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==========
Connectors
==========

This page describes how to use connectors in PyFlink and highlights the details to be aware of when
using Flink connectors in Python programs.

.. note::
    For general connector information and common
    configuration, please refer to the corresponding :flinkdoc:`Java/Scala documentation <docs/connectors/table/overview/>`.

Download connector and format jars
===================================

Since Flink is a Java/Scala-based project, for both connectors and formats, implementations
are available as jars that need to be specified as job dependencies (see :doc:`../dependency_management`).

.. code-block:: python

    table_env.get_config().set("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")

How to use connectors
=====================

In PyFlink's Table API, DDL is the recommended way to define sources and sinks, executed via the
``execute_sql()`` method on the ``TableEnvironment``.
This makes the table available for use by the application.

.. code-block:: python

    source_ddl = """
            CREATE TABLE source_table(
                a VARCHAR,
                b INT
            ) WITH (
              'connector' = 'kafka',
              'topic' = 'source_topic',
              'properties.bootstrap.servers' = 'kafka:9092',
              'properties.group.id' = 'test_3',
              'scan.startup.mode' = 'latest-offset',
              'format' = 'json'
            )
            """

    sink_ddl = """
            CREATE TABLE sink_table(
                a VARCHAR
            ) WITH (
              'connector' = 'kafka',
              'topic' = 'sink_topic',
              'properties.bootstrap.servers' = 'kafka:9092',
              'format' = 'json'
            )
            """

    t_env.execute_sql(source_ddl)
    t_env.execute_sql(sink_ddl)

    t_env.sql_query("SELECT a FROM source_table") \
        .execute_insert("sink_table").wait()

Below is a complete example of how to use a Kafka source/sink and the JSON format in PyFlink.

.. code-block:: python

    from pyflink.table import TableEnvironment, EnvironmentSettings

    def log_processing():
        env_settings = EnvironmentSettings.in_streaming_mode()
        t_env = TableEnvironment.create(env_settings)
        # specify connector and format jars
        t_env.get_config().set("pipeline.jars", "file:///my/jar/path/connector.jar;file:///my/jar/path/json.jar")

        source_ddl = """
                CREATE TABLE source_table(
                    a VARCHAR,
                    b INT
                ) WITH (
                  'connector' = 'kafka',
                  'topic' = 'source_topic',
                  'properties.bootstrap.servers' = 'kafka:9092',
                  'properties.group.id' = 'test_3',
                  'scan.startup.mode' = 'latest-offset',
                  'format' = 'json'
                )
                """

        sink_ddl = """
                CREATE TABLE sink_table(
                    a VARCHAR
                ) WITH (
                  'connector' = 'kafka',
                  'topic' = 'sink_topic',
                  'properties.bootstrap.servers' = 'kafka:9092',
                  'format' = 'json'
                )
                """

        t_env.execute_sql(source_ddl)
        t_env.execute_sql(sink_ddl)

        t_env.sql_query("SELECT a FROM source_table") \
            .execute_insert("sink_table").wait()


    if __name__ == '__main__':
        log_processing()

Predefined Sources and Sinks
=============================

Some data sources and sinks are built into Flink and are available out-of-the-box.
These predefined data sources include reading from Pandas DataFrame, or ingesting data from collections.
The predefined data sinks support writing to Pandas DataFrame.

from/to Pandas
--------------

PyFlink Tables support conversion to and from Pandas DataFrame.

.. code-block:: python

    from pyflink.table.expressions import col

    import pandas as pd
    import numpy as np

    # Create a PyFlink Table
    pdf = pd.DataFrame(np.random.rand(1000, 2))
    table = t_env.from_pandas(pdf, ["a", "b"]).filter(col('a') > 0.5)

    # Convert the PyFlink Table to a Pandas DataFrame
    pdf = table.to_pandas()

from_elements()
----------------

``from_elements()`` is used to create a table from a collection of elements. The element types must
be acceptable atomic types or acceptable composite types.

.. code-block:: python

    from pyflink.table import DataTypes

    table_env.from_elements([(1, 'Hi'), (2, 'Hello')])

    # use the second parameter to specify custom field names
    table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['a', 'b'])

    # use the second parameter to specify a custom table schema
    table_env.from_elements([(1, 'Hi'), (2, 'Hello')],
                            DataTypes.ROW([DataTypes.FIELD("a", DataTypes.INT()),
                                           DataTypes.FIELD("b", DataTypes.STRING())]))

The above query returns a Table like:

::

    +----+-------+
    | a  |   b   |
    +====+=======+
    | 1  |  Hi   |
    +----+-------+
    | 2  | Hello |
    +----+-------+

User-defined sources & sinks
==============================

In some cases, you may want to define custom sources and sinks. Currently, sources and sinks must
be implemented in Java/Scala, but you can define a ``TableFactory`` to support their use via DDL.
More details can be found in the :flinkdoc:`Java/Scala documentation <docs/dev/table/sourcessinks/>`.
