The Feldera Python SDK is meant to provide an easy and convenient way of interacting with Feldera.

Please submit any feature request / bug reports to: https://github.com/feldera/feldera

pip install git+https://github.com/feldera/feldera#subdirectory=python

Similarly, to install from a specific branch:

$ pip install git+https://github.com/feldera/feldera@{BRANCH_NAME}#subdirectory=python

Replace {BRANCH_NAME} with the name of the branch you want to install from.

• :class:`feldera.FelderaClient` or :class:`.Client`

  • This is the actual HTTP client used to make requests to your Feldera instance.

  • creating an instance of :class:`.Client` is usually the first thing you will do while working with Feldera.

  • Example:

    from feldera import FelderaClient
    
    client = FelderaClient("https://try.feldera.com", api_key="YOUR_API_KEY")

    • The API key may not be required if you are running Feldera locally.

• :class:`.SQLContext`

  • This represents the current context of your SQL program, data sources and sinks. In Feldera terminology, this represents both a Program and a Pipeline.

  • Example:

    from feldera import SQLContext
    
    sql = SQLContext("getting_started", client).get_or_create()

    • The first parameter is the name of this SQL context. By default, this is the name used in both Feldera Program and Pipeline.
    • The second parameter here is :class:`.Client` that we created above.

• :meth:`.SQLContext.run_to_completion`

  • Runs this Feldera pipeline to completion. Normally this means until the EoF has been reached for this input source.

  • Example:

    from feldera import SQLSchema
    
    tbl_name = "user_data"
    view_name = "select_view"
    
    # Declare input tables
    sql.register_table(tbl_name, SQLSchema({"name": "STRING"}))
    
    # Register Views based on your queries
    query = f"SELECT * FROM {tbl_name}"
    sql.register_view(view_name, query)
    
    # name for this connector
    in_con = "delta_input_conn"
    
    # the configuration for this input source
    in_cfg = {...}
    
    sql.connect_source_delta_table(tbl_name, in_con, in_cfg)
    
    # name for this connector
    out_con = "delta_output_con"
    
    # the configuration for this input source
    out_cfg = {...}
    
    sql.connect_sink_delta_table(view_name, out_con, out_cfg)
    
    sql.run_to_completion()

    • Here, we register a data table which receives data from input sources.
    • Then, we register a view that performs operations on this input data. You can also register other views on top of existing views.
    • Then, we connect a source delta table to the previously defined table.
    • Then, we connect a sink delta table to the previously defined view.
    • Finally, we run the pipeline to completion. Feldera will fetch data from the source, perform the query you supplied and passes this data to the sink delta table.

  Warning

  If the data source is streaming, this will block forever. In such cases, use :meth:`.SQLContext.start` instead.

• :meth:`.SQLContext.start`

  • Starts the Feldera Pipeline and keeps it running indefinitely.

  • Example:

    sql.start()

    • This tells Feldera to go ahead and start processing the data.

Checkout the :doc:`/examples`.