL | 49| 8 +Brand#43 |ECONOMY ANODIZED STEEL | 23| 8 +Brand#43 |ECONOMY ANODIZED STEEL | 36| 8 +Brand#43 |ECONOMY ANODIZED TIN | 49| 8 +Brand#43 |ECONOMY BRUSHED COPPER | 45| 8 +Brand#43 |ECONOMY BRUSHED NICKEL | 9| 8 +Brand#43 |ECONOMY BRUSHED NICKEL | 14| 8 +Brand#43 |ECONOMY BRUSHED NICKEL | 19| 8 +Brand#43 |ECONOMY BRUSHED NICKEL | 49| 8 +Brand#43 |ECONOMY BRUSHED TIN | 36| 8 +Brand#43 |ECONOMY BRUSHED TIN | 45| 8 +Brand#43 |ECONOMY BURNISHED BRASS | 19| 8 +Brand#43 |ECONOMY BURNISHED COPPER | 14| 8 +Brand#43 |ECONOMY BURNISHED COPPER | 36| 8 +Brand#43 |ECONOMY BURNISHED NICKEL | 9| 8 +Brand#43 |ECONOMY BURNISHED NICKEL | 14| 8 +Brand#43 |ECONOMY BURNISHED NICKEL | 23| 8 +Brand#43 |ECONOMY BURNISHED NICKEL | 45| 8 +Brand#43 |ECONOMY BURNISHED STEEL | 3| 8 +Brand#43 |ECONOMY BURNISHED STEEL | 36| 8 +Brand#43 |ECONOMY BURNISHED TIN | 3| 8 +Brand#43 |ECONOMY BURNISHED TIN | 49| 8 +Brand#43 |ECONOMY PLATED COPPER | 19| 8 +Brand#43 |ECONOMY PLATED NICKEL | 9| 8 +Brand#43 |ECONOMY PLATED STEEL | 19| 8 +Brand#43 |ECONOMY PLATED TIN | 9| 8 +Brand#43 |ECONOMY PLATED TIN | 19| 8 +Brand#43 |ECONOMY POLISHED BRASS | 19| 8 +Brand#43 |ECONOMY POLISHED COPPER | 19| 8 +Brand#43 |ECONOMY POLISHED COPPER | 36| 8 +Brand#43 |ECONOMY POLISHED NICKEL | 19| 8 +Brand#43 |ECONOMY POLISHED NICKEL | 36| 8 +Brand#43 |ECONOMY POLISHED STEEL | 3| 8 +Brand#43 |ECONOMY POLISHED TIN | 9| 8 +Brand#43 |ECONOMY POLISHED TIN | 36| 8 +Brand#43 |ECONOMY POLISHED TIN | 45| 8 +Brand#43 |LARGE ANODIZED BRASS | 14| 8 +Brand#43 |LARGE ANODIZED BRASS | 36| 8 +Brand#43 |LARGE ANODIZED COPPER | 19| 8 +Brand#43 |LARGE ANODIZED NICKEL | 3| 8 +Brand#43 |LARGE ANODIZED NICKEL | 23| 8 +Brand#43 |LARGE ANODIZED NICKEL | 36| 8 +Brand#43 |LARGE ANODIZED STEEL | 23| 8 +Brand#43 |LARGE ANODIZED STEEL | 49| 8 +Brand#43 |LARGE ANODIZED TIN | 19| 8 +Brand#43 |LARGE BRUSHED BRASS | 23| 8 +Brand#43 |LARGE BRUSHED COPPER | 19| 8 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BRASS | 36| 8 +Brand#43 |LARGE POLISHED COPPER | 3| 8 +Brand#43 |LARGE POLISHED COPPER | 14| 8 +Brand#43 |LARGE POLISHED COPPER | 36| 8 +Brand#43 |LARGE POLISHED NICKEL | 3| 8 +Brand#43 |LARGE POLISHED STEEL | 9| 8 +Brand#43 |LARGE POLISHED STEEL | 14| 8 +Brand#43 |LARGE POLISHED STEEL | 19| 8 +Brand#43 |MEDIUM ANODIZED BRASS | 49| 8 +Brand#43 |MEDIUM ANODIZED COPPER | 19| 8 +Brand#43 |MEDIUM ANODIZED COPPER | 23| 8 +Brand#43 |MEDIUM ANODIZED NICKEL | 3| 8 +Brand#43 |MEDIUM ANODIZED STEEL | 9| 8 +Brand#43 |MEDIUM ANODIZED STEEL | 19| 8 +Brand#43 |MEDIUM ANODIZED STEEL | 36| 8 +Brand#43 |MEDIUM BRUSHED BRASS | 9| 8 +Brand#43 |MEDIUM BRUSHED BRASS | 14| 8 +Brand#43 |MEDIUM BRUSHED COPPER | 45| 8 +Brand#43 |MEDIUM BRUSHED STEEL | 19| 8 +Brand#43 |MEDIUM BRUSHED STEEL | 49| 8 +Brand#43 |MEDIUM BRUSHED TIN | 49| 8 +Brand#43 |MEDIUM BURNISHED BRASS | 19| 8 +Brand#43 |MEDIUM BURNISHED NICKEL | 19| 8 +Brand#43 |MEDIUM BURNISHED NICKEL | 36| 8 +Brand#43 |MEDIUM BURNISHED STEEL | 23| 8 +Brand#43 |MEDIUM BURNISHED TIN | 14| 8 +Brand#43 |MEDIUM BURNISHED TIN | 36| 8 +Brand#43 |MEDIUM PLATED BRASS | 19| 8 +Brand#43 |MEDIUM PLATED BRASS | 36| 8 +Brand#43 |MEDIUM PLATED COPPER | 3| 8 +Brand#43 |MEDIUM PLATED COPPER | 49| 8 +Brand#43 |MEDIUM PLATED NICKEL | 36| 8 +Brand#43 |MEDIUM PLATED NICKEL | 45| 8 +Brand#43 |MEDIUM PLATED TIN | 45| 8 +Brand#43 |PROMO ANODIZED BRASS | 3| 8 +Brand#43 |PROMO ANODIZED BRASS | 9| 8 +Brand#43 |PROMO ANODIZED BRASS | 45| 8 +Brand#43 |PROMO ANODIZED NICKEL | 14| 8 +Brand#43 |PROMO ANODIZED NICKEL | 45| 8 +Brand#43 |PROMO ANODIZED STEEL | 49| 8 +Brand#43 |PROMO ANODIZED TIN | 3| 8 +Brand#43 |PROMO ANODIZED TIN | 14| 8 +Brand#43 |PROMO ANODIZED TIN | 19| 8 +Brand#43 |PROMO ANODIZED TIN | 49| 8 +Brand#43 |PROMO BRUSHED BRASS | 3| 8 +Brand#43 |PROMO BRUSHED BRASS | 45| 8 +Brand#43 |PROMO BRUSHED COPPER | 23| 8 +Brand#43 |PROMO BRUSHED NICKEL | 14| 8 +Brand#43 |PROMO BRUSHED NICKEL | 19| 8 +Brand#43 |PROMO BRUSHED STEEL | 14| 8 +Brand#43 |PROMO BURNISHED BRASS | 3| 8 +Brand#43 |PROMO BURNISHED BRASS | 49| 8 +Brand#43 |PROMO BURNISHED COPPER | 14| 8 +Brand#43 |PROMO BURNISHED NICKEL | 49| 8 +Brand#43 |PROMO BURNISHED STEEL | 49| 8 +Brand#43 |PROMO BURNISHED TIN | 9| 8 +Brand#43 |PROMO BURNISHED TIN | 36| 8 +Brand#43 |PROMO BURNISHED TIN | 49| 8 +Brand#43 |PROMO PLATED BRASS | 14| 8 +Brand#43 |PROMO PLATED COPPER | 45| 8 +Brand#43 |PROMO PLATED NICKEL | 45| 8 +Brand#43 |PROMO PLATED STEEL | 45| 8 +Brand#43 |PROMO PLATED TIN | 23| 8 +Brand#43 |PROMO POLISHED BRASS | 23| 8 +Brand#43 |PROMO POLISHED COPPER | 3| 8 +Brand#43 |PROMO POLISHED COPPER | 14| 8 +Brand#43 |PROMO POLISHED COPPER | 36| 8 +Brand#43 |PROMO POLISHED NICKEL | 14| 8 +Brand#43 |PROMO POLISHED NICKEL | 19| 8 +Brand#43 |PROMO POLISHED STEEL | 14| 8 +Brand#43 |PROMO POLISHED STEEL | 23| 8 +Brand#43 |PROMO POLISHED TIN | 3| 8 +Brand#43 |PROMO POLISHED TIN | 36| 8 +Brand#43 |SMALL ANODIZED BRASS | 19| 8 +Brand#43 |SMALL ANODIZED COPPER | 14| 8 +Brand#43 |SMALL ANODIZED COPPER | 19| 8 +Brand#43 |SMALL ANODIZED COPPER | 49| 8 +Brand#43 |SMALL ANODIZED NICKEL | 14| 8 +Brand#43 |SMALL ANODIZED NICKEL | 45| 8 +Brand#43 |SMALL ANODIZED STEEL | 49| 8 +Brand#43 |SMALL ANODIZED TIN | 49| 8 +Brand#43 |SMALL BRUSHED COPPER | 19| 8 +Brand#43 |SMALL BRUSHED COPPER | 49| 8 +Brand#43 |SMALL BRUSHED NICKEL | 9| 8 +Brand#43 |SMALL BRUSHED NICKEL | 49| 8 +Brand#43 |SMALL BRUSHED STEEL | 45| 8 +Brand#43 |SMALL BRUSHED TIN | 3| 8 +Brand#43 |SMALL BURNISHED COPPER | 23| 8 +Brand#43 |SMALL BURNISHED STEEL | 9| 8 +Brand#43 |SMALL BURNISHED STEEL | 45| 8 +Brand#43 |SMALL BURNISHED TIN | 9| 8 +Brand#43 |SMALL BURNISHED TIN | 49| 8 +Brand#43 |SMALL PLATED BRASS | 23| 8 +Brand#43 |SMALL PLATED BRASS | 45| 8 +Brand#43 |SMALL PLATED COPPER | 45| 8 +Brand#43 |SMALL PLATED NICKEL | 3| 8 +Brand#43 |SMALL PLATED NICKEL | 19| 8 +Brand#43 |SMALL PLATED NICKEL | 23| 8 +Brand#43 |SMALL PLATED NICKEL | 45| 8 +Brand#43 |SMALL PLATED NICKEL | 49| 8 +Brand#43 |SMALL PLATED STEEL | 14| 8 +Brand#43 |SMALL PLATED STEEL | 36| 8 +Brand#43 |SMALL PLATED TIN | 14| 8 +Brand#43 |SMALL POLISHED BRASS | 9| 8 +Brand#43 |SMALL POLISHED BRASS | 19| 8 +Brand#43 |SMALL POLISHED COPPER | 9| 8 +Brand#43 |SMALL POLISHED COPPER | 19| 8 +Brand#43 |SMALL POLISHED NICKEL | 3| 8 +Brand#43 |SMALL POLISHED NICKEL | 36| 8 +Brand#43 |SMALL POLISHED STEEL | 45| 8 +Brand#43 |SMALL POLISHED STEEL | 49| 8 +Brand#43 |SMALL POLISHED TIN | 36| 8 +Brand#43 |STANDARD ANODIZED COPPER | 3| 8 +Brand#43 |STANDARD ANODIZED COPPER | 9| 8 +Brand#43 |STANDARD ANODIZED COPPER | 14| 8 +Brand#43 |STANDARD ANODIZED COPPER | 49| 8 +Brand#43 |STANDARD ANODIZED NICKEL | 49| 8 +Brand#43 |STANDARD ANODIZED STEEL | 3| 8 +Brand#43 |STANDARD ANODIZED STEEL | 14| 8 +Brand#43 |STANDARD ANODIZED STEEL | 45| 8 +Brand#43 |STANDARD ANODIZED STEEL | 49| 8 +Brand#43 |STANDARD ANODIZED TIN | 14| 8 +Brand#43 |STANDARD BRUSHED BRASS | 14| 8 +Brand#43 |STANDARD BRUSHED BRASS | 36| 8 +Brand#43 |STANDARD BRUSHED NICKEL | 49| 8 +Brand#43 |STANDARD BRUSHED TIN | 19| 8 +Brand#43 |STANDARD BRUSHED TIN | 45| 8 +Brand#43 |STANDARD BRUSHED TIN | 49| 8 +Brand#43 |STANDARD BURNISHED BRASS | 23| 8 +Brand#43 |STANDARD BURNISHED BRASS | 49| 8 +Brand#43 |STANDARD BURNISHED COPPER| 9| 8 +Brand#43 |STANDARD BURNISHED COPPER| 14| 8 +Brand#43 |STANDARD BURNISHED COPPER| 45| 8 +Brand#43 |STANDARD BURNISHED NICKEL| 19| 8 +Brand#43 |STANDARD BURNISHED NICKEL| 49| 8 +Brand#43 |STANDARD BURNISHED STEEL | 9| 8 +Brand#43 |STANDARD BURNISHED STEEL | 19| 8 +Brand#43 |STANDARD BURNISHED STEEL | 45| 8 +Brand#43 |STANDARD BURNISHED TIN | 19| 8 +Brand#43 |STANDARD PLATED COPPER | 36| 8 +Brand#43 |STANDARD PLATED NICKEL | 19| 8 +Brand#43 |STANDARD PLATED NICKEL | 49| 8 +Brand#43 |STANDARD PLATED TIN | 14| 8 +Brand#43 |STANDARD PLATED TIN | 23| 8 +Brand#43 |STANDARD POLISHED BRASS | 19| 8 +Brand#43 |STANDARD POLISHED COPPER | 45| 8 +Brand#43 |STANDARD POLISHED NICKEL | 3| 8 +Brand#43 |STANDARD POLISHED NICKEL | 14| 8 +Brand#43 |STANDARD POLISHED NICKEL | 23| 8 +Brand#43 |STANDARD POLISHED NICKEL | 36| 8 +Brand#43 |STANDARD POLISHED NICKEL | 45| 8 +Brand#43 |STANDARD POLISHED STEEL | 14| 8 +Brand#43 |STANDARD POLISHED STEEL | 49| 8 +Brand#44 |ECONOMY ANODIZED BRASS | 3| 8 +Brand#44 |ECONOMY ANODIZED COPPER | 23| 8 +Brand#44 |ECONOMY ANODIZED COPPER | 49| 8 +Brand#44 |ECONOMY ANODIZED NICKEL | 23| 8 +Brand#44 |ECONOMY ANODIZED STEEL | 19| 8 +Brand#44 |ECONOMY ANODIZED STEEL | 45| 8 +Brand#44 |ECONOMY ANODIZED TIN | 14| 8 +Brand#44 |ECONOMY ANODIZED TIN | 36| 8 +Brand#44 |ECONOMY BRUSHED COPPER | 23| 8 +Brand#44 |ECONOMY BRUSHED STEEL | 9| 8 +Brand#44 |ECONOMY BRUSHED STEEL | 19| 8 +Brand#44 |ECONOMY BRUSHED TIN | 19| 8 +Brand#44 |ECONOMY BRUSHED TIN | 49| 8 +Brand#44 |ECONOMY BURNISHED COPPER | 3| 8 +Brand#44 |ECONOMY BURNISHED COPPER | 9| 8 +Brand#44 |ECONOMY BURNISHED COPPER | 14| 8 +Brand#44 |ECONOMY BURNISHED COPPER | 23| 8 +Brand#44 |ECONOMY BURNISHED COPPER | 49| 8 +Brand#44 |ECONOMY BURNISHED NICKEL | 23| 8 +Brand#44 |ECONOMY BURNISHED NICKEL | 49| 8 +Brand#44 |ECONOMY BURNISHED STEEL | 9| 8 +Brand#44 |ECONOMY BURNISHED STEEL | 19| 8 +Brand#44 |ECONOMY BURNISHED STEEL | 49| 8 +Brand#44 |ECONOMY BURNISHED TIN | 3| 8 +Brand#44 |ECONOMY BURNISHED TIN | 19| 8 +Brand#44 |ECONOMY BURNISHED TIN | 45| 8 +Brand#44 |ECONOMY PLATED COPPER | 45| 8 +Brand#44 |ECONOMY PLATED NICKEL | 23| 8 +Brand#44 |ECONOMY PLATED STEEL | 14| 8 +Brand#44 |ECONOMY PLATED STEEL | 23| 8 +Brand#44 |ECONOMY PLATED STEEL | 36| 8 +Brand#44 |ECONOMY PLATED TIN | 19| 8 +Brand#44 |ECONOMY POLISHED BRASS | 23| 8 +Brand#44 |ECONOMY POLISHED BRASS | 36| 8 +Brand#44 |ECONOMY POLISHED COPPER | 9| 8 +Brand#44 |ECONOMY POLISHED COPPER | 19| 8 +Brand#44 |ECONOMY POLISHED NICKEL | 23| 8 +Brand#44 |ECONOMY POLISHED NICKEL | 36| 8 +Brand#44 |ECONOMY POLISHED NICKEL | 45| 8 +Brand#44 |ECONOMY POLISHED NICKEL | 49| 8 +Brand#44 |ECONOMY POLISHED STEEL | 9| 8 +Brand#44 |ECONOMY POLISHED STEEL | 49| 8 +Brand#44 |ECONOMY POLISHED TIN | 3| 8 +Brand#44 |ECONOMY POLISHED TIN | 19| 8 +Brand#44 |LARGE ANODIZED BRASS | 3| 8 +Brand#44 |LARGE ANODIZED BRASS | 23| 8 +Brand#44 |LARGE ANODIZED BRASS | 49| 8 +Brand#44 |LARGE ANODIZED COPPER | 9| 8 +Brand#44 |LARGE ANODIZED COPPER | 45| 8 +Brand#44 |LARGE ANODIZED NICKEL | 49| 8 +Brand#44 |LARGE ANODIZED STEEL | 19| 8 +Brand#44 |LARGE ANODIZED TIN | 14| 8 +Brand#44 |LARGE BRUSHED BRASS | 14| 8 +Brand#44 |LARGE BRUSHED COPPER | 14| 8 +Brand#44 |LARGE BRUSHED NICKEL | 19| 8 +Brand#44 |LARGE BRUSHED NICKEL | 23| 8 +Brand#44 |LARGE BRUSHED NICKEL | 45| 8 +Brand#44 |LARGE BRUSHED TIN | 23| 8 +Brand#44 |LARGE BURNISHED COPPER | 9| 8 +Brand#44 |LARGE BURNISHED COPPER | 19| 8 +Brand#44 |LARGE BURNISHED COPPER | 23| 8 +Brand#44 |LARGE BURNISHED NICKEL | 36| 8 +Brand#44 |LARGE BURNISHED NICKEL | 49| 8 +Brand#44 |LARGE BURNISHED STEEL | 23| 8 +Brand#44 |LARGE BURNISHED STEEL | 49| 8 +Brand#44 |LARGE BURNISHED TIN | 14| 8 +Brand#44 |LARGE PLATED BRASS | 19| 8 +Brand#44 |LARGE PLATED COPPER | 14| 8 +Brand#44 |LARGE PLATED COPPER | 19| 8 +Brand#44 |LARGE PLATED NICKEL | 9| 8 +Brand#44 |LARGE PLATED NICKEL | 23| 8 +Brand#44 |LARGE PLATED STEEL | 23| 8 +Brand#44 |LARGE PLATED TIN | 14| 8 +Brand#44 |LARGE PLATED TIN | 19| 8 +Brand#44 |LARGE PLATED TIN | 36| 8 +Brand#44 |LARGE PLATED TIN | 49| 8 +Brand#44 |LARGE POLISHED BRASS | 9| 8 +Brand#44 |LARGE POLISHED BRASS | 19| 8 +Brand#44 |LARGE POLISHED BRASS | 23| 8 +Brand#44 |LARGE POLISHED COPPER | 9| 8 +Brand#44 |LARGE POLISHED COPPER | 49| 8 +Brand#44 |LARGE POLISHED NICKEL | 23| 8 +Brand#44 |LARGE POLISHED NICKEL | 36| 8 +Brand#44 |LARGE POLISHED STEEL | 45| 8 +Brand#44 |LARGE POLISHED TIN | 9| 8 +Brand#44 |MEDIUM ANODIZED BRASS | 36| 8 +Brand#44 |MEDIUM ANODIZED COPPER | 14| 8 +Brand#44 |MEDIUM ANODIZED COPPER | 49| 8 +Brand#44 |MEDIUM ANODIZED NICKEL | 19| 8 +Brand#44 |MEDIUM ANODIZED NICKEL | 45| 8 +Brand#44 |MEDIUM ANODIZED STEEL | 9| 8 +Brand#44 |MEDIUM ANODIZED STEEL | 23| 8 +Brand#44 |MEDIUM ANODIZED TIN | 45| 8 +Brand#44 |MEDIUM BRUSHED COPPER | 14| 8 +Brand#44 |MEDIUM BRUSHED NICKEL | 14| 8 +Brand#44 |MEDIUM BRUSHED STEEL | 14| 8 +Brand#44 |MEDIUM BRUSHED STEEL | 19| 8 +Brand#44 |MEDIUM BURNISHED BRASS | 3| 8 +Brand#44 |MEDIUM BURNISHED BRASS | 45| 8 +Brand#44 |MEDIUM BURNISHED COPPER | 45| 8 +Brand#44 |MEDIUM BURNISHED NICKEL | 3| 8 +Brand#44 |MEDIUM BURNISHED NICKEL | 14| 8 +Brand#44 |MEDIUM BURNISHED STEEL | 23| 8 +Brand#44 |MEDIUM BURNISHED TIN | 19| 8 +Brand#44 |MEDIUM BURNISHED TIN | 23| 8 +Brand#44 |MEDIUM PLATED BRASS | 3| 8 +Brand#44 |MEDIUM PLATED BRASS | 23| 8 +Brand#44 |MEDIUM PLATED COPPER | 3| 8 +Brand#44 |MEDIUM PLATED NICKEL | 23| 8 +Brand#44 |MEDIUM PLATED NICKEL | 49| 8 +Brand#44 |PROMO ANODIZED BRASS | 3| 8 +Brand#44 |PROMO ANODIZED BRASS | 14| 8 +Brand#44 |PROMO ANODIZED BRASS | 49| 8 +Brand#44 |PROMO ANODIZED COPPER | 23| 8 +Brand#44 |PROMO ANODIZED NICKEL | 23| 8 +Brand#44 |PROMO ANODIZED NICKEL | 36| 8 +Brand#44 |PROMO ANODIZED STEEL | 9| 8 +Brand#44 |PROMO ANODIZED STEEL | 49| 8 +Brand#44 |PROMO BRUSHED BRASS | 9| 8 +Brand#44 |PROMO BRUSHED COPPER | 9| 8 +Brand#44 |PROMO BRUSHED COPPER | 23| 8 +Brand#44 |PROMO BRUSHED COPPER | 36| 8 +Brand#44 |PROMO BRUSHED NICKEL | 23| 8 +Brand#44 |PROMO BRUSHED NICKEL | 45| 8 +Brand#44 |PROMO BRUSHED STEEL | 3| 8 +Brand#44 |PROMO BRUSHED STEEL | 9| 8 +Brand#44 |PROMO BRUSHED STEEL | 45| 8 +Brand#44 |PROMO BRUSHED STEEL | 49| 8 +Brand#44 |PROMO BRUSHED TIN | 3| 8 +Brand#44 |PROMO BRUSHED TIN | 19| 8 +Brand#44 |PROMO BRUSHED TIN | 45| 8 +Brand#44 |PROMO BURNISHED BRASS | 36| 8 +Brand#44 |PROMO BURNISHED NICKEL | 3| 8 +Brand#44 |PROMO BURNISHED STEEL | 9| 8 +Brand#44 |PROMO BURNISHED STEEL | 19| 8 +Brand#44 |PROMO BURNISHED STEEL | 49| 8 +Brand#44 |PROMO PLATED BRASS | 23| 8 +Brand#44 |PROMO PLATED NICKEL | 9| 8 +Brand#44 |PROMO PLATED NICKEL | 23| 8 +Brand#44 |PROMO PLATED STEEL | 23| 8 +Brand#44 |PROMO PLATED STEEL | 49| 8 +Brand#44 |PROMO PLATED TIN | 14| 8 +Brand#44 |PROMO PLATED TIN | 36| 8 +Brand#44 |PROMO POLISHED BRASS | 36| 8 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BURNISHED BRASS | 49| 8 +Brand#44 |SMALL BURNISHED NICKEL | 14| 8 +Brand#44 |SMALL BURNISHED NICKEL | 45| 8 +Brand#44 |SMALL BURNISHED TIN | 9| 8 +Brand#44 |SMALL BURNISHED TIN | 23| 8 +Brand#44 |SMALL BURNISHED TIN | 49| 8 +Brand#44 |SMALL PLATED BRASS | 36| 8 +Brand#44 |SMALL PLATED COPPER | 14| 8 +Brand#44 |SMALL PLATED NICKEL | 45| 8 +Brand#44 |SMALL PLATED NICKEL | 49| 8 +Brand#44 |SMALL PLATED TIN | 19| 8 +Brand#44 |SMALL POLISHED COPPER | 9| 8 +Brand#44 |SMALL POLISHED COPPER | 49| 8 +Brand#44 |SMALL POLISHED NICKEL | 9| 8 +Brand#44 |SMALL POLISHED NICKEL | 14| 8 +Brand#44 |SMALL POLISHED NICKEL | 19| 8 +Brand#44 |SMALL POLISHED NICKEL | 23| 8 +Brand#44 |SMALL POLISHED NICKEL | 45| 8 +Brand#44 |SMALL POLISHED STEEL | 3| 8 +Brand#44 |SMALL POLISHED TIN | 3| 8 +Brand#44 |SMALL POLISHED TIN | 14| 8 +Brand#44 |SMALL POLISHED TIN | 19| 8 +Brand#44 |SMALL POLISHED TIN | 23| 8 +Brand#44 |SMALL POLISHED TIN | 45| 8 +Brand#44 |STANDARD ANODIZED COPPER | 3| 8 +Brand#44 |STANDARD ANODIZED 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|STANDARD PLATED BRASS | 45| 8 +Brand#44 |STANDARD PLATED COPPER | 9| 8 +Brand#44 |STANDARD PLATED COPPER | 23| 8 +Brand#44 |STANDARD PLATED COPPER | 49| 8 +Brand#44 |STANDARD PLATED NICKEL | 14| 8 +Brand#44 |STANDARD PLATED NICKEL | 19| 8 +Brand#44 |STANDARD PLATED TIN | 19| 8 +Brand#44 |STANDARD PLATED TIN | 49| 8 +Brand#44 |STANDARD POLISHED COPPER | 14| 8 +Brand#44 |STANDARD POLISHED COPPER | 19| 8 +Brand#44 |STANDARD POLISHED COPPER | 45| 8 +Brand#44 |STANDARD POLISHED COPPER | 49| 8 +Brand#44 |STANDARD POLISHED NICKEL | 36| 8 +Brand#44 |STANDARD POLISHED TIN | 9| 8 +Brand#44 |STANDARD POLISHED TIN | 19| 8 +Brand#51 |ECONOMY ANODIZED BRASS | 49| 8 +Brand#51 |ECONOMY ANODIZED COPPER | 3| 8 +Brand#51 |ECONOMY ANODIZED NICKEL | 3| 8 +Brand#51 |ECONOMY ANODIZED NICKEL | 23| 8 +Brand#51 |ECONOMY ANODIZED STEEL | 36| 8 +Brand#51 |ECONOMY ANODIZED STEEL | 45| 8 +Brand#51 |ECONOMY ANODIZED STEEL | 49| 8 +Brand#51 |ECONOMY ANODIZED TIN | 23| 8 +Brand#51 |ECONOMY BRUSHED BRASS | 3| 8 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|ECONOMY POLISHED BRASS | 36| 8 +Brand#51 |ECONOMY POLISHED BRASS | 49| 8 +Brand#51 |ECONOMY POLISHED COPPER | 9| 8 +Brand#51 |ECONOMY POLISHED NICKEL | 19| 8 +Brand#51 |ECONOMY POLISHED NICKEL | 36| 8 +Brand#51 |ECONOMY POLISHED STEEL | 3| 8 +Brand#51 |ECONOMY POLISHED STEEL | 9| 8 +Brand#51 |ECONOMY POLISHED STEEL | 14| 8 +Brand#51 |ECONOMY POLISHED STEEL | 36| 8 +Brand#51 |ECONOMY POLISHED TIN | 14| 8 +Brand#51 |ECONOMY POLISHED TIN | 19| 8 +Brand#51 |LARGE ANODIZED BRASS | 19| 8 +Brand#51 |LARGE ANODIZED BRASS | 23| 8 +Brand#51 |LARGE ANODIZED COPPER | 36| 8 +Brand#51 |LARGE ANODIZED COPPER | 49| 8 +Brand#51 |LARGE ANODIZED NICKEL | 14| 8 +Brand#51 |LARGE ANODIZED NICKEL | 45| 8 +Brand#51 |LARGE ANODIZED STEEL | 45| 8 +Brand#51 |LARGE ANODIZED TIN | 19| 8 +Brand#51 |LARGE BRUSHED BRASS | 9| 8 +Brand#51 |LARGE BRUSHED BRASS | 23| 8 +Brand#51 |LARGE BRUSHED COPPER | 23| 8 +Brand#51 |LARGE BRUSHED COPPER | 49| 8 +Brand#51 |LARGE BRUSHED NICKEL | 9| 8 +Brand#51 |LARGE BRUSHED NICKEL | 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8 +Brand#52 |ECONOMY BRUSHED BRASS | 36| 8 +Brand#52 |ECONOMY BRUSHED STEEL | 3| 8 +Brand#52 |ECONOMY BRUSHED STEEL | 14| 8 +Brand#52 |ECONOMY BRUSHED TIN | 9| 8 +Brand#52 |ECONOMY BRUSHED TIN | 36| 8 +Brand#52 |ECONOMY BRUSHED TIN | 49| 8 +Brand#52 |ECONOMY BURNISHED COPPER | 45| 8 +Brand#52 |ECONOMY BURNISHED NICKEL | 23| 8 +Brand#52 |ECONOMY BURNISHED STEEL | 9| 8 +Brand#52 |ECONOMY BURNISHED STEEL | 36| 8 +Brand#52 |ECONOMY BURNISHED TIN | 3| 8 +Brand#52 |ECONOMY PLATED BRASS | 3| 8 +Brand#52 |ECONOMY PLATED BRASS | 14| 8 +Brand#52 |ECONOMY PLATED BRASS | 23| 8 +Brand#52 |ECONOMY PLATED BRASS | 45| 8 +Brand#52 |ECONOMY PLATED COPPER | 49| 8 +Brand#52 |ECONOMY PLATED NICKEL | 3| 8 +Brand#52 |ECONOMY PLATED NICKEL | 49| 8 +Brand#52 |ECONOMY PLATED STEEL | 3| 8 +Brand#52 |ECONOMY PLATED STEEL | 14| 8 +Brand#52 |ECONOMY PLATED TIN | 3| 8 +Brand#52 |ECONOMY PLATED TIN | 19| 8 +Brand#52 |ECONOMY PLATED TIN | 23| 8 +Brand#52 |ECONOMY POLISHED BRASS | 23| 8 +Brand#52 |ECONOMY POLISHED 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ANODIZED COPPER | 49| 8 +Brand#52 |MEDIUM ANODIZED NICKEL | 23| 8 +Brand#52 |MEDIUM ANODIZED NICKEL | 45| 8 +Brand#52 |MEDIUM ANODIZED STEEL | 19| 8 +Brand#52 |MEDIUM ANODIZED STEEL | 45| 8 +Brand#52 |MEDIUM ANODIZED TIN | 19| 8 +Brand#52 |MEDIUM ANODIZED TIN | 49| 8 +Brand#52 |MEDIUM BRUSHED BRASS | 9| 8 +Brand#52 |MEDIUM BRUSHED COPPER | 3| 8 +Brand#52 |MEDIUM BRUSHED COPPER | 9| 8 +Brand#52 |MEDIUM BRUSHED NICKEL | 49| 8 +Brand#52 |MEDIUM BRUSHED STEEL | 23| 8 +Brand#52 |MEDIUM BRUSHED STEEL | 36| 8 +Brand#52 |MEDIUM BRUSHED STEEL | 45| 8 +Brand#52 |MEDIUM BRUSHED STEEL | 49| 8 +Brand#52 |MEDIUM BRUSHED TIN | 19| 8 +Brand#52 |MEDIUM BRUSHED TIN | 23| 8 +Brand#52 |MEDIUM BRUSHED TIN | 49| 8 +Brand#52 |MEDIUM BURNISHED COPPER | 36| 8 +Brand#52 |MEDIUM BURNISHED NICKEL | 14| 8 +Brand#52 |MEDIUM BURNISHED NICKEL | 19| 8 +Brand#52 |MEDIUM BURNISHED TIN | 9| 8 +Brand#52 |MEDIUM BURNISHED TIN | 19| 8 +Brand#52 |MEDIUM BURNISHED TIN | 49| 8 +Brand#52 |MEDIUM PLATED COPPER | 14| 8 +Brand#52 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8 +Brand#54 |ECONOMY BRUSHED NICKEL | 9| 8 +Brand#54 |ECONOMY BRUSHED NICKEL | 23| 8 +Brand#54 |ECONOMY BRUSHED NICKEL | 36| 8 +Brand#54 |ECONOMY BRUSHED NICKEL | 49| 8 +Brand#54 |ECONOMY BRUSHED STEEL | 3| 8 +Brand#54 |ECONOMY BRUSHED STEEL | 14| 8 +Brand#54 |ECONOMY BURNISHED COPPER | 9| 8 +Brand#54 |ECONOMY BURNISHED COPPER | 36| 8 +Brand#54 |ECONOMY BURNISHED NICKEL | 36| 8 +Brand#54 |ECONOMY BURNISHED STEEL | 14| 8 +Brand#54 |ECONOMY BURNISHED STEEL | 36| 8 +Brand#54 |ECONOMY BURNISHED TIN | 9| 8 +Brand#54 |ECONOMY BURNISHED TIN | 14| 8 +Brand#54 |ECONOMY BURNISHED TIN | 23| 8 +Brand#54 |ECONOMY PLATED COPPER | 14| 8 +Brand#54 |ECONOMY PLATED COPPER | 19| 8 +Brand#54 |ECONOMY PLATED NICKEL | 23| 8 +Brand#54 |ECONOMY PLATED NICKEL | 45| 8 +Brand#54 |ECONOMY PLATED STEEL | 3| 8 +Brand#54 |ECONOMY PLATED STEEL | 19| 8 +Brand#54 |ECONOMY PLATED TIN | 23| 8 +Brand#54 |ECONOMY POLISHED BRASS | 23| 8 +Brand#54 |ECONOMY POLISHED BRASS | 36| 8 +Brand#54 |ECONOMY POLISHED BRASS | 49| 8 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8 +Brand#54 |MEDIUM ANODIZED NICKEL | 36| 8 +Brand#54 |MEDIUM ANODIZED STEEL | 3| 8 +Brand#54 |MEDIUM BRUSHED BRASS | 3| 8 +Brand#54 |MEDIUM BRUSHED BRASS | 14| 8 +Brand#54 |MEDIUM BRUSHED BRASS | 19| 8 +Brand#54 |MEDIUM BRUSHED BRASS | 45| 8 +Brand#54 |MEDIUM BRUSHED COPPER | 14| 8 +Brand#54 |MEDIUM BRUSHED COPPER | 19| 8 +Brand#54 |MEDIUM BRUSHED COPPER | 23| 8 +Brand#54 |MEDIUM BRUSHED NICKEL | 9| 8 +Brand#54 |MEDIUM BRUSHED STEEL | 9| 8 +Brand#54 |MEDIUM BRUSHED STEEL | 45| 8 +Brand#54 |MEDIUM BRUSHED TIN | 14| 8 +Brand#54 |MEDIUM BRUSHED TIN | 49| 8 +Brand#54 |MEDIUM BURNISHED BRASS | 23| 8 +Brand#54 |MEDIUM BURNISHED BRASS | 49| 8 +Brand#54 |MEDIUM BURNISHED COPPER | 3| 8 +Brand#54 |MEDIUM BURNISHED COPPER | 36| 8 +Brand#54 |MEDIUM BURNISHED COPPER | 45| 8 +Brand#54 |MEDIUM BURNISHED STEEL | 14| 8 +Brand#54 |MEDIUM BURNISHED STEEL | 19| 8 +Brand#54 |MEDIUM BURNISHED TIN | 9| 8 +Brand#54 |MEDIUM BURNISHED TIN | 19| 8 +Brand#54 |MEDIUM BURNISHED TIN | 36| 8 +Brand#54 |MEDIUM PLATED 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36| 8 +Brand#54 |SMALL POLISHED BRASS | 9| 8 +Brand#54 |SMALL POLISHED BRASS | 36| 8 +Brand#54 |SMALL POLISHED COPPER | 3| 8 +Brand#54 |SMALL POLISHED COPPER | 49| 8 +Brand#54 |SMALL POLISHED NICKEL | 3| 8 +Brand#54 |SMALL POLISHED NICKEL | 14| 8 +Brand#54 |SMALL POLISHED NICKEL | 23| 8 +Brand#54 |SMALL POLISHED STEEL | 3| 8 +Brand#54 |SMALL POLISHED STEEL | 23| 8 +Brand#54 |SMALL POLISHED TIN | 45| 8 +Brand#54 |STANDARD ANODIZED BRASS | 9| 8 +Brand#54 |STANDARD ANODIZED BRASS | 45| 8 +Brand#54 |STANDARD ANODIZED COPPER | 9| 8 +Brand#54 |STANDARD ANODIZED COPPER | 23| 8 +Brand#54 |STANDARD ANODIZED STEEL | 3| 8 +Brand#54 |STANDARD ANODIZED STEEL | 14| 8 +Brand#54 |STANDARD ANODIZED STEEL | 23| 8 +Brand#54 |STANDARD ANODIZED TIN | 45| 8 +Brand#54 |STANDARD BRUSHED BRASS | 36| 8 +Brand#54 |STANDARD BRUSHED COPPER | 36| 8 +Brand#54 |STANDARD BRUSHED NICKEL | 14| 8 +Brand#54 |STANDARD BRUSHED NICKEL | 49| 8 +Brand#54 |STANDARD BRUSHED STEEL | 9| 8 +Brand#54 |STANDARD BRUSHED STEEL | 36| 8 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| 19| 8 +Brand#55 |ECONOMY PLATED NICKEL | 36| 8 +Brand#55 |ECONOMY PLATED TIN | 23| 8 +Brand#55 |ECONOMY POLISHED BRASS | 19| 8 +Brand#55 |ECONOMY POLISHED BRASS | 23| 8 +Brand#55 |ECONOMY POLISHED COPPER | 23| 8 +Brand#55 |ECONOMY POLISHED COPPER | 45| 8 +Brand#55 |ECONOMY POLISHED NICKEL | 9| 8 +Brand#55 |ECONOMY POLISHED NICKEL | 14| 8 +Brand#55 |ECONOMY POLISHED NICKEL | 19| 8 +Brand#55 |ECONOMY POLISHED NICKEL | 45| 8 +Brand#55 |ECONOMY POLISHED TIN | 9| 8 +Brand#55 |LARGE ANODIZED BRASS | 36| 8 +Brand#55 |LARGE ANODIZED COPPER | 9| 8 +Brand#55 |LARGE ANODIZED COPPER | 36| 8 +Brand#55 |LARGE ANODIZED COPPER | 45| 8 +Brand#55 |LARGE ANODIZED NICKEL | 36| 8 +Brand#55 |LARGE ANODIZED STEEL | 9| 8 +Brand#55 |LARGE ANODIZED TIN | 14| 8 +Brand#55 |LARGE BRUSHED COPPER | 9| 8 +Brand#55 |LARGE BRUSHED COPPER | 19| 8 +Brand#55 |LARGE BRUSHED NICKEL | 14| 8 +Brand#55 |LARGE BRUSHED TIN | 9| 8 +Brand#55 |LARGE BURNISHED BRASS | 3| 8 +Brand#55 |LARGE BURNISHED BRASS | 49| 8 +Brand#55 |LARGE BURNISHED COPPER | 36| 8 +Brand#55 |LARGE BURNISHED COPPER | 49| 8 +Brand#55 |LARGE BURNISHED NICKEL | 19| 8 +Brand#55 |LARGE BURNISHED NICKEL | 45| 8 +Brand#55 |LARGE BURNISHED STEEL | 3| 8 +Brand#55 |LARGE BURNISHED STEEL | 23| 8 +Brand#55 |LARGE PLATED COPPER | 14| 8 +Brand#55 |LARGE PLATED NICKEL | 9| 8 +Brand#55 |LARGE PLATED STEEL | 19| 8 +Brand#55 |LARGE PLATED STEEL | 36| 8 +Brand#55 |LARGE PLATED STEEL | 49| 8 +Brand#55 |LARGE PLATED TIN | 9| 8 +Brand#55 |LARGE PLATED TIN | 14| 8 +Brand#55 |LARGE PLATED TIN | 36| 8 +Brand#55 |LARGE PLATED TIN | 45| 8 +Brand#55 |LARGE POLISHED BRASS | 3| 8 +Brand#55 |LARGE POLISHED COPPER | 9| 8 +Brand#55 |LARGE POLISHED COPPER | 36| 8 +Brand#55 |LARGE POLISHED TIN | 9| 8 +Brand#55 |LARGE POLISHED TIN | 45| 8 +Brand#55 |MEDIUM ANODIZED BRASS | 23| 8 +Brand#55 |MEDIUM ANODIZED COPPER | 14| 8 +Brand#55 |MEDIUM ANODIZED COPPER | 49| 8 +Brand#55 |MEDIUM ANODIZED NICKEL | 14| 8 +Brand#55 |MEDIUM ANODIZED NICKEL | 19| 8 +Brand#55 |MEDIUM ANODIZED 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BRASS | 49| 8 +Brand#55 |PROMO ANODIZED COPPER | 3| 8 +Brand#55 |PROMO ANODIZED COPPER | 45| 8 +Brand#55 |PROMO ANODIZED COPPER | 49| 8 +Brand#55 |PROMO ANODIZED NICKEL | 3| 8 +Brand#55 |PROMO ANODIZED NICKEL | 14| 8 +Brand#55 |PROMO ANODIZED NICKEL | 36| 8 +Brand#55 |PROMO ANODIZED STEEL | 3| 8 +Brand#55 |PROMO ANODIZED STEEL | 36| 8 +Brand#55 |PROMO ANODIZED STEEL | 49| 8 +Brand#55 |PROMO ANODIZED TIN | 36| 8 +Brand#55 |PROMO ANODIZED TIN | 49| 8 +Brand#55 |PROMO BRUSHED BRASS | 9| 8 +Brand#55 |PROMO BRUSHED COPPER | 9| 8 +Brand#55 |PROMO BRUSHED NICKEL | 36| 8 +Brand#55 |PROMO BRUSHED NICKEL | 49| 8 +Brand#55 |PROMO BRUSHED STEEL | 3| 8 +Brand#55 |PROMO BRUSHED STEEL | 9| 8 +Brand#55 |PROMO BRUSHED STEEL | 36| 8 +Brand#55 |PROMO BRUSHED STEEL | 45| 8 +Brand#55 |PROMO BRUSHED TIN | 49| 8 +Brand#55 |PROMO BURNISHED BRASS | 49| 8 +Brand#55 |PROMO BURNISHED COPPER | 14| 8 +Brand#55 |PROMO BURNISHED STEEL | 9| 8 +Brand#55 |PROMO BURNISHED TIN | 45| 8 +Brand#55 |PROMO BURNISHED TIN | 49| 8 +Brand#55 |PROMO PLATED BRASS | 9| 8 +Brand#55 |PROMO PLATED BRASS | 36| 8 +Brand#55 |PROMO PLATED BRASS | 45| 8 +Brand#55 |PROMO PLATED COPPER | 14| 8 +Brand#55 |PROMO PLATED COPPER | 23| 8 +Brand#55 |PROMO PLATED NICKEL | 14| 8 +Brand#55 |PROMO PLATED NICKEL | 49| 8 +Brand#55 |PROMO PLATED TIN | 36| 8 +Brand#55 |PROMO PLATED TIN | 45| 8 +Brand#55 |PROMO POLISHED BRASS | 3| 8 +Brand#55 |PROMO POLISHED COPPER | 36| 8 +Brand#55 |PROMO POLISHED STEEL | 3| 8 +Brand#55 |PROMO POLISHED STEEL | 14| 8 +Brand#55 |PROMO POLISHED STEEL | 36| 8 +Brand#55 |SMALL ANODIZED BRASS | 19| 8 +Brand#55 |SMALL ANODIZED COPPER | 14| 8 +Brand#55 |SMALL ANODIZED NICKEL | 3| 8 +Brand#55 |SMALL ANODIZED STEEL | 14| 8 +Brand#55 |SMALL ANODIZED STEEL | 19| 8 +Brand#55 |SMALL ANODIZED STEEL | 49| 8 +Brand#55 |SMALL ANODIZED TIN | 3| 8 +Brand#55 |SMALL BRUSHED BRASS | 19| 8 +Brand#55 |SMALL BRUSHED BRASS | 49| 8 +Brand#55 |SMALL BRUSHED COPPER | 14| 8 +Brand#55 |SMALL BRUSHED COPPER | 36| 8 +Brand#55 |SMALL BRUSHED COPPER | 45| 8 +Brand#55 |SMALL BRUSHED TIN | 23| 8 +Brand#55 |SMALL BURNISHED BRASS | 9| 8 +Brand#55 |SMALL BURNISHED COPPER | 45| 8 +Brand#55 |SMALL BURNISHED NICKEL | 3| 8 +Brand#55 |SMALL BURNISHED STEEL | 19| 8 +Brand#55 |SMALL BURNISHED STEEL | 23| 8 +Brand#55 |SMALL BURNISHED TIN | 3| 8 +Brand#55 |SMALL BURNISHED TIN | 14| 8 +Brand#55 |SMALL BURNISHED TIN | 19| 8 +Brand#55 |SMALL BURNISHED TIN | 36| 8 +Brand#55 |SMALL PLATED BRASS | 45| 8 +Brand#55 |SMALL PLATED COPPER | 9| 8 +Brand#55 |SMALL PLATED COPPER | 19| 8 +Brand#55 |SMALL PLATED COPPER | 23| 8 +Brand#55 |SMALL PLATED COPPER | 45| 8 +Brand#55 |SMALL PLATED NICKEL | 9| 8 +Brand#55 |SMALL PLATED NICKEL | 23| 8 +Brand#55 |SMALL PLATED STEEL | 49| 8 +Brand#55 |SMALL PLATED TIN | 3| 8 +Brand#55 |SMALL PLATED TIN | 9| 8 +Brand#55 |SMALL PLATED TIN | 14| 8 +Brand#55 |SMALL PLATED TIN | 49| 8 +Brand#55 |SMALL POLISHED BRASS | 14| 8 +Brand#55 |SMALL POLISHED COPPER | 3| 8 +Brand#55 |SMALL POLISHED TIN | 19| 8 +Brand#55 |SMALL POLISHED TIN | 49| 8 +Brand#55 |STANDARD ANODIZED BRASS | 14| 8 +Brand#55 |STANDARD ANODIZED BRASS | 36| 8 +Brand#55 |STANDARD ANODIZED COPPER | 23| 8 +Brand#55 |STANDARD ANODIZED NICKEL | 23| 8 +Brand#55 |STANDARD ANODIZED TIN | 19| 8 +Brand#55 |STANDARD ANODIZED TIN | 49| 8 +Brand#55 |STANDARD BRUSHED BRASS | 3| 8 +Brand#55 |STANDARD BRUSHED BRASS | 36| 8 +Brand#55 |STANDARD BRUSHED BRASS | 45| 8 +Brand#55 |STANDARD BRUSHED COPPER | 3| 8 +Brand#55 |STANDARD BRUSHED COPPER | 23| 8 +Brand#55 |STANDARD BRUSHED NICKEL | 19| 8 +Brand#55 |STANDARD BRUSHED TIN | 23| 8 +Brand#55 |STANDARD BURNISHED BRASS | 49| 8 +Brand#55 |STANDARD BURNISHED COPPER| 23| 8 +Brand#55 |STANDARD BURNISHED COPPER| 36| 8 +Brand#55 |STANDARD BURNISHED NICKEL| 3| 8 +Brand#55 |STANDARD BURNISHED NICKEL| 14| 8 +Brand#55 |STANDARD BURNISHED NICKEL| 36| 8 +Brand#55 |STANDARD BURNISHED NICKEL| 45| 8 +Brand#55 |STANDARD BURNISHED STEEL | 14| 8 +Brand#55 |STANDARD BURNISHED STEEL | 49| 8 +Brand#55 |STANDARD PLATED BRASS | 19| 8 +Brand#55 |STANDARD PLATED BRASS | 23| 8 +Brand#55 |STANDARD PLATED COPPER | 23| 8 +Brand#55 |STANDARD PLATED NICKEL | 49| 8 +Brand#55 |STANDARD PLATED TIN | 23| 8 +Brand#55 |STANDARD POLISHED BRASS | 19| 8 +Brand#55 |STANDARD POLISHED BRASS | 49| 8 +Brand#55 |STANDARD POLISHED COPPER | 9| 8 +Brand#55 |STANDARD POLISHED COPPER | 36| 8 +Brand#55 |STANDARD POLISHED STEEL | 9| 8 +Brand#55 |STANDARD POLISHED STEEL | 36| 8 +Brand#55 |STANDARD POLISHED STEEL | 45| 8 +Brand#55 |STANDARD POLISHED STEEL | 49| 8 +Brand#12 |PROMO ANODIZED NICKEL | 49| 7 +Brand#13 |LARGE PLATED TIN | 23| 7 +Brand#14 |PROMO PLATED BRASS | 19| 7 +Brand#22 |STANDARD POLISHED TIN | 3| 7 +Brand#23 |ECONOMY PLATED NICKEL | 19| 7 +Brand#23 |LARGE BURNISHED NICKEL | 14| 7 +Brand#24 |PROMO BRUSHED NICKEL | 14| 7 +Brand#31 |MEDIUM BURNISHED NICKEL | 23| 7 +Brand#32 |LARGE BRUSHED COPPER | 3| 7 +Brand#32 |LARGE POLISHED NICKEL | 23| 7 +Brand#32 |STANDARD BURNISHED STEEL | 19| 7 +Brand#33 |ECONOMY BRUSHED BRASS | 3| 7 +Brand#33 |PROMO PLATED NICKEL | 9| 7 +Brand#33 |SMALL ANODIZED COPPER | 23| 7 +Brand#41 |ECONOMY BRUSHED COPPER | 36| 7 +Brand#41 |PROMO POLISHED BRASS | 45| 7 +Brand#42 |MEDIUM PLATED STEEL | 45| 7 +Brand#42 |STANDARD PLATED COPPER | 19| 7 +Brand#43 |LARGE POLISHED COPPER | 19| 7 +Brand#44 |PROMO BURNISHED STEEL | 45| 7 +Brand#51 |STANDARD PLATED TIN | 45| 7 +Brand#52 |STANDARD ANODIZED STEEL | 14| 7 +Brand#53 |STANDARD ANODIZED NICKEL | 14| 7 +Brand#55 |ECONOMY POLISHED TIN | 19| 7 +Brand#55 |SMALL BURNISHED STEEL | 3| 7 +Brand#32 |MEDIUM BURNISHED STEEL | 3| 6 +Brand#11 |ECONOMY ANODIZED BRASS | 3| 4 +Brand#11 |ECONOMY ANODIZED BRASS | 45| 4 +Brand#11 |ECONOMY ANODIZED COPPER | 3| 4 +Brand#11 |ECONOMY ANODIZED COPPER | 19| 4 +Brand#11 |ECONOMY ANODIZED COPPER | 36| 4 +Brand#11 |ECONOMY ANODIZED COPPER | 45| 4 +Brand#11 |ECONOMY ANODIZED STEEL | 9| 4 +Brand#11 |ECONOMY ANODIZED STEEL | 14| 4 +Brand#11 |ECONOMY ANODIZED STEEL | 23| 4 +Brand#11 |ECONOMY ANODIZED STEEL | 45| 4 +Brand#11 |ECONOMY ANODIZED STEEL | 49| 4 +Brand#11 |ECONOMY ANODIZED TIN | 3| 4 +Brand#11 |ECONOMY ANODIZED TIN | 9| 4 +Brand#11 |ECONOMY ANODIZED TIN | 49| 4 +Brand#11 |ECONOMY BRUSHED BRASS | 3| 4 +Brand#11 |ECONOMY BRUSHED BRASS | 19| 4 +Brand#11 |ECONOMY BRUSHED COPPER | 3| 4 +Brand#11 |ECONOMY BRUSHED COPPER | 19| 4 +Brand#11 |ECONOMY BRUSHED NICKEL | 14| 4 +Brand#11 |ECONOMY BRUSHED STEEL | 3| 4 +Brand#11 |ECONOMY BRUSHED STEEL | 36| 4 +Brand#11 |ECONOMY BRUSHED TIN | 23| 4 +Brand#11 |ECONOMY BRUSHED TIN | 45| 4 +Brand#11 |ECONOMY BURNISHED BRASS | 3| 4 +Brand#11 |ECONOMY BURNISHED BRASS | 9| 4 +Brand#11 |ECONOMY BURNISHED BRASS | 14| 4 +Brand#11 |ECONOMY BURNISHED BRASS | 19| 4 +Brand#11 |ECONOMY BURNISHED BRASS | 49| 4 +Brand#11 |ECONOMY BURNISHED COPPER | 14| 4 +Brand#11 |ECONOMY BURNISHED COPPER | 23| 4 +Brand#11 |ECONOMY BURNISHED COPPER | 36| 4 +Brand#11 |ECONOMY BURNISHED NICKEL | 9| 4 +Brand#11 |ECONOMY BURNISHED NICKEL | 49| 4 +Brand#11 |ECONOMY BURNISHED STEEL | 14| 4 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19| 4 +Brand#11 |ECONOMY POLISHED COPPER | 23| 4 +Brand#11 |ECONOMY POLISHED NICKEL | 36| 4 +Brand#11 |ECONOMY POLISHED NICKEL | 49| 4 +Brand#11 |ECONOMY POLISHED STEEL | 9| 4 +Brand#11 |ECONOMY POLISHED STEEL | 14| 4 +Brand#11 |ECONOMY POLISHED STEEL | 23| 4 +Brand#11 |ECONOMY POLISHED STEEL | 36| 4 +Brand#11 |ECONOMY POLISHED STEEL | 45| 4 +Brand#11 |ECONOMY POLISHED TIN | 49| 4 +Brand#11 |LARGE ANODIZED BRASS | 3| 4 +Brand#11 |LARGE ANODIZED BRASS | 9| 4 +Brand#11 |LARGE ANODIZED BRASS | 19| 4 +Brand#11 |LARGE ANODIZED BRASS | 23| 4 +Brand#11 |LARGE ANODIZED COPPER | 3| 4 +Brand#11 |LARGE ANODIZED COPPER | 9| 4 +Brand#11 |LARGE ANODIZED COPPER | 36| 4 +Brand#11 |LARGE ANODIZED COPPER | 45| 4 +Brand#11 |LARGE ANODIZED NICKEL | 23| 4 +Brand#11 |LARGE ANODIZED STEEL | 14| 4 +Brand#11 |LARGE ANODIZED STEEL | 49| 4 +Brand#11 |LARGE ANODIZED TIN | 3| 4 +Brand#11 |LARGE ANODIZED TIN | 9| 4 +Brand#11 |LARGE ANODIZED TIN | 14| 4 +Brand#11 |LARGE ANODIZED TIN | 19| 4 +Brand#11 |LARGE BRUSHED BRASS | 36| 4 +Brand#11 |LARGE BRUSHED BRASS | 45| 4 +Brand#11 |LARGE BRUSHED COPPER | 3| 4 +Brand#11 |LARGE BRUSHED NICKEL | 9| 4 +Brand#11 |LARGE BRUSHED NICKEL | 36| 4 +Brand#11 |LARGE BRUSHED NICKEL | 49| 4 +Brand#11 |LARGE BRUSHED STEEL | 3| 4 +Brand#11 |LARGE BRUSHED STEEL | 9| 4 +Brand#11 |LARGE BRUSHED STEEL | 23| 4 +Brand#11 |LARGE BRUSHED STEEL | 45| 4 +Brand#11 |LARGE BRUSHED TIN | 3| 4 +Brand#11 |LARGE BURNISHED BRASS | 9| 4 +Brand#11 |LARGE BURNISHED BRASS | 19| 4 +Brand#11 |LARGE BURNISHED BRASS | 36| 4 +Brand#11 |LARGE BURNISHED COPPER | 19| 4 +Brand#11 |LARGE BURNISHED COPPER | 45| 4 +Brand#11 |LARGE BURNISHED NICKEL | 3| 4 +Brand#11 |LARGE BURNISHED NICKEL | 49| 4 +Brand#11 |LARGE BURNISHED STEEL | 14| 4 +Brand#11 |LARGE BURNISHED STEEL | 23| 4 +Brand#11 |LARGE BURNISHED STEEL | 45| 4 +Brand#11 |LARGE BURNISHED TIN | 3| 4 +Brand#11 |LARGE BURNISHED TIN | 9| 4 +Brand#11 |LARGE BURNISHED TIN | 36| 4 +Brand#11 |LARGE BURNISHED TIN | 45| 4 +Brand#11 |LARGE PLATED BRASS | 9| 4 +Brand#11 |LARGE PLATED BRASS | 36| 4 +Brand#11 |LARGE PLATED BRASS | 45| 4 +Brand#11 |LARGE PLATED BRASS | 49| 4 +Brand#11 |LARGE PLATED COPPER | 3| 4 +Brand#11 |LARGE PLATED COPPER | 9| 4 +Brand#11 |LARGE PLATED COPPER | 14| 4 +Brand#11 |LARGE PLATED COPPER | 19| 4 +Brand#11 |LARGE PLATED COPPER | 23| 4 +Brand#11 |LARGE PLATED COPPER | 36| 4 +Brand#11 |LARGE PLATED COPPER | 45| 4 +Brand#11 |LARGE PLATED COPPER | 49| 4 +Brand#11 |LARGE PLATED NICKEL | 9| 4 +Brand#11 |LARGE PLATED NICKEL | 14| 4 +Brand#11 |LARGE PLATED NICKEL | 19| 4 +Brand#11 |LARGE PLATED NICKEL | 49| 4 +Brand#11 |LARGE PLATED STEEL | 9| 4 +Brand#11 |LARGE PLATED STEEL | 49| 4 +Brand#11 |LARGE PLATED TIN | 23| 4 +Brand#11 |LARGE PLATED TIN | 45| 4 +Brand#11 |LARGE POLISHED BRASS | 3| 4 +Brand#11 |LARGE POLISHED BRASS | 14| 4 +Brand#11 |LARGE POLISHED BRASS | 19| 4 +Brand#11 |LARGE POLISHED BRASS | 23| 4 +Brand#11 |LARGE POLISHED BRASS | 45| 4 +Brand#11 |LARGE POLISHED COPPER | 3| 4 +Brand#11 |LARGE POLISHED COPPER | 19| 4 +Brand#11 |LARGE POLISHED NICKEL | 49| 4 +Brand#11 |LARGE POLISHED STEEL | 14| 4 +Brand#11 |LARGE POLISHED STEEL | 23| 4 +Brand#11 |LARGE POLISHED STEEL | 45| 4 +Brand#11 |LARGE POLISHED TIN | 9| 4 +Brand#11 |LARGE POLISHED TIN | 14| 4 +Brand#11 |LARGE POLISHED TIN | 45| 4 +Brand#11 |LARGE POLISHED TIN | 49| 4 +Brand#11 |MEDIUM ANODIZED BRASS | 19| 4 +Brand#11 |MEDIUM ANODIZED COPPER | 3| 4 +Brand#11 |MEDIUM ANODIZED COPPER | 45| 4 +Brand#11 |MEDIUM ANODIZED COPPER | 49| 4 +Brand#11 |MEDIUM ANODIZED STEEL | 14| 4 +Brand#11 |MEDIUM ANODIZED STEEL | 23| 4 +Brand#11 |MEDIUM ANODIZED TIN | 14| 4 +Brand#11 |MEDIUM ANODIZED TIN | 19| 4 +Brand#11 |MEDIUM BRUSHED BRASS | 14| 4 +Brand#11 |MEDIUM BRUSHED BRASS | 36| 4 +Brand#11 |MEDIUM BRUSHED BRASS | 49| 4 +Brand#11 |MEDIUM BRUSHED COPPER | 36| 4 +Brand#11 |MEDIUM BRUSHED COPPER | 49| 4 +Brand#11 |MEDIUM BRUSHED NICKEL | 3| 4 +Brand#11 |MEDIUM BRUSHED NICKEL | 19| 4 +Brand#11 |MEDIUM BRUSHED NICKEL | 49| 4 +Brand#11 |MEDIUM BRUSHED STEEL | 14| 4 +Brand#11 |MEDIUM BRUSHED TIN | 3| 4 +Brand#11 |MEDIUM BRUSHED TIN | 9| 4 +Brand#11 |MEDIUM BRUSHED TIN | 49| 4 +Brand#11 |MEDIUM BURNISHED BRASS | 9| 4 +Brand#11 |MEDIUM BURNISHED BRASS | 14| 4 +Brand#11 |MEDIUM BURNISHED BRASS | 36| 4 +Brand#11 |MEDIUM BURNISHED COPPER | 3| 4 +Brand#11 |MEDIUM BURNISHED COPPER | 36| 4 +Brand#11 |MEDIUM BURNISHED NICKEL | 14| 4 +Brand#11 |MEDIUM BURNISHED NICKEL | 19| 4 +Brand#11 |MEDIUM BURNISHED NICKEL | 36| 4 +Brand#11 |MEDIUM BURNISHED NICKEL | 45| 4 +Brand#11 |MEDIUM BURNISHED STEEL | 23| 4 +Brand#11 |MEDIUM BURNISHED STEEL | 45| 4 +Brand#11 |MEDIUM BURNISHED STEEL | 49| 4 +Brand#11 |MEDIUM BURNISHED TIN | 23| 4 +Brand#11 |MEDIUM BURNISHED TIN | 45| 4 +Brand#11 |MEDIUM PLATED BRASS | 19| 4 +Brand#11 |MEDIUM PLATED COPPER | 23| 4 +Brand#11 |MEDIUM PLATED COPPER | 45| 4 +Brand#11 |MEDIUM PLATED COPPER | 49| 4 +Brand#11 |MEDIUM PLATED NICKEL | 36| 4 +Brand#11 |MEDIUM PLATED NICKEL | 49| 4 +Brand#11 |MEDIUM PLATED STEEL | 49| 4 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|PROMO BRUSHED COPPER | 49| 4 +Brand#11 |PROMO BRUSHED NICKEL | 3| 4 +Brand#11 |PROMO BRUSHED NICKEL | 14| 4 +Brand#11 |PROMO BRUSHED NICKEL | 23| 4 +Brand#11 |PROMO BRUSHED NICKEL | 45| 4 +Brand#11 |PROMO BRUSHED NICKEL | 49| 4 +Brand#11 |PROMO BRUSHED STEEL | 3| 4 +Brand#11 |PROMO BRUSHED STEEL | 14| 4 +Brand#11 |PROMO BRUSHED STEEL | 19| 4 +Brand#11 |PROMO BRUSHED TIN | 3| 4 +Brand#11 |PROMO BRUSHED TIN | 9| 4 +Brand#11 |PROMO BRUSHED TIN | 23| 4 +Brand#11 |PROMO BRUSHED TIN | 49| 4 +Brand#11 |PROMO BURNISHED BRASS | 14| 4 +Brand#11 |PROMO BURNISHED BRASS | 45| 4 +Brand#11 |PROMO BURNISHED COPPER | 9| 4 +Brand#11 |PROMO BURNISHED COPPER | 19| 4 +Brand#11 |PROMO BURNISHED COPPER | 36| 4 +Brand#11 |PROMO BURNISHED NICKEL | 9| 4 +Brand#11 |PROMO BURNISHED NICKEL | 19| 4 +Brand#11 |PROMO BURNISHED NICKEL | 49| 4 +Brand#11 |PROMO BURNISHED STEEL | 3| 4 +Brand#11 |PROMO BURNISHED STEEL | 9| 4 +Brand#11 |PROMO BURNISHED TIN | 3| 4 +Brand#11 |PROMO BURNISHED TIN | 9| 4 +Brand#11 |PROMO BURNISHED TIN | 14| 4 +Brand#11 |PROMO BURNISHED TIN | 19| 4 +Brand#11 |PROMO BURNISHED TIN | 49| 4 +Brand#11 |PROMO PLATED BRASS | 3| 4 +Brand#11 |PROMO PLATED BRASS | 9| 4 +Brand#11 |PROMO PLATED BRASS | 36| 4 +Brand#11 |PROMO PLATED COPPER | 9| 4 +Brand#11 |PROMO PLATED COPPER | 23| 4 +Brand#11 |PROMO PLATED NICKEL | 19| 4 +Brand#11 |PROMO PLATED NICKEL | 23| 4 +Brand#11 |PROMO PLATED NICKEL | 36| 4 +Brand#11 |PROMO PLATED NICKEL | 45| 4 +Brand#11 |PROMO PLATED STEEL | 36| 4 +Brand#11 |PROMO PLATED STEEL | 45| 4 +Brand#11 |PROMO PLATED TIN | 45| 4 +Brand#11 |PROMO POLISHED BRASS | 9| 4 +Brand#11 |PROMO POLISHED BRASS | 45| 4 +Brand#11 |PROMO POLISHED BRASS | 49| 4 +Brand#11 |PROMO POLISHED COPPER | 3| 4 +Brand#11 |PROMO POLISHED COPPER | 36| 4 +Brand#11 |PROMO POLISHED COPPER | 49| 4 +Brand#11 |PROMO POLISHED NICKEL | 14| 4 +Brand#11 |PROMO POLISHED NICKEL | 19| 4 +Brand#11 |PROMO POLISHED STEEL | 9| 4 +Brand#11 |PROMO POLISHED STEEL | 14| 4 +Brand#11 |PROMO POLISHED STEEL | 36| 4 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|SMALL BRUSHED COPPER | 49| 4 +Brand#11 |SMALL BRUSHED STEEL | 9| 4 +Brand#11 |SMALL BRUSHED STEEL | 19| 4 +Brand#11 |SMALL BRUSHED STEEL | 36| 4 +Brand#11 |SMALL BRUSHED STEEL | 45| 4 +Brand#11 |SMALL BRUSHED TIN | 9| 4 +Brand#11 |SMALL BRUSHED TIN | 23| 4 +Brand#11 |SMALL BRUSHED TIN | 36| 4 +Brand#11 |SMALL BRUSHED TIN | 45| 4 +Brand#11 |SMALL BURNISHED BRASS | 3| 4 +Brand#11 |SMALL BURNISHED BRASS | 23| 4 +Brand#11 |SMALL BURNISHED BRASS | 36| 4 +Brand#11 |SMALL BURNISHED COPPER | 3| 4 +Brand#11 |SMALL BURNISHED COPPER | 14| 4 +Brand#11 |SMALL BURNISHED NICKEL | 36| 4 +Brand#11 |SMALL BURNISHED NICKEL | 45| 4 +Brand#11 |SMALL BURNISHED STEEL | 14| 4 +Brand#11 |SMALL BURNISHED STEEL | 23| 4 +Brand#11 |SMALL BURNISHED STEEL | 49| 4 +Brand#11 |SMALL BURNISHED TIN | 14| 4 +Brand#11 |SMALL BURNISHED TIN | 23| 4 +Brand#11 |SMALL BURNISHED TIN | 36| 4 +Brand#11 |SMALL BURNISHED TIN | 49| 4 +Brand#11 |SMALL PLATED BRASS | 9| 4 +Brand#11 |SMALL PLATED BRASS | 23| 4 +Brand#11 |SMALL PLATED COPPER | 3| 4 +Brand#11 |SMALL PLATED COPPER | 14| 4 +Brand#11 |SMALL PLATED COPPER | 36| 4 +Brand#11 |SMALL PLATED NICKEL | 3| 4 +Brand#11 |SMALL PLATED NICKEL | 14| 4 +Brand#11 |SMALL PLATED NICKEL | 19| 4 +Brand#11 |SMALL PLATED STEEL | 23| 4 +Brand#11 |SMALL PLATED STEEL | 36| 4 +Brand#11 |SMALL PLATED TIN | 49| 4 +Brand#11 |SMALL POLISHED BRASS | 36| 4 +Brand#11 |SMALL POLISHED BRASS | 45| 4 +Brand#11 |SMALL POLISHED BRASS | 49| 4 +Brand#11 |SMALL POLISHED COPPER | 3| 4 +Brand#11 |SMALL POLISHED COPPER | 14| 4 +Brand#11 |SMALL POLISHED COPPER | 19| 4 +Brand#11 |SMALL POLISHED COPPER | 49| 4 +Brand#11 |SMALL POLISHED NICKEL | 3| 4 +Brand#11 |SMALL POLISHED NICKEL | 14| 4 +Brand#11 |SMALL POLISHED NICKEL | 19| 4 +Brand#11 |SMALL POLISHED STEEL | 9| 4 +Brand#11 |SMALL POLISHED STEEL | 49| 4 +Brand#11 |SMALL POLISHED TIN | 14| 4 +Brand#11 |SMALL POLISHED TIN | 19| 4 +Brand#11 |SMALL POLISHED TIN | 36| 4 +Brand#11 |SMALL POLISHED TIN | 45| 4 +Brand#11 |SMALL POLISHED TIN | 49| 4 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BRUSHED COPPER | 19| 4 +Brand#11 |STANDARD BRUSHED COPPER | 49| 4 +Brand#11 |STANDARD BRUSHED NICKEL | 19| 4 +Brand#11 |STANDARD BRUSHED NICKEL | 23| 4 +Brand#11 |STANDARD BRUSHED NICKEL | 36| 4 +Brand#11 |STANDARD BRUSHED NICKEL | 49| 4 +Brand#11 |STANDARD BRUSHED STEEL | 23| 4 +Brand#11 |STANDARD BRUSHED STEEL | 36| 4 +Brand#11 |STANDARD BRUSHED TIN | 14| 4 +Brand#11 |STANDARD BRUSHED TIN | 45| 4 +Brand#11 |STANDARD BURNISHED BRASS | 3| 4 +Brand#11 |STANDARD BURNISHED BRASS | 14| 4 +Brand#11 |STANDARD BURNISHED BRASS | 45| 4 +Brand#11 |STANDARD BURNISHED COPPER| 3| 4 +Brand#11 |STANDARD BURNISHED COPPER| 45| 4 +Brand#11 |STANDARD BURNISHED NICKEL| 3| 4 +Brand#11 |STANDARD BURNISHED NICKEL| 9| 4 +Brand#11 |STANDARD BURNISHED NICKEL| 14| 4 +Brand#11 |STANDARD BURNISHED NICKEL| 19| 4 +Brand#11 |STANDARD BURNISHED STEEL | 9| 4 +Brand#11 |STANDARD BURNISHED STEEL | 14| 4 +Brand#11 |STANDARD BURNISHED STEEL | 19| 4 +Brand#11 |STANDARD BURNISHED STEEL | 49| 4 +Brand#11 |STANDARD BURNISHED TIN | 9| 4 +Brand#11 |STANDARD BURNISHED TIN | 19| 4 +Brand#11 |STANDARD BURNISHED TIN | 23| 4 +Brand#11 |STANDARD BURNISHED TIN | 36| 4 +Brand#11 |STANDARD PLATED BRASS | 3| 4 +Brand#11 |STANDARD PLATED BRASS | 14| 4 +Brand#11 |STANDARD PLATED BRASS | 36| 4 +Brand#11 |STANDARD PLATED COPPER | 9| 4 +Brand#11 |STANDARD PLATED COPPER | 14| 4 +Brand#11 |STANDARD PLATED COPPER | 45| 4 +Brand#11 |STANDARD PLATED NICKEL | 3| 4 +Brand#11 |STANDARD PLATED NICKEL | 9| 4 +Brand#11 |STANDARD PLATED NICKEL | 23| 4 +Brand#11 |STANDARD PLATED NICKEL | 49| 4 +Brand#11 |STANDARD PLATED STEEL | 9| 4 +Brand#11 |STANDARD PLATED STEEL | 36| 4 +Brand#11 |STANDARD PLATED TIN | 19| 4 +Brand#11 |STANDARD POLISHED BRASS | 19| 4 +Brand#11 |STANDARD POLISHED BRASS | 36| 4 +Brand#11 |STANDARD POLISHED BRASS | 49| 4 +Brand#11 |STANDARD POLISHED COPPER | 3| 4 +Brand#11 |STANDARD POLISHED COPPER | 45| 4 +Brand#11 |STANDARD POLISHED COPPER | 49| 4 +Brand#11 |STANDARD POLISHED NICKEL | 14| 4 +Brand#11 |STANDARD POLISHED NICKEL | 36| 4 +Brand#11 |STANDARD POLISHED NICKEL | 45| 4 +Brand#11 |STANDARD POLISHED STEEL | 14| 4 +Brand#11 |STANDARD POLISHED STEEL | 23| 4 +Brand#11 |STANDARD POLISHED STEEL | 36| 4 +Brand#11 |STANDARD POLISHED STEEL | 45| 4 +Brand#11 |STANDARD POLISHED TIN | 3| 4 +Brand#11 |STANDARD POLISHED TIN | 19| 4 +Brand#11 |STANDARD POLISHED TIN | 36| 4 +Brand#11 |STANDARD POLISHED TIN | 45| 4 +Brand#12 |ECONOMY ANODIZED BRASS | 9| 4 +Brand#12 |ECONOMY ANODIZED BRASS | 19| 4 +Brand#12 |ECONOMY ANODIZED BRASS | 23| 4 +Brand#12 |ECONOMY ANODIZED COPPER | 9| 4 +Brand#12 |ECONOMY ANODIZED COPPER | 19| 4 +Brand#12 |ECONOMY ANODIZED COPPER | 23| 4 +Brand#12 |ECONOMY ANODIZED COPPER | 36| 4 +Brand#12 |ECONOMY ANODIZED COPPER | 45| 4 +Brand#12 |ECONOMY ANODIZED COPPER | 49| 4 +Brand#12 |ECONOMY ANODIZED NICKEL | 3| 4 +Brand#12 |ECONOMY ANODIZED NICKEL | 9| 4 +Brand#12 |ECONOMY ANODIZED NICKEL | 23| 4 +Brand#12 |ECONOMY ANODIZED NICKEL | 49| 4 +Brand#12 |ECONOMY ANODIZED STEEL | 9| 4 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23| 4 +Brand#12 |LARGE ANODIZED NICKEL | 45| 4 +Brand#12 |LARGE ANODIZED STEEL | 14| 4 +Brand#12 |LARGE ANODIZED STEEL | 19| 4 +Brand#12 |LARGE ANODIZED STEEL | 45| 4 +Brand#12 |LARGE ANODIZED TIN | 9| 4 +Brand#12 |LARGE ANODIZED TIN | 36| 4 +Brand#12 |LARGE ANODIZED TIN | 45| 4 +Brand#12 |LARGE BRUSHED BRASS | 3| 4 +Brand#12 |LARGE BRUSHED COPPER | 3| 4 +Brand#12 |LARGE BRUSHED COPPER | 9| 4 +Brand#12 |LARGE BRUSHED COPPER | 45| 4 +Brand#12 |LARGE BRUSHED NICKEL | 3| 4 +Brand#12 |LARGE BRUSHED NICKEL | 19| 4 +Brand#12 |LARGE BRUSHED NICKEL | 45| 4 +Brand#12 |LARGE BRUSHED STEEL | 14| 4 +Brand#12 |LARGE BRUSHED TIN | 36| 4 +Brand#12 |LARGE BRUSHED TIN | 49| 4 +Brand#12 |LARGE BURNISHED BRASS | 3| 4 +Brand#12 |LARGE BURNISHED BRASS | 19| 4 +Brand#12 |LARGE BURNISHED BRASS | 23| 4 +Brand#12 |LARGE BURNISHED BRASS | 36| 4 +Brand#12 |LARGE BURNISHED BRASS | 49| 4 +Brand#12 |LARGE BURNISHED COPPER | 9| 4 +Brand#12 |LARGE BURNISHED COPPER | 14| 4 +Brand#12 |LARGE BURNISHED COPPER | 23| 4 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PLATED TIN | 23| 4 +Brand#12 |LARGE POLISHED BRASS | 14| 4 +Brand#12 |LARGE POLISHED BRASS | 36| 4 +Brand#12 |LARGE POLISHED BRASS | 45| 4 +Brand#12 |LARGE POLISHED COPPER | 14| 4 +Brand#12 |LARGE POLISHED COPPER | 45| 4 +Brand#12 |LARGE POLISHED NICKEL | 3| 4 +Brand#12 |LARGE POLISHED NICKEL | 9| 4 +Brand#12 |LARGE POLISHED STEEL | 3| 4 +Brand#12 |LARGE POLISHED STEEL | 19| 4 +Brand#12 |LARGE POLISHED STEEL | 45| 4 +Brand#12 |LARGE POLISHED TIN | 14| 4 +Brand#12 |LARGE POLISHED TIN | 23| 4 +Brand#12 |LARGE POLISHED TIN | 49| 4 +Brand#12 |MEDIUM ANODIZED BRASS | 9| 4 +Brand#12 |MEDIUM ANODIZED BRASS | 19| 4 +Brand#12 |MEDIUM ANODIZED BRASS | 36| 4 +Brand#12 |MEDIUM ANODIZED COPPER | 14| 4 +Brand#12 |MEDIUM ANODIZED COPPER | 36| 4 +Brand#12 |MEDIUM ANODIZED COPPER | 45| 4 +Brand#12 |MEDIUM ANODIZED NICKEL | 14| 4 +Brand#12 |MEDIUM ANODIZED NICKEL | 23| 4 +Brand#12 |MEDIUM ANODIZED NICKEL | 45| 4 +Brand#12 |MEDIUM ANODIZED NICKEL | 49| 4 +Brand#12 |MEDIUM ANODIZED STEEL | 23| 4 +Brand#12 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PLATED BRASS | 14| 4 +Brand#12 |PROMO PLATED BRASS | 23| 4 +Brand#12 |PROMO PLATED COPPER | 3| 4 +Brand#12 |PROMO PLATED COPPER | 19| 4 +Brand#12 |PROMO PLATED COPPER | 49| 4 +Brand#12 |PROMO PLATED NICKEL | 9| 4 +Brand#12 |PROMO PLATED NICKEL | 19| 4 +Brand#12 |PROMO PLATED NICKEL | 49| 4 +Brand#12 |PROMO PLATED STEEL | 9| 4 +Brand#12 |PROMO PLATED STEEL | 14| 4 +Brand#12 |PROMO PLATED STEEL | 23| 4 +Brand#12 |PROMO PLATED STEEL | 45| 4 +Brand#12 |PROMO PLATED TIN | 14| 4 +Brand#12 |PROMO PLATED TIN | 19| 4 +Brand#12 |PROMO PLATED TIN | 49| 4 +Brand#12 |PROMO POLISHED BRASS | 14| 4 +Brand#12 |PROMO POLISHED BRASS | 45| 4 +Brand#12 |PROMO POLISHED COPPER | 3| 4 +Brand#12 |PROMO POLISHED COPPER | 9| 4 +Brand#12 |PROMO POLISHED COPPER | 36| 4 +Brand#12 |PROMO POLISHED COPPER | 49| 4 +Brand#12 |PROMO POLISHED NICKEL | 9| 4 +Brand#12 |PROMO POLISHED NICKEL | 23| 4 +Brand#12 |PROMO POLISHED NICKEL | 45| 4 +Brand#12 |PROMO POLISHED STEEL | 9| 4 +Brand#12 |PROMO POLISHED STEEL | 14| 4 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|STANDARD PLATED BRASS | 14| 4 +Brand#12 |STANDARD PLATED BRASS | 23| 4 +Brand#12 |STANDARD PLATED BRASS | 36| 4 +Brand#12 |STANDARD PLATED BRASS | 45| 4 +Brand#12 |STANDARD PLATED COPPER | 3| 4 +Brand#12 |STANDARD PLATED COPPER | 9| 4 +Brand#12 |STANDARD PLATED COPPER | 19| 4 +Brand#12 |STANDARD PLATED COPPER | 45| 4 +Brand#12 |STANDARD PLATED NICKEL | 23| 4 +Brand#12 |STANDARD PLATED NICKEL | 36| 4 +Brand#12 |STANDARD PLATED NICKEL | 49| 4 +Brand#12 |STANDARD PLATED STEEL | 9| 4 +Brand#12 |STANDARD PLATED TIN | 14| 4 +Brand#12 |STANDARD PLATED TIN | 23| 4 +Brand#12 |STANDARD PLATED TIN | 49| 4 +Brand#12 |STANDARD POLISHED BRASS | 9| 4 +Brand#12 |STANDARD POLISHED BRASS | 19| 4 +Brand#12 |STANDARD POLISHED BRASS | 49| 4 +Brand#12 |STANDARD POLISHED COPPER | 14| 4 +Brand#12 |STANDARD POLISHED COPPER | 45| 4 +Brand#12 |STANDARD POLISHED COPPER | 49| 4 +Brand#12 |STANDARD POLISHED NICKEL | 9| 4 +Brand#12 |STANDARD POLISHED NICKEL | 14| 4 +Brand#12 |STANDARD POLISHED NICKEL | 19| 4 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4 +Brand#13 |ECONOMY BURNISHED NICKEL | 45| 4 +Brand#13 |ECONOMY BURNISHED NICKEL | 49| 4 +Brand#13 |ECONOMY BURNISHED STEEL | 9| 4 +Brand#13 |ECONOMY BURNISHED STEEL | 23| 4 +Brand#13 |ECONOMY BURNISHED STEEL | 49| 4 +Brand#13 |ECONOMY BURNISHED TIN | 3| 4 +Brand#13 |ECONOMY BURNISHED TIN | 9| 4 +Brand#13 |ECONOMY BURNISHED TIN | 19| 4 +Brand#13 |ECONOMY BURNISHED TIN | 45| 4 +Brand#13 |ECONOMY PLATED BRASS | 3| 4 +Brand#13 |ECONOMY PLATED BRASS | 19| 4 +Brand#13 |ECONOMY PLATED BRASS | 45| 4 +Brand#13 |ECONOMY PLATED COPPER | 23| 4 +Brand#13 |ECONOMY PLATED COPPER | 45| 4 +Brand#13 |ECONOMY PLATED NICKEL | 45| 4 +Brand#13 |ECONOMY PLATED STEEL | 9| 4 +Brand#13 |ECONOMY PLATED STEEL | 14| 4 +Brand#13 |ECONOMY PLATED STEEL | 49| 4 +Brand#13 |ECONOMY PLATED TIN | 19| 4 +Brand#13 |ECONOMY PLATED TIN | 36| 4 +Brand#13 |ECONOMY PLATED TIN | 49| 4 +Brand#13 |ECONOMY POLISHED BRASS | 19| 4 +Brand#13 |ECONOMY POLISHED COPPER | 3| 4 +Brand#13 |ECONOMY POLISHED COPPER | 14| 4 +Brand#13 |ECONOMY POLISHED COPPER | 23| 4 +Brand#13 |ECONOMY POLISHED NICKEL | 9| 4 +Brand#13 |ECONOMY POLISHED NICKEL | 14| 4 +Brand#13 |ECONOMY POLISHED NICKEL | 19| 4 +Brand#13 |ECONOMY POLISHED NICKEL | 36| 4 +Brand#13 |ECONOMY POLISHED NICKEL | 45| 4 +Brand#13 |ECONOMY POLISHED NICKEL | 49| 4 +Brand#13 |ECONOMY POLISHED STEEL | 14| 4 +Brand#13 |ECONOMY POLISHED TIN | 9| 4 +Brand#13 |ECONOMY POLISHED TIN | 14| 4 +Brand#13 |ECONOMY POLISHED TIN | 49| 4 +Brand#13 |LARGE ANODIZED BRASS | 3| 4 +Brand#13 |LARGE ANODIZED BRASS | 9| 4 +Brand#13 |LARGE ANODIZED BRASS | 14| 4 +Brand#13 |LARGE ANODIZED BRASS | 19| 4 +Brand#13 |LARGE ANODIZED BRASS | 23| 4 +Brand#13 |LARGE ANODIZED COPPER | 9| 4 +Brand#13 |LARGE ANODIZED COPPER | 14| 4 +Brand#13 |LARGE ANODIZED COPPER | 36| 4 +Brand#13 |LARGE ANODIZED COPPER | 45| 4 +Brand#13 |LARGE ANODIZED COPPER | 49| 4 +Brand#13 |LARGE ANODIZED NICKEL | 3| 4 +Brand#13 |LARGE ANODIZED NICKEL | 9| 4 +Brand#13 |LARGE ANODIZED NICKEL | 36| 4 +Brand#13 |LARGE ANODIZED STEEL | 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ANODIZED COPPER | 36| 4 +Brand#13 |PROMO ANODIZED COPPER | 49| 4 +Brand#13 |PROMO ANODIZED NICKEL | 14| 4 +Brand#13 |PROMO ANODIZED NICKEL | 19| 4 +Brand#13 |PROMO ANODIZED NICKEL | 23| 4 +Brand#13 |PROMO ANODIZED NICKEL | 36| 4 +Brand#13 |PROMO ANODIZED STEEL | 3| 4 +Brand#13 |PROMO ANODIZED STEEL | 9| 4 +Brand#13 |PROMO ANODIZED STEEL | 14| 4 +Brand#13 |PROMO ANODIZED STEEL | 23| 4 +Brand#13 |PROMO ANODIZED STEEL | 45| 4 +Brand#13 |PROMO ANODIZED STEEL | 49| 4 +Brand#13 |PROMO ANODIZED TIN | 3| 4 +Brand#13 |PROMO ANODIZED TIN | 9| 4 +Brand#13 |PROMO ANODIZED TIN | 14| 4 +Brand#13 |PROMO ANODIZED TIN | 19| 4 +Brand#13 |PROMO ANODIZED TIN | 23| 4 +Brand#13 |PROMO ANODIZED TIN | 45| 4 +Brand#13 |PROMO BRUSHED BRASS | 9| 4 +Brand#13 |PROMO BRUSHED BRASS | 14| 4 +Brand#13 |PROMO BRUSHED BRASS | 19| 4 +Brand#13 |PROMO BRUSHED COPPER | 9| 4 +Brand#13 |PROMO BRUSHED COPPER | 23| 4 +Brand#13 |PROMO BRUSHED COPPER | 45| 4 +Brand#13 |PROMO BRUSHED NICKEL | 3| 4 +Brand#13 |PROMO BRUSHED NICKEL | 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4 +Brand#13 |STANDARD BRUSHED NICKEL | 23| 4 +Brand#13 |STANDARD BRUSHED NICKEL | 45| 4 +Brand#13 |STANDARD BRUSHED STEEL | 3| 4 +Brand#13 |STANDARD BRUSHED STEEL | 14| 4 +Brand#13 |STANDARD BRUSHED STEEL | 19| 4 +Brand#13 |STANDARD BRUSHED STEEL | 23| 4 +Brand#13 |STANDARD BRUSHED TIN | 14| 4 +Brand#13 |STANDARD BRUSHED TIN | 36| 4 +Brand#13 |STANDARD BRUSHED TIN | 45| 4 +Brand#13 |STANDARD BURNISHED BRASS | 14| 4 +Brand#13 |STANDARD BURNISHED BRASS | 45| 4 +Brand#13 |STANDARD BURNISHED COPPER| 19| 4 +Brand#13 |STANDARD BURNISHED NICKEL| 36| 4 +Brand#13 |STANDARD BURNISHED NICKEL| 45| 4 +Brand#13 |STANDARD BURNISHED STEEL | 9| 4 +Brand#13 |STANDARD BURNISHED STEEL | 14| 4 +Brand#13 |STANDARD BURNISHED STEEL | 23| 4 +Brand#13 |STANDARD BURNISHED STEEL | 36| 4 +Brand#13 |STANDARD BURNISHED STEEL | 49| 4 +Brand#13 |STANDARD BURNISHED TIN | 14| 4 +Brand#13 |STANDARD BURNISHED TIN | 45| 4 +Brand#13 |STANDARD PLATED COPPER | 3| 4 +Brand#13 |STANDARD PLATED COPPER | 9| 4 +Brand#13 |STANDARD 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|SMALL ANODIZED TIN | 9| 4 +Brand#14 |SMALL ANODIZED TIN | 14| 4 +Brand#14 |SMALL ANODIZED TIN | 23| 4 +Brand#14 |SMALL ANODIZED TIN | 36| 4 +Brand#14 |SMALL ANODIZED TIN | 49| 4 +Brand#14 |SMALL BRUSHED BRASS | 3| 4 +Brand#14 |SMALL BRUSHED BRASS | 36| 4 +Brand#14 |SMALL BRUSHED COPPER | 9| 4 +Brand#14 |SMALL BRUSHED COPPER | 14| 4 +Brand#14 |SMALL BRUSHED COPPER | 19| 4 +Brand#14 |SMALL BRUSHED COPPER | 23| 4 +Brand#14 |SMALL BRUSHED COPPER | 45| 4 +Brand#14 |SMALL BRUSHED NICKEL | 3| 4 +Brand#14 |SMALL BRUSHED NICKEL | 14| 4 +Brand#14 |SMALL BRUSHED NICKEL | 23| 4 +Brand#14 |SMALL BRUSHED NICKEL | 45| 4 +Brand#14 |SMALL BRUSHED STEEL | 9| 4 +Brand#14 |SMALL BRUSHED STEEL | 19| 4 +Brand#14 |SMALL BRUSHED STEEL | 49| 4 +Brand#14 |SMALL BRUSHED TIN | 3| 4 +Brand#14 |SMALL BRUSHED TIN | 23| 4 +Brand#14 |SMALL BRUSHED TIN | 45| 4 +Brand#14 |SMALL BURNISHED BRASS | 9| 4 +Brand#14 |SMALL BURNISHED COPPER | 3| 4 +Brand#14 |SMALL BURNISHED COPPER | 9| 4 +Brand#14 |SMALL BURNISHED COPPER | 19| 4 +Brand#14 |SMALL BURNISHED COPPER | 23| 4 +Brand#14 |SMALL BURNISHED COPPER | 49| 4 +Brand#14 |SMALL BURNISHED NICKEL | 3| 4 +Brand#14 |SMALL BURNISHED NICKEL | 23| 4 +Brand#14 |SMALL BURNISHED STEEL | 3| 4 +Brand#14 |SMALL BURNISHED TIN | 3| 4 +Brand#14 |SMALL BURNISHED TIN | 9| 4 +Brand#14 |SMALL BURNISHED TIN | 14| 4 +Brand#14 |SMALL BURNISHED TIN | 36| 4 +Brand#14 |SMALL BURNISHED TIN | 45| 4 +Brand#14 |SMALL PLATED BRASS | 3| 4 +Brand#14 |SMALL PLATED BRASS | 19| 4 +Brand#14 |SMALL PLATED COPPER | 14| 4 +Brand#14 |SMALL PLATED COPPER | 36| 4 +Brand#14 |SMALL PLATED COPPER | 45| 4 +Brand#14 |SMALL PLATED NICKEL | 3| 4 +Brand#14 |SMALL PLATED NICKEL | 9| 4 +Brand#14 |SMALL PLATED NICKEL | 45| 4 +Brand#14 |SMALL PLATED NICKEL | 49| 4 +Brand#14 |SMALL PLATED STEEL | 3| 4 +Brand#14 |SMALL PLATED STEEL | 45| 4 +Brand#14 |SMALL PLATED TIN | 3| 4 +Brand#14 |SMALL PLATED TIN | 23| 4 +Brand#14 |SMALL PLATED TIN | 36| 4 +Brand#14 |SMALL POLISHED COPPER | 9| 4 +Brand#14 |SMALL POLISHED COPPER | 19| 4 +Brand#14 |SMALL POLISHED COPPER | 23| 4 +Brand#14 |SMALL POLISHED COPPER | 45| 4 +Brand#14 |SMALL POLISHED NICKEL | 14| 4 +Brand#14 |SMALL POLISHED NICKEL | 23| 4 +Brand#14 |SMALL POLISHED TIN | 23| 4 +Brand#14 |SMALL POLISHED TIN | 45| 4 +Brand#14 |STANDARD ANODIZED BRASS | 19| 4 +Brand#14 |STANDARD ANODIZED BRASS | 23| 4 +Brand#14 |STANDARD ANODIZED BRASS | 45| 4 +Brand#14 |STANDARD ANODIZED BRASS | 49| 4 +Brand#14 |STANDARD ANODIZED COPPER | 36| 4 +Brand#14 |STANDARD ANODIZED NICKEL | 9| 4 +Brand#14 |STANDARD ANODIZED NICKEL | 14| 4 +Brand#14 |STANDARD ANODIZED NICKEL | 23| 4 +Brand#14 |STANDARD ANODIZED NICKEL | 36| 4 +Brand#14 |STANDARD ANODIZED NICKEL | 45| 4 +Brand#14 |STANDARD ANODIZED NICKEL | 49| 4 +Brand#14 |STANDARD ANODIZED STEEL | 3| 4 +Brand#14 |STANDARD ANODIZED STEEL | 14| 4 +Brand#14 |STANDARD ANODIZED STEEL | 19| 4 +Brand#14 |STANDARD ANODIZED TIN | 9| 4 +Brand#14 |STANDARD ANODIZED TIN | 14| 4 +Brand#14 |STANDARD ANODIZED TIN | 19| 4 +Brand#14 |STANDARD ANODIZED TIN | 23| 4 +Brand#14 |STANDARD BRUSHED BRASS | 14| 4 +Brand#14 |STANDARD BRUSHED BRASS | 36| 4 +Brand#14 |STANDARD BRUSHED COPPER | 14| 4 +Brand#14 |STANDARD BRUSHED COPPER | 19| 4 +Brand#14 |STANDARD BRUSHED COPPER | 23| 4 +Brand#14 |STANDARD BRUSHED COPPER | 45| 4 +Brand#14 |STANDARD BRUSHED COPPER | 49| 4 +Brand#14 |STANDARD BRUSHED NICKEL | 9| 4 +Brand#14 |STANDARD BRUSHED NICKEL | 19| 4 +Brand#14 |STANDARD BRUSHED NICKEL | 36| 4 +Brand#14 |STANDARD BRUSHED NICKEL | 45| 4 +Brand#14 |STANDARD BRUSHED STEEL | 3| 4 +Brand#14 |STANDARD BRUSHED STEEL | 9| 4 +Brand#14 |STANDARD BRUSHED STEEL | 19| 4 +Brand#14 |STANDARD BRUSHED STEEL | 36| 4 +Brand#14 |STANDARD BRUSHED TIN | 3| 4 +Brand#14 |STANDARD BRUSHED TIN | 14| 4 +Brand#14 |STANDARD BRUSHED TIN | 36| 4 +Brand#14 |STANDARD BURNISHED COPPER| 36| 4 +Brand#14 |STANDARD BURNISHED COPPER| 45| 4 +Brand#14 |STANDARD BURNISHED COPPER| 49| 4 +Brand#14 |STANDARD BURNISHED NICKEL| 9| 4 +Brand#14 |STANDARD BURNISHED NICKEL| 14| 4 +Brand#14 |STANDARD BURNISHED NICKEL| 36| 4 +Brand#14 |STANDARD BURNISHED STEEL | 3| 4 +Brand#14 |STANDARD BURNISHED STEEL | 9| 4 +Brand#14 |STANDARD BURNISHED STEEL | 36| 4 +Brand#14 |STANDARD BURNISHED STEEL | 49| 4 +Brand#14 |STANDARD BURNISHED TIN | 23| 4 +Brand#14 |STANDARD BURNISHED TIN | 36| 4 +Brand#14 |STANDARD BURNISHED TIN | 45| 4 +Brand#14 |STANDARD PLATED BRASS | 23| 4 +Brand#14 |STANDARD PLATED BRASS | 36| 4 +Brand#14 |STANDARD PLATED COPPER | 3| 4 +Brand#14 |STANDARD PLATED COPPER | 9| 4 +Brand#14 |STANDARD PLATED COPPER | 19| 4 +Brand#14 |STANDARD PLATED NICKEL | 36| 4 +Brand#14 |STANDARD PLATED NICKEL | 45| 4 +Brand#14 |STANDARD PLATED STEEL | 14| 4 +Brand#14 |STANDARD PLATED STEEL | 19| 4 +Brand#14 |STANDARD PLATED STEEL | 45| 4 +Brand#14 |STANDARD PLATED STEEL | 49| 4 +Brand#14 |STANDARD PLATED TIN | 14| 4 +Brand#14 |STANDARD PLATED TIN | 23| 4 +Brand#14 |STANDARD PLATED TIN | 36| 4 +Brand#14 |STANDARD PLATED TIN | 45| 4 +Brand#14 |STANDARD POLISHED BRASS | 3| 4 +Brand#14 |STANDARD POLISHED BRASS | 36| 4 +Brand#14 |STANDARD POLISHED COPPER | 9| 4 +Brand#14 |STANDARD POLISHED COPPER | 23| 4 +Brand#14 |STANDARD POLISHED NICKEL | 14| 4 +Brand#14 |STANDARD POLISHED NICKEL | 23| 4 +Brand#14 |STANDARD POLISHED NICKEL | 45| 4 +Brand#14 |STANDARD POLISHED NICKEL | 49| 4 +Brand#14 |STANDARD POLISHED STEEL | 3| 4 +Brand#14 |STANDARD POLISHED STEEL | 9| 4 +Brand#14 |STANDARD POLISHED STEEL | 14| 4 +Brand#14 |STANDARD POLISHED STEEL | 19| 4 +Brand#14 |STANDARD POLISHED TIN | 19| 4 +Brand#14 |STANDARD POLISHED TIN | 23| 4 +Brand#14 |STANDARD POLISHED TIN | 36| 4 +Brand#15 |ECONOMY ANODIZED BRASS | 14| 4 +Brand#15 |ECONOMY ANODIZED BRASS | 19| 4 +Brand#15 |ECONOMY ANODIZED BRASS | 45| 4 +Brand#15 |ECONOMY ANODIZED BRASS | 49| 4 +Brand#15 |ECONOMY ANODIZED COPPER | 3| 4 +Brand#15 |ECONOMY ANODIZED COPPER | 14| 4 +Brand#15 |ECONOMY ANODIZED COPPER | 23| 4 +Brand#15 |ECONOMY ANODIZED COPPER | 36| 4 +Brand#15 |ECONOMY ANODIZED NICKEL | 14| 4 +Brand#15 |ECONOMY ANODIZED NICKEL | 45| 4 +Brand#15 |ECONOMY ANODIZED NICKEL | 49| 4 +Brand#15 |ECONOMY ANODIZED STEEL | 9| 4 +Brand#15 |ECONOMY ANODIZED STEEL | 19| 4 +Brand#15 |ECONOMY ANODIZED STEEL | 45| 4 +Brand#15 |ECONOMY ANODIZED STEEL | 49| 4 +Brand#15 |ECONOMY ANODIZED TIN | 3| 4 +Brand#15 |ECONOMY ANODIZED TIN | 14| 4 +Brand#15 |ECONOMY ANODIZED TIN | 23| 4 +Brand#15 |ECONOMY ANODIZED TIN | 45| 4 +Brand#15 |ECONOMY ANODIZED TIN | 49| 4 +Brand#15 |ECONOMY BRUSHED BRASS | 9| 4 +Brand#15 |ECONOMY BRUSHED BRASS | 14| 4 +Brand#15 |ECONOMY BRUSHED BRASS | 36| 4 +Brand#15 |ECONOMY BRUSHED BRASS | 45| 4 +Brand#15 |ECONOMY BRUSHED BRASS | 49| 4 +Brand#15 |ECONOMY BRUSHED COPPER | 14| 4 +Brand#15 |ECONOMY BRUSHED COPPER | 19| 4 +Brand#15 |ECONOMY BRUSHED COPPER | 45| 4 +Brand#15 |ECONOMY BRUSHED COPPER | 49| 4 +Brand#15 |ECONOMY BRUSHED NICKEL | 19| 4 +Brand#15 |ECONOMY BRUSHED STEEL | 3| 4 +Brand#15 |ECONOMY BRUSHED STEEL | 14| 4 +Brand#15 |ECONOMY BRUSHED TIN | 3| 4 +Brand#15 |ECONOMY BRUSHED TIN | 19| 4 +Brand#15 |ECONOMY BRUSHED TIN | 23| 4 +Brand#15 |ECONOMY BRUSHED TIN | 45| 4 +Brand#15 |ECONOMY BURNISHED BRASS | 23| 4 +Brand#15 |ECONOMY BURNISHED COPPER | 3| 4 +Brand#15 |ECONOMY BURNISHED NICKEL | 3| 4 +Brand#15 |ECONOMY BURNISHED NICKEL | 45| 4 +Brand#15 |ECONOMY BURNISHED STEEL | 14| 4 +Brand#15 |ECONOMY BURNISHED STEEL | 23| 4 +Brand#15 |ECONOMY BURNISHED STEEL | 36| 4 +Brand#15 |ECONOMY BURNISHED TIN | 3| 4 +Brand#15 |ECONOMY BURNISHED TIN | 14| 4 +Brand#15 |ECONOMY BURNISHED TIN | 19| 4 +Brand#15 |ECONOMY BURNISHED TIN | 36| 4 +Brand#15 |ECONOMY PLATED BRASS | 9| 4 +Brand#15 |ECONOMY PLATED BRASS | 19| 4 +Brand#15 |ECONOMY PLATED BRASS | 23| 4 +Brand#15 |ECONOMY PLATED BRASS | 45| 4 +Brand#15 |ECONOMY PLATED BRASS | 49| 4 +Brand#15 |ECONOMY PLATED COPPER | 14| 4 +Brand#15 |ECONOMY PLATED COPPER | 19| 4 +Brand#15 |ECONOMY PLATED NICKEL | 3| 4 +Brand#15 |ECONOMY PLATED NICKEL | 23| 4 +Brand#15 |ECONOMY PLATED NICKEL | 49| 4 +Brand#15 |ECONOMY PLATED STEEL | 9| 4 +Brand#15 |ECONOMY PLATED STEEL | 23| 4 +Brand#15 |ECONOMY PLATED STEEL | 36| 4 +Brand#15 |ECONOMY PLATED STEEL | 45| 4 +Brand#15 |ECONOMY PLATED STEEL | 49| 4 +Brand#15 |ECONOMY PLATED TIN | 3| 4 +Brand#15 |ECONOMY PLATED TIN | 19| 4 +Brand#15 |ECONOMY PLATED TIN | 23| 4 +Brand#15 |ECONOMY PLATED TIN | 36| 4 +Brand#15 |ECONOMY PLATED TIN | 45| 4 +Brand#15 |ECONOMY PLATED TIN | 49| 4 +Brand#15 |ECONOMY POLISHED BRASS | 9| 4 +Brand#15 |ECONOMY POLISHED BRASS | 23| 4 +Brand#15 |ECONOMY POLISHED BRASS | 45| 4 +Brand#15 |ECONOMY POLISHED BRASS | 49| 4 +Brand#15 |ECONOMY POLISHED COPPER | 14| 4 +Brand#15 |ECONOMY POLISHED COPPER | 19| 4 +Brand#15 |ECONOMY POLISHED COPPER | 23| 4 +Brand#15 |ECONOMY POLISHED NICKEL | 23| 4 +Brand#15 |ECONOMY POLISHED STEEL | 14| 4 +Brand#15 |ECONOMY POLISHED STEEL | 45| 4 +Brand#15 |ECONOMY POLISHED TIN | 19| 4 +Brand#15 |ECONOMY POLISHED TIN | 45| 4 +Brand#15 |ECONOMY POLISHED TIN | 49| 4 +Brand#15 |LARGE ANODIZED BRASS | 23| 4 +Brand#15 |LARGE ANODIZED BRASS | 45| 4 +Brand#15 |LARGE ANODIZED BRASS | 49| 4 +Brand#15 |LARGE ANODIZED COPPER | 3| 4 +Brand#15 |LARGE ANODIZED COPPER | 9| 4 +Brand#15 |LARGE ANODIZED NICKEL | 9| 4 +Brand#15 |LARGE ANODIZED NICKEL | 45| 4 +Brand#15 |LARGE ANODIZED STEEL | 9| 4 +Brand#15 |LARGE ANODIZED STEEL | 36| 4 +Brand#15 |LARGE ANODIZED STEEL | 49| 4 +Brand#15 |LARGE ANODIZED TIN | 3| 4 +Brand#15 |LARGE ANODIZED TIN | 9| 4 +Brand#15 |LARGE ANODIZED TIN | 19| 4 +Brand#15 |LARGE ANODIZED TIN | 45| 4 +Brand#15 |LARGE ANODIZED TIN | 49| 4 +Brand#15 |LARGE BRUSHED BRASS | 3| 4 +Brand#15 |LARGE BRUSHED COPPER | 23| 4 +Brand#15 |LARGE BRUSHED COPPER | 49| 4 +Brand#15 |LARGE BRUSHED NICKEL | 3| 4 +Brand#15 |LARGE BRUSHED NICKEL | 14| 4 +Brand#15 |LARGE BRUSHED NICKEL | 23| 4 +Brand#15 |LARGE BRUSHED NICKEL | 36| 4 +Brand#15 |LARGE BRUSHED STEEL | 3| 4 +Brand#15 |LARGE BRUSHED STEEL | 9| 4 +Brand#15 |LARGE BRUSHED STEEL | 36| 4 +Brand#15 |LARGE BRUSHED STEEL | 49| 4 +Brand#15 |LARGE BRUSHED TIN | 14| 4 +Brand#15 |LARGE BRUSHED TIN | 45| 4 +Brand#15 |LARGE BURNISHED BRASS | 49| 4 +Brand#15 |LARGE BURNISHED COPPER | 3| 4 +Brand#15 |LARGE BURNISHED COPPER | 14| 4 +Brand#15 |LARGE BURNISHED NICKEL | 14| 4 +Brand#15 |LARGE BURNISHED NICKEL | 23| 4 +Brand#15 |LARGE BURNISHED NICKEL | 45| 4 +Brand#15 |LARGE BURNISHED STEEL | 3| 4 +Brand#15 |LARGE BURNISHED TIN | 3| 4 +Brand#15 |LARGE BURNISHED TIN | 9| 4 +Brand#15 |LARGE BURNISHED TIN | 19| 4 +Brand#15 |LARGE BURNISHED TIN | 23| 4 +Brand#15 |LARGE BURNISHED TIN | 36| 4 +Brand#15 |LARGE BURNISHED TIN | 45| 4 +Brand#15 |LARGE PLATED BRASS | 3| 4 +Brand#15 |LARGE PLATED BRASS | 14| 4 +Brand#15 |LARGE PLATED BRASS | 19| 4 +Brand#15 |LARGE PLATED BRASS | 23| 4 +Brand#15 |LARGE PLATED BRASS | 49| 4 +Brand#15 |LARGE PLATED COPPER | 3| 4 +Brand#15 |LARGE PLATED COPPER | 14| 4 +Brand#15 |LARGE PLATED COPPER | 23| 4 +Brand#15 |LARGE PLATED NICKEL | 36| 4 +Brand#15 |LARGE PLATED STEEL | 3| 4 +Brand#15 |LARGE PLATED STEEL | 45| 4 +Brand#15 |LARGE PLATED STEEL | 49| 4 +Brand#15 |LARGE PLATED TIN | 9| 4 +Brand#15 |LARGE PLATED TIN | 19| 4 +Brand#15 |LARGE PLATED TIN | 36| 4 +Brand#15 |LARGE PLATED TIN | 45| 4 +Brand#15 |LARGE POLISHED BRASS | 3| 4 +Brand#15 |LARGE POLISHED BRASS | 9| 4 +Brand#15 |LARGE POLISHED BRASS | 14| 4 +Brand#15 |LARGE POLISHED COPPER | 9| 4 +Brand#15 |LARGE POLISHED COPPER | 14| 4 +Brand#15 |LARGE POLISHED COPPER | 19| 4 +Brand#15 |LARGE POLISHED COPPER | 45| 4 +Brand#15 |LARGE POLISHED NICKEL | 3| 4 +Brand#15 |LARGE POLISHED NICKEL | 14| 4 +Brand#15 |LARGE POLISHED NICKEL | 19| 4 +Brand#15 |LARGE POLISHED NICKEL | 23| 4 +Brand#15 |LARGE POLISHED NICKEL | 36| 4 +Brand#15 |LARGE POLISHED NICKEL | 49| 4 +Brand#15 |LARGE POLISHED STEEL | 3| 4 +Brand#15 |MEDIUM ANODIZED BRASS | 14| 4 +Brand#15 |MEDIUM ANODIZED BRASS | 45| 4 +Brand#15 |MEDIUM ANODIZED BRASS | 49| 4 +Brand#15 |MEDIUM ANODIZED COPPER | 3| 4 +Brand#15 |MEDIUM ANODIZED COPPER | 14| 4 +Brand#15 |MEDIUM ANODIZED COPPER | 23| 4 +Brand#15 |MEDIUM ANODIZED COPPER | 45| 4 +Brand#15 |MEDIUM ANODIZED COPPER | 49| 4 +Brand#15 |MEDIUM ANODIZED NICKEL | 14| 4 +Brand#15 |MEDIUM ANODIZED NICKEL | 19| 4 +Brand#15 |MEDIUM ANODIZED NICKEL | 23| 4 +Brand#15 |MEDIUM ANODIZED NICKEL | 49| 4 +Brand#15 |MEDIUM ANODIZED STEEL | 3| 4 +Brand#15 |MEDIUM ANODIZED STEEL | 14| 4 +Brand#15 |MEDIUM ANODIZED STEEL | 36| 4 +Brand#15 |MEDIUM ANODIZED TIN | 9| 4 +Brand#15 |MEDIUM ANODIZED TIN | 36| 4 +Brand#15 |MEDIUM ANODIZED TIN | 45| 4 +Brand#15 |MEDIUM BRUSHED BRASS | 9| 4 +Brand#15 |MEDIUM BRUSHED BRASS | 36| 4 +Brand#15 |MEDIUM BRUSHED COPPER | 19| 4 +Brand#15 |MEDIUM BRUSHED NICKEL | 36| 4 +Brand#15 |MEDIUM BRUSHED NICKEL | 45| 4 +Brand#15 |MEDIUM BRUSHED STEEL | 9| 4 +Brand#15 |MEDIUM BRUSHED STEEL | 14| 4 +Brand#15 |MEDIUM BRUSHED STEEL | 23| 4 +Brand#15 |MEDIUM BRUSHED TIN | 3| 4 +Brand#15 |MEDIUM BRUSHED TIN | 36| 4 +Brand#15 |MEDIUM BRUSHED TIN | 45| 4 +Brand#15 |MEDIUM BRUSHED TIN | 49| 4 +Brand#15 |MEDIUM BURNISHED BRASS | 3| 4 +Brand#15 |MEDIUM BURNISHED BRASS | 14| 4 +Brand#15 |MEDIUM BURNISHED BRASS | 19| 4 +Brand#15 |MEDIUM BURNISHED BRASS | 23| 4 +Brand#15 |MEDIUM BURNISHED BRASS | 49| 4 +Brand#15 |MEDIUM BURNISHED COPPER | 9| 4 +Brand#15 |MEDIUM BURNISHED COPPER | 19| 4 +Brand#15 |MEDIUM BURNISHED COPPER | 36| 4 +Brand#15 |MEDIUM BURNISHED NICKEL | 3| 4 +Brand#15 |MEDIUM BURNISHED NICKEL | 23| 4 +Brand#15 |MEDIUM BURNISHED STEEL | 9| 4 +Brand#15 |MEDIUM BURNISHED STEEL | 36| 4 +Brand#15 |MEDIUM BURNISHED STEEL | 45| 4 +Brand#15 |MEDIUM BURNISHED TIN | 3| 4 +Brand#15 |MEDIUM BURNISHED TIN | 19| 4 +Brand#15 |MEDIUM BURNISHED TIN | 23| 4 +Brand#15 |MEDIUM BURNISHED TIN | 36| 4 +Brand#15 |MEDIUM PLATED BRASS | 3| 4 +Brand#15 |MEDIUM PLATED BRASS | 9| 4 +Brand#15 |MEDIUM PLATED BRASS | 19| 4 +Brand#15 |MEDIUM PLATED BRASS | 23| 4 +Brand#15 |MEDIUM PLATED BRASS | 49| 4 +Brand#15 |MEDIUM PLATED COPPER | 9| 4 +Brand#15 |MEDIUM PLATED COPPER | 19| 4 +Brand#15 |MEDIUM PLATED COPPER | 36| 4 +Brand#15 |MEDIUM PLATED COPPER | 45| 4 +Brand#15 |MEDIUM PLATED COPPER | 49| 4 +Brand#15 |MEDIUM PLATED NICKEL | 3| 4 +Brand#15 |MEDIUM PLATED NICKEL | 9| 4 +Brand#15 |MEDIUM PLATED NICKEL | 14| 4 +Brand#15 |MEDIUM PLATED NICKEL | 19| 4 +Brand#15 |MEDIUM PLATED NICKEL | 36| 4 +Brand#15 |MEDIUM PLATED NICKEL | 45| 4 +Brand#15 |MEDIUM PLATED STEEL | 3| 4 +Brand#15 |MEDIUM PLATED STEEL | 14| 4 +Brand#15 |MEDIUM PLATED STEEL | 23| 4 +Brand#15 |MEDIUM PLATED STEEL | 36| 4 +Brand#15 |MEDIUM PLATED TIN | 14| 4 +Brand#15 |PROMO ANODIZED BRASS | 3| 4 +Brand#15 |PROMO ANODIZED BRASS | 9| 4 +Brand#15 |PROMO ANODIZED BRASS | 19| 4 +Brand#15 |PROMO ANODIZED BRASS | 49| 4 +Brand#15 |PROMO ANODIZED COPPER | 3| 4 +Brand#15 |PROMO ANODIZED COPPER | 19| 4 +Brand#15 |PROMO ANODIZED COPPER | 23| 4 +Brand#15 |PROMO ANODIZED COPPER | 49| 4 +Brand#15 |PROMO ANODIZED NICKEL | 19| 4 +Brand#15 |PROMO ANODIZED STEEL | 23| 4 +Brand#15 |PROMO ANODIZED STEEL | 45| 4 +Brand#15 |PROMO ANODIZED TIN | 23| 4 +Brand#15 |PROMO ANODIZED TIN | 36| 4 +Brand#15 |PROMO ANODIZED TIN | 45| 4 +Brand#15 |PROMO BRUSHED BRASS | 3| 4 +Brand#15 |PROMO BRUSHED BRASS | 23| 4 +Brand#15 |PROMO BRUSHED BRASS | 45| 4 +Brand#15 |PROMO BRUSHED COPPER | 14| 4 +Brand#15 |PROMO BRUSHED COPPER | 49| 4 +Brand#15 |PROMO BRUSHED NICKEL | 3| 4 +Brand#15 |PROMO BRUSHED NICKEL | 14| 4 +Brand#15 |PROMO BRUSHED NICKEL | 45| 4 +Brand#15 |PROMO BRUSHED STEEL | 3| 4 +Brand#15 |PROMO BRUSHED STEEL | 19| 4 +Brand#15 |PROMO BRUSHED TIN | 9| 4 +Brand#15 |PROMO BRUSHED TIN | 14| 4 +Brand#15 |PROMO BRUSHED TIN | 45| 4 +Brand#15 |PROMO BURNISHED BRASS | 3| 4 +Brand#15 |PROMO BURNISHED BRASS | 19| 4 +Brand#15 |PROMO BURNISHED BRASS | 45| 4 +Brand#15 |PROMO BURNISHED COPPER | 23| 4 +Brand#15 |PROMO BURNISHED COPPER | 49| 4 +Brand#15 |PROMO BURNISHED NICKEL | 45| 4 +Brand#15 |PROMO BURNISHED STEEL | 14| 4 +Brand#15 |PROMO BURNISHED STEEL | 45| 4 +Brand#15 |PROMO BURNISHED STEEL | 49| 4 +Brand#15 |PROMO BURNISHED TIN | 3| 4 +Brand#15 |PROMO BURNISHED TIN | 23| 4 +Brand#15 |PROMO PLATED BRASS | 3| 4 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| 36| 4 +Brand#15 |PROMO POLISHED STEEL | 45| 4 +Brand#15 |PROMO POLISHED TIN | 14| 4 +Brand#15 |PROMO POLISHED TIN | 19| 4 +Brand#15 |PROMO POLISHED TIN | 36| 4 +Brand#15 |SMALL ANODIZED BRASS | 3| 4 +Brand#15 |SMALL ANODIZED BRASS | 36| 4 +Brand#15 |SMALL ANODIZED COPPER | 3| 4 +Brand#15 |SMALL ANODIZED COPPER | 9| 4 +Brand#15 |SMALL ANODIZED COPPER | 14| 4 +Brand#15 |SMALL ANODIZED COPPER | 19| 4 +Brand#15 |SMALL ANODIZED COPPER | 36| 4 +Brand#15 |SMALL ANODIZED COPPER | 49| 4 +Brand#15 |SMALL ANODIZED NICKEL | 45| 4 +Brand#15 |SMALL ANODIZED NICKEL | 49| 4 +Brand#15 |SMALL ANODIZED STEEL | 19| 4 +Brand#15 |SMALL ANODIZED STEEL | 36| 4 +Brand#15 |SMALL ANODIZED TIN | 3| 4 +Brand#15 |SMALL ANODIZED TIN | 9| 4 +Brand#15 |SMALL ANODIZED TIN | 49| 4 +Brand#15 |SMALL BRUSHED COPPER | 3| 4 +Brand#15 |SMALL BRUSHED COPPER | 36| 4 +Brand#15 |SMALL BRUSHED COPPER | 49| 4 +Brand#15 |SMALL BRUSHED NICKEL | 3| 4 +Brand#15 |SMALL BRUSHED NICKEL | 45| 4 +Brand#15 |SMALL BRUSHED STEEL | 3| 4 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49| 4 +Brand#15 |STANDARD ANODIZED NICKEL | 14| 4 +Brand#15 |STANDARD ANODIZED NICKEL | 19| 4 +Brand#15 |STANDARD ANODIZED NICKEL | 49| 4 +Brand#15 |STANDARD ANODIZED STEEL | 23| 4 +Brand#15 |STANDARD ANODIZED STEEL | 49| 4 +Brand#15 |STANDARD ANODIZED TIN | 9| 4 +Brand#15 |STANDARD ANODIZED TIN | 14| 4 +Brand#15 |STANDARD ANODIZED TIN | 23| 4 +Brand#15 |STANDARD ANODIZED TIN | 49| 4 +Brand#15 |STANDARD BRUSHED BRASS | 9| 4 +Brand#15 |STANDARD BRUSHED BRASS | 14| 4 +Brand#15 |STANDARD BRUSHED BRASS | 23| 4 +Brand#15 |STANDARD BRUSHED COPPER | 3| 4 +Brand#15 |STANDARD BRUSHED COPPER | 19| 4 +Brand#15 |STANDARD BRUSHED COPPER | 36| 4 +Brand#15 |STANDARD BRUSHED NICKEL | 36| 4 +Brand#15 |STANDARD BRUSHED NICKEL | 45| 4 +Brand#15 |STANDARD BRUSHED NICKEL | 49| 4 +Brand#15 |STANDARD BRUSHED STEEL | 3| 4 +Brand#15 |STANDARD BRUSHED STEEL | 23| 4 +Brand#15 |STANDARD BRUSHED STEEL | 36| 4 +Brand#15 |STANDARD BRUSHED STEEL | 45| 4 +Brand#15 |STANDARD BRUSHED TIN | 3| 4 +Brand#15 |STANDARD BRUSHED TIN | 9| 4 +Brand#15 |STANDARD BRUSHED TIN | 14| 4 +Brand#15 |STANDARD BRUSHED TIN | 19| 4 +Brand#15 |STANDARD BRUSHED TIN | 36| 4 +Brand#15 |STANDARD BRUSHED TIN | 49| 4 +Brand#15 |STANDARD BURNISHED BRASS | 14| 4 +Brand#15 |STANDARD BURNISHED BRASS | 36| 4 +Brand#15 |STANDARD BURNISHED COPPER| 3| 4 +Brand#15 |STANDARD BURNISHED COPPER| 9| 4 +Brand#15 |STANDARD BURNISHED COPPER| 23| 4 +Brand#15 |STANDARD BURNISHED NICKEL| 3| 4 +Brand#15 |STANDARD BURNISHED NICKEL| 19| 4 +Brand#15 |STANDARD BURNISHED STEEL | 3| 4 +Brand#15 |STANDARD BURNISHED STEEL | 9| 4 +Brand#15 |STANDARD BURNISHED STEEL | 14| 4 +Brand#15 |STANDARD BURNISHED STEEL | 36| 4 +Brand#15 |STANDARD BURNISHED STEEL | 49| 4 +Brand#15 |STANDARD BURNISHED TIN | 19| 4 +Brand#15 |STANDARD BURNISHED TIN | 23| 4 +Brand#15 |STANDARD BURNISHED TIN | 36| 4 +Brand#15 |STANDARD PLATED BRASS | 19| 4 +Brand#15 |STANDARD PLATED BRASS | 49| 4 +Brand#15 |STANDARD PLATED COPPER | 3| 4 +Brand#15 |STANDARD PLATED COPPER | 19| 4 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POLISHED TIN | 19| 4 +Brand#15 |STANDARD POLISHED TIN | 45| 4 +Brand#21 |ECONOMY ANODIZED BRASS | 3| 4 +Brand#21 |ECONOMY ANODIZED BRASS | 9| 4 +Brand#21 |ECONOMY ANODIZED BRASS | 19| 4 +Brand#21 |ECONOMY ANODIZED COPPER | 9| 4 +Brand#21 |ECONOMY ANODIZED COPPER | 23| 4 +Brand#21 |ECONOMY ANODIZED NICKEL | 3| 4 +Brand#21 |ECONOMY ANODIZED NICKEL | 19| 4 +Brand#21 |ECONOMY ANODIZED NICKEL | 49| 4 +Brand#21 |ECONOMY ANODIZED STEEL | 9| 4 +Brand#21 |ECONOMY ANODIZED STEEL | 14| 4 +Brand#21 |ECONOMY ANODIZED STEEL | 23| 4 +Brand#21 |ECONOMY ANODIZED TIN | 14| 4 +Brand#21 |ECONOMY ANODIZED TIN | 19| 4 +Brand#21 |ECONOMY ANODIZED TIN | 23| 4 +Brand#21 |ECONOMY ANODIZED TIN | 45| 4 +Brand#21 |ECONOMY ANODIZED TIN | 49| 4 +Brand#21 |ECONOMY BRUSHED BRASS | 9| 4 +Brand#21 |ECONOMY BRUSHED BRASS | 23| 4 +Brand#21 |ECONOMY BRUSHED BRASS | 45| 4 +Brand#21 |ECONOMY BRUSHED BRASS | 49| 4 +Brand#21 |ECONOMY BRUSHED COPPER | 3| 4 +Brand#21 |ECONOMY BRUSHED COPPER | 23| 4 +Brand#21 |ECONOMY BRUSHED COPPER | 36| 4 +Brand#21 |ECONOMY BRUSHED COPPER | 49| 4 +Brand#21 |ECONOMY BRUSHED NICKEL | 3| 4 +Brand#21 |ECONOMY BRUSHED NICKEL | 45| 4 +Brand#21 |ECONOMY BRUSHED NICKEL | 49| 4 +Brand#21 |ECONOMY BRUSHED STEEL | 9| 4 +Brand#21 |ECONOMY BRUSHED STEEL | 14| 4 +Brand#21 |ECONOMY BRUSHED STEEL | 19| 4 +Brand#21 |ECONOMY BRUSHED STEEL | 23| 4 +Brand#21 |ECONOMY BRUSHED STEEL | 36| 4 +Brand#21 |ECONOMY BRUSHED TIN | 3| 4 +Brand#21 |ECONOMY BRUSHED TIN | 45| 4 +Brand#21 |ECONOMY BRUSHED TIN | 49| 4 +Brand#21 |ECONOMY BURNISHED BRASS | 23| 4 +Brand#21 |ECONOMY BURNISHED COPPER | 9| 4 +Brand#21 |ECONOMY BURNISHED COPPER | 14| 4 +Brand#21 |ECONOMY BURNISHED COPPER | 36| 4 +Brand#21 |ECONOMY BURNISHED NICKEL | 14| 4 +Brand#21 |ECONOMY BURNISHED NICKEL | 19| 4 +Brand#21 |ECONOMY BURNISHED NICKEL | 23| 4 +Brand#21 |ECONOMY BURNISHED NICKEL | 36| 4 +Brand#21 |ECONOMY BURNISHED STEEL | 3| 4 +Brand#21 |ECONOMY BURNISHED STEEL | 19| 4 +Brand#21 |ECONOMY BURNISHED STEEL | 49| 4 +Brand#21 |ECONOMY BURNISHED TIN | 23| 4 +Brand#21 |ECONOMY BURNISHED TIN | 36| 4 +Brand#21 |ECONOMY PLATED BRASS | 14| 4 +Brand#21 |ECONOMY PLATED BRASS | 19| 4 +Brand#21 |ECONOMY PLATED BRASS | 36| 4 +Brand#21 |ECONOMY PLATED BRASS | 45| 4 +Brand#21 |ECONOMY PLATED COPPER | 9| 4 +Brand#21 |ECONOMY PLATED COPPER | 19| 4 +Brand#21 |ECONOMY PLATED COPPER | 23| 4 +Brand#21 |ECONOMY PLATED NICKEL | 9| 4 +Brand#21 |ECONOMY PLATED NICKEL | 14| 4 +Brand#21 |ECONOMY PLATED NICKEL | 19| 4 +Brand#21 |ECONOMY PLATED STEEL | 36| 4 +Brand#21 |ECONOMY PLATED STEEL | 49| 4 +Brand#21 |ECONOMY PLATED TIN | 9| 4 +Brand#21 |ECONOMY PLATED TIN | 14| 4 +Brand#21 |ECONOMY PLATED TIN | 45| 4 +Brand#21 |ECONOMY PLATED TIN | 49| 4 +Brand#21 |ECONOMY POLISHED BRASS | 3| 4 +Brand#21 |ECONOMY POLISHED BRASS | 19| 4 +Brand#21 |ECONOMY POLISHED BRASS | 36| 4 +Brand#21 |ECONOMY POLISHED BRASS | 45| 4 +Brand#21 |ECONOMY POLISHED COPPER | 45| 4 +Brand#21 |ECONOMY POLISHED COPPER | 49| 4 +Brand#21 |ECONOMY POLISHED NICKEL | 9| 4 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45| 4 +Brand#21 |SMALL BRUSHED STEEL | 49| 4 +Brand#21 |SMALL BRUSHED TIN | 36| 4 +Brand#21 |SMALL BRUSHED TIN | 49| 4 +Brand#21 |SMALL BURNISHED BRASS | 3| 4 +Brand#21 |SMALL BURNISHED BRASS | 9| 4 +Brand#21 |SMALL BURNISHED BRASS | 19| 4 +Brand#21 |SMALL BURNISHED BRASS | 23| 4 +Brand#21 |SMALL BURNISHED BRASS | 45| 4 +Brand#21 |SMALL BURNISHED COPPER | 9| 4 +Brand#21 |SMALL BURNISHED COPPER | 23| 4 +Brand#21 |SMALL BURNISHED NICKEL | 3| 4 +Brand#21 |SMALL BURNISHED NICKEL | 19| 4 +Brand#21 |SMALL BURNISHED NICKEL | 23| 4 +Brand#21 |SMALL BURNISHED STEEL | 3| 4 +Brand#21 |SMALL BURNISHED STEEL | 14| 4 +Brand#21 |SMALL BURNISHED STEEL | 19| 4 +Brand#21 |SMALL BURNISHED STEEL | 36| 4 +Brand#21 |SMALL BURNISHED STEEL | 45| 4 +Brand#21 |SMALL BURNISHED TIN | 14| 4 +Brand#21 |SMALL BURNISHED TIN | 19| 4 +Brand#21 |SMALL BURNISHED TIN | 36| 4 +Brand#21 |SMALL BURNISHED TIN | 45| 4 +Brand#21 |SMALL BURNISHED TIN | 49| 4 +Brand#21 |SMALL PLATED BRASS | 19| 4 +Brand#21 |SMALL PLATED BRASS | 45| 4 +Brand#21 |SMALL PLATED BRASS | 49| 4 +Brand#21 |SMALL PLATED COPPER | 19| 4 +Brand#21 |SMALL PLATED COPPER | 49| 4 +Brand#21 |SMALL PLATED NICKEL | 19| 4 +Brand#21 |SMALL PLATED NICKEL | 49| 4 +Brand#21 |SMALL PLATED STEEL | 14| 4 +Brand#21 |SMALL PLATED STEEL | 36| 4 +Brand#21 |SMALL PLATED TIN | 3| 4 +Brand#21 |SMALL PLATED TIN | 9| 4 +Brand#21 |SMALL PLATED TIN | 14| 4 +Brand#21 |SMALL PLATED TIN | 23| 4 +Brand#21 |SMALL POLISHED BRASS | 3| 4 +Brand#21 |SMALL POLISHED BRASS | 9| 4 +Brand#21 |SMALL POLISHED BRASS | 23| 4 +Brand#21 |SMALL POLISHED BRASS | 45| 4 +Brand#21 |SMALL POLISHED COPPER | 3| 4 +Brand#21 |SMALL POLISHED COPPER | 9| 4 +Brand#21 |SMALL POLISHED COPPER | 19| 4 +Brand#21 |SMALL POLISHED COPPER | 45| 4 +Brand#21 |SMALL POLISHED NICKEL | 3| 4 +Brand#21 |SMALL POLISHED NICKEL | 14| 4 +Brand#21 |SMALL POLISHED NICKEL | 45| 4 +Brand#21 |SMALL POLISHED STEEL | 14| 4 +Brand#21 |SMALL POLISHED STEEL | 19| 4 +Brand#21 |SMALL POLISHED STEEL | 49| 4 +Brand#21 |SMALL POLISHED TIN | 3| 4 +Brand#21 |SMALL POLISHED TIN | 9| 4 +Brand#21 |SMALL POLISHED TIN | 23| 4 +Brand#21 |SMALL POLISHED TIN | 36| 4 +Brand#21 |SMALL POLISHED TIN | 45| 4 +Brand#21 |SMALL POLISHED TIN | 49| 4 +Brand#21 |STANDARD ANODIZED BRASS | 9| 4 +Brand#21 |STANDARD ANODIZED BRASS | 14| 4 +Brand#21 |STANDARD ANODIZED BRASS | 49| 4 +Brand#21 |STANDARD ANODIZED COPPER | 9| 4 +Brand#21 |STANDARD ANODIZED COPPER | 19| 4 +Brand#21 |STANDARD ANODIZED COPPER | 49| 4 +Brand#21 |STANDARD ANODIZED NICKEL | 14| 4 +Brand#21 |STANDARD ANODIZED NICKEL | 23| 4 +Brand#21 |STANDARD ANODIZED STEEL | 9| 4 +Brand#21 |STANDARD ANODIZED STEEL | 14| 4 +Brand#21 |STANDARD ANODIZED STEEL | 45| 4 +Brand#21 |STANDARD ANODIZED TIN | 14| 4 +Brand#21 |STANDARD ANODIZED TIN | 19| 4 +Brand#21 |STANDARD ANODIZED TIN | 23| 4 +Brand#21 |STANDARD ANODIZED TIN | 45| 4 +Brand#21 |STANDARD BRUSHED BRASS | 3| 4 +Brand#21 |STANDARD BRUSHED BRASS | 23| 4 +Brand#21 |STANDARD BRUSHED COPPER | 9| 4 +Brand#21 |STANDARD BRUSHED COPPER | 14| 4 +Brand#21 |STANDARD BRUSHED COPPER | 19| 4 +Brand#21 |STANDARD BRUSHED COPPER | 45| 4 +Brand#21 |STANDARD BRUSHED COPPER | 49| 4 +Brand#21 |STANDARD BRUSHED NICKEL | 3| 4 +Brand#21 |STANDARD BRUSHED NICKEL | 9| 4 +Brand#21 |STANDARD BRUSHED NICKEL | 36| 4 +Brand#21 |STANDARD BRUSHED NICKEL | 49| 4 +Brand#21 |STANDARD BRUSHED TIN | 3| 4 +Brand#21 |STANDARD BRUSHED TIN | 9| 4 +Brand#21 |STANDARD BRUSHED TIN | 14| 4 +Brand#21 |STANDARD BRUSHED TIN | 19| 4 +Brand#21 |STANDARD BRUSHED TIN | 49| 4 +Brand#21 |STANDARD BURNISHED BRASS | 9| 4 +Brand#21 |STANDARD BURNISHED BRASS | 23| 4 +Brand#21 |STANDARD BURNISHED COPPER| 23| 4 +Brand#21 |STANDARD BURNISHED COPPER| 36| 4 +Brand#21 |STANDARD BURNISHED COPPER| 45| 4 +Brand#21 |STANDARD BURNISHED COPPER| 49| 4 +Brand#21 |STANDARD BURNISHED NICKEL| 14| 4 +Brand#21 |STANDARD BURNISHED NICKEL| 19| 4 +Brand#21 |STANDARD BURNISHED NICKEL| 49| 4 +Brand#21 |STANDARD BURNISHED STEEL | 9| 4 +Brand#21 |STANDARD BURNISHED STEEL | 23| 4 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|STANDARD POLISHED NICKEL | 45| 4 +Brand#21 |STANDARD POLISHED NICKEL | 49| 4 +Brand#21 |STANDARD POLISHED STEEL | 9| 4 +Brand#21 |STANDARD POLISHED STEEL | 23| 4 +Brand#21 |STANDARD POLISHED STEEL | 45| 4 +Brand#21 |STANDARD POLISHED STEEL | 49| 4 +Brand#21 |STANDARD POLISHED TIN | 3| 4 +Brand#21 |STANDARD POLISHED TIN | 19| 4 +Brand#21 |STANDARD POLISHED TIN | 23| 4 +Brand#21 |STANDARD POLISHED TIN | 45| 4 +Brand#21 |STANDARD POLISHED TIN | 49| 4 +Brand#22 |ECONOMY ANODIZED BRASS | 14| 4 +Brand#22 |ECONOMY ANODIZED BRASS | 23| 4 +Brand#22 |ECONOMY ANODIZED BRASS | 45| 4 +Brand#22 |ECONOMY ANODIZED BRASS | 49| 4 +Brand#22 |ECONOMY ANODIZED COPPER | 3| 4 +Brand#22 |ECONOMY ANODIZED COPPER | 9| 4 +Brand#22 |ECONOMY ANODIZED COPPER | 19| 4 +Brand#22 |ECONOMY ANODIZED NICKEL | 9| 4 +Brand#22 |ECONOMY ANODIZED NICKEL | 14| 4 +Brand#22 |ECONOMY ANODIZED NICKEL | 49| 4 +Brand#22 |ECONOMY ANODIZED STEEL | 3| 4 +Brand#22 |ECONOMY ANODIZED STEEL | 9| 4 +Brand#22 |ECONOMY ANODIZED STEEL | 14| 4 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BURNISHED BRASS | 49| 4 +Brand#22 |ECONOMY BURNISHED COPPER | 19| 4 +Brand#22 |ECONOMY BURNISHED COPPER | 23| 4 +Brand#22 |ECONOMY BURNISHED COPPER | 36| 4 +Brand#22 |ECONOMY BURNISHED NICKEL | 19| 4 +Brand#22 |ECONOMY BURNISHED NICKEL | 45| 4 +Brand#22 |ECONOMY BURNISHED STEEL | 3| 4 +Brand#22 |ECONOMY BURNISHED STEEL | 14| 4 +Brand#22 |ECONOMY BURNISHED TIN | 3| 4 +Brand#22 |ECONOMY BURNISHED TIN | 14| 4 +Brand#22 |ECONOMY BURNISHED TIN | 36| 4 +Brand#22 |ECONOMY BURNISHED TIN | 45| 4 +Brand#22 |ECONOMY BURNISHED TIN | 49| 4 +Brand#22 |ECONOMY PLATED BRASS | 9| 4 +Brand#22 |ECONOMY PLATED BRASS | 14| 4 +Brand#22 |ECONOMY PLATED BRASS | 23| 4 +Brand#22 |ECONOMY PLATED COPPER | 14| 4 +Brand#22 |ECONOMY PLATED COPPER | 23| 4 +Brand#22 |ECONOMY PLATED COPPER | 36| 4 +Brand#22 |ECONOMY PLATED COPPER | 45| 4 +Brand#22 |ECONOMY PLATED COPPER | 49| 4 +Brand#22 |ECONOMY PLATED NICKEL | 19| 4 +Brand#22 |ECONOMY PLATED NICKEL | 23| 4 +Brand#22 |ECONOMY PLATED STEEL | 9| 4 +Brand#22 |ECONOMY PLATED STEEL | 36| 4 +Brand#22 |ECONOMY PLATED STEEL | 49| 4 +Brand#22 |ECONOMY PLATED TIN | 3| 4 +Brand#22 |ECONOMY PLATED TIN | 14| 4 +Brand#22 |ECONOMY PLATED TIN | 23| 4 +Brand#22 |ECONOMY PLATED TIN | 36| 4 +Brand#22 |ECONOMY PLATED TIN | 45| 4 +Brand#22 |ECONOMY POLISHED BRASS | 3| 4 +Brand#22 |ECONOMY POLISHED BRASS | 9| 4 +Brand#22 |ECONOMY POLISHED BRASS | 14| 4 +Brand#22 |ECONOMY POLISHED BRASS | 19| 4 +Brand#22 |ECONOMY POLISHED BRASS | 49| 4 +Brand#22 |ECONOMY POLISHED COPPER | 3| 4 +Brand#22 |ECONOMY POLISHED COPPER | 36| 4 +Brand#22 |ECONOMY POLISHED NICKEL | 3| 4 +Brand#22 |ECONOMY POLISHED NICKEL | 14| 4 +Brand#22 |ECONOMY POLISHED NICKEL | 19| 4 +Brand#22 |ECONOMY POLISHED NICKEL | 23| 4 +Brand#22 |ECONOMY POLISHED NICKEL | 36| 4 +Brand#22 |ECONOMY POLISHED NICKEL | 49| 4 +Brand#22 |ECONOMY POLISHED STEEL | 3| 4 +Brand#22 |ECONOMY POLISHED TIN | 3| 4 +Brand#22 |ECONOMY POLISHED TIN | 23| 4 +Brand#22 |LARGE ANODIZED BRASS | 3| 4 +Brand#22 |LARGE ANODIZED BRASS | 9| 4 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PLATED COPPER | 14| 4 +Brand#22 |LARGE PLATED COPPER | 49| 4 +Brand#22 |LARGE PLATED NICKEL | 14| 4 +Brand#22 |LARGE PLATED NICKEL | 49| 4 +Brand#22 |LARGE PLATED STEEL | 3| 4 +Brand#22 |LARGE PLATED STEEL | 36| 4 +Brand#22 |LARGE PLATED STEEL | 45| 4 +Brand#22 |LARGE PLATED STEEL | 49| 4 +Brand#22 |LARGE PLATED TIN | 9| 4 +Brand#22 |LARGE PLATED TIN | 19| 4 +Brand#22 |LARGE POLISHED BRASS | 9| 4 +Brand#22 |LARGE POLISHED BRASS | 19| 4 +Brand#22 |LARGE POLISHED COPPER | 14| 4 +Brand#22 |LARGE POLISHED COPPER | 45| 4 +Brand#22 |LARGE POLISHED NICKEL | 9| 4 +Brand#22 |LARGE POLISHED NICKEL | 36| 4 +Brand#22 |LARGE POLISHED STEEL | 14| 4 +Brand#22 |LARGE POLISHED STEEL | 19| 4 +Brand#22 |LARGE POLISHED STEEL | 23| 4 +Brand#22 |LARGE POLISHED STEEL | 36| 4 +Brand#22 |LARGE POLISHED TIN | 3| 4 +Brand#22 |LARGE POLISHED TIN | 19| 4 +Brand#22 |LARGE POLISHED TIN | 23| 4 +Brand#22 |MEDIUM ANODIZED BRASS | 3| 4 +Brand#22 |MEDIUM ANODIZED BRASS | 19| 4 +Brand#22 |MEDIUM ANODIZED BRASS | 36| 4 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| 19| 4 +Brand#22 |MEDIUM PLATED STEEL | 14| 4 +Brand#22 |MEDIUM PLATED STEEL | 36| 4 +Brand#22 |MEDIUM PLATED STEEL | 49| 4 +Brand#22 |MEDIUM PLATED TIN | 3| 4 +Brand#22 |MEDIUM PLATED TIN | 9| 4 +Brand#22 |MEDIUM PLATED TIN | 14| 4 +Brand#22 |PROMO ANODIZED BRASS | 14| 4 +Brand#22 |PROMO ANODIZED COPPER | 14| 4 +Brand#22 |PROMO ANODIZED COPPER | 36| 4 +Brand#22 |PROMO ANODIZED COPPER | 49| 4 +Brand#22 |PROMO ANODIZED NICKEL | 3| 4 +Brand#22 |PROMO ANODIZED NICKEL | 14| 4 +Brand#22 |PROMO ANODIZED NICKEL | 19| 4 +Brand#22 |PROMO ANODIZED NICKEL | 49| 4 +Brand#22 |PROMO ANODIZED STEEL | 3| 4 +Brand#22 |PROMO ANODIZED STEEL | 23| 4 +Brand#22 |PROMO ANODIZED STEEL | 45| 4 +Brand#22 |PROMO ANODIZED TIN | 3| 4 +Brand#22 |PROMO ANODIZED TIN | 9| 4 +Brand#22 |PROMO BRUSHED BRASS | 9| 4 +Brand#22 |PROMO BRUSHED COPPER | 3| 4 +Brand#22 |PROMO BRUSHED COPPER | 9| 4 +Brand#22 |PROMO BRUSHED COPPER | 14| 4 +Brand#22 |PROMO BRUSHED COPPER | 19| 4 +Brand#22 |PROMO BRUSHED NICKEL | 3| 4 +Brand#22 |PROMO BRUSHED NICKEL | 23| 4 +Brand#22 |PROMO BRUSHED STEEL | 9| 4 +Brand#22 |PROMO BRUSHED STEEL | 14| 4 +Brand#22 |PROMO BRUSHED STEEL | 19| 4 +Brand#22 |PROMO BRUSHED STEEL | 23| 4 +Brand#22 |PROMO BRUSHED STEEL | 49| 4 +Brand#22 |PROMO BRUSHED TIN | 14| 4 +Brand#22 |PROMO BRUSHED TIN | 23| 4 +Brand#22 |PROMO BRUSHED TIN | 45| 4 +Brand#22 |PROMO BRUSHED TIN | 49| 4 +Brand#22 |PROMO BURNISHED BRASS | 9| 4 +Brand#22 |PROMO BURNISHED BRASS | 19| 4 +Brand#22 |PROMO BURNISHED BRASS | 45| 4 +Brand#22 |PROMO BURNISHED COPPER | 3| 4 +Brand#22 |PROMO BURNISHED COPPER | 9| 4 +Brand#22 |PROMO BURNISHED COPPER | 19| 4 +Brand#22 |PROMO BURNISHED COPPER | 45| 4 +Brand#22 |PROMO BURNISHED NICKEL | 9| 4 +Brand#22 |PROMO BURNISHED NICKEL | 23| 4 +Brand#22 |PROMO BURNISHED NICKEL | 36| 4 +Brand#22 |PROMO BURNISHED NICKEL | 49| 4 +Brand#22 |PROMO BURNISHED STEEL | 9| 4 +Brand#22 |PROMO BURNISHED TIN | 9| 4 +Brand#22 |PROMO BURNISHED TIN | 19| 4 +Brand#22 |PROMO BURNISHED TIN | 23| 4 +Brand#22 |PROMO 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23| 4 +Brand#22 |SMALL POLISHED BRASS | 49| 4 +Brand#22 |SMALL POLISHED COPPER | 14| 4 +Brand#22 |SMALL POLISHED COPPER | 36| 4 +Brand#22 |SMALL POLISHED NICKEL | 36| 4 +Brand#22 |SMALL POLISHED STEEL | 3| 4 +Brand#22 |SMALL POLISHED STEEL | 19| 4 +Brand#22 |SMALL POLISHED STEEL | 23| 4 +Brand#22 |SMALL POLISHED STEEL | 36| 4 +Brand#22 |SMALL POLISHED TIN | 3| 4 +Brand#22 |SMALL POLISHED TIN | 9| 4 +Brand#22 |SMALL POLISHED TIN | 36| 4 +Brand#22 |STANDARD ANODIZED BRASS | 9| 4 +Brand#22 |STANDARD ANODIZED BRASS | 45| 4 +Brand#22 |STANDARD ANODIZED BRASS | 49| 4 +Brand#22 |STANDARD ANODIZED COPPER | 3| 4 +Brand#22 |STANDARD ANODIZED COPPER | 19| 4 +Brand#22 |STANDARD ANODIZED NICKEL | 19| 4 +Brand#22 |STANDARD ANODIZED NICKEL | 45| 4 +Brand#22 |STANDARD ANODIZED STEEL | 3| 4 +Brand#22 |STANDARD ANODIZED STEEL | 9| 4 +Brand#22 |STANDARD ANODIZED STEEL | 36| 4 +Brand#22 |STANDARD ANODIZED STEEL | 45| 4 +Brand#22 |STANDARD ANODIZED TIN | 19| 4 +Brand#22 |STANDARD ANODIZED TIN | 23| 4 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4 +Brand#23 |LARGE PLATED TIN | 14| 4 +Brand#23 |LARGE PLATED TIN | 19| 4 +Brand#23 |LARGE PLATED TIN | 23| 4 +Brand#23 |LARGE PLATED TIN | 36| 4 +Brand#23 |LARGE PLATED TIN | 45| 4 +Brand#23 |LARGE POLISHED BRASS | 3| 4 +Brand#23 |LARGE POLISHED BRASS | 14| 4 +Brand#23 |LARGE POLISHED BRASS | 23| 4 +Brand#23 |LARGE POLISHED BRASS | 36| 4 +Brand#23 |LARGE POLISHED BRASS | 45| 4 +Brand#23 |LARGE POLISHED BRASS | 49| 4 +Brand#23 |LARGE POLISHED COPPER | 19| 4 +Brand#23 |LARGE POLISHED NICKEL | 14| 4 +Brand#23 |LARGE POLISHED NICKEL | 19| 4 +Brand#23 |LARGE POLISHED NICKEL | 23| 4 +Brand#23 |LARGE POLISHED NICKEL | 45| 4 +Brand#23 |LARGE POLISHED STEEL | 9| 4 +Brand#23 |LARGE POLISHED STEEL | 14| 4 +Brand#23 |LARGE POLISHED STEEL | 19| 4 +Brand#23 |LARGE POLISHED STEEL | 36| 4 +Brand#23 |LARGE POLISHED TIN | 19| 4 +Brand#23 |LARGE POLISHED TIN | 23| 4 +Brand#23 |MEDIUM ANODIZED BRASS | 14| 4 +Brand#23 |MEDIUM ANODIZED BRASS | 19| 4 +Brand#23 |MEDIUM ANODIZED BRASS | 36| 4 +Brand#23 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14| 4 +Brand#24 |ECONOMY BRUSHED NICKEL | 19| 4 +Brand#24 |ECONOMY BRUSHED STEEL | 3| 4 +Brand#24 |ECONOMY BRUSHED STEEL | 19| 4 +Brand#24 |ECONOMY BRUSHED STEEL | 45| 4 +Brand#24 |ECONOMY BRUSHED TIN | 3| 4 +Brand#24 |ECONOMY BRUSHED TIN | 19| 4 +Brand#24 |ECONOMY BRUSHED TIN | 23| 4 +Brand#24 |ECONOMY BRUSHED TIN | 45| 4 +Brand#24 |ECONOMY BURNISHED BRASS | 3| 4 +Brand#24 |ECONOMY BURNISHED BRASS | 9| 4 +Brand#24 |ECONOMY BURNISHED BRASS | 36| 4 +Brand#24 |ECONOMY BURNISHED BRASS | 45| 4 +Brand#24 |ECONOMY BURNISHED COPPER | 9| 4 +Brand#24 |ECONOMY BURNISHED COPPER | 36| 4 +Brand#24 |ECONOMY BURNISHED NICKEL | 23| 4 +Brand#24 |ECONOMY BURNISHED NICKEL | 36| 4 +Brand#24 |ECONOMY BURNISHED NICKEL | 45| 4 +Brand#24 |ECONOMY BURNISHED NICKEL | 49| 4 +Brand#24 |ECONOMY BURNISHED STEEL | 14| 4 +Brand#24 |ECONOMY BURNISHED STEEL | 23| 4 +Brand#24 |ECONOMY BURNISHED TIN | 3| 4 +Brand#24 |ECONOMY BURNISHED TIN | 9| 4 +Brand#24 |ECONOMY BURNISHED TIN | 19| 4 +Brand#24 |ECONOMY BURNISHED TIN | 45| 4 +Brand#24 |ECONOMY PLATED BRASS | 3| 4 +Brand#24 |ECONOMY PLATED BRASS | 9| 4 +Brand#24 |ECONOMY PLATED BRASS | 23| 4 +Brand#24 |ECONOMY PLATED BRASS | 45| 4 +Brand#24 |ECONOMY PLATED COPPER | 3| 4 +Brand#24 |ECONOMY PLATED COPPER | 14| 4 +Brand#24 |ECONOMY PLATED COPPER | 23| 4 +Brand#24 |ECONOMY PLATED NICKEL | 45| 4 +Brand#24 |ECONOMY PLATED NICKEL | 49| 4 +Brand#24 |ECONOMY PLATED STEEL | 3| 4 +Brand#24 |ECONOMY PLATED STEEL | 23| 4 +Brand#24 |ECONOMY PLATED TIN | 14| 4 +Brand#24 |ECONOMY PLATED TIN | 19| 4 +Brand#24 |ECONOMY PLATED TIN | 23| 4 +Brand#24 |ECONOMY PLATED TIN | 45| 4 +Brand#24 |ECONOMY POLISHED BRASS | 19| 4 +Brand#24 |ECONOMY POLISHED BRASS | 49| 4 +Brand#24 |ECONOMY POLISHED COPPER | 9| 4 +Brand#24 |ECONOMY POLISHED COPPER | 14| 4 +Brand#24 |ECONOMY POLISHED COPPER | 45| 4 +Brand#24 |ECONOMY POLISHED NICKEL | 9| 4 +Brand#24 |ECONOMY POLISHED NICKEL | 19| 4 +Brand#24 |ECONOMY POLISHED NICKEL | 45| 4 +Brand#24 |ECONOMY POLISHED NICKEL | 49| 4 +Brand#24 |ECONOMY 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23| 4 +Brand#25 |LARGE PLATED STEEL | 14| 4 +Brand#25 |LARGE PLATED STEEL | 36| 4 +Brand#25 |LARGE PLATED TIN | 14| 4 +Brand#25 |LARGE POLISHED BRASS | 3| 4 +Brand#25 |LARGE POLISHED BRASS | 45| 4 +Brand#25 |LARGE POLISHED BRASS | 49| 4 +Brand#25 |LARGE POLISHED COPPER | 3| 4 +Brand#25 |LARGE POLISHED COPPER | 9| 4 +Brand#25 |LARGE POLISHED COPPER | 19| 4 +Brand#25 |LARGE POLISHED COPPER | 49| 4 +Brand#25 |LARGE POLISHED NICKEL | 3| 4 +Brand#25 |LARGE POLISHED NICKEL | 9| 4 +Brand#25 |LARGE POLISHED NICKEL | 23| 4 +Brand#25 |LARGE POLISHED STEEL | 3| 4 +Brand#25 |LARGE POLISHED STEEL | 14| 4 +Brand#25 |LARGE POLISHED TIN | 9| 4 +Brand#25 |LARGE POLISHED TIN | 19| 4 +Brand#25 |LARGE POLISHED TIN | 36| 4 +Brand#25 |MEDIUM ANODIZED BRASS | 23| 4 +Brand#25 |MEDIUM ANODIZED BRASS | 45| 4 +Brand#25 |MEDIUM ANODIZED COPPER | 3| 4 +Brand#25 |MEDIUM ANODIZED COPPER | 14| 4 +Brand#25 |MEDIUM ANODIZED COPPER | 19| 4 +Brand#25 |MEDIUM ANODIZED COPPER | 23| 4 +Brand#25 |MEDIUM ANODIZED NICKEL | 14| 4 +Brand#25 |MEDIUM ANODIZED NICKEL | 19| 4 +Brand#25 |MEDIUM ANODIZED STEEL | 9| 4 +Brand#25 |MEDIUM ANODIZED STEEL | 45| 4 +Brand#25 |MEDIUM ANODIZED STEEL | 49| 4 +Brand#25 |MEDIUM ANODIZED TIN | 14| 4 +Brand#25 |MEDIUM ANODIZED TIN | 19| 4 +Brand#25 |MEDIUM ANODIZED TIN | 23| 4 +Brand#25 |MEDIUM ANODIZED TIN | 36| 4 +Brand#25 |MEDIUM BRUSHED BRASS | 19| 4 +Brand#25 |MEDIUM BRUSHED BRASS | 23| 4 +Brand#25 |MEDIUM BRUSHED BRASS | 45| 4 +Brand#25 |MEDIUM BRUSHED COPPER | 19| 4 +Brand#25 |MEDIUM BRUSHED COPPER | 36| 4 +Brand#25 |MEDIUM BRUSHED NICKEL | 9| 4 +Brand#25 |MEDIUM BRUSHED NICKEL | 19| 4 +Brand#25 |MEDIUM BRUSHED NICKEL | 45| 4 +Brand#25 |MEDIUM BRUSHED STEEL | 14| 4 +Brand#25 |MEDIUM BRUSHED STEEL | 19| 4 +Brand#25 |MEDIUM BRUSHED STEEL | 23| 4 +Brand#25 |MEDIUM BRUSHED STEEL | 36| 4 +Brand#25 |MEDIUM BRUSHED STEEL | 49| 4 +Brand#25 |MEDIUM BRUSHED TIN | 3| 4 +Brand#25 |MEDIUM BRUSHED TIN | 36| 4 +Brand#25 |MEDIUM BRUSHED TIN | 45| 4 +Brand#25 |MEDIUM BURNISHED BRASS | 9| 4 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PLATED STEEL | 14| 4 +Brand#25 |MEDIUM PLATED STEEL | 19| 4 +Brand#25 |MEDIUM PLATED STEEL | 49| 4 +Brand#25 |MEDIUM PLATED TIN | 9| 4 +Brand#25 |MEDIUM PLATED TIN | 19| 4 +Brand#25 |MEDIUM PLATED TIN | 23| 4 +Brand#25 |PROMO ANODIZED BRASS | 3| 4 +Brand#25 |PROMO ANODIZED BRASS | 19| 4 +Brand#25 |PROMO ANODIZED COPPER | 9| 4 +Brand#25 |PROMO ANODIZED COPPER | 14| 4 +Brand#25 |PROMO ANODIZED NICKEL | 9| 4 +Brand#25 |PROMO ANODIZED NICKEL | 19| 4 +Brand#25 |PROMO ANODIZED STEEL | 3| 4 +Brand#25 |PROMO ANODIZED STEEL | 14| 4 +Brand#25 |PROMO ANODIZED STEEL | 36| 4 +Brand#25 |PROMO ANODIZED TIN | 45| 4 +Brand#25 |PROMO BRUSHED BRASS | 3| 4 +Brand#25 |PROMO BRUSHED BRASS | 9| 4 +Brand#25 |PROMO BRUSHED COPPER | 3| 4 +Brand#25 |PROMO BRUSHED COPPER | 36| 4 +Brand#25 |PROMO BRUSHED NICKEL | 23| 4 +Brand#25 |PROMO BRUSHED NICKEL | 49| 4 +Brand#25 |PROMO BRUSHED STEEL | 19| 4 +Brand#25 |PROMO BRUSHED STEEL | 36| 4 +Brand#25 |PROMO BRUSHED TIN | 3| 4 +Brand#25 |PROMO BRUSHED TIN | 23| 4 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PLATED TIN | 3| 4 +Brand#25 |PROMO PLATED TIN | 19| 4 +Brand#25 |PROMO PLATED TIN | 23| 4 +Brand#25 |PROMO PLATED TIN | 36| 4 +Brand#25 |PROMO PLATED TIN | 49| 4 +Brand#25 |PROMO POLISHED BRASS | 9| 4 +Brand#25 |PROMO POLISHED BRASS | 23| 4 +Brand#25 |PROMO POLISHED BRASS | 45| 4 +Brand#25 |PROMO POLISHED COPPER | 3| 4 +Brand#25 |PROMO POLISHED COPPER | 45| 4 +Brand#25 |PROMO POLISHED NICKEL | 3| 4 +Brand#25 |PROMO POLISHED NICKEL | 9| 4 +Brand#25 |PROMO POLISHED STEEL | 19| 4 +Brand#25 |PROMO POLISHED TIN | 3| 4 +Brand#25 |PROMO POLISHED TIN | 23| 4 +Brand#25 |PROMO POLISHED TIN | 45| 4 +Brand#25 |PROMO POLISHED TIN | 49| 4 +Brand#25 |SMALL ANODIZED BRASS | 45| 4 +Brand#25 |SMALL ANODIZED COPPER | 3| 4 +Brand#25 |SMALL ANODIZED COPPER | 9| 4 +Brand#25 |SMALL ANODIZED COPPER | 14| 4 +Brand#25 |SMALL ANODIZED COPPER | 19| 4 +Brand#25 |SMALL ANODIZED COPPER | 49| 4 +Brand#25 |SMALL ANODIZED NICKEL | 3| 4 +Brand#25 |SMALL ANODIZED NICKEL | 9| 4 +Brand#25 |SMALL ANODIZED NICKEL | 23| 4 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BRUSHED TIN | 9| 4 +Brand#25 |SMALL BRUSHED TIN | 14| 4 +Brand#25 |SMALL BRUSHED TIN | 19| 4 +Brand#25 |SMALL BURNISHED BRASS | 14| 4 +Brand#25 |SMALL BURNISHED BRASS | 19| 4 +Brand#25 |SMALL BURNISHED BRASS | 45| 4 +Brand#25 |SMALL BURNISHED BRASS | 49| 4 +Brand#25 |SMALL BURNISHED COPPER | 3| 4 +Brand#25 |SMALL BURNISHED COPPER | 14| 4 +Brand#25 |SMALL BURNISHED COPPER | 19| 4 +Brand#25 |SMALL BURNISHED COPPER | 23| 4 +Brand#25 |SMALL BURNISHED NICKEL | 14| 4 +Brand#25 |SMALL BURNISHED NICKEL | 19| 4 +Brand#25 |SMALL BURNISHED STEEL | 9| 4 +Brand#25 |SMALL BURNISHED STEEL | 19| 4 +Brand#25 |SMALL BURNISHED STEEL | 23| 4 +Brand#25 |SMALL BURNISHED STEEL | 36| 4 +Brand#25 |SMALL BURNISHED TIN | 9| 4 +Brand#25 |SMALL BURNISHED TIN | 14| 4 +Brand#25 |SMALL BURNISHED TIN | 23| 4 +Brand#25 |SMALL BURNISHED TIN | 36| 4 +Brand#25 |SMALL BURNISHED TIN | 49| 4 +Brand#25 |SMALL PLATED BRASS | 3| 4 +Brand#25 |SMALL PLATED BRASS | 23| 4 +Brand#25 |SMALL PLATED BRASS | 45| 4 +Brand#25 |SMALL PLATED COPPER | 3| 4 +Brand#25 |SMALL PLATED COPPER | 14| 4 +Brand#25 |SMALL PLATED NICKEL | 3| 4 +Brand#25 |SMALL PLATED NICKEL | 19| 4 +Brand#25 |SMALL PLATED NICKEL | 23| 4 +Brand#25 |SMALL PLATED NICKEL | 49| 4 +Brand#25 |SMALL PLATED STEEL | 3| 4 +Brand#25 |SMALL PLATED STEEL | 14| 4 +Brand#25 |SMALL PLATED TIN | 9| 4 +Brand#25 |SMALL PLATED TIN | 14| 4 +Brand#25 |SMALL PLATED TIN | 19| 4 +Brand#25 |SMALL PLATED TIN | 36| 4 +Brand#25 |SMALL PLATED TIN | 45| 4 +Brand#25 |SMALL POLISHED BRASS | 14| 4 +Brand#25 |SMALL POLISHED BRASS | 36| 4 +Brand#25 |SMALL POLISHED NICKEL | 36| 4 +Brand#25 |SMALL POLISHED NICKEL | 49| 4 +Brand#25 |SMALL POLISHED STEEL | 9| 4 +Brand#25 |SMALL POLISHED STEEL | 49| 4 +Brand#25 |SMALL POLISHED TIN | 14| 4 +Brand#25 |STANDARD ANODIZED BRASS | 14| 4 +Brand#25 |STANDARD ANODIZED BRASS | 23| 4 +Brand#25 |STANDARD ANODIZED BRASS | 36| 4 +Brand#25 |STANDARD ANODIZED COPPER | 9| 4 +Brand#25 |STANDARD ANODIZED COPPER | 14| 4 +Brand#25 |STANDARD ANODIZED COPPER | 19| 4 +Brand#25 |STANDARD ANODIZED COPPER | 36| 4 +Brand#25 |STANDARD ANODIZED COPPER | 49| 4 +Brand#25 |STANDARD ANODIZED NICKEL | 9| 4 +Brand#25 |STANDARD ANODIZED NICKEL | 19| 4 +Brand#25 |STANDARD ANODIZED NICKEL | 36| 4 +Brand#25 |STANDARD ANODIZED STEEL | 19| 4 +Brand#25 |STANDARD ANODIZED STEEL | 36| 4 +Brand#25 |STANDARD ANODIZED STEEL | 45| 4 +Brand#25 |STANDARD ANODIZED STEEL | 49| 4 +Brand#25 |STANDARD ANODIZED TIN | 36| 4 +Brand#25 |STANDARD ANODIZED TIN | 45| 4 +Brand#25 |STANDARD BRUSHED BRASS | 14| 4 +Brand#25 |STANDARD BRUSHED BRASS | 19| 4 +Brand#25 |STANDARD BRUSHED BRASS | 23| 4 +Brand#25 |STANDARD BRUSHED COPPER | 45| 4 +Brand#25 |STANDARD BRUSHED NICKEL | 3| 4 +Brand#25 |STANDARD BRUSHED NICKEL | 9| 4 +Brand#25 |STANDARD BRUSHED NICKEL | 45| 4 +Brand#25 |STANDARD BRUSHED STEEL | 14| 4 +Brand#25 |STANDARD BRUSHED STEEL | 23| 4 +Brand#25 |STANDARD BRUSHED STEEL | 45| 4 +Brand#25 |STANDARD BRUSHED TIN | 3| 4 +Brand#25 |STANDARD BRUSHED TIN | 9| 4 +Brand#25 |STANDARD BRUSHED TIN | 14| 4 +Brand#25 |STANDARD BURNISHED BRASS | 19| 4 +Brand#25 |STANDARD BURNISHED BRASS | 36| 4 +Brand#25 |STANDARD BURNISHED BRASS | 45| 4 +Brand#25 |STANDARD BURNISHED BRASS | 49| 4 +Brand#25 |STANDARD BURNISHED COPPER| 3| 4 +Brand#25 |STANDARD BURNISHED COPPER| 14| 4 +Brand#25 |STANDARD BURNISHED COPPER| 36| 4 +Brand#25 |STANDARD BURNISHED COPPER| 45| 4 +Brand#25 |STANDARD BURNISHED COPPER| 49| 4 +Brand#25 |STANDARD BURNISHED NICKEL| 14| 4 +Brand#25 |STANDARD BURNISHED NICKEL| 45| 4 +Brand#25 |STANDARD BURNISHED NICKEL| 49| 4 +Brand#25 |STANDARD BURNISHED STEEL | 3| 4 +Brand#25 |STANDARD BURNISHED STEEL | 9| 4 +Brand#25 |STANDARD BURNISHED STEEL | 19| 4 +Brand#25 |STANDARD BURNISHED STEEL | 49| 4 +Brand#25 |STANDARD BURNISHED TIN | 9| 4 +Brand#25 |STANDARD BURNISHED TIN | 45| 4 +Brand#25 |STANDARD PLATED BRASS | 3| 4 +Brand#25 |STANDARD PLATED BRASS | 36| 4 +Brand#25 |STANDARD PLATED COPPER | 3| 4 +Brand#25 |STANDARD PLATED COPPER | 19| 4 +Brand#25 |STANDARD PLATED NICKEL | 9| 4 +Brand#25 |STANDARD PLATED NICKEL | 19| 4 +Brand#25 |STANDARD PLATED STEEL | 23| 4 +Brand#25 |STANDARD PLATED TIN | 3| 4 +Brand#25 |STANDARD PLATED TIN | 9| 4 +Brand#25 |STANDARD PLATED TIN | 14| 4 +Brand#25 |STANDARD PLATED TIN | 19| 4 +Brand#25 |STANDARD PLATED TIN | 45| 4 +Brand#25 |STANDARD POLISHED BRASS | 3| 4 +Brand#25 |STANDARD POLISHED BRASS | 14| 4 +Brand#25 |STANDARD POLISHED BRASS | 23| 4 +Brand#25 |STANDARD POLISHED BRASS | 45| 4 +Brand#25 |STANDARD POLISHED COPPER | 9| 4 +Brand#25 |STANDARD POLISHED COPPER | 19| 4 +Brand#25 |STANDARD POLISHED COPPER | 45| 4 +Brand#25 |STANDARD POLISHED COPPER | 49| 4 +Brand#25 |STANDARD POLISHED NICKEL | 14| 4 +Brand#25 |STANDARD POLISHED NICKEL | 23| 4 +Brand#25 |STANDARD POLISHED NICKEL | 49| 4 +Brand#25 |STANDARD POLISHED STEEL | 49| 4 +Brand#25 |STANDARD POLISHED TIN | 9| 4 +Brand#25 |STANDARD POLISHED TIN | 23| 4 +Brand#31 |ECONOMY ANODIZED BRASS | 3| 4 +Brand#31 |ECONOMY ANODIZED BRASS | 9| 4 +Brand#31 |ECONOMY ANODIZED BRASS | 23| 4 +Brand#31 |ECONOMY ANODIZED COPPER | 3| 4 +Brand#31 |ECONOMY ANODIZED COPPER | 9| 4 +Brand#31 |ECONOMY ANODIZED COPPER | 14| 4 +Brand#31 |ECONOMY ANODIZED COPPER | 36| 4 +Brand#31 |ECONOMY ANODIZED COPPER | 45| 4 +Brand#31 |ECONOMY ANODIZED NICKEL | 19| 4 +Brand#31 |ECONOMY ANODIZED NICKEL | 23| 4 +Brand#31 |ECONOMY ANODIZED NICKEL | 36| 4 +Brand#31 |ECONOMY ANODIZED STEEL | 9| 4 +Brand#31 |ECONOMY ANODIZED STEEL | 19| 4 +Brand#31 |ECONOMY ANODIZED STEEL | 23| 4 +Brand#31 |ECONOMY ANODIZED STEEL | 49| 4 +Brand#31 |ECONOMY ANODIZED TIN | 14| 4 +Brand#31 |ECONOMY ANODIZED TIN | 45| 4 +Brand#31 |ECONOMY BRUSHED BRASS | 14| 4 +Brand#31 |ECONOMY BRUSHED BRASS | 23| 4 +Brand#31 |ECONOMY BRUSHED BRASS | 45| 4 +Brand#31 |ECONOMY BRUSHED BRASS | 49| 4 +Brand#31 |ECONOMY BRUSHED COPPER | 19| 4 +Brand#31 |ECONOMY BRUSHED COPPER | 23| 4 +Brand#31 |ECONOMY BRUSHED COPPER | 45| 4 +Brand#31 |ECONOMY BRUSHED COPPER | 49| 4 +Brand#31 |ECONOMY BRUSHED NICKEL | 23| 4 +Brand#31 |ECONOMY BRUSHED NICKEL | 36| 4 +Brand#31 |ECONOMY BRUSHED NICKEL | 45| 4 +Brand#31 |ECONOMY BRUSHED NICKEL | 49| 4 +Brand#31 |ECONOMY BRUSHED STEEL | 45| 4 +Brand#31 |ECONOMY BRUSHED TIN | 3| 4 +Brand#31 |ECONOMY BRUSHED TIN | 9| 4 +Brand#31 |ECONOMY BRUSHED TIN | 45| 4 +Brand#31 |ECONOMY BURNISHED BRASS | 9| 4 +Brand#31 |ECONOMY BURNISHED BRASS | 19| 4 +Brand#31 |ECONOMY BURNISHED BRASS | 36| 4 +Brand#31 |ECONOMY BURNISHED BRASS | 49| 4 +Brand#31 |ECONOMY BURNISHED COPPER | 3| 4 +Brand#31 |ECONOMY BURNISHED COPPER | 23| 4 +Brand#31 |ECONOMY BURNISHED COPPER | 36| 4 +Brand#31 |ECONOMY BURNISHED NICKEL | 3| 4 +Brand#31 |ECONOMY BURNISHED NICKEL | 9| 4 +Brand#31 |ECONOMY BURNISHED NICKEL | 14| 4 +Brand#31 |ECONOMY BURNISHED NICKEL | 23| 4 +Brand#31 |ECONOMY BURNISHED NICKEL | 49| 4 +Brand#31 |ECONOMY BURNISHED STEEL | 9| 4 +Brand#31 |ECONOMY BURNISHED STEEL | 23| 4 +Brand#31 |ECONOMY BURNISHED STEEL | 36| 4 +Brand#31 |ECONOMY BURNISHED STEEL | 45| 4 +Brand#31 |ECONOMY BURNISHED TIN | 36| 4 +Brand#31 |ECONOMY PLATED BRASS | 3| 4 +Brand#31 |ECONOMY PLATED BRASS | 9| 4 +Brand#31 |ECONOMY PLATED BRASS | 14| 4 +Brand#31 |ECONOMY PLATED BRASS | 19| 4 +Brand#31 |ECONOMY PLATED BRASS | 49| 4 +Brand#31 |ECONOMY PLATED COPPER | 9| 4 +Brand#31 |ECONOMY PLATED COPPER | 14| 4 +Brand#31 |ECONOMY PLATED COPPER | 23| 4 +Brand#31 |ECONOMY PLATED COPPER | 36| 4 +Brand#31 |ECONOMY PLATED COPPER | 45| 4 +Brand#31 |ECONOMY PLATED NICKEL | 3| 4 +Brand#31 |ECONOMY PLATED NICKEL | 14| 4 +Brand#31 |ECONOMY PLATED NICKEL | 19| 4 +Brand#31 |ECONOMY PLATED NICKEL | 23| 4 +Brand#31 |ECONOMY PLATED NICKEL | 45| 4 +Brand#31 |ECONOMY PLATED NICKEL | 49| 4 +Brand#31 |ECONOMY PLATED STEEL | 9| 4 +Brand#31 |ECONOMY PLATED STEEL | 14| 4 +Brand#31 |ECONOMY PLATED STEEL | 19| 4 +Brand#31 |ECONOMY PLATED STEEL | 36| 4 +Brand#31 |ECONOMY PLATED TIN | 9| 4 +Brand#31 |ECONOMY PLATED TIN | 14| 4 +Brand#31 |ECONOMY PLATED TIN | 49| 4 +Brand#31 |ECONOMY POLISHED BRASS | 19| 4 +Brand#31 |ECONOMY POLISHED BRASS | 49| 4 +Brand#31 |ECONOMY POLISHED COPPER | 9| 4 +Brand#31 |ECONOMY POLISHED COPPER | 23| 4 +Brand#31 |ECONOMY POLISHED COPPER | 36| 4 +Brand#31 |ECONOMY POLISHED COPPER | 45| 4 +Brand#31 |ECONOMY POLISHED NICKEL | 19| 4 +Brand#31 |ECONOMY POLISHED NICKEL | 23| 4 +Brand#31 |ECONOMY POLISHED NICKEL | 49| 4 +Brand#31 |ECONOMY POLISHED STEEL | 14| 4 +Brand#31 |ECONOMY POLISHED STEEL | 19| 4 +Brand#31 |ECONOMY POLISHED STEEL | 23| 4 +Brand#31 |ECONOMY POLISHED STEEL | 36| 4 +Brand#31 |ECONOMY POLISHED TIN | 9| 4 +Brand#31 |ECONOMY POLISHED TIN | 14| 4 +Brand#31 |ECONOMY POLISHED TIN | 19| 4 +Brand#31 |ECONOMY POLISHED TIN | 23| 4 +Brand#31 |ECONOMY POLISHED TIN | 49| 4 +Brand#31 |LARGE ANODIZED BRASS | 9| 4 +Brand#31 |LARGE ANODIZED BRASS | 19| 4 +Brand#31 |LARGE ANODIZED BRASS | 23| 4 +Brand#31 |LARGE ANODIZED BRASS | 49| 4 +Brand#31 |LARGE ANODIZED COPPER | 9| 4 +Brand#31 |LARGE ANODIZED NICKEL | 3| 4 +Brand#31 |LARGE ANODIZED NICKEL | 9| 4 +Brand#31 |LARGE ANODIZED NICKEL | 23| 4 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BURNISHED COPPER | 19| 4 +Brand#31 |LARGE BURNISHED COPPER | 49| 4 +Brand#31 |LARGE BURNISHED NICKEL | 3| 4 +Brand#31 |LARGE BURNISHED NICKEL | 23| 4 +Brand#31 |LARGE BURNISHED STEEL | 14| 4 +Brand#31 |LARGE BURNISHED STEEL | 19| 4 +Brand#31 |LARGE BURNISHED STEEL | 45| 4 +Brand#31 |LARGE BURNISHED TIN | 3| 4 +Brand#31 |LARGE BURNISHED TIN | 9| 4 +Brand#31 |LARGE BURNISHED TIN | 36| 4 +Brand#31 |LARGE BURNISHED TIN | 45| 4 +Brand#31 |LARGE PLATED BRASS | 19| 4 +Brand#31 |LARGE PLATED BRASS | 36| 4 +Brand#31 |LARGE PLATED COPPER | 9| 4 +Brand#31 |LARGE PLATED COPPER | 14| 4 +Brand#31 |LARGE PLATED COPPER | 36| 4 +Brand#31 |LARGE PLATED COPPER | 49| 4 +Brand#31 |LARGE PLATED NICKEL | 3| 4 +Brand#31 |LARGE PLATED NICKEL | 9| 4 +Brand#31 |LARGE PLATED NICKEL | 14| 4 +Brand#31 |LARGE PLATED STEEL | 3| 4 +Brand#31 |LARGE PLATED STEEL | 19| 4 +Brand#31 |LARGE PLATED STEEL | 36| 4 +Brand#31 |LARGE PLATED STEEL | 49| 4 +Brand#31 |LARGE PLATED TIN | 3| 4 +Brand#31 |LARGE PLATED TIN | 19| 4 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+Brand#31 |MEDIUM ANODIZED STEEL | 36| 4 +Brand#31 |MEDIUM ANODIZED TIN | 36| 4 +Brand#31 |MEDIUM ANODIZED TIN | 45| 4 +Brand#31 |MEDIUM ANODIZED TIN | 49| 4 +Brand#31 |MEDIUM BRUSHED BRASS | 49| 4 +Brand#31 |MEDIUM BRUSHED COPPER | 3| 4 +Brand#31 |MEDIUM BRUSHED COPPER | 45| 4 +Brand#31 |MEDIUM BRUSHED NICKEL | 3| 4 +Brand#31 |MEDIUM BRUSHED NICKEL | 23| 4 +Brand#31 |MEDIUM BRUSHED NICKEL | 45| 4 +Brand#31 |MEDIUM BRUSHED STEEL | 9| 4 +Brand#31 |MEDIUM BRUSHED STEEL | 14| 4 +Brand#31 |MEDIUM BRUSHED STEEL | 36| 4 +Brand#31 |MEDIUM BRUSHED STEEL | 45| 4 +Brand#31 |MEDIUM BRUSHED TIN | 19| 4 +Brand#31 |MEDIUM BRUSHED TIN | 36| 4 +Brand#31 |MEDIUM BRUSHED TIN | 45| 4 +Brand#31 |MEDIUM BURNISHED BRASS | 9| 4 +Brand#31 |MEDIUM BURNISHED BRASS | 36| 4 +Brand#31 |MEDIUM BURNISHED COPPER | 3| 4 +Brand#31 |MEDIUM BURNISHED COPPER | 9| 4 +Brand#31 |MEDIUM BURNISHED COPPER | 14| 4 +Brand#31 |MEDIUM BURNISHED COPPER | 23| 4 +Brand#31 |MEDIUM BURNISHED NICKEL | 36| 4 +Brand#31 |MEDIUM BURNISHED 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STEEL | 23| 4 +Brand#32 |STANDARD ANODIZED TIN | 3| 4 +Brand#32 |STANDARD ANODIZED TIN | 23| 4 +Brand#32 |STANDARD ANODIZED TIN | 49| 4 +Brand#32 |STANDARD BRUSHED BRASS | 3| 4 +Brand#32 |STANDARD BRUSHED BRASS | 9| 4 +Brand#32 |STANDARD BRUSHED BRASS | 19| 4 +Brand#32 |STANDARD BRUSHED BRASS | 23| 4 +Brand#32 |STANDARD BRUSHED COPPER | 14| 4 +Brand#32 |STANDARD BRUSHED COPPER | 19| 4 +Brand#32 |STANDARD BRUSHED COPPER | 23| 4 +Brand#32 |STANDARD BRUSHED COPPER | 49| 4 +Brand#32 |STANDARD BRUSHED NICKEL | 9| 4 +Brand#32 |STANDARD BRUSHED NICKEL | 23| 4 +Brand#32 |STANDARD BRUSHED NICKEL | 45| 4 +Brand#32 |STANDARD BRUSHED NICKEL | 49| 4 +Brand#32 |STANDARD BRUSHED TIN | 14| 4 +Brand#32 |STANDARD BRUSHED TIN | 19| 4 +Brand#32 |STANDARD BRUSHED TIN | 23| 4 +Brand#32 |STANDARD BURNISHED BRASS | 3| 4 +Brand#32 |STANDARD BURNISHED BRASS | 19| 4 +Brand#32 |STANDARD BURNISHED BRASS | 23| 4 +Brand#32 |STANDARD BURNISHED BRASS | 36| 4 +Brand#32 |STANDARD BURNISHED COPPER| 14| 4 +Brand#32 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|STANDARD POLISHED BRASS | 45| 4 +Brand#32 |STANDARD POLISHED BRASS | 49| 4 +Brand#32 |STANDARD POLISHED COPPER | 19| 4 +Brand#32 |STANDARD POLISHED COPPER | 45| 4 +Brand#32 |STANDARD POLISHED COPPER | 49| 4 +Brand#32 |STANDARD POLISHED NICKEL | 19| 4 +Brand#32 |STANDARD POLISHED NICKEL | 36| 4 +Brand#32 |STANDARD POLISHED NICKEL | 45| 4 +Brand#32 |STANDARD POLISHED STEEL | 3| 4 +Brand#32 |STANDARD POLISHED STEEL | 19| 4 +Brand#32 |STANDARD POLISHED STEEL | 45| 4 +Brand#32 |STANDARD POLISHED STEEL | 49| 4 +Brand#32 |STANDARD POLISHED TIN | 9| 4 +Brand#32 |STANDARD POLISHED TIN | 36| 4 +Brand#33 |ECONOMY ANODIZED BRASS | 9| 4 +Brand#33 |ECONOMY ANODIZED BRASS | 14| 4 +Brand#33 |ECONOMY ANODIZED BRASS | 23| 4 +Brand#33 |ECONOMY ANODIZED BRASS | 36| 4 +Brand#33 |ECONOMY ANODIZED COPPER | 14| 4 +Brand#33 |ECONOMY ANODIZED NICKEL | 9| 4 +Brand#33 |ECONOMY ANODIZED NICKEL | 49| 4 +Brand#33 |ECONOMY ANODIZED STEEL | 3| 4 +Brand#33 |ECONOMY ANODIZED STEEL | 14| 4 +Brand#33 |ECONOMY ANODIZED TIN | 9| 4 +Brand#33 |ECONOMY ANODIZED TIN | 14| 4 +Brand#33 |ECONOMY ANODIZED TIN | 36| 4 +Brand#33 |ECONOMY BRUSHED BRASS | 23| 4 +Brand#33 |ECONOMY BRUSHED COPPER | 49| 4 +Brand#33 |ECONOMY BRUSHED NICKEL | 3| 4 +Brand#33 |ECONOMY BRUSHED NICKEL | 36| 4 +Brand#33 |ECONOMY BRUSHED STEEL | 3| 4 +Brand#33 |ECONOMY BRUSHED STEEL | 9| 4 +Brand#33 |ECONOMY BRUSHED STEEL | 45| 4 +Brand#33 |ECONOMY BRUSHED TIN | 9| 4 +Brand#33 |ECONOMY BRUSHED TIN | 14| 4 +Brand#33 |ECONOMY BRUSHED TIN | 19| 4 +Brand#33 |ECONOMY BRUSHED TIN | 23| 4 +Brand#33 |ECONOMY BRUSHED TIN | 49| 4 +Brand#33 |ECONOMY BURNISHED BRASS | 23| 4 +Brand#33 |ECONOMY BURNISHED BRASS | 36| 4 +Brand#33 |ECONOMY BURNISHED COPPER | 9| 4 +Brand#33 |ECONOMY BURNISHED COPPER | 23| 4 +Brand#33 |ECONOMY BURNISHED COPPER | 36| 4 +Brand#33 |ECONOMY BURNISHED COPPER | 45| 4 +Brand#33 |ECONOMY BURNISHED COPPER | 49| 4 +Brand#33 |ECONOMY BURNISHED NICKEL | 3| 4 +Brand#33 |ECONOMY BURNISHED NICKEL | 23| 4 +Brand#33 |ECONOMY BURNISHED NICKEL | 36| 4 +Brand#33 |ECONOMY BURNISHED NICKEL | 49| 4 +Brand#33 |ECONOMY BURNISHED STEEL | 3| 4 +Brand#33 |ECONOMY BURNISHED STEEL | 14| 4 +Brand#33 |ECONOMY BURNISHED STEEL | 23| 4 +Brand#33 |ECONOMY BURNISHED STEEL | 45| 4 +Brand#33 |ECONOMY BURNISHED TIN | 14| 4 +Brand#33 |ECONOMY BURNISHED TIN | 19| 4 +Brand#33 |ECONOMY BURNISHED TIN | 36| 4 +Brand#33 |ECONOMY PLATED BRASS | 23| 4 +Brand#33 |ECONOMY PLATED COPPER | 3| 4 +Brand#33 |ECONOMY PLATED COPPER | 36| 4 +Brand#33 |ECONOMY PLATED COPPER | 45| 4 +Brand#33 |ECONOMY PLATED NICKEL | 9| 4 +Brand#33 |ECONOMY PLATED NICKEL | 14| 4 +Brand#33 |ECONOMY PLATED NICKEL | 23| 4 +Brand#33 |ECONOMY PLATED NICKEL | 45| 4 +Brand#33 |ECONOMY PLATED NICKEL | 49| 4 +Brand#33 |ECONOMY PLATED STEEL | 36| 4 +Brand#33 |ECONOMY PLATED STEEL | 49| 4 +Brand#33 |ECONOMY PLATED TIN | 14| 4 +Brand#33 |ECONOMY PLATED TIN | 19| 4 +Brand#33 |ECONOMY PLATED TIN | 23| 4 +Brand#33 |ECONOMY PLATED TIN | 36| 4 +Brand#33 |ECONOMY POLISHED BRASS | 9| 4 +Brand#33 |ECONOMY POLISHED COPPER | 9| 4 +Brand#33 |ECONOMY POLISHED COPPER | 23| 4 +Brand#33 |ECONOMY POLISHED NICKEL | 3| 4 +Brand#33 |ECONOMY POLISHED NICKEL | 23| 4 +Brand#33 |ECONOMY POLISHED NICKEL | 36| 4 +Brand#33 |ECONOMY POLISHED NICKEL | 49| 4 +Brand#33 |ECONOMY POLISHED STEEL | 14| 4 +Brand#33 |ECONOMY POLISHED STEEL | 19| 4 +Brand#33 |ECONOMY POLISHED STEEL | 23| 4 +Brand#33 |ECONOMY POLISHED STEEL | 45| 4 +Brand#33 |ECONOMY POLISHED TIN | 3| 4 +Brand#33 |ECONOMY POLISHED TIN | 9| 4 +Brand#33 |ECONOMY POLISHED TIN | 14| 4 +Brand#33 |ECONOMY POLISHED TIN | 23| 4 +Brand#33 |ECONOMY POLISHED TIN | 45| 4 +Brand#33 |LARGE ANODIZED BRASS | 14| 4 +Brand#33 |LARGE ANODIZED COPPER | 14| 4 +Brand#33 |LARGE ANODIZED COPPER | 45| 4 +Brand#33 |LARGE ANODIZED COPPER | 49| 4 +Brand#33 |LARGE ANODIZED NICKEL | 3| 4 +Brand#33 |LARGE ANODIZED NICKEL | 9| 4 +Brand#33 |LARGE ANODIZED NICKEL | 14| 4 +Brand#33 |LARGE ANODIZED NICKEL | 36| 4 +Brand#33 |LARGE ANODIZED STEEL | 14| 4 +Brand#33 |LARGE ANODIZED STEEL | 19| 4 +Brand#33 |LARGE ANODIZED STEEL | 36| 4 +Brand#33 |LARGE ANODIZED TIN | 14| 4 +Brand#33 |LARGE ANODIZED TIN | 19| 4 +Brand#33 |LARGE BRUSHED BRASS | 45| 4 +Brand#33 |LARGE BRUSHED COPPER | 3| 4 +Brand#33 |LARGE BRUSHED COPPER | 14| 4 +Brand#33 |LARGE BRUSHED COPPER | 23| 4 +Brand#33 |LARGE BRUSHED COPPER | 36| 4 +Brand#33 |LARGE BRUSHED COPPER | 45| 4 +Brand#33 |LARGE BRUSHED NICKEL | 3| 4 +Brand#33 |LARGE BRUSHED STEEL | 9| 4 +Brand#33 |LARGE BRUSHED STEEL | 14| 4 +Brand#33 |LARGE BRUSHED STEEL | 19| 4 +Brand#33 |LARGE BRUSHED TIN | 9| 4 +Brand#33 |LARGE BURNISHED COPPER | 3| 4 +Brand#33 |LARGE BURNISHED COPPER | 23| 4 +Brand#33 |LARGE BURNISHED COPPER | 36| 4 +Brand#33 |LARGE BURNISHED COPPER | 49| 4 +Brand#33 |LARGE BURNISHED NICKEL | 23| 4 +Brand#33 |LARGE BURNISHED NICKEL | 45| 4 +Brand#33 |LARGE BURNISHED STEEL | 19| 4 +Brand#33 |LARGE BURNISHED STEEL | 36| 4 +Brand#33 |LARGE BURNISHED TIN | 19| 4 +Brand#33 |LARGE BURNISHED TIN | 36| 4 +Brand#33 |LARGE PLATED BRASS | 9| 4 +Brand#33 |LARGE PLATED BRASS | 23| 4 +Brand#33 |LARGE PLATED BRASS | 36| 4 +Brand#33 |LARGE PLATED BRASS | 49| 4 +Brand#33 |LARGE PLATED COPPER | 9| 4 +Brand#33 |LARGE PLATED COPPER | 14| 4 +Brand#33 |LARGE PLATED COPPER | 19| 4 +Brand#33 |LARGE PLATED COPPER | 23| 4 +Brand#33 |LARGE PLATED COPPER | 45| 4 +Brand#33 |LARGE PLATED COPPER | 49| 4 +Brand#33 |LARGE PLATED NICKEL | 14| 4 +Brand#33 |LARGE PLATED NICKEL | 45| 4 +Brand#33 |LARGE PLATED STEEL | 9| 4 +Brand#33 |LARGE PLATED STEEL | 19| 4 +Brand#33 |LARGE PLATED STEEL | 23| 4 +Brand#33 |LARGE PLATED STEEL | 36| 4 +Brand#33 |LARGE PLATED STEEL | 45| 4 +Brand#33 |LARGE PLATED STEEL | 49| 4 +Brand#33 |LARGE PLATED TIN | 9| 4 +Brand#33 |LARGE PLATED TIN | 14| 4 +Brand#33 |LARGE PLATED TIN | 23| 4 +Brand#33 |LARGE PLATED TIN | 45| 4 +Brand#33 |LARGE PLATED TIN | 49| 4 +Brand#33 |LARGE POLISHED BRASS | 19| 4 +Brand#33 |LARGE POLISHED BRASS | 45| 4 +Brand#33 |LARGE POLISHED BRASS | 49| 4 +Brand#33 |LARGE POLISHED 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ANODIZED COPPER | 49| 4 +Brand#35 |SMALL ANODIZED NICKEL | 3| 4 +Brand#35 |SMALL ANODIZED NICKEL | 14| 4 +Brand#35 |SMALL ANODIZED NICKEL | 45| 4 +Brand#35 |SMALL ANODIZED STEEL | 3| 4 +Brand#35 |SMALL ANODIZED STEEL | 9| 4 +Brand#35 |SMALL ANODIZED STEEL | 23| 4 +Brand#35 |SMALL ANODIZED STEEL | 36| 4 +Brand#35 |SMALL ANODIZED TIN | 9| 4 +Brand#35 |SMALL ANODIZED TIN | 19| 4 +Brand#35 |SMALL ANODIZED TIN | 23| 4 +Brand#35 |SMALL ANODIZED TIN | 45| 4 +Brand#35 |SMALL BRUSHED BRASS | 3| 4 +Brand#35 |SMALL BRUSHED BRASS | 9| 4 +Brand#35 |SMALL BRUSHED BRASS | 19| 4 +Brand#35 |SMALL BRUSHED BRASS | 36| 4 +Brand#35 |SMALL BRUSHED COPPER | 14| 4 +Brand#35 |SMALL BRUSHED COPPER | 23| 4 +Brand#35 |SMALL BRUSHED COPPER | 36| 4 +Brand#35 |SMALL BRUSHED NICKEL | 36| 4 +Brand#35 |SMALL BRUSHED NICKEL | 45| 4 +Brand#35 |SMALL BRUSHED STEEL | 3| 4 +Brand#35 |SMALL BRUSHED STEEL | 14| 4 +Brand#35 |SMALL BRUSHED TIN | 3| 4 +Brand#35 |SMALL BRUSHED TIN | 36| 4 +Brand#35 |SMALL BURNISHED BRASS | 3| 4 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49| 4 +Brand#41 |PROMO ANODIZED NICKEL | 9| 4 +Brand#41 |PROMO ANODIZED NICKEL | 14| 4 +Brand#41 |PROMO ANODIZED NICKEL | 23| 4 +Brand#41 |PROMO ANODIZED NICKEL | 36| 4 +Brand#41 |PROMO ANODIZED STEEL | 3| 4 +Brand#41 |PROMO ANODIZED STEEL | 36| 4 +Brand#41 |PROMO ANODIZED STEEL | 45| 4 +Brand#41 |PROMO ANODIZED TIN | 3| 4 +Brand#41 |PROMO ANODIZED TIN | 14| 4 +Brand#41 |PROMO ANODIZED TIN | 19| 4 +Brand#41 |PROMO ANODIZED TIN | 23| 4 +Brand#41 |PROMO ANODIZED TIN | 45| 4 +Brand#41 |PROMO ANODIZED TIN | 49| 4 +Brand#41 |PROMO BRUSHED BRASS | 45| 4 +Brand#41 |PROMO BRUSHED BRASS | 49| 4 +Brand#41 |PROMO BRUSHED COPPER | 3| 4 +Brand#41 |PROMO BRUSHED COPPER | 9| 4 +Brand#41 |PROMO BRUSHED COPPER | 23| 4 +Brand#41 |PROMO BRUSHED NICKEL | 14| 4 +Brand#41 |PROMO BRUSHED NICKEL | 19| 4 +Brand#41 |PROMO BRUSHED NICKEL | 45| 4 +Brand#41 |PROMO BRUSHED STEEL | 14| 4 +Brand#41 |PROMO BRUSHED TIN | 3| 4 +Brand#41 |PROMO BRUSHED TIN | 19| 4 +Brand#41 |PROMO BRUSHED TIN | 23| 4 +Brand#41 |PROMO 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49| 4 +Brand#41 |SMALL BURNISHED STEEL | 14| 4 +Brand#41 |SMALL BURNISHED STEEL | 19| 4 +Brand#41 |SMALL BURNISHED STEEL | 36| 4 +Brand#41 |SMALL BURNISHED TIN | 9| 4 +Brand#41 |SMALL BURNISHED TIN | 19| 4 +Brand#41 |SMALL BURNISHED TIN | 36| 4 +Brand#41 |SMALL BURNISHED TIN | 45| 4 +Brand#41 |SMALL BURNISHED TIN | 49| 4 +Brand#41 |SMALL PLATED BRASS | 19| 4 +Brand#41 |SMALL PLATED BRASS | 45| 4 +Brand#41 |SMALL PLATED COPPER | 3| 4 +Brand#41 |SMALL PLATED COPPER | 36| 4 +Brand#41 |SMALL PLATED COPPER | 45| 4 +Brand#41 |SMALL PLATED COPPER | 49| 4 +Brand#41 |SMALL PLATED NICKEL | 14| 4 +Brand#41 |SMALL PLATED NICKEL | 45| 4 +Brand#41 |SMALL PLATED NICKEL | 49| 4 +Brand#41 |SMALL PLATED STEEL | 3| 4 +Brand#41 |SMALL PLATED STEEL | 19| 4 +Brand#41 |SMALL PLATED STEEL | 23| 4 +Brand#41 |SMALL PLATED TIN | 14| 4 +Brand#41 |SMALL PLATED TIN | 36| 4 +Brand#41 |SMALL PLATED TIN | 45| 4 +Brand#41 |SMALL POLISHED BRASS | 3| 4 +Brand#41 |SMALL POLISHED BRASS | 9| 4 +Brand#41 |SMALL POLISHED 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ANODIZED NICKEL | 36| 4 +Brand#42 |PROMO ANODIZED STEEL | 9| 4 +Brand#42 |PROMO ANODIZED STEEL | 14| 4 +Brand#42 |PROMO ANODIZED STEEL | 45| 4 +Brand#42 |PROMO ANODIZED TIN | 9| 4 +Brand#42 |PROMO ANODIZED TIN | 19| 4 +Brand#42 |PROMO ANODIZED TIN | 45| 4 +Brand#42 |PROMO BRUSHED BRASS | 3| 4 +Brand#42 |PROMO BRUSHED BRASS | 14| 4 +Brand#42 |PROMO BRUSHED BRASS | 23| 4 +Brand#42 |PROMO BRUSHED COPPER | 3| 4 +Brand#42 |PROMO BRUSHED COPPER | 19| 4 +Brand#42 |PROMO BRUSHED COPPER | 23| 4 +Brand#42 |PROMO BRUSHED COPPER | 36| 4 +Brand#42 |PROMO BRUSHED COPPER | 45| 4 +Brand#42 |PROMO BRUSHED COPPER | 49| 4 +Brand#42 |PROMO BRUSHED NICKEL | 9| 4 +Brand#42 |PROMO BRUSHED NICKEL | 14| 4 +Brand#42 |PROMO BRUSHED STEEL | 3| 4 +Brand#42 |PROMO BRUSHED STEEL | 14| 4 +Brand#42 |PROMO BRUSHED STEEL | 49| 4 +Brand#42 |PROMO BRUSHED TIN | 9| 4 +Brand#42 |PROMO BRUSHED TIN | 23| 4 +Brand#42 |PROMO BRUSHED TIN | 49| 4 +Brand#42 |PROMO BURNISHED BRASS | 9| 4 +Brand#42 |PROMO BURNISHED BRASS | 36| 4 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ANODIZED STEEL | 14| 4 +Brand#42 |SMALL ANODIZED STEEL | 45| 4 +Brand#42 |SMALL ANODIZED TIN | 9| 4 +Brand#42 |SMALL ANODIZED TIN | 14| 4 +Brand#42 |SMALL BRUSHED BRASS | 3| 4 +Brand#42 |SMALL BRUSHED BRASS | 9| 4 +Brand#42 |SMALL BRUSHED BRASS | 19| 4 +Brand#42 |SMALL BRUSHED BRASS | 23| 4 +Brand#42 |SMALL BRUSHED BRASS | 49| 4 +Brand#42 |SMALL BRUSHED COPPER | 23| 4 +Brand#42 |SMALL BRUSHED COPPER | 45| 4 +Brand#42 |SMALL BRUSHED NICKEL | 19| 4 +Brand#42 |SMALL BRUSHED NICKEL | 36| 4 +Brand#42 |SMALL BRUSHED NICKEL | 45| 4 +Brand#42 |SMALL BRUSHED TIN | 3| 4 +Brand#42 |SMALL BRUSHED TIN | 19| 4 +Brand#42 |SMALL BRUSHED TIN | 36| 4 +Brand#42 |SMALL BURNISHED BRASS | 14| 4 +Brand#42 |SMALL BURNISHED BRASS | 19| 4 +Brand#42 |SMALL BURNISHED BRASS | 45| 4 +Brand#42 |SMALL BURNISHED COPPER | 9| 4 +Brand#42 |SMALL BURNISHED COPPER | 14| 4 +Brand#42 |SMALL BURNISHED COPPER | 19| 4 +Brand#42 |SMALL BURNISHED COPPER | 23| 4 +Brand#42 |SMALL BURNISHED COPPER | 49| 4 +Brand#42 |SMALL BURNISHED NICKEL | 9| 4 +Brand#42 |SMALL BURNISHED NICKEL | 14| 4 +Brand#42 |SMALL BURNISHED STEEL | 9| 4 +Brand#42 |SMALL BURNISHED STEEL | 36| 4 +Brand#42 |SMALL BURNISHED STEEL | 45| 4 +Brand#42 |SMALL BURNISHED STEEL | 49| 4 +Brand#42 |SMALL BURNISHED TIN | 3| 4 +Brand#42 |SMALL BURNISHED TIN | 45| 4 +Brand#42 |SMALL PLATED BRASS | 3| 4 +Brand#42 |SMALL PLATED BRASS | 9| 4 +Brand#42 |SMALL PLATED BRASS | 23| 4 +Brand#42 |SMALL PLATED BRASS | 45| 4 +Brand#42 |SMALL PLATED BRASS | 49| 4 +Brand#42 |SMALL PLATED COPPER | 3| 4 +Brand#42 |SMALL PLATED COPPER | 45| 4 +Brand#42 |SMALL PLATED NICKEL | 9| 4 +Brand#42 |SMALL PLATED NICKEL | 14| 4 +Brand#42 |SMALL PLATED NICKEL | 36| 4 +Brand#42 |SMALL PLATED NICKEL | 45| 4 +Brand#42 |SMALL PLATED STEEL | 9| 4 +Brand#42 |SMALL PLATED STEEL | 14| 4 +Brand#42 |SMALL PLATED STEEL | 45| 4 +Brand#42 |SMALL PLATED TIN | 49| 4 +Brand#42 |SMALL POLISHED BRASS | 14| 4 +Brand#42 |SMALL POLISHED BRASS | 19| 4 +Brand#42 |SMALL POLISHED BRASS | 49| 4 +Brand#42 |SMALL POLISHED COPPER | 9| 4 +Brand#42 |SMALL POLISHED COPPER | 19| 4 +Brand#42 |SMALL POLISHED COPPER | 49| 4 +Brand#42 |SMALL POLISHED NICKEL | 3| 4 +Brand#42 |SMALL POLISHED NICKEL | 36| 4 +Brand#42 |SMALL POLISHED NICKEL | 49| 4 +Brand#42 |SMALL POLISHED STEEL | 3| 4 +Brand#42 |SMALL POLISHED STEEL | 19| 4 +Brand#42 |SMALL POLISHED TIN | 3| 4 +Brand#42 |SMALL POLISHED TIN | 19| 4 +Brand#42 |STANDARD ANODIZED BRASS | 3| 4 +Brand#42 |STANDARD ANODIZED BRASS | 14| 4 +Brand#42 |STANDARD ANODIZED BRASS | 19| 4 +Brand#42 |STANDARD ANODIZED BRASS | 49| 4 +Brand#42 |STANDARD ANODIZED COPPER | 3| 4 +Brand#42 |STANDARD ANODIZED COPPER | 9| 4 +Brand#42 |STANDARD ANODIZED COPPER | 23| 4 +Brand#42 |STANDARD ANODIZED COPPER | 49| 4 +Brand#42 |STANDARD ANODIZED NICKEL | 3| 4 +Brand#42 |STANDARD ANODIZED NICKEL | 23| 4 +Brand#42 |STANDARD ANODIZED NICKEL | 36| 4 +Brand#42 |STANDARD ANODIZED NICKEL | 45| 4 +Brand#42 |STANDARD ANODIZED NICKEL | 49| 4 +Brand#42 |STANDARD ANODIZED TIN | 14| 4 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BURNISHED NICKEL| 36| 4 +Brand#42 |STANDARD BURNISHED NICKEL| 45| 4 +Brand#42 |STANDARD BURNISHED STEEL | 3| 4 +Brand#42 |STANDARD BURNISHED STEEL | 14| 4 +Brand#42 |STANDARD BURNISHED STEEL | 23| 4 +Brand#42 |STANDARD BURNISHED STEEL | 45| 4 +Brand#42 |STANDARD BURNISHED TIN | 3| 4 +Brand#42 |STANDARD BURNISHED TIN | 14| 4 +Brand#42 |STANDARD BURNISHED TIN | 19| 4 +Brand#42 |STANDARD BURNISHED TIN | 36| 4 +Brand#42 |STANDARD PLATED BRASS | 3| 4 +Brand#42 |STANDARD PLATED BRASS | 9| 4 +Brand#42 |STANDARD PLATED BRASS | 19| 4 +Brand#42 |STANDARD PLATED BRASS | 23| 4 +Brand#42 |STANDARD PLATED BRASS | 36| 4 +Brand#42 |STANDARD PLATED BRASS | 49| 4 +Brand#42 |STANDARD PLATED COPPER | 36| 4 +Brand#42 |STANDARD PLATED NICKEL | 9| 4 +Brand#42 |STANDARD PLATED NICKEL | 36| 4 +Brand#42 |STANDARD PLATED NICKEL | 49| 4 +Brand#42 |STANDARD PLATED STEEL | 3| 4 +Brand#42 |STANDARD PLATED STEEL | 9| 4 +Brand#42 |STANDARD PLATED STEEL | 23| 4 +Brand#42 |STANDARD PLATED TIN | 19| 4 +Brand#42 |STANDARD 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45| 4 +Brand#43 |ECONOMY ANODIZED STEEL | 3| 4 +Brand#43 |ECONOMY ANODIZED STEEL | 9| 4 +Brand#43 |ECONOMY ANODIZED STEEL | 14| 4 +Brand#43 |ECONOMY ANODIZED STEEL | 19| 4 +Brand#43 |ECONOMY ANODIZED TIN | 19| 4 +Brand#43 |ECONOMY ANODIZED TIN | 23| 4 +Brand#43 |ECONOMY ANODIZED TIN | 36| 4 +Brand#43 |ECONOMY BRUSHED BRASS | 3| 4 +Brand#43 |ECONOMY BRUSHED BRASS | 23| 4 +Brand#43 |ECONOMY BRUSHED BRASS | 36| 4 +Brand#43 |ECONOMY BRUSHED BRASS | 49| 4 +Brand#43 |ECONOMY BRUSHED COPPER | 14| 4 +Brand#43 |ECONOMY BRUSHED COPPER | 19| 4 +Brand#43 |ECONOMY BRUSHED COPPER | 36| 4 +Brand#43 |ECONOMY BRUSHED NICKEL | 23| 4 +Brand#43 |ECONOMY BRUSHED NICKEL | 36| 4 +Brand#43 |ECONOMY BRUSHED NICKEL | 45| 4 +Brand#43 |ECONOMY BRUSHED STEEL | 19| 4 +Brand#43 |ECONOMY BRUSHED TIN | 3| 4 +Brand#43 |ECONOMY BRUSHED TIN | 14| 4 +Brand#43 |ECONOMY BRUSHED TIN | 19| 4 +Brand#43 |ECONOMY BRUSHED TIN | 23| 4 +Brand#43 |ECONOMY BURNISHED BRASS | 9| 4 +Brand#43 |ECONOMY BURNISHED BRASS | 14| 4 +Brand#43 |ECONOMY BURNISHED BRASS | 23| 4 +Brand#43 |ECONOMY BURNISHED BRASS | 36| 4 +Brand#43 |ECONOMY BURNISHED BRASS | 45| 4 +Brand#43 |ECONOMY BURNISHED COPPER | 3| 4 +Brand#43 |ECONOMY BURNISHED COPPER | 19| 4 +Brand#43 |ECONOMY BURNISHED COPPER | 23| 4 +Brand#43 |ECONOMY BURNISHED COPPER | 45| 4 +Brand#43 |ECONOMY BURNISHED NICKEL | 49| 4 +Brand#43 |ECONOMY BURNISHED STEEL | 14| 4 +Brand#43 |ECONOMY BURNISHED STEEL | 45| 4 +Brand#43 |ECONOMY BURNISHED TIN | 14| 4 +Brand#43 |ECONOMY BURNISHED TIN | 36| 4 +Brand#43 |ECONOMY PLATED BRASS | 3| 4 +Brand#43 |ECONOMY PLATED BRASS | 14| 4 +Brand#43 |ECONOMY PLATED BRASS | 19| 4 +Brand#43 |ECONOMY PLATED BRASS | 23| 4 +Brand#43 |ECONOMY PLATED BRASS | 36| 4 +Brand#43 |ECONOMY PLATED BRASS | 49| 4 +Brand#43 |ECONOMY PLATED COPPER | 14| 4 +Brand#43 |ECONOMY PLATED COPPER | 36| 4 +Brand#43 |ECONOMY PLATED NICKEL | 36| 4 +Brand#43 |ECONOMY PLATED NICKEL | 45| 4 +Brand#43 |ECONOMY PLATED STEEL | 9| 4 +Brand#43 |ECONOMY PLATED STEEL | 45| 4 +Brand#43 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ANODIZED COPPER | 36| 4 +Brand#43 |LARGE ANODIZED COPPER | 45| 4 +Brand#43 |LARGE ANODIZED NICKEL | 14| 4 +Brand#43 |LARGE ANODIZED STEEL | 3| 4 +Brand#43 |LARGE ANODIZED STEEL | 9| 4 +Brand#43 |LARGE ANODIZED STEEL | 14| 4 +Brand#43 |LARGE ANODIZED TIN | 3| 4 +Brand#43 |LARGE ANODIZED TIN | 49| 4 +Brand#43 |LARGE BRUSHED BRASS | 14| 4 +Brand#43 |LARGE BRUSHED BRASS | 19| 4 +Brand#43 |LARGE BRUSHED BRASS | 36| 4 +Brand#43 |LARGE BRUSHED COPPER | 3| 4 +Brand#43 |LARGE BRUSHED COPPER | 23| 4 +Brand#43 |LARGE BRUSHED COPPER | 45| 4 +Brand#43 |LARGE BRUSHED NICKEL | 3| 4 +Brand#43 |LARGE BRUSHED NICKEL | 45| 4 +Brand#43 |LARGE BRUSHED STEEL | 19| 4 +Brand#43 |LARGE BRUSHED STEEL | 49| 4 +Brand#43 |LARGE BRUSHED TIN | 3| 4 +Brand#43 |LARGE BRUSHED TIN | 14| 4 +Brand#43 |LARGE BRUSHED TIN | 45| 4 +Brand#43 |LARGE BRUSHED TIN | 49| 4 +Brand#43 |LARGE BURNISHED BRASS | 3| 4 +Brand#43 |LARGE BURNISHED BRASS | 19| 4 +Brand#43 |LARGE BURNISHED COPPER | 9| 4 +Brand#43 |LARGE BURNISHED COPPER | 19| 4 +Brand#43 |LARGE BURNISHED COPPER | 23| 4 +Brand#43 |LARGE BURNISHED COPPER | 49| 4 +Brand#43 |LARGE BURNISHED NICKEL | 9| 4 +Brand#43 |LARGE BURNISHED NICKEL | 19| 4 +Brand#43 |LARGE BURNISHED NICKEL | 45| 4 +Brand#43 |LARGE BURNISHED STEEL | 19| 4 +Brand#43 |LARGE BURNISHED STEEL | 23| 4 +Brand#43 |LARGE BURNISHED STEEL | 45| 4 +Brand#43 |LARGE BURNISHED STEEL | 49| 4 +Brand#43 |LARGE BURNISHED TIN | 9| 4 +Brand#43 |LARGE BURNISHED TIN | 49| 4 +Brand#43 |LARGE PLATED BRASS | 3| 4 +Brand#43 |LARGE PLATED BRASS | 36| 4 +Brand#43 |LARGE PLATED COPPER | 3| 4 +Brand#43 |LARGE PLATED COPPER | 14| 4 +Brand#43 |LARGE PLATED COPPER | 19| 4 +Brand#43 |LARGE PLATED COPPER | 23| 4 +Brand#43 |LARGE PLATED COPPER | 49| 4 +Brand#43 |LARGE PLATED NICKEL | 19| 4 +Brand#43 |LARGE PLATED NICKEL | 23| 4 +Brand#43 |LARGE PLATED NICKEL | 36| 4 +Brand#43 |LARGE PLATED STEEL | 9| 4 +Brand#43 |LARGE PLATED STEEL | 19| 4 +Brand#43 |LARGE PLATED STEEL | 45| 4 +Brand#43 |LARGE PLATED TIN | 3| 4 +Brand#43 |LARGE PLATED TIN | 49| 4 +Brand#43 |LARGE POLISHED BRASS | 19| 4 +Brand#43 |LARGE POLISHED BRASS | 23| 4 +Brand#43 |LARGE POLISHED BRASS | 45| 4 +Brand#43 |LARGE POLISHED BRASS | 49| 4 +Brand#43 |LARGE POLISHED COPPER | 9| 4 +Brand#43 |LARGE POLISHED COPPER | 45| 4 +Brand#43 |LARGE POLISHED NICKEL | 14| 4 +Brand#43 |LARGE POLISHED NICKEL | 19| 4 +Brand#43 |LARGE POLISHED NICKEL | 36| 4 +Brand#43 |LARGE POLISHED NICKEL | 45| 4 +Brand#43 |LARGE POLISHED NICKEL | 49| 4 +Brand#43 |LARGE POLISHED STEEL | 3| 4 +Brand#43 |LARGE POLISHED STEEL | 23| 4 +Brand#43 |LARGE POLISHED STEEL | 45| 4 +Brand#43 |LARGE POLISHED STEEL | 49| 4 +Brand#43 |LARGE POLISHED TIN | 3| 4 +Brand#43 |LARGE POLISHED TIN | 19| 4 +Brand#43 |LARGE POLISHED TIN | 23| 4 +Brand#43 |LARGE POLISHED TIN | 36| 4 +Brand#43 |MEDIUM ANODIZED BRASS | 9| 4 +Brand#43 |MEDIUM ANODIZED BRASS | 23| 4 +Brand#43 |MEDIUM ANODIZED BRASS | 45| 4 +Brand#43 |MEDIUM ANODIZED COPPER | 36| 4 +Brand#43 |MEDIUM ANODIZED NICKEL | 19| 4 +Brand#43 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NICKEL | 9| 4 +Brand#44 |LARGE ANODIZED NICKEL | 45| 4 +Brand#44 |LARGE ANODIZED STEEL | 3| 4 +Brand#44 |LARGE ANODIZED STEEL | 9| 4 +Brand#44 |LARGE ANODIZED STEEL | 14| 4 +Brand#44 |LARGE ANODIZED STEEL | 36| 4 +Brand#44 |LARGE ANODIZED STEEL | 45| 4 +Brand#44 |LARGE ANODIZED STEEL | 49| 4 +Brand#44 |LARGE ANODIZED TIN | 9| 4 +Brand#44 |LARGE ANODIZED TIN | 19| 4 +Brand#44 |LARGE ANODIZED TIN | 36| 4 +Brand#44 |LARGE ANODIZED TIN | 45| 4 +Brand#44 |LARGE ANODIZED TIN | 49| 4 +Brand#44 |LARGE BRUSHED BRASS | 3| 4 +Brand#44 |LARGE BRUSHED BRASS | 23| 4 +Brand#44 |LARGE BRUSHED BRASS | 36| 4 +Brand#44 |LARGE BRUSHED BRASS | 45| 4 +Brand#44 |LARGE BRUSHED BRASS | 49| 4 +Brand#44 |LARGE BRUSHED COPPER | 3| 4 +Brand#44 |LARGE BRUSHED COPPER | 19| 4 +Brand#44 |LARGE BRUSHED COPPER | 45| 4 +Brand#44 |LARGE BRUSHED COPPER | 49| 4 +Brand#44 |LARGE BRUSHED NICKEL | 36| 4 +Brand#44 |LARGE BRUSHED NICKEL | 49| 4 +Brand#44 |LARGE BRUSHED STEEL | 19| 4 +Brand#44 |LARGE BRUSHED STEEL | 45| 4 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14| 4 +Brand#44 |MEDIUM BURNISHED TIN | 36| 4 +Brand#44 |MEDIUM PLATED COPPER | 14| 4 +Brand#44 |MEDIUM PLATED COPPER | 23| 4 +Brand#44 |MEDIUM PLATED COPPER | 36| 4 +Brand#44 |MEDIUM PLATED NICKEL | 9| 4 +Brand#44 |MEDIUM PLATED NICKEL | 14| 4 +Brand#44 |MEDIUM PLATED NICKEL | 19| 4 +Brand#44 |MEDIUM PLATED NICKEL | 36| 4 +Brand#44 |MEDIUM PLATED STEEL | 3| 4 +Brand#44 |MEDIUM PLATED STEEL | 36| 4 +Brand#44 |MEDIUM PLATED TIN | 19| 4 +Brand#44 |MEDIUM PLATED TIN | 45| 4 +Brand#44 |PROMO ANODIZED BRASS | 23| 4 +Brand#44 |PROMO ANODIZED BRASS | 45| 4 +Brand#44 |PROMO ANODIZED COPPER | 3| 4 +Brand#44 |PROMO ANODIZED COPPER | 9| 4 +Brand#44 |PROMO ANODIZED COPPER | 14| 4 +Brand#44 |PROMO ANODIZED COPPER | 49| 4 +Brand#44 |PROMO ANODIZED NICKEL | 3| 4 +Brand#44 |PROMO ANODIZED NICKEL | 49| 4 +Brand#44 |PROMO ANODIZED STEEL | 14| 4 +Brand#44 |PROMO ANODIZED STEEL | 19| 4 +Brand#44 |PROMO ANODIZED STEEL | 45| 4 +Brand#44 |PROMO ANODIZED TIN | 9| 4 +Brand#44 |PROMO ANODIZED TIN | 14| 4 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4 +Brand#44 |SMALL ANODIZED TIN | 36| 4 +Brand#44 |SMALL ANODIZED TIN | 49| 4 +Brand#44 |SMALL BRUSHED BRASS | 3| 4 +Brand#44 |SMALL BRUSHED BRASS | 9| 4 +Brand#44 |SMALL BRUSHED BRASS | 36| 4 +Brand#44 |SMALL BRUSHED COPPER | 9| 4 +Brand#44 |SMALL BRUSHED COPPER | 14| 4 +Brand#44 |SMALL BRUSHED NICKEL | 14| 4 +Brand#44 |SMALL BRUSHED NICKEL | 36| 4 +Brand#44 |SMALL BRUSHED NICKEL | 49| 4 +Brand#44 |SMALL BRUSHED STEEL | 3| 4 +Brand#44 |SMALL BRUSHED STEEL | 9| 4 +Brand#44 |SMALL BRUSHED STEEL | 45| 4 +Brand#44 |SMALL BRUSHED STEEL | 49| 4 +Brand#44 |SMALL BRUSHED TIN | 9| 4 +Brand#44 |SMALL BRUSHED TIN | 23| 4 +Brand#44 |SMALL BURNISHED BRASS | 9| 4 +Brand#44 |SMALL BURNISHED BRASS | 14| 4 +Brand#44 |SMALL BURNISHED BRASS | 19| 4 +Brand#44 |SMALL BURNISHED BRASS | 23| 4 +Brand#44 |SMALL BURNISHED BRASS | 45| 4 +Brand#44 |SMALL BURNISHED COPPER | 9| 4 +Brand#44 |SMALL BURNISHED COPPER | 19| 4 +Brand#44 |SMALL BURNISHED COPPER | 36| 4 +Brand#44 |SMALL BURNISHED COPPER | 45| 4 +Brand#44 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4 +Brand#44 |SMALL POLISHED BRASS | 19| 4 +Brand#44 |SMALL POLISHED BRASS | 23| 4 +Brand#44 |SMALL POLISHED BRASS | 36| 4 +Brand#44 |SMALL POLISHED COPPER | 3| 4 +Brand#44 |SMALL POLISHED COPPER | 19| 4 +Brand#44 |SMALL POLISHED COPPER | 45| 4 +Brand#44 |SMALL POLISHED NICKEL | 36| 4 +Brand#44 |SMALL POLISHED STEEL | 23| 4 +Brand#44 |SMALL POLISHED STEEL | 36| 4 +Brand#44 |SMALL POLISHED STEEL | 45| 4 +Brand#44 |SMALL POLISHED STEEL | 49| 4 +Brand#44 |STANDARD ANODIZED BRASS | 23| 4 +Brand#44 |STANDARD ANODIZED BRASS | 36| 4 +Brand#44 |STANDARD ANODIZED COPPER | 14| 4 +Brand#44 |STANDARD ANODIZED COPPER | 23| 4 +Brand#44 |STANDARD ANODIZED COPPER | 36| 4 +Brand#44 |STANDARD ANODIZED NICKEL | 3| 4 +Brand#44 |STANDARD ANODIZED NICKEL | 14| 4 +Brand#44 |STANDARD ANODIZED NICKEL | 23| 4 +Brand#44 |STANDARD ANODIZED NICKEL | 49| 4 +Brand#44 |STANDARD ANODIZED STEEL | 49| 4 +Brand#44 |STANDARD ANODIZED TIN | 3| 4 +Brand#44 |STANDARD ANODIZED TIN | 14| 4 +Brand#44 |STANDARD ANODIZED TIN | 19| 4 +Brand#44 |STANDARD BRUSHED BRASS | 19| 4 +Brand#44 |STANDARD BRUSHED BRASS | 49| 4 +Brand#44 |STANDARD BRUSHED COPPER | 3| 4 +Brand#44 |STANDARD BRUSHED COPPER | 45| 4 +Brand#44 |STANDARD BRUSHED NICKEL | 3| 4 +Brand#44 |STANDARD BRUSHED NICKEL | 19| 4 +Brand#44 |STANDARD BRUSHED NICKEL | 36| 4 +Brand#44 |STANDARD BRUSHED NICKEL | 45| 4 +Brand#44 |STANDARD BRUSHED NICKEL | 49| 4 +Brand#44 |STANDARD BRUSHED STEEL | 9| 4 +Brand#44 |STANDARD BRUSHED STEEL | 14| 4 +Brand#44 |STANDARD BRUSHED STEEL | 36| 4 +Brand#44 |STANDARD BRUSHED TIN | 14| 4 +Brand#44 |STANDARD BRUSHED TIN | 36| 4 +Brand#44 |STANDARD BURNISHED BRASS | 3| 4 +Brand#44 |STANDARD BURNISHED BRASS | 14| 4 +Brand#44 |STANDARD BURNISHED BRASS | 23| 4 +Brand#44 |STANDARD BURNISHED BRASS | 36| 4 +Brand#44 |STANDARD BURNISHED BRASS | 45| 4 +Brand#44 |STANDARD BURNISHED COPPER| 9| 4 +Brand#44 |STANDARD BURNISHED COPPER| 14| 4 +Brand#44 |STANDARD BURNISHED COPPER| 23| 4 +Brand#44 |STANDARD BURNISHED NICKEL| 3| 4 +Brand#44 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23| 4 +Brand#51 |ECONOMY ANODIZED TIN | 3| 4 +Brand#51 |ECONOMY ANODIZED TIN | 45| 4 +Brand#51 |ECONOMY ANODIZED TIN | 49| 4 +Brand#51 |ECONOMY BRUSHED BRASS | 9| 4 +Brand#51 |ECONOMY BRUSHED BRASS | 14| 4 +Brand#51 |ECONOMY BRUSHED BRASS | 19| 4 +Brand#51 |ECONOMY BRUSHED BRASS | 23| 4 +Brand#51 |ECONOMY BRUSHED BRASS | 36| 4 +Brand#51 |ECONOMY BRUSHED BRASS | 45| 4 +Brand#51 |ECONOMY BRUSHED COPPER | 9| 4 +Brand#51 |ECONOMY BRUSHED COPPER | 19| 4 +Brand#51 |ECONOMY BRUSHED NICKEL | 3| 4 +Brand#51 |ECONOMY BRUSHED NICKEL | 23| 4 +Brand#51 |ECONOMY BRUSHED NICKEL | 45| 4 +Brand#51 |ECONOMY BRUSHED STEEL | 3| 4 +Brand#51 |ECONOMY BRUSHED STEEL | 19| 4 +Brand#51 |ECONOMY BRUSHED TIN | 3| 4 +Brand#51 |ECONOMY BRUSHED TIN | 9| 4 +Brand#51 |ECONOMY BRUSHED TIN | 14| 4 +Brand#51 |ECONOMY BRUSHED TIN | 49| 4 +Brand#51 |ECONOMY BURNISHED BRASS | 9| 4 +Brand#51 |ECONOMY BURNISHED BRASS | 19| 4 +Brand#51 |ECONOMY BURNISHED BRASS | 45| 4 +Brand#51 |ECONOMY BURNISHED BRASS | 49| 4 +Brand#51 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4 +Brand#51 |LARGE PLATED BRASS | 19| 4 +Brand#51 |LARGE PLATED COPPER | 3| 4 +Brand#51 |LARGE PLATED COPPER | 14| 4 +Brand#51 |LARGE PLATED COPPER | 19| 4 +Brand#51 |LARGE PLATED NICKEL | 14| 4 +Brand#51 |LARGE PLATED STEEL | 9| 4 +Brand#51 |LARGE PLATED STEEL | 14| 4 +Brand#51 |LARGE PLATED STEEL | 19| 4 +Brand#51 |LARGE PLATED STEEL | 23| 4 +Brand#51 |LARGE PLATED TIN | 45| 4 +Brand#51 |LARGE PLATED TIN | 49| 4 +Brand#51 |LARGE POLISHED BRASS | 14| 4 +Brand#51 |LARGE POLISHED BRASS | 19| 4 +Brand#51 |LARGE POLISHED BRASS | 49| 4 +Brand#51 |LARGE POLISHED COPPER | 9| 4 +Brand#51 |LARGE POLISHED COPPER | 19| 4 +Brand#51 |LARGE POLISHED COPPER | 49| 4 +Brand#51 |LARGE POLISHED NICKEL | 3| 4 +Brand#51 |LARGE POLISHED NICKEL | 23| 4 +Brand#51 |LARGE POLISHED NICKEL | 36| 4 +Brand#51 |LARGE POLISHED NICKEL | 45| 4 +Brand#51 |LARGE POLISHED STEEL | 19| 4 +Brand#51 |LARGE POLISHED STEEL | 23| 4 +Brand#51 |LARGE POLISHED STEEL | 36| 4 +Brand#51 |LARGE POLISHED TIN | 19| 4 +Brand#51 |MEDIUM 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4 +Brand#51 |PROMO ANODIZED TIN | 3| 4 +Brand#51 |PROMO ANODIZED TIN | 45| 4 +Brand#51 |PROMO BRUSHED BRASS | 3| 4 +Brand#51 |PROMO BRUSHED BRASS | 14| 4 +Brand#51 |PROMO BRUSHED BRASS | 36| 4 +Brand#51 |PROMO BRUSHED BRASS | 49| 4 +Brand#51 |PROMO BRUSHED COPPER | 3| 4 +Brand#51 |PROMO BRUSHED COPPER | 9| 4 +Brand#51 |PROMO BRUSHED COPPER | 14| 4 +Brand#51 |PROMO BRUSHED COPPER | 45| 4 +Brand#51 |PROMO BRUSHED NICKEL | 45| 4 +Brand#51 |PROMO BRUSHED STEEL | 3| 4 +Brand#51 |PROMO BRUSHED STEEL | 14| 4 +Brand#51 |PROMO BRUSHED STEEL | 23| 4 +Brand#51 |PROMO BRUSHED STEEL | 45| 4 +Brand#51 |PROMO BRUSHED TIN | 9| 4 +Brand#51 |PROMO BRUSHED TIN | 19| 4 +Brand#51 |PROMO BRUSHED TIN | 49| 4 +Brand#51 |PROMO BURNISHED BRASS | 36| 4 +Brand#51 |PROMO BURNISHED BRASS | 49| 4 +Brand#51 |PROMO BURNISHED COPPER | 14| 4 +Brand#51 |PROMO BURNISHED COPPER | 36| 4 +Brand#51 |PROMO BURNISHED COPPER | 45| 4 +Brand#51 |PROMO BURNISHED COPPER | 49| 4 +Brand#51 |PROMO BURNISHED NICKEL | 9| 4 +Brand#51 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23| 4 +Brand#53 |MEDIUM BRUSHED NICKEL | 36| 4 +Brand#53 |MEDIUM BRUSHED STEEL | 9| 4 +Brand#53 |MEDIUM BRUSHED STEEL | 19| 4 +Brand#53 |MEDIUM BRUSHED TIN | 14| 4 +Brand#53 |MEDIUM BRUSHED TIN | 49| 4 +Brand#53 |MEDIUM BURNISHED BRASS | 9| 4 +Brand#53 |MEDIUM BURNISHED BRASS | 19| 4 +Brand#53 |MEDIUM BURNISHED BRASS | 23| 4 +Brand#53 |MEDIUM BURNISHED BRASS | 36| 4 +Brand#53 |MEDIUM BURNISHED BRASS | 45| 4 +Brand#53 |MEDIUM BURNISHED COPPER | 23| 4 +Brand#53 |MEDIUM BURNISHED COPPER | 36| 4 +Brand#53 |MEDIUM BURNISHED STEEL | 3| 4 +Brand#53 |MEDIUM BURNISHED STEEL | 45| 4 +Brand#53 |MEDIUM BURNISHED TIN | 3| 4 +Brand#53 |MEDIUM BURNISHED TIN | 19| 4 +Brand#53 |MEDIUM BURNISHED TIN | 23| 4 +Brand#53 |MEDIUM BURNISHED TIN | 36| 4 +Brand#53 |MEDIUM BURNISHED TIN | 49| 4 +Brand#53 |MEDIUM PLATED BRASS | 3| 4 +Brand#53 |MEDIUM PLATED BRASS | 23| 4 +Brand#53 |MEDIUM PLATED COPPER | 36| 4 +Brand#53 |MEDIUM PLATED COPPER | 45| 4 +Brand#53 |MEDIUM PLATED COPPER | 49| 4 +Brand#53 |MEDIUM PLATED 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|PROMO ANODIZED TIN | 9| 4 +Brand#53 |PROMO ANODIZED TIN | 14| 4 +Brand#53 |PROMO ANODIZED TIN | 23| 4 +Brand#53 |PROMO BRUSHED BRASS | 3| 4 +Brand#53 |PROMO BRUSHED BRASS | 9| 4 +Brand#53 |PROMO BRUSHED BRASS | 14| 4 +Brand#53 |PROMO BRUSHED BRASS | 19| 4 +Brand#53 |PROMO BRUSHED BRASS | 23| 4 +Brand#53 |PROMO BRUSHED COPPER | 19| 4 +Brand#53 |PROMO BRUSHED COPPER | 45| 4 +Brand#53 |PROMO BRUSHED NICKEL | 36| 4 +Brand#53 |PROMO BRUSHED NICKEL | 45| 4 +Brand#53 |PROMO BRUSHED STEEL | 9| 4 +Brand#53 |PROMO BRUSHED STEEL | 36| 4 +Brand#53 |PROMO BRUSHED STEEL | 45| 4 +Brand#53 |PROMO BRUSHED TIN | 3| 4 +Brand#53 |PROMO BRUSHED TIN | 45| 4 +Brand#53 |PROMO BURNISHED BRASS | 3| 4 +Brand#53 |PROMO BURNISHED BRASS | 9| 4 +Brand#53 |PROMO BURNISHED BRASS | 45| 4 +Brand#53 |PROMO BURNISHED COPPER | 3| 4 +Brand#53 |PROMO BURNISHED COPPER | 19| 4 +Brand#53 |PROMO BURNISHED COPPER | 23| 4 +Brand#53 |PROMO BURNISHED NICKEL | 3| 4 +Brand#53 |PROMO BURNISHED NICKEL | 23| 4 +Brand#53 |PROMO BURNISHED 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POLISHED NICKEL | 36| 4 +Brand#53 |PROMO POLISHED STEEL | 14| 4 +Brand#53 |PROMO POLISHED STEEL | 19| 4 +Brand#53 |PROMO POLISHED STEEL | 23| 4 +Brand#53 |PROMO POLISHED TIN | 3| 4 +Brand#53 |PROMO POLISHED TIN | 9| 4 +Brand#53 |PROMO POLISHED TIN | 19| 4 +Brand#53 |PROMO POLISHED TIN | 23| 4 +Brand#53 |SMALL ANODIZED BRASS | 14| 4 +Brand#53 |SMALL ANODIZED BRASS | 36| 4 +Brand#53 |SMALL ANODIZED COPPER | 14| 4 +Brand#53 |SMALL ANODIZED COPPER | 45| 4 +Brand#53 |SMALL ANODIZED COPPER | 49| 4 +Brand#53 |SMALL ANODIZED NICKEL | 14| 4 +Brand#53 |SMALL ANODIZED STEEL | 14| 4 +Brand#53 |SMALL ANODIZED STEEL | 36| 4 +Brand#53 |SMALL ANODIZED TIN | 14| 4 +Brand#53 |SMALL ANODIZED TIN | 19| 4 +Brand#53 |SMALL ANODIZED TIN | 23| 4 +Brand#53 |SMALL BRUSHED BRASS | 3| 4 +Brand#53 |SMALL BRUSHED BRASS | 19| 4 +Brand#53 |SMALL BRUSHED BRASS | 23| 4 +Brand#53 |SMALL BRUSHED BRASS | 45| 4 +Brand#53 |SMALL BRUSHED BRASS | 49| 4 +Brand#53 |SMALL BRUSHED COPPER | 9| 4 +Brand#53 |SMALL BRUSHED COPPER | 19| 4 +Brand#53 |SMALL BRUSHED COPPER | 23| 4 +Brand#53 |SMALL BRUSHED COPPER | 45| 4 +Brand#53 |SMALL BRUSHED NICKEL | 9| 4 +Brand#53 |SMALL BRUSHED NICKEL | 14| 4 +Brand#53 |SMALL BRUSHED NICKEL | 19| 4 +Brand#53 |SMALL BRUSHED NICKEL | 23| 4 +Brand#53 |SMALL BRUSHED NICKEL | 36| 4 +Brand#53 |SMALL BRUSHED STEEL | 14| 4 +Brand#53 |SMALL BRUSHED STEEL | 19| 4 +Brand#53 |SMALL BRUSHED TIN | 9| 4 +Brand#53 |SMALL BRUSHED TIN | 36| 4 +Brand#53 |SMALL BURNISHED BRASS | 19| 4 +Brand#53 |SMALL BURNISHED NICKEL | 3| 4 +Brand#53 |SMALL BURNISHED NICKEL | 19| 4 +Brand#53 |SMALL BURNISHED STEEL | 3| 4 +Brand#53 |SMALL BURNISHED STEEL | 14| 4 +Brand#53 |SMALL BURNISHED STEEL | 23| 4 +Brand#53 |SMALL BURNISHED STEEL | 45| 4 +Brand#53 |SMALL BURNISHED TIN | 9| 4 +Brand#53 |SMALL BURNISHED TIN | 19| 4 +Brand#53 |SMALL BURNISHED TIN | 36| 4 +Brand#53 |SMALL BURNISHED TIN | 45| 4 +Brand#53 |SMALL BURNISHED TIN | 49| 4 +Brand#53 |SMALL PLATED BRASS | 14| 4 +Brand#53 |SMALL PLATED BRASS | 19| 4 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STEEL | 19| 4 +Brand#53 |STANDARD BRUSHED STEEL | 36| 4 +Brand#53 |STANDARD BRUSHED TIN | 3| 4 +Brand#53 |STANDARD BRUSHED TIN | 9| 4 +Brand#53 |STANDARD BRUSHED TIN | 23| 4 +Brand#53 |STANDARD BURNISHED BRASS | 3| 4 +Brand#53 |STANDARD BURNISHED BRASS | 14| 4 +Brand#53 |STANDARD BURNISHED BRASS | 23| 4 +Brand#53 |STANDARD BURNISHED BRASS | 45| 4 +Brand#53 |STANDARD BURNISHED COPPER| 9| 4 +Brand#53 |STANDARD BURNISHED COPPER| 14| 4 +Brand#53 |STANDARD BURNISHED COPPER| 49| 4 +Brand#53 |STANDARD BURNISHED NICKEL| 3| 4 +Brand#53 |STANDARD BURNISHED NICKEL| 9| 4 +Brand#53 |STANDARD BURNISHED NICKEL| 14| 4 +Brand#53 |STANDARD BURNISHED NICKEL| 19| 4 +Brand#53 |STANDARD BURNISHED STEEL | 9| 4 +Brand#53 |STANDARD BURNISHED STEEL | 14| 4 +Brand#53 |STANDARD BURNISHED STEEL | 45| 4 +Brand#53 |STANDARD BURNISHED TIN | 9| 4 +Brand#53 |STANDARD BURNISHED TIN | 23| 4 +Brand#53 |STANDARD BURNISHED TIN | 45| 4 +Brand#53 |STANDARD BURNISHED TIN | 49| 4 +Brand#53 |STANDARD PLATED BRASS | 14| 4 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COPPER | 36| 4 +Brand#54 |LARGE BRUSHED COPPER | 49| 4 +Brand#54 |LARGE BRUSHED NICKEL | 14| 4 +Brand#54 |LARGE BRUSHED NICKEL | 19| 4 +Brand#54 |LARGE BRUSHED NICKEL | 45| 4 +Brand#54 |LARGE BRUSHED NICKEL | 49| 4 +Brand#54 |LARGE BRUSHED STEEL | 3| 4 +Brand#54 |LARGE BRUSHED STEEL | 9| 4 +Brand#54 |LARGE BRUSHED STEEL | 19| 4 +Brand#54 |LARGE BRUSHED STEEL | 23| 4 +Brand#54 |LARGE BRUSHED STEEL | 45| 4 +Brand#54 |LARGE BRUSHED TIN | 14| 4 +Brand#54 |LARGE BRUSHED TIN | 19| 4 +Brand#54 |LARGE BRUSHED TIN | 45| 4 +Brand#54 |LARGE BURNISHED BRASS | 14| 4 +Brand#54 |LARGE BURNISHED BRASS | 19| 4 +Brand#54 |LARGE BURNISHED BRASS | 36| 4 +Brand#54 |LARGE BURNISHED NICKEL | 3| 4 +Brand#54 |LARGE BURNISHED NICKEL | 19| 4 +Brand#54 |LARGE BURNISHED NICKEL | 45| 4 +Brand#54 |LARGE BURNISHED STEEL | 9| 4 +Brand#54 |LARGE BURNISHED STEEL | 36| 4 +Brand#54 |LARGE BURNISHED STEEL | 45| 4 +Brand#54 |LARGE BURNISHED TIN | 9| 4 +Brand#54 |LARGE BURNISHED TIN | 23| 4 +Brand#54 |LARGE BURNISHED TIN | 36| 4 +Brand#54 |LARGE PLATED BRASS | 3| 4 +Brand#54 |LARGE PLATED BRASS | 14| 4 +Brand#54 |LARGE PLATED COPPER | 14| 4 +Brand#54 |LARGE PLATED COPPER | 36| 4 +Brand#54 |LARGE PLATED NICKEL | 9| 4 +Brand#54 |LARGE PLATED NICKEL | 14| 4 +Brand#54 |LARGE PLATED NICKEL | 19| 4 +Brand#54 |LARGE PLATED NICKEL | 45| 4 +Brand#54 |LARGE PLATED NICKEL | 49| 4 +Brand#54 |LARGE PLATED STEEL | 45| 4 +Brand#54 |LARGE PLATED TIN | 3| 4 +Brand#54 |LARGE PLATED TIN | 14| 4 +Brand#54 |LARGE PLATED TIN | 49| 4 +Brand#54 |LARGE POLISHED BRASS | 3| 4 +Brand#54 |LARGE POLISHED BRASS | 14| 4 +Brand#54 |LARGE POLISHED BRASS | 19| 4 +Brand#54 |LARGE POLISHED BRASS | 36| 4 +Brand#54 |LARGE POLISHED COPPER | 14| 4 +Brand#54 |LARGE POLISHED COPPER | 23| 4 +Brand#54 |LARGE POLISHED COPPER | 36| 4 +Brand#54 |LARGE POLISHED COPPER | 49| 4 +Brand#54 |LARGE POLISHED NICKEL | 45| 4 +Brand#54 |LARGE POLISHED NICKEL | 49| 4 +Brand#54 |LARGE POLISHED STEEL | 9| 4 +Brand#54 |LARGE POLISHED STEEL | 23| 4 +Brand#54 |LARGE POLISHED STEEL | 36| 4 +Brand#54 |LARGE POLISHED TIN | 3| 4 +Brand#54 |LARGE POLISHED TIN | 9| 4 +Brand#54 |LARGE POLISHED TIN | 23| 4 +Brand#54 |MEDIUM ANODIZED BRASS | 19| 4 +Brand#54 |MEDIUM ANODIZED BRASS | 23| 4 +Brand#54 |MEDIUM ANODIZED BRASS | 45| 4 +Brand#54 |MEDIUM ANODIZED COPPER | 3| 4 +Brand#54 |MEDIUM ANODIZED COPPER | 14| 4 +Brand#54 |MEDIUM ANODIZED COPPER | 36| 4 +Brand#54 |MEDIUM ANODIZED COPPER | 45| 4 +Brand#54 |MEDIUM ANODIZED NICKEL | 9| 4 +Brand#54 |MEDIUM ANODIZED STEEL | 14| 4 +Brand#54 |MEDIUM ANODIZED STEEL | 45| 4 +Brand#54 |MEDIUM ANODIZED TIN | 14| 4 +Brand#54 |MEDIUM ANODIZED TIN | 49| 4 +Brand#54 |MEDIUM BRUSHED BRASS | 36| 4 +Brand#54 |MEDIUM BRUSHED COPPER | 9| 4 +Brand#54 |MEDIUM BRUSHED COPPER | 45| 4 +Brand#54 |MEDIUM BRUSHED COPPER | 49| 4 +Brand#54 |MEDIUM BRUSHED NICKEL | 3| 4 +Brand#54 |MEDIUM BRUSHED NICKEL | 19| 4 +Brand#54 |MEDIUM BRUSHED NICKEL | 45| 4 +Brand#54 |MEDIUM BRUSHED NICKEL | 49| 4 +Brand#54 |MEDIUM BRUSHED STEEL | 3| 4 +Brand#54 |MEDIUM BRUSHED STEEL | 14| 4 +Brand#54 |MEDIUM BRUSHED STEEL | 19| 4 +Brand#54 |MEDIUM BRUSHED STEEL | 23| 4 +Brand#54 |MEDIUM BRUSHED TIN | 3| 4 +Brand#54 |MEDIUM BRUSHED TIN | 19| 4 +Brand#54 |MEDIUM BRUSHED TIN | 45| 4 +Brand#54 |MEDIUM BURNISHED BRASS | 3| 4 +Brand#54 |MEDIUM BURNISHED BRASS | 9| 4 +Brand#54 |MEDIUM BURNISHED BRASS | 14| 4 +Brand#54 |MEDIUM BURNISHED BRASS | 19| 4 +Brand#54 |MEDIUM BURNISHED BRASS | 45| 4 +Brand#54 |MEDIUM BURNISHED COPPER | 9| 4 +Brand#54 |MEDIUM BURNISHED COPPER | 49| 4 +Brand#54 |MEDIUM BURNISHED NICKEL | 3| 4 +Brand#54 |MEDIUM BURNISHED NICKEL | 14| 4 +Brand#54 |MEDIUM BURNISHED NICKEL | 23| 4 +Brand#54 |MEDIUM BURNISHED NICKEL | 36| 4 +Brand#54 |MEDIUM BURNISHED STEEL | 9| 4 +Brand#54 |MEDIUM BURNISHED STEEL | 23| 4 +Brand#54 |MEDIUM BURNISHED STEEL | 36| 4 +Brand#54 |MEDIUM BURNISHED TIN | 14| 4 +Brand#54 |MEDIUM BURNISHED TIN | 49| 4 +Brand#54 |MEDIUM PLATED BRASS | 9| 4 +Brand#54 |MEDIUM PLATED BRASS | 14| 4 +Brand#54 |MEDIUM PLATED BRASS 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ANODIZED TIN | 23| 4 +Brand#54 |PROMO ANODIZED TIN | 36| 4 +Brand#54 |PROMO BRUSHED BRASS | 3| 4 +Brand#54 |PROMO BRUSHED BRASS | 36| 4 +Brand#54 |PROMO BRUSHED BRASS | 45| 4 +Brand#54 |PROMO BRUSHED COPPER | 9| 4 +Brand#54 |PROMO BRUSHED COPPER | 19| 4 +Brand#54 |PROMO BRUSHED COPPER | 36| 4 +Brand#54 |PROMO BRUSHED NICKEL | 14| 4 +Brand#54 |PROMO BRUSHED NICKEL | 36| 4 +Brand#54 |PROMO BRUSHED NICKEL | 45| 4 +Brand#54 |PROMO BRUSHED NICKEL | 49| 4 +Brand#54 |PROMO BRUSHED STEEL | 9| 4 +Brand#54 |PROMO BRUSHED STEEL | 23| 4 +Brand#54 |PROMO BRUSHED TIN | 19| 4 +Brand#54 |PROMO BRUSHED TIN | 23| 4 +Brand#54 |PROMO BRUSHED TIN | 36| 4 +Brand#54 |PROMO BURNISHED BRASS | 3| 4 +Brand#54 |PROMO BURNISHED BRASS | 23| 4 +Brand#54 |PROMO BURNISHED BRASS | 45| 4 +Brand#54 |PROMO BURNISHED COPPER | 3| 4 +Brand#54 |PROMO BURNISHED COPPER | 19| 4 +Brand#54 |PROMO BURNISHED COPPER | 23| 4 +Brand#54 |PROMO BURNISHED COPPER | 36| 4 +Brand#54 |PROMO BURNISHED COPPER | 45| 4 +Brand#54 |PROMO BURNISHED 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POLISHED BRASS | 49| 4 +Brand#54 |PROMO POLISHED COPPER | 9| 4 +Brand#54 |PROMO POLISHED COPPER | 49| 4 +Brand#54 |PROMO POLISHED NICKEL | 3| 4 +Brand#54 |PROMO POLISHED NICKEL | 9| 4 +Brand#54 |PROMO POLISHED NICKEL | 45| 4 +Brand#54 |PROMO POLISHED NICKEL | 49| 4 +Brand#54 |PROMO POLISHED TIN | 9| 4 +Brand#54 |PROMO POLISHED TIN | 36| 4 +Brand#54 |SMALL ANODIZED BRASS | 3| 4 +Brand#54 |SMALL ANODIZED BRASS | 36| 4 +Brand#54 |SMALL ANODIZED BRASS | 49| 4 +Brand#54 |SMALL ANODIZED COPPER | 9| 4 +Brand#54 |SMALL ANODIZED COPPER | 36| 4 +Brand#54 |SMALL ANODIZED NICKEL | 3| 4 +Brand#54 |SMALL ANODIZED NICKEL | 9| 4 +Brand#54 |SMALL ANODIZED NICKEL | 36| 4 +Brand#54 |SMALL ANODIZED STEEL | 14| 4 +Brand#54 |SMALL ANODIZED STEEL | 19| 4 +Brand#54 |SMALL ANODIZED TIN | 3| 4 +Brand#54 |SMALL ANODIZED TIN | 9| 4 +Brand#54 |SMALL ANODIZED TIN | 19| 4 +Brand#54 |SMALL ANODIZED TIN | 23| 4 +Brand#54 |SMALL ANODIZED TIN | 45| 4 +Brand#54 |SMALL ANODIZED TIN | 49| 4 +Brand#54 |SMALL BRUSHED BRASS | 3| 4 +Brand#54 |SMALL BRUSHED BRASS | 14| 4 +Brand#54 |SMALL BRUSHED BRASS | 45| 4 +Brand#54 |SMALL BRUSHED COPPER | 3| 4 +Brand#54 |SMALL BRUSHED COPPER | 14| 4 +Brand#54 |SMALL BRUSHED COPPER | 36| 4 +Brand#54 |SMALL BRUSHED COPPER | 49| 4 +Brand#54 |SMALL BRUSHED NICKEL | 3| 4 +Brand#54 |SMALL BRUSHED NICKEL | 9| 4 +Brand#54 |SMALL BRUSHED NICKEL | 19| 4 +Brand#54 |SMALL BRUSHED NICKEL | 23| 4 +Brand#54 |SMALL BRUSHED NICKEL | 49| 4 +Brand#54 |SMALL BRUSHED STEEL | 3| 4 +Brand#54 |SMALL BRUSHED STEEL | 9| 4 +Brand#54 |SMALL BRUSHED STEEL | 45| 4 +Brand#54 |SMALL BRUSHED STEEL | 49| 4 +Brand#54 |SMALL BRUSHED TIN | 9| 4 +Brand#54 |SMALL BURNISHED BRASS | 19| 4 +Brand#54 |SMALL BURNISHED BRASS | 36| 4 +Brand#54 |SMALL BURNISHED BRASS | 49| 4 +Brand#54 |SMALL BURNISHED COPPER | 9| 4 +Brand#54 |SMALL BURNISHED COPPER | 36| 4 +Brand#54 |SMALL BURNISHED NICKEL | 9| 4 +Brand#54 |SMALL BURNISHED NICKEL | 19| 4 +Brand#54 |SMALL BURNISHED NICKEL | 23| 4 +Brand#54 |SMALL BURNISHED NICKEL | 45| 4 +Brand#54 |SMALL BURNISHED STEEL | 14| 4 +Brand#54 |SMALL BURNISHED STEEL | 23| 4 +Brand#54 |SMALL BURNISHED STEEL | 36| 4 +Brand#54 |SMALL BURNISHED STEEL | 49| 4 +Brand#54 |SMALL BURNISHED TIN | 3| 4 +Brand#54 |SMALL BURNISHED TIN | 14| 4 +Brand#54 |SMALL BURNISHED TIN | 36| 4 +Brand#54 |SMALL BURNISHED TIN | 45| 4 +Brand#54 |SMALL PLATED BRASS | 36| 4 +Brand#54 |SMALL PLATED BRASS | 45| 4 +Brand#54 |SMALL PLATED BRASS | 49| 4 +Brand#54 |SMALL PLATED COPPER | 9| 4 +Brand#54 |SMALL PLATED COPPER | 49| 4 +Brand#54 |SMALL PLATED NICKEL | 3| 4 +Brand#54 |SMALL PLATED NICKEL | 19| 4 +Brand#54 |SMALL PLATED NICKEL | 49| 4 +Brand#54 |SMALL PLATED STEEL | 3| 4 +Brand#54 |SMALL PLATED STEEL | 9| 4 +Brand#54 |SMALL PLATED STEEL | 19| 4 +Brand#54 |SMALL PLATED STEEL | 36| 4 +Brand#54 |SMALL PLATED STEEL | 45| 4 +Brand#54 |SMALL PLATED TIN | 9| 4 +Brand#54 |SMALL PLATED TIN | 49| 4 +Brand#54 |SMALL POLISHED BRASS | 14| 4 +Brand#54 |SMALL POLISHED BRASS | 23| 4 +Brand#54 |SMALL POLISHED BRASS | 49| 4 +Brand#54 |SMALL POLISHED COPPER | 9| 4 +Brand#54 |SMALL POLISHED COPPER | 23| 4 +Brand#54 |SMALL POLISHED NICKEL | 9| 4 +Brand#54 |SMALL POLISHED NICKEL | 19| 4 +Brand#54 |SMALL POLISHED NICKEL | 45| 4 +Brand#54 |SMALL POLISHED STEEL | 14| 4 +Brand#54 |SMALL POLISHED STEEL | 19| 4 +Brand#54 |SMALL POLISHED STEEL | 36| 4 +Brand#54 |SMALL POLISHED STEEL | 45| 4 +Brand#54 |SMALL POLISHED TIN | 3| 4 +Brand#54 |SMALL POLISHED TIN | 9| 4 +Brand#54 |SMALL POLISHED TIN | 14| 4 +Brand#54 |SMALL POLISHED TIN | 19| 4 +Brand#54 |STANDARD ANODIZED BRASS | 14| 4 +Brand#54 |STANDARD ANODIZED BRASS | 19| 4 +Brand#54 |STANDARD ANODIZED BRASS | 36| 4 +Brand#54 |STANDARD ANODIZED BRASS | 49| 4 +Brand#54 |STANDARD ANODIZED COPPER | 3| 4 +Brand#54 |STANDARD ANODIZED COPPER | 19| 4 +Brand#54 |STANDARD ANODIZED COPPER | 45| 4 +Brand#54 |STANDARD ANODIZED NICKEL | 14| 4 +Brand#54 |STANDARD ANODIZED NICKEL | 45| 4 +Brand#54 |STANDARD ANODIZED STEEL | 9| 4 +Brand#54 |STANDARD ANODIZED STEEL | 19| 4 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|ECONOMY BRUSHED TIN | 9| 4 +Brand#55 |ECONOMY BRUSHED TIN | 14| 4 +Brand#55 |ECONOMY BRUSHED TIN | 19| 4 +Brand#55 |ECONOMY BRUSHED TIN | 49| 4 +Brand#55 |ECONOMY BURNISHED BRASS | 36| 4 +Brand#55 |ECONOMY BURNISHED BRASS | 45| 4 +Brand#55 |ECONOMY BURNISHED BRASS | 49| 4 +Brand#55 |ECONOMY BURNISHED COPPER | 3| 4 +Brand#55 |ECONOMY BURNISHED COPPER | 14| 4 +Brand#55 |ECONOMY BURNISHED COPPER | 36| 4 +Brand#55 |ECONOMY BURNISHED NICKEL | 9| 4 +Brand#55 |ECONOMY BURNISHED NICKEL | 36| 4 +Brand#55 |ECONOMY BURNISHED NICKEL | 45| 4 +Brand#55 |ECONOMY BURNISHED STEEL | 3| 4 +Brand#55 |ECONOMY BURNISHED STEEL | 14| 4 +Brand#55 |ECONOMY BURNISHED STEEL | 36| 4 +Brand#55 |ECONOMY BURNISHED STEEL | 45| 4 +Brand#55 |ECONOMY BURNISHED TIN | 14| 4 +Brand#55 |ECONOMY PLATED BRASS | 3| 4 +Brand#55 |ECONOMY PLATED BRASS | 9| 4 +Brand#55 |ECONOMY PLATED BRASS | 14| 4 +Brand#55 |ECONOMY PLATED BRASS | 23| 4 +Brand#55 |ECONOMY PLATED BRASS | 36| 4 +Brand#55 |ECONOMY PLATED COPPER | 3| 4 +Brand#55 |ECONOMY PLATED COPPER | 9| 4 +Brand#55 |ECONOMY PLATED COPPER | 14| 4 +Brand#55 |ECONOMY PLATED NICKEL | 45| 4 +Brand#55 |ECONOMY PLATED STEEL | 3| 4 +Brand#55 |ECONOMY PLATED STEEL | 19| 4 +Brand#55 |ECONOMY PLATED STEEL | 36| 4 +Brand#55 |ECONOMY PLATED TIN | 3| 4 +Brand#55 |ECONOMY PLATED TIN | 14| 4 +Brand#55 |ECONOMY PLATED TIN | 36| 4 +Brand#55 |ECONOMY PLATED TIN | 45| 4 +Brand#55 |ECONOMY PLATED TIN | 49| 4 +Brand#55 |ECONOMY POLISHED BRASS | 3| 4 +Brand#55 |ECONOMY POLISHED BRASS | 9| 4 +Brand#55 |ECONOMY POLISHED BRASS | 14| 4 +Brand#55 |ECONOMY POLISHED BRASS | 36| 4 +Brand#55 |ECONOMY POLISHED BRASS | 45| 4 +Brand#55 |ECONOMY POLISHED COPPER | 14| 4 +Brand#55 |ECONOMY POLISHED NICKEL | 3| 4 +Brand#55 |ECONOMY POLISHED NICKEL | 23| 4 +Brand#55 |ECONOMY POLISHED NICKEL | 36| 4 +Brand#55 |ECONOMY POLISHED STEEL | 9| 4 +Brand#55 |ECONOMY POLISHED STEEL | 36| 4 +Brand#55 |ECONOMY POLISHED STEEL | 45| 4 +Brand#55 |ECONOMY POLISHED TIN | 3| 4 +Brand#55 |ECONOMY POLISHED TIN | 23| 4 +Brand#55 |ECONOMY POLISHED TIN | 45| 4 +Brand#55 |ECONOMY POLISHED TIN | 49| 4 +Brand#55 |LARGE ANODIZED BRASS | 3| 4 +Brand#55 |LARGE ANODIZED BRASS | 14| 4 +Brand#55 |LARGE ANODIZED BRASS | 19| 4 +Brand#55 |LARGE ANODIZED BRASS | 45| 4 +Brand#55 |LARGE ANODIZED BRASS | 49| 4 +Brand#55 |LARGE ANODIZED COPPER | 19| 4 +Brand#55 |LARGE ANODIZED COPPER | 49| 4 +Brand#55 |LARGE ANODIZED NICKEL | 3| 4 +Brand#55 |LARGE ANODIZED NICKEL | 49| 4 +Brand#55 |LARGE ANODIZED STEEL | 3| 4 +Brand#55 |LARGE ANODIZED STEEL | 19| 4 +Brand#55 |LARGE ANODIZED STEEL | 36| 4 +Brand#55 |LARGE ANODIZED STEEL | 45| 4 +Brand#55 |LARGE ANODIZED STEEL | 49| 4 +Brand#55 |LARGE ANODIZED TIN | 9| 4 +Brand#55 |LARGE ANODIZED TIN | 23| 4 +Brand#55 |LARGE BRUSHED BRASS | 19| 4 +Brand#55 |LARGE BRUSHED BRASS | 23| 4 +Brand#55 |LARGE BRUSHED BRASS | 36| 4 +Brand#55 |LARGE BRUSHED BRASS | 45| 4 +Brand#55 |LARGE BRUSHED COPPER | 36| 4 +Brand#55 |LARGE BRUSHED COPPER | 45| 4 +Brand#55 |LARGE BRUSHED NICKEL | 9| 4 +Brand#55 |LARGE BRUSHED NICKEL | 45| 4 +Brand#55 |LARGE BRUSHED NICKEL | 49| 4 +Brand#55 |LARGE BRUSHED STEEL | 3| 4 +Brand#55 |LARGE BRUSHED STEEL | 19| 4 +Brand#55 |LARGE BRUSHED STEEL | 36| 4 +Brand#55 |LARGE BRUSHED STEEL | 45| 4 +Brand#55 |LARGE BRUSHED TIN | 3| 4 +Brand#55 |LARGE BRUSHED TIN | 14| 4 +Brand#55 |LARGE BRUSHED TIN | 23| 4 +Brand#55 |LARGE BRUSHED TIN | 36| 4 +Brand#55 |LARGE BURNISHED BRASS | 9| 4 +Brand#55 |LARGE BURNISHED BRASS | 23| 4 +Brand#55 |LARGE BURNISHED BRASS | 36| 4 +Brand#55 |LARGE BURNISHED COPPER | 23| 4 +Brand#55 |LARGE BURNISHED COPPER | 45| 4 +Brand#55 |LARGE BURNISHED NICKEL | 3| 4 +Brand#55 |LARGE BURNISHED NICKEL | 9| 4 +Brand#55 |LARGE BURNISHED STEEL | 14| 4 +Brand#55 |LARGE BURNISHED TIN | 23| 4 +Brand#55 |LARGE BURNISHED TIN | 45| 4 +Brand#55 |LARGE PLATED BRASS | 19| 4 +Brand#55 |LARGE PLATED BRASS | 36| 4 +Brand#55 |LARGE PLATED BRASS | 49| 4 +Brand#55 |LARGE PLATED COPPER | 3| 4 +Brand#55 |LARGE PLATED COPPER | 19| 4 +Brand#55 |LARGE PLATED COPPER | 36| 4 +Brand#55 |LARGE PLATED NICKEL | 3| 4 +Brand#55 |LARGE PLATED NICKEL | 23| 4 +Brand#55 |LARGE PLATED NICKEL | 45| 4 +Brand#55 |LARGE PLATED NICKEL | 49| 4 +Brand#55 |LARGE PLATED STEEL | 9| 4 +Brand#55 |LARGE PLATED STEEL | 45| 4 +Brand#55 |LARGE PLATED TIN | 3| 4 +Brand#55 |LARGE PLATED TIN | 23| 4 +Brand#55 |LARGE PLATED TIN | 49| 4 +Brand#55 |LARGE POLISHED BRASS | 9| 4 +Brand#55 |LARGE POLISHED BRASS | 14| 4 +Brand#55 |LARGE POLISHED BRASS | 19| 4 +Brand#55 |LARGE POLISHED COPPER | 3| 4 +Brand#55 |LARGE POLISHED COPPER | 14| 4 +Brand#55 |LARGE POLISHED COPPER | 19| 4 +Brand#55 |LARGE POLISHED COPPER | 23| 4 +Brand#55 |LARGE POLISHED COPPER | 45| 4 +Brand#55 |LARGE POLISHED COPPER | 49| 4 +Brand#55 |LARGE POLISHED NICKEL | 23| 4 +Brand#55 |LARGE POLISHED NICKEL | 45| 4 +Brand#55 |LARGE POLISHED STEEL | 23| 4 +Brand#55 |LARGE POLISHED STEEL | 36| 4 +Brand#55 |LARGE POLISHED STEEL | 49| 4 +Brand#55 |LARGE POLISHED TIN | 3| 4 +Brand#55 |LARGE POLISHED TIN | 19| 4 +Brand#55 |LARGE POLISHED TIN | 23| 4 +Brand#55 |LARGE POLISHED TIN | 49| 4 +Brand#55 |MEDIUM ANODIZED BRASS | 3| 4 +Brand#55 |MEDIUM ANODIZED BRASS | 14| 4 +Brand#55 |MEDIUM ANODIZED BRASS | 19| 4 +Brand#55 |MEDIUM ANODIZED BRASS | 45| 4 +Brand#55 |MEDIUM ANODIZED COPPER | 9| 4 +Brand#55 |MEDIUM ANODIZED COPPER | 23| 4 +Brand#55 |MEDIUM ANODIZED COPPER | 45| 4 +Brand#55 |MEDIUM ANODIZED NICKEL | 3| 4 +Brand#55 |MEDIUM ANODIZED NICKEL | 9| 4 +Brand#55 |MEDIUM ANODIZED NICKEL | 23| 4 +Brand#55 |MEDIUM ANODIZED STEEL | 9| 4 +Brand#55 |MEDIUM ANODIZED STEEL | 23| 4 +Brand#55 |MEDIUM ANODIZED STEEL | 36| 4 +Brand#55 |MEDIUM ANODIZED STEEL | 45| 4 +Brand#55 |MEDIUM ANODIZED STEEL | 49| 4 +Brand#55 |MEDIUM ANODIZED TIN | 3| 4 +Brand#55 |MEDIUM ANODIZED TIN | 9| 4 +Brand#55 |MEDIUM ANODIZED TIN | 14| 4 +Brand#55 |MEDIUM ANODIZED TIN | 19| 4 +Brand#55 |MEDIUM ANODIZED TIN | 36| 4 +Brand#55 |MEDIUM BRUSHED BRASS | 3| 4 +Brand#55 |MEDIUM BRUSHED BRASS | 9| 4 +Brand#55 |MEDIUM BRUSHED BRASS | 36| 4 +Brand#55 |MEDIUM BRUSHED BRASS | 45| 4 +Brand#55 |MEDIUM BRUSHED COPPER | 9| 4 +Brand#55 |MEDIUM BRUSHED COPPER | 14| 4 +Brand#55 |MEDIUM BRUSHED COPPER | 19| 4 +Brand#55 |MEDIUM BRUSHED COPPER | 36| 4 +Brand#55 |MEDIUM BRUSHED NICKEL | 14| 4 +Brand#55 |MEDIUM BRUSHED NICKEL | 49| 4 +Brand#55 |MEDIUM BRUSHED STEEL | 3| 4 +Brand#55 |MEDIUM BRUSHED TIN | 23| 4 +Brand#55 |MEDIUM BRUSHED TIN | 49| 4 +Brand#55 |MEDIUM BURNISHED BRASS | 9| 4 +Brand#55 |MEDIUM BURNISHED BRASS | 23| 4 +Brand#55 |MEDIUM BURNISHED BRASS | 45| 4 +Brand#55 |MEDIUM BURNISHED BRASS | 49| 4 +Brand#55 |MEDIUM BURNISHED COPPER | 9| 4 +Brand#55 |MEDIUM BURNISHED COPPER | 14| 4 +Brand#55 |MEDIUM BURNISHED COPPER | 19| 4 +Brand#55 |MEDIUM BURNISHED NICKEL | 3| 4 +Brand#55 |MEDIUM BURNISHED NICKEL | 9| 4 +Brand#55 |MEDIUM BURNISHED NICKEL | 14| 4 +Brand#55 |MEDIUM BURNISHED NICKEL | 36| 4 +Brand#55 |MEDIUM BURNISHED STEEL | 9| 4 +Brand#55 |MEDIUM BURNISHED TIN | 3| 4 +Brand#55 |MEDIUM BURNISHED TIN | 23| 4 +Brand#55 |MEDIUM BURNISHED TIN | 49| 4 +Brand#55 |MEDIUM PLATED BRASS | 9| 4 +Brand#55 |MEDIUM PLATED BRASS | 14| 4 +Brand#55 |MEDIUM PLATED BRASS | 36| 4 +Brand#55 |MEDIUM PLATED BRASS | 49| 4 +Brand#55 |MEDIUM PLATED COPPER | 49| 4 +Brand#55 |MEDIUM PLATED NICKEL | 9| 4 +Brand#55 |MEDIUM PLATED NICKEL | 14| 4 +Brand#55 |MEDIUM PLATED NICKEL | 45| 4 +Brand#55 |MEDIUM PLATED STEEL | 9| 4 +Brand#55 |MEDIUM PLATED STEEL | 23| 4 +Brand#55 |MEDIUM PLATED STEEL | 49| 4 +Brand#55 |MEDIUM PLATED TIN | 45| 4 +Brand#55 |MEDIUM PLATED TIN | 49| 4 +Brand#55 |PROMO ANODIZED BRASS | 9| 4 +Brand#55 |PROMO ANODIZED COPPER | 19| 4 +Brand#55 |PROMO ANODIZED NICKEL | 23| 4 +Brand#55 |PROMO ANODIZED NICKEL | 45| 4 +Brand#55 |PROMO ANODIZED STEEL | 9| 4 +Brand#55 |PROMO ANODIZED STEEL | 14| 4 +Brand#55 |PROMO ANODIZED STEEL | 23| 4 +Brand#55 |PROMO ANODIZED TIN | 9| 4 +Brand#55 |PROMO ANODIZED TIN | 23| 4 +Brand#55 |PROMO BRUSHED BRASS | 14| 4 +Brand#55 |PROMO BRUSHED BRASS | 19| 4 +Brand#55 |PROMO BRUSHED BRASS | 49| 4 +Brand#55 |PROMO BRUSHED COPPER | 14| 4 +Brand#55 |PROMO BRUSHED COPPER | 23| 4 +Brand#55 |PROMO BRUSHED COPPER | 36| 4 +Brand#55 |PROMO BRUSHED COPPER | 45| 4 +Brand#55 |PROMO BRUSHED COPPER | 49| 4 +Brand#55 |PROMO BRUSHED NICKEL | 3| 4 +Brand#55 |PROMO BRUSHED NICKEL | 19| 4 +Brand#55 |PROMO BRUSHED STEEL | 14| 4 +Brand#55 |PROMO BRUSHED STEEL | 19| 4 +Brand#55 |PROMO BRUSHED TIN | 19| 4 +Brand#55 |PROMO BURNISHED BRASS | 9| 4 +Brand#55 |PROMO BURNISHED BRASS | 14| 4 +Brand#55 |PROMO BURNISHED BRASS | 19| 4 +Brand#55 |PROMO BURNISHED COPPER | 3| 4 +Brand#55 |PROMO BURNISHED COPPER | 49| 4 +Brand#55 |PROMO BURNISHED NICKEL | 14| 4 +Brand#55 |PROMO BURNISHED NICKEL | 19| 4 +Brand#55 |PROMO BURNISHED NICKEL | 23| 4 +Brand#55 |PROMO BURNISHED NICKEL | 45| 4 +Brand#55 |PROMO BURNISHED NICKEL | 49| 4 +Brand#55 |PROMO BURNISHED STEEL | 19| 4 +Brand#55 |PROMO BURNISHED STEEL | 36| 4 +Brand#55 |PROMO BURNISHED TIN | 3| 4 +Brand#55 |PROMO BURNISHED TIN | 14| 4 +Brand#55 |PROMO BURNISHED TIN | 23| 4 +Brand#55 |PROMO PLATED BRASS | 3| 4 +Brand#55 |PROMO PLATED BRASS | 49| 4 +Brand#55 |PROMO PLATED COPPER | 3| 4 +Brand#55 |PROMO PLATED COPPER | 45| 4 +Brand#55 |PROMO PLATED NICKEL | 3| 4 +Brand#55 |PROMO PLATED NICKEL | 23| 4 +Brand#55 |PROMO PLATED STEEL | 3| 4 +Brand#55 |PROMO PLATED STEEL | 23| 4 +Brand#55 |PROMO PLATED STEEL | 36| 4 +Brand#55 |PROMO PLATED STEEL | 45| 4 +Brand#55 |PROMO PLATED STEEL | 49| 4 +Brand#55 |PROMO PLATED TIN | 3| 4 +Brand#55 |PROMO PLATED TIN | 19| 4 +Brand#55 |PROMO PLATED TIN | 23| 4 +Brand#55 |PROMO POLISHED BRASS | 14| 4 +Brand#55 |PROMO POLISHED COPPER | 3| 4 +Brand#55 |PROMO POLISHED COPPER | 19| 4 +Brand#55 |PROMO POLISHED COPPER | 45| 4 +Brand#55 |PROMO POLISHED COPPER | 49| 4 +Brand#55 |PROMO POLISHED NICKEL | 3| 4 +Brand#55 |PROMO POLISHED NICKEL | 14| 4 +Brand#55 |PROMO POLISHED NICKEL | 19| 4 +Brand#55 |PROMO POLISHED NICKEL | 23| 4 +Brand#55 |PROMO POLISHED NICKEL | 36| 4 +Brand#55 |PROMO POLISHED STEEL | 19| 4 +Brand#55 |PROMO POLISHED STEEL | 45| 4 +Brand#55 |PROMO POLISHED STEEL | 49| 4 +Brand#55 |PROMO POLISHED TIN | 3| 4 +Brand#55 |PROMO POLISHED TIN | 9| 4 +Brand#55 |PROMO POLISHED TIN | 14| 4 +Brand#55 |PROMO POLISHED TIN | 19| 4 +Brand#55 |PROMO POLISHED TIN | 23| 4 +Brand#55 |PROMO POLISHED TIN | 36| 4 +Brand#55 |PROMO POLISHED TIN | 45| 4 +Brand#55 |PROMO POLISHED TIN | 49| 4 +Brand#55 |SMALL ANODIZED BRASS | 23| 4 +Brand#55 |SMALL ANODIZED BRASS | 36| 4 +Brand#55 |SMALL ANODIZED BRASS | 45| 4 +Brand#55 |SMALL ANODIZED COPPER | 9| 4 +Brand#55 |SMALL ANODIZED COPPER | 19| 4 +Brand#55 |SMALL ANODIZED COPPER | 23| 4 +Brand#55 |SMALL ANODIZED NICKEL | 9| 4 +Brand#55 |SMALL ANODIZED NICKEL | 14| 4 +Brand#55 |SMALL ANODIZED NICKEL | 23| 4 +Brand#55 |SMALL ANODIZED NICKEL | 36| 4 +Brand#55 |SMALL ANODIZED NICKEL | 45| 4 +Brand#55 |SMALL ANODIZED STEEL | 36| 4 +Brand#55 |SMALL ANODIZED TIN | 9| 4 +Brand#55 |SMALL ANODIZED TIN | 36| 4 +Brand#55 |SMALL ANODIZED TIN | 45| 4 +Brand#55 |SMALL ANODIZED TIN | 49| 4 +Brand#55 |SMALL BRUSHED BRASS | 9| 4 +Brand#55 |SMALL BRUSHED BRASS | 36| 4 +Brand#55 |SMALL BRUSHED COPPER | 3| 4 +Brand#55 |SMALL BRUSHED COPPER | 9| 4 +Brand#55 |SMALL BRUSHED COPPER | 19| 4 +Brand#55 |SMALL BRUSHED COPPER | 23| 4 +Brand#55 |SMALL BRUSHED NICKEL | 3| 4 +Brand#55 |SMALL BRUSHED NICKEL | 9| 4 +Brand#55 |SMALL BRUSHED NICKEL | 19| 4 +Brand#55 |SMALL BRUSHED NICKEL | 23| 4 +Brand#55 |SMALL BRUSHED NICKEL | 45| 4 +Brand#55 |SMALL BRUSHED NICKEL | 49| 4 +Brand#55 |SMALL BRUSHED STEEL | 3| 4 +Brand#55 |SMALL BRUSHED STEEL | 14| 4 +Brand#55 |SMALL BRUSHED STEEL | 19| 4 +Brand#55 |SMALL BRUSHED STEEL | 23| 4 +Brand#55 |SMALL BRUSHED STEEL | 45| 4 +Brand#55 |SMALL BRUSHED STEEL | 49| 4 +Brand#55 |SMALL BRUSHED TIN | 9| 4 +Brand#55 |SMALL BRUSHED TIN | 49| 4 +Brand#55 |SMALL BURNISHED BRASS | 14| 4 +Brand#55 |SMALL BURNISHED BRASS | 23| 4 +Brand#55 |SMALL BURNISHED COPPER | 3| 4 +Brand#55 |SMALL BURNISHED COPPER | 9| 4 +Brand#55 |SMALL BURNISHED COPPER | 36| 4 +Brand#55 |SMALL BURNISHED NICKEL | 9| 4 +Brand#55 |SMALL BURNISHED NICKEL | 19| 4 +Brand#55 |SMALL BURNISHED NICKEL | 36| 4 +Brand#55 |SMALL BURNISHED NICKEL | 45| 4 +Brand#55 |SMALL BURNISHED STEEL | 14| 4 +Brand#55 |SMALL BURNISHED TIN | 9| 4 +Brand#55 |SMALL BURNISHED TIN | 23| 4 +Brand#55 |SMALL PLATED COPPER | 3| 4 +Brand#55 |SMALL PLATED COPPER | 14| 4 +Brand#55 |SMALL PLATED COPPER | 36| 4 +Brand#55 |SMALL PLATED COPPER | 49| 4 +Brand#55 |SMALL PLATED NICKEL | 14| 4 +Brand#55 |SMALL PLATED NICKEL | 49| 4 +Brand#55 |SMALL PLATED STEEL | 3| 4 +Brand#55 |SMALL PLATED STEEL | 23| 4 +Brand#55 |SMALL PLATED STEEL | 36| 4 +Brand#55 |SMALL PLATED TIN | 36| 4 +Brand#55 |SMALL PLATED TIN | 45| 4 +Brand#55 |SMALL POLISHED BRASS | 9| 4 +Brand#55 |SMALL POLISHED BRASS | 19| 4 +Brand#55 |SMALL POLISHED BRASS | 49| 4 +Brand#55 |SMALL POLISHED COPPER | 19| 4 +Brand#55 |SMALL POLISHED COPPER | 23| 4 +Brand#55 |SMALL POLISHED COPPER | 36| 4 +Brand#55 |SMALL POLISHED COPPER | 45| 4 +Brand#55 |SMALL POLISHED COPPER | 49| 4 +Brand#55 |SMALL POLISHED NICKEL | 9| 4 +Brand#55 |SMALL POLISHED NICKEL | 14| 4 +Brand#55 |SMALL POLISHED NICKEL | 19| 4 +Brand#55 |SMALL POLISHED NICKEL | 23| 4 +Brand#55 |SMALL POLISHED NICKEL | 45| 4 +Brand#55 |SMALL POLISHED NICKEL | 49| 4 +Brand#55 |SMALL POLISHED STEEL | 19| 4 +Brand#55 |SMALL POLISHED STEEL | 45| 4 +Brand#55 |SMALL POLISHED TIN | 14| 4 +Brand#55 |SMALL POLISHED TIN | 23| 4 +Brand#55 |SMALL POLISHED TIN | 45| 4 +Brand#55 |STANDARD ANODIZED BRASS | 9| 4 +Brand#55 |STANDARD ANODIZED BRASS | 23| 4 +Brand#55 |STANDARD ANODIZED BRASS | 49| 4 +Brand#55 |STANDARD ANODIZED COPPER | 9| 4 +Brand#55 |STANDARD ANODIZED COPPER | 14| 4 +Brand#55 |STANDARD ANODIZED COPPER | 45| 4 +Brand#55 |STANDARD ANODIZED NICKEL | 3| 4 +Brand#55 |STANDARD ANODIZED NICKEL | 14| 4 +Brand#55 |STANDARD ANODIZED NICKEL | 45| 4 +Brand#55 |STANDARD ANODIZED NICKEL | 49| 4 +Brand#55 |STANDARD ANODIZED STEEL | 3| 4 +Brand#55 |STANDARD ANODIZED STEEL | 14| 4 +Brand#55 |STANDARD ANODIZED TIN | 14| 4 +Brand#55 |STANDARD ANODIZED TIN | 36| 4 +Brand#55 |STANDARD ANODIZED TIN | 45| 4 +Brand#55 |STANDARD BRUSHED BRASS | 9| 4 +Brand#55 |STANDARD BRUSHED BRASS | 19| 4 +Brand#55 |STANDARD BRUSHED COPPER | 14| 4 +Brand#55 |STANDARD BRUSHED COPPER | 19| 4 +Brand#55 |STANDARD BRUSHED NICKEL | 3| 4 +Brand#55 |STANDARD BRUSHED NICKEL | 36| 4 +Brand#55 |STANDARD BRUSHED STEEL | 9| 4 +Brand#55 |STANDARD BRUSHED STEEL | 14| 4 +Brand#55 |STANDARD BRUSHED STEEL | 19| 4 +Brand#55 |STANDARD BRUSHED STEEL | 49| 4 +Brand#55 |STANDARD BRUSHED TIN | 19| 4 +Brand#55 |STANDARD BRUSHED TIN | 49| 4 +Brand#55 |STANDARD BURNISHED BRASS | 9| 4 +Brand#55 |STANDARD BURNISHED BRASS | 19| 4 +Brand#55 |STANDARD BURNISHED BRASS | 23| 4 +Brand#55 |STANDARD BURNISHED BRASS | 36| 4 +Brand#55 |STANDARD BURNISHED COPPER| 3| 4 +Brand#55 |STANDARD BURNISHED NICKEL| 9| 4 +Brand#55 |STANDARD BURNISHED NICKEL| 49| 4 +Brand#55 |STANDARD BURNISHED STEEL | 19| 4 +Brand#55 |STANDARD BURNISHED STEEL | 23| 4 +Brand#55 |STANDARD BURNISHED STEEL | 36| 4 +Brand#55 |STANDARD BURNISHED STEEL | 45| 4 +Brand#55 |STANDARD BURNISHED TIN | 9| 4 +Brand#55 |STANDARD BURNISHED TIN | 19| 4 +Brand#55 |STANDARD BURNISHED TIN | 36| 4 +Brand#55 |STANDARD BURNISHED TIN | 49| 4 +Brand#55 |STANDARD PLATED BRASS | 9| 4 +Brand#55 |STANDARD PLATED BRASS | 45| 4 +Brand#55 |STANDARD PLATED BRASS | 49| 4 +Brand#55 |STANDARD PLATED COPPER | 9| 4 +Brand#55 |STANDARD PLATED COPPER | 45| 4 +Brand#55 |STANDARD PLATED NICKEL | 3| 4 +Brand#55 |STANDARD PLATED NICKEL | 19| 4 +Brand#55 |STANDARD PLATED NICKEL | 45| 4 +Brand#55 |STANDARD PLATED STEEL | 14| 4 +Brand#55 |STANDARD PLATED STEEL | 23| 4 +Brand#55 |STANDARD PLATED STEEL | 49| 4 +Brand#55 |STANDARD PLATED TIN | 9| 4 +Brand#55 |STANDARD PLATED TIN | 14| 4 +Brand#55 |STANDARD PLATED TIN | 36| 4 +Brand#55 |STANDARD POLISHED BRASS | 3| 4 +Brand#55 |STANDARD POLISHED BRASS | 9| 4 +Brand#55 |STANDARD POLISHED BRASS | 23| 4 +Brand#55 |STANDARD POLISHED COPPER | 3| 4 +Brand#55 |STANDARD POLISHED COPPER | 23| 4 +Brand#55 |STANDARD POLISHED COPPER | 45| 4 +Brand#55 |STANDARD POLISHED NICKEL | 3| 4 +Brand#55 |STANDARD POLISHED NICKEL | 23| 4 +Brand#55 |STANDARD POLISHED NICKEL | 36| 4 +Brand#55 |STANDARD POLISHED NICKEL | 45| 4 +Brand#55 |STANDARD POLISHED NICKEL | 49| 4 +Brand#55 |STANDARD POLISHED STEEL | 14| 4 +Brand#55 |STANDARD POLISHED STEEL | 23| 4 +Brand#55 |STANDARD POLISHED TIN | 9| 4 +Brand#55 |STANDARD POLISHED TIN | 19| 4 +Brand#55 |STANDARD POLISHED TIN | 36| 4 +Brand#11 |SMALL BRUSHED TIN | 19| 3 +Brand#15 |LARGE PLATED NICKEL | 45| 3 +Brand#15 |LARGE POLISHED NICKEL | 9| 3 +Brand#21 |PROMO BURNISHED STEEL | 45| 3 +Brand#22 |STANDARD PLATED STEEL | 23| 3 +Brand#25 |LARGE PLATED STEEL | 19| 3 +Brand#32 |STANDARD ANODIZED COPPER | 23| 3 +Brand#33 |SMALL ANODIZED BRASS | 9| 3 +Brand#35 |MEDIUM ANODIZED TIN | 19| 3 +Brand#51 |SMALL PLATED BRASS | 23| 3 +Brand#52 |MEDIUM BRUSHED BRASS | 45| 3 +Brand#53 |MEDIUM BRUSHED TIN | 45| 3 +Brand#54 |ECONOMY POLISHED BRASS | 9| 3 +Brand#55 |PROMO PLATED BRASS | 19| 3 +Brand#55 |STANDARD PLATED TIN | 49| 3 diff --git a/examples/tpch/answers_sf1/q17.tbl b/examples/tpch/answers_sf1/q17.tbl new file mode 100644 index 000000000..c7e570a0f --- /dev/null +++ b/examples/tpch/answers_sf1/q17.tbl @@ -0,0 +1,2 @@ +avg_yearly +348406.02 diff --git a/examples/tpch/answers_sf1/q18.tbl b/examples/tpch/answers_sf1/q18.tbl new file mode 100644 index 000000000..9e036c6b1 --- /dev/null +++ b/examples/tpch/answers_sf1/q18.tbl @@ -0,0 +1,58 @@ +c_name |c_custkey |o_orderkey |o_orderdat|o_totalprice |col6 +Customer#000128120 | 128120| 4722021|1994-04-07|544089.09|323.00 +Customer#000144617 | 144617| 3043270|1997-02-12|530604.44|317.00 +Customer#000013940 | 13940| 2232932|1997-04-13|522720.61|304.00 +Customer#000066790 | 66790| 2199712|1996-09-30|515531.82|327.00 +Customer#000046435 | 46435| 4745607|1997-07-03|508047.99|309.00 +Customer#000015272 | 15272| 3883783|1993-07-28|500241.33|302.00 +Customer#000146608 | 146608| 3342468|1994-06-12|499794.58|303.00 +Customer#000096103 | 96103| 5984582|1992-03-16|494398.79|312.00 +Customer#000024341 | 24341| 1474818|1992-11-15|491348.26|302.00 +Customer#000137446 | 137446| 5489475|1997-05-23|487763.25|311.00 +Customer#000107590 | 107590| 4267751|1994-11-04|485141.38|301.00 +Customer#000050008 | 50008| 2366755|1996-12-09|483891.26|302.00 +Customer#000015619 | 15619| 3767271|1996-08-07|480083.96|318.00 +Customer#000077260 | 77260| 1436544|1992-09-12|479499.43|307.00 +Customer#000109379 | 109379| 5746311|1996-10-10|478064.11|302.00 +Customer#000054602 | 54602| 5832321|1997-02-09|471220.08|307.00 +Customer#000105995 | 105995| 2096705|1994-07-03|469692.58|307.00 +Customer#000148885 | 148885| 2942469|1992-05-31|469630.44|313.00 +Customer#000114586 | 114586| 551136|1993-05-19|469605.59|308.00 +Customer#000105260 | 105260| 5296167|1996-09-06|469360.57|303.00 +Customer#000147197 | 147197| 1263015|1997-02-02|467149.67|320.00 +Customer#000064483 | 64483| 2745894|1996-07-04|466991.35|304.00 +Customer#000136573 | 136573| 2761378|1996-05-31|461282.73|301.00 +Customer#000016384 | 16384| 502886|1994-04-12|458378.92|312.00 +Customer#000117919 | 117919| 2869152|1996-06-20|456815.92|317.00 +Customer#000012251 | 12251| 735366|1993-11-24|455107.26|309.00 +Customer#000120098 | 120098| 1971680|1995-06-14|453451.23|308.00 +Customer#000066098 | 66098| 5007490|1992-08-07|453436.16|304.00 +Customer#000117076 | 117076| 4290656|1997-02-05|449545.85|301.00 +Customer#000129379 | 129379| 4720454|1997-06-07|448665.79|303.00 +Customer#000126865 | 126865| 4702759|1994-11-07|447606.65|320.00 +Customer#000088876 | 88876| 983201|1993-12-30|446717.46|304.00 +Customer#000036619 | 36619| 4806726|1995-01-17|446704.09|328.00 +Customer#000141823 | 141823| 2806245|1996-12-29|446269.12|310.00 +Customer#000053029 | 53029| 2662214|1993-08-13|446144.49|302.00 +Customer#000018188 | 18188| 3037414|1995-01-25|443807.22|308.00 +Customer#000066533 | 66533| 29158|1995-10-21|443576.50|305.00 +Customer#000037729 | 37729| 4134341|1995-06-29|441082.97|309.00 +Customer#000003566 | 3566| 2329187|1998-01-04|439803.36|304.00 +Customer#000045538 | 45538| 4527553|1994-05-22|436275.31|305.00 +Customer#000081581 | 81581| 4739650|1995-11-04|435405.90|305.00 +Customer#000119989 | 119989| 1544643|1997-09-20|434568.25|320.00 +Customer#000003680 | 3680| 3861123|1998-07-03|433525.97|301.00 +Customer#000113131 | 113131| 967334|1995-12-15|432957.75|301.00 +Customer#000141098 | 141098| 565574|1995-09-24|430986.69|301.00 +Customer#000093392 | 93392| 5200102|1997-01-22|425487.51|304.00 +Customer#000015631 | 15631| 1845057|1994-05-12|419879.59|302.00 +Customer#000112987 | 112987| 4439686|1996-09-17|418161.49|305.00 +Customer#000012599 | 12599| 4259524|1998-02-12|415200.61|304.00 +Customer#000105410 | 105410| 4478371|1996-03-05|412754.51|302.00 +Customer#000149842 | 149842| 5156581|1994-05-30|411329.35|302.00 +Customer#000010129 | 10129| 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requests! unusual dolphins play sl +8968.42|Supplier#000010000 |ROMANIA | 119999|Manufacturer#5 |aTGLEusCiL4F PDBdv665XBJhPyCOB0i |29-578-432-2146|ly regular foxes boost slyly. quickly special waters boost carefully ironi +8936.82|Supplier#000007043 |UNITED KINGDOM | 109512|Manufacturer#1 |FVajceZInZdbJE6Z9XsRUxrUEpiwHDrOXi,1Rz |33-784-177-8208|efully regular courts. furiousl +8929.42|Supplier#000008770 |FRANCE | 173735|Manufacturer#4 |R7cG26TtXrHAP9 HckhfRi |16-242-746-9248|cajole furiously unusual requests. quickly stealthy requests are. +8920.59|Supplier#000003967 |ROMANIA | 26460|Manufacturer#1 |eHoAXe62SY9 |29-194-731-3944|aters. express, pending instructions sleep. brave, r +8920.59|Supplier#000003967 |ROMANIA | 173966|Manufacturer#2 |eHoAXe62SY9 |29-194-731-3944|aters. express, pending instructions sleep. brave, r +8913.96|Supplier#000004603 |UNITED KINGDOM | 137063|Manufacturer#2 |OUzlvMUr7n,utLxmPNeYKSf3T24OXskxB5 |33-789-255-7342| haggle slyly above the furiously regular pinto beans. even +8877.82|Supplier#000007967 |FRANCE | 167966|Manufacturer#5 |A3pi1BARM4nx6R,qrwFoRPU |16-442-147-9345|ously foxes. express, ironic requests im +8862.24|Supplier#000003323 |ROMANIA | 73322|Manufacturer#3 |W9 lYcsC9FwBqk3ItL |29-736-951-3710|ly pending ideas sleep about the furiously unu +8841.59|Supplier#000005750 |ROMANIA | 100729|Manufacturer#5 |Erx3lAgu0g62iaHF9x50uMH4EgeN9hEG |29-344-502-5481|gainst the pinto beans. fluffily unusual dependencies affix slyly even deposits. +8781.71|Supplier#000003121 |ROMANIA | 13120|Manufacturer#5 |wNqTogx238ZYCamFb,50v,bj 4IbNFW9Bvw1xP |29-707-291-5144|s wake quickly ironic ideas +8754.24|Supplier#000009407 |UNITED KINGDOM | 179406|Manufacturer#4 |CHRCbkaWcf5B |33-903-970-9604|e ironic requests. carefully even foxes above the furious +8691.06|Supplier#000004429 |UNITED KINGDOM | 126892|Manufacturer#2 |k,BQms5UhoAF1B2Asi,fLib |33-964-337-5038|efully express deposits kindle after the deposits. final +8655.99|Supplier#000006330 |RUSSIA | 193810|Manufacturer#2 |UozlaENr0ytKe2w6CeIEWFWn iO3S8Rae7Ou |32-561-198-3705|symptotes use about the express dolphins. requests use after the express platelets. final, ex +8638.36|Supplier#000002920 |RUSSIA | 75398|Manufacturer#1 |Je2a8bszf3L |32-122-621-7549|ly quickly ironic requests. even requests whithout t +8638.36|Supplier#000002920 |RUSSIA | 170402|Manufacturer#3 |Je2a8bszf3L |32-122-621-7549|ly quickly ironic requests. even requests whithout t +8607.69|Supplier#000006003 |UNITED KINGDOM | 76002|Manufacturer#2 |EH9wADcEiuenM0NR08zDwMidw,52Y2RyILEiA |33-416-807-5206|ar, pending accounts. pending depende +8569.52|Supplier#000005936 |RUSSIA | 5935|Manufacturer#5 |jXaNZ6vwnEWJ2ksLZJpjtgt0bY2a3AU |32-644-251-7916|. regular foxes nag carefully atop the regular, silent deposits. quickly regular packages +8564.12|Supplier#000000033 |GERMANY | 110032|Manufacturer#1 |gfeKpYw3400L0SDywXA6Ya1Qmq1w6YB9f3R |17-138-897-9374|n sauternes along the regular asymptotes are regularly along the +8553.82|Supplier#000003979 |ROMANIA | 143978|Manufacturer#4 |BfmVhCAnCMY3jzpjUMy4CNWs9 HzpdQR7INJU |29-124-646-4897|ic requests wake against the blithely unusual accounts. fluffily r +8517.23|Supplier#000009529 |RUSSIA | 37025|Manufacturer#5 |e44R8o7JAIS9iMcr |32-565-297-8775|ove the even courts. furiously special platelets +8517.23|Supplier#000009529 |RUSSIA | 59528|Manufacturer#2 |e44R8o7JAIS9iMcr |32-565-297-8775|ove the even courts. furiously special platelets +8503.70|Supplier#000006830 |RUSSIA | 44325|Manufacturer#4 |BC4WFCYRUZyaIgchU 4S |32-147-878-5069|pades cajole. furious packages among the carefully express excuses boost furiously across th +8457.09|Supplier#000009456 |UNITED KINGDOM | 19455|Manufacturer#1 |7SBhZs8gP1cJjT0Qf433YBk |33-858-440-4349|cing requests along the furiously unusual deposits promise among the furiously unus +8441.40|Supplier#000003817 |FRANCE | 141302|Manufacturer#2 |hU3fz3xL78 |16-339-356-5115|ely even ideas. ideas wake slyly furiously unusual instructions. pinto beans sleep ag +8432.89|Supplier#000003990 |RUSSIA | 191470|Manufacturer#1 |wehBBp1RQbfxAYDASS75MsywmsKHRVdkrvNe6m |32-839-509-9301|ep furiously. packages should have to haggle slyly across the deposits. furiously regu +8431.40|Supplier#000002675 |ROMANIA | 5174|Manufacturer#1 |HJFStOu9R5NGPOegKhgbzBdyvrG2yh8w |29-474-643-1443|ithely express pinto beans. blithely even foxes haggle. furiously regular theodol +8407.04|Supplier#000005406 |RUSSIA | 162889|Manufacturer#4 |j7 gYF5RW8DC5UrjKC |32-626-152-4621|r the blithely regular packages. slyly ironic theodoli +8386.08|Supplier#000008518 |FRANCE | 36014|Manufacturer#3 |2jqzqqAVe9crMVGP,n9nTsQXulNLTUYoJjEDcqWV|16-618-780-7481|blithely bold pains are carefully platelets. finally regular pinto beans sleep carefully special +8376.52|Supplier#000005306 |UNITED KINGDOM | 190267|Manufacturer#5 |9t8Y8 QqSIsoADPt6NLdk,TP5zyRx41oBUlgoGc9|33-632-514-7931|ly final accounts sleep special, regular requests. furiously regular +8348.74|Supplier#000008851 |FRANCE | 66344|Manufacturer#4 |nWxi7GwEbjhw1 |16-796-240-2472| boldly final deposits. regular, even instructions detect slyly. fluffily unusual pinto bea +8338.58|Supplier#000007269 |FRANCE | 17268|Manufacturer#4 |ZwhJSwABUoiB04,3 |16-267-277-4365|iously final accounts. even pinto beans cajole slyly regular +8328.46|Supplier#000001744 |ROMANIA | 69237|Manufacturer#5 |oLo3fV64q2,FKHa3p,qHnS7Yzv,ps8 |29-330-728-5873|ep carefully-- even, careful packages are slyly along t +8307.93|Supplier#000003142 |GERMANY | 18139|Manufacturer#1 |dqblvV8dCNAorGlJ |17-595-447-6026|olites wake furiously regular decoys. final requests nod +8231.61|Supplier#000009558 |RUSSIA | 192000|Manufacturer#2 |mcdgen,yT1iJDHDS5fV |32-762-137-5858| foxes according to the furi +8152.61|Supplier#000002731 |ROMANIA | 15227|Manufacturer#4 | nluXJCuY1tu |29-805-463-2030| special requests. even, regular warhorses affix among the final gr +8109.09|Supplier#000009186 |FRANCE | 99185|Manufacturer#1 |wgfosrVPexl9pEXWywaqlBMDYYf |16-668-570-1402|tions haggle slyly about the sil +8102.62|Supplier#000003347 |UNITED KINGDOM | 18344|Manufacturer#5 |m CtXS2S16i |33-454-274-8532|egrate with the slyly bold instructions. special foxes haggle silently among the +8046.07|Supplier#000008780 |FRANCE | 191222|Manufacturer#3 |AczzuE0UK9osj ,Lx0Jmh |16-473-215-6395|onic platelets cajole after the regular instructions. permanently bold excuses +8042.09|Supplier#000003245 |RUSSIA | 135705|Manufacturer#4 |Dh8Ikg39onrbOL4DyTfGw8a9oKUX3d9Y |32-836-132-8872|osits. packages cajole slyly. furiously regular deposits cajole slyly. q +8042.09|Supplier#000003245 |RUSSIA | 150729|Manufacturer#1 |Dh8Ikg39onrbOL4DyTfGw8a9oKUX3d9Y |32-836-132-8872|osits. packages cajole slyly. furiously regular deposits cajole slyly. q +7992.40|Supplier#000006108 |FRANCE | 118574|Manufacturer#1 |8tBydnTDwUqfBfFV4l3 |16-974-998-8937| ironic ideas? fluffily even instructions wake. blithel +7980.65|Supplier#000001288 |FRANCE | 13784|Manufacturer#4 |zE,7HgVPrCn |16-646-464-8247|ully bold courts. escapades nag slyly. furiously fluffy theodo +7950.37|Supplier#000008101 |GERMANY | 33094|Manufacturer#5 |kkYvL6IuvojJgTNG IKkaXQDYgx8ILohj |17-627-663-8014|arefully unusual requests x-ray above the quickly final deposits. +7937.93|Supplier#000009012 |ROMANIA | 83995|Manufacturer#2 |iUiTziH,Ek3i4lwSgunXMgrcTzwdb |29-250-925-9690|to the blithely ironic deposits nag sly +7914.45|Supplier#000001013 |RUSSIA | 125988|Manufacturer#2 |riRcntps4KEDtYScjpMIWeYF6mNnR |32-194-698-3365| busily bold packages are dolphi +7912.91|Supplier#000004211 |GERMANY | 159180|Manufacturer#5 |2wQRVovHrm3,v03IKzfTd,1PYsFXQFFOG |17-266-947-7315|ay furiously regular platelets. cou +7912.91|Supplier#000004211 |GERMANY | 184210|Manufacturer#4 |2wQRVovHrm3,v03IKzfTd,1PYsFXQFFOG |17-266-947-7315|ay furiously regular platelets. cou +7894.56|Supplier#000007981 |GERMANY | 85472|Manufacturer#4 |NSJ96vMROAbeXP |17-963-404-3760|ic platelets affix after the 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+Supplier#000006394 | 13 +Supplier#000006624 | 13 +Supplier#000006629 | 13 +Supplier#000006682 | 13 +Supplier#000006737 | 13 +Supplier#000006825 | 13 +Supplier#000007021 | 13 +Supplier#000007417 | 13 +Supplier#000007497 | 13 +Supplier#000007602 | 13 +Supplier#000008134 | 13 +Supplier#000008234 | 13 +Supplier#000009435 | 13 +Supplier#000009436 | 13 +Supplier#000009564 | 13 +Supplier#000009896 | 13 +Supplier#000000379 | 12 +Supplier#000000673 | 12 +Supplier#000000762 | 12 +Supplier#000000811 | 12 +Supplier#000000821 | 12 +Supplier#000001337 | 12 +Supplier#000001916 | 12 +Supplier#000001925 | 12 +Supplier#000002039 | 12 +Supplier#000002357 | 12 +Supplier#000002483 | 12 diff --git a/examples/tpch/answers_sf1/q22.tbl b/examples/tpch/answers_sf1/q22.tbl new file mode 100644 index 000000000..de4bb472c --- /dev/null +++ b/examples/tpch/answers_sf1/q22.tbl @@ -0,0 +1,8 @@ +cntrycode |numcust |totacctbal +13 | 888|6737713.99 +17 | 861|6460573.72 +18 | 964|7236687.40 +23 | 892|6701457.95 +29 | 948|7158866.63 +30 | 909|6808436.13 +31 | 922|6806670.18 diff --git a/examples/tpch/answers_sf1/q3.tbl b/examples/tpch/answers_sf1/q3.tbl new file mode 100644 index 000000000..69771b8be --- /dev/null +++ b/examples/tpch/answers_sf1/q3.tbl @@ -0,0 +1,11 @@ +l_orderkey |revenue |o_orderdat|o_shippriority + 2456423|406181.01|1995-03-05| 0 + 3459808|405838.70|1995-03-04| 0 + 492164|390324.06|1995-02-19| 0 + 1188320|384537.94|1995-03-09| 0 + 2435712|378673.06|1995-02-26| 0 + 4878020|378376.80|1995-03-12| 0 + 5521732|375153.92|1995-03-13| 0 + 2628192|373133.31|1995-02-22| 0 + 993600|371407.46|1995-03-05| 0 + 2300070|367371.15|1995-03-13| 0 diff --git a/examples/tpch/answers_sf1/q4.tbl b/examples/tpch/answers_sf1/q4.tbl new file mode 100644 index 000000000..91bc1da24 --- /dev/null +++ b/examples/tpch/answers_sf1/q4.tbl @@ -0,0 +1,6 @@ +o_orderpriority|order_count +1-URGENT | 10594 +2-HIGH | 10476 +3-MEDIUM | 10410 +4-NOT SPECIFIED| 10556 +5-LOW | 10487 diff --git a/examples/tpch/answers_sf1/q5.tbl b/examples/tpch/answers_sf1/q5.tbl new file mode 100644 index 000000000..5dac463df --- /dev/null +++ b/examples/tpch/answers_sf1/q5.tbl @@ -0,0 +1,6 @@ +n_name |revenue +INDONESIA |55502041.17 +VIETNAM |55295087.00 +CHINA |53724494.26 +INDIA |52035512.00 +JAPAN |45410175.70 diff --git a/examples/tpch/answers_sf1/q6.tbl b/examples/tpch/answers_sf1/q6.tbl new file mode 100644 index 000000000..703a8084c --- /dev/null +++ b/examples/tpch/answers_sf1/q6.tbl @@ -0,0 +1,2 @@ +revenue +123141078.23 diff --git a/examples/tpch/answers_sf1/q7.tbl b/examples/tpch/answers_sf1/q7.tbl new file mode 100644 index 000000000..7164bfac2 --- /dev/null +++ b/examples/tpch/answers_sf1/q7.tbl @@ -0,0 +1,5 @@ +supp_nation |cust_nation |l_year |revenue +FRANCE |GERMANY | 1995|54639732.73 +FRANCE |GERMANY | 1996|54633083.31 +GERMANY |FRANCE | 1995|52531746.67 +GERMANY |FRANCE | 1996|52520549.02 diff --git a/examples/tpch/answers_sf1/q8.tbl b/examples/tpch/answers_sf1/q8.tbl new file mode 100644 index 000000000..618439d1a --- /dev/null +++ b/examples/tpch/answers_sf1/q8.tbl @@ -0,0 +1,3 @@ +o_year |mkt_share + 1995|0.03 + 1996|0.04 diff --git a/examples/tpch/answers_sf1/q9.tbl b/examples/tpch/answers_sf1/q9.tbl new file mode 100644 index 000000000..16ab17f13 --- /dev/null +++ b/examples/tpch/answers_sf1/q9.tbl @@ -0,0 +1,176 @@ +nation |o_year |sum_profit +ALGERIA | 1998|27136900.18 +ALGERIA | 1997|48611833.50 +ALGERIA | 1996|48285482.68 +ALGERIA | 1995|44402273.60 +ALGERIA | 1994|48694008.07 +ALGERIA | 1993|46044207.78 +ALGERIA | 1992|45636849.49 +ARGENTINA | 1998|28341663.78 +ARGENTINA | 1997|47143964.12 +ARGENTINA | 1996|45255278.60 +ARGENTINA | 1995|45631769.21 +ARGENTINA | 1994|48268856.35 +ARGENTINA | 1993|48605593.62 +ARGENTINA | 1992|46654240.75 +BRAZIL | 1998|26527736.40 +BRAZIL | 1997|45640660.77 +BRAZIL | 1996|45090647.16 +BRAZIL | 1995|44015888.51 +BRAZIL | 1994|44854218.89 +BRAZIL | 1993|45766603.74 +BRAZIL | 1992|45280216.80 +CANADA | 1998|26828985.39 +CANADA | 1997|44849954.32 +CANADA | 1996|46307936.11 +CANADA | 1995|47311993.04 +CANADA | 1994|46691491.96 +CANADA | 1993|46634791.11 +CANADA | 1992|45873849.69 +CHINA | 1998|27510180.17 +CHINA | 1997|46123865.41 +CHINA | 1996|49532807.06 +CHINA | 1995|46734651.48 +CHINA | 1994|46397896.61 +CHINA | 1993|49634673.95 +CHINA | 1992|46949457.64 +EGYPT | 1998|28401491.80 +EGYPT | 1997|47674857.68 +EGYPT | 1996|47745727.55 +EGYPT | 1995|45897160.68 +EGYPT | 1994|47194895.23 +EGYPT | 1993|49133627.65 +EGYPT | 1992|47000574.50 +ETHIOPIA | 1998|25135046.14 +ETHIOPIA | 1997|43010596.08 +ETHIOPIA | 1996|43636287.19 +ETHIOPIA | 1995|43575757.33 +ETHIOPIA | 1994|41597208.53 +ETHIOPIA | 1993|42622804.16 +ETHIOPIA | 1992|44385735.68 +FRANCE | 1998|26210392.28 +FRANCE | 1997|42392969.47 +FRANCE | 1996|43306317.97 +FRANCE | 1995|46377408.43 +FRANCE | 1994|43447352.99 +FRANCE | 1993|43729961.06 +FRANCE | 1992|44052308.43 +GERMANY | 1998|25991257.11 +GERMANY | 1997|43968355.81 +GERMANY | 1996|45882074.80 +GERMANY | 1995|43314338.31 +GERMANY | 1994|44616995.44 +GERMANY | 1993|45126645.91 +GERMANY | 1992|44361141.21 +INDIA | 1998|29626417.24 +INDIA | 1997|51386111.34 +INDIA | 1996|47571018.51 +INDIA | 1995|49344062.28 +INDIA | 1994|50106952.43 +INDIA | 1993|48112766.70 +INDIA | 1992|47914303.12 +INDONESIA | 1998|27734909.68 +INDONESIA | 1997|44593812.99 +INDONESIA | 1996|44746729.81 +INDONESIA | 1995|45593622.70 +INDONESIA | 1994|45988483.88 +INDONESIA | 1993|46147963.79 +INDONESIA | 1992|45185777.07 +IRAN | 1998|26661608.93 +IRAN | 1997|45019114.17 +IRAN | 1996|45891397.10 +IRAN | 1995|44414285.23 +IRAN | 1994|43696360.48 +IRAN | 1993|45362775.81 +IRAN | 1992|43052338.41 +IRAQ | 1998|31188498.19 +IRAQ | 1997|48585307.52 +IRAQ | 1996|50036593.84 +IRAQ | 1995|48774801.73 +IRAQ | 1994|48795847.23 +IRAQ | 1993|47435691.51 +IRAQ | 1992|47562355.66 +JAPAN | 1998|24694102.17 +JAPAN | 1997|42377052.35 +JAPAN | 1996|40267778.91 +JAPAN | 1995|40925317.47 +JAPAN | 1994|41159518.31 +JAPAN | 1993|39589074.28 +JAPAN | 1992|39113493.91 +JORDAN | 1998|23489867.79 +JORDAN | 1997|41615962.66 +JORDAN | 1996|41860855.47 +JORDAN | 1995|39931672.09 +JORDAN | 1994|40707555.46 +JORDAN | 1993|39060405.47 +JORDAN | 1992|41657604.27 +KENYA | 1998|25566337.43 +KENYA | 1997|43108847.90 +KENYA | 1996|43482953.54 +KENYA | 1995|42517988.98 +KENYA | 1994|43612479.45 +KENYA | 1993|42724038.76 +KENYA | 1992|43217106.21 +MOROCCO | 1998|24915496.88 +MOROCCO | 1997|42698382.85 +MOROCCO | 1996|42986113.50 +MOROCCO | 1995|42316089.16 +MOROCCO | 1994|43458604.60 +MOROCCO | 1993|42672288.07 +MOROCCO | 1992|42800781.64 +MOZAMBIQUE | 1998|28279876.03 +MOZAMBIQUE | 1997|51159216.23 +MOZAMBIQUE | 1996|48072525.06 +MOZAMBIQUE | 1995|48905200.60 +MOZAMBIQUE | 1994|46092076.28 +MOZAMBIQUE | 1993|48555926.27 +MOZAMBIQUE | 1992|47809075.12 +PERU | 1998|26713966.27 +PERU | 1997|48324008.60 +PERU | 1996|50310008.86 +PERU | 1995|49647080.96 +PERU | 1994|46420910.28 +PERU | 1993|51536906.25 +PERU | 1992|47711665.31 +ROMANIA | 1998|27271993.10 +ROMANIA | 1997|45063059.20 +ROMANIA | 1996|47492335.03 +ROMANIA | 1995|45710636.29 +ROMANIA | 1994|46088041.11 +ROMANIA | 1993|47515092.56 +ROMANIA | 1992|44111439.80 +RUSSIA | 1998|27935323.73 +RUSSIA | 1997|48222347.29 +RUSSIA | 1996|47553559.49 +RUSSIA | 1995|46755990.10 +RUSSIA | 1994|48000515.62 +RUSSIA | 1993|48569624.51 +RUSSIA | 1992|47672831.53 +SAUDI ARABIA | 1998|27113516.84 +SAUDI ARABIA | 1997|46690468.96 +SAUDI ARABIA | 1996|47775782.67 +SAUDI ARABIA | 1995|46657107.83 +SAUDI ARABIA | 1994|48181672.81 +SAUDI ARABIA | 1993|45692556.44 +SAUDI ARABIA | 1992|48924913.27 +UNITED KINGDOM | 1998|26366682.88 +UNITED KINGDOM | 1997|44518130.19 +UNITED KINGDOM | 1996|45539729.62 +UNITED KINGDOM | 1995|46845879.34 +UNITED KINGDOM | 1994|43081609.57 +UNITED KINGDOM | 1993|44770146.76 +UNITED KINGDOM | 1992|44123402.55 +UNITED STATES | 1998|27826593.68 +UNITED STATES | 1997|46638572.36 +UNITED STATES | 1996|46688280.55 +UNITED STATES | 1995|48951591.62 +UNITED STATES | 1994|45099092.06 +UNITED STATES | 1993|46181600.53 +UNITED STATES | 1992|46168214.09 +VIETNAM | 1998|27281931.00 +VIETNAM | 1997|48735914.18 +VIETNAM | 1996|47824595.90 +VIETNAM | 1995|48235135.80 +VIETNAM | 1994|47729256.33 +VIETNAM | 1993|45352676.87 +VIETNAM | 1992|47846355.65 diff --git a/examples/tpch/q01_pricing_summary_report.py b/examples/tpch/q01_pricing_summary_report.py index 3f97f00dc..105f1632d 100644 --- a/examples/tpch/q01_pricing_summary_report.py +++ b/examples/tpch/q01_pricing_summary_report.py @@ -27,6 +27,30 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + l_returnflag, + l_linestatus, + sum(l_quantity) as sum_qty, + sum(l_extendedprice) as sum_base_price, + sum(l_extendedprice * (1 - l_discount)) as sum_disc_price, + sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge, + avg(l_quantity) as avg_qty, + avg(l_extendedprice) as avg_price, + avg(l_discount) as avg_disc, + count(*) as count_order + from + lineitem + where + l_shipdate <= date '1998-12-01' - interval '90 days' + group by + l_returnflag, + l_linestatus + order by + l_returnflag, + l_linestatus; """ import pyarrow as pa @@ -58,31 +82,25 @@ # Aggregate the results +disc_price = col("l_extendedprice") * (lit(1) - col("l_discount")) + df = df.aggregate( - [col("l_returnflag"), col("l_linestatus")], + ["l_returnflag", "l_linestatus"], [ F.sum(col("l_quantity")).alias("sum_qty"), F.sum(col("l_extendedprice")).alias("sum_base_price"), - F.sum(col("l_extendedprice") * (lit(1) - col("l_discount"))).alias( - "sum_disc_price" - ), - F.sum( - col("l_extendedprice") - * (lit(1) - col("l_discount")) - * (lit(1) + col("l_tax")) - ).alias("sum_charge"), + F.sum(disc_price).alias("sum_disc_price"), + F.sum(disc_price * (lit(1) + col("l_tax"))).alias("sum_charge"), F.avg(col("l_quantity")).alias("avg_qty"), F.avg(col("l_extendedprice")).alias("avg_price"), F.avg(col("l_discount")).alias("avg_disc"), - F.count(col("l_returnflag")).alias( - "count_order" - ), # Counting any column should return same result + F.count_star().alias("count_order"), ], ) # Sort per the expected result -df = df.sort(col("l_returnflag").sort(), col("l_linestatus").sort()) +df = df.sort_by("l_returnflag", "l_linestatus") # Note: There appears to be a discrepancy between what is returned here and what is in the generated # answers file for the case of return flag N and line status O, but I did not investigate further. diff --git a/examples/tpch/q02_minimum_cost_supplier.py b/examples/tpch/q02_minimum_cost_supplier.py index 7390d0892..c5c6b9c0b 100644 --- a/examples/tpch/q02_minimum_cost_supplier.py +++ b/examples/tpch/q02_minimum_cost_supplier.py @@ -27,11 +27,58 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + s_acctbal, + s_name, + n_name, + p_partkey, + p_mfgr, + s_address, + s_phone, + s_comment + from + part, + supplier, + partsupp, + nation, + region + where + p_partkey = ps_partkey + and s_suppkey = ps_suppkey + and p_size = 15 + and p_type like '%BRASS' + and s_nationkey = n_nationkey + and n_regionkey = r_regionkey + and r_name = 'EUROPE' + and ps_supplycost = ( + select + min(ps_supplycost) + from + partsupp, + supplier, + nation, + region + where + p_partkey = ps_partkey + and s_suppkey = ps_suppkey + and s_nationkey = n_nationkey + and n_regionkey = r_regionkey + and r_name = 'EUROPE' + ) + order by + s_acctbal desc, + n_name, + s_name, + p_partkey limit 100; """ import datafusion from datafusion import SessionContext, col, lit from datafusion import functions as F +from datafusion.expr import Window from util import get_data_path # This is the part we're looking for. Values selected here differ from the spec in order to run @@ -66,35 +113,30 @@ "r_regionkey", "r_name" ) -# Filter down parts. Part names contain the type of interest, so we can use strpos to find where -# in the p_type column the word is. `strpos` will return 0 if not found, otherwise the position -# in the string where it is located. +# Filter down parts. The reference SQL uses ``p_type like '%BRASS'`` which +# is an ``ends_with`` check; use the dedicated string function rather than +# a manual substring match. df_part = df_part.filter( - F.strpos(col("p_type"), lit(TYPE_OF_INTEREST)) > lit(0) -).filter(col("p_size") == lit(SIZE_OF_INTEREST)) + F.ends_with(col("p_type"), lit(TYPE_OF_INTEREST)), + col("p_size") == SIZE_OF_INTEREST, +) # Filter regions down to the one of interest -df_region = df_region.filter(col("r_name") == lit(REGION_OF_INTEREST)) +df_region = df_region.filter(col("r_name") == REGION_OF_INTEREST) # Now that we have the region, find suppliers in that region. Suppliers are tied to their nation # and nations are tied to the region. -df_nation = df_nation.join( - df_region, left_on=["n_regionkey"], right_on=["r_regionkey"], how="inner" -) -df_supplier = df_supplier.join( - df_nation, left_on=["s_nationkey"], right_on=["n_nationkey"], how="inner" -) +df_nation = df_nation.join(df_region, left_on="n_regionkey", right_on="r_regionkey") +df_supplier = df_supplier.join(df_nation, left_on="s_nationkey", right_on="n_nationkey") # Now that we know who the potential suppliers are for the part, we can limit out part # supplies table down. We can further join down to the specific parts we've identified # as matching the request -df = df_partsupp.join( - df_supplier, left_on=["ps_suppkey"], right_on=["s_suppkey"], how="inner" -) +df = df_partsupp.join(df_supplier, left_on="ps_suppkey", right_on="s_suppkey") # Locate the minimum cost across all suppliers. There are multiple ways you could do this, # but one way is to create a window function across all suppliers, find the minimum, and @@ -106,17 +148,14 @@ window_frame = datafusion.WindowFrame("rows", None, None) df = df.with_column( "min_cost", - F.window( - "min", - [col("ps_supplycost")], - partition_by=[col("ps_partkey")], - window_frame=window_frame, + F.min(col("ps_supplycost")).over( + Window(partition_by=[col("ps_partkey")], window_frame=window_frame) ), ) -df = df.filter(col("min_cost") == col("ps_supplycost")) - -df = df.join(df_part, left_on=["ps_partkey"], right_on=["p_partkey"], how="inner") +df = df.filter(col("min_cost") == col("ps_supplycost")).join( + df_part, left_on="ps_partkey", right_on="p_partkey" +) # From the problem statement, these are the values we wish to output @@ -134,12 +173,10 @@ # Sort and display 100 entries df = df.sort( col("s_acctbal").sort(ascending=False), - col("n_name").sort(), - col("s_name").sort(), - col("p_partkey").sort(), -) - -df = df.limit(100) + "n_name", + "s_name", + "p_partkey", +).limit(100) # Show results diff --git a/examples/tpch/q03_shipping_priority.py b/examples/tpch/q03_shipping_priority.py index fc1231e0a..880c7435f 100644 --- a/examples/tpch/q03_shipping_priority.py +++ b/examples/tpch/q03_shipping_priority.py @@ -25,6 +25,31 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + l_orderkey, + sum(l_extendedprice * (1 - l_discount)) as revenue, + o_orderdate, + o_shippriority + from + customer, + orders, + lineitem + where + c_mktsegment = 'BUILDING' + and c_custkey = o_custkey + and l_orderkey = o_orderkey + and o_orderdate < date '1995-03-15' + and l_shipdate > date '1995-03-15' + group by + l_orderkey, + o_orderdate, + o_shippriority + order by + revenue desc, + o_orderdate limit 10; """ from datafusion import SessionContext, col, lit @@ -50,20 +75,20 @@ # Limit dataframes to the rows of interest -df_customer = df_customer.filter(col("c_mktsegment") == lit(SEGMENT_OF_INTEREST)) +df_customer = df_customer.filter(col("c_mktsegment") == SEGMENT_OF_INTEREST) df_orders = df_orders.filter(col("o_orderdate") < lit(DATE_OF_INTEREST)) df_lineitem = df_lineitem.filter(col("l_shipdate") > lit(DATE_OF_INTEREST)) # Join all 3 dataframes -df = df_customer.join( - df_orders, left_on=["c_custkey"], right_on=["o_custkey"], how="inner" -).join(df_lineitem, left_on=["o_orderkey"], right_on=["l_orderkey"], how="inner") +df = df_customer.join(df_orders, left_on="c_custkey", right_on="o_custkey").join( + df_lineitem, left_on="o_orderkey", right_on="l_orderkey" +) # Compute the revenue df = df.aggregate( - [col("l_orderkey")], + ["l_orderkey"], [ F.first_value(col("o_orderdate")).alias("o_orderdate"), F.first_value(col("o_shippriority")).alias("o_shippriority"), @@ -71,17 +96,13 @@ ], ) -# Sort by priority - -df = df.sort(col("revenue").sort(ascending=False), col("o_orderdate").sort()) - -# Only return 10 results +# Sort by priority, take 10, and project in the order expected by the spec. -df = df.limit(10) - -# Change the order that the columns are reported in just to match the spec - -df = df.select("l_orderkey", "revenue", "o_orderdate", "o_shippriority") +df = ( + df.sort(col("revenue").sort(ascending=False), "o_orderdate") + .limit(10) + .select("l_orderkey", "revenue", "o_orderdate", "o_shippriority") +) # Show result diff --git a/examples/tpch/q04_order_priority_checking.py b/examples/tpch/q04_order_priority_checking.py index 426338aea..6f11c1383 100644 --- a/examples/tpch/q04_order_priority_checking.py +++ b/examples/tpch/q04_order_priority_checking.py @@ -24,18 +24,40 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + o_orderpriority, + count(*) as order_count + from + orders + where + o_orderdate >= date '1993-07-01' + and o_orderdate < date '1993-07-01' + interval '3' month + and exists ( + select + * + from + lineitem + where + l_orderkey = o_orderkey + and l_commitdate < l_receiptdate + ) + group by + o_orderpriority + order by + o_orderpriority; """ -from datetime import datetime +from datetime import date -import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path -# Ideally we could put 3 months into the interval. See note below. -INTERVAL_DAYS = 92 -DATE_OF_INTEREST = "1993-07-01" +QUARTER_START = date(1993, 7, 1) +QUARTER_END = date(1993, 10, 1) # Load the dataframes we need @@ -48,36 +70,23 @@ "l_orderkey", "l_commitdate", "l_receiptdate" ) -# Create a date object from the string -date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date() - -interval = pa.scalar((0, INTERVAL_DAYS, 0), type=pa.month_day_nano_interval()) - -# Limit results to cases where commitment date before receipt date -# Aggregate the results so we only get one row to join with the order table. -# Alternately, and likely more idiomatic is instead of `.aggregate` you could -# do `.select("l_orderkey").distinct()`. The goal here is to show -# multiple examples of how to use Data Fusion. -df_lineitem = df_lineitem.filter(col("l_commitdate") < col("l_receiptdate")).aggregate( - [col("l_orderkey")], [] +# Keep only orders in the quarter of interest, then restrict to those that +# have at least one late lineitem via a semi join (the DataFrame form of +# ``EXISTS`` from the reference SQL). +df_orders = df_orders.filter( + col("o_orderdate") >= lit(QUARTER_START), + col("o_orderdate") < lit(QUARTER_END), ) -# Limit orders to date range of interest -df_orders = df_orders.filter(col("o_orderdate") >= lit(date)).filter( - col("o_orderdate") < lit(date) + lit(interval) -) +late_lineitems = df_lineitem.filter(col("l_commitdate") < col("l_receiptdate")) -# Perform the join to find only orders for which there are lineitems outside of expected range df = df_orders.join( - df_lineitem, left_on=["o_orderkey"], right_on=["l_orderkey"], how="inner" + late_lineitems, left_on="o_orderkey", right_on="l_orderkey", how="semi" ) -# Based on priority, find the number of entries -df = df.aggregate( - [col("o_orderpriority")], [F.count(col("o_orderpriority")).alias("order_count")] +# Count the number of orders in each priority group and sort. +df = df.aggregate(["o_orderpriority"], [F.count_star().alias("order_count")]).sort_by( + "o_orderpriority" ) -# Sort the results -df = df.sort(col("o_orderpriority").sort()) - df.show() diff --git a/examples/tpch/q05_local_supplier_volume.py b/examples/tpch/q05_local_supplier_volume.py index fa2b01dea..bfdba5d4c 100644 --- a/examples/tpch/q05_local_supplier_volume.py +++ b/examples/tpch/q05_local_supplier_volume.py @@ -27,23 +27,45 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + n_name, + sum(l_extendedprice * (1 - l_discount)) as revenue + from + customer, + orders, + lineitem, + supplier, + nation, + region + where + c_custkey = o_custkey + and l_orderkey = o_orderkey + and l_suppkey = s_suppkey + and c_nationkey = s_nationkey + and s_nationkey = n_nationkey + and n_regionkey = r_regionkey + and r_name = 'ASIA' + and o_orderdate >= date '1994-01-01' + and o_orderdate < date '1994-01-01' + interval '1' year + group by + n_name + order by + revenue desc; """ -from datetime import datetime +from datetime import date -import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path -DATE_OF_INTEREST = "1994-01-01" -INTERVAL_DAYS = 365 +YEAR_START = date(1994, 1, 1) +YEAR_END = date(1995, 1, 1) REGION_OF_INTEREST = "ASIA" -date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date() - -interval = pa.scalar((0, INTERVAL_DAYS, 0), type=pa.month_day_nano_interval()) - # Load the dataframes we need ctx = SessionContext() @@ -68,38 +90,32 @@ ) # Restrict dataframes to cases of interest -df_orders = df_orders.filter(col("o_orderdate") >= lit(date)).filter( - col("o_orderdate") < lit(date) + lit(interval) +df_orders = df_orders.filter( + col("o_orderdate") >= lit(YEAR_START), + col("o_orderdate") < lit(YEAR_END), ) -df_region = df_region.filter(col("r_name") == lit(REGION_OF_INTEREST)) +df_region = df_region.filter(col("r_name") == REGION_OF_INTEREST) # Join all the dataframes df = ( - df_customer.join( - df_orders, left_on=["c_custkey"], right_on=["o_custkey"], how="inner" - ) - .join(df_lineitem, left_on=["o_orderkey"], right_on=["l_orderkey"], how="inner") + df_customer.join(df_orders, left_on="c_custkey", right_on="o_custkey") + .join(df_lineitem, left_on="o_orderkey", right_on="l_orderkey") .join( df_supplier, left_on=["l_suppkey", "c_nationkey"], right_on=["s_suppkey", "s_nationkey"], - how="inner", ) - .join(df_nation, left_on=["s_nationkey"], right_on=["n_nationkey"], how="inner") - .join(df_region, left_on=["n_regionkey"], right_on=["r_regionkey"], how="inner") + .join(df_nation, left_on="s_nationkey", right_on="n_nationkey") + .join(df_region, left_on="n_regionkey", right_on="r_regionkey") ) -# Compute the final result +# Compute the final result, then sort in descending order. df = df.aggregate( - [col("n_name")], + ["n_name"], [F.sum(col("l_extendedprice") * (lit(1.0) - col("l_discount"))).alias("revenue")], -) - -# Sort in descending order - -df = df.sort(col("revenue").sort(ascending=False)) +).sort(col("revenue").sort(ascending=False)) df.show() diff --git a/examples/tpch/q06_forecasting_revenue_change.py b/examples/tpch/q06_forecasting_revenue_change.py index 1de5848b1..ed54d22a4 100644 --- a/examples/tpch/q06_forecasting_revenue_change.py +++ b/examples/tpch/q06_forecasting_revenue_change.py @@ -27,28 +27,34 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + sum(l_extendedprice * l_discount) as revenue + from + lineitem + where + l_shipdate >= date '1994-01-01' + and l_shipdate < date '1994-01-01' + interval '1' year + and l_discount between 0.06 - 0.01 and 0.06 + 0.01 + and l_quantity < 24; """ -from datetime import datetime +from datetime import date -import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path # Variables from the example query -DATE_OF_INTEREST = "1994-01-01" +YEAR_START = date(1994, 1, 1) +YEAR_END = date(1995, 1, 1) DISCOUT = 0.06 DELTA = 0.01 QUANTITY = 24 -INTERVAL_DAYS = 365 - -date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date() - -interval = pa.scalar((0, INTERVAL_DAYS, 0), type=pa.month_day_nano_interval()) - # Load the dataframes we need ctx = SessionContext() @@ -59,12 +65,11 @@ # Filter down to lineitems of interest -df = ( - df_lineitem.filter(col("l_shipdate") >= lit(date)) - .filter(col("l_shipdate") < lit(date) + lit(interval)) - .filter(col("l_discount") >= lit(DISCOUT) - lit(DELTA)) - .filter(col("l_discount") <= lit(DISCOUT) + lit(DELTA)) - .filter(col("l_quantity") < lit(QUANTITY)) +df = df_lineitem.filter( + col("l_shipdate") >= lit(YEAR_START), + col("l_shipdate") < lit(YEAR_END), + col("l_discount").between(lit(DISCOUT - DELTA), lit(DISCOUT + DELTA)), + col("l_quantity") < QUANTITY, ) # Add up all the "lost" revenue diff --git a/examples/tpch/q07_volume_shipping.py b/examples/tpch/q07_volume_shipping.py index ff2f891f1..df1c2ae0d 100644 --- a/examples/tpch/q07_volume_shipping.py +++ b/examples/tpch/q07_volume_shipping.py @@ -26,9 +26,51 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + supp_nation, + cust_nation, + l_year, + sum(volume) as revenue + from + ( + select + n1.n_name as supp_nation, + n2.n_name as cust_nation, + extract(year from l_shipdate) as l_year, + l_extendedprice * (1 - l_discount) as volume + from + supplier, + lineitem, + orders, + customer, + nation n1, + nation n2 + where + s_suppkey = l_suppkey + and o_orderkey = l_orderkey + and c_custkey = o_custkey + and s_nationkey = n1.n_nationkey + and c_nationkey = n2.n_nationkey + and ( + (n1.n_name = 'FRANCE' and n2.n_name = 'GERMANY') + or (n1.n_name = 'GERMANY' and n2.n_name = 'FRANCE') + ) + and l_shipdate between date '1995-01-01' and date '1996-12-31' + ) as shipping + group by + supp_nation, + cust_nation, + l_year + order by + supp_nation, + cust_nation, + l_year; """ -from datetime import datetime +from datetime import date import pyarrow as pa from datafusion import SessionContext, col, lit @@ -40,11 +82,8 @@ nation_1 = lit("FRANCE") nation_2 = lit("GERMANY") -START_DATE = "1995-01-01" -END_DATE = "1996-12-31" - -start_date = lit(datetime.strptime(START_DATE, "%Y-%m-%d").date()) -end_date = lit(datetime.strptime(END_DATE, "%Y-%m-%d").date()) +START_DATE = date(1995, 1, 1) +END_DATE = date(1996, 12, 31) # Load the dataframes we need @@ -69,60 +108,44 @@ # Filter to time of interest -df_lineitem = df_lineitem.filter(col("l_shipdate") >= start_date).filter( - col("l_shipdate") <= end_date +df_lineitem = df_lineitem.filter( + col("l_shipdate") >= lit(START_DATE), col("l_shipdate") <= lit(END_DATE) ) -# A simpler way to do the following operation is to use a filter, but we also want to demonstrate -# how to use case statements. Here we are assigning `n_name` to be itself when it is either of -# the two nations of interest. Since there is no `otherwise()` statement, any values that do -# not match these will result in a null value and then get filtered out. -# -# To do the same using a simple filter would be: -# df_nation = df_nation.filter((F.col("n_name") == nation_1) | (F.col("n_name") == nation_2)) # noqa: ERA001 -df_nation = df_nation.with_column( - "n_name", - F.case(col("n_name")) - .when(nation_1, col("n_name")) - .when(nation_2, col("n_name")) - .end(), -).filter(~col("n_name").is_null()) +# Limit the nation table to the two nations of interest. +df_nation = df_nation.filter(F.in_list(col("n_name"), [nation_1, nation_2])) # Limit suppliers to either nation df_supplier = df_supplier.join( - df_nation, left_on=["s_nationkey"], right_on=["n_nationkey"], how="inner" -).select(col("s_suppkey"), col("n_name").alias("supp_nation")) + df_nation, left_on="s_nationkey", right_on="n_nationkey" +).select("s_suppkey", col("n_name").alias("supp_nation")) # Limit customers to either nation df_customer = df_customer.join( - df_nation, left_on=["c_nationkey"], right_on=["n_nationkey"], how="inner" -).select(col("c_custkey"), col("n_name").alias("cust_nation")) + df_nation, left_on="c_nationkey", right_on="n_nationkey" +).select("c_custkey", col("n_name").alias("cust_nation")) # Join up all the data frames from line items, and make sure the supplier and customer are in # different nations. df = ( - df_lineitem.join( - df_orders, left_on=["l_orderkey"], right_on=["o_orderkey"], how="inner" - ) - .join(df_customer, left_on=["o_custkey"], right_on=["c_custkey"], how="inner") - .join(df_supplier, left_on=["l_suppkey"], right_on=["s_suppkey"], how="inner") + df_lineitem.join(df_orders, left_on="l_orderkey", right_on="o_orderkey") + .join(df_customer, left_on="o_custkey", right_on="c_custkey") + .join(df_supplier, left_on="l_suppkey", right_on="s_suppkey") .filter(col("cust_nation") != col("supp_nation")) ) # Extract out two values for every line item -df = df.with_column( - "l_year", F.datepart(lit("year"), col("l_shipdate")).cast(pa.int32()) -).with_column("volume", col("l_extendedprice") * (lit(1.0) - col("l_discount"))) +df = df.with_columns( + l_year=F.datepart(lit("year"), col("l_shipdate")).cast(pa.int32()), + volume=col("l_extendedprice") * (lit(1.0) - col("l_discount")), +) -# Aggregate the results +# Aggregate and sort per the spec. df = df.aggregate( - [col("supp_nation"), col("cust_nation"), col("l_year")], + ["supp_nation", "cust_nation", "l_year"], [F.sum(col("volume")).alias("revenue")], -) - -# Sort based on problem statement requirements -df = df.sort(col("supp_nation").sort(), col("cust_nation").sort(), col("l_year").sort()) +).sort_by("supp_nation", "cust_nation", "l_year") df.show() diff --git a/examples/tpch/q08_market_share.py b/examples/tpch/q08_market_share.py index 4bf50efba..dd7bacedb 100644 --- a/examples/tpch/q08_market_share.py +++ b/examples/tpch/q08_market_share.py @@ -25,24 +25,61 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + o_year, + sum(case + when nation = 'BRAZIL' then volume + else 0 + end) / sum(volume) as mkt_share + from + ( + select + extract(year from o_orderdate) as o_year, + l_extendedprice * (1 - l_discount) as volume, + n2.n_name as nation + from + part, + supplier, + lineitem, + orders, + customer, + nation n1, + nation n2, + region + where + p_partkey = l_partkey + and s_suppkey = l_suppkey + and l_orderkey = o_orderkey + and o_custkey = c_custkey + and c_nationkey = n1.n_nationkey + and n1.n_regionkey = r_regionkey + and r_name = 'AMERICA' + and s_nationkey = n2.n_nationkey + and o_orderdate between date '1995-01-01' and date '1996-12-31' + and p_type = 'ECONOMY ANODIZED STEEL' + ) as all_nations + group by + o_year + order by + o_year; """ -from datetime import datetime +from datetime import date import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path -supplier_nation = lit("BRAZIL") -customer_region = lit("AMERICA") -part_of_interest = lit("ECONOMY ANODIZED STEEL") - -START_DATE = "1995-01-01" -END_DATE = "1996-12-31" +supplier_nation = "BRAZIL" +customer_region = "AMERICA" +part_of_interest = "ECONOMY ANODIZED STEEL" -start_date = lit(datetime.strptime(START_DATE, "%Y-%m-%d").date()) -end_date = lit(datetime.strptime(END_DATE, "%Y-%m-%d").date()) +START_DATE = date(1995, 1, 1) +END_DATE = date(1996, 12, 31) # Load the dataframes we need @@ -74,105 +111,57 @@ # Limit orders to those in the specified range -df_orders = df_orders.filter(col("o_orderdate") >= start_date).filter( - col("o_orderdate") <= end_date -) - -# Part 1: Find customers in the region - -# We want customers in region specified by region_of_interest. This will be used to compute -# the total sales of the part of interest. We want to know of those sales what fraction -# was supplied by the nation of interest. There is no guarantee that the nation of -# interest is within the region of interest. - -# First we find all the sales that make up the basis. - -df_regional_customers = df_region.filter(col("r_name") == customer_region) - -# After this join we have all of the possible sales nations -df_regional_customers = df_regional_customers.join( - df_nation, left_on=["r_regionkey"], right_on=["n_regionkey"], how="inner" -) - -# Now find the possible customers -df_regional_customers = df_regional_customers.join( - df_customer, left_on=["n_nationkey"], right_on=["c_nationkey"], how="inner" -) - -# Next find orders for these customers -df_regional_customers = df_regional_customers.join( - df_orders, left_on=["c_custkey"], right_on=["o_custkey"], how="inner" -) - -# Find all line items from these orders -df_regional_customers = df_regional_customers.join( - df_lineitem, left_on=["o_orderkey"], right_on=["l_orderkey"], how="inner" -) - -# Limit to the part of interest -df_regional_customers = df_regional_customers.join( - df_part, left_on=["l_partkey"], right_on=["p_partkey"], how="inner" -) - -# Compute the volume for each line item -df_regional_customers = df_regional_customers.with_column( - "volume", col("l_extendedprice") * (lit(1.0) - col("l_discount")) -) - -# Part 2: Find suppliers from the nation - -# Now that we have all of the sales of that part in the specified region, we need -# to determine which of those came from suppliers in the nation we are interested in. - -df_national_suppliers = df_nation.filter(col("n_name") == supplier_nation) - -# Determine the suppliers by the limited nation key we have in our single row df above -df_national_suppliers = df_national_suppliers.join( - df_supplier, left_on=["n_nationkey"], right_on=["s_nationkey"], how="inner" -) - -# When we join to the customer dataframe, we don't want to confuse other columns, so only -# select the supplier key that we need -df_national_suppliers = df_national_suppliers.select("s_suppkey") - - -# Part 3: Combine suppliers and customers and compute the market share - -# Now we can do a left outer join on the suppkey. Those line items from other suppliers -# will get a null value. We can check for the existence of this null to compute a volume -# column only from suppliers in the nation we are evaluating. - -df = df_regional_customers.join( - df_national_suppliers, left_on=["l_suppkey"], right_on=["s_suppkey"], how="left" -) - -# Use a case statement to compute the volume sold by suppliers in the nation of interest -df = df.with_column( - "national_volume", - F.case(col("s_suppkey").is_null()) - .when(lit(value=False), col("volume")) - .otherwise(lit(0.0)), -) - -df = df.with_column( - "o_year", F.datepart(lit("year"), col("o_orderdate")).cast(pa.int32()) -) - - -# Lastly, sum up the results - -df = df.aggregate( - [col("o_year")], - [ - F.sum(col("volume")).alias("volume"), - F.sum(col("national_volume")).alias("national_volume"), - ], +df_orders = df_orders.filter( + col("o_orderdate") >= lit(START_DATE), col("o_orderdate") <= lit(END_DATE) +) + +# Pair each supplier with its nation name so every regional-customer row +# below carries the supplier's nation and can be filtered inside the +# aggregate with ``F.sum(..., filter=...)``. + +df_supplier_with_nation = df_supplier.join( + df_nation, left_on="s_nationkey", right_on="n_nationkey" +).select("s_suppkey", col("n_name").alias("supp_nation")) + +# Build every (part, lineitem, order, customer) row for customers in the +# target region ordering the target part. Each row carries the supplier's +# nation so we can aggregate on it below. + +df = ( + df_region.filter(col("r_name") == customer_region) + .join(df_nation, left_on="r_regionkey", right_on="n_regionkey") + .join(df_customer, left_on="n_nationkey", right_on="c_nationkey") + .join(df_orders, left_on="c_custkey", right_on="o_custkey") + .join(df_lineitem, left_on="o_orderkey", right_on="l_orderkey") + .join(df_part, left_on="l_partkey", right_on="p_partkey") + .join(df_supplier_with_nation, left_on="l_suppkey", right_on="s_suppkey") + .with_columns( + volume=col("l_extendedprice") * (lit(1.0) - col("l_discount")), + o_year=F.datepart(lit("year"), col("o_orderdate")).cast(pa.int32()), + ) +) + +# Aggregate the total and national volumes per year via the ``filter`` +# kwarg on ``F.sum`` (DataFrame form of SQL ``sum(... ) FILTER (WHERE ...)``). +# ``coalesce`` handles the case where no sale came from the target nation +# for a given year. +df = ( + df.aggregate( + ["o_year"], + [ + F.sum(col("volume"), filter=col("supp_nation") == supplier_nation).alias( + "national_volume" + ), + F.sum(col("volume")).alias("total_volume"), + ], + ) + .select( + "o_year", + (F.coalesce(col("national_volume"), lit(0.0)) / col("total_volume")).alias( + "mkt_share" + ), + ) + .sort_by("o_year") ) -df = df.select( - col("o_year"), (F.col("national_volume") / F.col("volume")).alias("mkt_share") -) - -df = df.sort(col("o_year").sort()) - df.show() diff --git a/examples/tpch/q09_product_type_profit_measure.py b/examples/tpch/q09_product_type_profit_measure.py index e2abbd095..ec68a2ab7 100644 --- a/examples/tpch/q09_product_type_profit_measure.py +++ b/examples/tpch/q09_product_type_profit_measure.py @@ -27,6 +27,41 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + nation, + o_year, + sum(amount) as sum_profit + from + ( + select + n_name as nation, + extract(year from o_orderdate) as o_year, + l_extendedprice * (1 - l_discount) - ps_supplycost * l_quantity as amount + from + part, + supplier, + lineitem, + partsupp, + orders, + nation + where + s_suppkey = l_suppkey + and ps_suppkey = l_suppkey + and ps_partkey = l_partkey + and p_partkey = l_partkey + and o_orderkey = l_orderkey + and s_nationkey = n_nationkey + and p_name like '%green%' + ) as profit + group by + nation, + o_year + order by + nation, + o_year desc; """ import pyarrow as pa @@ -34,7 +69,7 @@ from datafusion import functions as F from util import get_data_path -part_color = lit("green") +part_color = "green" # Load the dataframes we need @@ -62,37 +97,35 @@ "n_nationkey", "n_name", "n_regionkey" ) -# Limit possible parts to the color specified -df = df_part.filter(F.strpos(col("p_name"), part_color) > lit(0)) - -# We have a series of joins that get us to limit down to the line items we need -df = df.join(df_lineitem, left_on=["p_partkey"], right_on=["l_partkey"], how="inner") -df = df.join(df_supplier, left_on=["l_suppkey"], right_on=["s_suppkey"], how="inner") -df = df.join(df_orders, left_on=["l_orderkey"], right_on=["o_orderkey"], how="inner") -df = df.join( - df_partsupp, - left_on=["l_suppkey", "l_partkey"], - right_on=["ps_suppkey", "ps_partkey"], - how="inner", +# Limit possible parts to the color specified, then walk the joins down to the +# line-item rows we need and attach the supplier's nation. ``F.contains`` +# maps directly to the reference SQL's ``p_name like '%green%'``. +df = ( + df_part.filter(F.contains(col("p_name"), lit(part_color))) + .join(df_lineitem, left_on="p_partkey", right_on="l_partkey") + .join(df_supplier, left_on="l_suppkey", right_on="s_suppkey") + .join(df_orders, left_on="l_orderkey", right_on="o_orderkey") + .join( + df_partsupp, + left_on=["l_suppkey", "l_partkey"], + right_on=["ps_suppkey", "ps_partkey"], + ) + .join(df_nation, left_on="s_nationkey", right_on="n_nationkey") ) -df = df.join(df_nation, left_on=["s_nationkey"], right_on=["n_nationkey"], how="inner") # Compute the intermediate values and limit down to the expressions we need df = df.select( col("n_name").alias("nation"), F.datepart(lit("year"), col("o_orderdate")).cast(pa.int32()).alias("o_year"), ( - (col("l_extendedprice") * (lit(1) - col("l_discount"))) - - (col("ps_supplycost") * col("l_quantity")) + col("l_extendedprice") * (lit(1) - col("l_discount")) + - col("ps_supplycost") * col("l_quantity") ).alias("amount"), ) -# Sum up the values by nation and year -df = df.aggregate( - [col("nation"), col("o_year")], [F.sum(col("amount")).alias("profit")] +# Sum up the values by nation and year, then sort per the spec. +df = df.aggregate(["nation", "o_year"], [F.sum(col("amount")).alias("profit")]).sort( + "nation", col("o_year").sort(ascending=False) ) -# Sort according to the problem specification -df = df.sort(col("nation").sort(), col("o_year").sort(ascending=False)) - df.show() diff --git a/examples/tpch/q10_returned_item_reporting.py b/examples/tpch/q10_returned_item_reporting.py index ed822e264..e6532517e 100644 --- a/examples/tpch/q10_returned_item_reporting.py +++ b/examples/tpch/q10_returned_item_reporting.py @@ -27,20 +27,50 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + c_custkey, + c_name, + sum(l_extendedprice * (1 - l_discount)) as revenue, + c_acctbal, + n_name, + c_address, + c_phone, + c_comment + from + customer, + orders, + lineitem, + nation + where + c_custkey = o_custkey + and l_orderkey = o_orderkey + and o_orderdate >= date '1993-10-01' + and o_orderdate < date '1993-10-01' + interval '3' month + and l_returnflag = 'R' + and c_nationkey = n_nationkey + group by + c_custkey, + c_name, + c_acctbal, + c_phone, + n_name, + c_address, + c_comment + order by + revenue desc limit 20; """ -from datetime import datetime +from datetime import date -import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path -DATE_START_OF_QUARTER = "1993-10-01" - -date_start_of_quarter = lit(datetime.strptime(DATE_START_OF_QUARTER, "%Y-%m-%d").date()) - -interval_one_quarter = lit(pa.scalar((0, 92, 0), type=pa.month_day_nano_interval())) +QUARTER_START = date(1993, 10, 1) +QUARTER_END = date(1994, 1, 1) # Load the dataframes we need @@ -66,44 +96,40 @@ ) # limit to returns -df_lineitem = df_lineitem.filter(col("l_returnflag") == lit("R")) +df_lineitem = df_lineitem.filter(col("l_returnflag") == "R") # Rather than aggregate by all of the customer fields as you might do looking at the specification, # we can aggregate by o_custkey and then join in the customer data at the end. -df = df_orders.filter(col("o_orderdate") >= date_start_of_quarter).filter( - col("o_orderdate") < date_start_of_quarter + interval_one_quarter +df = ( + df_orders.filter( + col("o_orderdate") >= lit(QUARTER_START), + col("o_orderdate") < lit(QUARTER_END), + ) + .join(df_lineitem, left_on="o_orderkey", right_on="l_orderkey") + .aggregate( + ["o_custkey"], + [F.sum(col("l_extendedprice") * (lit(1) - col("l_discount"))).alias("revenue")], + ) ) -df = df.join(df_lineitem, left_on=["o_orderkey"], right_on=["l_orderkey"], how="inner") - -# Compute the revenue -df = df.aggregate( - [col("o_custkey")], - [F.sum(col("l_extendedprice") * (lit(1) - col("l_discount"))).alias("revenue")], +# Now join in the customer data, project the spec's output columns, and take the top 20. +df = ( + df.join(df_customer, left_on="o_custkey", right_on="c_custkey") + .join(df_nation, left_on="c_nationkey", right_on="n_nationkey") + .select( + "c_custkey", + "c_name", + "revenue", + "c_acctbal", + "n_name", + "c_address", + "c_phone", + "c_comment", + ) + .sort(col("revenue").sort(ascending=False)) + .limit(20) ) -# Now join in the customer data -df = df.join(df_customer, left_on=["o_custkey"], right_on=["c_custkey"], how="inner") -df = df.join(df_nation, left_on=["c_nationkey"], right_on=["n_nationkey"], how="inner") - -# These are the columns the problem statement requires -df = df.select( - "c_custkey", - "c_name", - "revenue", - "c_acctbal", - "n_name", - "c_address", - "c_phone", - "c_comment", -) - -# Sort the results in descending order -df = df.sort(col("revenue").sort(ascending=False)) - -# Only return the top 20 results -df = df.limit(20) - df.show() diff --git a/examples/tpch/q11_important_stock_identification.py b/examples/tpch/q11_important_stock_identification.py index 22829ab7c..1f40bbdad 100644 --- a/examples/tpch/q11_important_stock_identification.py +++ b/examples/tpch/q11_important_stock_identification.py @@ -25,10 +25,41 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + ps_partkey, + sum(ps_supplycost * ps_availqty) as value + from + partsupp, + supplier, + nation + where + ps_suppkey = s_suppkey + and s_nationkey = n_nationkey + and n_name = 'GERMANY' + group by + ps_partkey having + sum(ps_supplycost * ps_availqty) > ( + select + sum(ps_supplycost * ps_availqty) * 0.0001000000 + from + partsupp, + supplier, + nation + where + ps_suppkey = s_suppkey + and s_nationkey = n_nationkey + and n_name = 'GERMANY' + ) + order by + value desc; """ from datafusion import SessionContext, WindowFrame, col, lit from datafusion import functions as F +from datafusion.expr import Window from util import get_data_path NATION = "GERMANY" @@ -48,39 +79,30 @@ "n_nationkey", "n_name" ) -# limit to returns -df_nation = df_nation.filter(col("n_name") == lit(NATION)) - -# Find part supplies of within this target nation - -df = df_nation.join( - df_supplier, left_on=["n_nationkey"], right_on=["s_nationkey"], how="inner" +# Restrict to the target nation, then walk to partsupp rows via the supplier +# join. Aggregate the per-part inventory value. +df = ( + df_nation.filter(col("n_name") == NATION) + .join(df_supplier, left_on="n_nationkey", right_on="s_nationkey") + .join(df_partsupp, left_on="s_suppkey", right_on="ps_suppkey") + .with_column("value", col("ps_supplycost") * col("ps_availqty")) + .aggregate(["ps_partkey"], [F.sum(col("value")).alias("value")]) ) -df = df.join(df_partsupp, left_on=["s_suppkey"], right_on=["ps_suppkey"], how="inner") - - -# Compute the value of individual parts -df = df.with_column("value", col("ps_supplycost") * col("ps_availqty")) - -# Compute total value of specific parts -df = df.aggregate([col("ps_partkey")], [F.sum(col("value")).alias("value")]) - -# By default window functions go from unbounded preceding to current row, but we want -# to compute this sum across all rows -window_frame = WindowFrame("rows", None, None) - -df = df.with_column( - "total_value", F.window("sum", [col("value")], window_frame=window_frame) +# A window function evaluated over the entire output produces a scalar grand +# total that can be referenced row-by-row in the filter — a DataFrame-native +# stand-in for the SQL HAVING ... > (SELECT SUM(...) * FRACTION ...) pattern. +# The default frame is "UNBOUNDED PRECEDING to CURRENT ROW"; override to the +# full partition for the grand total. +whole_frame = WindowFrame("rows", None, None) + +df = ( + df.with_column( + "total_value", F.sum(col("value")).over(Window(window_frame=whole_frame)) + ) + .filter(col("value") / col("total_value") >= lit(FRACTION)) + .select("ps_partkey", "value") + .sort(col("value").sort(ascending=False)) ) -# Limit to the parts for which there is a significant value based on the fraction of the total -df = df.filter(col("value") / col("total_value") >= lit(FRACTION)) - -# We only need to report on these two columns -df = df.select("ps_partkey", "value") - -# Sort in descending order of value -df = df.sort(col("value").sort(ascending=False)) - df.show() diff --git a/examples/tpch/q12_ship_mode_order_priority.py b/examples/tpch/q12_ship_mode_order_priority.py index 9071597f0..fb78fe3c2 100644 --- a/examples/tpch/q12_ship_mode_order_priority.py +++ b/examples/tpch/q12_ship_mode_order_priority.py @@ -27,18 +27,49 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + l_shipmode, + sum(case + when o_orderpriority = '1-URGENT' + or o_orderpriority = '2-HIGH' + then 1 + else 0 + end) as high_line_count, + sum(case + when o_orderpriority <> '1-URGENT' + and o_orderpriority <> '2-HIGH' + then 1 + else 0 + end) as low_line_count + from + orders, + lineitem + where + o_orderkey = l_orderkey + and l_shipmode in ('MAIL', 'SHIP') + and l_commitdate < l_receiptdate + and l_shipdate < l_commitdate + and l_receiptdate >= date '1994-01-01' + and l_receiptdate < date '1994-01-01' + interval '1' year + group by + l_shipmode + order by + l_shipmode; """ -from datetime import datetime +from datetime import date -import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path SHIP_MODE_1 = "MAIL" SHIP_MODE_2 = "SHIP" -DATE_OF_INTEREST = "1994-01-01" +YEAR_START = date(1994, 1, 1) +YEAR_END = date(1995, 1, 1) # Load the dataframes we need @@ -51,63 +82,30 @@ "l_orderkey", "l_shipmode", "l_commitdate", "l_shipdate", "l_receiptdate" ) -date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date() - -interval = pa.scalar((0, 365, 0), type=pa.month_day_nano_interval()) - - -df = df_lineitem.filter(col("l_receiptdate") >= lit(date)).filter( - col("l_receiptdate") < lit(date) + lit(interval) -) - -# Note: It is not recommended to use array_has because it treats the second argument as an argument -# so if you pass it col("l_shipmode") it will pass the entire array to process which is very slow. -# Instead check the position of the entry is not null. -df = df.filter( - ~F.array_position( - F.make_array(lit(SHIP_MODE_1), lit(SHIP_MODE_2)), col("l_shipmode") - ).is_null() -) - -# Since we have only two values, it's much easier to do this as a filter where the l_shipmode -# matches either of the two values, but we want to show doing some array operations in this -# example. If you want to see this done with filters, comment out the above line and uncomment -# this one. -# df = df.filter((col("l_shipmode") == lit(SHIP_MODE_1)) | (col("l_shipmode") == lit(SHIP_MODE_2))) # noqa: ERA001 +df = df_lineitem.filter( + col("l_receiptdate") >= lit(YEAR_START), + col("l_receiptdate") < lit(YEAR_END), + # ``in_list`` maps directly to ``l_shipmode in (...)`` from the SQL. + F.in_list(col("l_shipmode"), [lit(SHIP_MODE_1), lit(SHIP_MODE_2)]), + col("l_shipdate") < col("l_commitdate"), + col("l_commitdate") < col("l_receiptdate"), +).join(df_orders, left_on="l_orderkey", right_on="o_orderkey") -# We need order priority, so join order df to line item -df = df.join(df_orders, left_on=["l_orderkey"], right_on=["o_orderkey"], how="inner") +# Flag each line item as belonging to a high-priority order or not. +high_priorities = [lit("1-URGENT"), lit("2-HIGH")] +is_high = F.in_list(col("o_orderpriority"), high_priorities) +is_low = F.in_list(col("o_orderpriority"), high_priorities, negated=True) -# Restrict to line items we care about based on the problem statement. -df = df.filter(col("l_commitdate") < col("l_receiptdate")) - -df = df.filter(col("l_shipdate") < col("l_commitdate")) - -df = df.with_column( - "high_line_value", - F.case(col("o_orderpriority")) - .when(lit("1-URGENT"), lit(1)) - .when(lit("2-HIGH"), lit(1)) - .otherwise(lit(0)), -) - -# Aggregate the results +# Count the high-priority and low-priority lineitems per ship mode via the +# ``filter`` kwarg on ``F.count`` (DataFrame form of SQL's ``count(*) +# FILTER (WHERE ...)``). df = df.aggregate( - [col("l_shipmode")], + ["l_shipmode"], [ - F.sum(col("high_line_value")).alias("high_line_count"), - F.count(col("high_line_value")).alias("all_lines_count"), + F.count(col("o_orderkey"), filter=is_high).alias("high_line_count"), + F.count(col("o_orderkey"), filter=is_low).alias("low_line_count"), ], -) - -# Compute the final output -df = df.select( - col("l_shipmode"), - col("high_line_count"), - (col("all_lines_count") - col("high_line_count")).alias("low_line_count"), -) - -df = df.sort(col("l_shipmode").sort()) +).sort_by("l_shipmode") df.show() diff --git a/examples/tpch/q13_customer_distribution.py b/examples/tpch/q13_customer_distribution.py index 93f082ea3..37c0b93f6 100644 --- a/examples/tpch/q13_customer_distribution.py +++ b/examples/tpch/q13_customer_distribution.py @@ -26,6 +26,29 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + c_count, + count(*) as custdist + from + ( + select + c_custkey, + count(o_orderkey) + from + customer left outer join orders on + c_custkey = o_custkey + and o_comment not like '%special%requests%' + group by + c_custkey + ) as c_orders (c_custkey, c_count) + group by + c_count + order by + custdist desc, + c_count desc; """ from datafusion import SessionContext, col, lit @@ -49,20 +72,16 @@ F.regexp_match(col("o_comment"), lit(f"{WORD_1}.?*{WORD_2}")).is_null() ) -# Since we may have customers with no orders we must do a left join -df = df_customer.join( - df_orders, left_on=["c_custkey"], right_on=["o_custkey"], how="left" -) - -# Find the number of orders for each customer -df = df.aggregate([col("c_custkey")], [F.count(col("o_custkey")).alias("c_count")]) - -# Ultimately we want to know the number of customers that have that customer count -df = df.aggregate([col("c_count")], [F.count(col("c_count")).alias("custdist")]) - -# We want to order the results by the highest number of customers per count -df = df.sort( - col("custdist").sort(ascending=False), col("c_count").sort(ascending=False) +# Customers with no orders still participate, so this is a left join. Count the +# orders per customer, then count customers per order-count value. +df = ( + df_customer.join(df_orders, left_on="c_custkey", right_on="o_custkey", how="left") + .aggregate(["c_custkey"], [F.count(col("o_custkey")).alias("c_count")]) + .aggregate(["c_count"], [F.count_star().alias("custdist")]) + .sort( + col("custdist").sort(ascending=False), + col("c_count").sort(ascending=False), + ) ) df.show() diff --git a/examples/tpch/q14_promotion_effect.py b/examples/tpch/q14_promotion_effect.py index d62f76e3c..08f4f054d 100644 --- a/examples/tpch/q14_promotion_effect.py +++ b/examples/tpch/q14_promotion_effect.py @@ -24,20 +24,32 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + 100.00 * sum(case + when p_type like 'PROMO%' + then l_extendedprice * (1 - l_discount) + else 0 + end) / sum(l_extendedprice * (1 - l_discount)) as promo_revenue + from + lineitem, + part + where + l_partkey = p_partkey + and l_shipdate >= date '1995-09-01' + and l_shipdate < date '1995-09-01' + interval '1' month; """ -from datetime import datetime +from datetime import date -import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path -DATE = "1995-09-01" - -date_of_interest = lit(datetime.strptime(DATE, "%Y-%m-%d").date()) - -interval_one_month = lit(pa.scalar((0, 30, 0), type=pa.month_day_nano_interval())) +MONTH_START = date(1995, 9, 1) +MONTH_END = date(1995, 10, 1) # Load the dataframes we need @@ -49,37 +61,30 @@ df_part = ctx.read_parquet(get_data_path("part.parquet")).select("p_partkey", "p_type") -# Check part type begins with PROMO -df_part = df_part.filter( - F.substring(col("p_type"), lit(0), lit(6)) == lit("PROMO") -).with_column("promo_factor", lit(1.0)) - -df_lineitem = df_lineitem.filter(col("l_shipdate") >= date_of_interest).filter( - col("l_shipdate") < date_of_interest + interval_one_month -) - -# Left join so we can sum up the promo parts different from other parts -df = df_lineitem.join( - df_part, left_on=["l_partkey"], right_on=["p_partkey"], how="left" -) - -# Make a factor of 1.0 if it is a promotion, 0.0 otherwise -df = df.with_column("promo_factor", F.coalesce(col("promo_factor"), lit(0.0))) -df = df.with_column("revenue", col("l_extendedprice") * (lit(1.0) - col("l_discount"))) - - -# Sum up the promo and total revenue -df = df.aggregate( - [], - [ - F.sum(col("promo_factor") * col("revenue")).alias("promo_revenue"), - F.sum(col("revenue")).alias("total_revenue"), - ], -) - -# Return the percentage of revenue from promotions -df = df.select( - (lit(100.0) * col("promo_revenue") / col("total_revenue")).alias("promo_revenue") +# Restrict the line items to the month of interest, join the matching part +# rows, and aggregate revenue totals with a ``filter`` clause on the promo +# sum — the DataFrame form of SQL ``sum(... ) FILTER (WHERE ...)``. +revenue = col("l_extendedprice") * (lit(1.0) - col("l_discount")) +is_promo = F.starts_with(col("p_type"), lit("PROMO")) + +df = ( + df_lineitem.filter( + col("l_shipdate") >= lit(MONTH_START), + col("l_shipdate") < lit(MONTH_END), + ) + .join(df_part, left_on="l_partkey", right_on="p_partkey") + .aggregate( + [], + [ + F.sum(revenue, filter=is_promo).alias("promo_revenue"), + F.sum(revenue).alias("total_revenue"), + ], + ) + .select( + (lit(100.0) * col("promo_revenue") / col("total_revenue")).alias( + "promo_revenue" + ) + ) ) df.show() diff --git a/examples/tpch/q15_top_supplier.py b/examples/tpch/q15_top_supplier.py index c321048f2..01c38b9f8 100644 --- a/examples/tpch/q15_top_supplier.py +++ b/examples/tpch/q15_top_supplier.py @@ -24,20 +24,50 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + create view revenue0 (supplier_no, total_revenue) as + select + l_suppkey, + sum(l_extendedprice * (1 - l_discount)) + from + lineitem + where + l_shipdate >= date '1996-01-01' + and l_shipdate < date '1996-01-01' + interval '3' month + group by + l_suppkey; + select + s_suppkey, + s_name, + s_address, + s_phone, + total_revenue + from + supplier, + revenue0 + where + s_suppkey = supplier_no + and total_revenue = ( + select + max(total_revenue) + from + revenue0 + ) + order by + s_suppkey; + drop view revenue0; """ -from datetime import datetime +from datetime import date -import pyarrow as pa -from datafusion import SessionContext, WindowFrame, col, lit +from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path -DATE = "1996-01-01" - -date_of_interest = lit(datetime.strptime(DATE, "%Y-%m-%d").date()) - -interval_3_months = lit(pa.scalar((0, 91, 0), type=pa.month_day_nano_interval())) +QUARTER_START = date(1996, 1, 1) +QUARTER_END = date(1996, 4, 1) # Load the dataframes we need @@ -53,37 +83,29 @@ "s_phone", ) -# Limit line items to the quarter of interest -df_lineitem = df_lineitem.filter(col("l_shipdate") >= date_of_interest).filter( - col("l_shipdate") < date_of_interest + interval_3_months -) +# Per-supplier revenue over the quarter of interest. +revenue = col("l_extendedprice") * (lit(1) - col("l_discount")) -df = df_lineitem.aggregate( - [col("l_suppkey")], - [ - F.sum(col("l_extendedprice") * (lit(1) - col("l_discount"))).alias( - "total_revenue" - ) - ], -) +per_supplier_revenue = df_lineitem.filter( + col("l_shipdate") >= lit(QUARTER_START), + col("l_shipdate") < lit(QUARTER_END), +).aggregate(["l_suppkey"], [F.sum(revenue).alias("total_revenue")]) -# Use a window function to find the maximum revenue across the entire dataframe -window_frame = WindowFrame("rows", None, None) -df = df.with_column( - "max_revenue", F.window("max", [col("total_revenue")], window_frame=window_frame) +# Compute the grand maximum revenue separately and join on equality — the +# DataFrame stand-in for the reference SQL's +# ``total_revenue = (select max(total_revenue) from revenue0)`` subquery. +max_revenue = per_supplier_revenue.aggregate( + [], [F.max(col("total_revenue")).alias("max_rev")] ) -# Find all suppliers whose total revenue is the same as the maximum -df = df.filter(col("total_revenue") == col("max_revenue")) - -# Now that we know the supplier(s) with maximum revenue, get the rest of their information -# from the supplier table -df = df.join(df_supplier, left_on=["l_suppkey"], right_on=["s_suppkey"], how="inner") +top_suppliers = per_supplier_revenue.join_on( + max_revenue, col("total_revenue") == col("max_rev") +).select("l_suppkey", "total_revenue") -# Return only the columns requested -df = df.select("s_suppkey", "s_name", "s_address", "s_phone", "total_revenue") - -# If we have more than one, sort by supplier number (suppkey) -df = df.sort(col("s_suppkey").sort()) +df = ( + df_supplier.join(top_suppliers, left_on="s_suppkey", right_on="l_suppkey") + .select("s_suppkey", "s_name", "s_address", "s_phone", "total_revenue") + .sort_by("s_suppkey") +) df.show() diff --git a/examples/tpch/q16_part_supplier_relationship.py b/examples/tpch/q16_part_supplier_relationship.py index 65043ffda..ddeadff5f 100644 --- a/examples/tpch/q16_part_supplier_relationship.py +++ b/examples/tpch/q16_part_supplier_relationship.py @@ -26,9 +26,41 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + p_brand, + p_type, + p_size, + count(distinct ps_suppkey) as supplier_cnt + from + partsupp, + part + where + p_partkey = ps_partkey + and p_brand <> 'Brand#45' + and p_type not like 'MEDIUM POLISHED%' + and p_size in (49, 14, 23, 45, 19, 3, 36, 9) + and ps_suppkey not in ( + select + s_suppkey + from + supplier + where + s_comment like '%Customer%Complaints%' + ) + group by + p_brand, + p_type, + p_size + order by + supplier_cnt desc, + p_brand, + p_type, + p_size; """ -import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path @@ -52,39 +84,36 @@ ) df_unwanted_suppliers = df_supplier.filter( - ~F.regexp_match(col("s_comment"), lit("Customer.?*Complaints")).is_null() + F.regexp_like(col("s_comment"), lit("Customer.*Complaints")) ) -# Remove unwanted suppliers +# Remove unwanted suppliers via an anti join (DataFrame form of NOT IN). df_partsupp = df_partsupp.join( - df_unwanted_suppliers, left_on=["ps_suppkey"], right_on=["s_suppkey"], how="anti" + df_unwanted_suppliers, left_on="ps_suppkey", right_on="s_suppkey", how="anti" ) -# Select the parts we are interested in -df_part = df_part.filter(col("p_brand") != lit(BRAND)) +# Select the parts we are interested in. df_part = df_part.filter( - F.substring(col("p_type"), lit(0), lit(len(TYPE_TO_IGNORE) + 1)) - != lit(TYPE_TO_IGNORE) -) - -# Python conversion of integer to literal casts it to int64 but the data for -# part size is stored as an int32, so perform a cast. Then check to find if the part -# size is within the array of possible sizes by checking the position of it is not -# null. -p_sizes = F.make_array(*[lit(s).cast(pa.int32()) for s in SIZES_OF_INTEREST]) -df_part = df_part.filter(~F.array_position(p_sizes, col("p_size")).is_null()) - -df = df_part.join( - df_partsupp, left_on=["p_partkey"], right_on=["ps_partkey"], how="inner" + col("p_brand") != BRAND, + ~F.starts_with(col("p_type"), lit(TYPE_TO_IGNORE)), + F.in_list(col("p_size"), [lit(s) for s in SIZES_OF_INTEREST]), ) -df = df.select("p_brand", "p_type", "p_size", "ps_suppkey").distinct() - -df = df.aggregate( - [col("p_brand"), col("p_type"), col("p_size")], - [F.count(col("ps_suppkey")).alias("supplier_cnt")], +# For each (brand, type, size), count the distinct suppliers remaining. +df = ( + df_part.join(df_partsupp, left_on="p_partkey", right_on="ps_partkey") + .select("p_brand", "p_type", "p_size", "ps_suppkey") + .distinct() + .aggregate( + ["p_brand", "p_type", "p_size"], + [F.count(col("ps_suppkey")).alias("supplier_cnt")], + ) + .sort( + col("supplier_cnt").sort(ascending=False), + "p_brand", + "p_type", + "p_size", + ) ) -df = df.sort(col("supplier_cnt").sort(ascending=False)) - df.show() diff --git a/examples/tpch/q17_small_quantity_order.py b/examples/tpch/q17_small_quantity_order.py index 6d76fe506..f2229171f 100644 --- a/examples/tpch/q17_small_quantity_order.py +++ b/examples/tpch/q17_small_quantity_order.py @@ -26,10 +26,31 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + sum(l_extendedprice) / 7.0 as avg_yearly + from + lineitem, + part + where + p_partkey = l_partkey + and p_brand = 'Brand#23' + and p_container = 'MED BOX' + and l_quantity < ( + select + 0.2 * avg(l_quantity) + from + lineitem + where + l_partkey = p_partkey + ); """ from datafusion import SessionContext, WindowFrame, col, lit from datafusion import functions as F +from datafusion.expr import Window from util import get_data_path BRAND = "Brand#23" @@ -46,32 +67,23 @@ "l_partkey", "l_quantity", "l_extendedprice" ) -# Limit to the problem statement's brand and container types -df = df_part.filter(col("p_brand") == lit(BRAND)).filter( - col("p_container") == lit(CONTAINER) -) - -# Combine data -df = df.join(df_lineitem, left_on=["p_partkey"], right_on=["l_partkey"], how="inner") +# Limit to parts of the target brand/container, join their line items, and +# attach the per-part average quantity via a partitioned window function — +# the DataFrame form of the SQL's correlated ``avg(l_quantity)`` subquery. +whole_frame = WindowFrame("rows", None, None) -# Find the average quantity -window_frame = WindowFrame("rows", None, None) -df = df.with_column( - "avg_quantity", - F.window( - "avg", - [col("l_quantity")], - window_frame=window_frame, - partition_by=[col("l_partkey")], - ), +df = ( + df_part.filter(col("p_brand") == BRAND, col("p_container") == CONTAINER) + .join(df_lineitem, left_on="p_partkey", right_on="l_partkey") + .with_column( + "avg_quantity", + F.avg(col("l_quantity")).over( + Window(partition_by=[col("l_partkey")], window_frame=whole_frame) + ), + ) + .filter(col("l_quantity") < lit(0.2) * col("avg_quantity")) + .aggregate([], [F.sum(col("l_extendedprice")).alias("total")]) + .select((col("total") / lit(7.0)).alias("avg_yearly")) ) -df = df.filter(col("l_quantity") < lit(0.2) * col("avg_quantity")) - -# Compute the total -df = df.aggregate([], [F.sum(col("l_extendedprice")).alias("total")]) - -# Divide by number of years in the problem statement to get average -df = df.select((col("total") / lit(7)).alias("avg_yearly")) - df.show() diff --git a/examples/tpch/q18_large_volume_customer.py b/examples/tpch/q18_large_volume_customer.py index 834d181c9..23132d60d 100644 --- a/examples/tpch/q18_large_volume_customer.py +++ b/examples/tpch/q18_large_volume_customer.py @@ -24,9 +24,44 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + c_name, + c_custkey, + o_orderkey, + o_orderdate, + o_totalprice, + sum(l_quantity) + from + customer, + orders, + lineitem + where + o_orderkey in ( + select + l_orderkey + from + lineitem + group by + l_orderkey having + sum(l_quantity) > 300 + ) + and c_custkey = o_custkey + and o_orderkey = l_orderkey + group by + c_name, + c_custkey, + o_orderkey, + o_orderdate, + o_totalprice + order by + o_totalprice desc, + o_orderdate limit 100; """ -from datafusion import SessionContext, col, lit +from datafusion import SessionContext, col from datafusion import functions as F from util import get_data_path @@ -46,22 +81,24 @@ "l_orderkey", "l_quantity", "l_extendedprice" ) -df = df_lineitem.aggregate( - [col("l_orderkey")], [F.sum(col("l_quantity")).alias("total_quantity")] +# Find orders whose total quantity exceeds the threshold, then join in the +# order + customer details the problem statement requires and sort. +df = ( + df_lineitem.aggregate( + ["l_orderkey"], [F.sum(col("l_quantity")).alias("total_quantity")] + ) + .filter(col("total_quantity") > QUANTITY) + .join(df_orders, left_on="l_orderkey", right_on="o_orderkey") + .join(df_customer, left_on="o_custkey", right_on="c_custkey") + .select( + "c_name", + "c_custkey", + "o_orderkey", + "o_orderdate", + "o_totalprice", + "total_quantity", + ) + .sort(col("o_totalprice").sort(ascending=False), "o_orderdate") ) -# Limit to orders in which the total quantity is above a threshold -df = df.filter(col("total_quantity") > lit(QUANTITY)) - -# We've identified the orders of interest, now join the additional data -# we are required to report on -df = df.join(df_orders, left_on=["l_orderkey"], right_on=["o_orderkey"], how="inner") -df = df.join(df_customer, left_on=["o_custkey"], right_on=["c_custkey"], how="inner") - -df = df.select( - "c_name", "c_custkey", "o_orderkey", "o_orderdate", "o_totalprice", "total_quantity" -) - -df = df.sort(col("o_totalprice").sort(ascending=False), col("o_orderdate").sort()) - df.show() diff --git a/examples/tpch/q19_discounted_revenue.py b/examples/tpch/q19_discounted_revenue.py index bd492aac0..a2be1c1b7 100644 --- a/examples/tpch/q19_discounted_revenue.py +++ b/examples/tpch/q19_discounted_revenue.py @@ -24,10 +24,47 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + sum(l_extendedprice* (1 - l_discount)) as revenue + from + lineitem, + part + where + ( + p_partkey = l_partkey + and p_brand = 'Brand#12' + and p_container in ('SM CASE', 'SM BOX', 'SM PACK', 'SM PKG') + and l_quantity >= 1 and l_quantity <= 1 + 10 + and p_size between 1 and 5 + and l_shipmode in ('AIR', 'AIR REG') + and l_shipinstruct = 'DELIVER IN PERSON' + ) + or + ( + p_partkey = l_partkey + and p_brand = 'Brand#23' + and p_container in ('MED BAG', 'MED BOX', 'MED PKG', 'MED PACK') + and l_quantity >= 10 and l_quantity <= 10 + 10 + and p_size between 1 and 10 + and l_shipmode in ('AIR', 'AIR REG') + and l_shipinstruct = 'DELIVER IN PERSON' + ) + or + ( + p_partkey = l_partkey + and p_brand = 'Brand#34' + and p_container in ('LG CASE', 'LG BOX', 'LG PACK', 'LG PKG') + and l_quantity >= 20 and l_quantity <= 20 + 10 + and p_size between 1 and 15 + and l_shipmode in ('AIR', 'AIR REG') + and l_shipinstruct = 'DELIVER IN PERSON' + ); """ -import pyarrow as pa -from datafusion import SessionContext, col, lit, udf +from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path @@ -65,72 +102,41 @@ "l_discount", ) -# These limitations apply to all line items, so go ahead and do them first - -df = df_lineitem.filter(col("l_shipinstruct") == lit("DELIVER IN PERSON")) - -df = df.filter( - (col("l_shipmode") == lit("AIR")) | (col("l_shipmode") == lit("AIR REG")) -) +# Filter conditions that apply to every disjunct of the reference SQL's WHERE +# clause — pull them out up front so the per-brand predicate stays focused on +# the brand-specific parts. +df = df_lineitem.filter( + col("l_shipinstruct") == "DELIVER IN PERSON", + F.in_list(col("l_shipmode"), [lit("AIR"), lit("AIR REG")]), +).join(df_part, left_on="l_partkey", right_on="p_partkey") + + +# Build one OR-combined predicate per brand. Each disjunct encodes the +# brand-specific container list, quantity window, and size range from the +# reference SQL. This mirrors the SQL ``where (... brand A ...) or (... brand +# B ...) or (... brand C ...)`` form directly, without a UDF. +def _brand_predicate( + brand: str, min_quantity: int, containers: list[str], max_size: int +): + return ( + (col("p_brand") == brand) + & F.in_list(col("p_container"), [lit(c) for c in containers]) + & col("l_quantity").between(lit(min_quantity), lit(min_quantity + 10)) + & col("p_size").between(lit(1), lit(max_size)) + ) -df = df.join(df_part, left_on=["l_partkey"], right_on=["p_partkey"], how="inner") - - -# Create the user defined function (UDF) definition that does the work -def is_of_interest( - brand_arr: pa.Array, - container_arr: pa.Array, - quantity_arr: pa.Array, - size_arr: pa.Array, -) -> pa.Array: - """ - The purpose of this function is to demonstrate how a UDF works, taking as input a pyarrow Array - and generating a resultant Array. The length of the inputs should match and there should be the - same number of rows in the output. - """ - result = [] - for idx, brand_val in enumerate(brand_arr): - brand = brand_val.as_py() - if brand in items_of_interest: - values_of_interest = items_of_interest[brand] - - container_matches = ( - container_arr[idx].as_py() in values_of_interest["containers"] - ) - - quantity = quantity_arr[idx].as_py() - quantity_matches = ( - values_of_interest["min_quantity"] - <= quantity - <= values_of_interest["min_quantity"] + 10 - ) - - size = size_arr[idx].as_py() - size_matches = 1 <= size <= values_of_interest["max_size"] - - result.append(container_matches and quantity_matches and size_matches) - else: - result.append(False) - - return pa.array(result) - - -# Turn the above function into a UDF that DataFusion can understand -is_of_interest_udf = udf( - is_of_interest, - [pa.utf8(), pa.utf8(), pa.decimal128(15, 2), pa.int32()], - pa.bool_(), - "stable", -) -# Filter results using the above UDF -df = df.filter( - is_of_interest_udf( - col("p_brand"), col("p_container"), col("l_quantity"), col("p_size") +predicate = None +for brand, params in items_of_interest.items(): + part_predicate = _brand_predicate( + brand, + params["min_quantity"], + params["containers"], + params["max_size"], ) -) + predicate = part_predicate if predicate is None else predicate | part_predicate -df = df.aggregate( +df = df.filter(predicate).aggregate( [], [F.sum(col("l_extendedprice") * (lit(1) - col("l_discount"))).alias("revenue")], ) diff --git a/examples/tpch/q20_potential_part_promotion.py b/examples/tpch/q20_potential_part_promotion.py index a25188d31..18f96da97 100644 --- a/examples/tpch/q20_potential_part_promotion.py +++ b/examples/tpch/q20_potential_part_promotion.py @@ -25,17 +25,57 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + s_name, + s_address + from + supplier, + nation + where + s_suppkey in ( + select + ps_suppkey + from + partsupp + where + ps_partkey in ( + select + p_partkey + from + part + where + p_name like 'forest%' + ) + and ps_availqty > ( + select + 0.5 * sum(l_quantity) + from + lineitem + where + l_partkey = ps_partkey + and l_suppkey = ps_suppkey + and l_shipdate >= date '1994-01-01' + and l_shipdate < date '1994-01-01' + interval '1' year + ) + ) + and s_nationkey = n_nationkey + and n_name = 'CANADA' + order by + s_name; """ -from datetime import datetime +from datetime import date -import pyarrow as pa from datafusion import SessionContext, col, lit from datafusion import functions as F from util import get_data_path COLOR_OF_INTEREST = "forest" -DATE_OF_INTEREST = "1994-01-01" +YEAR_START = date(1994, 1, 1) +YEAR_END = date(1995, 1, 1) NATION_OF_INTEREST = "CANADA" # Load the dataframes we need @@ -56,46 +96,48 @@ "n_nationkey", "n_name" ) -date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date() - -interval = pa.scalar((0, 365, 0), type=pa.month_day_nano_interval()) - -# Filter down dataframes -df_nation = df_nation.filter(col("n_name") == lit(NATION_OF_INTEREST)) -df_part = df_part.filter( - F.substring(col("p_name"), lit(0), lit(len(COLOR_OF_INTEREST) + 1)) - == lit(COLOR_OF_INTEREST) -) - -df = df_lineitem.filter(col("l_shipdate") >= lit(date)).filter( - col("l_shipdate") < lit(date) + lit(interval) +# Filter down dataframes. ``starts_with`` reads more naturally than an +# explicit substring slice and maps directly to the reference SQL's +# ``p_name like 'forest%'`` clause. +df_nation = df_nation.filter(col("n_name") == NATION_OF_INTEREST) +df_part = df_part.filter(F.starts_with(col("p_name"), lit(COLOR_OF_INTEREST))) + +# Compute the total quantity of interesting parts shipped by each (part, +# supplier) pair within the year of interest. +totals = ( + df_lineitem.filter( + col("l_shipdate") >= lit(YEAR_START), + col("l_shipdate") < lit(YEAR_END), + ) + .join(df_part, left_on="l_partkey", right_on="p_partkey") + .aggregate( + ["l_partkey", "l_suppkey"], + [F.sum(col("l_quantity")).alias("total_sold")], + ) ) -# This will filter down the line items to the parts of interest -df = df.join(df_part, left_on="l_partkey", right_on="p_partkey", how="inner") - -# Compute the total sold and limit ourselves to individual supplier/part combinations -df = df.aggregate( - [col("l_partkey"), col("l_suppkey")], [F.sum(col("l_quantity")).alias("total_sold")] +# Keep only (part, supplier) pairs whose available quantity exceeds 50% of +# the total shipped. The result already contains one row per supplier of +# interest, so we can semi-join the supplier table rather than inner-join +# and deduplicate afterwards. +excess_suppliers = ( + df_partsupp.join( + totals, + left_on=["ps_partkey", "ps_suppkey"], + right_on=["l_partkey", "l_suppkey"], + ) + .filter(col("ps_availqty") > lit(0.5) * col("total_sold")) + .select(col("ps_suppkey").alias("suppkey")) + .distinct() ) -df = df.join( - df_partsupp, - left_on=["l_partkey", "l_suppkey"], - right_on=["ps_partkey", "ps_suppkey"], - how="inner", +# Limit to suppliers in the nation of interest and pick out the two +# requested columns. +df = ( + df_supplier.join(df_nation, left_on="s_nationkey", right_on="n_nationkey") + .join(excess_suppliers, left_on="s_suppkey", right_on="suppkey", how="semi") + .select("s_name", "s_address") + .sort_by("s_name") ) -# Find cases of excess quantity -df.filter(col("ps_availqty") > lit(0.5) * col("total_sold")) - -# We could do these joins earlier, but now limit to the nation of interest suppliers -df = df.join(df_supplier, left_on=["ps_suppkey"], right_on=["s_suppkey"], how="inner") -df = df.join(df_nation, left_on=["s_nationkey"], right_on=["n_nationkey"], how="inner") - -# Restrict to the requested data per the problem statement -df = df.select("s_name", "s_address").distinct() - -df = df.sort(col("s_name").sort()) - df.show() diff --git a/examples/tpch/q21_suppliers_kept_orders_waiting.py b/examples/tpch/q21_suppliers_kept_orders_waiting.py index 619c4406b..d98f76ce7 100644 --- a/examples/tpch/q21_suppliers_kept_orders_waiting.py +++ b/examples/tpch/q21_suppliers_kept_orders_waiting.py @@ -24,9 +24,51 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + s_name, + count(*) as numwait + from + supplier, + lineitem l1, + orders, + nation + where + s_suppkey = l1.l_suppkey + and o_orderkey = l1.l_orderkey + and o_orderstatus = 'F' + and l1.l_receiptdate > l1.l_commitdate + and exists ( + select + * + from + lineitem l2 + where + l2.l_orderkey = l1.l_orderkey + and l2.l_suppkey <> l1.l_suppkey + ) + and not exists ( + select + * + from + lineitem l3 + where + l3.l_orderkey = l1.l_orderkey + and l3.l_suppkey <> l1.l_suppkey + and l3.l_receiptdate > l3.l_commitdate + ) + and s_nationkey = n_nationkey + and n_name = 'SAUDI ARABIA' + group by + s_name + order by + numwait desc, + s_name limit 100; """ -from datafusion import SessionContext, col, lit +from datafusion import SessionContext, col from datafusion import functions as F from util import get_data_path @@ -50,69 +92,57 @@ ) # Limit to suppliers in the nation of interest -df_suppliers_of_interest = df_nation.filter(col("n_name") == lit(NATION_OF_INTEREST)) - -df_suppliers_of_interest = df_suppliers_of_interest.join( - df_supplier, left_on="n_nationkey", right_on="s_nationkey", how="inner" +df_suppliers_of_interest = df_nation.filter(col("n_name") == NATION_OF_INTEREST).join( + df_supplier, left_on="n_nationkey", right_on="s_nationkey" ) -# Find the failed orders and all their line items -df = df_orders.filter(col("o_orderstatus") == lit("F")) - -df = df_lineitem.join(df, left_on="l_orderkey", right_on="o_orderkey", how="inner") - -# Identify the line items for which the order is failed due to. -df = df.with_column( - "failed_supp", - F.case(col("l_receiptdate") > col("l_commitdate")) - .when(lit(value=True), col("l_suppkey")) - .end(), +# Line items for orders that have status 'F'. This is the candidate set of +# (order, supplier) pairs we reason about below. +failed_order_lineitems = df_lineitem.join( + df_orders.filter(col("o_orderstatus") == "F"), + left_on="l_orderkey", + right_on="o_orderkey", ) -# There are other ways we could do this but the purpose of this example is to work with rows where -# an element is an array of values. In this case, we will create two columns of arrays. One will be -# an array of all of the suppliers who made up this order. That way we can filter the dataframe for -# only orders where this array is larger than one for multiple supplier orders. The second column -# is all of the suppliers who failed to make their commitment. We can filter the second column for -# arrays with size one. That combination will give us orders that had multiple suppliers where only -# one failed. Use distinct=True in the blow aggregation so we don't get multiple line items from the -# same supplier reported in either array. -df = df.aggregate( - [col("o_orderkey")], - [ - F.array_agg(col("l_suppkey"), distinct=True).alias("all_suppliers"), - F.array_agg(col("failed_supp"), distinct=True).alias("failed_suppliers"), - ], +# Line items whose receipt was late. This corresponds to ``l1`` in the +# reference SQL. +late_lineitems = failed_order_lineitems.filter( + col("l_receiptdate") > col("l_commitdate") ) -# Remove the null entries that will get returned by array_agg so we can test to see where we only -# have a single failed supplier in a multiple supplier order -df = df.with_column( - "failed_suppliers", F.array_remove(col("failed_suppliers"), lit(None)) +# Orders that had more than one distinct supplier. Expressed as +# ``count(distinct l_suppkey) > 1``. Stands in for the reference SQL's +# ``exists (... l2.l_suppkey <> l1.l_suppkey ...)`` subquery. +multi_supplier_orders = ( + failed_order_lineitems.select("l_orderkey", "l_suppkey") + .distinct() + .aggregate(["l_orderkey"], [F.count_star().alias("n_suppliers")]) + .filter(col("n_suppliers") > 1) + .select("l_orderkey") ) -# This is the check described above which will identify single failed supplier in a multiple -# supplier order. -df = df.filter(F.array_length(col("failed_suppliers")) == lit(1)).filter( - F.array_length(col("all_suppliers")) > lit(1) +# Orders where exactly one distinct supplier was late. Stands in for the +# reference SQL's ``not exists (... l3.l_suppkey <> l1.l_suppkey and l3 is +# also late ...)`` subquery: if only one supplier on the order was late, +# nobody else on the same order was late. +single_late_supplier_orders = ( + late_lineitems.select("l_orderkey", "l_suppkey") + .distinct() + .aggregate(["l_orderkey"], [F.count_star().alias("n_late_suppliers")]) + .filter(col("n_late_suppliers") == 1) + .select("l_orderkey") ) -# Since we have an array we know is exactly one element long, we can extract that single value. -df = df.select( - col("o_orderkey"), F.array_element(col("failed_suppliers"), lit(1)).alias("suppkey") +# Keep late line items whose order qualifies on both counts, attach the +# supplier name for suppliers in the nation of interest, count one row per +# qualifying order, and return the top 100. +df = ( + late_lineitems.join(multi_supplier_orders, on="l_orderkey", how="semi") + .join(single_late_supplier_orders, on="l_orderkey", how="semi") + .join(df_suppliers_of_interest, left_on="l_suppkey", right_on="s_suppkey") + .aggregate(["s_name"], [F.count_star().alias("numwait")]) + .sort(col("numwait").sort(ascending=False), "s_name") + .limit(100) ) -# Join to the supplier of interest list for the nation of interest -df = df.join( - df_suppliers_of_interest, left_on=["suppkey"], right_on=["s_suppkey"], how="inner" -) - -# Count how many orders that supplier is the only failed supplier for -df = df.aggregate([col("s_name")], [F.count(col("o_orderkey")).alias("numwait")]) - -# Return in descending order -df = df.sort(col("numwait").sort(ascending=False), col("s_name").sort()) - -df = df.limit(100) - df.show() diff --git a/examples/tpch/q22_global_sales_opportunity.py b/examples/tpch/q22_global_sales_opportunity.py index c4d115b74..5043eeb51 100644 --- a/examples/tpch/q22_global_sales_opportunity.py +++ b/examples/tpch/q22_global_sales_opportunity.py @@ -24,10 +24,51 @@ The above problem statement text is copyrighted by the Transaction Processing Performance Council as part of their TPC Benchmark H Specification revision 2.18.0. + +Reference SQL (from TPC-H specification, used by the benchmark suite):: + + select + cntrycode, + count(*) as numcust, + sum(c_acctbal) as totacctbal + from + ( + select + substring(c_phone from 1 for 2) as cntrycode, + c_acctbal + from + customer + where + substring(c_phone from 1 for 2) in + ('13', '31', '23', '29', '30', '18', '17') + and c_acctbal > ( + select + avg(c_acctbal) + from + customer + where + c_acctbal > 0.00 + and substring(c_phone from 1 for 2) in + ('13', '31', '23', '29', '30', '18', '17') + ) + and not exists ( + select + * + from + orders + where + o_custkey = c_custkey + ) + ) as custsale + group by + cntrycode + order by + cntrycode; """ from datafusion import SessionContext, WindowFrame, col, lit from datafusion import functions as F +from datafusion.expr import Window from util import get_data_path NATION_CODES = [13, 31, 23, 29, 30, 18, 17] @@ -41,39 +82,36 @@ ) df_orders = ctx.read_parquet(get_data_path("orders.parquet")).select("o_custkey") -# The nation code is a two digit number, but we need to convert it to a string literal -nation_codes = F.make_array(*[lit(str(n)) for n in NATION_CODES]) - -# Use the substring operation to extract the first two characters of the phone number -df = df_customer.with_column("cntrycode", F.substring(col("c_phone"), lit(0), lit(3))) - -# Limit our search to customers with some balance and in the country code above -df = df.filter(col("c_acctbal") > lit(0.0)) -df = df.filter(~F.array_position(nation_codes, col("cntrycode")).is_null()) - -# Compute the average balance. By default, the window frame is from unbounded preceding to the -# current row. We want our frame to cover the entire data frame. -window_frame = WindowFrame("rows", None, None) -df = df.with_column( - "avg_balance", F.window("avg", [col("c_acctbal")], window_frame=window_frame) -) - -df.show() -# Limit results to customers with above average balance -df = df.filter(col("c_acctbal") > col("avg_balance")) - -# Limit results to customers with no orders -df = df.join(df_orders, left_on="c_custkey", right_on="o_custkey", how="anti") - -# Count up the customers and the balances -df = df.aggregate( - [col("cntrycode")], - [ - F.count(col("c_custkey")).alias("numcust"), - F.sum(col("c_acctbal")).alias("totacctbal"), - ], +# Country code is the two-digit prefix of the phone number. +nation_codes = [lit(str(n)) for n in NATION_CODES] + +# Start from customers with a positive balance in one of the target country +# codes, then attach the grand-mean balance via a whole-frame window so we +# can filter per row — DataFrame stand-in for the SQL's scalar ``(select +# avg(c_acctbal) ... )`` subquery. +whole_frame = WindowFrame("rows", None, None) + +df = ( + df_customer.with_column("cntrycode", F.left(col("c_phone"), lit(2))) + .filter( + col("c_acctbal") > 0.0, + F.in_list(col("cntrycode"), nation_codes), + ) + .with_column( + "avg_balance", + F.avg(col("c_acctbal")).over(Window(window_frame=whole_frame)), + ) + .filter(col("c_acctbal") > col("avg_balance")) + # Keep only customers with no orders (anti join = NOT EXISTS). + .join(df_orders, left_on="c_custkey", right_on="o_custkey", how="anti") + .aggregate( + ["cntrycode"], + [ + F.count_star().alias("numcust"), + F.sum(col("c_acctbal")).alias("totacctbal"), + ], + ) + .sort_by("cntrycode") ) -df = df.sort(col("cntrycode").sort()) - df.show() diff --git a/examples/tpch/util.py b/examples/tpch/util.py index ec53bcd15..0b52c4e09 100644 --- a/examples/tpch/util.py +++ b/examples/tpch/util.py @@ -31,4 +31,4 @@ def get_data_path(filename: str) -> Path: def get_answer_file(answer_file: str) -> Path: path = Path(__file__).resolve().parent - return path / "../../benchmarks/tpch/data/answers" / f"{answer_file}.out" + return path / "answers_sf1" / f"{answer_file}.tbl" diff --git a/pyproject.toml b/pyproject.toml index 9ad7dab8d..951f7adc3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,28 +25,28 @@ description = "Build and run queries against data" readme = "README.md" license = { file = "LICENSE.txt" } requires-python = ">=3.10" -keywords = ["datafusion", "dataframe", "rust", "query-engine"] +keywords = ["dataframe", "datafusion", "query-engine", "rust"] classifiers = [ - "Development Status :: 2 - Pre-Alpha", - "Intended Audience :: Developers", - "License :: OSI Approved :: Apache Software License", - "License :: OSI Approved", - "Operating System :: MacOS", - "Operating System :: Microsoft :: Windows", - "Operating System :: POSIX :: Linux", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", - "Programming Language :: Python :: 3.14", - "Programming Language :: Python", - "Programming Language :: Rust", + "Development Status :: 2 - Pre-Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "License :: OSI Approved", + "Operating System :: MacOS", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Programming Language :: Python", + "Programming Language :: Rust", ] dependencies = [ - "pyarrow>=16.0.0;python_version<'3.14'", - "pyarrow>=22.0.0;python_version>='3.14'", - "typing-extensions;python_version<'3.13'" + "pyarrow>=16.0.0;python_version<'3.14'", + "pyarrow>=22.0.0;python_version>='3.14'", + "typing-extensions;python_version<'3.13'", ] dynamic = ["version"] @@ -59,10 +59,11 @@ repository = "https://github.com/apache/datafusion-python" profile = "black" [tool.maturin] +manifest-path = "crates/core/Cargo.toml" python-source = "python" module-name = "datafusion._internal" include = [{ path = "Cargo.lock", format = "sdist" }] -exclude = [".github/**", "ci/**", ".asf.yaml"] +exclude = [".asf.yaml", ".github/**", "ci/**"] # Require Cargo.lock is up to date locked = true features = ["substrait"] @@ -70,26 +71,28 @@ features = ["substrait"] [tool.pytest.ini_options] asyncio_mode = "auto" asyncio_default_fixture_loop_scope = "function" +addopts = "--doctest-modules" +doctest_optionflags = ["NORMALIZE_WHITESPACE", "ELLIPSIS"] +testpaths = ["python/tests", "python/datafusion"] # Enable docstring linting using the google style guide [tool.ruff.lint] -select = ["ALL" ] +select = ["ALL"] ignore = [ - "A001", # Allow using words like min as variable names - "A002", # Allow using words like filter as variable names - "ANN401", # Allow Any for wrapper classes - "COM812", # Recommended to ignore these rules when using with ruff-format - "FIX002", # Allow TODO lines - consider removing at some point - "FBT001", # Allow boolean positional args - "FBT002", # Allow boolean positional args - "ISC001", # Recommended to ignore these rules when using with ruff-format - "SLF001", # Allow accessing private members - "TD002", # Do not require author names in TODO statements - "TD003", # Allow TODO lines - "PLR0913", # Allow many arguments in function definition - "PD901", # Allow variable name df - "N812", # Allow importing functions as `F` - "A005", # Allow module named io + "A001", # Allow using words like min as variable names + "A002", # Allow using words like filter as variable names + "A005", # Allow module named io + "ANN401", # Allow Any for wrapper classes + "COM812", # Recommended to ignore these rules when using with ruff-format + "FBT001", # Allow boolean positional args + "FBT002", # Allow boolean positional args + "FIX002", # Allow TODO lines - consider removing at some point + "ISC001", # Recommended to ignore these rules when using with ruff-format + "N812", # Allow importing functions as `F` + "PLR0913", # Allow many arguments in function definition + "SLF001", # Allow accessing private members + "TD002", # Do not require author names in TODO statements + "TD003", # Allow TODO lines ] [tool.ruff.lint.pydocstyle] @@ -99,65 +102,113 @@ convention = "google" max-doc-length = 88 [tool.ruff.lint.flake8-boolean-trap] -extend-allowed-calls = ["lit", "datafusion.lit"] +extend-allowed-calls = ["datafusion.lit", "lit"] # Disable docstring checking for these directories [tool.ruff.lint.per-file-ignores] "python/tests/*" = [ - "ANN", - "ARG", - "BLE001", - "D", - "S101", - "SLF", - "PD", - "PLR2004", - "PT011", - "RUF015", - "S608", - "PLR0913", - "PT004", + "ANN", + "ARG", + "BLE001", + "D", + "FBT003", + "PD", + "PLC0415", + "PLR0913", + "PLR2004", + "PT004", + "PT011", + "RUF015", + "S101", + "S608", + "SLF", +] +"examples/*" = [ + "ANN001", + "ANN202", + "D", + "DTZ007", + "E501", + "INP001", + "PLR2004", + "RUF015", + "S101", + "T201", + "W505", +] +"dev/*" = [ + "ANN001", + "C", + "D", + "E", + "ERA001", + "EXE", + "N817", + "PLR", + "S", + "SIM", + "T", + "UP", +] +"benchmarks/*" = [ + "ANN001", + "BLE", + "D", + "E", + "ERA001", + "EXE", + "F", + "FURB", + "INP001", + "PLR", + "S", + "SIM", + "T", + "TD", + "TRY", + "UP", ] -"examples/*" = ["D", "W505", "E501", "T201", "S101", "PLR2004", "ANN001", "ANN202", "INP001", "DTZ007", "RUF015"] -"dev/*" = ["D", "E", "T", "S", "PLR", "C", "SIM", "UP", "EXE", "N817", "ERA001", "ANN001"] -"benchmarks/*" = ["D", "F", "T", "BLE", "FURB", "PLR", "E", "TD", "TRY", "S", "SIM", "EXE", "UP", "ERA001", "ANN001", "INP001"] "docs/*" = ["D"] -"docs/source/conf.py" = ["ERA001", "ANN001", "INP001"] +"docs/source/conf.py" = ["ANN001", "ERA001", "INP001"] +# CI and pre-commit invoke codespell with different paths, so we have a little +# redundancy here, and we intentionally drop python in the path. [tool.codespell] skip = [ - "./target", - "uv.lock", - "./python/tests/test_functions.py" + "*/tests/test_functions.py", + "*/target", + "./uv.lock", + "uv.lock", + "*/tpch/answers_sf1/*", ] count = true -ignore-words-list = [ - "ans", - "IST" -] +ignore-words-list = ["IST", "ans"] [dependency-groups] dev = [ - "maturin>=1.8.1", - "numpy>1.25.0;python_version<'3.14'", - "numpy>=2.3.2;python_version>='3.14'", - "pre-commit>=4.3.0", - "pyyaml>=6.0.3", - "pytest>=7.4.4", - "pytest-asyncio>=0.23.3", - "ruff>=0.9.1", - "toml>=0.10.2", - "pygithub==2.5.0", - "codespell==2.4.1", + "arro3-core==0.6.5", + "codespell==2.4.1", + "maturin>=1.8.1", + "nanoarrow==0.8.0", + "numpy>1.25.0;python_version<'3.14'", + "numpy>=2.3.2;python_version>='3.14'", + "pre-commit>=4.3.0", + "pyarrow>=19.0.0", + "pygithub==2.5.0", + "pytest-asyncio>=0.23.3", + "pytest>=7.4.4", + "pyyaml>=6.0.3", + "ruff>=0.15.1", + "toml>=0.10.2", ] docs = [ - "sphinx>=7.1.2", - "pydata-sphinx-theme==0.8.0", - "myst-parser>=3.0.1", - "jinja2>=3.1.5", - "ipython>=8.12.3", - "pandas>=2.0.3", - "pickleshare>=0.7.5", - "sphinx-autoapi>=3.4.0", - "setuptools>=75.3.0", + "ipython>=8.12.3", + "jinja2>=3.1.5", + "myst-parser>=3.0.1", + "pandas>=2.0.3", + "pickleshare>=0.7.5", + "pydata-sphinx-theme==0.8.0", + "setuptools>=75.3.0", + "sphinx-autoapi>=3.4.0", + "sphinx>=7.1.2", ] diff --git a/python/datafusion/__init__.py b/python/datafusion/__init__.py index 784d4ccc6..e4972411a 100644 --- a/python/datafusion/__init__.py +++ b/python/datafusion/__init__.py @@ -15,10 +15,44 @@ # specific language governing permissions and limitations # under the License. -"""DataFusion python package. - -This is a Python library that binds to Apache Arrow in-memory query engine DataFusion. -See https://datafusion.apache.org/python for more information. +"""DataFusion: an in-process query engine built on Apache Arrow. + +DataFusion is not a database -- it has no server and no external dependencies. +You create a :py:class:`SessionContext`, point it at data sources (Parquet, CSV, +JSON, Arrow IPC, Pandas, Polars, or raw Python dicts/lists), and run queries +using either SQL or the DataFrame API. + +Core abstractions +----------------- +- **SessionContext** -- entry point for loading data, running SQL, and creating + DataFrames. +- **DataFrame** -- lazy query builder. Every method returns a new DataFrame; + call :py:meth:`~datafusion.dataframe.DataFrame.collect` or a ``to_*`` + method to execute. +- **Expr** -- expression tree node for column references, literals, and function + calls. Build with :py:func:`col` and :py:func:`lit`. +- **functions** -- 290+ built-in scalar, aggregate, and window functions. + +Quick start +----------- + +>>> from datafusion import SessionContext, col +>>> from datafusion import functions as F +>>> ctx = SessionContext() +>>> df = ctx.from_pydict({"a": [1, 2, 3], "b": [4, 5, 6]}) +>>> result = ( +... df.filter(col("a") > 1) +... .with_column("total", col("a") + col("b")) +... .aggregate([], [F.sum(col("total")).alias("grand_total")]) +... ) +>>> result.to_pydict() +{'grand_total': [16]} + +User guide and full documentation: https://datafusion.apache.org/python + +AI agent reference (SQL-to-DataFrame mappings, expression-building patterns, +common pitfalls), written in a dense, skill-oriented format: +https://github.com/apache/datafusion-python/blob/main/SKILL.md """ from __future__ import annotations @@ -35,7 +69,7 @@ # The following imports are okay to remain as opaque to the user. from ._internal import Config -from .catalog import Catalog, Database, Table +from .catalog import Catalog, Table from .col import col, column from .common import DFSchema from .context import ( @@ -47,6 +81,7 @@ from .dataframe import ( DataFrame, DataFrameWriteOptions, + ExplainFormat, InsertOp, ParquetColumnOptions, ParquetWriterOptions, @@ -54,7 +89,8 @@ from .dataframe_formatter import configure_formatter from .expr import Expr, WindowFrame from .io import read_avro, read_csv, read_json, read_parquet -from .plan import ExecutionPlan, LogicalPlan +from .options import CsvReadOptions +from .plan import ExecutionPlan, LogicalPlan, Metric, MetricsSet from .record_batch import RecordBatch, RecordBatchStream from .user_defined import ( Accumulator, @@ -75,14 +111,17 @@ "AggregateUDF", "Catalog", "Config", + "CsvReadOptions", "DFSchema", "DataFrame", "DataFrameWriteOptions", - "Database", "ExecutionPlan", + "ExplainFormat", "Expr", "InsertOp", "LogicalPlan", + "Metric", + "MetricsSet", "ParquetColumnOptions", "ParquetWriterOptions", "RecordBatch", @@ -106,6 +145,7 @@ "lit", "literal", "object_store", + "options", "read_avro", "read_csv", "read_json", diff --git a/python/datafusion/catalog.py b/python/datafusion/catalog.py index da54d233d..20da5e671 100644 --- a/python/datafusion/catalog.py +++ b/python/datafusion/catalog.py @@ -27,8 +27,9 @@ if TYPE_CHECKING: import pyarrow as pa - from datafusion import DataFrame + from datafusion import DataFrame, SessionContext from datafusion.context import TableProviderExportable + from datafusion.expr import CreateExternalTable try: from warnings import deprecated # Python 3.13+ @@ -38,13 +39,61 @@ __all__ = [ "Catalog", + "CatalogList", "CatalogProvider", + "CatalogProviderList", "Schema", "SchemaProvider", "Table", ] +class CatalogList: + """DataFusion data catalog list.""" + + def __init__(self, catalog_list: df_internal.catalog.RawCatalogList) -> None: + """This constructor is not typically called by the end user.""" + self.catalog_list = catalog_list + + def __repr__(self) -> str: + """Print a string representation of the catalog list.""" + return self.catalog_list.__repr__() + + def names(self) -> set[str]: + """This is an alias for `catalog_names`.""" + return self.catalog_names() + + def catalog_names(self) -> set[str]: + """Returns the list of schemas in this catalog.""" + return self.catalog_list.catalog_names() + + @staticmethod + def memory_catalog(ctx: SessionContext | None = None) -> CatalogList: + """Create an in-memory catalog provider list.""" + catalog_list = df_internal.catalog.RawCatalogList.memory_catalog(ctx) + return CatalogList(catalog_list) + + def catalog(self, name: str = "datafusion") -> Catalog: + """Returns the catalog with the given ``name`` from this catalog.""" + catalog = self.catalog_list.catalog(name) + + return ( + Catalog(catalog) + if isinstance(catalog, df_internal.catalog.RawCatalog) + else catalog + ) + + def register_catalog( + self, + name: str, + catalog: Catalog | CatalogProvider | CatalogProviderExportable, + ) -> Catalog | None: + """Register a catalog with this catalog list.""" + if isinstance(catalog, Catalog): + return self.catalog_list.register_catalog(name, catalog.catalog) + return self.catalog_list.register_catalog(name, catalog) + + class Catalog: """DataFusion data catalog.""" @@ -65,9 +114,9 @@ def schema_names(self) -> set[str]: return self.catalog.schema_names() @staticmethod - def memory_catalog() -> Catalog: + def memory_catalog(ctx: SessionContext | None = None) -> Catalog: """Create an in-memory catalog provider.""" - catalog = df_internal.catalog.RawCatalog.memory_catalog() + catalog = df_internal.catalog.RawCatalog.memory_catalog(ctx) return Catalog(catalog) def schema(self, name: str = "public") -> Schema: @@ -80,11 +129,6 @@ def schema(self, name: str = "public") -> Schema: else schema ) - @deprecated("Use `schema` instead.") - def database(self, name: str = "public") -> Schema: - """Returns the database with the given ``name`` from this catalog.""" - return self.schema(name) - def register_schema( self, name: str, @@ -112,9 +156,9 @@ def __repr__(self) -> str: return self._raw_schema.__repr__() @staticmethod - def memory_schema() -> Schema: + def memory_schema(ctx: SessionContext | None = None) -> Schema: """Create an in-memory schema provider.""" - schema = df_internal.catalog.RawSchema.memory_schema() + schema = df_internal.catalog.RawSchema.memory_schema(ctx) return Schema(schema) def names(self) -> set[str]: @@ -141,10 +185,9 @@ def deregister_table(self, name: str) -> None: """Deregister a table provider from this schema.""" return self._raw_schema.deregister_table(name) - -@deprecated("Use `Schema` instead.") -class Database(Schema): - """See `Schema`.""" + def table_exist(self, name: str) -> bool: + """Determines if a table exists in this schema.""" + return self._raw_schema.table_exist(name) class Table: @@ -163,10 +206,12 @@ class Table: __slots__ = ("_inner",) def __init__( - self, table: Table | TableProviderExportable | DataFrame | pa.dataset.Dataset + self, + table: Table | TableProviderExportable | DataFrame | pa.dataset.Dataset, + ctx: SessionContext | None = None, ) -> None: """Constructor.""" - self._inner = df_internal.catalog.RawTable(table) + self._inner = df_internal.catalog.RawTable(table, ctx) def __repr__(self) -> str: """Print a string representation of the table.""" @@ -189,6 +234,58 @@ def kind(self) -> str: return self._inner.kind +class TableProviderFactory(ABC): + """Abstract class for defining a Python based Table Provider Factory.""" + + @abstractmethod + def create(self, cmd: CreateExternalTable) -> Table: + """Create a table using the :class:`CreateExternalTable`.""" + ... + + +class TableProviderFactoryExportable(Protocol): + """Type hint for object that has __datafusion_table_provider_factory__ PyCapsule. + + https://docs.rs/datafusion/latest/datafusion/catalog/trait.TableProviderFactory.html + """ + + def __datafusion_table_provider_factory__(self, session: Any) -> object: ... + + +class CatalogProviderList(ABC): + """Abstract class for defining a Python based Catalog Provider List.""" + + @abstractmethod + def catalog_names(self) -> set[str]: + """Set of the names of all catalogs in this catalog list.""" + ... + + @abstractmethod + def catalog( + self, name: str + ) -> CatalogProviderExportable | CatalogProvider | Catalog | None: + """Retrieve a specific catalog from this catalog list.""" + ... + + def register_catalog( # noqa: B027 + self, name: str, catalog: CatalogProviderExportable | CatalogProvider | Catalog + ) -> None: + """Add a catalog to this catalog list. + + This method is optional. If your catalog provides a fixed list of catalogs, you + do not need to implement this method. + """ + + +class CatalogProviderListExportable(Protocol): + """Type hint for object that has __datafusion_catalog_provider_list__ PyCapsule. + + https://docs.rs/datafusion/latest/datafusion/catalog/trait.CatalogProviderList.html + """ + + def __datafusion_catalog_provider_list__(self, session: Any) -> object: ... + + class CatalogProvider(ABC): """Abstract class for defining a Python based Catalog Provider.""" @@ -223,6 +320,15 @@ def deregister_schema(self, name: str, cascade: bool) -> None: # noqa: B027 """ +class CatalogProviderExportable(Protocol): + """Type hint for object that has __datafusion_catalog_provider__ PyCapsule. + + https://docs.rs/datafusion/latest/datafusion/catalog/trait.CatalogProvider.html + """ + + def __datafusion_catalog_provider__(self, session: Any) -> object: ... + + class SchemaProvider(ABC): """Abstract class for defining a Python based Schema Provider.""" @@ -271,4 +377,4 @@ class SchemaProviderExportable(Protocol): https://docs.rs/datafusion/latest/datafusion/catalog/trait.SchemaProvider.html """ - def __datafusion_schema_provider__(self) -> object: ... + def __datafusion_schema_provider__(self, session: Any) -> object: ... diff --git a/python/datafusion/context.py b/python/datafusion/context.py index 7dc06eb17..dd6790402 100644 --- a/python/datafusion/context.py +++ b/python/datafusion/context.py @@ -15,7 +15,32 @@ # specific language governing permissions and limitations # under the License. -"""Session Context and it's associated configuration.""" +""":py:class:`SessionContext` — entry point for running DataFusion queries. + +A :py:class:`SessionContext` holds registered tables, catalogs, and +configuration for the current session. It is the first object most programs +create: from it you register data, run SQL strings +(:py:meth:`SessionContext.sql`), read files +(:py:meth:`SessionContext.read_csv`, +:py:meth:`SessionContext.read_parquet`, ...), and construct +:py:class:`~datafusion.dataframe.DataFrame` objects in memory +(:py:meth:`SessionContext.from_pydict`, +:py:meth:`SessionContext.from_arrow`). + +Session behavior (memory limits, batch size, configured optimizer passes, +...) is controlled by :py:class:`SessionConfig` and +:py:class:`RuntimeEnvBuilder`; SQL dialect limits are controlled by +:py:class:`SQLOptions`. + +Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> ctx.sql("SELECT 1 AS n").to_pydict() + {'n': [1]} + +See :ref:`user_guide_concepts` in the online documentation for the broader +execution model. +""" from __future__ import annotations @@ -31,9 +56,22 @@ import pyarrow as pa -from datafusion.catalog import Catalog +from datafusion.catalog import ( + Catalog, + CatalogList, + CatalogProviderExportable, + CatalogProviderList, + CatalogProviderListExportable, + TableProviderFactory, + TableProviderFactoryExportable, +) from datafusion.dataframe import DataFrame from datafusion.expr import sort_list_to_raw_sort_list +from datafusion.options import ( + DEFAULT_MAX_INFER_SCHEMA, + CsvReadOptions, + _convert_table_partition_cols, +) from datafusion.record_batch import RecordBatchStream from ._internal import RuntimeEnvBuilder as RuntimeEnvBuilderInternal @@ -50,7 +88,8 @@ import polars as pl # type: ignore[import] from datafusion.catalog import CatalogProvider, Table - from datafusion.expr import SortKey + from datafusion.common import DFSchema + from datafusion.expr import Expr, SortKey from datafusion.plan import ExecutionPlan, LogicalPlan from datafusion.user_defined import ( AggregateUDF, @@ -88,16 +127,7 @@ class TableProviderExportable(Protocol): https://datafusion.apache.org/python/user-guide/io/table_provider.html """ - def __datafusion_table_provider__(self) -> object: ... # noqa: D105 - - -class CatalogProviderExportable(Protocol): - """Type hint for object that has __datafusion_catalog_provider__ PyCapsule. - - https://docs.rs/datafusion/latest/datafusion/catalog/trait.CatalogProvider.html - """ - - def __datafusion_catalog_provider__(self) -> object: ... # noqa: D105 + def __datafusion_table_provider__(self, session: Any) -> object: ... # noqa: D105 class SessionConfig: @@ -292,6 +322,19 @@ def set(self, key: str, value: str) -> SessionConfig: self.config_internal = self.config_internal.set(key, value) return self + def with_extension(self, extension: Any) -> SessionConfig: + """Create a new configuration using an extension. + + Args: + extension: A custom configuration extension object. These are + shared from another DataFusion extension library. + + Returns: + A new :py:class:`SessionConfig` object with the updated setting. + """ + self.config_internal = self.config_internal.with_extension(extension) + return self + class RuntimeEnvBuilder: """Runtime configuration options.""" @@ -367,9 +410,8 @@ def with_fair_spill_pool(self, size: int) -> RuntimeEnvBuilder: Returns: A new :py:class:`RuntimeEnvBuilder` object with the updated setting. - Examples usage:: - - config = RuntimeEnvBuilder().with_fair_spill_pool(1024) + Examples: + >>> config = dfn.RuntimeEnvBuilder().with_fair_spill_pool(1024) """ self.config_internal = self.config_internal.with_fair_spill_pool(size) return self @@ -387,9 +429,8 @@ def with_greedy_memory_pool(self, size: int) -> RuntimeEnvBuilder: Returns: A new :py:class:`RuntimeEnvBuilder` object with the updated setting. - Example usage:: - - config = RuntimeEnvBuilder().with_greedy_memory_pool(1024) + Examples: + >>> config = dfn.RuntimeEnvBuilder().with_greedy_memory_pool(1024) """ self.config_internal = self.config_internal.with_greedy_memory_pool(size) return self @@ -403,19 +444,13 @@ def with_temp_file_path(self, path: str | pathlib.Path) -> RuntimeEnvBuilder: Returns: A new :py:class:`RuntimeEnvBuilder` object with the updated setting. - Example usage:: - - config = RuntimeEnvBuilder().with_temp_file_path("/tmp") + Examples: + >>> config = dfn.RuntimeEnvBuilder().with_temp_file_path("/tmp") """ self.config_internal = self.config_internal.with_temp_file_path(str(path)) return self -@deprecated("Use `RuntimeEnvBuilder` instead.") -class RuntimeConfig(RuntimeEnvBuilder): - """See `RuntimeEnvBuilder`.""" - - class SQLOptions: """Options to be used when performing SQL queries.""" @@ -440,9 +475,8 @@ def with_allow_ddl(self, allow: bool = True) -> SQLOptions: Returns: A new :py:class:`SQLOptions` object with the updated setting. - Example usage:: - - options = SQLOptions().with_allow_ddl(True) + Examples: + >>> options = dfn.SQLOptions().with_allow_ddl(True) """ self.options_internal = self.options_internal.with_allow_ddl(allow) return self @@ -458,9 +492,8 @@ def with_allow_dml(self, allow: bool = True) -> SQLOptions: Returns: A new :py:class:`SQLOptions` object with the updated setting. - Example usage:: - - options = SQLOptions().with_allow_dml(True) + Examples: + >>> options = dfn.SQLOptions().with_allow_dml(True) """ self.options_internal = self.options_internal.with_allow_dml(allow) return self @@ -474,9 +507,8 @@ def with_allow_statements(self, allow: bool = True) -> SQLOptions: Returns: A new :py:class:SQLOptions` object with the updated setting. - Example usage:: - - options = SQLOptions().with_allow_statements(True) + Examples: + >>> options = dfn.SQLOptions().with_allow_statements(True) """ self.options_internal = self.options_internal.with_allow_statements(allow) return self @@ -557,6 +589,15 @@ def register_object_store( """ self.ctx.register_object_store(schema, store, host) + def deregister_object_store(self, schema: str, host: str | None = None) -> None: + """Remove an object store from the session. + + Args: + schema: The data source schema (e.g. ``"s3://"``). + host: URL for the host (e.g. bucket name). + """ + self.ctx.deregister_object_store(schema, host) + def register_listing_table( self, name: str, @@ -584,7 +625,7 @@ def register_listing_table( """ if table_partition_cols is None: table_partition_cols = [] - table_partition_cols = self._convert_table_partition_cols(table_partition_cols) + table_partition_cols = _convert_table_partition_cols(table_partition_cols) self.ctx.register_listing_table( name, str(path), @@ -764,14 +805,6 @@ def from_arrow( """ return DataFrame(self.ctx.from_arrow(data, name)) - @deprecated("Use ``from_arrow`` instead.") - def from_arrow_table(self, data: pa.Table, name: str | None = None) -> DataFrame: - """Create a :py:class:`~datafusion.dataframe.DataFrame` from an Arrow table. - - This is an alias for :py:func:`from_arrow`. - """ - return self.from_arrow(data, name) - def from_pandas(self, data: pd.DataFrame, name: str | None = None) -> DataFrame: """Create a :py:class:`~datafusion.dataframe.DataFrame` from a Pandas DataFrame. @@ -828,10 +861,36 @@ def deregister_table(self, name: str) -> None: """Remove a table from the session.""" self.ctx.deregister_table(name) + def register_table_factory( + self, + format: str, + factory: TableProviderFactory | TableProviderFactoryExportable, + ) -> None: + """Register a :py:class:`~datafusion.TableProviderFactoryExportable`. + + The registered factory can be referenced from SQL DDL statements executed + against this context. + + Args: + format: The value to be used in `STORED AS ${format}` clause. + factory: A PyCapsule that implements :class:`TableProviderFactoryExportable` + """ + self.ctx.register_table_factory(format, factory) + def catalog_names(self) -> set[str]: """Returns the list of catalogs in this context.""" return self.ctx.catalog_names() + def register_catalog_provider_list( + self, + provider: CatalogProviderListExportable | CatalogProviderList | CatalogList, + ) -> None: + """Register a catalog provider list.""" + if isinstance(provider, CatalogList): + self.ctx.register_catalog_provider_list(provider.catalog) + else: + self.ctx.register_catalog_provider_list(provider) + def register_catalog_provider( self, name: str, provider: CatalogProviderExportable | CatalogProvider | Catalog ) -> None: @@ -857,6 +916,35 @@ def register_udtf(self, func: TableFunction) -> None: """Register a user defined table function.""" self.ctx.register_udtf(func._udtf) + def register_batch(self, name: str, batch: pa.RecordBatch) -> None: + """Register a single :py:class:`pa.RecordBatch` as a table. + + Args: + name: Name of the resultant table. + batch: Record batch to register as a table. + + Examples: + >>> ctx = dfn.SessionContext() + >>> batch = pa.RecordBatch.from_pydict({"a": [1, 2, 3]}) + >>> ctx.register_batch("batch_tbl", batch) + >>> ctx.sql("SELECT * FROM batch_tbl").collect()[0].column(0) + + [ + 1, + 2, + 3 + ] + """ + self.ctx.register_batch(name, batch) + + def deregister_udtf(self, name: str) -> None: + """Remove a user-defined table function from the session. + + Args: + name: Name of the UDTF to deregister. + """ + self.ctx.deregister_udtf(name) + def register_record_batches( self, name: str, partitions: list[list[pa.RecordBatch]] ) -> None: @@ -905,7 +993,7 @@ def register_parquet( """ if table_partition_cols is None: table_partition_cols = [] - table_partition_cols = self._convert_table_partition_cols(table_partition_cols) + table_partition_cols = _convert_table_partition_cols(table_partition_cols) self.ctx.register_parquet( name, str(path), @@ -924,9 +1012,10 @@ def register_csv( schema: pa.Schema | None = None, has_header: bool = True, delimiter: str = ",", - schema_infer_max_records: int = 1000, + schema_infer_max_records: int = DEFAULT_MAX_INFER_SCHEMA, file_extension: str = ".csv", file_compression_type: str | None = None, + options: CsvReadOptions | None = None, ) -> None: """Register a CSV file as a table. @@ -946,18 +1035,46 @@ def register_csv( file_extension: File extension; only files with this extension are selected for data input. file_compression_type: File compression type. + options: Set advanced options for CSV reading. This cannot be + combined with any of the other options in this method. """ - path = [str(p) for p in path] if isinstance(path, list) else str(path) + path_arg = [str(p) for p in path] if isinstance(path, list) else str(path) + + if options is not None and ( + schema is not None + or not has_header + or delimiter != "," + or schema_infer_max_records != DEFAULT_MAX_INFER_SCHEMA + or file_extension != ".csv" + or file_compression_type is not None + ): + message = ( + "Combining CsvReadOptions parameter with additional options " + "is not supported. Use CsvReadOptions to set parameters." + ) + warnings.warn( + message, + category=UserWarning, + stacklevel=2, + ) + + options = ( + options + if options is not None + else CsvReadOptions( + schema=schema, + has_header=has_header, + delimiter=delimiter, + schema_infer_max_records=schema_infer_max_records, + file_extension=file_extension, + file_compression_type=file_compression_type, + ) + ) self.ctx.register_csv( name, - path, - schema, - has_header, - delimiter, - schema_infer_max_records, - file_extension, - file_compression_type, + path_arg, + options.to_inner(), ) def register_json( @@ -988,7 +1105,7 @@ def register_json( """ if table_partition_cols is None: table_partition_cols = [] - table_partition_cols = self._convert_table_partition_cols(table_partition_cols) + table_partition_cols = _convert_table_partition_cols(table_partition_cols) self.ctx.register_json( name, str(path), @@ -1021,11 +1138,91 @@ def register_avro( """ if table_partition_cols is None: table_partition_cols = [] - table_partition_cols = self._convert_table_partition_cols(table_partition_cols) + table_partition_cols = _convert_table_partition_cols(table_partition_cols) self.ctx.register_avro( name, str(path), schema, file_extension, table_partition_cols ) + def register_arrow( + self, + name: str, + path: str | pathlib.Path, + schema: pa.Schema | None = None, + file_extension: str = ".arrow", + table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None, + ) -> None: + """Register an Arrow IPC file as a table. + + The registered table can be referenced from SQL statements executed + against this context. + + Args: + name: Name of the table to register. + path: Path to the Arrow IPC file. + schema: The data source schema. + file_extension: File extension to select. + table_partition_cols: Partition columns. + + Examples: + >>> import tempfile, os + >>> ctx = dfn.SessionContext() + >>> table = pa.table({"x": [10, 20, 30]}) + >>> with tempfile.TemporaryDirectory() as tmpdir: + ... path = os.path.join(tmpdir, "data.arrow") + ... with pa.ipc.new_file(path, table.schema) as writer: + ... writer.write_table(table) + ... ctx.register_arrow("arrow_tbl", path) + ... ctx.sql("SELECT * FROM arrow_tbl").collect()[0].column(0) + + [ + 10, + 20, + 30 + ] + + Provide an explicit ``schema`` to override schema inference: + + >>> with tempfile.TemporaryDirectory() as tmpdir: + ... path = os.path.join(tmpdir, "data.arrow") + ... with pa.ipc.new_file(path, table.schema) as writer: + ... writer.write_table(table) + ... ctx.register_arrow( + ... "arrow_schema", + ... path, + ... schema=pa.schema([("x", pa.int64())]), + ... ) + ... ctx.sql("SELECT * FROM arrow_schema").collect()[0].column(0) + + [ + 10, + 20, + 30 + ] + + Use ``file_extension`` to read files with a non-default extension: + + >>> with tempfile.TemporaryDirectory() as tmpdir: + ... path = os.path.join(tmpdir, "data.ipc") + ... with pa.ipc.new_file(path, table.schema) as writer: + ... writer.write_table(table) + ... ctx.register_arrow( + ... "arrow_ipc", path, file_extension=".ipc" + ... ) + ... ctx.sql("SELECT * FROM arrow_ipc").collect()[0].column(0) + + [ + 10, + 20, + 30 + ] + """ + if table_partition_cols is None: + table_partition_cols = [] + table_partition_cols = _convert_table_partition_cols(table_partition_cols) + self.ctx.register_arrow( + name, str(path), schema, file_extension, table_partition_cols + ) + def register_dataset(self, name: str, dataset: pa.dataset.Dataset) -> None: """Register a :py:class:`pa.dataset.Dataset` as a table. @@ -1039,26 +1236,42 @@ def register_udf(self, udf: ScalarUDF) -> None: """Register a user-defined function (UDF) with the context.""" self.ctx.register_udf(udf._udf) + def deregister_udf(self, name: str) -> None: + """Remove a user-defined scalar function from the session. + + Args: + name: Name of the UDF to deregister. + """ + self.ctx.deregister_udf(name) + def register_udaf(self, udaf: AggregateUDF) -> None: """Register a user-defined aggregation function (UDAF) with the context.""" self.ctx.register_udaf(udaf._udaf) + def deregister_udaf(self, name: str) -> None: + """Remove a user-defined aggregate function from the session. + + Args: + name: Name of the UDAF to deregister. + """ + self.ctx.deregister_udaf(name) + def register_udwf(self, udwf: WindowUDF) -> None: """Register a user-defined window function (UDWF) with the context.""" self.ctx.register_udwf(udwf._udwf) + def deregister_udwf(self, name: str) -> None: + """Remove a user-defined window function from the session. + + Args: + name: Name of the UDWF to deregister. + """ + self.ctx.deregister_udwf(name) + def catalog(self, name: str = "datafusion") -> Catalog: """Retrieve a catalog by name.""" return Catalog(self.ctx.catalog(name)) - @deprecated( - "Use the catalog provider interface ``SessionContext.Catalog`` to " - "examine available catalogs, schemas and tables" - ) - def tables(self) -> set[str]: - """Deprecated.""" - return self.ctx.tables() - def table(self, name: str) -> DataFrame: """Retrieve a previously registered table by name.""" return DataFrame(self.ctx.table(name)) @@ -1075,6 +1288,121 @@ def session_id(self) -> str: """Return an id that uniquely identifies this :py:class:`SessionContext`.""" return self.ctx.session_id() + def session_start_time(self) -> str: + """Return the session start time as an RFC 3339 formatted string. + + Examples: + >>> ctx = SessionContext() + >>> ctx.session_start_time() # doctest: +SKIP + '2026-01-01T12:34:56.123456789+00:00' + """ + return self.ctx.session_start_time() + + def enable_ident_normalization(self) -> bool: + """Return whether identifier normalization (lowercasing) is enabled. + + Examples: + >>> ctx = SessionContext() + >>> ctx.enable_ident_normalization() + True + """ + return self.ctx.enable_ident_normalization() + + def parse_sql_expr(self, sql: str, schema: DFSchema) -> Expr: + """Parse a SQL expression string into a logical expression. + + Args: + sql: SQL expression string. + schema: Schema to use for resolving column references. + + Returns: + Parsed expression. + + Examples: + >>> from datafusion.common import DFSchema + >>> ctx = SessionContext() + >>> schema = DFSchema.empty() + >>> ctx.parse_sql_expr("1 + 2", schema=schema) + Expr(Int64(1) + Int64(2)) + """ + from datafusion.expr import Expr # noqa: PLC0415 + + return Expr(self.ctx.parse_sql_expr(sql, schema)) + + def execute_logical_plan(self, plan: LogicalPlan) -> DataFrame: + """Execute a :py:class:`~datafusion.plan.LogicalPlan` and return a DataFrame. + + Args: + plan: Logical plan to execute. + + Returns: + DataFrame resulting from the execution. + + Examples: + >>> ctx = SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> plan = df.logical_plan() + >>> df2 = ctx.execute_logical_plan(plan) + >>> df2.collect()[0].column(0) + + [ + 1, + 2, + 3 + ] + """ + return DataFrame(self.ctx.execute_logical_plan(plan._raw_plan)) + + def refresh_catalogs(self) -> None: + """Refresh catalog metadata. + + Examples: + >>> ctx = SessionContext() + >>> ctx.refresh_catalogs() + """ + self.ctx.refresh_catalogs() + + def remove_optimizer_rule(self, name: str) -> bool: + """Remove an optimizer rule by name. + + Args: + name: Name of the optimizer rule to remove. + + Returns: + True if a rule with the given name was found and removed. + + Examples: + >>> ctx = SessionContext() + >>> ctx.remove_optimizer_rule("nonexistent_rule") + False + """ + return self.ctx.remove_optimizer_rule(name) + + def table_provider(self, name: str) -> Table: + """Return the :py:class:`~datafusion.catalog.Table` for the given table name. + + Args: + name: Name of the table. + + Returns: + The table provider. + + Raises: + KeyError: If the table is not found. + + Examples: + >>> import pyarrow as pa + >>> ctx = SessionContext() + >>> batch = pa.RecordBatch.from_pydict({"x": [1, 2]}) + >>> ctx.register_record_batches("my_table", [[batch]]) + >>> tbl = ctx.table_provider("my_table") + >>> tbl.schema + x: int64 + """ + from datafusion.catalog import Table # noqa: PLC0415 + + return Table(self.ctx.table_provider(name)) + def read_json( self, path: str | pathlib.Path, @@ -1101,7 +1429,7 @@ def read_json( """ if table_partition_cols is None: table_partition_cols = [] - table_partition_cols = self._convert_table_partition_cols(table_partition_cols) + table_partition_cols = _convert_table_partition_cols(table_partition_cols) return DataFrame( self.ctx.read_json( str(path), @@ -1119,10 +1447,11 @@ def read_csv( schema: pa.Schema | None = None, has_header: bool = True, delimiter: str = ",", - schema_infer_max_records: int = 1000, + schema_infer_max_records: int = DEFAULT_MAX_INFER_SCHEMA, file_extension: str = ".csv", table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None, file_compression_type: str | None = None, + options: CsvReadOptions | None = None, ) -> DataFrame: """Read a CSV data source. @@ -1140,26 +1469,51 @@ def read_csv( selected for data input. table_partition_cols: Partition columns. file_compression_type: File compression type. + options: Set advanced options for CSV reading. This cannot be + combined with any of the other options in this method. Returns: DataFrame representation of the read CSV files """ - if table_partition_cols is None: - table_partition_cols = [] - table_partition_cols = self._convert_table_partition_cols(table_partition_cols) + path_arg = [str(p) for p in path] if isinstance(path, list) else str(path) + + if options is not None and ( + schema is not None + or not has_header + or delimiter != "," + or schema_infer_max_records != DEFAULT_MAX_INFER_SCHEMA + or file_extension != ".csv" + or table_partition_cols is not None + or file_compression_type is not None + ): + message = ( + "Combining CsvReadOptions parameter with additional options " + "is not supported. Use CsvReadOptions to set parameters." + ) + warnings.warn( + message, + category=UserWarning, + stacklevel=2, + ) - path = [str(p) for p in path] if isinstance(path, list) else str(path) + options = ( + options + if options is not None + else CsvReadOptions( + schema=schema, + has_header=has_header, + delimiter=delimiter, + schema_infer_max_records=schema_infer_max_records, + file_extension=file_extension, + table_partition_cols=table_partition_cols, + file_compression_type=file_compression_type, + ) + ) return DataFrame( self.ctx.read_csv( - path, - schema, - has_header, - delimiter, - schema_infer_max_records, - file_extension, - table_partition_cols, - file_compression_type, + path_arg, + options.to_inner(), ) ) @@ -1197,7 +1551,7 @@ def read_parquet( """ if table_partition_cols is None: table_partition_cols = [] - table_partition_cols = self._convert_table_partition_cols(table_partition_cols) + table_partition_cols = _convert_table_partition_cols(table_partition_cols) file_sort_order = self._convert_file_sort_order(file_sort_order) return DataFrame( self.ctx.read_parquet( @@ -1231,11 +1585,91 @@ def read_avro( """ if file_partition_cols is None: file_partition_cols = [] - file_partition_cols = self._convert_table_partition_cols(file_partition_cols) + file_partition_cols = _convert_table_partition_cols(file_partition_cols) return DataFrame( self.ctx.read_avro(str(path), schema, file_partition_cols, file_extension) ) + def read_arrow( + self, + path: str | pathlib.Path, + schema: pa.Schema | None = None, + file_extension: str = ".arrow", + file_partition_cols: list[tuple[str, str | pa.DataType]] | None = None, + ) -> DataFrame: + """Create a :py:class:`DataFrame` for reading an Arrow IPC data source. + + Args: + path: Path to the Arrow IPC file. + schema: The data source schema. + file_extension: File extension to select. + file_partition_cols: Partition columns. + + Returns: + DataFrame representation of the read Arrow IPC file. + + Examples: + >>> import tempfile, os + >>> ctx = dfn.SessionContext() + >>> table = pa.table({"a": [1, 2, 3]}) + >>> with tempfile.TemporaryDirectory() as tmpdir: + ... path = os.path.join(tmpdir, "data.arrow") + ... with pa.ipc.new_file(path, table.schema) as writer: + ... writer.write_table(table) + ... df = ctx.read_arrow(path) + ... df.collect()[0].column(0) + + [ + 1, + 2, + 3 + ] + + Provide an explicit ``schema`` to override schema inference: + + >>> with tempfile.TemporaryDirectory() as tmpdir: + ... path = os.path.join(tmpdir, "data.arrow") + ... with pa.ipc.new_file(path, table.schema) as writer: + ... writer.write_table(table) + ... df = ctx.read_arrow(path, schema=pa.schema([("a", pa.int64())])) + ... df.collect()[0].column(0) + + [ + 1, + 2, + 3 + ] + + Use ``file_extension`` to read files with a non-default extension: + + >>> with tempfile.TemporaryDirectory() as tmpdir: + ... path = os.path.join(tmpdir, "data.ipc") + ... with pa.ipc.new_file(path, table.schema) as writer: + ... writer.write_table(table) + ... df = ctx.read_arrow(path, file_extension=".ipc") + ... df.collect()[0].column(0) + + [ + 1, + 2, + 3 + ] + """ + if file_partition_cols is None: + file_partition_cols = [] + file_partition_cols = _convert_table_partition_cols(file_partition_cols) + return DataFrame( + self.ctx.read_arrow(str(path), schema, file_extension, file_partition_cols) + ) + + def read_empty(self) -> DataFrame: + """Create an empty :py:class:`DataFrame` with no columns or rows. + + See Also: + This is an alias for :meth:`empty_table`. + """ + return self.empty_table() + def read_table( self, table: Table | TableProviderExportable | DataFrame | pa.dataset.Dataset ) -> DataFrame: @@ -1301,3 +1735,19 @@ def _convert_table_partition_cols( ) return converted_table_partition_cols + + def __datafusion_task_context_provider__(self) -> Any: + """Access the PyCapsule FFI_TaskContextProvider.""" + return self.ctx.__datafusion_task_context_provider__() + + def __datafusion_logical_extension_codec__(self) -> Any: + """Access the PyCapsule FFI_LogicalExtensionCodec.""" + return self.ctx.__datafusion_logical_extension_codec__() + + def with_logical_extension_codec(self, codec: Any) -> SessionContext: + """Create a new session context with specified codec. + + This only supports codecs that have been implemented using the + FFI interface. + """ + return self.ctx.with_logical_extension_codec(codec) diff --git a/python/datafusion/dataframe.py b/python/datafusion/dataframe.py index d302c12a5..2b07861da 100644 --- a/python/datafusion/dataframe.py +++ b/python/datafusion/dataframe.py @@ -14,9 +14,32 @@ # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. -""":py:class:`DataFrame` is one of the core concepts in DataFusion. - -See :ref:`user_guide_concepts` in the online documentation for more information. +""":py:class:`DataFrame` — lazy, chainable query representation. + +A :py:class:`DataFrame` is a logical plan over one or more data sources. +Methods that reshape the plan (:py:meth:`DataFrame.select`, +:py:meth:`DataFrame.filter`, :py:meth:`DataFrame.aggregate`, +:py:meth:`DataFrame.sort`, :py:meth:`DataFrame.join`, +:py:meth:`DataFrame.limit`, the set-operation methods, ...) return a new +:py:class:`DataFrame` and do no work until a terminal method such as +:py:meth:`DataFrame.collect`, :py:meth:`DataFrame.to_pydict`, +:py:meth:`DataFrame.show`, or one of the ``write_*`` methods is called. + +DataFrames are produced from a +:py:class:`~datafusion.context.SessionContext`, typically via +:py:meth:`~datafusion.context.SessionContext.sql`, +:py:meth:`~datafusion.context.SessionContext.read_csv`, +:py:meth:`~datafusion.context.SessionContext.read_parquet`, or +:py:meth:`~datafusion.context.SessionContext.from_pydict`. + +Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3], "b": [10, 20, 30]}) + >>> df.filter(col("a") > 1).select("b").to_pydict() + {'b': [20, 30]} + +See :ref:`user_guide_concepts` in the online documentation for a high-level +overview of the execution model. """ from __future__ import annotations @@ -44,6 +67,7 @@ Expr, SortExpr, SortKey, + _to_raw_expr, ensure_expr, ensure_expr_list, expr_list_to_raw_expr_list, @@ -65,6 +89,25 @@ from enum import Enum +class ExplainFormat(Enum): + """Output format for explain plans. + + Controls how the query plan is rendered in :py:meth:`DataFrame.explain`. + """ + + INDENT = "indent" + """Default indented text format.""" + + TREE = "tree" + """Tree-style visual format with box-drawing characters.""" + + PGJSON = "pgjson" + """PostgreSQL-compatible JSON format for use with visualization tools.""" + + GRAPHVIZ = "graphviz" + """Graphviz DOT format for graph rendering.""" + + # excerpt from deltalake # https://github.com/apache/datafusion-python/pull/981#discussion_r1905619163 class Compression(Enum): @@ -327,10 +370,11 @@ def into_view(self, temporary: bool = False) -> Table: >>> df = ctx.sql("SELECT 1 AS value") >>> view = df.into_view() >>> ctx.register_table("values_view", view) - >>> df.collect() # The DataFrame is still usable - >>> ctx.sql("SELECT value FROM values_view").collect() + >>> result = ctx.sql("SELECT value FROM values_view").collect() + >>> result[0].column("value").to_pylist() + [1] """ - from datafusion.catalog import Table as _Table + from datafusion.catalog import Table as _Table # noqa: PLC0415 return _Table(self.df.into_view(temporary)) @@ -394,16 +438,79 @@ def schema(self) -> pa.Schema: """ return self.df.schema() - @deprecated( - "select_columns() is deprecated. Use :py:meth:`~DataFrame.select` instead" - ) - def select_columns(self, *args: str) -> DataFrame: - """Filter the DataFrame by columns. + def column(self, name: str) -> Expr: + """Return a fully qualified column expression for ``name``. + + Resolves an unqualified column name against this DataFrame's schema + and returns an :py:class:`Expr` whose underlying column reference + includes the table qualifier. This is especially useful after joins, + where the same column name may appear in multiple relations. + + Args: + name: Unqualified column name to look up. Returns: - DataFrame only containing the specified columns. + A fully qualified column expression. + + Raises: + Exception: If the column is not found or is ambiguous (exists in + multiple relations). + + Examples: + Resolve a column from a simple DataFrame: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2], "b": [3, 4]}) + >>> expr = df.column("a") + >>> df.select(expr).to_pydict() + {'a': [1, 2]} + + Resolve qualified columns after a join: + + >>> left = ctx.from_pydict({"id": [1, 2], "x": [10, 20]}) + >>> right = ctx.from_pydict({"id": [1, 2], "y": [30, 40]}) + >>> joined = left.join(right, on="id", how="inner") + >>> expr = joined.column("y") + >>> joined.select("id", expr).sort("id").to_pydict() + {'id': [1, 2], 'y': [30, 40]} """ - return self.select(*args) + return self.find_qualified_columns(name)[0] + + def col(self, name: str) -> Expr: + """Alias for :py:meth:`column`. + + See Also: + :py:meth:`column` + """ + return self.column(name) + + def find_qualified_columns(self, *names: str) -> list[Expr]: + """Return fully qualified column expressions for the given names. + + This is a batch version of :py:meth:`column` — it resolves each + unqualified name against the DataFrame's schema and returns a list + of qualified column expressions. + + Args: + names: Unqualified column names to look up. + + Returns: + List of fully qualified column expressions, one per name. + + Raises: + Exception: If any column is not found or is ambiguous. + + Examples: + Resolve multiple columns at once: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2], "b": [3, 4], "c": [5, 6]}) + >>> exprs = df.find_qualified_columns("a", "c") + >>> df.select(*exprs).to_pydict() + {'a': [1, 2], 'c': [5, 6]} + """ + raw_exprs = self.df.find_qualified_columns(list(names)) + return [Expr(e) for e in raw_exprs] def select_exprs(self, *args: str) -> DataFrame: """Project arbitrary list of expression strings into a new DataFrame. @@ -419,21 +526,29 @@ def select_exprs(self, *args: str) -> DataFrame: def select(self, *exprs: Expr | str) -> DataFrame: """Project arbitrary expressions into a new :py:class:`DataFrame`. + String arguments are treated as column names; :py:class:`~datafusion.expr.Expr` + arguments can reshape, rename, or compute new columns. + Args: exprs: Either column names or :py:class:`~datafusion.expr.Expr` to select. Returns: DataFrame after projection. It has one column for each expression. - Example usage: + Examples: + Select columns by name: - The following example will return 3 columns from the original dataframe. - The first two columns will be the original column ``a`` and ``b`` since the - string "a" is assumed to refer to column selection. Also a duplicate of - column ``a`` will be returned with the column name ``alternate_a``:: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3], "b": [10, 20, 30]}) + >>> df.select("a").to_pydict() + {'a': [1, 2, 3]} - df = df.select("a", col("b"), col("a").alias("alternate_a")) + Mix column names, expressions, and aliases. The string ``"a"`` selects + column ``a`` directly; ``col("a").alias("alternate_a")`` returns a + duplicate under a new name: + >>> df.select("a", col("b"), col("a").alias("alternate_a")).to_pydict() + {'a': [1, 2, 3], 'b': [10, 20, 30], 'alternate_a': [1, 2, 3]} """ exprs_internal = expr_list_to_raw_expr_list(exprs) return DataFrame(self.df.select(*exprs_internal)) @@ -441,30 +556,61 @@ def select(self, *exprs: Expr | str) -> DataFrame: def drop(self, *columns: str) -> DataFrame: """Drop arbitrary amount of columns. - Column names are case-sensitive and do not require double quotes like - other operations such as `select`. Leading and trailing double quotes - are allowed and will be automatically stripped if present. + Column names are case-sensitive and require double quotes to be dropped + if the original name is not strictly lower case. Args: - columns: Column names to drop from the dataframe. Both ``column_name`` - and ``"column_name"`` are accepted. + columns: Column names to drop from the dataframe. Returns: DataFrame with those columns removed in the projection. - Example Usage:: + Examples: + To drop a lower-cased column 'a' + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2], "b": [3, 4]}) + >>> df.drop("a").schema().names + ['b'] + + Or to drop an upper-cased column 'A' - df.drop('ID_For_Students') # Works - df.drop('"ID_For_Students"') # Also works (quotes stripped) + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"A": [1, 2], "b": [3, 4]}) + >>> df.drop('"A"').schema().names + ['b'] """ - normalized_columns = [] - for col in columns: - if col.startswith('"') and col.endswith('"'): - normalized_columns.append(col.strip('"')) # Strip double quotes - else: - normalized_columns.append(col) + return DataFrame(self.df.drop(*columns)) + + def window(self, *exprs: Expr) -> DataFrame: + """Add window function columns to the DataFrame. - return DataFrame(self.df.drop(*normalized_columns)) + Applies the given window function expressions and appends the results + as new columns. + + Args: + exprs: Window function expressions to evaluate. + + Returns: + DataFrame with new window function columns appended. + + Examples: + Add a row number within each group: + + >>> import datafusion.functions as f + >>> from datafusion import col + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3], "b": ["x", "x", "y"]}) + >>> df = df.window( + ... f.row_number( + ... partition_by=[col("b")], order_by=[col("a")] + ... ).alias("rn") + ... ) + >>> "rn" in df.schema().names + True + """ + raw = expr_list_to_raw_expr_list(exprs) + return DataFrame(self.df.window(*raw)) def filter(self, *predicates: Expr | str) -> DataFrame: """Return a DataFrame for which ``predicate`` evaluates to ``True``. @@ -477,11 +623,13 @@ def filter(self, *predicates: Expr | str) -> DataFrame: that will be parsed against the DataFrame schema. If more complex logic is required, see the logical operations in :py:mod:`~datafusion.functions`. - Example:: - - from datafusion import col, lit - df.filter(col("a") > lit(1)) - df.filter("a > 1") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> df.filter(col("a") > lit(1)).to_pydict() + {'a': [2, 3]} + >>> df.filter("a > 1").to_pydict() + {'a': [2, 3]} Args: predicates: Predicate expression(s) or SQL strings to filter the DataFrame. @@ -504,14 +652,12 @@ def parse_sql_expr(self, expr: str) -> Expr: The expression is created and processed against the current schema. - Example:: - - from datafusion import col, lit - df.parse_sql_expr("a > 1") - - should produce: - - col("a") > lit(1) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> expr = df.parse_sql_expr("a > 1") + >>> df.filter(expr).to_pydict() + {'a': [2, 3]} Args: expr: Expression string to be converted to datafusion expression @@ -528,10 +674,11 @@ def with_column(self, name: str, expr: Expr | str) -> DataFrame: :func:`datafusion.col` or :func:`datafusion.lit`, or a SQL expression string that will be parsed against the DataFrame schema. - Example:: - - from datafusion import col, lit - df.with_column("b", col("a") + lit(1)) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2]}) + >>> df.with_column("b", col("a") + lit(10)).to_pydict() + {'a': [1, 2], 'b': [11, 12]} Args: name: Name of the column to add. @@ -630,12 +777,44 @@ def aggregate( ) -> DataFrame: """Aggregates the rows of the current DataFrame. + By default each unique combination of the ``group_by`` columns + produces one row. To get multiple levels of subtotals in a + single pass, pass a + :py:class:`~datafusion.expr.GroupingSet` expression + (created via + :py:meth:`~datafusion.expr.GroupingSet.rollup`, + :py:meth:`~datafusion.expr.GroupingSet.cube`, or + :py:meth:`~datafusion.expr.GroupingSet.grouping_sets`) + as the ``group_by`` argument. See the + :ref:`aggregation` user guide for detailed examples. + Args: - group_by: Sequence of expressions or column names to group by. + group_by: Sequence of expressions or column names to group + by. A :py:class:`~datafusion.expr.GroupingSet` + expression may be included to produce multiple grouping + levels (rollup, cube, or explicit grouping sets). aggs: Sequence of expressions to aggregate. Returns: DataFrame after aggregation. + + Examples: + Aggregate without grouping — an empty ``group_by`` produces a + single row: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict( + ... {"team": ["x", "x", "y"], "score": [1, 2, 5]} + ... ) + >>> df.aggregate([], [F.sum(col("score")).alias("total")]).to_pydict() + {'total': [8]} + + Group by a column and produce one row per group: + + >>> df.aggregate( + ... ["team"], [F.sum(col("score")).alias("total")] + ... ).sort("team").to_pydict() + {'team': ['x', 'y'], 'total': [3, 5]} """ group_by_list = ( list(group_by) @@ -656,13 +835,27 @@ def sort(self, *exprs: SortKey) -> DataFrame: """Sort the DataFrame by the specified sorting expressions or column names. Note that any expression can be turned into a sort expression by - calling its ``sort`` method. + calling its ``sort`` method. For ascending-only sorts, the shorter + :py:meth:`sort_by` is usually more convenient. Args: exprs: Sort expressions or column names, applied in order. Returns: DataFrame after sorting. + + Examples: + Sort ascending by a column name: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [3, 1, 2], "b": [10, 20, 30]}) + >>> df.sort("a").to_pydict() + {'a': [1, 2, 3], 'b': [20, 30, 10]} + + Sort descending using :py:meth:`Expr.sort`: + + >>> df.sort(col("a").sort(ascending=False)).to_pydict() + {'a': [3, 2, 1], 'b': [10, 30, 20]} """ exprs_raw = sort_list_to_raw_sort_list(exprs) return DataFrame(self.df.sort(*exprs_raw)) @@ -682,12 +875,28 @@ def cast(self, mapping: dict[str, pa.DataType[Any]]) -> DataFrame: def limit(self, count: int, offset: int = 0) -> DataFrame: """Return a new :py:class:`DataFrame` with a limited number of rows. + Results are returned in unspecified order unless the DataFrame is + explicitly sorted first via :py:meth:`sort` or :py:meth:`sort_by`. + Args: count: Number of rows to limit the DataFrame to. offset: Number of rows to skip. Returns: DataFrame after limiting. + + Examples: + Take the first two rows: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3, 4]}).sort("a") + >>> df.limit(2).to_pydict() + {'a': [1, 2]} + + Skip the first row then take two (paging): + + >>> df.limit(2, offset=1).to_pydict() + {'a': [2, 3]} """ return DataFrame(self.df.limit(count, offset)) @@ -820,7 +1029,13 @@ def join( ) -> DataFrame: """Join this :py:class:`DataFrame` with another :py:class:`DataFrame`. - `on` has to be provided or both `left_on` and `right_on` in conjunction. + ``on`` has to be provided or both ``left_on`` and ``right_on`` in + conjunction. + + When non-key columns share the same name in both DataFrames, use + :py:meth:`DataFrame.col` on each DataFrame **before** the join to + obtain fully qualified column references that can disambiguate them. + See :py:meth:`join_on` for an example. Args: right: Other DataFrame to join with. @@ -836,6 +1051,28 @@ def join( Returns: DataFrame after join. + + Examples: + Inner-join two DataFrames on a shared column: + + >>> ctx = dfn.SessionContext() + >>> left = ctx.from_pydict({"id": [1, 2, 3], "val": [10, 20, 30]}) + >>> right = ctx.from_pydict({"id": [2, 3, 4], "label": ["b", "c", "d"]}) + >>> left.join(right, on="id").sort("id").to_pydict() + {'id': [2, 3], 'val': [20, 30], 'label': ['b', 'c']} + + Left join to keep all rows from the left side: + + >>> left.join(right, on="id", how="left").sort("id").to_pydict() + {'id': [1, 2, 3], 'val': [10, 20, 30], 'label': [None, 'b', 'c']} + + Use ``left_on`` / ``right_on`` when the key columns differ in name: + + >>> right2 = ctx.from_pydict({"rid": [2, 3], "label": ["b", "c"]}) + >>> left.join( + ... right2, left_on="id", right_on="rid" + ... ).sort("id").to_pydict() + {'id': [2, 3], 'val': [20, 30], 'rid': [2, 3], 'label': ['b', 'c']} """ if join_keys is not None: warnings.warn( @@ -894,10 +1131,33 @@ def join_on( built with :func:`datafusion.col`. On expressions are used to support in-equality predicates. Equality predicates are correctly optimized. - Example:: + Use :py:meth:`DataFrame.col` on each DataFrame **before** the join to + obtain fully qualified column references. These qualified references + can then be used in the join predicate and to disambiguate columns + with the same name when selecting from the result. - from datafusion import col - df.join_on(other_df, col("id") == col("other_id")) + Examples: + Join with unique column names: + + >>> ctx = dfn.SessionContext() + >>> left = ctx.from_pydict({"a": [1, 2], "x": ["a", "b"]}) + >>> right = ctx.from_pydict({"b": [1, 2], "y": ["c", "d"]}) + >>> left.join_on( + ... right, col("a") == col("b") + ... ).sort(col("x")).to_pydict() + {'a': [1, 2], 'x': ['a', 'b'], 'b': [1, 2], 'y': ['c', 'd']} + + Use :py:meth:`col` to disambiguate shared column names: + + >>> left = ctx.from_pydict({"id": [1, 2], "val": [10, 20]}) + >>> right = ctx.from_pydict({"id": [1, 2], "val": [30, 40]}) + >>> joined = left.join_on( + ... right, left.col("id") == right.col("id"), how="inner" + ... ) + >>> joined.select( + ... left.col("id"), left.col("val"), right.col("val").alias("rval") + ... ).sort(left.col("id")).to_pydict() + {'id': [1, 2], 'val': [10, 20], 'rval': [30, 40]} Args: right: Other DataFrame to join with. @@ -911,7 +1171,12 @@ def join_on( exprs = [ensure_expr(expr) for expr in on_exprs] return DataFrame(self.df.join_on(right.df, exprs, how)) - def explain(self, verbose: bool = False, analyze: bool = False) -> None: + def explain( + self, + verbose: bool = False, + analyze: bool = False, + format: ExplainFormat | None = None, + ) -> None: """Print an explanation of the DataFrame's plan so far. If ``analyze`` is specified, runs the plan and reports metrics. @@ -919,8 +1184,23 @@ def explain(self, verbose: bool = False, analyze: bool = False) -> None: Args: verbose: If ``True``, more details will be included. analyze: If ``True``, the plan will run and metrics reported. + format: Output format for the plan. Defaults to + :py:attr:`ExplainFormat.INDENT`. + + Examples: + Show the plan in tree format: + + >>> from datafusion import ExplainFormat + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> df.explain(format=ExplainFormat.TREE) # doctest: +SKIP + + Show plan with runtime metrics: + + >>> df.explain(analyze=True) # doctest: +SKIP """ - self.df.explain(verbose, analyze) + fmt = format.value if format is not None else None + self.df.explain(verbose, analyze, fmt) def logical_plan(self) -> LogicalPlan: """Return the unoptimized ``LogicalPlan``. @@ -986,48 +1266,187 @@ def union(self, other: DataFrame, distinct: bool = False) -> DataFrame: Returns: DataFrame after union. + + Examples: + Stack rows from both DataFrames, preserving duplicates: + + >>> ctx = dfn.SessionContext() + >>> df1 = ctx.from_pydict({"a": [1, 2]}) + >>> df2 = ctx.from_pydict({"a": [2, 3]}) + >>> df1.union(df2).sort("a").to_pydict() + {'a': [1, 2, 2, 3]} + + Deduplicate the combined result with ``distinct=True``: + + >>> df1.union(df2, distinct=True).sort("a").to_pydict() + {'a': [1, 2, 3]} """ return DataFrame(self.df.union(other.df, distinct)) + @deprecated( + "union_distinct() is deprecated. Use union(other, distinct=True) instead." + ) def union_distinct(self, other: DataFrame) -> DataFrame: """Calculate the distinct union of two :py:class:`DataFrame`. + See Also: + :py:meth:`union` + """ + return self.union(other, distinct=True) + + def intersect(self, other: DataFrame, distinct: bool = False) -> DataFrame: + """Calculate the intersection of two :py:class:`DataFrame`. + The two :py:class:`DataFrame` must have exactly the same schema. - Any duplicate rows are discarded. Args: - other: DataFrame to union with. + other: DataFrame to intersect with. + distinct: If ``True``, duplicate rows are removed from the result. Returns: - DataFrame after union. + DataFrame after intersection. + + Examples: + Find rows common to both DataFrames: + + >>> ctx = dfn.SessionContext() + >>> df1 = ctx.from_pydict({"a": [1, 2, 3], "b": [10, 20, 30]}) + >>> df2 = ctx.from_pydict({"a": [1, 4], "b": [10, 40]}) + >>> df1.intersect(df2).to_pydict() + {'a': [1], 'b': [10]} + + Intersect with deduplication: + + >>> df1 = ctx.from_pydict({"a": [1, 1, 2], "b": [10, 10, 20]}) + >>> df2 = ctx.from_pydict({"a": [1, 1], "b": [10, 10]}) + >>> df1.intersect(df2, distinct=True).to_pydict() + {'a': [1], 'b': [10]} """ - return DataFrame(self.df.union_distinct(other.df)) + return DataFrame(self.df.intersect(other.df, distinct)) - def intersect(self, other: DataFrame) -> DataFrame: - """Calculate the intersection of two :py:class:`DataFrame`. + def except_all(self, other: DataFrame, distinct: bool = False) -> DataFrame: + """Calculate the set difference of two :py:class:`DataFrame`. + + Returns rows that are in this DataFrame but not in ``other``. The two :py:class:`DataFrame` must have exactly the same schema. Args: - other: DataFrame to intersect with. + other: DataFrame to calculate exception with. + distinct: If ``True``, duplicate rows are removed from the result. Returns: - DataFrame after intersection. + DataFrame after set difference. + + Examples: + Remove rows present in ``df2``: + + >>> ctx = dfn.SessionContext() + >>> df1 = ctx.from_pydict({"a": [1, 2, 3], "b": [10, 20, 30]}) + >>> df2 = ctx.from_pydict({"a": [1, 2], "b": [10, 20]}) + >>> df1.except_all(df2).sort("a").to_pydict() + {'a': [3], 'b': [30]} + + Remove rows present in ``df2`` and deduplicate: + + >>> df1.except_all(df2, distinct=True).sort("a").to_pydict() + {'a': [3], 'b': [30]} """ - return DataFrame(self.df.intersect(other.df)) + return DataFrame(self.df.except_all(other.df, distinct)) - def except_all(self, other: DataFrame) -> DataFrame: - """Calculate the exception of two :py:class:`DataFrame`. + def union_by_name(self, other: DataFrame, distinct: bool = False) -> DataFrame: + """Union two :py:class:`DataFrame` matching columns by name. - The two :py:class:`DataFrame` must have exactly the same schema. + Unlike :py:meth:`union` which matches columns by position, this method + matches columns by their names, allowing DataFrames with different + column orders to be combined. Args: - other: DataFrame to calculate exception with. + other: DataFrame to union with. + distinct: If ``True``, duplicate rows are removed from the result. Returns: - DataFrame after exception. + DataFrame after union by name. + + Examples: + Combine DataFrames with different column orders: + + >>> ctx = dfn.SessionContext() + >>> df1 = ctx.from_pydict({"a": [1], "b": [10]}) + >>> df2 = ctx.from_pydict({"b": [20], "a": [2]}) + >>> df1.union_by_name(df2).sort("a").to_pydict() + {'a': [1, 2], 'b': [10, 20]} + + Union by name with deduplication: + + >>> df1 = ctx.from_pydict({"a": [1, 1], "b": [10, 10]}) + >>> df2 = ctx.from_pydict({"b": [10], "a": [1]}) + >>> df1.union_by_name(df2, distinct=True).to_pydict() + {'a': [1], 'b': [10]} + """ + return DataFrame(self.df.union_by_name(other.df, distinct)) + + def distinct_on( + self, + on_expr: list[Expr], + select_expr: list[Expr], + sort_expr: list[SortKey] | None = None, + ) -> DataFrame: + """Deduplicate rows based on specific columns. + + Returns a new DataFrame with one row per unique combination of the + ``on_expr`` columns, keeping the first row per group as determined by + ``sort_expr``. + + Args: + on_expr: Expressions that determine uniqueness. + select_expr: Expressions to include in the output. + sort_expr: Optional sort expressions to determine which row to keep. + + Returns: + DataFrame after deduplication. + + Examples: + Keep the row with the smallest ``b`` for each unique ``a``: + + >>> from datafusion import col + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 1, 2, 2], "b": [10, 20, 30, 40]}) + >>> df.distinct_on( + ... [col("a")], + ... [col("a"), col("b")], + ... [col("a").sort(ascending=True), col("b").sort(ascending=True)], + ... ).sort("a").to_pydict() + {'a': [1, 2], 'b': [10, 30]} + """ + on_raw = expr_list_to_raw_expr_list(on_expr) + select_raw = expr_list_to_raw_expr_list(select_expr) + sort_raw = sort_list_to_raw_sort_list(sort_expr) if sort_expr else None + return DataFrame(self.df.distinct_on(on_raw, select_raw, sort_raw)) + + def sort_by(self, *exprs: Expr | str) -> DataFrame: + """Sort the DataFrame by column expressions in ascending order. + + This is a convenience method that sorts the DataFrame by the given + expressions in ascending order with nulls last. For more control over + sort direction and null ordering, use :py:meth:`sort` instead. + + Args: + exprs: Expressions or column names to sort by. + + Returns: + DataFrame after sorting. + + Examples: + Sort by a single column: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [3, 1, 2]}) + >>> df.sort_by("a").to_pydict() + {'a': [1, 2, 3]} """ - return DataFrame(self.df.except_all(other.df)) + raw = [_to_raw_expr(e) for e in exprs] + return DataFrame(self.df.sort_by(raw)) def write_csv( self, @@ -1288,24 +1707,44 @@ def count(self) -> int: """ return self.df.count() - @deprecated("Use :py:func:`unnest_columns` instead.") - def unnest_column(self, column: str, preserve_nulls: bool = True) -> DataFrame: - """See :py:func:`unnest_columns`.""" - return DataFrame(self.df.unnest_column(column, preserve_nulls=preserve_nulls)) - - def unnest_columns(self, *columns: str, preserve_nulls: bool = True) -> DataFrame: + def unnest_columns( + self, + *columns: str, + preserve_nulls: bool = True, + recursions: list[tuple[str, str, int]] | None = None, + ) -> DataFrame: """Expand columns of arrays into a single row per array element. Args: columns: Column names to perform unnest operation on. preserve_nulls: If False, rows with null entries will not be returned. + recursions: Optional list of ``(input_column, output_column, depth)`` + tuples that control how deeply nested columns are unnested. Any + column not mentioned here is unnested with depth 1. Returns: A DataFrame with the columns expanded. + + Examples: + Unnest an array column: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2], [3]], "b": ["x", "y"]}) + >>> df.unnest_columns("a").to_pydict() + {'a': [1, 2, 3], 'b': ['x', 'x', 'y']} + + With explicit recursion depth: + + >>> df.unnest_columns("a", recursions=[("a", "a", 1)]).to_pydict() + {'a': [1, 2, 3], 'b': ['x', 'x', 'y']} """ columns = list(columns) - return DataFrame(self.df.unnest_columns(columns, preserve_nulls=preserve_nulls)) + return DataFrame( + self.df.unnest_columns( + columns, preserve_nulls=preserve_nulls, recursions=recursions + ) + ) def __arrow_c_stream__(self, requested_schema: object | None = None) -> object: """Export the DataFrame as an Arrow C Stream. @@ -1359,15 +1798,17 @@ def __aiter__(self) -> AsyncIterator[RecordBatch]: def transform(self, func: Callable[..., DataFrame], *args: Any) -> DataFrame: """Apply a function to the current DataFrame which returns another DataFrame. - This is useful for chaining together multiple functions. For example:: - - def add_3(df: DataFrame) -> DataFrame: - return df.with_column("modified", lit(3)) - - def within_limit(df: DataFrame, limit: int) -> DataFrame: - return df.filter(col("a") < lit(limit)).distinct() + This is useful for chaining together multiple functions. - df = df.transform(modify_df).transform(within_limit, 4) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> def add_3(df): + ... return df.with_column("modified", dfn.lit(3)) + >>> def within_limit(df: DataFrame, limit: int) -> DataFrame: + ... return df.filter(col("a") < lit(limit)).distinct() + >>> df.transform(add_3).transform(within_limit, 4).sort("a").to_pydict() + {'a': [1, 2, 3], 'modified': [3, 3, 3]} Args: func: A callable function that takes a DataFrame as it's first argument @@ -1389,9 +1830,12 @@ def fill_null(self, value: Any, subset: list[str] | None = None) -> DataFrame: DataFrame with null values replaced where type casting is possible Examples: - >>> df = df.fill_null(0) # Fill all nulls with 0 where possible - >>> # Fill nulls in specific string columns - >>> df = df.fill_null("missing", subset=["name", "category"]) + >>> from datafusion import SessionContext, col + >>> ctx = SessionContext() + >>> df = ctx.from_pydict({"a": [1, None, 3], "b": [None, 5, 6]}) + >>> filled = df.fill_null(0) + >>> filled.sort(col("a")).collect()[0].column("a").to_pylist() + [0, 1, 3] Notes: - Only fills nulls in columns where the value can be cast to the column type diff --git a/python/datafusion/dataframe_formatter.py b/python/datafusion/dataframe_formatter.py index bb53d323e..fd2da99f0 100644 --- a/python/datafusion/dataframe_formatter.py +++ b/python/datafusion/dataframe_formatter.py @@ -18,6 +18,7 @@ from __future__ import annotations +import warnings from typing import ( TYPE_CHECKING, Any, @@ -61,6 +62,93 @@ def _validate_bool(value: Any, param_name: str) -> None: raise TypeError(msg) +def _validate_formatter_parameters( + max_cell_length: int, + max_width: int, + max_height: int, + max_memory_bytes: int, + min_rows: int, + max_rows: int | None, + repr_rows: int | None, + enable_cell_expansion: bool, + show_truncation_message: bool, + use_shared_styles: bool, + custom_css: str | None, + style_provider: Any, +) -> int: + """Validate all formatter parameters and return resolved max_rows value. + + Args: + max_cell_length: Maximum cell length value to validate + max_width: Maximum width value to validate + max_height: Maximum height value to validate + max_memory_bytes: Maximum memory bytes value to validate + min_rows: Minimum rows to display value to validate + max_rows: Maximum rows value to validate (None means use default) + repr_rows: Deprecated repr_rows value to validate + enable_cell_expansion: Boolean expansion flag to validate + show_truncation_message: Boolean message flag to validate + use_shared_styles: Boolean styles flag to validate + custom_css: Custom CSS string to validate + style_provider: Style provider object to validate + + Returns: + The resolved max_rows value after handling repr_rows deprecation + + Raises: + ValueError: If any numeric parameter is invalid or constraints are violated + TypeError: If any parameter has invalid type + DeprecationWarning: If repr_rows parameter is used + """ + # Validate numeric parameters + _validate_positive_int(max_cell_length, "max_cell_length") + _validate_positive_int(max_width, "max_width") + _validate_positive_int(max_height, "max_height") + _validate_positive_int(max_memory_bytes, "max_memory_bytes") + _validate_positive_int(min_rows, "min_rows") + + # Handle deprecated repr_rows parameter + if repr_rows is not None: + warnings.warn( + "repr_rows parameter is deprecated, use max_rows instead", + DeprecationWarning, + stacklevel=4, + ) + _validate_positive_int(repr_rows, "repr_rows") + if max_rows is not None and repr_rows != max_rows: + msg = "Cannot specify both repr_rows and max_rows; use max_rows only" + raise ValueError(msg) + max_rows = repr_rows + + # Use default if max_rows was not provided + if max_rows is None: + max_rows = 10 + + _validate_positive_int(max_rows, "max_rows") + + # Validate constraint: min_rows <= max_rows + if min_rows > max_rows: + msg = "min_rows must be less than or equal to max_rows" + raise ValueError(msg) + + # Validate boolean parameters + _validate_bool(enable_cell_expansion, "enable_cell_expansion") + _validate_bool(show_truncation_message, "show_truncation_message") + _validate_bool(use_shared_styles, "use_shared_styles") + + # Validate custom_css + if custom_css is not None and not isinstance(custom_css, str): + msg = "custom_css must be None or a string" + raise TypeError(msg) + + # Validate style_provider + if style_provider is not None and not isinstance(style_provider, StyleProvider): + msg = "style_provider must implement the StyleProvider protocol" + raise TypeError(msg) + + return max_rows + + @runtime_checkable class CellFormatter(Protocol): """Protocol for cell value formatters.""" @@ -126,8 +214,9 @@ class DataFrameHtmlFormatter: max_width: Maximum width of the HTML table in pixels max_height: Maximum height of the HTML table in pixels max_memory_bytes: Maximum memory in bytes for rendered data (default: 2MB) - min_rows_display: Minimum number of rows to display - repr_rows: Default number of rows to display in repr output + min_rows: Minimum number of rows to display (must be <= max_rows) + max_rows: Maximum number of rows to display in repr output + repr_rows: Deprecated alias for max_rows enable_cell_expansion: Whether to add expand/collapse buttons for long cell values custom_css: Additional CSS to include in the HTML output @@ -143,8 +232,9 @@ def __init__( max_width: int = 1000, max_height: int = 300, max_memory_bytes: int = 2 * 1024 * 1024, # 2 MB - min_rows_display: int = 20, - repr_rows: int = 10, + min_rows: int = 10, + max_rows: int | None = None, + repr_rows: int | None = None, enable_cell_expansion: bool = True, custom_css: str | None = None, show_truncation_message: bool = True, @@ -155,71 +245,70 @@ def __init__( Parameters ---------- - max_cell_length : int, default 25 + max_cell_length Maximum length of cell content before truncation. - max_width : int, default 1000 + max_width Maximum width of the displayed table in pixels. - max_height : int, default 300 + max_height Maximum height of the displayed table in pixels. - max_memory_bytes : int, default 2097152 (2MB) - Maximum memory in bytes for rendered data. - min_rows_display : int, default 20 - Minimum number of rows to display. - repr_rows : int, default 10 - Default number of rows to display in repr output. - enable_cell_expansion : bool, default True + max_memory_bytes + Maximum memory in bytes for rendered data. Helps prevent performance + issues with large datasets. + min_rows + Minimum number of rows to display even if memory limit is reached. + Must not exceed ``max_rows``. + max_rows + Maximum number of rows to display. Takes precedence over memory limits + when fewer rows are requested. + repr_rows + Deprecated alias for ``max_rows``. Use ``max_rows`` instead. + enable_cell_expansion Whether to allow cells to expand when clicked. - custom_css : str, optional + custom_css Custom CSS to apply to the HTML table. - show_truncation_message : bool, default True + show_truncation_message Whether to show a message indicating that content has been truncated. - style_provider : StyleProvider, optional + style_provider Provider of CSS styles for the HTML table. If None, DefaultStyleProvider is used. - use_shared_styles : bool, default True - Whether to use shared styles across multiple tables. + use_shared_styles + Whether to use shared styles across multiple tables. This improves + performance when displaying many DataFrames in a single notebook. Raises: ------ ValueError If max_cell_length, max_width, max_height, max_memory_bytes, - min_rows_display, or repr_rows is not a positive integer. + min_rows or max_rows is not a positive integer, or if min_rows + exceeds max_rows. TypeError If enable_cell_expansion, show_truncation_message, or use_shared_styles is - not a boolean, - or if custom_css is provided but is not a string, - or if style_provider is provided but does not implement the StyleProvider + not a boolean, or if custom_css is provided but is not a string, or if + style_provider is provided but does not implement the StyleProvider protocol. """ - # Validate numeric parameters - _validate_positive_int(max_cell_length, "max_cell_length") - _validate_positive_int(max_width, "max_width") - _validate_positive_int(max_height, "max_height") - _validate_positive_int(max_memory_bytes, "max_memory_bytes") - _validate_positive_int(min_rows_display, "min_rows_display") - _validate_positive_int(repr_rows, "repr_rows") - - # Validate boolean parameters - _validate_bool(enable_cell_expansion, "enable_cell_expansion") - _validate_bool(show_truncation_message, "show_truncation_message") - _validate_bool(use_shared_styles, "use_shared_styles") - - # Validate custom_css - if custom_css is not None and not isinstance(custom_css, str): - msg = "custom_css must be None or a string" - raise TypeError(msg) - - # Validate style_provider - if style_provider is not None and not isinstance(style_provider, StyleProvider): - msg = "style_provider must implement the StyleProvider protocol" - raise TypeError(msg) + # Validate all parameters and get resolved max_rows + resolved_max_rows = _validate_formatter_parameters( + max_cell_length, + max_width, + max_height, + max_memory_bytes, + min_rows, + max_rows, + repr_rows, + enable_cell_expansion, + show_truncation_message, + use_shared_styles, + custom_css, + style_provider, + ) self.max_cell_length = max_cell_length self.max_width = max_width self.max_height = max_height self.max_memory_bytes = max_memory_bytes - self.min_rows_display = min_rows_display - self.repr_rows = repr_rows + self.min_rows = min_rows + self._max_rows = resolved_max_rows self.enable_cell_expansion = enable_cell_expansion self.custom_css = custom_css self.show_truncation_message = show_truncation_message @@ -231,6 +320,55 @@ def __init__( self._custom_cell_builder: Callable[[Any, int, int, str], str] | None = None self._custom_header_builder: Callable[[Any], str] | None = None + @property + def max_rows(self) -> int: + """Get the maximum number of rows to display. + + Returns: + The maximum number of rows to display in repr output + """ + return self._max_rows + + @max_rows.setter + def max_rows(self, value: int) -> None: + """Set the maximum number of rows to display. + + Args: + value: The maximum number of rows + """ + self._max_rows = value + + @property + def repr_rows(self) -> int: + """Get the maximum number of rows (deprecated name). + + .. deprecated:: + Use :attr:`max_rows` instead. This property is provided for + backward compatibility. + + Returns: + The maximum number of rows to display + """ + return self._max_rows + + @repr_rows.setter + def repr_rows(self, value: int) -> None: + """Set the maximum number of rows using deprecated name. + + .. deprecated:: + Use :attr:`max_rows` setter instead. This property is provided for + backward compatibility. + + Args: + value: The maximum number of rows + """ + warnings.warn( + "repr_rows is deprecated, use max_rows instead", + DeprecationWarning, + stacklevel=2, + ) + self._max_rows = value + def register_formatter(self, type_class: type, formatter: CellFormatter) -> None: """Register a custom formatter for a specific data type. @@ -610,7 +748,7 @@ def get_formatter() -> DataFrameHtmlFormatter: The global HTML formatter instance Example: - >>> from datafusion.html_formatter import get_formatter + >>> from datafusion.dataframe_formatter import get_formatter >>> formatter = get_formatter() >>> formatter.max_cell_length = 50 # Increase cell length """ @@ -624,7 +762,7 @@ def set_formatter(formatter: DataFrameHtmlFormatter) -> None: formatter: The formatter instance to use globally Example: - >>> from datafusion.html_formatter import get_formatter, set_formatter + >>> from datafusion.dataframe_formatter import get_formatter, set_formatter >>> custom_formatter = DataFrameHtmlFormatter(max_cell_length=100) >>> set_formatter(custom_formatter) """ @@ -645,7 +783,7 @@ def configure_formatter(**kwargs: Any) -> None: ValueError: If any invalid parameters are provided Example: - >>> from datafusion.html_formatter import configure_formatter + >>> from datafusion.dataframe_formatter import configure_formatter >>> configure_formatter( ... max_cell_length=50, ... max_height=500, @@ -659,7 +797,8 @@ def configure_formatter(**kwargs: Any) -> None: "max_width", "max_height", "max_memory_bytes", - "min_rows_display", + "min_rows", + "max_rows", "repr_rows", "enable_cell_expansion", "custom_css", @@ -688,7 +827,7 @@ def reset_formatter() -> None: and sets it as the global formatter for all DataFrames. Example: - >>> from datafusion.html_formatter import reset_formatter + >>> from datafusion.dataframe_formatter import reset_formatter >>> reset_formatter() # Reset formatter to default settings """ formatter = DataFrameHtmlFormatter() diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index 695fe7c49..0f7f3ab5a 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -15,21 +15,40 @@ # specific language governing permissions and limitations # under the License. -"""This module supports expressions, one of the core concepts in DataFusion. - -See :ref:`Expressions` in the online documentation for more details. +""":py:class:`Expr` — the logical expression type used to build DataFusion queries. + +An :py:class:`Expr` represents a computation over columns or literals: a +column reference (``col("a")``), a literal (``lit(5)``), an operator +combination (``col("a") + lit(1)``), or the output of a function from +:py:mod:`datafusion.functions`. Expressions are passed to +:py:class:`~datafusion.dataframe.DataFrame` methods such as +:py:meth:`~datafusion.dataframe.DataFrame.select`, +:py:meth:`~datafusion.dataframe.DataFrame.filter`, +:py:meth:`~datafusion.dataframe.DataFrame.aggregate`, and +:py:meth:`~datafusion.dataframe.DataFrame.sort`. + +Convenience constructors are re-exported at the package level: +:py:func:`datafusion.col` / :py:func:`datafusion.column` for column references +and :py:func:`datafusion.lit` / :py:func:`datafusion.literal` for scalar +literals. + +Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> df.select((col("a") * lit(10)).alias("ten_a")).to_pydict() + {'ten_a': [10, 20, 30]} + +See :ref:`expressions` in the online documentation for details on available +operators and helpers. """ +# ruff: noqa: PLC0415 + from __future__ import annotations from collections.abc import Iterable, Sequence from typing import TYPE_CHECKING, Any, ClassVar -try: - from warnings import deprecated # Python 3.13+ -except ImportError: - from typing_extensions import deprecated # Python 3.12 - import pyarrow as pa from ._internal import expr as expr_internal @@ -89,7 +108,6 @@ Extension = expr_internal.Extension FileType = expr_internal.FileType Filter = expr_internal.Filter -GroupingSet = expr_internal.GroupingSet Join = expr_internal.Join ILike = expr_internal.ILike InList = expr_internal.InList @@ -225,6 +243,8 @@ "WindowExpr", "WindowFrame", "WindowFrameBound", + "coerce_to_expr", + "coerce_to_expr_or_none", "ensure_expr", "ensure_expr_list", ] @@ -237,6 +257,10 @@ def ensure_expr(value: Expr | Any) -> expr_internal.Expr: higher level APIs consistently require explicit :func:`~datafusion.col` or :func:`~datafusion.lit` expressions. + See Also: + :func:`coerce_to_expr` — the opposite behavior: *wraps* non-``Expr`` + values as literals instead of rejecting them. + Args: value: Candidate expression or other object. @@ -281,6 +305,41 @@ def _iter( return list(_iter(exprs)) +def coerce_to_expr(value: Any) -> Expr: + """Coerce a native Python value to an ``Expr`` literal, passing ``Expr`` through. + + This is the complement of :func:`ensure_expr`: where ``ensure_expr`` + *rejects* non-``Expr`` values, ``coerce_to_expr`` *wraps* them via + :meth:`Expr.literal` so that functions can accept native Python types + (``int``, ``float``, ``str``, ``bool``, etc.) alongside ``Expr``. + + Args: + value: An ``Expr`` instance (returned as-is) or a Python literal to wrap. + + Returns: + An ``Expr`` representing the value. + """ + if isinstance(value, Expr): + return value + return Expr.literal(value) + + +def coerce_to_expr_or_none(value: Any | None) -> Expr | None: + """Coerce a value to ``Expr`` or pass ``None`` through unchanged. + + Same as :func:`coerce_to_expr` but accepts ``None`` for optional parameters. + + Args: + value: An ``Expr`` instance, a Python literal to wrap, or ``None``. + + Returns: + An ``Expr`` representing the value, or ``None``. + """ + if value is None: + return None + return coerce_to_expr(value) + + def _to_raw_expr(value: Expr | str) -> expr_internal.Expr: """Convert a Python expression or column name to its raw variant. @@ -340,7 +399,7 @@ def sort_list_to_raw_sort_list( return raw_sort_list -class Expr: +class Expr: # noqa: PLW1641 """Expression object. Expressions are one of the core concepts in DataFusion. See @@ -355,16 +414,6 @@ def to_variant(self) -> Any: """Convert this expression into a python object if possible.""" return self.expr.to_variant() - @deprecated( - "display_name() is deprecated. Use :py:meth:`~Expr.schema_name` instead" - ) - def display_name(self) -> str: - """Returns the name of this expression as it should appear in a schema. - - This name will not include any CAST expressions. - """ - return self.schema_name() - def schema_name(self) -> str: """Returns the name of this expression as it should appear in a schema. @@ -497,6 +546,8 @@ def __eq__(self, rhs: object) -> Expr: Accepts either an expression or any valid PyArrow scalar literal value. """ + if rhs is None: + return self.is_null() if not isinstance(rhs, Expr): rhs = Expr.literal(rhs) return Expr(self.expr.__eq__(rhs.expr)) @@ -506,6 +557,8 @@ def __ne__(self, rhs: object) -> Expr: Accepts either an expression or any valid PyArrow scalar literal value. """ + if rhs is None: + return self.is_not_null() if not isinstance(rhs, Expr): rhs = Expr.literal(rhs) return Expr(self.expr.__ne__(rhs.expr)) @@ -562,8 +615,6 @@ def literal(value: Any) -> Expr: """ if isinstance(value, str): value = pa.scalar(value, type=pa.string_view()) - if not isinstance(value, pa.Scalar): - value = pa.scalar(value) return Expr(expr_internal.RawExpr.literal(value)) @staticmethod @@ -576,7 +627,6 @@ def literal_with_metadata(value: Any, metadata: dict[str, str]) -> Expr: """ if isinstance(value, str): value = pa.scalar(value, type=pa.string_view()) - value = value if isinstance(value, pa.Scalar) else pa.scalar(value) return Expr(expr_internal.RawExpr.literal_with_metadata(value, metadata)) @@ -1368,16 +1418,18 @@ def is_unbounded(self) -> bool: class CaseBuilder: """Builder class for constructing case statements. - An example usage would be as follows:: - - import datafusion.functions as f - from datafusion import lit, col - df.select( - f.case(col("column_a")) - .when(lit(1), lit("One")) - .when(lit(2), lit("Two")) - .otherwise(lit("Unknown")) - ) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.select( + ... dfn.functions.case(dfn.col("a")) + ... .when(dfn.lit(1), dfn.lit("One")) + ... .when(dfn.lit(2), dfn.lit("Two")) + ... .otherwise(dfn.lit("Other")) + ... .alias("label") + ... ) + >>> result.to_pydict() + {'label': ['One', 'Two', 'Other']} """ def __init__(self, case_builder: expr_internal.CaseBuilder) -> None: @@ -1429,3 +1481,129 @@ def __repr__(self) -> str: SortKey = Expr | SortExpr | str + + +class GroupingSet: + """Factory for creating grouping set expressions. + + Grouping sets control how + :py:meth:`~datafusion.dataframe.DataFrame.aggregate` groups rows. + Instead of a single ``GROUP BY``, they produce multiple grouping + levels in one pass — subtotals, cross-tabulations, or arbitrary + column subsets. + + Use :py:func:`~datafusion.functions.grouping` in the aggregate list + to tell which columns are aggregated across in each result row. + """ + + @staticmethod + def rollup(*exprs: Expr | str) -> Expr: + """Create a ``ROLLUP`` grouping set for use with ``aggregate()``. + + ``ROLLUP`` generates all prefixes of the given column list as + grouping sets. For example, ``rollup(a, b)`` produces grouping + sets ``(a, b)``, ``(a)``, and ``()`` (grand total). + + This is equivalent to ``GROUP BY ROLLUP(a, b)`` in SQL. + + Args: + *exprs: Column expressions or column name strings to + include in the rollup. + + Examples: + >>> from datafusion.expr import GroupingSet + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 1, 2], "b": [10, 20, 30]}) + >>> result = df.aggregate( + ... [GroupingSet.rollup(dfn.col("a"))], + ... [dfn.functions.sum(dfn.col("b")).alias("s"), + ... dfn.functions.grouping(dfn.col("a"))], + ... ).sort(dfn.col("a").sort(nulls_first=False)) + >>> result.collect_column("s").to_pylist() + [30, 30, 60] + + See Also: + :py:meth:`cube`, :py:meth:`grouping_sets`, + :py:func:`~datafusion.functions.grouping` + """ + args = [_to_raw_expr(e) for e in exprs] + return Expr(expr_internal.GroupingSet.rollup(*args)) + + @staticmethod + def cube(*exprs: Expr | str) -> Expr: + """Create a ``CUBE`` grouping set for use with ``aggregate()``. + + ``CUBE`` generates all possible subsets of the given column list + as grouping sets. For example, ``cube(a, b)`` produces grouping + sets ``(a, b)``, ``(a)``, ``(b)``, and ``()`` (grand total). + + This is equivalent to ``GROUP BY CUBE(a, b)`` in SQL. + + Args: + *exprs: Column expressions or column name strings to + include in the cube. + + Examples: + With a single column, ``cube`` behaves identically to + :py:meth:`rollup`: + + >>> from datafusion.expr import GroupingSet + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 1, 2], "b": [10, 20, 30]}) + >>> result = df.aggregate( + ... [GroupingSet.cube(dfn.col("a"))], + ... [dfn.functions.sum(dfn.col("b")).alias("s"), + ... dfn.functions.grouping(dfn.col("a"))], + ... ).sort(dfn.col("a").sort(nulls_first=False)) + >>> result.collect_column("s").to_pylist() + [30, 30, 60] + + See Also: + :py:meth:`rollup`, :py:meth:`grouping_sets`, + :py:func:`~datafusion.functions.grouping` + """ + args = [_to_raw_expr(e) for e in exprs] + return Expr(expr_internal.GroupingSet.cube(*args)) + + @staticmethod + def grouping_sets(*expr_lists: list[Expr | str]) -> Expr: + """Create explicit grouping sets for use with ``aggregate()``. + + Each argument is a list of column expressions or column name + strings representing one grouping set. For example, + ``grouping_sets([a], [b])`` groups by ``a`` alone and by ``b`` + alone in a single query. + + This is equivalent to ``GROUP BY GROUPING SETS ((a), (b))`` in + SQL. + + Args: + *expr_lists: Each positional argument is a list of + expressions or column name strings forming one + grouping set. + + Examples: + >>> from datafusion.expr import GroupingSet + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict( + ... {"a": ["x", "x", "y"], "b": ["m", "n", "m"], + ... "c": [1, 2, 3]}) + >>> result = df.aggregate( + ... [GroupingSet.grouping_sets( + ... [dfn.col("a")], [dfn.col("b")])], + ... [dfn.functions.sum(dfn.col("c")).alias("s"), + ... dfn.functions.grouping(dfn.col("a")), + ... dfn.functions.grouping(dfn.col("b"))], + ... ).sort( + ... dfn.col("a").sort(nulls_first=False), + ... dfn.col("b").sort(nulls_first=False), + ... ) + >>> result.collect_column("s").to_pylist() + [3, 3, 4, 2] + + See Also: + :py:meth:`rollup`, :py:meth:`cube`, + :py:func:`~datafusion.functions.grouping` + """ + raw_lists = [[_to_raw_expr(e) for e in lst] for lst in expr_lists] + return Expr(expr_internal.GroupingSet.grouping_sets(*raw_lists)) diff --git a/python/datafusion/functions.py b/python/datafusion/functions.py index 7ae59c000..08062851a 100644 --- a/python/datafusion/functions.py +++ b/python/datafusion/functions.py @@ -14,11 +14,31 @@ # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. -"""User functions for operating on :py:class:`~datafusion.expr.Expr`.""" +"""Scalar, aggregate, and window functions for :py:class:`~datafusion.expr.Expr`. + +Each function returns an :py:class:`~datafusion.expr.Expr` that can be combined +with other expressions and passed to +:py:class:`~datafusion.dataframe.DataFrame` methods such as +:py:meth:`~datafusion.dataframe.DataFrame.select`, +:py:meth:`~datafusion.dataframe.DataFrame.filter`, +:py:meth:`~datafusion.dataframe.DataFrame.aggregate`, and +:py:meth:`~datafusion.dataframe.DataFrame.window`. The module is conventionally +imported as ``F`` so calls read like ``F.sum(col("price"))``. + +Examples: + >>> from datafusion import functions as F + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3, 4]}) + >>> df.aggregate([], [F.sum(col("a")).alias("total")]).to_pydict() + {'total': [10]} + +See :ref:`aggregation` and :ref:`window_functions` in the online documentation +for categorized catalogs of aggregate and window functions. +""" from __future__ import annotations -from typing import TYPE_CHECKING, Any +from typing import Any import pyarrow as pa @@ -29,20 +49,13 @@ Expr, SortExpr, SortKey, - WindowFrame, + coerce_to_expr, + coerce_to_expr_or_none, expr_list_to_raw_expr_list, sort_list_to_raw_sort_list, sort_or_default, ) -try: - from warnings import deprecated # Python 3.13+ -except ImportError: - from typing_extensions import deprecated # Python 3.12 - -if TYPE_CHECKING: - from datafusion.context import SessionContext - __all__ = [ "abs", "acos", @@ -54,10 +67,13 @@ "approx_percentile_cont_with_weight", "array", "array_agg", + "array_any_value", "array_append", "array_cat", "array_concat", + "array_contains", "array_dims", + "array_distance", "array_distinct", "array_element", "array_empty", @@ -70,6 +86,8 @@ "array_intersect", "array_join", "array_length", + "array_max", + "array_min", "array_ndims", "array_pop_back", "array_pop_front", @@ -86,11 +104,15 @@ "array_replace_all", "array_replace_n", "array_resize", + "array_reverse", "array_slice", "array_sort", "array_to_string", "array_union", + "arrays_overlap", + "arrays_zip", "arrow_cast", + "arrow_metadata", "arrow_typeof", "ascii", "asin", @@ -117,6 +139,7 @@ "col", "concat", "concat_ws", + "contains", "corr", "cos", "cosh", @@ -129,7 +152,9 @@ "cume_dist", "current_date", "current_time", + "current_timestamp", "date_bin", + "date_format", "date_part", "date_trunc", "datepart", @@ -138,6 +163,7 @@ "degrees", "dense_rank", "digest", + "element_at", "empty", "encode", "ends_with", @@ -150,6 +176,12 @@ "floor", "from_unixtime", "gcd", + "gen_series", + "generate_series", + "get_field", + "greatest", + "grouping", + "ifnull", "in_list", "initcap", "isnan", @@ -158,22 +190,35 @@ "last_value", "lcm", "lead", + "least", "left", "length", "levenshtein", + "list_any_value", "list_append", "list_cat", "list_concat", + "list_contains", "list_dims", + "list_distance", "list_distinct", "list_element", + "list_empty", "list_except", "list_extract", + "list_has", + "list_has_all", + "list_has_any", "list_indexof", "list_intersect", "list_join", "list_length", + "list_max", + "list_min", "list_ndims", + "list_overlap", + "list_pop_back", + "list_pop_front", "list_position", "list_positions", "list_prepend", @@ -187,10 +232,12 @@ "list_replace_all", "list_replace_n", "list_resize", + "list_reverse", "list_slice", "list_sort", "list_to_string", "list_union", + "list_zip", "ln", "log", "log2", @@ -201,6 +248,12 @@ "make_array", "make_date", "make_list", + "make_map", + "make_time", + "map_entries", + "map_extract", + "map_keys", + "map_values", "max", "md5", "mean", @@ -213,18 +266,22 @@ "ntile", "nullif", "nvl", + "nvl2", "octet_length", "order_by", "overlay", "percent_rank", + "percentile_cont", "pi", "pow", "power", + "quantile_cont", "radians", "random", "range", "rank", "regexp_count", + "regexp_instr", "regexp_like", "regexp_match", "regexp_replace", @@ -242,6 +299,7 @@ "reverse", "right", "round", + "row", "row_number", "rpad", "rtrim", @@ -259,6 +317,8 @@ "stddev_pop", "stddev_samp", "string_agg", + "string_to_array", + "string_to_list", "strpos", "struct", "substr", @@ -267,30 +327,45 @@ "sum", "tan", "tanh", + "to_char", + "to_date", "to_hex", + "to_local_time", + "to_time", "to_timestamp", "to_timestamp_micros", "to_timestamp_millis", "to_timestamp_nanos", "to_timestamp_seconds", "to_unixtime", + "today", "translate", "trim", "trunc", + "union_extract", + "union_tag", "upper", "uuid", "var", "var_pop", + "var_population", "var_samp", "var_sample", + "version", "when", - # Window Functions - "window", ] def isnan(expr: Expr) -> Expr: - """Returns true if a given number is +NaN or -NaN otherwise returns false.""" + """Returns true if a given number is +NaN or -NaN otherwise returns false. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, np.nan]}) + >>> result = df.select(dfn.functions.isnan(dfn.col("a")).alias("isnan")) + >>> result.collect_column("isnan")[1].as_py() + True + """ return Expr(f.isnan(expr.expr)) @@ -298,68 +373,163 @@ def nullif(expr1: Expr, expr2: Expr) -> Expr: """Returns NULL if expr1 equals expr2; otherwise it returns expr1. This can be used to perform the inverse operation of the COALESCE expression. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2], "b": [1, 3]}) + >>> result = df.select( + ... dfn.functions.nullif(dfn.col("a"), dfn.col("b")).alias("nullif")) + >>> result.collect_column("nullif").to_pylist() + [None, 2] """ return Expr(f.nullif(expr1.expr, expr2.expr)) -def encode(expr: Expr, encoding: Expr) -> Expr: - """Encode the ``input``, using the ``encoding``. encoding can be base64 or hex.""" +def encode(expr: Expr, encoding: Expr | str) -> Expr: + """Encode the ``input``, using the ``encoding``. encoding can be base64 or hex. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.encode(dfn.col("a"), "base64").alias("enc")) + >>> result.collect_column("enc")[0].as_py() + 'aGVsbG8' + """ + encoding = coerce_to_expr(encoding) return Expr(f.encode(expr.expr, encoding.expr)) -def decode(expr: Expr, encoding: Expr) -> Expr: - """Decode the ``input``, using the ``encoding``. encoding can be base64 or hex.""" +def decode(expr: Expr, encoding: Expr | str) -> Expr: + """Decode the ``input``, using the ``encoding``. encoding can be base64 or hex. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["aGVsbG8="]}) + >>> result = df.select( + ... dfn.functions.decode(dfn.col("a"), "base64").alias("dec")) + >>> result.collect_column("dec")[0].as_py() + b'hello' + """ + encoding = coerce_to_expr(encoding) return Expr(f.decode(expr.expr, encoding.expr)) -def array_to_string(expr: Expr, delimiter: Expr) -> Expr: - """Converts each element to its text representation.""" +def array_to_string(expr: Expr, delimiter: Expr | str) -> Expr: + """Converts each element to its text representation. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_to_string(dfn.col("a"), ",").alias("s")) + >>> result.collect_column("s")[0].as_py() + '1,2,3' + """ + delimiter = coerce_to_expr(delimiter) return Expr(f.array_to_string(expr.expr, delimiter.expr.cast(pa.string()))) -def array_join(expr: Expr, delimiter: Expr) -> Expr: +def array_join(expr: Expr, delimiter: Expr | str) -> Expr: """Converts each element to its text representation. - This is an alias for :py:func:`array_to_string`. + See Also: + This is an alias for :py:func:`array_to_string`. """ return array_to_string(expr, delimiter) -def list_to_string(expr: Expr, delimiter: Expr) -> Expr: +def list_to_string(expr: Expr, delimiter: Expr | str) -> Expr: """Converts each element to its text representation. - This is an alias for :py:func:`array_to_string`. + See Also: + This is an alias for :py:func:`array_to_string`. """ return array_to_string(expr, delimiter) -def list_join(expr: Expr, delimiter: Expr) -> Expr: +def list_join(expr: Expr, delimiter: Expr | str) -> Expr: """Converts each element to its text representation. - This is an alias for :py:func:`array_to_string`. + See Also: + This is an alias for :py:func:`array_to_string`. """ return array_to_string(expr, delimiter) def in_list(arg: Expr, values: list[Expr], negated: bool = False) -> Expr: - """Returns whether the argument is contained within the list ``values``.""" + """Returns whether the argument is contained within the list ``values``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.select( + ... dfn.functions.in_list( + ... dfn.col("a"), [dfn.lit(1), dfn.lit(3)] + ... ).alias("in") + ... ) + >>> result.collect_column("in").to_pylist() + [True, False, True] + + >>> result = df.select( + ... dfn.functions.in_list( + ... dfn.col("a"), [dfn.lit(1), dfn.lit(3)], + ... negated=True, + ... ).alias("not_in") + ... ) + >>> result.collect_column("not_in").to_pylist() + [False, True, False] + """ values = [v.expr for v in values] return Expr(f.in_list(arg.expr, values, negated)) -def digest(value: Expr, method: Expr) -> Expr: +def digest(value: Expr, method: Expr | str) -> Expr: """Computes the binary hash of an expression using the specified algorithm. Standard algorithms are md5, sha224, sha256, sha384, sha512, blake2s, blake2b, and blake3. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.digest(dfn.col("a"), "md5").alias("d")) + >>> len(result.collect_column("d")[0].as_py()) > 0 + True """ + method = coerce_to_expr(method) return Expr(f.digest(value.expr, method.expr)) +def contains(string: Expr, search_str: Expr | str) -> Expr: + """Returns true if ``search_str`` is found within ``string`` (case-sensitive). + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["the quick brown fox"]}) + >>> result = df.select( + ... dfn.functions.contains(dfn.col("a"), "brown").alias("c")) + >>> result.collect_column("c")[0].as_py() + True + """ + search_str = coerce_to_expr(search_str) + return Expr(f.contains(string.expr, search_str.expr)) + + def concat(*args: Expr) -> Expr: """Concatenates the text representations of all the arguments. NULL arguments are ignored. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"], "b": [" world"]}) + >>> result = df.select( + ... dfn.functions.concat(dfn.col("a"), dfn.col("b")).alias("c") + ... ) + >>> result.collect_column("c")[0].as_py() + 'hello world' """ args = [arg.expr for arg in args] return Expr(f.concat(args)) @@ -369,13 +539,33 @@ def concat_ws(separator: str, *args: Expr) -> Expr: """Concatenates the list ``args`` with the separator. ``NULL`` arguments are ignored. ``separator`` should not be ``NULL``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"], "b": ["world"]}) + >>> result = df.select( + ... dfn.functions.concat_ws("-", dfn.col("a"), dfn.col("b")).alias("c")) + >>> result.collect_column("c")[0].as_py() + 'hello-world' """ args = [arg.expr for arg in args] return Expr(f.concat_ws(separator, args)) def order_by(expr: Expr, ascending: bool = True, nulls_first: bool = True) -> SortExpr: - """Creates a new sort expression.""" + """Creates a new sort expression. + + Examples: + >>> sort_expr = dfn.functions.order_by( + ... dfn.col("a"), ascending=False) + >>> sort_expr.ascending() + False + + >>> sort_expr = dfn.functions.order_by( + ... dfn.col("a"), ascending=True, nulls_first=False) + >>> sort_expr.nulls_first() + False + """ return SortExpr(expr, ascending=ascending, nulls_first=nulls_first) @@ -387,14 +577,39 @@ def alias(expr: Expr, name: str, metadata: dict[str, str] | None = None) -> Expr name: The alias name metadata: Optional metadata to attach to the column - Returns: - An expression with the given alias + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2]}) + >>> result = df.select( + ... dfn.functions.alias( + ... dfn.col("a"), "b" + ... ) + ... ) + >>> result.collect_column("b")[0].as_py() + 1 + + >>> result = df.select( + ... dfn.functions.alias( + ... dfn.col("a"), "b", metadata={"info": "test"} + ... ) + ... ) + >>> result.schema() + b: int64 + -- field metadata -- + info: 'test' """ return Expr(f.alias(expr.expr, name, metadata)) def col(name: str) -> Expr: - """Creates a column reference expression.""" + """Creates a column reference expression. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> df.select(dfn.functions.col("a")).collect_column("a")[0].as_py() + 1 + """ return Expr(f.col(name)) @@ -408,6 +623,22 @@ def count_star(filter: Expr | None = None) -> Expr: Args: filter: If provided, only count rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.count_star( + ... ).alias("cnt")]) + >>> result.collect_column("cnt")[0].as_py() + 3 + + >>> result = df.aggregate( + ... [], [dfn.functions.count_star( + ... filter=dfn.col("a") > dfn.lit(1) + ... ).alias("cnt")]) + >>> result.collect_column("cnt")[0].as_py() + 2 """ return count(Expr.literal(1), filter=filter) @@ -418,6 +649,15 @@ def case(expr: Expr) -> CaseBuilder: Create a :py:class:`~datafusion.expr.CaseBuilder` to match cases for the expression ``expr``. See :py:class:`~datafusion.expr.CaseBuilder` for detailed usage. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.select( + ... dfn.functions.case(dfn.col("a")).when(dfn.lit(1), + ... dfn.lit("one")).otherwise(dfn.lit("other")).alias("c")) + >>> result.collect_column("c")[0].as_py() + 'one' """ return CaseBuilder(f.case(expr.expr)) @@ -428,61 +668,29 @@ def when(when: Expr, then: Expr) -> CaseBuilder: Create a :py:class:`~datafusion.expr.CaseBuilder` to match cases for the expression ``expr``. See :py:class:`~datafusion.expr.CaseBuilder` for detailed usage. - """ - return CaseBuilder(f.when(when.expr, then.expr)) - - -@deprecated("Prefer to call Expr.over() instead") -def window( - name: str, - args: list[Expr], - partition_by: list[Expr] | Expr | None = None, - order_by: list[SortKey] | SortKey | None = None, - window_frame: WindowFrame | None = None, - filter: Expr | None = None, - distinct: bool = False, - ctx: SessionContext | None = None, -) -> Expr: - """Creates a new Window function expression. - This interface will soon be deprecated. Instead of using this interface, - users should call the window functions directly. For example, to perform a - lag use:: - - df.select(functions.lag(col("a")).partition_by(col("b")).build()) - - The ``order_by`` parameter accepts column names or expressions, e.g.:: - - window("lag", [col("a")], order_by="ts") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.select( + ... dfn.functions.when(dfn.col("a") > dfn.lit(2), + ... dfn.lit("big")).otherwise(dfn.lit("small")).alias("c")) + >>> result.collect_column("c")[2].as_py() + 'big' """ - args = [a.expr for a in args] - partition_by_raw = expr_list_to_raw_expr_list(partition_by) - order_by_raw = sort_list_to_raw_sort_list(order_by) - window_frame = window_frame.window_frame if window_frame is not None else None - ctx = ctx.ctx if ctx is not None else None - filter_raw = filter.expr if filter is not None else None - return Expr( - f.window( - name, - args, - partition_by=partition_by_raw, - order_by=order_by_raw, - window_frame=window_frame, - ctx=ctx, - filter=filter_raw, - distinct=distinct, - ) - ) + return CaseBuilder(f.when(when.expr, then.expr)) # scalar functions def abs(arg: Expr) -> Expr: """Return the absolute value of a given number. - Returns: - -------- - Expr - A new expression representing the absolute value of the input expression. + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [-1, 0, 1]}) + >>> result = df.select(dfn.functions.abs(dfn.col("a")).alias("abs")) + >>> result.collect_column("abs")[0].as_py() + 1 """ return Expr(f.abs(arg.expr)) @@ -490,482 +698,1371 @@ def abs(arg: Expr) -> Expr: def acos(arg: Expr) -> Expr: """Returns the arc cosine or inverse cosine of a number. - Returns: - -------- - Expr - A new expression representing the arc cosine of the input expression. + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0]}) + >>> result = df.select(dfn.functions.acos(dfn.col("a")).alias("acos")) + >>> result.collect_column("acos")[0].as_py() + 0.0 """ return Expr(f.acos(arg.expr)) def acosh(arg: Expr) -> Expr: - """Returns inverse hyperbolic cosine.""" + """Returns inverse hyperbolic cosine. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0]}) + >>> result = df.select(dfn.functions.acosh(dfn.col("a")).alias("acosh")) + >>> result.collect_column("acosh")[0].as_py() + 0.0 + """ return Expr(f.acosh(arg.expr)) def ascii(arg: Expr) -> Expr: - """Returns the numeric code of the first character of the argument.""" + """Returns the numeric code of the first character of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["a","b","c"]}) + >>> ascii_df = df.select(dfn.functions.ascii(dfn.col("a")).alias("ascii")) + >>> ascii_df.collect_column("ascii")[0].as_py() + 97 + """ return Expr(f.ascii(arg.expr)) def asin(arg: Expr) -> Expr: - """Returns the arc sine or inverse sine of a number.""" + """Returns the arc sine or inverse sine of a number. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.asin(dfn.col("a")).alias("asin")) + >>> result.collect_column("asin")[0].as_py() + 0.0 + """ return Expr(f.asin(arg.expr)) def asinh(arg: Expr) -> Expr: - """Returns inverse hyperbolic sine.""" + """Returns inverse hyperbolic sine. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.asinh(dfn.col("a")).alias("asinh")) + >>> result.collect_column("asinh")[0].as_py() + 0.0 + """ return Expr(f.asinh(arg.expr)) def atan(arg: Expr) -> Expr: - """Returns inverse tangent of a number.""" + """Returns inverse tangent of a number. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.atan(dfn.col("a")).alias("atan")) + >>> result.collect_column("atan")[0].as_py() + 0.0 + """ return Expr(f.atan(arg.expr)) def atanh(arg: Expr) -> Expr: - """Returns inverse hyperbolic tangent.""" + """Returns inverse hyperbolic tangent. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.atanh(dfn.col("a")).alias("atanh")) + >>> result.collect_column("atanh")[0].as_py() + 0.0 + """ return Expr(f.atanh(arg.expr)) def atan2(y: Expr, x: Expr) -> Expr: - """Returns inverse tangent of a division given in the argument.""" + """Returns inverse tangent of a division given in the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [0.0], "x": [1.0]}) + >>> result = df.select( + ... dfn.functions.atan2(dfn.col("y"), dfn.col("x")).alias("atan2")) + >>> result.collect_column("atan2")[0].as_py() + 0.0 + """ return Expr(f.atan2(y.expr, x.expr)) def bit_length(arg: Expr) -> Expr: - """Returns the number of bits in the string argument.""" + """Returns the number of bits in the string argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["a","b","c"]}) + >>> bit_df = df.select(dfn.functions.bit_length(dfn.col("a")).alias("bit_len")) + >>> bit_df.collect_column("bit_len")[0].as_py() + 8 + """ return Expr(f.bit_length(arg.expr)) def btrim(arg: Expr) -> Expr: - """Removes all characters, spaces by default, from both sides of a string.""" + """Removes all characters, spaces by default, from both sides of a string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [" a "]}) + >>> trim_df = df.select(dfn.functions.btrim(dfn.col("a")).alias("trimmed")) + >>> trim_df.collect_column("trimmed")[0].as_py() + 'a' + """ return Expr(f.btrim(arg.expr)) def cbrt(arg: Expr) -> Expr: - """Returns the cube root of a number.""" + """Returns the cube root of a number. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [27]}) + >>> cbrt_df = df.select(dfn.functions.cbrt(dfn.col("a")).alias("cbrt")) + >>> cbrt_df.collect_column("cbrt")[0].as_py() + 3.0 + """ return Expr(f.cbrt(arg.expr)) def ceil(arg: Expr) -> Expr: - """Returns the nearest integer greater than or equal to argument.""" + """Returns the nearest integer greater than or equal to argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.9]}) + >>> ceil_df = df.select(dfn.functions.ceil(dfn.col("a")).alias("ceil")) + >>> ceil_df.collect_column("ceil")[0].as_py() + 2.0 + """ return Expr(f.ceil(arg.expr)) def character_length(arg: Expr) -> Expr: - """Returns the number of characters in the argument.""" + """Returns the number of characters in the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["abc","b","c"]}) + >>> char_len_df = df.select( + ... dfn.functions.character_length(dfn.col("a")).alias("char_len")) + >>> char_len_df.collect_column("char_len")[0].as_py() + 3 + """ return Expr(f.character_length(arg.expr)) def length(string: Expr) -> Expr: - """The number of characters in the ``string``.""" + """The number of characters in the ``string``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select(dfn.functions.length(dfn.col("a")).alias("len")) + >>> result.collect_column("len")[0].as_py() + 5 + """ return Expr(f.length(string.expr)) def char_length(string: Expr) -> Expr: - """The number of characters in the ``string``.""" + """The number of characters in the ``string``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select(dfn.functions.char_length(dfn.col("a")).alias("len")) + >>> result.collect_column("len")[0].as_py() + 5 + """ return Expr(f.char_length(string.expr)) def chr(arg: Expr) -> Expr: - """Converts the Unicode code point to a UTF8 character.""" + """Converts the Unicode code point to a UTF8 character. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [65]}) + >>> result = df.select(dfn.functions.chr(dfn.col("a")).alias("chr")) + >>> result.collect_column("chr")[0].as_py() + 'A' + """ return Expr(f.chr(arg.expr)) def coalesce(*args: Expr) -> Expr: - """Returns the value of the first expr in ``args`` which is not NULL.""" + """Returns the value of the first expr in ``args`` which is not NULL. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [None, 1], "b": [2, 3]}) + >>> result = df.select( + ... dfn.functions.coalesce(dfn.col("a"), dfn.col("b")).alias("c")) + >>> result.collect_column("c")[0].as_py() + 2 + """ args = [arg.expr for arg in args] return Expr(f.coalesce(*args)) def cos(arg: Expr) -> Expr: - """Returns the cosine of the argument.""" + """Returns the cosine of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0,-1,1]}) + >>> cos_df = df.select(dfn.functions.cos(dfn.col("a")).alias("cos")) + >>> cos_df.collect_column("cos")[0].as_py() + 1.0 + """ return Expr(f.cos(arg.expr)) def cosh(arg: Expr) -> Expr: - """Returns the hyperbolic cosine of the argument.""" + """Returns the hyperbolic cosine of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0,-1,1]}) + >>> cosh_df = df.select(dfn.functions.cosh(dfn.col("a")).alias("cosh")) + >>> cosh_df.collect_column("cosh")[0].as_py() + 1.0 + """ return Expr(f.cosh(arg.expr)) def cot(arg: Expr) -> Expr: - """Returns the cotangent of the argument.""" + """Returns the cotangent of the argument. + + Examples: + >>> from math import pi + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [pi / 4]}) + >>> result = df.select( + ... dfn.functions.cot(dfn.col("a")).alias("cot") + ... ) + >>> result.collect_column("cot")[0].as_py() + 1.0... + """ return Expr(f.cot(arg.expr)) def degrees(arg: Expr) -> Expr: - """Converts the argument from radians to degrees.""" + """Converts the argument from radians to degrees. + + Examples: + >>> from math import pi + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0,pi,2*pi]}) + >>> deg_df = df.select(dfn.functions.degrees(dfn.col("a")).alias("deg")) + >>> deg_df.collect_column("deg")[2].as_py() + 360.0 + """ return Expr(f.degrees(arg.expr)) -def ends_with(arg: Expr, suffix: Expr) -> Expr: - """Returns true if the ``string`` ends with the ``suffix``, false otherwise.""" +def ends_with(arg: Expr, suffix: Expr | str) -> Expr: + """Returns true if the ``string`` ends with the ``suffix``, false otherwise. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["abc","b","c"]}) + >>> ends_with_df = df.select( + ... dfn.functions.ends_with(dfn.col("a"), "c").alias("ends_with")) + >>> ends_with_df.collect_column("ends_with")[0].as_py() + True + """ + suffix = coerce_to_expr(suffix) return Expr(f.ends_with(arg.expr, suffix.expr)) def exp(arg: Expr) -> Expr: - """Returns the exponential of the argument.""" + """Returns the exponential of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.exp(dfn.col("a")).alias("exp")) + >>> result.collect_column("exp")[0].as_py() + 1.0 + """ return Expr(f.exp(arg.expr)) def factorial(arg: Expr) -> Expr: - """Returns the factorial of the argument.""" + """Returns the factorial of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [3]}) + >>> result = df.select( + ... dfn.functions.factorial(dfn.col("a")).alias("factorial") + ... ) + >>> result.collect_column("factorial")[0].as_py() + 6 + """ return Expr(f.factorial(arg.expr)) -def find_in_set(string: Expr, string_list: Expr) -> Expr: +def find_in_set(string: Expr, string_list: Expr | str) -> Expr: """Find a string in a list of strings. Returns a value in the range of 1 to N if the string is in the string list ``string_list`` consisting of N substrings. The string list is a string composed of substrings separated by ``,`` characters. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["b"]}) + >>> result = df.select( + ... dfn.functions.find_in_set(dfn.col("a"), "a,b,c").alias("pos")) + >>> result.collect_column("pos")[0].as_py() + 2 """ + string_list = coerce_to_expr(string_list) return Expr(f.find_in_set(string.expr, string_list.expr)) def floor(arg: Expr) -> Expr: - """Returns the nearest integer less than or equal to the argument.""" + """Returns the nearest integer less than or equal to the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.9]}) + >>> floor_df = df.select(dfn.functions.floor(dfn.col("a")).alias("floor")) + >>> floor_df.collect_column("floor")[0].as_py() + 1.0 + """ return Expr(f.floor(arg.expr)) def gcd(x: Expr, y: Expr) -> Expr: - """Returns the greatest common divisor.""" + """Returns the greatest common divisor. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [12], "b": [8]}) + >>> result = df.select( + ... dfn.functions.gcd(dfn.col("a"), dfn.col("b")).alias("gcd") + ... ) + >>> result.collect_column("gcd")[0].as_py() + 4 + """ return Expr(f.gcd(x.expr, y.expr)) +def greatest(*args: Expr) -> Expr: + """Returns the greatest value from a list of expressions. + + Returns NULL if all expressions are NULL. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 3], "b": [2, 1]}) + >>> result = df.select( + ... dfn.functions.greatest(dfn.col("a"), dfn.col("b")).alias("greatest")) + >>> result.collect_column("greatest")[0].as_py() + 2 + >>> result.collect_column("greatest")[1].as_py() + 3 + """ + exprs = [arg.expr for arg in args] + return Expr(f.greatest(*exprs)) + + +def ifnull(x: Expr, y: Expr) -> Expr: + """Returns ``x`` if ``x`` is not NULL. Otherwise returns ``y``. + + See Also: + This is an alias for :py:func:`nvl`. + """ + return nvl(x, y) + + def initcap(string: Expr) -> Expr: """Set the initial letter of each word to capital. Converts the first letter of each word in ``string`` to uppercase and the remaining characters to lowercase. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["the cat"]}) + >>> cap_df = df.select(dfn.functions.initcap(dfn.col("a")).alias("cap")) + >>> cap_df.collect_column("cap")[0].as_py() + 'The Cat' """ return Expr(f.initcap(string.expr)) -def instr(string: Expr, substring: Expr) -> Expr: +def instr(string: Expr, substring: Expr | str) -> Expr: """Finds the position from where the ``substring`` matches the ``string``. - This is an alias for :py:func:`strpos`. + See Also: + This is an alias for :py:func:`strpos`. """ return strpos(string, substring) def iszero(arg: Expr) -> Expr: - """Returns true if a given number is +0.0 or -0.0 otherwise returns false.""" + """Returns true if a given number is +0.0 or -0.0 otherwise returns false. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0, 1.0]}) + >>> result = df.select(dfn.functions.iszero(dfn.col("a")).alias("iz")) + >>> result.collect_column("iz")[0].as_py() + True + """ return Expr(f.iszero(arg.expr)) def lcm(x: Expr, y: Expr) -> Expr: - """Returns the least common multiple.""" + """Returns the least common multiple. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [4], "b": [6]}) + >>> result = df.select( + ... dfn.functions.lcm(dfn.col("a"), dfn.col("b")).alias("lcm") + ... ) + >>> result.collect_column("lcm")[0].as_py() + 12 + """ return Expr(f.lcm(x.expr, y.expr)) -def left(string: Expr, n: Expr) -> Expr: - """Returns the first ``n`` characters in the ``string``.""" +def least(*args: Expr) -> Expr: + """Returns the least value from a list of expressions. + + Returns NULL if all expressions are NULL. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 3], "b": [2, 1]}) + >>> result = df.select( + ... dfn.functions.least(dfn.col("a"), dfn.col("b")).alias("least")) + >>> result.collect_column("least")[0].as_py() + 1 + >>> result.collect_column("least")[1].as_py() + 1 + """ + exprs = [arg.expr for arg in args] + return Expr(f.least(*exprs)) + + +def left(string: Expr, n: Expr | int) -> Expr: + """Returns the first ``n`` characters in the ``string``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["the cat"]}) + >>> left_df = df.select( + ... dfn.functions.left(dfn.col("a"), 3).alias("left")) + >>> left_df.collect_column("left")[0].as_py() + 'the' + """ + n = coerce_to_expr(n) return Expr(f.left(string.expr, n.expr)) -def levenshtein(string1: Expr, string2: Expr) -> Expr: - """Returns the Levenshtein distance between the two given strings.""" +def levenshtein(string1: Expr, string2: Expr | str) -> Expr: + """Returns the Levenshtein distance between the two given strings. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["kitten"]}) + >>> result = df.select( + ... dfn.functions.levenshtein(dfn.col("a"), "sitting").alias("d")) + >>> result.collect_column("d")[0].as_py() + 3 + """ + string2 = coerce_to_expr(string2) return Expr(f.levenshtein(string1.expr, string2.expr)) def ln(arg: Expr) -> Expr: - """Returns the natural logarithm (base e) of the argument.""" + """Returns the natural logarithm (base e) of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0]}) + >>> result = df.select(dfn.functions.ln(dfn.col("a")).alias("ln")) + >>> result.collect_column("ln")[0].as_py() + 0.0 + """ return Expr(f.ln(arg.expr)) -def log(base: Expr, num: Expr) -> Expr: - """Returns the logarithm of a number for a particular ``base``.""" +def log(base: Expr | int | float, num: Expr) -> Expr: # noqa: PYI041 + """Returns the logarithm of a number for a particular ``base``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [100.0]}) + >>> result = df.select( + ... dfn.functions.log(10.0, dfn.col("a")).alias("log") + ... ) + >>> result.collect_column("log")[0].as_py() + 2.0 + """ + base = coerce_to_expr(base) return Expr(f.log(base.expr, num.expr)) def log10(arg: Expr) -> Expr: - """Base 10 logarithm of the argument.""" + """Base 10 logarithm of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [100.0]}) + >>> result = df.select(dfn.functions.log10(dfn.col("a")).alias("log10")) + >>> result.collect_column("log10")[0].as_py() + 2.0 + """ return Expr(f.log10(arg.expr)) def log2(arg: Expr) -> Expr: - """Base 2 logarithm of the argument.""" + """Base 2 logarithm of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [8.0]}) + >>> result = df.select(dfn.functions.log2(dfn.col("a")).alias("log2")) + >>> result.collect_column("log2")[0].as_py() + 3.0 + """ return Expr(f.log2(arg.expr)) def lower(arg: Expr) -> Expr: - """Converts a string to lowercase.""" + """Converts a string to lowercase. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["THE CaT"]}) + >>> lower_df = df.select(dfn.functions.lower(dfn.col("a")).alias("lower")) + >>> lower_df.collect_column("lower")[0].as_py() + 'the cat' + """ return Expr(f.lower(arg.expr)) -def lpad(string: Expr, count: Expr, characters: Expr | None = None) -> Expr: +def lpad(string: Expr, count: Expr | int, characters: Expr | str | None = None) -> Expr: """Add left padding to a string. Extends the string to length length by prepending the characters fill (a space by default). If the string is already longer than length then it is truncated (on the right). + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["the cat", "a hat"]}) + >>> lpad_df = df.select( + ... dfn.functions.lpad(dfn.col("a"), 6).alias("lpad")) + >>> lpad_df.collect_column("lpad")[0].as_py() + 'the ca' + >>> lpad_df.collect_column("lpad")[1].as_py() + ' a hat' + + >>> result = df.select( + ... dfn.functions.lpad( + ... dfn.col("a"), 10, characters="." + ... ).alias("lpad")) + >>> result.collect_column("lpad")[0].as_py() + '...the cat' """ - characters = characters if characters is not None else Expr.literal(" ") + count = coerce_to_expr(count) + characters = coerce_to_expr(characters if characters is not None else " ") return Expr(f.lpad(string.expr, count.expr, characters.expr)) def ltrim(arg: Expr) -> Expr: - """Removes all characters, spaces by default, from the beginning of a string.""" + """Removes all characters, spaces by default, from the beginning of a string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [" a "]}) + >>> trim_df = df.select(dfn.functions.ltrim(dfn.col("a")).alias("trimmed")) + >>> trim_df.collect_column("trimmed")[0].as_py() + 'a ' + """ return Expr(f.ltrim(arg.expr)) def md5(arg: Expr) -> Expr: - """Computes an MD5 128-bit checksum for a string expression.""" + """Computes an MD5 128-bit checksum for a string expression. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select(dfn.functions.md5(dfn.col("a")).alias("md5")) + >>> result.collect_column("md5")[0].as_py() + '5d41402abc4b2a76b9719d911017c592' + """ return Expr(f.md5(arg.expr)) def nanvl(x: Expr, y: Expr) -> Expr: - """Returns ``x`` if ``x`` is not ``NaN``. Otherwise returns ``y``.""" + """Returns ``x`` if ``x`` is not ``NaN``. Otherwise returns ``y``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [np.nan, 1.0], "b": [0.0, 0.0]}) + >>> nanvl_df = df.select( + ... dfn.functions.nanvl(dfn.col("a"), dfn.col("b")).alias("nanvl")) + >>> nanvl_df.collect_column("nanvl")[0].as_py() + 0.0 + >>> nanvl_df.collect_column("nanvl")[1].as_py() + 1.0 + """ return Expr(f.nanvl(x.expr, y.expr)) def nvl(x: Expr, y: Expr) -> Expr: - """Returns ``x`` if ``x`` is not ``NULL``. Otherwise returns ``y``.""" + """Returns ``x`` if ``x`` is not ``NULL``. Otherwise returns ``y``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [None, 1], "b": [0, 0]}) + >>> nvl_df = df.select( + ... dfn.functions.nvl(dfn.col("a"), dfn.col("b")).alias("nvl") + ... ) + >>> nvl_df.collect_column("nvl")[0].as_py() + 0 + >>> nvl_df.collect_column("nvl")[1].as_py() + 1 + """ return Expr(f.nvl(x.expr, y.expr)) +def nvl2(x: Expr, y: Expr, z: Expr) -> Expr: + """Returns ``y`` if ``x`` is not NULL. Otherwise returns ``z``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [None, 1], "b": [10, 20], "c": [30, 40]}) + >>> result = df.select( + ... dfn.functions.nvl2( + ... dfn.col("a"), dfn.col("b"), dfn.col("c")).alias("nvl2") + ... ) + >>> result.collect_column("nvl2")[0].as_py() + 30 + >>> result.collect_column("nvl2")[1].as_py() + 20 + """ + return Expr(f.nvl2(x.expr, y.expr, z.expr)) + + def octet_length(arg: Expr) -> Expr: - """Returns the number of bytes of a string.""" + """Returns the number of bytes of a string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select(dfn.functions.octet_length(dfn.col("a")).alias("len")) + >>> result.collect_column("len")[0].as_py() + 5 + """ return Expr(f.octet_length(arg.expr)) def overlay( - string: Expr, substring: Expr, start: Expr, length: Expr | None = None + string: Expr, + substring: Expr | str, + start: Expr | int, + length: Expr | int | None = None, ) -> Expr: """Replace a substring with a new substring. Replace the substring of string that starts at the ``start``'th character and extends for ``length`` characters with new substring. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["abcdef"]}) + >>> result = df.select( + ... dfn.functions.overlay(dfn.col("a"), "XY", 3, 2).alias("o")) + >>> result.collect_column("o")[0].as_py() + 'abXYef' """ + substring = coerce_to_expr(substring) + start = coerce_to_expr(start) if length is None: return Expr(f.overlay(string.expr, substring.expr, start.expr)) + length = coerce_to_expr(length) return Expr(f.overlay(string.expr, substring.expr, start.expr, length.expr)) def pi() -> Expr: - """Returns an approximate value of π.""" + """Returns an approximate value of π. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> from math import pi + >>> result = df.select( + ... dfn.functions.pi().alias("pi") + ... ) + >>> result.collect_column("pi")[0].as_py() == pi + True + """ return Expr(f.pi()) -def position(string: Expr, substring: Expr) -> Expr: +def position(string: Expr, substring: Expr | str) -> Expr: """Finds the position from where the ``substring`` matches the ``string``. - This is an alias for :py:func:`strpos`. + See Also: + This is an alias for :py:func:`strpos`. """ return strpos(string, substring) -def power(base: Expr, exponent: Expr) -> Expr: - """Returns ``base`` raised to the power of ``exponent``.""" +def power(base: Expr, exponent: Expr | int | float) -> Expr: # noqa: PYI041 + """Returns ``base`` raised to the power of ``exponent``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [2.0]}) + >>> result = df.select( + ... dfn.functions.power(dfn.col("a"), 3.0).alias("pow") + ... ) + >>> result.collect_column("pow")[0].as_py() + 8.0 + """ + exponent = coerce_to_expr(exponent) return Expr(f.power(base.expr, exponent.expr)) -def pow(base: Expr, exponent: Expr) -> Expr: +def pow(base: Expr, exponent: Expr | int | float) -> Expr: # noqa: PYI041 """Returns ``base`` raised to the power of ``exponent``. - This is an alias of :py:func:`power`. + See Also: + This is an alias of :py:func:`power`. """ return power(base, exponent) def radians(arg: Expr) -> Expr: - """Converts the argument from degrees to radians.""" + """Converts the argument from degrees to radians. + + Examples: + >>> from math import pi + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [180.0]}) + >>> result = df.select( + ... dfn.functions.radians(dfn.col("a")).alias("rad") + ... ) + >>> result.collect_column("rad")[0].as_py() == pi + True + """ return Expr(f.radians(arg.expr)) -def regexp_like(string: Expr, regex: Expr, flags: Expr | None = None) -> Expr: - """Find if any regular expression (regex) matches exist. +def regexp_like( + string: Expr, regex: Expr | str, flags: Expr | str | None = None +) -> Expr: + r"""Find if any regular expression (regex) matches exist. Tests a string using a regular expression returning true if at least one match, false otherwise. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello123"]}) + >>> result = df.select( + ... dfn.functions.regexp_like(dfn.col("a"), "\\d+").alias("m") + ... ) + >>> result.collect_column("m")[0].as_py() + True + + Use ``flags`` for case-insensitive matching: + + >>> result = df.select( + ... dfn.functions.regexp_like( + ... dfn.col("a"), "HELLO", flags="i", + ... ).alias("m") + ... ) + >>> result.collect_column("m")[0].as_py() + True """ - if flags is not None: - flags = flags.expr - return Expr(f.regexp_like(string.expr, regex.expr, flags)) + regex = coerce_to_expr(regex) + flags = coerce_to_expr_or_none(flags) + return Expr( + f.regexp_like( + string.expr, regex.expr, flags.expr if flags is not None else None + ) + ) -def regexp_match(string: Expr, regex: Expr, flags: Expr | None = None) -> Expr: - """Perform regular expression (regex) matching. +def regexp_match( + string: Expr, regex: Expr | str, flags: Expr | str | None = None +) -> Expr: + r"""Perform regular expression (regex) matching. Returns an array with each element containing the leftmost-first match of the corresponding index in ``regex`` to string in ``string``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello 42 world"]}) + >>> result = df.select( + ... dfn.functions.regexp_match(dfn.col("a"), "(\\d+)").alias("m") + ... ) + >>> result.collect_column("m")[0].as_py() + ['42'] + + Use ``flags`` for case-insensitive matching: + + >>> result = df.select( + ... dfn.functions.regexp_match( + ... dfn.col("a"), "(HELLO)", flags="i", + ... ).alias("m") + ... ) + >>> result.collect_column("m")[0].as_py() + ['hello'] """ - if flags is not None: - flags = flags.expr - return Expr(f.regexp_match(string.expr, regex.expr, flags)) + regex = coerce_to_expr(regex) + flags = coerce_to_expr_or_none(flags) + return Expr( + f.regexp_match( + string.expr, regex.expr, flags.expr if flags is not None else None + ) + ) def regexp_replace( - string: Expr, pattern: Expr, replacement: Expr, flags: Expr | None = None + string: Expr, + pattern: Expr | str, + replacement: Expr | str, + flags: Expr | str | None = None, ) -> Expr: - """Replaces substring(s) matching a PCRE-like regular expression. + r"""Replaces substring(s) matching a PCRE-like regular expression. The full list of supported features and syntax can be found at Supported flags with the addition of 'g' can be found at + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello 42"]}) + >>> result = df.select( + ... dfn.functions.regexp_replace( + ... dfn.col("a"), "\\d+", "XX" + ... ).alias("r") + ... ) + >>> result.collect_column("r")[0].as_py() + 'hello XX' + + Use the ``g`` flag to replace all occurrences: + + >>> df = ctx.from_pydict({"a": ["a1 b2 c3"]}) + >>> result = df.select( + ... dfn.functions.regexp_replace( + ... dfn.col("a"), "\\d+", "X", flags="g", + ... ).alias("r") + ... ) + >>> result.collect_column("r")[0].as_py() + 'aX bX cX' """ - if flags is not None: - flags = flags.expr - return Expr(f.regexp_replace(string.expr, pattern.expr, replacement.expr, flags)) + pattern = coerce_to_expr(pattern) + replacement = coerce_to_expr(replacement) + flags = coerce_to_expr_or_none(flags) + return Expr( + f.regexp_replace( + string.expr, + pattern.expr, + replacement.expr, + flags.expr if flags is not None else None, + ) + ) def regexp_count( - string: Expr, pattern: Expr, start: Expr, flags: Expr | None = None + string: Expr, + pattern: Expr | str, + start: Expr | int | None = None, + flags: Expr | str | None = None, ) -> Expr: """Returns the number of matches in a string. Optional start position (the first position is 1) to search for the regular expression. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["abcabc"]}) + >>> result = df.select( + ... dfn.functions.regexp_count(dfn.col("a"), "abc").alias("c")) + >>> result.collect_column("c")[0].as_py() + 2 + + Use ``start`` to begin searching from a position, and + ``flags`` for case-insensitive matching: + + >>> result = df.select( + ... dfn.functions.regexp_count( + ... dfn.col("a"), "ABC", start=4, flags="i", + ... ).alias("c")) + >>> result.collect_column("c")[0].as_py() + 1 """ - if flags is not None: - flags = flags.expr - start = start.expr if start is not None else Expr.expr - return Expr(f.regexp_count(string.expr, pattern.expr, start, flags)) + pattern = coerce_to_expr(pattern) + start = coerce_to_expr_or_none(start) + flags = coerce_to_expr_or_none(flags) + return Expr( + f.regexp_count( + string.expr, + pattern.expr, + start.expr if start is not None else None, + flags.expr if flags is not None else None, + ) + ) -def repeat(string: Expr, n: Expr) -> Expr: - """Repeats the ``string`` to ``n`` times.""" +def regexp_instr( + values: Expr, + regex: Expr | str, + start: Expr | int | None = None, + n: Expr | int | None = None, + flags: Expr | str | None = None, + sub_expr: Expr | int | None = None, +) -> Expr: + r"""Returns the position of a regular expression match in a string. + + Args: + values: Data to search for the regular expression match. + regex: Regular expression to search for. + start: Optional position to start the search (the first position is 1). + n: Optional occurrence of the match to find (the first occurrence is 1). + flags: Optional regular expression flags to control regex behavior. + sub_expr: Optionally capture group position instead of the entire match. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello 42 world"]}) + >>> result = df.select( + ... dfn.functions.regexp_instr(dfn.col("a"), "\\d+").alias("pos") + ... ) + >>> result.collect_column("pos")[0].as_py() + 7 + + Use ``start`` to search from a position, ``n`` for the + nth occurrence, and ``flags`` for case-insensitive mode: + + >>> df = ctx.from_pydict({"a": ["abc ABC abc"]}) + >>> result = df.select( + ... dfn.functions.regexp_instr( + ... dfn.col("a"), "abc", + ... start=2, n=1, flags="i", + ... ).alias("pos") + ... ) + >>> result.collect_column("pos")[0].as_py() + 5 + + Use ``sub_expr`` to get the position of a capture group: + + >>> result = df.select( + ... dfn.functions.regexp_instr( + ... dfn.col("a"), "(abc)", sub_expr=1, + ... ).alias("pos") + ... ) + >>> result.collect_column("pos")[0].as_py() + 1 + """ + regex = coerce_to_expr(regex) + start = coerce_to_expr_or_none(start) + n = coerce_to_expr_or_none(n) + flags = coerce_to_expr_or_none(flags) + sub_expr = coerce_to_expr_or_none(sub_expr) + + return Expr( + f.regexp_instr( + values.expr, + regex.expr, + start.expr if start is not None else None, + n.expr if n is not None else None, + flags.expr if flags is not None else None, + sub_expr.expr if sub_expr is not None else None, + ) + ) + + +def repeat(string: Expr, n: Expr | int) -> Expr: + """Repeats the ``string`` to ``n`` times. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["ha"]}) + >>> result = df.select( + ... dfn.functions.repeat(dfn.col("a"), 3).alias("r")) + >>> result.collect_column("r")[0].as_py() + 'hahaha' + """ + n = coerce_to_expr(n) return Expr(f.repeat(string.expr, n.expr)) -def replace(string: Expr, from_val: Expr, to_val: Expr) -> Expr: - """Replaces all occurrences of ``from_val`` with ``to_val`` in the ``string``.""" +def replace(string: Expr, from_val: Expr | str, to_val: Expr | str) -> Expr: + """Replaces all occurrences of ``from_val`` with ``to_val`` in the ``string``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello world"]}) + >>> result = df.select( + ... dfn.functions.replace(dfn.col("a"), "world", "there").alias("r")) + >>> result.collect_column("r")[0].as_py() + 'hello there' + """ + from_val = coerce_to_expr(from_val) + to_val = coerce_to_expr(to_val) return Expr(f.replace(string.expr, from_val.expr, to_val.expr)) def reverse(arg: Expr) -> Expr: - """Reverse the string argument.""" + """Reverse the string argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select(dfn.functions.reverse(dfn.col("a")).alias("r")) + >>> result.collect_column("r")[0].as_py() + 'olleh' + """ return Expr(f.reverse(arg.expr)) -def right(string: Expr, n: Expr) -> Expr: - """Returns the last ``n`` characters in the ``string``.""" +def right(string: Expr, n: Expr | int) -> Expr: + """Returns the last ``n`` characters in the ``string``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select(dfn.functions.right(dfn.col("a"), 3).alias("r")) + >>> result.collect_column("r")[0].as_py() + 'llo' + """ + n = coerce_to_expr(n) return Expr(f.right(string.expr, n.expr)) -def round(value: Expr, decimal_places: Expr | None = None) -> Expr: +def round(value: Expr, decimal_places: Expr | int | None = None) -> Expr: """Round the argument to the nearest integer. If the optional ``decimal_places`` is specified, round to the nearest number of decimal places. You can specify a negative number of decimal places. For example - ``round(lit(125.2345), lit(-2))`` would yield a value of ``100.0``. + ``round(lit(125.2345), -2)`` would yield a value of ``100.0``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.567]}) + >>> result = df.select(dfn.functions.round(dfn.col("a"), 2).alias("r")) + >>> result.collect_column("r")[0].as_py() + 1.57 """ - if decimal_places is None: - decimal_places = Expr.literal(0) + decimal_places = coerce_to_expr(decimal_places if decimal_places is not None else 0) return Expr(f.round(value.expr, decimal_places.expr)) -def rpad(string: Expr, count: Expr, characters: Expr | None = None) -> Expr: +def rpad(string: Expr, count: Expr | int, characters: Expr | str | None = None) -> Expr: """Add right padding to a string. Extends the string to length length by appending the characters fill (a space by default). If the string is already longer than length then it is truncated. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hi"]}) + >>> result = df.select( + ... dfn.functions.rpad(dfn.col("a"), 5, "!").alias("r")) + >>> result.collect_column("r")[0].as_py() + 'hi!!!' """ - characters = characters if characters is not None else Expr.literal(" ") + count = coerce_to_expr(count) + characters = coerce_to_expr(characters if characters is not None else " ") return Expr(f.rpad(string.expr, count.expr, characters.expr)) def rtrim(arg: Expr) -> Expr: - """Removes all characters, spaces by default, from the end of a string.""" + """Removes all characters, spaces by default, from the end of a string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [" a "]}) + >>> trim_df = df.select(dfn.functions.rtrim(dfn.col("a")).alias("trimmed")) + >>> trim_df.collect_column("trimmed")[0].as_py() + ' a' + """ return Expr(f.rtrim(arg.expr)) def sha224(arg: Expr) -> Expr: - """Computes the SHA-224 hash of a binary string.""" + """Computes the SHA-224 hash of a binary string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.sha224(dfn.col("a")).alias("h") + ... ) + >>> result.collect_column("h")[0].as_py().hex() + 'ea09ae9cc6768c50fcee903ed054556e5bfc8347907f12598aa24193' + """ return Expr(f.sha224(arg.expr)) def sha256(arg: Expr) -> Expr: - """Computes the SHA-256 hash of a binary string.""" + """Computes the SHA-256 hash of a binary string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.sha256(dfn.col("a")).alias("h") + ... ) + >>> result.collect_column("h")[0].as_py().hex() + '2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824' + """ return Expr(f.sha256(arg.expr)) def sha384(arg: Expr) -> Expr: - """Computes the SHA-384 hash of a binary string.""" + """Computes the SHA-384 hash of a binary string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.sha384(dfn.col("a")).alias("h") + ... ) + >>> result.collect_column("h")[0].as_py().hex() + '59e1748777448c69de6b800d7a33bbfb9ff1b... + """ return Expr(f.sha384(arg.expr)) def sha512(arg: Expr) -> Expr: - """Computes the SHA-512 hash of a binary string.""" + """Computes the SHA-512 hash of a binary string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.sha512(dfn.col("a")).alias("h") + ... ) + >>> result.collect_column("h")[0].as_py().hex() + '9b71d224bd62f3785d96d46ad3ea3d73319bfb... + """ return Expr(f.sha512(arg.expr)) def signum(arg: Expr) -> Expr: - """Returns the sign of the argument (-1, 0, +1).""" + """Returns the sign of the argument (-1, 0, +1). + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [-5.0, 0.0, 5.0]}) + >>> result = df.select(dfn.functions.signum(dfn.col("a")).alias("s")) + >>> result.collect_column("s").to_pylist() + [-1.0, 0.0, 1.0] + """ return Expr(f.signum(arg.expr)) def sin(arg: Expr) -> Expr: - """Returns the sine of the argument.""" + """Returns the sine of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.sin(dfn.col("a")).alias("sin")) + >>> result.collect_column("sin")[0].as_py() + 0.0 + """ return Expr(f.sin(arg.expr)) def sinh(arg: Expr) -> Expr: - """Returns the hyperbolic sine of the argument.""" + """Returns the hyperbolic sine of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.sinh(dfn.col("a")).alias("sinh")) + >>> result.collect_column("sinh")[0].as_py() + 0.0 + """ return Expr(f.sinh(arg.expr)) -def split_part(string: Expr, delimiter: Expr, index: Expr) -> Expr: +def split_part(string: Expr, delimiter: Expr | str, index: Expr | int) -> Expr: """Split a string and return one part. Splits a string based on a delimiter and picks out the desired field based on the index. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["a,b,c"]}) + >>> result = df.select( + ... dfn.functions.split_part(dfn.col("a"), ",", 2).alias("s")) + >>> result.collect_column("s")[0].as_py() + 'b' """ + delimiter = coerce_to_expr(delimiter) + index = coerce_to_expr(index) return Expr(f.split_part(string.expr, delimiter.expr, index.expr)) def sqrt(arg: Expr) -> Expr: - """Returns the square root of the argument.""" + """Returns the square root of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [9.0]}) + >>> result = df.select(dfn.functions.sqrt(dfn.col("a")).alias("sqrt")) + >>> result.collect_column("sqrt")[0].as_py() + 3.0 + """ return Expr(f.sqrt(arg.expr)) -def starts_with(string: Expr, prefix: Expr) -> Expr: - """Returns true if string starts with prefix.""" +def starts_with(string: Expr, prefix: Expr | str) -> Expr: + """Returns true if string starts with prefix. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello_from_datafusion"]}) + >>> result = df.select( + ... dfn.functions.starts_with(dfn.col("a"), "hello").alias("sw")) + >>> result.collect_column("sw")[0].as_py() + True + """ + prefix = coerce_to_expr(prefix) return Expr(f.starts_with(string.expr, prefix.expr)) -def strpos(string: Expr, substring: Expr) -> Expr: - """Finds the position from where the ``substring`` matches the ``string``.""" +def strpos(string: Expr, substring: Expr | str) -> Expr: + """Finds the position from where the ``substring`` matches the ``string``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.strpos(dfn.col("a"), "llo").alias("pos")) + >>> result.collect_column("pos")[0].as_py() + 3 + """ + substring = coerce_to_expr(substring) return Expr(f.strpos(string.expr, substring.expr)) -def substr(string: Expr, position: Expr) -> Expr: - """Substring from the ``position`` to the end.""" +def substr(string: Expr, position: Expr | int) -> Expr: + """Substring from the ``position`` to the end. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.substr(dfn.col("a"), 3).alias("s")) + >>> result.collect_column("s")[0].as_py() + 'llo' + """ + position = coerce_to_expr(position) return Expr(f.substr(string.expr, position.expr)) -def substr_index(string: Expr, delimiter: Expr, count: Expr) -> Expr: +def substr_index(string: Expr, delimiter: Expr | str, count: Expr | int) -> Expr: """Returns an indexed substring. The return will be the ``string`` from before ``count`` occurrences of ``delimiter``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["a.b.c"]}) + >>> result = df.select( + ... dfn.functions.substr_index(dfn.col("a"), ".", 2).alias("s")) + >>> result.collect_column("s")[0].as_py() + 'a.b' """ + delimiter = coerce_to_expr(delimiter) + count = coerce_to_expr(count) return Expr(f.substr_index(string.expr, delimiter.expr, count.expr)) -def substring(string: Expr, position: Expr, length: Expr) -> Expr: - """Substring from the ``position`` with ``length`` characters.""" +def substring(string: Expr, position: Expr | int, length: Expr | int) -> Expr: + """Substring from the ``position`` with ``length`` characters. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello world"]}) + >>> result = df.select( + ... dfn.functions.substring(dfn.col("a"), 1, 5).alias("s")) + >>> result.collect_column("s")[0].as_py() + 'hello' + """ + position = coerce_to_expr(position) + length = coerce_to_expr(length) return Expr(f.substring(string.expr, position.expr, length.expr)) def tan(arg: Expr) -> Expr: - """Returns the tangent of the argument.""" + """Returns the tangent of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.tan(dfn.col("a")).alias("tan")) + >>> result.collect_column("tan")[0].as_py() + 0.0 + """ return Expr(f.tan(arg.expr)) def tanh(arg: Expr) -> Expr: - """Returns the hyperbolic tangent of the argument.""" + """Returns the hyperbolic tangent of the argument. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0]}) + >>> result = df.select(dfn.functions.tanh(dfn.col("a")).alias("tanh")) + >>> result.collect_column("tanh")[0].as_py() + 0.0 + """ return Expr(f.tanh(arg.expr)) def to_hex(arg: Expr) -> Expr: - """Converts an integer to a hexadecimal string.""" + """Converts an integer to a hexadecimal string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [255]}) + >>> result = df.select(dfn.functions.to_hex(dfn.col("a")).alias("hex")) + >>> result.collect_column("hex")[0].as_py() + 'ff' + """ return Expr(f.to_hex(arg.expr)) @@ -973,144 +2070,466 @@ def now() -> Expr: """Returns the current timestamp in nanoseconds. This will use the same value for all instances of now() in same statement. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.now().alias("now") + ... ) + + Use .value instead of .as_py() because nanosecond timestamps + require pandas to convert to Python datetime objects. + + >>> result.collect_column("now")[0].value > 0 + True """ return Expr(f.now()) +def current_timestamp() -> Expr: + """Returns the current timestamp in nanoseconds. + + See Also: + This is an alias for :py:func:`now`. + """ + return now() + + +def to_char(arg: Expr, formatter: Expr | str) -> Expr: + """Returns a string representation of a date, time, timestamp or duration. + + For usage of ``formatter`` see the rust chrono package ``strftime`` package. + + [Documentation here.](https://docs.rs/chrono/latest/chrono/format/strftime/index.html) + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-01-01T00:00:00"]}) + >>> result = df.select( + ... dfn.functions.to_char( + ... dfn.functions.to_timestamp(dfn.col("a")), + ... "%Y/%m/%d", + ... ).alias("formatted") + ... ) + >>> result.collect_column("formatted")[0].as_py() + '2021/01/01' + """ + formatter = coerce_to_expr(formatter) + return Expr(f.to_char(arg.expr, formatter.expr)) + + +def date_format(arg: Expr, formatter: Expr | str) -> Expr: + """Returns a string representation of a date, time, timestamp or duration. + + See Also: + This is an alias for :py:func:`to_char`. + """ + return to_char(arg, formatter) + + +def _unwrap_exprs(args: tuple[Expr, ...]) -> list: + return [arg.expr for arg in args] + + +def to_date(arg: Expr, *formatters: Expr) -> Expr: + """Converts a value to a date (YYYY-MM-DD). + + Supports strings, numeric and timestamp types as input. + Integers and doubles are interpreted as days since the unix epoch. + Strings are parsed as YYYY-MM-DD (e.g. '2023-07-20') + if ``formatters`` are not provided. + + For usage of ``formatters`` see the rust chrono package ``strftime`` package. + + [Documentation here.](https://docs.rs/chrono/latest/chrono/format/strftime/index.html) + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-07-20"]}) + >>> result = df.select( + ... dfn.functions.to_date(dfn.col("a")).alias("dt")) + >>> str(result.collect_column("dt")[0].as_py()) + '2021-07-20' + """ + return Expr(f.to_date(arg.expr, *_unwrap_exprs(formatters))) + + +def to_local_time(*args: Expr) -> Expr: + """Converts a timestamp with a timezone to a timestamp without a timezone. + + This function handles daylight saving time changes. + """ + return Expr(f.to_local_time(*_unwrap_exprs(args))) + + +def to_time(arg: Expr, *formatters: Expr) -> Expr: + """Converts a value to a time. Supports strings and timestamps as input. + + If ``formatters`` is not provided strings are parsed as HH:MM:SS, HH:MM or + HH:MM:SS.nnnnnnnnn; + + For usage of ``formatters`` see the rust chrono package ``strftime`` package. + + [Documentation here.](https://docs.rs/chrono/latest/chrono/format/strftime/index.html) + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["14:30:00"]}) + >>> result = df.select( + ... dfn.functions.to_time(dfn.col("a")).alias("t")) + >>> str(result.collect_column("t")[0].as_py()) + '14:30:00' + """ + return Expr(f.to_time(arg.expr, *_unwrap_exprs(formatters))) + + def to_timestamp(arg: Expr, *formatters: Expr) -> Expr: """Converts a string and optional formats to a ``Timestamp`` in nanoseconds. For usage of ``formatters`` see the rust chrono package ``strftime`` package. [Documentation here.](https://docs.rs/chrono/latest/chrono/format/strftime/index.html) - """ - if formatters is None: - return f.to_timestamp(arg.expr) - formatters = [f.expr for f in formatters] - return Expr(f.to_timestamp(arg.expr, *formatters)) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-01-01T00:00:00"]}) + >>> result = df.select( + ... dfn.functions.to_timestamp( + ... dfn.col("a") + ... ).alias("ts") + ... ) + >>> str(result.collect_column("ts")[0].as_py()) + '2021-01-01 00:00:00' + """ + return Expr(f.to_timestamp(arg.expr, *_unwrap_exprs(formatters))) def to_timestamp_millis(arg: Expr, *formatters: Expr) -> Expr: """Converts a string and optional formats to a ``Timestamp`` in milliseconds. See :py:func:`to_timestamp` for a description on how to use formatters. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-01-01T00:00:00"]}) + >>> result = df.select( + ... dfn.functions.to_timestamp_millis( + ... dfn.col("a") + ... ).alias("ts") + ... ) + >>> str(result.collect_column("ts")[0].as_py()) + '2021-01-01 00:00:00' """ - formatters = [f.expr for f in formatters] - return Expr(f.to_timestamp_millis(arg.expr, *formatters)) + return Expr(f.to_timestamp_millis(arg.expr, *_unwrap_exprs(formatters))) def to_timestamp_micros(arg: Expr, *formatters: Expr) -> Expr: """Converts a string and optional formats to a ``Timestamp`` in microseconds. See :py:func:`to_timestamp` for a description on how to use formatters. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-01-01T00:00:00"]}) + >>> result = df.select( + ... dfn.functions.to_timestamp_micros( + ... dfn.col("a") + ... ).alias("ts") + ... ) + >>> str(result.collect_column("ts")[0].as_py()) + '2021-01-01 00:00:00' """ - formatters = [f.expr for f in formatters] - return Expr(f.to_timestamp_micros(arg.expr, *formatters)) + return Expr(f.to_timestamp_micros(arg.expr, *_unwrap_exprs(formatters))) def to_timestamp_nanos(arg: Expr, *formatters: Expr) -> Expr: """Converts a string and optional formats to a ``Timestamp`` in nanoseconds. See :py:func:`to_timestamp` for a description on how to use formatters. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-01-01T00:00:00"]}) + >>> result = df.select( + ... dfn.functions.to_timestamp_nanos( + ... dfn.col("a") + ... ).alias("ts") + ... ) + >>> str(result.collect_column("ts")[0].as_py()) + '2021-01-01 00:00:00' """ - formatters = [f.expr for f in formatters] - return Expr(f.to_timestamp_nanos(arg.expr, *formatters)) + return Expr(f.to_timestamp_nanos(arg.expr, *_unwrap_exprs(formatters))) def to_timestamp_seconds(arg: Expr, *formatters: Expr) -> Expr: """Converts a string and optional formats to a ``Timestamp`` in seconds. See :py:func:`to_timestamp` for a description on how to use formatters. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-01-01T00:00:00"]}) + >>> result = df.select( + ... dfn.functions.to_timestamp_seconds( + ... dfn.col("a") + ... ).alias("ts") + ... ) + >>> str(result.collect_column("ts")[0].as_py()) + '2021-01-01 00:00:00' """ - formatters = [f.expr for f in formatters] - return Expr(f.to_timestamp_seconds(arg.expr, *formatters)) + return Expr(f.to_timestamp_seconds(arg.expr, *_unwrap_exprs(formatters))) def to_unixtime(string: Expr, *format_arguments: Expr) -> Expr: - """Converts a string and optional formats to a Unixtime.""" - args = [f.expr for f in format_arguments] - return Expr(f.to_unixtime(string.expr, *args)) + """Converts a string and optional formats to a Unixtime. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["1970-01-01T00:00:00"]}) + >>> result = df.select(dfn.functions.to_unixtime(dfn.col("a")).alias("u")) + >>> result.collect_column("u")[0].as_py() + 0 + """ + return Expr(f.to_unixtime(string.expr, *_unwrap_exprs(format_arguments))) def current_date() -> Expr: - """Returns current UTC date as a Date32 value.""" + """Returns current UTC date as a Date32 value. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.current_date().alias("d") + ... ) + >>> result.collect_column("d")[0].as_py() is not None + True + """ return Expr(f.current_date()) +today = current_date + + def current_time() -> Expr: - """Returns current UTC time as a Time64 value.""" + """Returns current UTC time as a Time64 value. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.current_time().alias("t") + ... ) + + Use .value instead of .as_py() because nanosecond timestamps + require pandas to convert to Python datetime objects. + + >>> result.collect_column("t")[0].value > 0 + True + """ return Expr(f.current_time()) -def datepart(part: Expr, date: Expr) -> Expr: +def datepart(part: Expr | str, date: Expr) -> Expr: """Return a specified part of a date. - This is an alias for :py:func:`date_part`. + See Also: + This is an alias for :py:func:`date_part`. """ return date_part(part, date) -def date_part(part: Expr, date: Expr) -> Expr: - """Extracts a subfield from the date.""" +def date_part(part: Expr | str, date: Expr) -> Expr: + """Extracts a subfield from the date. + + Args: + part: The part of the date to extract. Must be one of ``"year"``, + ``"month"``, ``"day"``, ``"hour"``, ``"minute"``, ``"second"``, etc. + date: The date expression to extract from. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-07-15T00:00:00"]}) + >>> df = df.select(dfn.functions.to_timestamp(dfn.col("a")).alias("a")) + >>> result = df.select( + ... dfn.functions.date_part("year", dfn.col("a")).alias("y")) + >>> result.collect_column("y")[0].as_py() + 2021 + """ + part = coerce_to_expr(part) return Expr(f.date_part(part.expr, date.expr)) -def extract(part: Expr, date: Expr) -> Expr: +def extract(part: Expr | str, date: Expr) -> Expr: """Extracts a subfield from the date. - This is an alias for :py:func:`date_part`. + See Also: + This is an alias for :py:func:`date_part`. """ return date_part(part, date) -def date_trunc(part: Expr, date: Expr) -> Expr: - """Truncates the date to a specified level of precision.""" +def date_trunc(part: Expr | str, date: Expr) -> Expr: + """Truncates the date to a specified level of precision. + + Args: + part: The precision to truncate to. Must be one of ``"year"``, + ``"month"``, ``"day"``, ``"hour"``, ``"minute"``, ``"second"``, etc. + date: The date expression to truncate. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["2021-07-15T12:34:56"]}) + >>> df = df.select(dfn.functions.to_timestamp(dfn.col("a")).alias("a")) + >>> result = df.select( + ... dfn.functions.date_trunc("month", dfn.col("a")).alias("t") + ... ) + >>> str(result.collect_column("t")[0].as_py()) + '2021-07-01 00:00:00' + """ + part = coerce_to_expr(part) return Expr(f.date_trunc(part.expr, date.expr)) -def datetrunc(part: Expr, date: Expr) -> Expr: +def datetrunc(part: Expr | str, date: Expr) -> Expr: """Truncates the date to a specified level of precision. - This is an alias for :py:func:`date_trunc`. + See Also: + This is an alias for :py:func:`date_trunc`. """ return date_trunc(part, date) def date_bin(stride: Expr, source: Expr, origin: Expr) -> Expr: - """Coerces an arbitrary timestamp to the start of the nearest specified interval.""" + """Coerces an arbitrary timestamp to the start of the nearest specified interval. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"timestamp": ['2021-07-15 12:34:56', '2021-01-01']}) + >>> result = df.select( + ... dfn.functions.date_bin( + ... dfn.string_literal("15 minutes"), + ... dfn.col("timestamp"), + ... dfn.string_literal("2001-01-01 00:00:00") + ... ).alias("b") + ... ) + >>> str(result.collect_column("b")[0].as_py()) + '2021-07-15 12:30:00' + >>> str(result.collect_column("b")[1].as_py()) + '2021-01-01 00:00:00' + """ return Expr(f.date_bin(stride.expr, source.expr, origin.expr)) def make_date(year: Expr, month: Expr, day: Expr) -> Expr: - """Make a date from year, month and day component parts.""" + """Make a date from year, month and day component parts. + + Examples: + >>> from datetime import date + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [2024], "m": [1], "d": [15]}) + >>> result = df.select( + ... dfn.functions.make_date(dfn.col("y"), dfn.col("m"), + ... dfn.col("d")).alias("dt")) + >>> result.collect_column("dt")[0].as_py() + datetime.date(2024, 1, 15) + """ return Expr(f.make_date(year.expr, month.expr, day.expr)) -def translate(string: Expr, from_val: Expr, to_val: Expr) -> Expr: - """Replaces the characters in ``from_val`` with the counterpart in ``to_val``.""" +def make_time(hour: Expr, minute: Expr, second: Expr) -> Expr: + """Make a time from hour, minute and second component parts. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"h": [12], "m": [30], "s": [0]}) + >>> result = df.select( + ... dfn.functions.make_time(dfn.col("h"), dfn.col("m"), + ... dfn.col("s")).alias("t")) + >>> result.collect_column("t")[0].as_py() + datetime.time(12, 30) + """ + return Expr(f.make_time(hour.expr, minute.expr, second.expr)) + + +def translate(string: Expr, from_val: Expr | str, to_val: Expr | str) -> Expr: + """Replaces the characters in ``from_val`` with the counterpart in ``to_val``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select( + ... dfn.functions.translate(dfn.col("a"), "helo", "HELO").alias("t")) + >>> result.collect_column("t")[0].as_py() + 'HELLO' + """ + from_val = coerce_to_expr(from_val) + to_val = coerce_to_expr(to_val) return Expr(f.translate(string.expr, from_val.expr, to_val.expr)) def trim(arg: Expr) -> Expr: - """Removes all characters, spaces by default, from both sides of a string.""" + """Removes all characters, spaces by default, from both sides of a string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [" hello "]}) + >>> result = df.select(dfn.functions.trim(dfn.col("a")).alias("t")) + >>> result.collect_column("t")[0].as_py() + 'hello' + """ return Expr(f.trim(arg.expr)) -def trunc(num: Expr, precision: Expr | None = None) -> Expr: - """Truncate the number toward zero with optional precision.""" +def trunc(num: Expr, precision: Expr | int | None = None) -> Expr: + """Truncate the number toward zero with optional precision. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.567]}) + >>> result = df.select( + ... dfn.functions.trunc(dfn.col("a")).alias("t")) + >>> result.collect_column("t")[0].as_py() + 1.0 + + >>> result = df.select( + ... dfn.functions.trunc(dfn.col("a"), precision=2).alias("t")) + >>> result.collect_column("t")[0].as_py() + 1.56 + """ if precision is not None: + precision = coerce_to_expr(precision) return Expr(f.trunc(num.expr, precision.expr)) return Expr(f.trunc(num.expr)) def upper(arg: Expr) -> Expr: - """Converts a string to uppercase.""" + """Converts a string to uppercase. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello"]}) + >>> result = df.select(dfn.functions.upper(dfn.col("a")).alias("u")) + >>> result.collect_column("u")[0].as_py() + 'HELLO' + """ return Expr(f.upper(arg.expr)) def make_array(*args: Expr) -> Expr: - """Returns an array using the specified input expressions.""" + """Returns an array using the specified input expressions. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.make_array( + ... dfn.lit(1), dfn.lit(2), dfn.lit(3) + ... ).alias("arr")) + >>> result.collect_column("arr")[0].as_py() + [1, 2, 3] + """ args = [arg.expr for arg in args] return Expr(f.make_array(args)) @@ -1118,7 +2537,8 @@ def make_array(*args: Expr) -> Expr: def make_list(*args: Expr) -> Expr: """Returns an array using the specified input expressions. - This is an alias for :py:func:`make_array`. + See Also: + This is an alias for :py:func:`make_array`. """ return make_array(*args) @@ -1126,29 +2546,77 @@ def make_list(*args: Expr) -> Expr: def array(*args: Expr) -> Expr: """Returns an array using the specified input expressions. - This is an alias for :py:func:`make_array`. + See Also: + This is an alias for :py:func:`make_array`. """ return make_array(*args) def range(start: Expr, stop: Expr, step: Expr) -> Expr: - """Create a list of values in the range between start and stop.""" + """Create a list of values in the range between start and stop. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.range(dfn.lit(0), dfn.lit(5), dfn.lit(2)).alias("r")) + >>> result.collect_column("r")[0].as_py() + [0, 2, 4] + """ return Expr(f.range(start.expr, stop.expr, step.expr)) def uuid() -> Expr: - """Returns uuid v4 as a string value.""" + """Returns uuid v4 as a string value. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.uuid().alias("u") + ... ) + >>> len(result.collect_column("u")[0].as_py()) == 36 + True + """ return Expr(f.uuid()) def struct(*args: Expr) -> Expr: - """Returns a struct with the given arguments.""" + """Returns a struct with the given arguments. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1], "b": [2]}) + >>> result = df.select( + ... dfn.functions.struct( + ... dfn.col("a"), dfn.col("b") + ... ).alias("s") + ... ) + + Children in the new struct will always be `c0`, ..., `cN-1` + for `N` children. + + >>> result.collect_column("s")[0].as_py() == {"c0": 1, "c1": 2} + True + """ args = [arg.expr for arg in args] return Expr(f.struct(*args)) def named_struct(name_pairs: list[tuple[str, Expr]]) -> Expr: - """Returns a struct with the given names and arguments pairs.""" + """Returns a struct with the given names and arguments pairs. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.named_struct( + ... [("x", dfn.lit(10)), ("y", dfn.lit(20))] + ... ).alias("s") + ... ) + >>> result.collect_column("s")[0].as_py() == {"x": 10, "y": 20} + True + """ name_pair_exprs = [ [Expr.literal(pa.scalar(pair[0], type=pa.string())), pair[1]] for pair in name_pairs @@ -1160,34 +2628,244 @@ def named_struct(name_pairs: list[tuple[str, Expr]]) -> Expr: def from_unixtime(arg: Expr) -> Expr: - """Converts an integer to RFC3339 timestamp format string.""" + """Converts an integer to RFC3339 timestamp format string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0]}) + >>> result = df.select( + ... dfn.functions.from_unixtime( + ... dfn.col("a") + ... ).alias("ts") + ... ) + >>> str(result.collect_column("ts")[0].as_py()) + '1970-01-01 00:00:00' + """ return Expr(f.from_unixtime(arg.expr)) def arrow_typeof(arg: Expr) -> Expr: - """Returns the Arrow type of the expression.""" + """Returns the Arrow type of the expression. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select(dfn.functions.arrow_typeof(dfn.col("a")).alias("t")) + >>> result.collect_column("t")[0].as_py() + 'Int64' + """ return Expr(f.arrow_typeof(arg.expr)) -def arrow_cast(expr: Expr, data_type: Expr) -> Expr: - """Casts an expression to a specified data type.""" +def arrow_cast(expr: Expr, data_type: Expr | str | pa.DataType) -> Expr: + """Casts an expression to a specified data type. + + The ``data_type`` can be a string, a ``pyarrow.DataType``, or an + ``Expr``. For simple types, :py:meth:`Expr.cast() + ` is more concise + (e.g., ``col("a").cast(pa.float64())``). Use ``arrow_cast`` when + you want to specify the target type as a string using DataFusion's + type syntax, which can be more readable for complex types like + ``"Timestamp(Nanosecond, None)"``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.arrow_cast(dfn.col("a"), "Float64").alias("c") + ... ) + >>> result.collect_column("c")[0].as_py() + 1.0 + + >>> result = df.select( + ... dfn.functions.arrow_cast( + ... dfn.col("a"), data_type=pa.float64() + ... ).alias("c") + ... ) + >>> result.collect_column("c")[0].as_py() + 1.0 + """ + if isinstance(data_type, pa.DataType): + return expr.cast(data_type) + if isinstance(data_type, str): + data_type = Expr.string_literal(data_type) return Expr(f.arrow_cast(expr.expr, data_type.expr)) +def arrow_metadata(expr: Expr, key: Expr | str | None = None) -> Expr: + """Returns the metadata of the input expression. + + If called with one argument, returns a Map of all metadata key-value pairs. + If called with two arguments, returns the value for the specified metadata key. + + Examples: + >>> field = pa.field("val", pa.int64(), metadata={"k": "v"}) + >>> schema = pa.schema([field]) + >>> batch = pa.RecordBatch.from_arrays([pa.array([1])], schema=schema) + >>> ctx = dfn.SessionContext() + >>> df = ctx.create_dataframe([[batch]]) + >>> result = df.select( + ... dfn.functions.arrow_metadata(dfn.col("val")).alias("meta") + ... ) + >>> ("k", "v") in result.collect_column("meta")[0].as_py() + True + + >>> result = df.select( + ... dfn.functions.arrow_metadata( + ... dfn.col("val"), key="k" + ... ).alias("meta_val") + ... ) + >>> result.collect_column("meta_val")[0].as_py() + 'v' + """ + if key is None: + return Expr(f.arrow_metadata(expr.expr)) + if isinstance(key, str): + key = Expr.string_literal(key) + return Expr(f.arrow_metadata(expr.expr, key.expr)) + + +def get_field(expr: Expr, name: Expr | str) -> Expr: + """Extracts a field from a struct or map by name. + + When the field name is a static string, the bracket operator + ``expr["field"]`` is a convenient shorthand. Use ``get_field`` + when the field name is a dynamic expression. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1], "b": [2]}) + >>> df = df.with_column( + ... "s", + ... dfn.functions.named_struct( + ... [("x", dfn.col("a")), ("y", dfn.col("b"))] + ... ), + ... ) + >>> result = df.select( + ... dfn.functions.get_field(dfn.col("s"), "x").alias("x_val") + ... ) + >>> result.collect_column("x_val")[0].as_py() + 1 + + Equivalent using bracket syntax: + + >>> result = df.select( + ... dfn.col("s")["x"].alias("x_val") + ... ) + >>> result.collect_column("x_val")[0].as_py() + 1 + """ + if isinstance(name, str): + name = Expr.string_literal(name) + return Expr(f.get_field(expr.expr, name.expr)) + + +def union_extract(union_expr: Expr, field_name: Expr | str) -> Expr: + """Extracts a value from a union type by field name. + + Returns the value of the named field if it is the currently selected + variant, otherwise returns NULL. + + Examples: + >>> ctx = dfn.SessionContext() + >>> types = pa.array([0, 1, 0], type=pa.int8()) + >>> offsets = pa.array([0, 0, 1], type=pa.int32()) + >>> arr = pa.UnionArray.from_dense( + ... types, offsets, [pa.array([1, 2]), pa.array(["hi"])], + ... ["int", "str"], [0, 1], + ... ) + >>> batch = pa.RecordBatch.from_arrays([arr], names=["u"]) + >>> df = ctx.create_dataframe([[batch]]) + >>> result = df.select( + ... dfn.functions.union_extract(dfn.col("u"), "int").alias("val") + ... ) + >>> result.collect_column("val").to_pylist() + [1, None, 2] + """ + if isinstance(field_name, str): + field_name = Expr.string_literal(field_name) + return Expr(f.union_extract(union_expr.expr, field_name.expr)) + + +def union_tag(union_expr: Expr) -> Expr: + """Returns the tag (active field name) of a union type. + + Examples: + >>> ctx = dfn.SessionContext() + >>> types = pa.array([0, 1, 0], type=pa.int8()) + >>> offsets = pa.array([0, 0, 1], type=pa.int32()) + >>> arr = pa.UnionArray.from_dense( + ... types, offsets, [pa.array([1, 2]), pa.array(["hi"])], + ... ["int", "str"], [0, 1], + ... ) + >>> batch = pa.RecordBatch.from_arrays([arr], names=["u"]) + >>> df = ctx.create_dataframe([[batch]]) + >>> result = df.select( + ... dfn.functions.union_tag(dfn.col("u")).alias("tag") + ... ) + >>> result.collect_column("tag").to_pylist() + ['int', 'str', 'int'] + """ + return Expr(f.union_tag(union_expr.expr)) + + +def version() -> Expr: + """Returns the DataFusion version string. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.empty_table() + >>> result = df.select(dfn.functions.version().alias("v")) + >>> "Apache DataFusion" in result.collect_column("v")[0].as_py() + True + """ + return Expr(f.version()) + + +def row(*args: Expr) -> Expr: + """Returns a struct with the given arguments. + + See Also: + This is an alias for :py:func:`struct`. + """ + return struct(*args) + + def random() -> Expr: - """Returns a random value in the range ``0.0 <= x < 1.0``.""" + """Returns a random value in the range ``0.0 <= x < 1.0``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.random().alias("r") + ... ) + >>> val = result.collect_column("r")[0].as_py() + >>> 0.0 <= val < 1.0 + True + """ return Expr(f.random()) def array_append(array: Expr, element: Expr) -> Expr: - """Appends an element to the end of an array.""" + """Appends an element to the end of an array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_append(dfn.col("a"), dfn.lit(4)).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 2, 3, 4] + """ return Expr(f.array_append(array.expr, element.expr)) def array_push_back(array: Expr, element: Expr) -> Expr: """Appends an element to the end of an array. - This is an alias for :py:func:`array_append`. + See Also: + This is an alias for :py:func:`array_append`. """ return array_append(array, element) @@ -1195,7 +2873,8 @@ def array_push_back(array: Expr, element: Expr) -> Expr: def list_append(array: Expr, element: Expr) -> Expr: """Appends an element to the end of an array. - This is an alias for :py:func:`array_append`. + See Also: + This is an alias for :py:func:`array_append`. """ return array_append(array, element) @@ -1203,13 +2882,23 @@ def list_append(array: Expr, element: Expr) -> Expr: def list_push_back(array: Expr, element: Expr) -> Expr: """Appends an element to the end of an array. - This is an alias for :py:func:`array_append`. + See Also: + This is an alias for :py:func:`array_append`. """ return array_append(array, element) def array_concat(*args: Expr) -> Expr: - """Concatenates the input arrays.""" + """Concatenates the input arrays. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2]], "b": [[3, 4]]}) + >>> result = df.select( + ... dfn.functions.array_concat(dfn.col("a"), dfn.col("b")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 2, 3, 4] + """ args = [arg.expr for arg in args] return Expr(f.array_concat(args)) @@ -1217,25 +2906,49 @@ def array_concat(*args: Expr) -> Expr: def array_cat(*args: Expr) -> Expr: """Concatenates the input arrays. - This is an alias for :py:func:`array_concat`. + See Also: + This is an alias for :py:func:`array_concat`. """ return array_concat(*args) def array_dims(array: Expr) -> Expr: - """Returns an array of the array's dimensions.""" + """Returns an array of the array's dimensions. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select(dfn.functions.array_dims(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [3] + """ return Expr(f.array_dims(array.expr)) def array_distinct(array: Expr) -> Expr: - """Returns distinct values from the array after removing duplicates.""" + """Returns distinct values from the array after removing duplicates. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_distinct( + ... dfn.col("a") + ... ).alias("result") + ... ) + >>> sorted( + ... result.collect_column("result")[0].as_py() + ... ) + [1, 2, 3] + """ return Expr(f.array_distinct(array.expr)) def list_cat(*args: Expr) -> Expr: """Concatenates the input arrays. - This is an alias for :py:func:`array_concat`, :py:func:`array_cat`. + See Also: + This is an alias for :py:func:`array_concat`, :py:func:`array_cat`. """ return array_concat(*args) @@ -1243,7 +2956,8 @@ def list_cat(*args: Expr) -> Expr: def list_concat(*args: Expr) -> Expr: """Concatenates the input arrays. - This is an alias for :py:func:`array_concat`, :py:func:`array_cat`. + See Also: + This is an alias for :py:func:`array_concat`, :py:func:`array_cat`. """ return array_concat(*args) @@ -1251,7 +2965,8 @@ def list_concat(*args: Expr) -> Expr: def list_distinct(array: Expr) -> Expr: """Returns distinct values from the array after removing duplicates. - This is an alias for :py:func:`array_distinct`. + See Also: + This is an alias for :py:func:`array_distinct`. """ return array_distinct(array) @@ -1259,60 +2974,109 @@ def list_distinct(array: Expr) -> Expr: def list_dims(array: Expr) -> Expr: """Returns an array of the array's dimensions. - This is an alias for :py:func:`array_dims`. + See Also: + This is an alias for :py:func:`array_dims`. """ return array_dims(array) -def array_element(array: Expr, n: Expr) -> Expr: - """Extracts the element with the index n from the array.""" +def array_element(array: Expr, n: Expr | int) -> Expr: + """Extracts the element with the index n from the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[10, 20, 30]]}) + >>> result = df.select( + ... dfn.functions.array_element(dfn.col("a"), 2).alias("result")) + >>> result.collect_column("result")[0].as_py() + 20 + """ + n = coerce_to_expr(n) return Expr(f.array_element(array.expr, n.expr)) def array_empty(array: Expr) -> Expr: - """Returns a boolean indicating whether the array is empty.""" + """Returns a boolean indicating whether the array is empty. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2]]}) + >>> result = df.select(dfn.functions.array_empty(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + False + """ return Expr(f.array_empty(array.expr)) -def array_extract(array: Expr, n: Expr) -> Expr: +def list_empty(array: Expr) -> Expr: + """Returns a boolean indicating whether the array is empty. + + See Also: + This is an alias for :py:func:`array_empty`. + """ + return array_empty(array) + + +def array_extract(array: Expr, n: Expr | int) -> Expr: """Extracts the element with the index n from the array. - This is an alias for :py:func:`array_element`. + See Also: + This is an alias for :py:func:`array_element`. """ return array_element(array, n) -def list_element(array: Expr, n: Expr) -> Expr: +def list_element(array: Expr, n: Expr | int) -> Expr: """Extracts the element with the index n from the array. - This is an alias for :py:func:`array_element`. + See Also: + This is an alias for :py:func:`array_element`. """ return array_element(array, n) -def list_extract(array: Expr, n: Expr) -> Expr: +def list_extract(array: Expr, n: Expr | int) -> Expr: """Extracts the element with the index n from the array. - This is an alias for :py:func:`array_element`. + See Also: + This is an alias for :py:func:`array_element`. """ return array_element(array, n) def array_length(array: Expr) -> Expr: - """Returns the length of the array.""" + """Returns the length of the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select(dfn.functions.array_length(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + 3 + """ return Expr(f.array_length(array.expr)) def list_length(array: Expr) -> Expr: """Returns the length of the array. - This is an alias for :py:func:`array_length`. + See Also: + This is an alias for :py:func:`array_length`. """ return array_length(array) def array_has(first_array: Expr, second_array: Expr) -> Expr: - """Returns true if the element appears in the first array, otherwise false.""" + """Returns true if the element appears in the first array, otherwise false. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_has(dfn.col("a"), dfn.lit(2)).alias("result")) + >>> result.collect_column("result")[0].as_py() + True + """ return Expr(f.array_has(first_array.expr, second_array.expr)) @@ -1321,6 +3085,14 @@ def array_has_all(first_array: Expr, second_array: Expr) -> Expr: Returns true if each element of the second array appears in the first array. Otherwise, it returns false. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]], "b": [[1, 2]]}) + >>> result = df.select( + ... dfn.functions.array_has_all(dfn.col("a"), dfn.col("b")).alias("result")) + >>> result.collect_column("result")[0].as_py() + True """ return Expr(f.array_has_all(first_array.expr, second_array.expr)) @@ -1330,19 +3102,112 @@ def array_has_any(first_array: Expr, second_array: Expr) -> Expr: Returns true if at least one element of the second array appears in the first array. Otherwise, it returns false. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]], "b": [[2, 5]]}) + >>> result = df.select( + ... dfn.functions.array_has_any(dfn.col("a"), dfn.col("b")).alias("result")) + >>> result.collect_column("result")[0].as_py() + True """ return Expr(f.array_has_any(first_array.expr, second_array.expr)) -def array_position(array: Expr, element: Expr, index: int | None = 1) -> Expr: - """Return the position of the first occurrence of ``element`` in ``array``.""" +def array_contains(array: Expr, element: Expr) -> Expr: + """Returns true if the element appears in the array, otherwise false. + + See Also: + This is an alias for :py:func:`array_has`. + """ + return array_has(array, element) + + +def list_has(array: Expr, element: Expr) -> Expr: + """Returns true if the element appears in the array, otherwise false. + + See Also: + This is an alias for :py:func:`array_has`. + """ + return array_has(array, element) + + +def list_has_all(first_array: Expr, second_array: Expr) -> Expr: + """Determines if there is complete overlap ``second_array`` in ``first_array``. + + See Also: + This is an alias for :py:func:`array_has_all`. + """ + return array_has_all(first_array, second_array) + + +def list_has_any(first_array: Expr, second_array: Expr) -> Expr: + """Determine if there is an overlap between ``first_array`` and ``second_array``. + + See Also: + This is an alias for :py:func:`array_has_any`. + """ + return array_has_any(first_array, second_array) + + +def arrays_overlap(first_array: Expr, second_array: Expr) -> Expr: + """Returns true if any element appears in both arrays. + + See Also: + This is an alias for :py:func:`array_has_any`. + """ + return array_has_any(first_array, second_array) + + +def list_overlap(first_array: Expr, second_array: Expr) -> Expr: + """Returns true if any element appears in both arrays. + + See Also: + This is an alias for :py:func:`array_has_any`. + """ + return array_has_any(first_array, second_array) + + +def list_contains(array: Expr, element: Expr) -> Expr: + """Returns true if the element appears in the array, otherwise false. + + See Also: + This is an alias for :py:func:`array_has`. + """ + return array_has(array, element) + + +def array_position(array: Expr, element: Expr, index: int | None = 1) -> Expr: + """Return the position of the first occurrence of ``element`` in ``array``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[10, 20, 30]]}) + >>> result = df.select( + ... dfn.functions.array_position( + ... dfn.col("a"), dfn.lit(20) + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + 2 + + Use ``index`` to start searching from a given position: + + >>> df = ctx.from_pydict({"a": [[10, 20, 10, 20]]}) + >>> result = df.select( + ... dfn.functions.array_position( + ... dfn.col("a"), dfn.lit(20), index=3, + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + 4 + """ return Expr(f.array_position(array.expr, element.expr, index)) def array_indexof(array: Expr, element: Expr, index: int | None = 1) -> Expr: """Return the position of the first occurrence of ``element`` in ``array``. - This is an alias for :py:func:`array_position`. + See Also: + This is an alias for :py:func:`array_position`. """ return array_position(array, element, index) @@ -1350,7 +3215,8 @@ def array_indexof(array: Expr, element: Expr, index: int | None = 1) -> Expr: def list_position(array: Expr, element: Expr, index: int | None = 1) -> Expr: """Return the position of the first occurrence of ``element`` in ``array``. - This is an alias for :py:func:`array_position`. + See Also: + This is an alias for :py:func:`array_position`. """ return array_position(array, element, index) @@ -1358,46 +3224,76 @@ def list_position(array: Expr, element: Expr, index: int | None = 1) -> Expr: def list_indexof(array: Expr, element: Expr, index: int | None = 1) -> Expr: """Return the position of the first occurrence of ``element`` in ``array``. - This is an alias for :py:func:`array_position`. + See Also: + This is an alias for :py:func:`array_position`. """ return array_position(array, element, index) def array_positions(array: Expr, element: Expr) -> Expr: - """Searches for an element in the array and returns all occurrences.""" + """Searches for an element in the array and returns all occurrences. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 1]]}) + >>> result = df.select( + ... dfn.functions.array_positions(dfn.col("a"), dfn.lit(1)).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 3] + """ return Expr(f.array_positions(array.expr, element.expr)) def list_positions(array: Expr, element: Expr) -> Expr: """Searches for an element in the array and returns all occurrences. - This is an alias for :py:func:`array_positions`. + See Also: + This is an alias for :py:func:`array_positions`. """ return array_positions(array, element) def array_ndims(array: Expr) -> Expr: - """Returns the number of dimensions of the array.""" + """Returns the number of dimensions of the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select(dfn.functions.array_ndims(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + 1 + """ return Expr(f.array_ndims(array.expr)) def list_ndims(array: Expr) -> Expr: """Returns the number of dimensions of the array. - This is an alias for :py:func:`array_ndims`. + See Also: + This is an alias for :py:func:`array_ndims`. """ return array_ndims(array) def array_prepend(element: Expr, array: Expr) -> Expr: - """Prepends an element to the beginning of an array.""" + """Prepends an element to the beginning of an array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2]]}) + >>> result = df.select( + ... dfn.functions.array_prepend(dfn.lit(0), dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [0, 1, 2] + """ return Expr(f.array_prepend(element.expr, array.expr)) def array_push_front(element: Expr, array: Expr) -> Expr: """Prepends an element to the beginning of an array. - This is an alias for :py:func:`array_prepend`. + See Also: + This is an alias for :py:func:`array_prepend`. """ return array_prepend(element, array) @@ -1405,7 +3301,8 @@ def array_push_front(element: Expr, array: Expr) -> Expr: def list_prepend(element: Expr, array: Expr) -> Expr: """Prepends an element to the beginning of an array. - This is an alias for :py:func:`array_prepend`. + See Also: + This is an alias for :py:func:`array_prepend`. """ return array_prepend(element, array) @@ -1413,115 +3310,232 @@ def list_prepend(element: Expr, array: Expr) -> Expr: def list_push_front(element: Expr, array: Expr) -> Expr: """Prepends an element to the beginning of an array. - This is an alias for :py:func:`array_prepend`. + See Also: + This is an alias for :py:func:`array_prepend`. """ return array_prepend(element, array) def array_pop_back(array: Expr) -> Expr: - """Returns the array without the last element.""" + """Returns the array without the last element. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_pop_back(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 2] + """ return Expr(f.array_pop_back(array.expr)) def array_pop_front(array: Expr) -> Expr: - """Returns the array without the first element.""" + """Returns the array without the first element. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_pop_front(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [2, 3] + """ return Expr(f.array_pop_front(array.expr)) +def list_pop_back(array: Expr) -> Expr: + """Returns the array without the last element. + + See Also: + This is an alias for :py:func:`array_pop_back`. + """ + return array_pop_back(array) + + +def list_pop_front(array: Expr) -> Expr: + """Returns the array without the first element. + + See Also: + This is an alias for :py:func:`array_pop_front`. + """ + return array_pop_front(array) + + def array_remove(array: Expr, element: Expr) -> Expr: - """Removes the first element from the array equal to the given value.""" + """Removes the first element from the array equal to the given value. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 1]]}) + >>> result = df.select( + ... dfn.functions.array_remove(dfn.col("a"), dfn.lit(1)).alias("result")) + >>> result.collect_column("result")[0].as_py() + [2, 1] + """ return Expr(f.array_remove(array.expr, element.expr)) def list_remove(array: Expr, element: Expr) -> Expr: """Removes the first element from the array equal to the given value. - This is an alias for :py:func:`array_remove`. + See Also: + This is an alias for :py:func:`array_remove`. """ return array_remove(array, element) -def array_remove_n(array: Expr, element: Expr, max: Expr) -> Expr: - """Removes the first ``max`` elements from the array equal to the given value.""" +def array_remove_n(array: Expr, element: Expr, max: Expr | int) -> Expr: + """Removes the first ``max`` elements from the array equal to the given value. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 1, 1]]}) + >>> result = df.select( + ... dfn.functions.array_remove_n( + ... dfn.col("a"), dfn.lit(1), 2 + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + [2, 1] + """ + max = coerce_to_expr(max) return Expr(f.array_remove_n(array.expr, element.expr, max.expr)) -def list_remove_n(array: Expr, element: Expr, max: Expr) -> Expr: +def list_remove_n(array: Expr, element: Expr, max: Expr | int) -> Expr: """Removes the first ``max`` elements from the array equal to the given value. - This is an alias for :py:func:`array_remove_n`. + See Also: + This is an alias for :py:func:`array_remove_n`. """ return array_remove_n(array, element, max) def array_remove_all(array: Expr, element: Expr) -> Expr: - """Removes all elements from the array equal to the given value.""" + """Removes all elements from the array equal to the given value. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 1]]}) + >>> result = df.select( + ... dfn.functions.array_remove_all( + ... dfn.col("a"), dfn.lit(1) + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + [2] + """ return Expr(f.array_remove_all(array.expr, element.expr)) def list_remove_all(array: Expr, element: Expr) -> Expr: """Removes all elements from the array equal to the given value. - This is an alias for :py:func:`array_remove_all`. + See Also: + This is an alias for :py:func:`array_remove_all`. """ return array_remove_all(array, element) -def array_repeat(element: Expr, count: Expr) -> Expr: - """Returns an array containing ``element`` ``count`` times.""" +def array_repeat(element: Expr, count: Expr | int) -> Expr: + """Returns an array containing ``element`` ``count`` times. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.array_repeat(dfn.lit(3), 3).alias("result")) + >>> result.collect_column("result")[0].as_py() + [3, 3, 3] + """ + count = coerce_to_expr(count) return Expr(f.array_repeat(element.expr, count.expr)) -def list_repeat(element: Expr, count: Expr) -> Expr: +def list_repeat(element: Expr, count: Expr | int) -> Expr: """Returns an array containing ``element`` ``count`` times. - This is an alias for :py:func:`array_repeat`. + See Also: + This is an alias for :py:func:`array_repeat`. """ return array_repeat(element, count) def array_replace(array: Expr, from_val: Expr, to_val: Expr) -> Expr: - """Replaces the first occurrence of ``from_val`` with ``to_val``.""" + """Replaces the first occurrence of ``from_val`` with ``to_val``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 1]]}) + >>> result = df.select( + ... dfn.functions.array_replace(dfn.col("a"), dfn.lit(1), + ... dfn.lit(9)).alias("result")) + >>> result.collect_column("result")[0].as_py() + [9, 2, 1] + """ return Expr(f.array_replace(array.expr, from_val.expr, to_val.expr)) def list_replace(array: Expr, from_val: Expr, to_val: Expr) -> Expr: """Replaces the first occurrence of ``from_val`` with ``to_val``. - This is an alias for :py:func:`array_replace`. + See Also: + This is an alias for :py:func:`array_replace`. """ return array_replace(array, from_val, to_val) -def array_replace_n(array: Expr, from_val: Expr, to_val: Expr, max: Expr) -> Expr: +def array_replace_n(array: Expr, from_val: Expr, to_val: Expr, max: Expr | int) -> Expr: """Replace ``n`` occurrences of ``from_val`` with ``to_val``. Replaces the first ``max`` occurrences of the specified element with another specified element. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 1, 1]]}) + >>> result = df.select( + ... dfn.functions.array_replace_n( + ... dfn.col("a"), dfn.lit(1), dfn.lit(9), 2 + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + [9, 2, 9, 1] """ + max = coerce_to_expr(max) return Expr(f.array_replace_n(array.expr, from_val.expr, to_val.expr, max.expr)) -def list_replace_n(array: Expr, from_val: Expr, to_val: Expr, max: Expr) -> Expr: +def list_replace_n(array: Expr, from_val: Expr, to_val: Expr, max: Expr | int) -> Expr: """Replace ``n`` occurrences of ``from_val`` with ``to_val``. Replaces the first ``max`` occurrences of the specified element with another specified element. - This is an alias for :py:func:`array_replace_n`. + See Also: + This is an alias for :py:func:`array_replace_n`. """ return array_replace_n(array, from_val, to_val, max) def array_replace_all(array: Expr, from_val: Expr, to_val: Expr) -> Expr: - """Replaces all occurrences of ``from_val`` with ``to_val``.""" + """Replaces all occurrences of ``from_val`` with ``to_val``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 1]]}) + >>> result = df.select( + ... dfn.functions.array_replace_all(dfn.col("a"), dfn.lit(1), + ... dfn.lit(9)).alias("result")) + >>> result.collect_column("result")[0].as_py() + [9, 2, 9] + """ return Expr(f.array_replace_all(array.expr, from_val.expr, to_val.expr)) def list_replace_all(array: Expr, from_val: Expr, to_val: Expr) -> Expr: """Replaces all occurrences of ``from_val`` with ``to_val``. - This is an alias for :py:func:`array_replace_all`. + See Also: + This is an alias for :py:func:`array_replace_all`. """ return array_replace_all(array, from_val, to_val) @@ -1533,6 +3547,24 @@ def array_sort(array: Expr, descending: bool = False, null_first: bool = False) array: The input array to sort. descending: If True, sorts in descending order. null_first: If True, nulls will be returned at the beginning of the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[3, 1, 2]]}) + >>> result = df.select( + ... dfn.functions.array_sort( + ... dfn.col("a") + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 2, 3] + + >>> df = ctx.from_pydict({"a": [[3, None, 1]]}) + >>> result = df.select( + ... dfn.functions.array_sort( + ... dfn.col("a"), descending=True, null_first=True, + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + [None, 3, 1] """ desc = "DESC" if descending else "ASC" nulls_first = "NULLS FIRST" if null_first else "NULLS LAST" @@ -1546,36 +3578,87 @@ def array_sort(array: Expr, descending: bool = False, null_first: bool = False) def list_sort(array: Expr, descending: bool = False, null_first: bool = False) -> Expr: - """This is an alias for :py:func:`array_sort`.""" + """Sorts the array. + + See Also: + This is an alias for :py:func:`array_sort`. + """ return array_sort(array, descending=descending, null_first=null_first) def array_slice( - array: Expr, begin: Expr, end: Expr, stride: Expr | None = None + array: Expr, + begin: Expr | int, + end: Expr | int, + stride: Expr | int | None = None, ) -> Expr: - """Returns a slice of the array.""" - if stride is not None: - stride = stride.expr - return Expr(f.array_slice(array.expr, begin.expr, end.expr, stride)) + """Returns a slice of the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3, 4]]}) + >>> result = df.select( + ... dfn.functions.array_slice(dfn.col("a"), 2, 3).alias("result")) + >>> result.collect_column("result")[0].as_py() + [2, 3] + + Use ``stride`` to skip elements: + + >>> result = df.select( + ... dfn.functions.array_slice( + ... dfn.col("a"), 1, 4, stride=2, + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 3] + """ + begin = coerce_to_expr(begin) + end = coerce_to_expr(end) + stride = coerce_to_expr_or_none(stride) + return Expr( + f.array_slice( + array.expr, + begin.expr, + end.expr, + stride.expr if stride is not None else None, + ) + ) -def list_slice(array: Expr, begin: Expr, end: Expr, stride: Expr | None = None) -> Expr: +def list_slice( + array: Expr, begin: Expr | int, end: Expr | int, stride: Expr | int | None = None +) -> Expr: """Returns a slice of the array. - This is an alias for :py:func:`array_slice`. + See Also: + This is an alias for :py:func:`array_slice`. """ return array_slice(array, begin, end, stride) def array_intersect(array1: Expr, array2: Expr) -> Expr: - """Returns the intersection of ``array1`` and ``array2``.""" + """Returns the intersection of ``array1`` and ``array2``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]], "b": [[2, 3, 4]]}) + >>> result = df.select( + ... dfn.functions.array_intersect( + ... dfn.col("a"), dfn.col("b") + ... ).alias("result") + ... ) + >>> sorted( + ... result.collect_column("result")[0].as_py() + ... ) + [2, 3] + """ return Expr(f.array_intersect(array1.expr, array2.expr)) def list_intersect(array1: Expr, array2: Expr) -> Expr: """Returns an the intersection of ``array1`` and ``array2``. - This is an alias for :py:func:`array_intersect`. + See Also: + This is an alias for :py:func:`array_intersect`. """ return array_intersect(array1, array2) @@ -1584,6 +3667,19 @@ def array_union(array1: Expr, array2: Expr) -> Expr: """Returns an array of the elements in the union of array1 and array2. Duplicate rows will not be returned. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]], "b": [[2, 3, 4]]}) + >>> result = df.select( + ... dfn.functions.array_union( + ... dfn.col("a"), dfn.col("b") + ... ).alias("result") + ... ) + >>> sorted( + ... result.collect_column("result")[0].as_py() + ... ) + [1, 2, 3, 4] """ return Expr(f.array_union(array1.expr, array2.expr)) @@ -1593,57 +3689,478 @@ def list_union(array1: Expr, array2: Expr) -> Expr: Duplicate rows will not be returned. - This is an alias for :py:func:`array_union`. + See Also: + This is an alias for :py:func:`array_union`. """ return array_union(array1, array2) def array_except(array1: Expr, array2: Expr) -> Expr: - """Returns the elements that appear in ``array1`` but not in ``array2``.""" + """Returns the elements that appear in ``array1`` but not in ``array2``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]], "b": [[2, 3, 4]]}) + >>> result = df.select( + ... dfn.functions.array_except(dfn.col("a"), dfn.col("b")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1] + """ return Expr(f.array_except(array1.expr, array2.expr)) def list_except(array1: Expr, array2: Expr) -> Expr: """Returns the elements that appear in ``array1`` but not in the ``array2``. - This is an alias for :py:func:`array_except`. + See Also: + This is an alias for :py:func:`array_except`. """ return array_except(array1, array2) -def array_resize(array: Expr, size: Expr, value: Expr) -> Expr: +def array_resize(array: Expr, size: Expr | int, value: Expr) -> Expr: """Returns an array with the specified size filled. If ``size`` is greater than the ``array`` length, the additional entries will be filled with the given ``value``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2]]}) + >>> result = df.select( + ... dfn.functions.array_resize(dfn.col("a"), 4, dfn.lit(0)).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 2, 0, 0] """ + size = coerce_to_expr(size) return Expr(f.array_resize(array.expr, size.expr, value.expr)) -def list_resize(array: Expr, size: Expr, value: Expr) -> Expr: +def list_resize(array: Expr, size: Expr | int, value: Expr) -> Expr: """Returns an array with the specified size filled. If ``size`` is greater than the ``array`` length, the additional entries will be - filled with the given ``value``. This is an alias for :py:func:`array_resize`. + filled with the given ``value``. + + See Also: + This is an alias for :py:func:`array_resize`. """ return array_resize(array, size, value) +def array_any_value(array: Expr) -> Expr: + """Returns the first non-null element in the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[None, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_any_value(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + 2 + """ + return Expr(f.array_any_value(array.expr)) + + +def list_any_value(array: Expr) -> Expr: + """Returns the first non-null element in the array. + + See Also: + This is an alias for :py:func:`array_any_value`. + """ + return array_any_value(array) + + +def array_distance(array1: Expr, array2: Expr) -> Expr: + """Returns the Euclidean distance between two numeric arrays. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1.0, 2.0]], "b": [[1.0, 4.0]]}) + >>> result = df.select( + ... dfn.functions.array_distance( + ... dfn.col("a"), dfn.col("b"), + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + 2.0 + """ + return Expr(f.array_distance(array1.expr, array2.expr)) + + +def list_distance(array1: Expr, array2: Expr) -> Expr: + """Returns the Euclidean distance between two numeric arrays. + + See Also: + This is an alias for :py:func:`array_distance`. + """ + return array_distance(array1, array2) + + +def array_max(array: Expr) -> Expr: + """Returns the maximum value in the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_max(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + 3 + """ + return Expr(f.array_max(array.expr)) + + +def list_max(array: Expr) -> Expr: + """Returns the maximum value in the array. + + See Also: + This is an alias for :py:func:`array_max`. + """ + return array_max(array) + + +def array_min(array: Expr) -> Expr: + """Returns the minimum value in the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_min(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + 1 + """ + return Expr(f.array_min(array.expr)) + + +def list_min(array: Expr) -> Expr: + """Returns the minimum value in the array. + + See Also: + This is an alias for :py:func:`array_min`. + """ + return array_min(array) + + +def array_reverse(array: Expr) -> Expr: + """Reverses the order of elements in the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select( + ... dfn.functions.array_reverse(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [3, 2, 1] + """ + return Expr(f.array_reverse(array.expr)) + + +def list_reverse(array: Expr) -> Expr: + """Reverses the order of elements in the array. + + See Also: + This is an alias for :py:func:`array_reverse`. + """ + return array_reverse(array) + + +def arrays_zip(*arrays: Expr) -> Expr: + """Combines multiple arrays into a single array of structs. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2]], "b": [[3, 4]]}) + >>> result = df.select( + ... dfn.functions.arrays_zip(dfn.col("a"), dfn.col("b")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [{'c0': 1, 'c1': 3}, {'c0': 2, 'c1': 4}] + """ + args = [a.expr for a in arrays] + return Expr(f.arrays_zip(args)) + + +def list_zip(*arrays: Expr) -> Expr: + """Combines multiple arrays into a single array of structs. + + See Also: + This is an alias for :py:func:`arrays_zip`. + """ + return arrays_zip(*arrays) + + +def string_to_array( + string: Expr, delimiter: Expr | str, null_string: Expr | str | None = None +) -> Expr: + """Splits a string based on a delimiter and returns an array of parts. + + Any parts matching the optional ``null_string`` will be replaced with ``NULL``. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["hello,world"]}) + >>> result = df.select( + ... dfn.functions.string_to_array(dfn.col("a"), ",").alias("result")) + >>> result.collect_column("result")[0].as_py() + ['hello', 'world'] + + Replace parts matching a ``null_string`` with ``NULL``: + + >>> result = df.select( + ... dfn.functions.string_to_array( + ... dfn.col("a"), ",", null_string="world", + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + ['hello', None] + """ + delimiter = coerce_to_expr(delimiter) + null_string = coerce_to_expr_or_none(null_string) + return Expr( + f.string_to_array( + string.expr, + delimiter.expr, + null_string.expr if null_string is not None else None, + ) + ) + + +def string_to_list( + string: Expr, delimiter: Expr | str, null_string: Expr | str | None = None +) -> Expr: + """Splits a string based on a delimiter and returns an array of parts. + + See Also: + This is an alias for :py:func:`string_to_array`. + """ + return string_to_array(string, delimiter, null_string) + + +def gen_series(start: Expr, stop: Expr, step: Expr | None = None) -> Expr: + """Creates a list of values in the range between start and stop. + + Unlike :py:func:`range`, this includes the upper bound. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0]}) + >>> result = df.select( + ... dfn.functions.gen_series( + ... dfn.lit(1), dfn.lit(5), + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 2, 3, 4, 5] + + Specify a custom ``step``: + + >>> result = df.select( + ... dfn.functions.gen_series( + ... dfn.lit(1), dfn.lit(10), step=dfn.lit(3), + ... ).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 4, 7, 10] + """ + step_expr = step.expr if step is not None else None + return Expr(f.gen_series(start.expr, stop.expr, step_expr)) + + +def generate_series(start: Expr, stop: Expr, step: Expr | None = None) -> Expr: + """Creates a list of values in the range between start and stop. + + Unlike :py:func:`range`, this includes the upper bound. + + See Also: + This is an alias for :py:func:`gen_series`. + """ + return gen_series(start, stop, step) + + def flatten(array: Expr) -> Expr: - """Flattens an array of arrays into a single array.""" + """Flattens an array of arrays into a single array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[[1, 2], [3, 4]]]}) + >>> result = df.select(dfn.functions.flatten(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + [1, 2, 3, 4] + """ return Expr(f.flatten(array.expr)) def cardinality(array: Expr) -> Expr: - """Returns the total number of elements in the array.""" + """Returns the total number of elements in the array. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [[1, 2, 3]]}) + >>> result = df.select(dfn.functions.cardinality(dfn.col("a")).alias("result")) + >>> result.collect_column("result")[0].as_py() + 3 + """ return Expr(f.cardinality(array.expr)) def empty(array: Expr) -> Expr: - """This is an alias for :py:func:`array_empty`.""" + """Returns true if the array is empty. + + See Also: + This is an alias for :py:func:`array_empty`. + """ return array_empty(array) +# map functions + + +def make_map(*args: Any) -> Expr: + """Returns a map expression. + + Supports three calling conventions: + + - ``make_map({"a": 1, "b": 2})`` — from a Python dictionary. + - ``make_map([keys], [values])`` — from a list of keys and a list of + their associated values. Both lists must be the same length. + - ``make_map(k1, v1, k2, v2, ...)`` — from alternating keys and their + associated values. + + Keys and values that are not already :py:class:`~datafusion.expr.Expr` + are automatically converted to literal expressions. + + Examples: + From a dictionary: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.make_map({"a": 1, "b": 2}).alias("m")) + >>> result.collect_column("m")[0].as_py() + [('a', 1), ('b', 2)] + + From two lists: + + >>> df = ctx.from_pydict({"key": ["x", "y"], "val": [10, 20]}) + >>> df = df.select( + ... dfn.functions.make_map( + ... [dfn.col("key")], [dfn.col("val")] + ... ).alias("m")) + >>> df.collect_column("m")[0].as_py() + [('x', 10)] + + From alternating keys and values: + + >>> df = ctx.from_pydict({"a": [1]}) + >>> result = df.select( + ... dfn.functions.make_map("x", 1, "y", 2).alias("m")) + >>> result.collect_column("m")[0].as_py() + [('x', 1), ('y', 2)] + """ + if len(args) == 1 and isinstance(args[0], dict): + key_list = list(args[0].keys()) + value_list = list(args[0].values()) + elif ( + len(args) == 2 # noqa: PLR2004 + and isinstance(args[0], list) + and isinstance(args[1], list) + ): + if len(args[0]) != len(args[1]): + msg = "make_map requires key and value lists to be the same length" + raise ValueError(msg) + key_list = args[0] + value_list = args[1] + elif len(args) >= 2 and len(args) % 2 == 0: # noqa: PLR2004 + key_list = list(args[0::2]) + value_list = list(args[1::2]) + else: + msg = ( + "make_map expects a dict, two lists, or an even number of " + "key-value arguments" + ) + raise ValueError(msg) + + key_exprs = [k if isinstance(k, Expr) else Expr.literal(k) for k in key_list] + val_exprs = [v if isinstance(v, Expr) else Expr.literal(v) for v in value_list] + return Expr(f.make_map([k.expr for k in key_exprs], [v.expr for v in val_exprs])) + + +def map_keys(map: Expr) -> Expr: + """Returns a list of all keys in the map. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> df = df.select( + ... dfn.functions.make_map({"x": 1, "y": 2}).alias("m")) + >>> result = df.select( + ... dfn.functions.map_keys(dfn.col("m")).alias("keys")) + >>> result.collect_column("keys")[0].as_py() + ['x', 'y'] + """ + return Expr(f.map_keys(map.expr)) + + +def map_values(map: Expr) -> Expr: + """Returns a list of all values in the map. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> df = df.select( + ... dfn.functions.make_map({"x": 1, "y": 2}).alias("m")) + >>> result = df.select( + ... dfn.functions.map_values(dfn.col("m")).alias("vals")) + >>> result.collect_column("vals")[0].as_py() + [1, 2] + """ + return Expr(f.map_values(map.expr)) + + +def map_extract(map: Expr, key: Expr) -> Expr: + """Returns the value for a given key in the map. + + Returns ``[None]`` if the key is absent. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> df = df.select( + ... dfn.functions.make_map({"x": 1, "y": 2}).alias("m")) + >>> result = df.select( + ... dfn.functions.map_extract( + ... dfn.col("m"), dfn.lit("x") + ... ).alias("val")) + >>> result.collect_column("val")[0].as_py() + [1] + """ + return Expr(f.map_extract(map.expr, key.expr)) + + +def map_entries(map: Expr) -> Expr: + """Returns a list of all entries (key-value struct pairs) in the map. + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1]}) + >>> df = df.select( + ... dfn.functions.make_map({"x": 1, "y": 2}).alias("m")) + >>> result = df.select( + ... dfn.functions.map_entries(dfn.col("m")).alias("entries")) + >>> result.collect_column("entries")[0].as_py() + [{'key': 'x', 'value': 1}, {'key': 'y', 'value': 2}] + """ + return Expr(f.map_entries(map.expr)) + + +def element_at(map: Expr, key: Expr) -> Expr: + """Returns the value for a given key in the map. + + Returns ``[None]`` if the key is absent. + + See Also: + This is an alias for :py:func:`map_extract`. + """ + return map_extract(map, key) + + # aggregate functions def approx_distinct( expression: Expr, @@ -1661,6 +4178,24 @@ def approx_distinct( Args: expression: Values to check for distinct entries filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.approx_distinct( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() == 3 + True + + >>> result = df.aggregate( + ... [], [dfn.functions.approx_distinct( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(1) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() == 2 + True """ filter_raw = filter.expr if filter is not None else None @@ -1679,6 +4214,24 @@ def approx_median(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: Values to find the median for filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.approx_median( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.approx_median( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.5 """ filter_raw = filter.expr if filter is not None else None return Expr(f.approx_median(expression.expr, filter=filter_raw)) @@ -1710,6 +4263,25 @@ def approx_percentile_cont( percentile: This must be between 0.0 and 1.0, inclusive num_centroids: Max bin size for the t-digest algorithm filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0, 4.0, 5.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.approx_percentile_cont( + ... dfn.col("a"), 0.5 + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 3.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.approx_percentile_cont( + ... dfn.col("a"), 0.5, + ... num_centroids=10, + ... filter=dfn.col("a") > dfn.lit(1.0), + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 3.5 """ sort_expr_raw = sort_or_default(sort_expression) filter_raw = filter.expr if filter is not None else None @@ -1742,6 +4314,24 @@ def approx_percentile_cont_with_weight( num_centroids: Max bin size for the t-digest algorithm filter: If provided, only compute against rows for which the filter is True + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0], "w": [1.0, 1.0, 1.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.approx_percentile_cont_with_weight( + ... dfn.col("a"), dfn.col("w"), 0.5 + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.approx_percentile_cont_with_weight( + ... dfn.col("a"), dfn.col("w"), 0.5, + ... num_centroids=10, + ... filter=dfn.col("a") > dfn.lit(1.0), + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.5 """ sort_expr_raw = sort_or_default(sort_expression) filter_raw = filter.expr if filter is not None else None @@ -1756,6 +4346,60 @@ def approx_percentile_cont_with_weight( ) +def percentile_cont( + sort_expression: Expr | SortExpr, + percentile: float, + filter: Expr | None = None, +) -> Expr: + """Computes the exact percentile of input values using continuous interpolation. + + Unlike :py:func:`approx_percentile_cont`, this function computes the exact + percentile value rather than an approximation. + + If using the builder functions described in ref:`_aggregation` this function ignores + the options ``order_by``, ``null_treatment``, and ``distinct``. + + Args: + sort_expression: Values for which to find the percentile + percentile: This must be between 0.0 and 1.0, inclusive + filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0, 4.0, 5.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.percentile_cont( + ... dfn.col("a"), 0.5 + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 3.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.percentile_cont( + ... dfn.col("a"), 0.5, + ... filter=dfn.col("a") > dfn.lit(1.0), + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 3.5 + """ + sort_expr_raw = sort_or_default(sort_expression) + filter_raw = filter.expr if filter is not None else None + return Expr(f.percentile_cont(sort_expr_raw, percentile, filter=filter_raw)) + + +def quantile_cont( + sort_expression: Expr | SortExpr, + percentile: float, + filter: Expr | None = None, +) -> Expr: + """Computes the exact percentile of input values using continuous interpolation. + + See Also: + This is an alias for :py:func:`percentile_cont`. + """ + return percentile_cont(sort_expression, percentile, filter) + + def array_agg( expression: Expr, distinct: bool = False, @@ -1777,9 +4421,32 @@ def array_agg( filter: If provided, only compute against rows for which the filter is True order_by: Order the resultant array values. Accepts column names or expressions. - For example:: - - df.select(array_agg(col("a"), order_by="b")) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.array_agg( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + [1, 2, 3] + + >>> df = ctx.from_pydict({"a": [3, 1, 2, 1]}) + >>> result = df.aggregate( + ... [], [dfn.functions.array_agg( + ... dfn.col("a"), distinct=True, + ... ).alias("v")]) + >>> sorted(result.collect_column("v")[0].as_py()) + [1, 2, 3] + + >>> result = df.aggregate( + ... [], [dfn.functions.array_agg( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(1), + ... order_by="a", + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + [2, 3] """ order_by_raw = sort_list_to_raw_sort_list(order_by) filter_raw = filter.expr if filter is not None else None @@ -1791,6 +4458,65 @@ def array_agg( ) +def grouping( + expression: Expr, + distinct: bool = False, + filter: Expr | None = None, +) -> Expr: + """Indicates whether a column is aggregated across in the current row. + + Returns 0 when the column is part of the grouping key for that row + (i.e., the row contains per-group results for that column). Returns 1 + when the column is *not* part of the grouping key (i.e., the row's + aggregate spans all values of that column). + + This function is meaningful with + :py:meth:`GroupingSet.rollup `, + :py:meth:`GroupingSet.cube `, or + :py:meth:`GroupingSet.grouping_sets `, + where different rows are grouped by different subsets of columns. In a + default aggregation without grouping sets every column is always part + of the key, so ``grouping()`` always returns 0. + + .. warning:: + + Due to an upstream DataFusion limitation + (`#21411 `_), + ``.alias()`` cannot be applied directly to a ``grouping()`` + expression. Doing so will raise an error at execution time. To + rename the column, use + :py:meth:`~datafusion.dataframe.DataFrame.with_column_renamed` + on the result DataFrame instead. + + Args: + expression: The column to check grouping status for + distinct: If True, compute on distinct values only + filter: If provided, only compute against rows for which the filter is True + + Examples: + With :py:meth:`~datafusion.expr.GroupingSet.rollup`, the result + includes both per-group rows (``grouping(a) = 0``) and a + grand-total row where ``a`` is aggregated across + (``grouping(a) = 1``): + + >>> from datafusion.expr import GroupingSet + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 1, 2], "b": [10, 20, 30]}) + >>> result = df.aggregate( + ... [GroupingSet.rollup(dfn.col("a"))], + ... [dfn.functions.sum(dfn.col("b")).alias("s"), + ... dfn.functions.grouping(dfn.col("a"))], + ... ).sort(dfn.col("a").sort(nulls_first=False)) + >>> result.collect_column("s").to_pylist() + [30, 30, 60] + + See Also: + :py:class:`~datafusion.expr.GroupingSet` + """ + filter_raw = filter.expr if filter is not None else None + return Expr(f.grouping(expression.expr, distinct=distinct, filter=filter_raw)) + + def avg( expression: Expr, filter: Expr | None = None, @@ -1805,6 +4531,24 @@ def avg( Args: expression: Values to combine into an array filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.avg( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.avg( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.5 """ filter_raw = filter.expr if filter is not None else None return Expr(f.avg(expression.expr, filter=filter_raw)) @@ -1822,6 +4566,24 @@ def corr(value_y: Expr, value_x: Expr, filter: Expr | None = None) -> Expr: value_y: The dependent variable for correlation value_x: The independent variable for correlation filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0], "b": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.corr( + ... dfn.col("a"), dfn.col("b") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.corr( + ... dfn.col("a"), dfn.col("b"), + ... filter=dfn.col("a") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.0 """ filter_raw = filter.expr if filter is not None else None return Expr(f.corr(value_y.expr, value_x.expr, filter=filter_raw)) @@ -1843,6 +4605,25 @@ def count( expressions: Argument to perform bitwise calculation on distinct: If True, a single entry for each distinct value will be in the result filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.count( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 3 + + >>> df = ctx.from_pydict({"a": [1, 1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.count( + ... dfn.col("a"), distinct=True, + ... filter=dfn.col("a") > dfn.lit(1), + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2 """ filter_raw = filter.expr if filter is not None else None @@ -1868,6 +4649,30 @@ def covar_pop(value_y: Expr, value_x: Expr, filter: Expr | None = None) -> Expr: value_y: The dependent variable for covariance value_x: The independent variable for covariance filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 5.0, 10.0], "b": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], + ... [dfn.functions.covar_pop( + ... dfn.col("a"), dfn.col("b") + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 3.0 + + >>> df = ctx.from_pydict( + ... {"a": [0.0, 1.0, 3.0], "b": [0.0, 1.0, 3.0]}) + >>> result = df.aggregate( + ... [], + ... [dfn.functions.covar_pop( + ... dfn.col("a"), dfn.col("b"), + ... filter=dfn.col("a") > dfn.lit(0.0) + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 1.0 """ filter_raw = filter.expr if filter is not None else None return Expr(f.covar_pop(value_y.expr, value_x.expr, filter=filter_raw)) @@ -1885,6 +4690,24 @@ def covar_samp(value_y: Expr, value_x: Expr, filter: Expr | None = None) -> Expr value_y: The dependent variable for covariance value_x: The independent variable for covariance filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0], "b": [4.0, 5.0, 6.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.covar_samp( + ... dfn.col("a"), dfn.col("b") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.covar_samp( + ... dfn.col("a"), dfn.col("b"), + ... filter=dfn.col("a") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 0.5 """ filter_raw = filter.expr if filter is not None else None return Expr(f.covar_samp(value_y.expr, value_x.expr, filter=filter_raw)) @@ -1893,7 +4716,8 @@ def covar_samp(value_y: Expr, value_x: Expr, filter: Expr | None = None) -> Expr def covar(value_y: Expr, value_x: Expr, filter: Expr | None = None) -> Expr: """Computes the sample covariance. - This is an alias for :py:func:`covar_samp`. + See Also: + This is an alias for :py:func:`covar_samp`. """ return covar_samp(value_y, value_x, filter) @@ -1907,6 +4731,24 @@ def max(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: The value to find the maximum of filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.max( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 3 + + >>> result = df.aggregate( + ... [], [dfn.functions.max( + ... dfn.col("a"), + ... filter=dfn.col("a") < dfn.lit(3) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2 """ filter_raw = filter.expr if filter is not None else None return Expr(f.max(expression.expr, filter=filter_raw)) @@ -1915,7 +4757,8 @@ def max(expression: Expr, filter: Expr | None = None) -> Expr: def mean(expression: Expr, filter: Expr | None = None) -> Expr: """Returns the average (mean) value of the argument. - This is an alias for :py:func:`avg`. + See Also: + This is an alias for :py:func:`avg`. """ return avg(expression, filter) @@ -1935,13 +4778,32 @@ def median( expression: The value to compute the median of distinct: If True, a single entry for each distinct value will be in the result filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.median( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> df = ctx.from_pydict({"a": [1.0, 1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.median( + ... dfn.col("a"), distinct=True, + ... filter=dfn.col("a") < dfn.lit(3.0), + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.5 """ filter_raw = filter.expr if filter is not None else None return Expr(f.median(expression.expr, distinct=distinct, filter=filter_raw)) def min(expression: Expr, filter: Expr | None = None) -> Expr: - """Returns the minimum value of the argument. + """Aggregate function that returns the minimum value of the argument. If using the builder functions described in ref:`_aggregation` this function ignores the options ``order_by``, ``null_treatment``, and ``distinct``. @@ -1949,6 +4811,24 @@ def min(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: The value to find the minimum of filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.min( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1 + + >>> result = df.aggregate( + ... [], [dfn.functions.min( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(1) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2 """ filter_raw = filter.expr if filter is not None else None return Expr(f.min(expression.expr, filter=filter_raw)) @@ -1968,6 +4848,24 @@ def sum( Args: expression: Values to combine into an array filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.sum( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 6 + + >>> result = df.aggregate( + ... [], [dfn.functions.sum( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(1) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 5 """ filter_raw = filter.expr if filter is not None else None return Expr(f.sum(expression.expr, filter=filter_raw)) @@ -1982,6 +4880,24 @@ def stddev(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: The value to find the minimum of filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [2.0, 4.0, 6.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.stddev( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.stddev( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(2.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.41... """ filter_raw = filter.expr if filter is not None else None return Expr(f.stddev(expression.expr, filter=filter_raw)) @@ -1996,6 +4912,27 @@ def stddev_pop(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: The value to find the minimum of filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [0.0, 1.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.stddev_pop( + ... dfn.col("a") + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 1.247... + + >>> df = ctx.from_pydict({"a": [0.0, 1.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.stddev_pop( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(0.0) + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 1.0 """ filter_raw = filter.expr if filter is not None else None return Expr(f.stddev_pop(expression.expr, filter=filter_raw)) @@ -2004,7 +4941,8 @@ def stddev_pop(expression: Expr, filter: Expr | None = None) -> Expr: def stddev_samp(arg: Expr, filter: Expr | None = None) -> Expr: """Computes the sample standard deviation of the argument. - This is an alias for :py:func:`stddev`. + See Also: + This is an alias for :py:func:`stddev`. """ return stddev(arg, filter=filter) @@ -2012,7 +4950,8 @@ def stddev_samp(arg: Expr, filter: Expr | None = None) -> Expr: def var(expression: Expr, filter: Expr | None = None) -> Expr: """Computes the sample variance of the argument. - This is an alias for :py:func:`var_samp`. + See Also: + This is an alias for :py:func:`var_samp`. """ return var_samp(expression, filter) @@ -2026,11 +4965,38 @@ def var_pop(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: The variable to compute the variance for filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [-1.0, 0.0, 2.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.var_pop( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.555... + + >>> result = df.aggregate( + ... [], [dfn.functions.var_pop( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(-1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.0 """ filter_raw = filter.expr if filter is not None else None return Expr(f.var_pop(expression.expr, filter=filter_raw)) +def var_population(expression: Expr, filter: Expr | None = None) -> Expr: + """Computes the population variance of the argument. + + See Also: + This is an alias for :py:func:`var_pop`. + """ + return var_pop(expression, filter) + + def var_samp(expression: Expr, filter: Expr | None = None) -> Expr: """Computes the sample variance of the argument. @@ -2040,6 +5006,24 @@ def var_samp(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: The variable to compute the variance for filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.var_samp( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.var_samp( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 0.5 """ filter_raw = filter.expr if filter is not None else None return Expr(f.var_sample(expression.expr, filter=filter_raw)) @@ -2048,7 +5032,8 @@ def var_samp(expression: Expr, filter: Expr | None = None) -> Expr: def var_sample(expression: Expr, filter: Expr | None = None) -> Expr: """Computes the sample variance of the argument. - This is an alias for :py:func:`var_samp`. + See Also: + This is an alias for :py:func:`var_samp`. """ return var_samp(expression, filter) @@ -2070,6 +5055,24 @@ def regr_avgx( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [1.0, 2.0, 3.0], "x": [4.0, 5.0, 6.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.regr_avgx( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 5.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.regr_avgx( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 5.5 """ filter_raw = filter.expr if filter is not None else None @@ -2093,6 +5096,24 @@ def regr_avgy( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [1.0, 2.0, 3.0], "x": [4.0, 5.0, 6.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.regr_avgy( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.regr_avgy( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.5 """ filter_raw = filter.expr if filter is not None else None @@ -2116,6 +5137,24 @@ def regr_count( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [1.0, 2.0, 3.0], "x": [4.0, 5.0, 6.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.regr_count( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 3 + + >>> result = df.aggregate( + ... [], [dfn.functions.regr_count( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2 """ filter_raw = filter.expr if filter is not None else None @@ -2139,6 +5178,26 @@ def regr_intercept( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [2.0, 4.0, 6.0], "x": [4.0, 16.0, 36.0]}) + >>> result = df.aggregate( + ... [], + ... [dfn.functions.regr_intercept( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.714... + + >>> result = df.aggregate( + ... [], + ... [dfn.functions.regr_intercept( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(2.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.4 """ filter_raw = filter.expr if filter is not None else None @@ -2162,6 +5221,24 @@ def regr_r2( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [2.0, 4.0, 6.0], "x": [4.0, 16.0, 36.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.regr_r2( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 0.9795... + + >>> result = df.aggregate( + ... [], [dfn.functions.regr_r2( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(2.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 1.0 """ filter_raw = filter.expr if filter is not None else None @@ -2185,6 +5262,24 @@ def regr_slope( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [2.0, 4.0, 6.0], "x": [4.0, 16.0, 36.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.regr_slope( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 0.122... + + >>> result = df.aggregate( + ... [], [dfn.functions.regr_slope( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(2.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 0.1 """ filter_raw = filter.expr if filter is not None else None @@ -2208,6 +5303,24 @@ def regr_sxx( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [1.0, 2.0, 3.0], "x": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.regr_sxx( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.regr_sxx( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 0.5 """ filter_raw = filter.expr if filter is not None else None @@ -2231,6 +5344,24 @@ def regr_sxy( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [1.0, 2.0, 3.0], "x": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.regr_sxy( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.regr_sxy( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 0.5 """ filter_raw = filter.expr if filter is not None else None @@ -2254,6 +5385,24 @@ def regr_syy( y: The linear regression dependent variable x: The linear regression independent variable filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"y": [1.0, 2.0, 3.0], "x": [1.0, 2.0, 3.0]}) + >>> result = df.aggregate( + ... [], [dfn.functions.regr_syy( + ... dfn.col("y"), dfn.col("x") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 2.0 + + >>> result = df.aggregate( + ... [], [dfn.functions.regr_syy( + ... dfn.col("y"), dfn.col("x"), + ... filter=dfn.col("y") > dfn.lit(1.0) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 0.5 """ filter_raw = filter.expr if filter is not None else None @@ -2280,9 +5429,28 @@ def first_value( column names or expressions. null_treatment: Assign whether to respect or ignore null values. - For example:: - - df.select(first_value(col("a"), order_by="ts")) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 20, 30]}) + >>> result = df.aggregate( + ... [], [dfn.functions.first_value( + ... dfn.col("a") + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 10 + + >>> df = ctx.from_pydict({"a": [None, 20, 10]}) + >>> result = df.aggregate( + ... [], [dfn.functions.first_value( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(10), + ... order_by="a", + ... null_treatment=dfn.common.NullTreatment.IGNORE_NULLS, + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 20 """ order_by_raw = sort_list_to_raw_sort_list(order_by) filter_raw = filter.expr if filter is not None else None @@ -2317,9 +5485,28 @@ def last_value( column names or expressions. null_treatment: Assign whether to respect or ignore null values. - For example:: - - df.select(last_value(col("a"), order_by="ts")) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 20, 30]}) + >>> result = df.aggregate( + ... [], [dfn.functions.last_value( + ... dfn.col("a") + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 30 + + >>> df = ctx.from_pydict({"a": [None, 20, 10]}) + >>> result = df.aggregate( + ... [], [dfn.functions.last_value( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(10), + ... order_by="a", + ... null_treatment=dfn.common.NullTreatment.IGNORE_NULLS, + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 20 """ order_by_raw = sort_list_to_raw_sort_list(order_by) filter_raw = filter.expr if filter is not None else None @@ -2356,9 +5543,27 @@ def nth_value( column names or expressions. null_treatment: Assign whether to respect or ignore null values. - For example:: - - df.select(nth_value(col("a"), 2, order_by="ts")) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 20, 30]}) + >>> result = df.aggregate( + ... [], [dfn.functions.nth_value( + ... dfn.col("a"), 1 + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 10 + + >>> result = df.aggregate( + ... [], [dfn.functions.nth_value( + ... dfn.col("a"), 1, + ... filter=dfn.col("a") > dfn.lit(10), + ... order_by="a", + ... null_treatment=dfn.common.NullTreatment.IGNORE_NULLS, + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 20 """ order_by_raw = sort_list_to_raw_sort_list(order_by) filter_raw = filter.expr if filter is not None else None @@ -2385,6 +5590,25 @@ def bit_and(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: Argument to perform bitwise calculation on filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [7, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bit_and( + ... dfn.col("a") + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 3 + + >>> df = ctx.from_pydict({"a": [7, 5, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bit_and( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(3) + ... ).alias("v")]) + >>> result.collect_column("v")[0].as_py() + 5 """ filter_raw = filter.expr if filter is not None else None return Expr(f.bit_and(expression.expr, filter=filter_raw)) @@ -2401,6 +5625,27 @@ def bit_or(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: Argument to perform bitwise calculation on filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bit_or( + ... dfn.col("a") + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 3 + + >>> df = ctx.from_pydict({"a": [1, 2, 4]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bit_or( + ... dfn.col("a"), + ... filter=dfn.col("a") > dfn.lit(1) + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 6 """ filter_raw = filter.expr if filter is not None else None return Expr(f.bit_or(expression.expr, filter=filter_raw)) @@ -2420,6 +5665,27 @@ def bit_xor( expression: Argument to perform bitwise calculation on distinct: If True, evaluate each unique value of expression only once filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [5, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bit_xor( + ... dfn.col("a") + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 6 + + >>> df = ctx.from_pydict({"a": [5, 5, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bit_xor( + ... dfn.col("a"), distinct=True, + ... filter=dfn.col("a") > dfn.lit(3), + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + 5 """ filter_raw = filter.expr if filter is not None else None return Expr(f.bit_xor(expression.expr, distinct=distinct, filter=filter_raw)) @@ -2437,6 +5703,28 @@ def bool_and(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: Argument to perform calculation on filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [True, True, False]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bool_and( + ... dfn.col("a") + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + False + + >>> df = ctx.from_pydict( + ... {"a": [True, True, False], "b": [1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bool_and( + ... dfn.col("a"), + ... filter=dfn.col("b") < dfn.lit(3) + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + True """ filter_raw = filter.expr if filter is not None else None return Expr(f.bool_and(expression.expr, filter=filter_raw)) @@ -2454,6 +5742,28 @@ def bool_or(expression: Expr, filter: Expr | None = None) -> Expr: Args: expression: Argument to perform calculation on filter: If provided, only compute against rows for which the filter is True + + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [False, False, True]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bool_or( + ... dfn.col("a") + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + True + + >>> df = ctx.from_pydict( + ... {"a": [False, False, True], "b": [1, 2, 3]}) + >>> result = df.aggregate( + ... [], [dfn.functions.bool_or( + ... dfn.col("a"), + ... filter=dfn.col("b") < dfn.lit(3) + ... ).alias("v")] + ... ) + >>> result.collect_column("v")[0].as_py() + False """ filter_raw = filter.expr if filter is not None else None return Expr(f.bool_or(expression.expr, filter=filter_raw)) @@ -2496,9 +5806,27 @@ def lead( order_by: Set ordering within the window frame. Accepts column names or expressions. - For example:: - - lead(col("b"), order_by="ts") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.select( + ... dfn.col("a"), + ... dfn.functions.lead( + ... dfn.col("a"), shift_offset=1, + ... default_value=0, order_by="a" + ... ).alias("lead")) + >>> result.sort(dfn.col("a")).collect_column("lead").to_pylist() + [2, 3, 0] + + >>> df = ctx.from_pydict({"g": ["a", "a", "b"], "v": [1, 2, 3]}) + >>> result = df.select( + ... dfn.col("g"), dfn.col("v"), + ... dfn.functions.lead( + ... dfn.col("v"), shift_offset=1, default_value=0, + ... partition_by=dfn.col("g"), order_by="v", + ... ).alias("lead")) + >>> result.sort(dfn.col("g"), dfn.col("v")).collect_column("lead").to_pylist() + [2, 0, 0] """ if not isinstance(default_value, pa.Scalar) and default_value is not None: default_value = pa.scalar(default_value) @@ -2551,9 +5879,27 @@ def lag( order_by: Set ordering within the window frame. Accepts column names or expressions. - For example:: - - lag(col("b"), order_by="ts") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1, 2, 3]}) + >>> result = df.select( + ... dfn.col("a"), + ... dfn.functions.lag( + ... dfn.col("a"), shift_offset=1, + ... default_value=0, order_by="a" + ... ).alias("lag")) + >>> result.sort(dfn.col("a")).collect_column("lag").to_pylist() + [0, 1, 2] + + >>> df = ctx.from_pydict({"g": ["a", "a", "b"], "v": [1, 2, 3]}) + >>> result = df.select( + ... dfn.col("g"), dfn.col("v"), + ... dfn.functions.lag( + ... dfn.col("v"), shift_offset=1, default_value=0, + ... partition_by=dfn.col("g"), order_by="v", + ... ).alias("lag")) + >>> result.sort(dfn.col("g"), dfn.col("v")).collect_column("lag").to_pylist() + [0, 1, 0] """ if not isinstance(default_value, pa.Scalar): default_value = pa.scalar(default_value) @@ -2596,9 +5942,26 @@ def row_number( order_by: Set ordering within the window frame. Accepts column names or expressions. - For example:: - - row_number(order_by="points") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 20, 30]}) + >>> result = df.select( + ... dfn.col("a"), + ... dfn.functions.row_number( + ... order_by="a" + ... ).alias("rn")) + >>> result.sort(dfn.col("a")).collect_column("rn").to_pylist() + [1, 2, 3] + + >>> df = ctx.from_pydict( + ... {"g": ["a", "a", "b", "b"], "v": [1, 2, 3, 4]}) + >>> result = df.select( + ... dfn.col("g"), dfn.col("v"), + ... dfn.functions.row_number( + ... partition_by=dfn.col("g"), order_by="v", + ... ).alias("rn")) + >>> result.sort(dfn.col("g"), dfn.col("v")).collect_column("rn").to_pylist() + [1, 2, 1, 2] """ partition_by_raw = expr_list_to_raw_expr_list(partition_by) order_by_raw = sort_list_to_raw_sort_list(order_by) @@ -2640,9 +6003,27 @@ def rank( order_by: Set ordering within the window frame. Accepts column names or expressions. - For example:: - - rank(order_by="points") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 10, 20]}) + >>> result = df.select( + ... dfn.col("a"), + ... dfn.functions.rank( + ... order_by="a" + ... ).alias("rnk") + ... ) + >>> result.sort(dfn.col("a")).collect_column("rnk").to_pylist() + [1, 1, 3] + + >>> df = ctx.from_pydict( + ... {"g": ["a", "a", "b", "b"], "v": [1, 1, 2, 3]}) + >>> result = df.select( + ... dfn.col("g"), dfn.col("v"), + ... dfn.functions.rank( + ... partition_by=dfn.col("g"), order_by="v", + ... ).alias("rnk")) + >>> result.sort(dfn.col("g"), dfn.col("v")).collect_column("rnk").to_pylist() + [1, 1, 1, 2] """ partition_by_raw = expr_list_to_raw_expr_list(partition_by) order_by_raw = sort_list_to_raw_sort_list(order_by) @@ -2679,9 +6060,26 @@ def dense_rank( order_by: Set ordering within the window frame. Accepts column names or expressions. - For example:: - - dense_rank(order_by="points") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 10, 20]}) + >>> result = df.select( + ... dfn.col("a"), + ... dfn.functions.dense_rank( + ... order_by="a" + ... ).alias("dr")) + >>> result.sort(dfn.col("a")).collect_column("dr").to_pylist() + [1, 1, 2] + + >>> df = ctx.from_pydict( + ... {"g": ["a", "a", "b", "b"], "v": [1, 1, 2, 3]}) + >>> result = df.select( + ... dfn.col("g"), dfn.col("v"), + ... dfn.functions.dense_rank( + ... partition_by=dfn.col("g"), order_by="v", + ... ).alias("dr")) + >>> result.sort(dfn.col("g"), dfn.col("v")).collect_column("dr").to_pylist() + [1, 1, 1, 2] """ partition_by_raw = expr_list_to_raw_expr_list(partition_by) order_by_raw = sort_list_to_raw_sort_list(order_by) @@ -2719,9 +6117,27 @@ def percent_rank( order_by: Set ordering within the window frame. Accepts column names or expressions. - For example:: - percent_rank(order_by="points") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 20, 30]}) + >>> result = df.select( + ... dfn.col("a"), + ... dfn.functions.percent_rank( + ... order_by="a" + ... ).alias("pr")) + >>> result.sort(dfn.col("a")).collect_column("pr").to_pylist() + [0.0, 0.5, 1.0] + + >>> df = ctx.from_pydict( + ... {"g": ["a", "a", "a", "b", "b"], "v": [1, 2, 3, 4, 5]}) + >>> result = df.select( + ... dfn.col("g"), dfn.col("v"), + ... dfn.functions.percent_rank( + ... partition_by=dfn.col("g"), order_by="v", + ... ).alias("pr")) + >>> result.sort(dfn.col("g"), dfn.col("v")).collect_column("pr").to_pylist() + [0.0, 0.5, 1.0, 0.0, 1.0] """ partition_by_raw = expr_list_to_raw_expr_list(partition_by) order_by_raw = sort_list_to_raw_sort_list(order_by) @@ -2759,9 +6175,27 @@ def cume_dist( order_by: Set ordering within the window frame. Accepts column names or expressions. - For example:: - - cume_dist(order_by="points") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1., 2., 2., 3.]}) + >>> result = df.select( + ... dfn.col("a"), + ... dfn.functions.cume_dist( + ... order_by="a" + ... ).alias("cd") + ... ) + >>> result.collect_column("cd").to_pylist() + [0.25..., 0.75..., 0.75..., 1.0...] + + >>> df = ctx.from_pydict( + ... {"g": ["a", "a", "b", "b"], "v": [1, 2, 3, 4]}) + >>> result = df.select( + ... dfn.col("g"), dfn.col("v"), + ... dfn.functions.cume_dist( + ... partition_by=dfn.col("g"), order_by="v", + ... ).alias("cd")) + >>> result.sort(dfn.col("g"), dfn.col("v")).collect_column("cd").to_pylist() + [0.5, 1.0, 0.5, 1.0] """ partition_by_raw = expr_list_to_raw_expr_list(partition_by) order_by_raw = sort_list_to_raw_sort_list(order_by) @@ -2803,9 +6237,26 @@ def ntile( order_by: Set ordering within the window frame. Accepts column names or expressions. - For example:: - - ntile(3, order_by="points") + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 20, 30, 40]}) + >>> result = df.select( + ... dfn.col("a"), + ... dfn.functions.ntile( + ... 2, order_by="a" + ... ).alias("nt")) + >>> result.sort(dfn.col("a")).collect_column("nt").to_pylist() + [1, 1, 2, 2] + + >>> df = ctx.from_pydict( + ... {"g": ["a", "a", "b", "b"], "v": [1, 2, 3, 4]}) + >>> result = df.select( + ... dfn.col("g"), dfn.col("v"), + ... dfn.functions.ntile( + ... 2, partition_by=dfn.col("g"), order_by="v", + ... ).alias("nt")) + >>> result.sort(dfn.col("g"), dfn.col("v")).collect_column("nt").to_pylist() + [1, 2, 1, 2] """ partition_by_raw = expr_list_to_raw_expr_list(partition_by) order_by_raw = sort_list_to_raw_sort_list(order_by) @@ -2841,9 +6292,24 @@ def string_agg( order_by: Set the ordering of the expression to evaluate. Accepts column names or expressions. - For example:: - - df.select(string_agg(col("a"), ",", order_by="b")) + Examples: + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": ["x", "y", "z"]}) + >>> result = df.aggregate( + ... [], [dfn.functions.string_agg( + ... dfn.col("a"), ",", order_by="a" + ... ).alias("s")]) + >>> result.collect_column("s")[0].as_py() + 'x,y,z' + + >>> result = df.aggregate( + ... [], [dfn.functions.string_agg( + ... dfn.col("a"), ",", + ... filter=dfn.col("a") > dfn.lit("x"), + ... order_by="a", + ... ).alias("s")]) + >>> result.collect_column("s")[0].as_py() + 'y,z' """ order_by_raw = sort_list_to_raw_sort_list(order_by) filter_raw = filter.expr if filter is not None else None diff --git a/python/datafusion/input/location.py b/python/datafusion/input/location.py index b804ac18b..779d94d23 100644 --- a/python/datafusion/input/location.py +++ b/python/datafusion/input/location.py @@ -46,7 +46,7 @@ def build_table( num_rows = 0 # Total number of rows in the file. Used for statistics columns = [] if file_format == "parquet": - import pyarrow.parquet as pq + import pyarrow.parquet as pq # noqa: PLC0415 # Read the Parquet metadata metadata = pq.read_metadata(input_item) @@ -61,7 +61,7 @@ def build_table( ] elif format == "csv": - import csv + import csv # noqa: PLC0415 # Consume header row and count number of rows for statistics. # TODO: Possibly makes sense to have the eager number of rows diff --git a/python/datafusion/io.py b/python/datafusion/io.py index 67dbc730f..4f9c3c516 100644 --- a/python/datafusion/io.py +++ b/python/datafusion/io.py @@ -31,6 +31,8 @@ from datafusion.dataframe import DataFrame from datafusion.expr import Expr + from .options import CsvReadOptions + def read_parquet( path: str | pathlib.Path, @@ -126,6 +128,7 @@ def read_csv( file_extension: str = ".csv", table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None, file_compression_type: str | None = None, + options: CsvReadOptions | None = None, ) -> DataFrame: """Read a CSV data source. @@ -147,15 +150,12 @@ def read_csv( selected for data input. table_partition_cols: Partition columns. file_compression_type: File compression type. + options: Set advanced options for CSV reading. This cannot be + combined with any of the other options in this method. Returns: DataFrame representation of the read CSV files """ - if table_partition_cols is None: - table_partition_cols = [] - - path = [str(p) for p in path] if isinstance(path, list) else str(path) - return SessionContext.global_ctx().read_csv( path, schema, @@ -165,6 +165,7 @@ def read_csv( file_extension, table_partition_cols, file_compression_type, + options, ) diff --git a/python/datafusion/options.py b/python/datafusion/options.py new file mode 100644 index 000000000..ec19f37d0 --- /dev/null +++ b/python/datafusion/options.py @@ -0,0 +1,284 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Options for reading various file formats.""" + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import pyarrow as pa + +from datafusion.expr import sort_list_to_raw_sort_list + +if TYPE_CHECKING: + from datafusion.expr import SortExpr + +from ._internal import options + +__all__ = ["CsvReadOptions"] + +DEFAULT_MAX_INFER_SCHEMA = 1000 + + +class CsvReadOptions: + """Options for reading CSV files. + + This class provides a builder pattern for configuring CSV reading options. + All methods starting with ``with_`` return ``self`` to allow method chaining. + """ + + def __init__( + self, + *, + has_header: bool = True, + delimiter: str = ",", + quote: str = '"', + terminator: str | None = None, + escape: str | None = None, + comment: str | None = None, + newlines_in_values: bool = False, + schema: pa.Schema | None = None, + schema_infer_max_records: int = DEFAULT_MAX_INFER_SCHEMA, + file_extension: str = ".csv", + table_partition_cols: list[tuple[str, pa.DataType]] | None = None, + file_compression_type: str = "", + file_sort_order: list[list[SortExpr]] | None = None, + null_regex: str | None = None, + truncated_rows: bool = False, + ) -> None: + """Initialize CsvReadOptions. + + Args: + has_header: Does the CSV file have a header row? If schema inference + is run on a file with no headers, default column names are created. + delimiter: Column delimiter character. Must be a single ASCII character. + quote: Quote character for fields containing delimiters or newlines. + Must be a single ASCII character. + terminator: Optional line terminator character. If ``None``, uses CRLF. + Must be a single ASCII character. + escape: Optional escape character for quotes. Must be a single ASCII + character. + comment: If specified, lines beginning with this character are ignored. + Must be a single ASCII character. + newlines_in_values: Whether newlines in quoted values are supported. + Parsing newlines in quoted values may be affected by execution + behavior such as parallel file scanning. Setting this to ``True`` + ensures that newlines in values are parsed successfully, which may + reduce performance. + schema: Optional PyArrow schema representing the CSV files. If ``None``, + the CSV reader will try to infer it based on data in the file. + schema_infer_max_records: Maximum number of rows to read from CSV files + for schema inference if needed. + file_extension: File extension; only files with this extension are + selected for data input. + table_partition_cols: Partition columns as a list of tuples of + (column_name, data_type). + file_compression_type: File compression type. Supported values are + ``"gzip"``, ``"bz2"``, ``"xz"``, ``"zstd"``, or empty string for + uncompressed. + file_sort_order: Optional sort order of the files as a list of sort + expressions per file. + null_regex: Optional regex pattern to match null values in the CSV. + truncated_rows: Whether to allow truncated rows when parsing. By default + this is ``False`` and will error if the CSV rows have different + lengths. When set to ``True``, it will allow records with less than + the expected number of columns and fill the missing columns with + nulls. If the record's schema is not nullable, it will still return + an error. + """ + validate_single_character("delimiter", delimiter) + validate_single_character("quote", quote) + validate_single_character("terminator", terminator) + validate_single_character("escape", escape) + validate_single_character("comment", comment) + + self.has_header = has_header + self.delimiter = delimiter + self.quote = quote + self.terminator = terminator + self.escape = escape + self.comment = comment + self.newlines_in_values = newlines_in_values + self.schema = schema + self.schema_infer_max_records = schema_infer_max_records + self.file_extension = file_extension + self.table_partition_cols = table_partition_cols or [] + self.file_compression_type = file_compression_type + self.file_sort_order = file_sort_order or [] + self.null_regex = null_regex + self.truncated_rows = truncated_rows + + def with_has_header(self, has_header: bool) -> CsvReadOptions: + """Configure whether the CSV has a header row.""" + self.has_header = has_header + return self + + def with_delimiter(self, delimiter: str) -> CsvReadOptions: + """Configure the column delimiter.""" + self.delimiter = delimiter + return self + + def with_quote(self, quote: str) -> CsvReadOptions: + """Configure the quote character.""" + self.quote = quote + return self + + def with_terminator(self, terminator: str | None) -> CsvReadOptions: + """Configure the line terminator character.""" + self.terminator = terminator + return self + + def with_escape(self, escape: str | None) -> CsvReadOptions: + """Configure the escape character.""" + self.escape = escape + return self + + def with_comment(self, comment: str | None) -> CsvReadOptions: + """Configure the comment character.""" + self.comment = comment + return self + + def with_newlines_in_values(self, newlines_in_values: bool) -> CsvReadOptions: + """Configure whether newlines in values are supported.""" + self.newlines_in_values = newlines_in_values + return self + + def with_schema(self, schema: pa.Schema | None) -> CsvReadOptions: + """Configure the schema.""" + self.schema = schema + return self + + def with_schema_infer_max_records( + self, schema_infer_max_records: int + ) -> CsvReadOptions: + """Configure maximum records for schema inference.""" + self.schema_infer_max_records = schema_infer_max_records + return self + + def with_file_extension(self, file_extension: str) -> CsvReadOptions: + """Configure the file extension filter.""" + self.file_extension = file_extension + return self + + def with_table_partition_cols( + self, table_partition_cols: list[tuple[str, pa.DataType]] + ) -> CsvReadOptions: + """Configure table partition columns.""" + self.table_partition_cols = table_partition_cols + return self + + def with_file_compression_type(self, file_compression_type: str) -> CsvReadOptions: + """Configure file compression type.""" + self.file_compression_type = file_compression_type + return self + + def with_file_sort_order( + self, file_sort_order: list[list[SortExpr]] + ) -> CsvReadOptions: + """Configure file sort order.""" + self.file_sort_order = file_sort_order + return self + + def with_null_regex(self, null_regex: str | None) -> CsvReadOptions: + """Configure null value regex pattern.""" + self.null_regex = null_regex + return self + + def with_truncated_rows(self, truncated_rows: bool) -> CsvReadOptions: + """Configure whether to allow truncated rows.""" + self.truncated_rows = truncated_rows + return self + + def to_inner(self) -> options.CsvReadOptions: + """Convert this object into the underlying Rust structure. + + This is intended for internal use only. + """ + file_sort_order = ( + [] + if self.file_sort_order is None + else [ + sort_list_to_raw_sort_list(sort_list) + for sort_list in self.file_sort_order + ] + ) + + return options.CsvReadOptions( + has_header=self.has_header, + delimiter=ord(self.delimiter[0]) if self.delimiter else ord(","), + quote=ord(self.quote[0]) if self.quote else ord('"'), + terminator=ord(self.terminator[0]) if self.terminator else None, + escape=ord(self.escape[0]) if self.escape else None, + comment=ord(self.comment[0]) if self.comment else None, + newlines_in_values=self.newlines_in_values, + schema=self.schema, + schema_infer_max_records=self.schema_infer_max_records, + file_extension=self.file_extension, + table_partition_cols=_convert_table_partition_cols( + self.table_partition_cols + ), + file_compression_type=self.file_compression_type or "", + file_sort_order=file_sort_order, + null_regex=self.null_regex, + truncated_rows=self.truncated_rows, + ) + + +def validate_single_character(name: str, value: str | None) -> None: + if value is not None and len(value) != 1: + message = f"{name} must be a single character" + raise ValueError(message) + + +def _convert_table_partition_cols( + table_partition_cols: list[tuple[str, str | pa.DataType]], +) -> list[tuple[str, pa.DataType]]: + warn = False + converted_table_partition_cols = [] + + for col, data_type in table_partition_cols: + if isinstance(data_type, str): + warn = True + if data_type == "string": + converted_data_type = pa.string() + elif data_type == "int": + converted_data_type = pa.int32() + else: + message = ( + f"Unsupported literal data type '{data_type}' for partition " + "column. Supported types are 'string' and 'int'" + ) + raise ValueError(message) + else: + converted_data_type = data_type + + converted_table_partition_cols.append((col, converted_data_type)) + + if warn: + message = ( + "using literals for table_partition_cols data types is deprecated," + "use pyarrow types instead" + ) + warnings.warn( + message, + category=DeprecationWarning, + stacklevel=2, + ) + + return converted_table_partition_cols diff --git a/python/datafusion/plan.py b/python/datafusion/plan.py index 0b7bebcb3..c0cfd523f 100644 --- a/python/datafusion/plan.py +++ b/python/datafusion/plan.py @@ -24,15 +24,19 @@ import datafusion._internal as df_internal if TYPE_CHECKING: + import datetime + from datafusion.context import SessionContext __all__ = [ "ExecutionPlan", "LogicalPlan", + "Metric", + "MetricsSet", ] -class LogicalPlan: +class LogicalPlan: # noqa: PLW1641 """Logical Plan. A `LogicalPlan` is a node in a tree of relational operators (such as @@ -98,6 +102,12 @@ def to_proto(self) -> bytes: """ return self._raw_plan.to_proto() + def __eq__(self, other: LogicalPlan) -> bool: + """Test equality.""" + if not isinstance(other, LogicalPlan): + return False + return self._raw_plan.__eq__(other._raw_plan) + class ExecutionPlan: """Represent nodes in the DataFusion Physical Plan.""" @@ -145,3 +155,176 @@ def to_proto(self) -> bytes: Tables created in memory from record batches are currently not supported. """ return self._raw_plan.to_proto() + + def metrics(self) -> MetricsSet | None: + """Return metrics for this plan node, or None if this plan has no MetricsSet. + + Some operators (e.g. DataSourceExec) eagerly initialize a MetricsSet + when the plan is created, so this may return a set even before + execution. Metric *values* (such as ``output_rows``) are only + meaningful after the DataFrame has been executed. + """ + raw = self._raw_plan.metrics() + if raw is None: + return None + return MetricsSet(raw) + + def collect_metrics(self) -> list[tuple[str, MetricsSet]]: + """Return runtime statistics for each step of the query execution. + + DataFusion executes a query as a pipeline of operators — for example a + data source scan, followed by a filter, followed by a projection. After + the DataFrame has been executed (via + :py:meth:`~datafusion.DataFrame.collect`, + :py:meth:`~datafusion.DataFrame.execute_stream`, etc.), each operator + records statistics such as how many rows it produced and how much CPU + time it consumed. + + Each entry in the returned list corresponds to one operator that + recorded metrics. The first element of the tuple is the operator's + description string — the same text shown by + :py:meth:`display_indent` — which identifies both the operator type + and its key parameters, for example ``"FilterExec: column1@0 > 1"`` + or ``"DataSourceExec: partitions=1"``. + + Returns: + A list of ``(description, MetricsSet)`` tuples ordered from the + outermost operator (top of the execution tree) down to the + data-source leaves. Only operators that recorded at least one + metric are included. Returns an empty list if called before the + DataFrame has been executed. + """ + result: list[tuple[str, MetricsSet]] = [] + + def _walk(node: ExecutionPlan) -> None: + ms = node.metrics() + if ms is not None: + result.append((node.display(), ms)) + for child in node.children(): + _walk(child) + + _walk(self) + return result + + +class MetricsSet: + """A set of metrics for a single execution plan operator. + + A physical plan operator runs independently across one or more partitions. + :py:meth:`metrics` returns the raw per-partition :py:class:`Metric` objects. + The convenience properties (:py:attr:`output_rows`, :py:attr:`elapsed_compute`, + etc.) automatically sum the named metric across *all* partitions, giving a + single aggregate value for the operator as a whole. + """ + + def __init__(self, raw: df_internal.MetricsSet) -> None: + """This constructor should not be called by the end user.""" + self._raw = raw + + def metrics(self) -> list[Metric]: + """Return all individual metrics in this set.""" + return [Metric(m) for m in self._raw.metrics()] + + @property + def output_rows(self) -> int | None: + """Sum of output_rows across all partitions.""" + return self._raw.output_rows() + + @property + def elapsed_compute(self) -> int | None: + """Total CPU time (in nanoseconds) spent inside this operator's execute loop. + + Summed across all partitions. Returns ``None`` if no ``elapsed_compute`` + metric was recorded. + """ + return self._raw.elapsed_compute() + + @property + def spill_count(self) -> int | None: + """Number of times this operator spilled data to disk due to memory pressure. + + This is a count of spill events, not a byte count. Summed across all + partitions. Returns ``None`` if no ``spill_count`` metric was recorded. + """ + return self._raw.spill_count() + + @property + def spilled_bytes(self) -> int | None: + """Sum of spilled_bytes across all partitions.""" + return self._raw.spilled_bytes() + + @property + def spilled_rows(self) -> int | None: + """Sum of spilled_rows across all partitions.""" + return self._raw.spilled_rows() + + def sum_by_name(self, name: str) -> int | None: + """Sum the named metric across all partitions. + + Useful for accessing any metric not exposed as a first-class property. + Returns ``None`` if no metric with the given name was recorded. + + Args: + name: The metric name, e.g. ``"output_rows"`` or ``"elapsed_compute"``. + """ + return self._raw.sum_by_name(name) + + def __repr__(self) -> str: + """Return a string representation of the metrics set.""" + return repr(self._raw) + + +class Metric: + """A single execution metric with name, value, partition, and labels.""" + + def __init__(self, raw: df_internal.Metric) -> None: + """This constructor should not be called by the end user.""" + self._raw = raw + + @property + def name(self) -> str: + """The name of this metric (e.g. ``output_rows``).""" + return self._raw.name + + @property + def value(self) -> int | datetime.datetime | None: + """The value of this metric. + + Returns an ``int`` for counters, gauges, and time-based metrics + (nanoseconds), a :py:class:`~datetime.datetime` (UTC) for + ``start_timestamp`` / ``end_timestamp`` metrics, or ``None`` + when the value has not been set or is not representable. + """ + return self._raw.value + + @property + def value_as_datetime(self) -> datetime.datetime | None: + """The value as a UTC :py:class:`~datetime.datetime` for timestamp metrics. + + Returns ``None`` for all non-timestamp metrics and for timestamp + metrics whose value has not been set (e.g. before execution). + """ + return self._raw.value_as_datetime + + @property + def partition(self) -> int | None: + """The 0-based partition index this metric applies to. + + Returns ``None`` for metrics that are not partition-specific (i.e. they + apply globally across all partitions of the operator). + """ + return self._raw.partition + + def labels(self) -> dict[str, str]: + """Return the labels associated with this metric. + + Labels provide additional context for a metric. For example:: + + metric.labels() + # {'output_type': 'final'} + """ + return self._raw.labels() + + def __repr__(self) -> str: + """Return a string representation of the metric.""" + return repr(self._raw) diff --git a/python/datafusion/substrait.py b/python/datafusion/substrait.py index f10adfb0c..6353ef8cc 100644 --- a/python/datafusion/substrait.py +++ b/python/datafusion/substrait.py @@ -25,11 +25,6 @@ from typing import TYPE_CHECKING -try: - from warnings import deprecated # Python 3.13+ -except ImportError: - from typing_extensions import deprecated # Python 3.12 - from datafusion.plan import LogicalPlan from ._internal import substrait as substrait_internal @@ -67,10 +62,25 @@ def encode(self) -> bytes: """ return self.plan_internal.encode() + def to_json(self) -> str: + """Get the JSON representation of the Substrait plan. + + Returns: + A JSON representation of the Substrait plan. + """ + return self.plan_internal.to_json() + + @staticmethod + def from_json(json: str) -> Plan: + """Parse a plan from a JSON string representation. + + Args: + json: JSON representation of a Substrait plan. -@deprecated("Use `Plan` instead.") -class plan(Plan): # noqa: N801 - """See `Plan`.""" + Returns: + Plan object representing the Substrait plan. + """ + return Plan(substrait_internal.Plan.from_json(json)) class Serde: @@ -138,11 +148,6 @@ def deserialize_bytes(proto_bytes: bytes) -> Plan: return Plan(substrait_internal.Serde.deserialize_bytes(proto_bytes)) -@deprecated("Use `Serde` instead.") -class serde(Serde): # noqa: N801 - """See `Serde` instead.""" - - class Producer: """Generates substrait plans from a logical plan.""" @@ -164,11 +169,6 @@ def to_substrait_plan(logical_plan: LogicalPlan, ctx: SessionContext) -> Plan: ) -@deprecated("Use `Producer` instead.") -class producer(Producer): # noqa: N801 - """Use `Producer` instead.""" - - class Consumer: """Generates a logical plan from a substrait plan.""" @@ -186,8 +186,3 @@ def from_substrait_plan(ctx: SessionContext, plan: Plan) -> LogicalPlan: return LogicalPlan( substrait_internal.Consumer.from_substrait_plan(ctx.ctx, plan.plan_internal) ) - - -@deprecated("Use `Consumer` instead.") -class consumer(Consumer): # noqa: N801 - """Use `Consumer` instead.""" diff --git a/python/datafusion/user_defined.py b/python/datafusion/user_defined.py index 43a72c805..848ab4cee 100644 --- a/python/datafusion/user_defined.py +++ b/python/datafusion/user_defined.py @@ -27,13 +27,14 @@ import pyarrow as pa import datafusion._internal as df_internal +from datafusion import SessionContext from datafusion.expr import Expr if TYPE_CHECKING: from _typeshed import CapsuleType as _PyCapsule _R = TypeVar("_R", bound=pa.DataType) - from collections.abc import Callable + from collections.abc import Callable, Sequence class Volatility(Enum): @@ -80,6 +81,27 @@ def __str__(self) -> str: return self.name.lower() +def data_type_or_field_to_field(value: pa.DataType | pa.Field, name: str) -> pa.Field: + """Helper function to return a Field from either a Field or DataType.""" + if isinstance(value, pa.Field): + return value + return pa.field(name, type=value) + + +def data_types_or_fields_to_field_list( + inputs: Sequence[pa.Field | pa.DataType] | pa.Field | pa.DataType, +) -> list[pa.Field]: + """Helper function to return a list of Fields.""" + if isinstance(inputs, pa.DataType): + return [pa.field("value", type=inputs)] + if isinstance(inputs, pa.Field): + return [inputs] + + return [ + data_type_or_field_to_field(v, f"value_{idx}") for (idx, v) in enumerate(inputs) + ] + + class ScalarUDFExportable(Protocol): """Type hint for object that has __datafusion_scalar_udf__ PyCapsule.""" @@ -102,8 +124,8 @@ def __init__( self, name: str, func: Callable[..., _R], - input_types: pa.DataType | list[pa.DataType], - return_type: _R, + input_fields: list[pa.Field], + return_field: _R, volatility: Volatility | str, ) -> None: """Instantiate a scalar user-defined function (UDF). @@ -113,10 +135,10 @@ def __init__( if hasattr(func, "__datafusion_scalar_udf__"): self._udf = df_internal.ScalarUDF.from_pycapsule(func) return - if isinstance(input_types, pa.DataType): - input_types = [input_types] + if isinstance(input_fields, pa.DataType): + input_fields = [input_fields] self._udf = df_internal.ScalarUDF( - name, func, input_types, return_type, str(volatility) + name, func, input_fields, return_field, str(volatility) ) def __repr__(self) -> str: @@ -135,8 +157,8 @@ def __call__(self, *args: Expr) -> Expr: @overload @staticmethod def udf( - input_types: list[pa.DataType], - return_type: _R, + input_fields: Sequence[pa.DataType | pa.Field] | pa.DataType | pa.Field, + return_field: pa.DataType | pa.Field, volatility: Volatility | str, name: str | None = None, ) -> Callable[..., ScalarUDF]: ... @@ -145,8 +167,8 @@ def udf( @staticmethod def udf( func: Callable[..., _R], - input_types: list[pa.DataType], - return_type: _R, + input_fields: Sequence[pa.DataType | pa.Field] | pa.DataType | pa.Field, + return_field: pa.DataType | pa.Field, volatility: Volatility | str, name: str | None = None, ) -> ScalarUDF: ... @@ -162,20 +184,25 @@ def udf(*args: Any, **kwargs: Any): # noqa: D417 This class can be used both as either a function or a decorator. Usage: - - As a function: ``udf(func, input_types, return_type, volatility, name)``. - - As a decorator: ``@udf(input_types, return_type, volatility, name)``. + - As a function: ``udf(func, input_fields, return_field, + volatility, name)``. + - As a decorator: ``@udf(input_fields, return_field, volatility, name)``. When used a decorator, do **not** pass ``func`` explicitly. + In lieu of passing a PyArrow Field, you can pass a DataType for simplicity. + When you do so, it will be assumed that the nullability of the inputs and + output are True and that they have no metadata. + Args: func (Callable, optional): Only needed when calling as a function. Skip this argument when using `udf` as a decorator. If you have a Rust backed ScalarUDF within a PyCapsule, you can pass this parameter and ignore the rest. They will be determined directly from the underlying function. See the online documentation for more information. - input_types (list[pa.DataType]): The data types of the arguments - to ``func``. This list must be of the same length as the number of - arguments. - return_type (_R): The data type of the return value from the function. + input_fields (list[pa.Field | pa.DataType]): The data types or Fields + of the arguments to ``func``. This list must be of the same length + as the number of arguments. + return_field (_R): The field of the return value from the function. volatility (Volatility | str): See `Volatility` for allowed values. name (Optional[str]): A descriptive name for the function. @@ -183,24 +210,37 @@ def udf(*args: Any, **kwargs: Any): # noqa: D417 A user-defined function that can be used in SQL expressions, data aggregation, or window function calls. - Example: Using ``udf`` as a function:: + Examples: + Using ``udf`` as a function: + + >>> import pyarrow.compute as pc + >>> from datafusion.user_defined import ScalarUDF + >>> def double_func(x): + ... return pc.multiply(x, 2) + >>> double_udf = ScalarUDF.udf( + ... double_func, [pa.int64()], pa.int64(), + ... "volatile", "double_it") + + Using ``udf`` as a decorator: - def double_func(x): - return x * 2 - double_udf = udf(double_func, [pa.int32()], pa.int32(), - "volatile", "double_it") + >>> @ScalarUDF.udf([pa.int64()], pa.int64(), "volatile") + ... def decorator_double_udf(x): + ... return pc.multiply(x, 3) - Example: Using ``udf`` as a decorator:: + Apply to a dataframe: - @udf([pa.int32()], pa.int32(), "volatile", "double_it") - def double_udf(x): - return x * 2 + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"x": [1, 2, 3]}) + >>> df.select(double_udf(col("x")).alias("result")).to_pydict() + {'result': [2, 4, 6]} + >>> df.select(decorator_double_udf(col("x")).alias("result")).to_pydict() + {'result': [3, 6, 9]} """ def _function( func: Callable[..., _R], - input_types: list[pa.DataType], - return_type: _R, + input_fields: Sequence[pa.DataType | pa.Field] | pa.DataType | pa.Field, + return_field: pa.DataType | pa.Field, volatility: Volatility | str, name: str | None = None, ) -> ScalarUDF: @@ -212,23 +252,25 @@ def _function( name = func.__qualname__.lower() else: name = func.__class__.__name__.lower() + input_fields = data_types_or_fields_to_field_list(input_fields) + return_field = data_type_or_field_to_field(return_field, "value") return ScalarUDF( name=name, func=func, - input_types=input_types, - return_type=return_type, + input_fields=input_fields, + return_field=return_field, volatility=volatility, ) def _decorator( - input_types: list[pa.DataType], - return_type: _R, + input_fields: Sequence[pa.DataType | pa.Field] | pa.DataType | pa.Field, + return_field: _R, volatility: Volatility | str, name: str | None = None, ) -> Callable: def decorator(func: Callable) -> Callable: udf_caller = ScalarUDF.udf( - func, input_types, return_type, volatility, name + func, input_fields, return_field, volatility, name ) @functools.wraps(func) @@ -259,8 +301,8 @@ def from_pycapsule(func: ScalarUDFExportable) -> ScalarUDF: return ScalarUDF( name=name, func=func, - input_types=None, - return_type=None, + input_fields=None, + return_field=None, volatility=None, ) @@ -270,7 +312,16 @@ class Accumulator(metaclass=ABCMeta): @abstractmethod def state(self) -> list[pa.Scalar]: - """Return the current state.""" + """Return the current state. + + While this function template expects PyArrow Scalar values return type, + you can return any value that can be converted into a Scalar. This + includes basic Python data types such as integers and strings. In + addition to primitive types, we currently support PyArrow, nanoarrow, + and arro3 objects in addition to primitive data types. Other objects + that support the Arrow FFI standard will be given a "best attempt" at + conversion to scalar objects. + """ @abstractmethod def update(self, *values: pa.Array) -> None: @@ -282,7 +333,16 @@ def merge(self, states: list[pa.Array]) -> None: @abstractmethod def evaluate(self) -> pa.Scalar: - """Return the resultant value.""" + """Return the resultant value. + + While this function template expects a PyArrow Scalar value return type, + you can return any value that can be converted into a Scalar. This + includes basic Python data types such as integers and strings. In + addition to primitive types, we currently support PyArrow, nanoarrow, + and arro3 objects in addition to primitive data types. Other objects + that support the Arrow FFI standard will be given a "best attempt" at + conversion to scalar objects. + """ class AggregateUDFExportable(Protocol): @@ -412,48 +472,71 @@ def udaf(*args: Any, **kwargs: Any): # noqa: D417, C901 - As a decorator: ``@udaf(input_types, return_type, state_type, volatility, name)``. When using ``udaf`` as a decorator, do not pass ``accum`` explicitly. - Function example: - If your :py:class:`Accumulator` can be instantiated with no arguments, you - can simply pass it's type as `accum`. If you need to pass additional - arguments to it's constructor, you can define a lambda or a factory method. + can simply pass its type as ``accum``. If you need to pass additional + arguments to its constructor, you can define a lambda or a factory method. During runtime the :py:class:`Accumulator` will be constructed for every - instance in which this UDAF is used. The following examples are all valid:: - - import pyarrow as pa - import pyarrow.compute as pc - - class Summarize(Accumulator): - def __init__(self, bias: float = 0.0): - self._sum = pa.scalar(bias) - - def state(self) -> list[pa.Scalar]: - return [self._sum] - - def update(self, values: pa.Array) -> None: - self._sum = pa.scalar(self._sum.as_py() + pc.sum(values).as_py()) - - def merge(self, states: list[pa.Array]) -> None: - self._sum = pa.scalar(self._sum.as_py() + pc.sum(states[0]).as_py()) - - def evaluate(self) -> pa.Scalar: - return self._sum - - def sum_bias_10() -> Summarize: - return Summarize(10.0) - - udaf1 = udaf(Summarize, pa.float64(), pa.float64(), [pa.float64()], - "immutable") - udaf2 = udaf(sum_bias_10, pa.float64(), pa.float64(), [pa.float64()], - "immutable") - udaf3 = udaf(lambda: Summarize(20.0), pa.float64(), pa.float64(), - [pa.float64()], "immutable") - - Decorator example::: - - @udaf(pa.float64(), pa.float64(), [pa.float64()], "immutable") - def udf4() -> Summarize: - return Summarize(10.0) + instance in which this UDAF is used. + + Examples: + >>> import pyarrow.compute as pc + >>> from datafusion.user_defined import AggregateUDF, Accumulator, udaf + >>> class Summarize(Accumulator): + ... def __init__(self, bias: float = 0.0): + ... self._sum = pa.scalar(bias) + ... def state(self): + ... return [self._sum] + ... def update(self, values): + ... self._sum = pa.scalar( + ... self._sum.as_py() + pc.sum(values).as_py()) + ... def merge(self, states): + ... self._sum = pa.scalar( + ... self._sum.as_py() + pc.sum(states[0]).as_py()) + ... def evaluate(self): + ... return self._sum + + Using ``udaf`` as a function: + + >>> udaf1 = AggregateUDF.udaf( + ... Summarize, pa.float64(), pa.float64(), + ... [pa.float64()], "immutable") + + Wrapping ``udaf`` with a function: + + >>> def sum_bias_10() -> Summarize: + ... return Summarize(10.0) + >>> udaf2 = udaf(sum_bias_10, pa.float64(), pa.float64(), [pa.float64()], + ... "immutable") + + Using ``udaf`` with lambda: + + >>> udaf3 = udaf(lambda: Summarize(20.0), pa.float64(), pa.float64(), + ... [pa.float64()], "immutable") + + Using ``udaf`` as a decorator: + + >>> @AggregateUDF.udaf( + ... pa.float64(), pa.float64(), + ... [pa.float64()], "immutable") + ... def udaf4(): + ... return Summarize(10.0) + + Apply to a dataframe: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0]}) + >>> df.aggregate([], [udaf1(col("a")).alias("total")]).collect_column( + ... "total")[0].as_py() + 6.0 + >>> df.aggregate([], [udaf2(col("a")).alias("total")]).collect_column( + ... "total")[0].as_py() + 16.0 + >>> df.aggregate([], [udaf3(col("a")).alias("total")]).collect_column( + ... "total")[0].as_py() + 26.0 + >>> df.aggregate([], [udaf4(col("a")).alias("total")]).collect_column( + ... "total")[0].as_py() + 16.0 Args: accum: The accumulator python function. Only needed when calling as a @@ -537,11 +620,11 @@ def from_pycapsule(func: AggregateUDFExportable | _PyCapsule) -> AggregateUDF: AggregateUDF that is exported via the FFI bindings. """ if _is_pycapsule(func): - aggregate = cast(AggregateUDF, object.__new__(AggregateUDF)) + aggregate = cast("AggregateUDF", object.__new__(AggregateUDF)) aggregate._udaf = df_internal.AggregateUDF.from_pycapsule(func) return aggregate - capsule = cast(AggregateUDFExportable, func) + capsule = cast("AggregateUDFExportable", func) name = str(capsule.__class__) return AggregateUDF( name=name, @@ -788,31 +871,44 @@ def udwf(*args: Any, **kwargs: Any): # noqa: D417 - As a decorator: ``@udwf(input_types, return_type, volatility, name)``. When using ``udwf`` as a decorator, do not pass ``func`` explicitly. - Function example:: - - import pyarrow as pa - - class BiasedNumbers(WindowEvaluator): - def __init__(self, start: int = 0) -> None: - self.start = start - - def evaluate_all(self, values: list[pa.Array], - num_rows: int) -> pa.Array: - return pa.array([self.start + i for i in range(num_rows)]) - - def bias_10() -> BiasedNumbers: - return BiasedNumbers(10) - - udwf1 = udwf(BiasedNumbers, pa.int64(), pa.int64(), "immutable") - udwf2 = udwf(bias_10, pa.int64(), pa.int64(), "immutable") - udwf3 = udwf(lambda: BiasedNumbers(20), pa.int64(), pa.int64(), "immutable") - - - Decorator example:: - - @udwf(pa.int64(), pa.int64(), "immutable") - def biased_numbers() -> BiasedNumbers: - return BiasedNumbers(10) + Examples: + >>> from datafusion.user_defined import WindowUDF, WindowEvaluator, udwf + >>> class BiasedNumbers(WindowEvaluator): + ... def __init__(self, start: int = 0): + ... self.start = start + ... def evaluate_all(self, values, num_rows): + ... return pa.array( + ... [self.start + i for i in range(num_rows)]) + + Using ``udwf`` as a function: + + >>> udwf1 = WindowUDF.udwf( + ... BiasedNumbers, pa.int64(), pa.int64(), "immutable") + >>> def bias_10() -> BiasedNumbers: + ... return BiasedNumbers(10) + >>> udwf2 = udwf(bias_10, pa.int64(), pa.int64(), "immutable") + >>> udwf3 = udwf( + ... lambda: BiasedNumbers(20), pa.int64(), pa.int64(), "immutable" + ... ) + + Using ``udwf`` as a decorator: + + >>> @WindowUDF.udwf(pa.int64(), pa.int64(), "immutable") + ... def biased_numbers(): + ... return BiasedNumbers(10) + + Apply to a dataframe: + + >>> ctx = dfn.SessionContext() + >>> df = ctx.from_pydict({"a": [10, 20, 30]}) + >>> df.select(udwf1(col("a")).alias("result")).to_pydict() + {'result': [0, 1, 2]} + >>> df.select(udwf2(col("a")).alias("result")).to_pydict() + {'result': [10, 11, 12]} + >>> df.select(udwf3(col("a")).alias("result")).to_pydict() + {'result': [20, 21, 22]} + >>> df.select(biased_numbers(col("a")).alias("result")).to_pydict() + {'result': [10, 11, 12]} Args: func: Only needed when calling as a function. Skip this argument when @@ -923,16 +1019,14 @@ class TableFunction: """ def __init__( - self, - name: str, - func: Callable[[], any], + self, name: str, func: Callable[[], any], ctx: SessionContext | None = None ) -> None: """Instantiate a user-defined table function (UDTF). See :py:func:`udtf` for a convenience function and argument descriptions. """ - self._udtf = df_internal.TableFunction(name, func) + self._udtf = df_internal.TableFunction(name, func, ctx) def __call__(self, *args: Expr) -> Any: """Execute the UDTF and return a table provider.""" diff --git a/python/tests/test_aggregation.py b/python/tests/test_aggregation.py index f595127fa..240332848 100644 --- a/python/tests/test_aggregation.py +++ b/python/tests/test_aggregation.py @@ -141,14 +141,14 @@ def test_aggregation_stats(df, agg_expr, calc_expected): ), ( f.approx_percentile_cont_with_weight(column("b"), lit(0.6), 0.5), - pa.array([6], type=pa.float64()), + pa.array([4], type=pa.float64()), False, ), ( f.approx_percentile_cont_with_weight( column("b").sort(ascending=False, nulls_first=False), lit(0.6), 0.5 ), - pa.array([6], type=pa.float64()), + pa.array([4], type=pa.float64()), False, ), ( diff --git a/python/tests/test_catalog.py b/python/tests/test_catalog.py index 08f494dee..c89da36bf 100644 --- a/python/tests/test_catalog.py +++ b/python/tests/test_catalog.py @@ -16,11 +16,16 @@ # under the License. from __future__ import annotations +from typing import TYPE_CHECKING + import datafusion as dfn import pyarrow as pa import pyarrow.dataset as ds import pytest -from datafusion import SessionContext, Table, udtf +from datafusion import Catalog, SessionContext, Table, udtf + +if TYPE_CHECKING: + from datafusion.catalog import CatalogProvider, CatalogProviderExportable # Note we take in `database` as a variable even though we don't use @@ -76,6 +81,12 @@ def table_exist(self, name: str) -> bool: return name in self.tables +class CustomErrorSchemaProvider(CustomSchemaProvider): + def table(self, name: str) -> Table | None: + message = f"{name} is not an acceptable name" + raise ValueError(message) + + class CustomCatalogProvider(dfn.catalog.CatalogProvider): def __init__(self): self.schemas = {"my_schema": CustomSchemaProvider()} @@ -93,6 +104,40 @@ def deregister_schema(self, name, cascade: bool): del self.schemas[name] +class CustomCatalogProviderList(dfn.catalog.CatalogProviderList): + def __init__(self): + self.catalogs = {"my_catalog": CustomCatalogProvider()} + + def catalog_names(self) -> set[str]: + return set(self.catalogs.keys()) + + def catalog(self, name: str) -> Catalog | None: + return self.catalogs[name] + + def register_catalog( + self, name: str, catalog: CatalogProviderExportable | CatalogProvider | Catalog + ) -> None: + self.catalogs[name] = catalog + + +class CustomTableProviderFactory(dfn.catalog.TableProviderFactory): + def create(self, cmd: dfn.expr.CreateExternalTable): + assert cmd.name() == "test_table_factory" + return create_dataset() + + +def test_python_catalog_provider_list(ctx: SessionContext): + ctx.register_catalog_provider_list(CustomCatalogProviderList()) + + # Ensure `datafusion` catalog does not exist since + # we replaced the catalog list + assert ctx.catalog_names() == {"my_catalog"} + + # Ensure registering works + ctx.register_catalog_provider("second_catalog", Catalog.memory_catalog()) + assert ctx.catalog_names() == {"my_catalog", "second_catalog"} + + def test_python_catalog_provider(ctx: SessionContext): ctx.register_catalog_provider("my_catalog", CustomCatalogProvider()) @@ -186,6 +231,33 @@ def test_schema_register_table_with_pyarrow_dataset(ctx: SessionContext): schema.deregister_table(table_name) +def test_exception_not_mangled(ctx: SessionContext): + """Test registering all python providers and running a query against them.""" + + catalog_name = "custom_catalog" + schema_name = "custom_schema" + + ctx.register_catalog_provider(catalog_name, CustomCatalogProvider()) + + catalog = ctx.catalog(catalog_name) + + # Clean out previous schemas if they exist so we can start clean + for schema_name in catalog.schema_names(): + catalog.deregister_schema(schema_name, cascade=False) + + catalog.register_schema(schema_name, CustomErrorSchemaProvider()) + + schema = catalog.schema(schema_name) + + for table_name in schema.table_names(): + schema.deregister_table(table_name) + + schema.register_table("test_table", create_dataset()) + + with pytest.raises(ValueError, match=r"^test_table is not an acceptable name$"): + ctx.sql(f"select * from {catalog_name}.{schema_name}.test_table") + + def test_in_end_to_end_python_providers(ctx: SessionContext): """Test registering all python providers and running a query against them.""" @@ -248,3 +320,24 @@ def my_table_function_udtf() -> Table: assert len(result[0]) == 1 assert len(result[0][0]) == 1 assert result[0][0][0].as_py() == 3 + + +def test_register_python_table_provider_factory(ctx: SessionContext): + ctx.register_table_factory("CUSTOM_FACTORY", CustomTableProviderFactory()) + + ctx.sql(""" + CREATE EXTERNAL TABLE test_table_factory + STORED AS CUSTOM_FACTORY + LOCATION foo; + """).collect() + + result = ctx.sql("SELECT * FROM test_table_factory;").collect() + + expect = [ + pa.RecordBatch.from_arrays( + [pa.array([1, 2, 3]), pa.array([4, 5, 6])], + names=["a", "b"], + ) + ] + + assert result == expect diff --git a/python/tests/test_context.py b/python/tests/test_context.py index bd65305ed..e0ebdbae5 100644 --- a/python/tests/test_context.py +++ b/python/tests/test_context.py @@ -22,6 +22,7 @@ import pyarrow.dataset as ds import pytest from datafusion import ( + CsvReadOptions, DataFrame, RuntimeEnvBuilder, SessionConfig, @@ -30,6 +31,7 @@ Table, column, literal, + udf, ) @@ -350,6 +352,125 @@ def test_deregister_table(ctx, database): assert public.names() == {"csv1", "csv2"} +def test_deregister_udf(): + ctx = SessionContext() + + is_null = udf( + lambda x: x.is_null(), + [pa.float64()], + pa.bool_(), + volatility="immutable", + name="my_is_null", + ) + ctx.register_udf(is_null) + + # Verify it works + df = ctx.from_pydict({"a": [1.0, None]}) + ctx.register_table("t", df.into_view()) + result = ctx.sql("SELECT my_is_null(a) FROM t").collect() + assert result[0].column(0) == pa.array([False, True]) + + # Deregister and verify it's gone + ctx.deregister_udf("my_is_null") + with pytest.raises(ValueError): + ctx.sql("SELECT my_is_null(a) FROM t").collect() + + +def test_deregister_udaf(): + import pyarrow.compute as pc + + ctx = SessionContext() + from datafusion import Accumulator, udaf + + class MySum(Accumulator): + def __init__(self): + self._sum = 0.0 + + def update(self, values: pa.Array) -> None: + self._sum += pc.sum(values).as_py() + + def merge(self, states: list[pa.Array]) -> None: + self._sum += pc.sum(states[0]).as_py() + + def state(self) -> list: + return [self._sum] + + def evaluate(self) -> pa.Scalar: + return self._sum + + my_sum = udaf( + MySum, + [pa.float64()], + pa.float64(), + [pa.float64()], + volatility="immutable", + name="my_sum", + ) + ctx.register_udaf(my_sum) + df = ctx.from_pydict({"a": [1.0, 2.0, 3.0]}) + ctx.register_table("t", df.into_view()) + + result = ctx.sql("SELECT my_sum(a) FROM t").collect() + assert result[0].column(0) == pa.array([6.0]) + + ctx.deregister_udaf("my_sum") + with pytest.raises(ValueError): + ctx.sql("SELECT my_sum(a) FROM t").collect() + + +def test_deregister_udwf(): + ctx = SessionContext() + from datafusion import udwf + from datafusion.user_defined import WindowEvaluator + + class MyRowNumber(WindowEvaluator): + def __init__(self): + self._row = 0 + + def evaluate_all(self, values, num_rows): + return pa.array(list(range(1, num_rows + 1)), type=pa.uint64()) + + my_row_number = udwf( + MyRowNumber, + [pa.float64()], + pa.uint64(), + volatility="immutable", + name="my_row_number", + ) + ctx.register_udwf(my_row_number) + df = ctx.from_pydict({"a": [1.0, 2.0, 3.0]}) + ctx.register_table("t", df.into_view()) + + result = ctx.sql("SELECT my_row_number(a) OVER () FROM t").collect() + assert result[0].column(0) == pa.array([1, 2, 3], type=pa.uint64()) + + ctx.deregister_udwf("my_row_number") + with pytest.raises(ValueError): + ctx.sql("SELECT my_row_number(a) OVER () FROM t").collect() + + +def test_deregister_udtf(): + import pyarrow.dataset as ds + + ctx = SessionContext() + from datafusion import Table, udtf + + class MyTable: + def __call__(self): + batch = pa.RecordBatch.from_pydict({"x": [1, 2, 3]}) + return Table(ds.dataset([batch])) + + my_table = udtf(MyTable(), "my_table") + ctx.register_udtf(my_table) + + result = ctx.sql("SELECT * FROM my_table()").collect() + assert result[0].column(0) == pa.array([1, 2, 3]) + + ctx.deregister_udtf("my_table") + with pytest.raises(ValueError): + ctx.sql("SELECT * FROM my_table()").collect() + + def test_register_table_from_dataframe(ctx): df = ctx.from_pydict({"a": [1, 2]}) ctx.register_table("df_tbl", df) @@ -550,6 +671,61 @@ def test_table_not_found(ctx): ctx.table(f"not-found-{uuid4()}") +def test_session_start_time(ctx): + import datetime + import re + + st = ctx.session_start_time() + assert isinstance(st, str) + # Truncate nanoseconds to microseconds for Python 3.10 compat + st = re.sub(r"(\.\d{6})\d+", r"\1", st) + dt = datetime.datetime.fromisoformat(st) + assert dt.isoformat() + + +def test_enable_ident_normalization(ctx): + assert ctx.enable_ident_normalization() is True + ctx.sql("SET datafusion.sql_parser.enable_ident_normalization = false") + assert ctx.enable_ident_normalization() is False + + +def test_parse_sql_expr(ctx): + from datafusion.common import DFSchema + + schema = DFSchema.empty() + expr = ctx.parse_sql_expr("1 + 2", schema) + assert str(expr) == "Expr(Int64(1) + Int64(2))" + + +def test_execute_logical_plan(ctx): + df = ctx.from_pydict({"a": [1, 2, 3]}) + plan = df.logical_plan() + df2 = ctx.execute_logical_plan(plan) + result = df2.collect() + assert result[0].column(0) == pa.array([1, 2, 3]) + + +def test_refresh_catalogs(ctx): + ctx.refresh_catalogs() + + +def test_remove_optimizer_rule(ctx): + assert ctx.remove_optimizer_rule("push_down_filter") is True + assert ctx.remove_optimizer_rule("nonexistent_rule") is False + + +def test_table_provider(ctx): + batch = pa.RecordBatch.from_pydict({"x": [10, 20, 30]}) + ctx.register_record_batches("provider_test", [[batch]]) + tbl = ctx.table_provider("provider_test") + assert tbl.schema == pa.schema([("x", pa.int64())]) + + +def test_table_provider_not_found(ctx): + with pytest.raises(KeyError): + ctx.table_provider("nonexistent_table") + + def test_read_json(ctx): path = pathlib.Path(__file__).parent.resolve() @@ -626,6 +802,8 @@ def test_read_csv_list(ctx): def test_read_csv_compressed(ctx, tmp_path): test_data_path = pathlib.Path("testing/data/csv/aggregate_test_100.csv") + expected = ctx.read_csv(test_data_path).collect() + # File compression type gzip_path = tmp_path / "aggregate_test_100.csv.gz" @@ -636,7 +814,13 @@ def test_read_csv_compressed(ctx, tmp_path): gzipped_file.writelines(csv_file) csv_df = ctx.read_csv(gzip_path, file_extension=".gz", file_compression_type="gz") - csv_df.select(column("c1")).show() + assert csv_df.collect() == expected + + csv_df = ctx.read_csv( + gzip_path, + options=CsvReadOptions(file_extension=".gz", file_compression_type="gz"), + ) + assert csv_df.collect() == expected def test_read_parquet(ctx): @@ -659,6 +843,68 @@ def test_read_avro(ctx): assert avro_df is not None +def test_read_arrow(ctx, tmp_path): + # Write an Arrow IPC file, then read it back + table = pa.table({"a": [1, 2, 3], "b": ["x", "y", "z"]}) + arrow_path = tmp_path / "test.arrow" + with pa.ipc.new_file(str(arrow_path), table.schema) as writer: + writer.write_table(table) + + df = ctx.read_arrow(str(arrow_path)) + result = df.collect() + assert result[0].column(0) == pa.array([1, 2, 3]) + assert result[0].column(1) == pa.array(["x", "y", "z"]) + + # Also verify pathlib.Path works + df = ctx.read_arrow(arrow_path) + result = df.collect() + assert result[0].column(0) == pa.array([1, 2, 3]) + + +def test_read_empty(ctx): + df = ctx.read_empty() + result = df.collect() + assert len(result) == 1 + assert result[0].num_columns == 0 + + df = ctx.empty_table() + result = df.collect() + assert len(result) == 1 + assert result[0].num_columns == 0 + + +def test_register_arrow(ctx, tmp_path): + # Write an Arrow IPC file, then register and query it + table = pa.table({"x": [10, 20, 30]}) + arrow_path = tmp_path / "test.arrow" + with pa.ipc.new_file(str(arrow_path), table.schema) as writer: + writer.write_table(table) + + ctx.register_arrow("arrow_tbl", str(arrow_path)) + result = ctx.sql("SELECT * FROM arrow_tbl").collect() + assert result[0].column(0) == pa.array([10, 20, 30]) + + # Also verify pathlib.Path works + ctx.register_arrow("arrow_tbl_path", arrow_path) + result = ctx.sql("SELECT * FROM arrow_tbl_path").collect() + assert result[0].column(0) == pa.array([10, 20, 30]) + + +def test_register_batch(ctx): + batch = pa.RecordBatch.from_pydict({"a": [1, 2, 3], "b": [4, 5, 6]}) + ctx.register_batch("batch_tbl", batch) + result = ctx.sql("SELECT * FROM batch_tbl").collect() + assert result[0].column(0) == pa.array([1, 2, 3]) + assert result[0].column(1) == pa.array([4, 5, 6]) + + +def test_register_batch_empty(ctx): + batch = pa.RecordBatch.from_pydict({"a": pa.array([], type=pa.int64())}) + ctx.register_batch("empty_batch_tbl", batch) + result = ctx.sql("SELECT * FROM empty_batch_tbl").collect() + assert result[0].num_rows == 0 + + def test_create_sql_options(): SQLOptions() @@ -710,3 +956,154 @@ def test_create_dataframe_with_global_ctx(batch): result = df.collect()[0].column(0) assert result == pa.array([4, 5, 6]) + + +def test_csv_read_options_builder_pattern(): + """Test CsvReadOptions builder pattern.""" + from datafusion import CsvReadOptions + + options = ( + CsvReadOptions() + .with_has_header(False) + .with_delimiter("|") + .with_quote("'") + .with_schema_infer_max_records(2000) + .with_truncated_rows(True) + .with_newlines_in_values(True) + .with_file_extension(".tsv") + ) + assert options.has_header is False + assert options.delimiter == "|" + assert options.quote == "'" + assert options.schema_infer_max_records == 2000 + assert options.truncated_rows is True + assert options.newlines_in_values is True + assert options.file_extension == ".tsv" + + +def read_csv_with_options_inner( + tmp_path: pathlib.Path, + csv_content: str, + options: CsvReadOptions, + expected: pa.RecordBatch, + as_read: bool, + global_ctx: bool, +) -> None: + from datafusion import SessionContext + + # Create a test CSV file + group_dir = tmp_path / "group=a" + group_dir.mkdir(exist_ok=True) + + csv_path = group_dir / "test.csv" + csv_path.write_text(csv_content, newline="\n") + + ctx = SessionContext() + + if as_read: + if global_ctx: + from datafusion.io import read_csv + + df = read_csv(str(tmp_path), options=options) + else: + df = ctx.read_csv(str(tmp_path), options=options) + else: + ctx.register_csv("test_table", str(tmp_path), options=options) + df = ctx.sql("SELECT * FROM test_table") + df.show() + + # Verify the data + result = df.collect() + assert len(result) == 1 + assert result[0] == expected + + +@pytest.mark.parametrize( + ("as_read", "global_ctx"), + [ + (True, True), + (True, False), + (False, False), + ], +) +def test_read_csv_with_options(tmp_path, as_read, global_ctx): + """Test reading CSV with CsvReadOptions.""" + + csv_content = "Alice;30;|New York; NY|\nBob;25\n#Charlie;35;Paris\nPhil;75;Detroit' MI\nKarin;50;|Stockholm\nSweden|" # noqa: E501 + + # Some of the read options are difficult to test in combination + # such as schema and schema_infer_max_records so run multiple tests + # file_sort_order doesn't impact reading, but included here to ensure + # all options parse correctly + options = CsvReadOptions( + has_header=False, + delimiter=";", + quote="|", + terminator="\n", + escape="\\", + comment="#", + newlines_in_values=True, + schema_infer_max_records=1, + null_regex="[pP]+aris", + truncated_rows=True, + file_sort_order=[[column("column_1").sort(), column("column_2")], ["column_3"]], + ) + + expected = pa.RecordBatch.from_arrays( + [ + pa.array(["Alice", "Bob", "Phil", "Karin"]), + pa.array([30, 25, 75, 50]), + pa.array(["New York; NY", None, "Detroit' MI", "Stockholm\nSweden"]), + ], + names=["column_1", "column_2", "column_3"], + ) + + read_csv_with_options_inner( + tmp_path, csv_content, options, expected, as_read, global_ctx + ) + + schema = pa.schema( + [ + pa.field("name", pa.string(), nullable=False), + pa.field("age", pa.float32(), nullable=False), + pa.field("location", pa.string(), nullable=True), + ] + ) + options.with_schema(schema) + + expected = pa.RecordBatch.from_arrays( + [ + pa.array(["Alice", "Bob", "Phil", "Karin"]), + pa.array([30.0, 25.0, 75.0, 50.0]), + pa.array(["New York; NY", None, "Detroit' MI", "Stockholm\nSweden"]), + ], + schema=schema, + ) + + read_csv_with_options_inner( + tmp_path, csv_content, options, expected, as_read, global_ctx + ) + + csv_content = "name,age\nAlice,30\nBob,25\nCharlie,35\nDiego,40\nEmily,15" + + expected = pa.RecordBatch.from_arrays( + [ + pa.array(["Alice", "Bob", "Charlie", "Diego", "Emily"]), + pa.array([30, 25, 35, 40, 15]), + pa.array(["a", "a", "a", "a", "a"]), + ], + schema=pa.schema( + [ + pa.field("name", pa.string(), nullable=True), + pa.field("age", pa.int64(), nullable=True), + pa.field("group", pa.string(), nullable=False), + ] + ), + ) + options = CsvReadOptions( + table_partition_cols=[("group", pa.string())], + ) + + read_csv_with_options_inner( + tmp_path, csv_content, options, expected, as_read, global_ctx + ) diff --git a/python/tests/test_dataframe.py b/python/tests/test_dataframe.py index 30f9ab903..9e2f791ea 100644 --- a/python/tests/test_dataframe.py +++ b/python/tests/test_dataframe.py @@ -29,6 +29,7 @@ import pytest from datafusion import ( DataFrame, + ExplainFormat, InsertOp, ParquetColumnOptions, ParquetWriterOptions, @@ -91,6 +92,39 @@ def large_df(): return ctx.from_arrow(batch) +@pytest.fixture +def large_multi_batch_df(): + """Create a DataFrame with multiple record batches for testing stream behavior. + + This fixture creates 10 batches of 10,000 rows each (100,000 rows total), + ensuring the DataFrame spans multiple batches. This is essential for testing + that memory limits actually cause early stream termination rather than + truncating all collected data. + """ + ctx = SessionContext() + + # Create multiple batches, each with 10,000 rows + batches = [] + rows_per_batch = 10000 + num_batches = 10 + + for batch_idx in range(num_batches): + start_row = batch_idx * rows_per_batch + end_row = start_row + rows_per_batch + data = { + "a": list(range(start_row, end_row)), + "b": [f"s-{i}" for i in range(start_row, end_row)], + "c": [float(i + 0.1) for i in range(start_row, end_row)], + } + batch = pa.record_batch(data) + batches.append(batch) + + # Register as record batches to maintain multi-batch structure + # Using [batches] wraps list in another list as required by register_record_batches + ctx.register_record_batches("large_multi_batch_data", [batches]) + return ctx.table("large_multi_batch_data") + + @pytest.fixture def struct_df(): ctx = SessionContext() @@ -262,10 +296,17 @@ def test_drop_quoted_columns(): ctx = SessionContext() batch = pa.RecordBatch.from_arrays([pa.array([1, 2, 3])], names=["ID_For_Students"]) df = ctx.create_dataframe([[batch]]) - - # Both should work + # here we must quote to match the original column name assert df.drop('"ID_For_Students"').schema().names == [] - assert df.drop("ID_For_Students").schema().names == [] + + batch = pa.RecordBatch.from_arrays( + [pa.array([1, 2, 3]), pa.array([4, 5, 6])], names=["a", "b"] + ) + df = ctx.create_dataframe([[batch]]) + # with a lower case column, both 'a' and '"a"' work + assert df.drop("a").schema().names == ["b"] + df = ctx.create_dataframe([[batch]]) + assert df.drop('"a"').schema().names == ["b"] def test_select_mixed_expr_string(df): @@ -371,6 +412,16 @@ def test_show_empty(df, capsys): assert "DataFrame has no rows" in captured.out +def test_show_on_explain(ctx, capsys): + ctx.sql("explain select 1").show() + captured = capsys.readouterr() + assert "1 as Int64(1)" in captured.out + + ctx.sql("explain analyze select 1").show() + captured = capsys.readouterr() + assert "1 as Int64(1)" in captured.out + + def test_sort(df): df = df.sort(column("b").sort(ascending=False)) @@ -1438,7 +1489,7 @@ def get_header_style(self) -> str: def test_html_formatter_memory(df, clean_formatter_state): """Test the memory and row control parameters in DataFrameHtmlFormatter.""" - configure_formatter(max_memory_bytes=10, min_rows_display=1) + configure_formatter(max_memory_bytes=10, min_rows=1) html_output = df._repr_html_() # Count the number of table rows in the output @@ -1448,7 +1499,7 @@ def test_html_formatter_memory(df, clean_formatter_state): assert tr_count == 2 # 1 for header row, 1 for data row assert "data truncated" in html_output.lower() - configure_formatter(max_memory_bytes=10 * MB, min_rows_display=1) + configure_formatter(max_memory_bytes=10 * MB, min_rows=1) html_output = df._repr_html_() # With larger memory limit and min_rows=2, should display all rows tr_count = count_table_rows(html_output) @@ -1458,15 +1509,136 @@ def test_html_formatter_memory(df, clean_formatter_state): assert "data truncated" not in html_output.lower() -def test_html_formatter_repr_rows(df, clean_formatter_state): - configure_formatter(min_rows_display=2, repr_rows=2) +def test_html_formatter_memory_boundary_conditions(large_df, clean_formatter_state): + """Test memory limit behavior at boundary conditions with large dataset. + + This test validates that the formatter correctly handles edge cases when + the memory limit is reached with a large dataset (100,000 rows), ensuring + that min_rows constraint is properly respected while respecting memory limits. + Uses large_df to actually test memory limit behavior with realistic data sizes. + """ + + # Get the raw size of the data to test boundary conditions + # First, capture output with no limits + # NOTE: max_rows=200000 is set well above the dataset size (100k rows) to ensure + # we're testing memory limits, not row limits. Default max_rows=10 would + # truncate before memory limit is reached. + configure_formatter(max_memory_bytes=10 * MB, min_rows=1, max_rows=200000) + unrestricted_output = large_df._repr_html_() + unrestricted_rows = count_table_rows(unrestricted_output) + + # Test 1: Very small memory limit should still respect min_rows + # With large dataset, this should definitely hit memory limit before min_rows + configure_formatter(max_memory_bytes=10, min_rows=1) + html_output = large_df._repr_html_() + tr_count = count_table_rows(html_output) + assert tr_count >= 2 # At least header + 1 data row (minimum) + # Should show truncation since we limited memory so aggressively + assert "data truncated" in html_output.lower() + + # Test 2: Memory limit at default size (2MB) should truncate the large dataset + # Default max_rows would truncate at 10 rows, so we don't set it here to test + # that memory limit is respected even with default row limit + configure_formatter(max_memory_bytes=2 * MB, min_rows=1) + html_output = large_df._repr_html_() + tr_count = count_table_rows(html_output) + assert tr_count >= 2 # At least header + min_rows + # Should be truncated since full dataset is much larger than 2MB + assert tr_count < unrestricted_rows + + # Test 3: Very large memory limit should show much more data + # NOTE: max_rows=200000 is critical here - without it, default max_rows=10 + # would limit output to 10 rows even though we have 100MB of memory available + configure_formatter(max_memory_bytes=100 * MB, min_rows=1, max_rows=200000) + html_output = large_df._repr_html_() + tr_count = count_table_rows(html_output) + # Should show significantly more rows, possibly all + assert tr_count > 100 # Should show substantially more rows + + # Test 4: Min rows should override memory limit + # With tiny memory and larger min_rows, min_rows should win + configure_formatter(max_memory_bytes=10, min_rows=2) + html_output = large_df._repr_html_() + tr_count = count_table_rows(html_output) + assert tr_count >= 3 # At least header + 2 data rows (min_rows) + # Should show truncation message despite min_rows being satisfied + assert "data truncated" in html_output.lower() + + # Test 5: With reasonable memory and min_rows settings + # NOTE: max_rows=200000 ensures we test memory limit behavior, not row limit + configure_formatter(max_memory_bytes=2 * MB, min_rows=10, max_rows=200000) + html_output = large_df._repr_html_() + tr_count = count_table_rows(html_output) + assert tr_count >= 11 # header + at least 10 data rows (min_rows) + # Should be truncated due to memory limit + assert tr_count < unrestricted_rows + + +def test_html_formatter_stream_early_termination( + large_multi_batch_df, clean_formatter_state +): + """Test that memory limits cause early stream termination with multi-batch data. + + This test specifically validates that the formatter stops collecting data when + the memory limit is reached, rather than collecting all data and then truncating. + The large_multi_batch_df fixture creates 10 record batches, allowing us to verify + that not all batches are consumed when memory limit is hit. + + Key difference from test_html_formatter_memory_boundary_conditions: + - Uses multi-batch DataFrame to verify stream termination behavior + - Tests with memory limit exceeded by 2-3 batches but not 1 batch + - Verifies partial data + truncation message + respects min_rows + """ + + # Get baseline: how much data fits without memory limit + configure_formatter(max_memory_bytes=100 * MB, min_rows=1, max_rows=200000) + unrestricted_output = large_multi_batch_df._repr_html_() + unrestricted_rows = count_table_rows(unrestricted_output) + + # Test 1: Memory limit exceeded by ~2 batches (each batch ~10k rows) + # With 1 batch (~1-2MB), we should have space. With 2-3 batches, we exceed limit. + # Set limit to ~3MB to ensure we collect ~1 batch before hitting limit + configure_formatter(max_memory_bytes=3 * MB, min_rows=1, max_rows=200000) + html_output = large_multi_batch_df._repr_html_() + tr_count = count_table_rows(html_output) + + # Should show significant truncation (not all 100k rows) + assert tr_count < unrestricted_rows, "Should be truncated by memory limit" + assert tr_count >= 2, "Should respect min_rows" + assert "data truncated" in html_output.lower(), "Should indicate truncation" + + # Test 2: Very tight memory limit should still respect min_rows + # Even with tiny memory (10 bytes), should show at least min_rows + configure_formatter(max_memory_bytes=10, min_rows=5, max_rows=200000) + html_output = large_multi_batch_df._repr_html_() + tr_count = count_table_rows(html_output) + + assert tr_count >= 6, "Should show header + at least min_rows (5)" + assert "data truncated" in html_output.lower(), "Should indicate truncation" + + # Test 3: Memory limit should take precedence over max_rows in early termination + # With max_rows=100 but small memory limit, should terminate early due to memory + configure_formatter(max_memory_bytes=2 * MB, min_rows=1, max_rows=100) + html_output = large_multi_batch_df._repr_html_() + tr_count = count_table_rows(html_output) + + # Should be truncated by memory limit (showing more than max_rows would suggest + # but less than unrestricted) + assert tr_count >= 2, "Should respect min_rows" + assert tr_count < unrestricted_rows, "Should be truncated" + # Output should indicate why truncation occurred + assert "data truncated" in html_output.lower() + + +def test_html_formatter_max_rows(df, clean_formatter_state): + configure_formatter(min_rows=2, max_rows=2) html_output = df._repr_html_() tr_count = count_table_rows(html_output) # Table should have header row (1) + 2 data rows = 3 rows assert tr_count == 3 - configure_formatter(min_rows_display=2, repr_rows=3) + configure_formatter(min_rows=2, max_rows=3) html_output = df._repr_html_() tr_count = count_table_rows(html_output) @@ -1492,17 +1664,42 @@ def test_html_formatter_validation(): with pytest.raises(ValueError, match="max_memory_bytes must be a positive integer"): DataFrameHtmlFormatter(max_memory_bytes=-100) - with pytest.raises(ValueError, match="min_rows_display must be a positive integer"): - DataFrameHtmlFormatter(min_rows_display=0) + with pytest.raises(ValueError, match="min_rows must be a positive integer"): + DataFrameHtmlFormatter(min_rows=0) + + with pytest.raises(ValueError, match="min_rows must be a positive integer"): + DataFrameHtmlFormatter(min_rows=-5) - with pytest.raises(ValueError, match="min_rows_display must be a positive integer"): - DataFrameHtmlFormatter(min_rows_display=-5) + with pytest.raises(ValueError, match="max_rows must be a positive integer"): + DataFrameHtmlFormatter(max_rows=0) - with pytest.raises(ValueError, match="repr_rows must be a positive integer"): - DataFrameHtmlFormatter(repr_rows=0) + with pytest.raises(ValueError, match="max_rows must be a positive integer"): + DataFrameHtmlFormatter(max_rows=-10) - with pytest.raises(ValueError, match="repr_rows must be a positive integer"): - DataFrameHtmlFormatter(repr_rows=-10) + with pytest.raises( + ValueError, match="min_rows must be less than or equal to max_rows" + ): + DataFrameHtmlFormatter(min_rows=5, max_rows=4) + + +def test_repr_rows_backward_compatibility(clean_formatter_state): + """Test that repr_rows parameter still works as deprecated alias.""" + # Should work when not conflicting with max_rows + with pytest.warns(DeprecationWarning, match="repr_rows parameter is deprecated"): + formatter = DataFrameHtmlFormatter(repr_rows=15, min_rows=10) + assert formatter.max_rows == 15 + assert formatter.repr_rows == 15 + + # Should fail when conflicting with max_rows + with pytest.raises(ValueError, match="Cannot specify both repr_rows and max_rows"): + DataFrameHtmlFormatter(repr_rows=5, max_rows=10) + + # Setting repr_rows via property should warn + formatter2 = DataFrameHtmlFormatter() + with pytest.warns(DeprecationWarning, match="repr_rows is deprecated"): + formatter2.repr_rows = 7 + assert formatter2.max_rows == 7 + assert formatter2.repr_rows == 7 def test_configure_formatter(df, clean_formatter_state): @@ -1514,8 +1711,8 @@ def test_configure_formatter(df, clean_formatter_state): max_width = 500 max_height = 30 max_memory_bytes = 3 * MB - min_rows_display = 2 - repr_rows = 2 + min_rows = 2 + max_rows = 2 enable_cell_expansion = False show_truncation_message = False use_shared_styles = False @@ -1527,8 +1724,8 @@ def test_configure_formatter(df, clean_formatter_state): assert formatter_default.max_width != max_width assert formatter_default.max_height != max_height assert formatter_default.max_memory_bytes != max_memory_bytes - assert formatter_default.min_rows_display != min_rows_display - assert formatter_default.repr_rows != repr_rows + assert formatter_default.min_rows != min_rows + assert formatter_default.max_rows != max_rows assert formatter_default.enable_cell_expansion != enable_cell_expansion assert formatter_default.show_truncation_message != show_truncation_message assert formatter_default.use_shared_styles != use_shared_styles @@ -1539,8 +1736,8 @@ def test_configure_formatter(df, clean_formatter_state): max_width=max_width, max_height=max_height, max_memory_bytes=max_memory_bytes, - min_rows_display=min_rows_display, - repr_rows=repr_rows, + min_rows=min_rows, + max_rows=max_rows, enable_cell_expansion=enable_cell_expansion, show_truncation_message=show_truncation_message, use_shared_styles=use_shared_styles, @@ -1550,8 +1747,8 @@ def test_configure_formatter(df, clean_formatter_state): assert formatter_custom.max_width == max_width assert formatter_custom.max_height == max_height assert formatter_custom.max_memory_bytes == max_memory_bytes - assert formatter_custom.min_rows_display == min_rows_display - assert formatter_custom.repr_rows == repr_rows + assert formatter_custom.min_rows == min_rows + assert formatter_custom.max_rows == max_rows assert formatter_custom.enable_cell_expansion == enable_cell_expansion assert formatter_custom.show_truncation_message == show_truncation_message assert formatter_custom.use_shared_styles == use_shared_styles @@ -1666,7 +1863,6 @@ def test_execution_plan(aggregate_df): # indent plan will be different for everyone due to absolute path # to filename, so we just check for some expected content assert "AggregateExec:" in indent - assert "CoalesceBatchesExec:" in indent assert "RepartitionExec:" in indent assert "DataSourceExec:" in indent assert "file_type=csv" in indent @@ -2435,9 +2631,7 @@ def test_write_parquet_with_options_writer_version( @pytest.mark.parametrize("writer_version", ["1.2.3", "custom-version", "0"]) def test_write_parquet_with_options_wrong_writer_version(df, tmp_path, writer_version): """Test that invalid writer versions in Parquet throw an exception.""" - with pytest.raises( - Exception, match="Unknown or unsupported parquet writer version" - ): + with pytest.raises(Exception, match="Invalid parquet writer version"): df.write_parquet_with_options( tmp_path, ParquetWriterOptions(writer_version=writer_version) ) @@ -2614,7 +2808,7 @@ def test_write_parquet_with_options_encoding(tmp_path, encoding, data_types, res def test_write_parquet_with_options_unsupported_encoding(df, tmp_path, encoding): """Test that unsupported Parquet encodings do not work.""" # BaseException is used since this throws a Rust panic: https://github.com/PyO3/pyo3/issues/3519 - with pytest.raises(BaseException, match="Encoding .*? is not supported"): + with pytest.raises(BaseException, match=r"Encoding .*? is not supported"): df.write_parquet_with_options(tmp_path, ParquetWriterOptions(encoding=encoding)) @@ -2958,6 +3152,47 @@ def test_html_formatter_manual_format_html(clean_formatter_state): assert "