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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/>.