# Copyright 2019 The Feast Authors

#

# Licensed 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

#

# https://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.

import os

import warnings

from collections import Counter, OrderedDict, defaultdict

from datetime import datetime, timedelta

from pathlib import Path

from typing import Any, Dict, Iterable, List, Optional, Tuple, Union

import pandas as pd

from colorama import Fore, Style

from tqdm import tqdm

from feast import utils

from feast.entity import Entity

from feast.errors import (

EntityNotFoundException,

FeatureNameCollisionError,

FeatureViewNotFoundException,

)

from feast.feature_service import FeatureService

from feast.feature_table import FeatureTable

from feast.feature_view import FeatureView

from feast.inference import (

update_data_sources_with_inferred_event_timestamp_col,

update_entities_with_inferred_types_from_feature_views,

)

from feast.infra.provider import Provider, RetrievalJob, get_provider

from feast.online_response import OnlineResponse, _infer_online_entity_rows

from feast.protos.feast.serving.ServingService_pb2 import (

GetOnlineFeaturesRequestV2,

GetOnlineFeaturesResponse,

)

from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto

from feast.registry import Registry

from feast.repo_config import RepoConfig, load_repo_config

from feast.usage import log_exceptions, log_exceptions_and_usage

from feast.version import get_version

warnings.simplefilter("once", DeprecationWarning)

class FeatureStore:

"""

A FeatureStore object is used to define, create, and retrieve features.

Args:

repo_path (optional): Path to a `feature_store.yaml` used to configure the

feature store.

config (optional): Configuration object used to configure the feature store.

"""

config: RepoConfig

repo_path: Path

_registry: Registry

@log_exceptions

def __init__(

self, repo_path: Optional[str] = None, config: Optional[RepoConfig] = None,

):

"""

Creates a FeatureStore object.

Raises:

ValueError: If both or neither of repo_path and config are specified.

"""

if repo_path is not None and config is not None:

raise ValueError("You cannot specify both repo_path and config.")

if config is not None:

self.repo_path = Path(os.getcwd())

self.config = config

elif repo_path is not None:

self.repo_path = Path(repo_path)

self.config = load_repo_config(Path(repo_path))

else:

raise ValueError("Please specify one of repo_path or config.")

registry_config = self.config.get_registry_config()

self._registry = Registry(

registry_path=registry_config.path,

repo_path=self.repo_path,

cache_ttl=timedelta(seconds=registry_config.cache_ttl_seconds),

)

@log_exceptions

def version(self) -> str:

"""Returns the version of the current Feast SDK/CLI."""

return get_version()

@property

def registry(self) -> Registry:

"""Gets the registry of this feature store."""

return self._registry

@property

def project(self) -> str:

"""Gets the project of this feature store."""

return self.config.project

def _get_provider(self) -> Provider:

# TODO: Bake self.repo_path into self.config so that we dont only have one interface to paths

return get_provider(self.config, self.repo_path)

@log_exceptions_and_usage

def refresh_registry(self):

"""Fetches and caches a copy of the feature registry in memory.

Explicitly calling this method allows for direct control of the state of the registry cache. Every time this

method is called the complete registry state will be retrieved from the remote registry store backend

(e.g., GCS, S3), and the cache timer will be reset. If refresh_registry() is run before get_online_features()

is called, then get_online_feature() will use the cached registry instead of retrieving (and caching) the

registry itself.

Additionally, the TTL for the registry cache can be set to infinity (by setting it to 0), which means that

refresh_registry() will become the only way to update the cached registry. If the TTL is set to a value

greater than 0, then once the cache becomes stale (more time than the TTL has passed), a new cache will be

downloaded synchronously, which may increase latencies if the triggering method is get_online_features()

"""

registry_config = self.config.get_registry_config()

self._registry = Registry(

registry_path=registry_config.path,

repo_path=self.repo_path,

cache_ttl=timedelta(seconds=registry_config.cache_ttl_seconds),

)

self._registry.refresh()

@log_exceptions_and_usage

def list_entities(self, allow_cache: bool = False) -> List[Entity]:

"""

Retrieves the list of entities from the registry.

Args:

allow_cache: Whether to allow returning entities from a cached registry.

Returns:

A list of entities.

"""

return self._registry.list_entities(self.project, allow_cache=allow_cache)

@log_exceptions_and_usage

def list_feature_services(self) -> List[FeatureService]:

"""

Retrieves the list of feature services from the registry.

Returns:

A list of feature services.

"""

return self._registry.list_feature_services(self.project)

@log_exceptions_and_usage

def list_feature_views(self) -> List[FeatureView]:

"""

Retrieves the list of feature views from the registry.

Returns:

A list of feature views.

"""

return self._registry.list_feature_views(self.project)

@log_exceptions_and_usage

def get_entity(self, name: str) -> Entity:

"""

Retrieves an entity.

Args:

name: Name of entity.

Returns:

The specified entity.

Raises:

EntityNotFoundException: The entity could not be found.

"""

return self._registry.get_entity(name, self.project)

@log_exceptions_and_usage

def get_feature_service(self, name: str) -> FeatureService:

"""

Retrieves a feature service.

Args:

name: Name of feature service.

Returns:

The specified feature service.

Raises:

FeatureServiceNotFoundException: The feature service could not be found.

"""

return self._registry.get_feature_service(name, self.project)

@log_exceptions_and_usage

def get_feature_view(self, name: str) -> FeatureView:

"""

Retrieves a feature view.

Args:

name: Name of feature view.

Returns:

The specified feature view.

Raises:

FeatureViewNotFoundException: The feature view could not be found.

"""

return self._registry.get_feature_view(name, self.project)

@log_exceptions_and_usage

def delete_feature_view(self, name: str):

"""

Deletes a feature view.

Args:

name: Name of feature view.

Raises:

FeatureViewNotFoundException: The feature view could not be found.

"""

return self._registry.delete_feature_view(name, self.project)

@log_exceptions_and_usage

def delete_feature_service(self, name: str):

"""

Deletes a feature service.

Args:

name: Name of feature service.

Raises:

FeatureServiceNotFoundException: The feature view could not be found.

"""

return self._registry.delete_feature_service(name, self.project)

def _get_features(

self,

features: Optional[Union[List[str], FeatureService]],

feature_refs: Optional[List[str]],

) -> List[str]:

_features = features or feature_refs

if not _features:

raise ValueError("No features specified for retrieval")

_feature_refs: List[str]

if isinstance(_features, FeatureService):

# Get the latest value of the feature service, in case the object passed in has been updated underneath us.

_feature_refs = _get_feature_refs_from_feature_services(

self.get_feature_service(_features.name)

)

else:

_feature_refs = _features

return _feature_refs

@log_exceptions_and_usage

def apply(

self,

objects: Union[

Entity,

FeatureView,

FeatureService,

List[Union[FeatureView, Entity, FeatureService]],

],

commit: bool = True,

):

"""Register objects to metadata store and update related infrastructure.

The apply method registers one or more definitions (e.g., Entity, FeatureView) and registers or updates these

objects in the Feast registry. Once the registry has been updated, the apply method will update related

infrastructure (e.g., create tables in an online store) in order to reflect these new definitions. All

operations are idempotent, meaning they can safely be rerun.

Args:

objects: A single object, or a list of objects that should be registered with the Feature Store.

commit: whether to commit changes to the registry

Raises:

ValueError: The 'objects' parameter could not be parsed properly.

Examples:

Register an Entity and a FeatureView.

>>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig

>>> from datetime import timedelta

>>> fs = FeatureStore(config=RepoConfig(registry="feature_repo/data/registry.db", project="feature_repo", provider="local"))

>>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id")

>>> driver_hourly_stats = FileSource(

... path="feature_repo/data/driver_stats.parquet",

... event_timestamp_column="event_timestamp",

... created_timestamp_column="created",

... )

>>> driver_hourly_stats_view = FeatureView(

... name="driver_hourly_stats",

... entities=["driver_id"],

... ttl=timedelta(seconds=86400 * 1),

... batch_source=driver_hourly_stats,

... )

>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view

"""

# TODO: Add locking

if not isinstance(objects, Iterable):

objects = [objects]

assert isinstance(objects, list)

views_to_update = [ob for ob in objects if isinstance(ob, FeatureView)]

_validate_feature_views(views_to_update)

entities_to_update = [ob for ob in objects if isinstance(ob, Entity)]

services_to_update = [ob for ob in objects if isinstance(ob, FeatureService)]

# Make inferences

update_entities_with_inferred_types_from_feature_views(

entities_to_update, views_to_update, self.config

)

update_data_sources_with_inferred_event_timestamp_col(

[view.batch_source for view in views_to_update], self.config

)

for view in views_to_update:

view.infer_features_from_batch_source(self.config)

if len(views_to_update) + len(entities_to_update) + len(

services_to_update

) != len(objects):

raise ValueError("Unknown object type provided as part of apply() call")

for view in views_to_update:

self._registry.apply_feature_view(view, project=self.project, commit=False)

for ent in entities_to_update:

self._registry.apply_entity(ent, project=self.project, commit=False)

for feature_service in services_to_update:

self._registry.apply_feature_service(feature_service, project=self.project)

self._get_provider().update_infra(

project=self.project,

tables_to_delete=[],

tables_to_keep=views_to_update,

entities_to_delete=[],

entities_to_keep=entities_to_update,

partial=True,

)

if commit:

self._registry.commit()

@log_exceptions_and_usage

def teardown(self):

"""Tears down all local and cloud resources for the feature store."""

tables: List[Union[FeatureView, FeatureTable]] = []

feature_views = self.list_feature_views()

feature_tables = self._registry.list_feature_tables(self.project)

tables.extend(feature_views)

tables.extend(feature_tables)

entities = self.list_entities()

self._get_provider().teardown_infra(self.project, tables, entities)

self._registry.teardown()

@log_exceptions_and_usage

def get_historical_features(

self,

entity_df: Union[pd.DataFrame, str],

features: Optional[Union[List[str], FeatureService]] = None,

feature_refs: Optional[List[str]] = None,

full_feature_names: bool = False,

) -> RetrievalJob:

"""Enrich an entity dataframe with historical feature values for either training or batch scoring.

This method joins historical feature data from one or more feature views to an entity dataframe by using a time

travel join.

Each feature view is joined to the entity dataframe using all entities configured for the respective feature

view. All configured entities must be available in the entity dataframe. Therefore, the entity dataframe must

contain all entities found in all feature views, but the individual feature views can have different entities.

Time travel is based on the configured TTL for each feature view. A shorter TTL will limit the

amount of scanning that will be done in order to find feature data for a specific entity key. Setting a short

TTL may result in null values being returned.

Args:

entity_df (Union[pd.DataFrame, str]): An entity dataframe is a collection of rows containing all entity

columns (e.g., customer_id, driver_id) on which features need to be joined, as well as a event_timestamp

column used to ensure point-in-time correctness. Either a Pandas DataFrame can be provided or a string

SQL query. The query must be of a format supported by the configured offline store (e.g., BigQuery)

features: A list of features, that should be retrieved from the offline store.

Either a list of string feature references can be provided or a FeatureService object.

Feature references are of the format "feature_view:feature", e.g., "customer_fv:daily_transactions".

full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names,

changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to

"customer_fv__daily_transactions"). By default, this value is set to False.

Returns:

RetrievalJob which can be used to materialize the results.

Raises:

ValueError: Both or neither of features and feature_refs are specified.

Examples:

Retrieve historical features from a local offline store.

>>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig

>>> from datetime import timedelta

>>> import pandas as pd

>>> fs = FeatureStore(config=RepoConfig(registry="feature_repo/data/registry.db", project="feature_repo", provider="local"))

>>> # Before retrieving historical features, we must register the appropriate entity and featureview.

>>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id")

>>> driver_hourly_stats = FileSource(

... path="feature_repo/data/driver_stats.parquet",

... event_timestamp_column="event_timestamp",

... created_timestamp_column="created",

... )

>>> driver_hourly_stats_view = FeatureView(

... name="driver_hourly_stats",

... entities=["driver_id"],

... ttl=timedelta(seconds=86400 * 1),

... features=[

... Feature(name="conv_rate", dtype=ValueType.FLOAT),

... Feature(name="acc_rate", dtype=ValueType.FLOAT),

... Feature(name="avg_daily_trips", dtype=ValueType.INT64),

... ],

... batch_source=driver_hourly_stats,

... )

>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view

>>> entity_df = pd.DataFrame.from_dict(

... {

... "driver_id": [1001, 1002],

... "event_timestamp": [

... datetime(2021, 4, 12, 10, 59, 42),

... datetime(2021, 4, 12, 8, 12, 10),

... ],

... }

... )

>>> retrieval_job = fs.get_historical_features(

... entity_df=entity_df,

... features=[

... "driver_hourly_stats:conv_rate",

... "driver_hourly_stats:acc_rate",

... "driver_hourly_stats:avg_daily_trips",

... ],

... )

>>> feature_data = retrieval_job.to_df()

"""

if (features is not None and feature_refs is not None) or (

features is None and feature_refs is None

):

raise ValueError(

"You must specify exactly one of features and feature_refs."

)

if feature_refs:

warnings.warn(

(

"The argument 'feature_refs' is being deprecated. Please use 'features' "

"instead. Feast 0.13 and onwards will not support the argument 'feature_refs'."

),

DeprecationWarning,

)

_feature_refs = self._get_features(features, feature_refs)

all_feature_views = self._registry.list_feature_views(project=self.project)

feature_views = list(

view for view, _ in _group_feature_refs(_feature_refs, all_feature_views)

)

_validate_feature_refs(_feature_refs, full_feature_names)

provider = self._get_provider()

job = provider.get_historical_features(

self.config,

feature_views,

_feature_refs,

entity_df,

self._registry,

self.project,

full_feature_names,

)

return job

@log_exceptions_and_usage

def materialize_incremental(

self, end_date: datetime, feature_views: Optional[List[str]] = None,

) -> None:

"""

Materialize incremental new data from the offline store into the online store.

This method loads incremental new feature data up to the specified end time from either

the specified feature views, or all feature views if none are specified,

into the online store where it is available for online serving. The start time of

the interval materialized is either the most recent end time of a prior materialization or

(now - ttl) if no such prior materialization exists.

Args:

end_date (datetime): End date for time range of data to materialize into the online store

feature_views (List[str]): Optional list of feature view names. If selected, will only run

materialization for the specified feature views.

Raises:

Exception: A feature view being materialized does not have a TTL set.

Examples:

Materialize all features into the online store up to 5 minutes ago.

>>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig

>>> from datetime import timedelta

>>> fs = FeatureStore(config=RepoConfig(registry="feature_repo/data/registry.db", project="feature_repo", provider="local"))

>>> # Before materializing, we must register the appropriate entity and featureview.

>>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)

>>> driver_hourly_stats = FileSource(

... path="feature_repo/data/driver_stats.parquet",

... event_timestamp_column="event_timestamp",

... created_timestamp_column="created",

... )

>>> driver_hourly_stats_view = FeatureView(

... name="driver_hourly_stats",

... entities=["driver_id"],

... ttl=timedelta(seconds=86400 * 1),

... features=[

... Feature(name="conv_rate", dtype=ValueType.FLOAT),

... Feature(name="acc_rate", dtype=ValueType.FLOAT),

... Feature(name="avg_daily_trips", dtype=ValueType.INT64),

... ],

... batch_source=driver_hourly_stats,

... )

>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view

>>> fs.materialize_incremental(end_date=datetime.utcnow() - timedelta(minutes=5))

Materializing...

<BLANKLINE>

...

"""

feature_views_to_materialize = []

if feature_views is None:

feature_views_to_materialize = self._registry.list_feature_views(

self.project

)

else:

for name in feature_views:

feature_view = self._registry.get_feature_view(name, self.project)

feature_views_to_materialize.append(feature_view)

_print_materialization_log(

None,

end_date,

len(feature_views_to_materialize),

self.config.online_store.type,

)

# TODO paging large loads

for feature_view in feature_views_to_materialize:

start_date = feature_view.most_recent_end_time

if start_date is None:

if feature_view.ttl is None:

raise Exception(

f"No start time found for feature view {feature_view.name}. materialize_incremental() requires"

f" either a ttl to be set or for materialize() to have been run at least once."

)

start_date = datetime.utcnow() - feature_view.ttl

provider = self._get_provider()

print(

f"{Style.BRIGHT + Fore.GREEN}{feature_view.name}{Style.RESET_ALL}"

f" from {Style.BRIGHT + Fore.GREEN}{start_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}"

f" to {Style.BRIGHT + Fore.GREEN}{end_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}:"

)

def tqdm_builder(length):

return tqdm(total=length, ncols=100)

start_date = utils.make_tzaware(start_date)

end_date = utils.make_tzaware(end_date)

provider.materialize_single_feature_view(

config=self.config,

feature_view=feature_view,

start_date=start_date,

end_date=end_date,

registry=self._registry,

project=self.project,

tqdm_builder=tqdm_builder,

)

self._registry.apply_materialization(

feature_view, self.project, start_date, end_date

)

@log_exceptions_and_usage

def materialize(

self,

start_date: datetime,

end_date: datetime,

feature_views: Optional[List[str]] = None,

) -> None:

"""

Materialize data from the offline store into the online store.

This method loads feature data in the specified interval from either

the specified feature views, or all feature views if none are specified,

into the online store where it is available for online serving.

Args:

start_date (datetime): Start date for time range of data to materialize into the online store

end_date (datetime): End date for time range of data to materialize into the online store

feature_views (List[str]): Optional list of feature view names. If selected, will only run

materialization for the specified feature views.

Examples:

Materialize all features into the online store over the interval

from 3 hours ago to 10 minutes ago.

>>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig

>>> from datetime import timedelta

>>> fs = FeatureStore(config=RepoConfig(registry="feature_repo/data/registry.db", project="feature_repo", provider="local"))

>>> # Before materializing, we must register the appropriate entity and featureview.

>>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)

>>> driver_hourly_stats = FileSource(

... path="feature_repo/data/driver_stats.parquet",

... event_timestamp_column="event_timestamp",

... created_timestamp_column="created",

... )

>>> driver_hourly_stats_view = FeatureView(

... name="driver_hourly_stats",

... entities=["driver_id"],

... ttl=timedelta(seconds=86400 * 1),

... features=[

... Feature(name="conv_rate", dtype=ValueType.FLOAT),

... Feature(name="acc_rate", dtype=ValueType.FLOAT),

... Feature(name="avg_daily_trips", dtype=ValueType.INT64),

... ],

... batch_source=driver_hourly_stats,

... )

>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view

>>> fs.materialize(

... start_date=datetime.utcnow() - timedelta(hours=3), end_date=datetime.utcnow() - timedelta(minutes=10)

... )

Materializing...

<BLANKLINE>

...

"""

if utils.make_tzaware(start_date) > utils.make_tzaware(end_date):

raise ValueError(

f"The given start_date {start_date} is greater than the given end_date {end_date}."

)

feature_views_to_materialize = []

if feature_views is None:

feature_views_to_materialize = self._registry.list_feature_views(

self.project

)

else:

for name in feature_views:

feature_view = self._registry.get_feature_view(name, self.project)

feature_views_to_materialize.append(feature_view)

_print_materialization_log(

start_date,

end_date,

len(feature_views_to_materialize),

self.config.online_store.type,

)

# TODO paging large loads

for feature_view in feature_views_to_materialize:

provider = self._get_provider()

print(f"{Style.BRIGHT + Fore.GREEN}{feature_view.name}{Style.RESET_ALL}:")

def tqdm_builder(length):

return tqdm(total=length, ncols=100)

start_date = utils.make_tzaware(start_date)

end_date = utils.make_tzaware(end_date)

provider.materialize_single_feature_view(

config=self.config,

feature_view=feature_view,

start_date=start_date,

end_date=end_date,

registry=self._registry,

project=self.project,

tqdm_builder=tqdm_builder,

)

self._registry.apply_materialization(

feature_view, self.project, start_date, end_date

)

@log_exceptions_and_usage

def get_online_features(

self,

features: Union[List[str], FeatureService],

entity_rows: List[Dict[str, Any]],

feature_refs: Optional[List[str]] = None,

full_feature_names: bool = False,

) -> OnlineResponse:

"""

Retrieves the latest online feature data.

Note: This method will download the full feature registry the first time it is run. If you are using a

remote registry like GCS or S3 then that may take a few seconds. The registry remains cached up to a TTL

duration (which can be set to infinity). If the cached registry is stale (more time than the TTL has

passed), then a new registry will be downloaded synchronously by this method. This download may

introduce latency to online feature retrieval. In order to avoid synchronous downloads, please call

refresh_registry() prior to the TTL being reached. Remember it is possible to set the cache TTL to

infinity (cache forever).

Args:

features: List of feature references that will be returned for each entity.

Each feature reference should have the following format:

"feature_table:feature" where "feature_table" & "feature" refer to

the feature and feature table names respectively.

Only the feature name is required.

entity_rows: A list of dictionaries where each key-value is an entity-name, entity-value pair.

Returns:

OnlineResponse containing the feature data in records.

Raises:

Exception: No entity with the specified name exists.

Examples:

Materialize all features into the online store over the interval

from 3 hours ago to 10 minutes ago, and then retrieve these online features.

>>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig

>>> from datetime import timedelta

>>> import pandas as pd

>>> fs = FeatureStore(config=RepoConfig(registry="feature_repo/data/registry.db", project="feature_repo", provider="local"))

>>> # Before getting online features, we must register the appropriate entity and featureview and then materialize the features.

>>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)

>>> driver_hourly_stats = FileSource(

... path="feature_repo/data/driver_stats.parquet",

... event_timestamp_column="event_timestamp",

... created_timestamp_column="created",

... )

>>> driver_hourly_stats_view = FeatureView(

... name="driver_hourly_stats",

... entities=["driver_id"],

... ttl=timedelta(seconds=86400 * 1),

... features=[

... Feature(name="conv_rate", dtype=ValueType.FLOAT),

... Feature(name="acc_rate", dtype=ValueType.FLOAT),

... Feature(name="avg_daily_trips", dtype=ValueType.INT64),

... ],

... batch_source=driver_hourly_stats,

... )

>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view

>>> fs.materialize(

... start_date=datetime.utcnow() - timedelta(hours=3), end_date=datetime.utcnow() - timedelta(minutes=10)

... )

Materializing...

<BLANKLINE>

...

>>> online_response = fs.get_online_features(

... features=[

... "driver_hourly_stats:conv_rate",

... "driver_hourly_stats:acc_rate",

... "driver_hourly_stats:avg_daily_trips",

... ],

... entity_rows=[{"driver_id": 1001}, {"driver_id": 1002}, {"driver_id": 1003}, {"driver_id": 1004}],

... )

>>> online_response_dict = online_response.to_dict()

"""

_feature_refs = self._get_features(features, feature_refs)

provider = self._get_provider()

entities = self.list_entities(allow_cache=True)

entity_name_to_join_key_map = {}

for entity in entities:

entity_name_to_join_key_map[entity.name] = entity.join_key

join_key_rows = []

for row in entity_rows:

join_key_row = {}

for entity_name, entity_value in row.items():

try:

join_key = entity_name_to_join_key_map[entity_name]

except KeyError:

raise EntityNotFoundException(entity_name, self.project)

join_key_row[join_key] = entity_value

join_key_rows.append(join_key_row)

entity_row_proto_list = _infer_online_entity_rows(join_key_rows)

union_of_entity_keys = []

result_rows: List[GetOnlineFeaturesResponse.FieldValues] = []

for entity_row_proto in entity_row_proto_list:

union_of_entity_keys.append(_entity_row_to_key(entity_row_proto))

result_rows.append(_entity_row_to_field_values(entity_row_proto))

all_feature_views = self._registry.list_feature_views(

project=self.project, allow_cache=True

)

_validate_feature_refs(_feature_refs, full_feature_names)

grouped_refs = _group_feature_refs(_feature_refs, all_feature_views)

for table, requested_features in grouped_refs:

entity_keys = _get_table_entity_keys(

table, union_of_entity_keys, entity_name_to_join_key_map

)

read_rows = provider.online_read(

config=self.config,

table=table,

entity_keys=entity_keys,

requested_features=requested_features,

)

for row_idx, read_row in enumerate(read_rows):

row_ts, feature_data = read_row

result_row = result_rows[row_idx]

if feature_data is None:

for feature_name in requested_features:

feature_ref = (

f"{table.name}__{feature_name}"

if full_feature_names

else feature_name

)

result_row.statuses[

feature_ref

] = GetOnlineFeaturesResponse.FieldStatus.NOT_FOUND

else:

for feature_name in feature_data:

feature_ref = (

f"{table.name}__{feature_name}"

if full_feature_names

else feature_name

)

if feature_name in requested_features:

result_row.fields[feature_ref].CopyFrom(

feature_data[feature_name]

)

result_row.statuses[

feature_ref

] = GetOnlineFeaturesResponse.FieldStatus.PRESENT

return OnlineResponse(GetOnlineFeaturesResponse(field_values=result_rows))

def _entity_row_to_key(row: GetOnlineFeaturesRequestV2.EntityRow) -> EntityKeyProto:

names, values = zip(*row.fields.items())

return EntityKeyProto(join_keys=names, entity_values=values)

def _entity_row_to_field_values(

row: GetOnlineFeaturesRequestV2.EntityRow,

) -> GetOnlineFeaturesResponse.FieldValues:

result = GetOnlineFeaturesResponse.FieldValues()

for k in row.fields:

result.fields[k].CopyFrom(row.fields[k])

result.statuses[k] = GetOnlineFeaturesResponse.FieldStatus.PRESENT

return result

def _validate_feature_refs(feature_refs: List[str], full_feature_names: bool = False):

collided_feature_refs = []

if full_feature_names:

collided_feature_refs = [

ref for ref, occurrences in Counter(feature_refs).items() if occurrences > 1

]

else:

feature_names = [ref.split(":")[1] for ref in feature_refs]

collided_feature_names = [

ref

for ref, occurrences in Counter(feature_names).items()

if occurrences > 1

]

for feature_name in collided_feature_names:

collided_feature_refs.extend(

[ref for ref in feature_refs if ref.endswith(":" + feature_name)]

)

if len(collided_feature_refs) > 0:

raise FeatureNameCollisionError(collided_feature_refs, full_feature_names)

def _group_feature_refs(

features: Union[List[str], FeatureService], all_feature_views: List[FeatureView]

) -> List[Tuple[FeatureView, List[str]]]:

""" Get list of feature views and corresponding feature names based on feature references"""

# view name to view proto

view_index = {view.name: view for view in all_feature_views}

# view name to feature names

views_features = defaultdict(list)

if isinstance(features, list) and isinstance(features[0], str):

for ref in features:

view_name, feat_name = ref.split(":")

if view_name not in view_index:

raise FeatureViewNotFoundException(view_name)

views_features[view_name].append(feat_name)

elif isinstance(features, FeatureService):

for feature_projection in features.features:

projected_features = feature_projection.features

views_features[feature_projection.name].extend(

[f.name for f in projected_features]

)

result = []

for view_name, feature_names in views_features.items():

result.append((view_index[view_name], feature_names))

return result

def _get_feature_refs_from_feature_services(

feature_service: FeatureService,

) -> List[str]:

feature_refs = []

for projection in feature_service.features:

feature_refs.extend(

[f"{projection.name}:{f.name}" for f in projection.features]

)

return feature_refs

def _get_table_entity_keys(

table: FeatureView, entity_keys: List[EntityKeyProto], join_key_map: Dict[str, str],

) -> List[EntityKeyProto]:

table_join_keys = [join_key_map[entity_name] for entity_name in table.entities]

required_entities = OrderedDict.fromkeys(sorted(table_join_keys))

entity_key_protos = []

for entity_key in entity_keys:

required_entities_to_values = required_entities.copy()

for i in range(len(entity_key.join_keys)):

entity_name = entity_key.join_keys[i]

entity_value = entity_key.entity_values[i]

if entity_name in required_entities_to_values:

if required_entities_to_values[entity_name] is not None:

raise ValueError(

f"Duplicate entity keys detected. Table {table.name} expects {table_join_keys}. The entity "

f"{entity_name} was provided at least twice"

)

required_entities_to_values[entity_name] = entity_value

entity_names = []

entity_values = []

for entity_name, entity_value in required_entities_to_values.items():

if entity_value is None:

raise ValueError(

f"Table {table.name} expects entity field {table_join_keys}. No entity value was found for "

f"{entity_name}"

)

entity_names.append(entity_name)

entity_values.append(entity_value)

entity_key_protos.append(

EntityKeyProto(join_keys=entity_names, entity_values=entity_values)

)

return entity_key_protos

def _print_materialization_log(

start_date, end_date, num_feature_views: int, online_store: str

):

if start_date:

print(

f"Materializing {Style.BRIGHT + Fore.GREEN}{num_feature_views}{Style.RESET_ALL} feature views"

f" from {Style.BRIGHT + Fore.GREEN}{start_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}"

f" to {Style.BRIGHT + Fore.GREEN}{end_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}"

f" into the {Style.BRIGHT + Fore.GREEN}{online_store}{Style.RESET_ALL} online store.\n"

)

else:

print(

f"Materializing {Style.BRIGHT + Fore.GREEN}{num_feature_views}{Style.RESET_ALL} feature views"

f" to {Style.BRIGHT + Fore.GREEN}{end_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}"

f" into the {Style.BRIGHT + Fore.GREEN}{online_store}{Style.RESET_ALL} online store.\n"

)

def _validate_feature_views(feature_views: List[FeatureView]):

""" Verify feature views have unique names"""

name_to_fv_dict = {}

for fv in feature_views:

if fv.name in name_to_fv_dict:

raise ValueError(

f"More than one feature view with name {fv.name} found. Please ensure that all feature view names are unique. It may be necessary to ignore certain files in your feature repository by using a .feastignore file."

)

else:

name_to_fv_dict[fv.name] = fv