flink-python/docs/user_guide/python_repl.rst

.. ################################################################################
     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.
   ################################################################################

===========
Python REPL
===========

Flink comes with an integrated interactive Python Shell.
It can be used in a local setup as well as in a cluster setup.
See the :flinkdoc:`standalone resource provider page <docs/deployment/resource-providers/standalone/overview/>` for more information about how to setup a local Flink.
You can also `build a local setup from source <https://github.com/apache/flink#building-apache-flink-from-source>`_.

.. note::
    The Python Shell will run the command "python". Please refer to the
    :flinkdoc:`First Steps guide <docs/getting-started/local_installation/>` for PyFlink installation instructions.

To use the shell with an integrated Flink cluster, you can simply install PyFlink with PyPi and execute the shell directly:

.. code-block:: bash

    # install PyFlink
    $ python -m pip install apache-flink
    # execute the shell
    $ pyflink-shell.sh local

To run the shell on a cluster, please see the Setup section below.

Usage
=====

The shell only supports Table API currently.
The Table Environments are automatically prebound after startup.
Use "bt_env" and "st_env" to access BatchTableEnvironment and StreamTableEnvironment respectively.

Table API
---------

The example below is a simple program in the Python shell:

**Streaming:**

.. code-block:: python

    >>> import tempfile
    >>> import os
    >>> import shutil
    >>> sink_path = tempfile.gettempdir() + '/streaming.csv'
    >>> if os.path.exists(sink_path):
    ...     if os.path.isfile(sink_path):
    ...         os.remove(sink_path)
    ...     else:
    ...         shutil.rmtree(sink_path)
    >>> s_env.set_parallelism(1)
    >>> t = st_env.from_elements([(1, 'hi', 'hello'), (2, 'hi', 'hello')], ['a', 'b', 'c'])
    >>> st_env.create_temporary_table("stream_sink", TableDescriptor.for_connector("filesystem")
    ...     .schema(Schema.new_builder()
    ...         .column("a", DataTypes.BIGINT())
    ...         .column("b", DataTypes.STRING())
    ...         .column("c", DataTypes.STRING())
    ...         .build())
    ...     .option("path", path)
    ...     .format(FormatDescriptor.for_format("csv")
    ...         .option("field-delimiter", ",")
    ...         .build())
    ...     .build())
    >>> t.select("a + 1, b, c")\
    ...     .execute_insert("stream_sink").wait()
    >>> # If the job runs in local mode, you can exec following code in Python shell to see the result:
    >>> with open(os.path.join(sink_path, os.listdir(sink_path)[0]), 'r') as f:
    ...     print(f.read())

**Batch:**

.. code-block:: python

    >>> import tempfile
    >>> import os
    >>> import shutil
    >>> sink_path = tempfile.gettempdir() + '/batch.csv'
    >>> if os.path.exists(sink_path):
    ...     if os.path.isfile(sink_path):
    ...         os.remove(sink_path)
    ...     else:
    ...         shutil.rmtree(sink_path)
    >>> b_env.set_parallelism(1)
    >>> t = bt_env.from_elements([(1, 'hi', 'hello'), (2, 'hi', 'hello')], ['a', 'b', 'c'])
    >>> st_env.create_temporary_table("batch_sink", TableDescriptor.for_connector("filesystem")
    ...     .schema(Schema.new_builder()
    ...         .column("a", DataTypes.BIGINT())
    ...         .column("b", DataTypes.STRING())
    ...         .column("c", DataTypes.STRING())
    ...         .build())
    ...     .option("path", path)
    ...     .format(FormatDescriptor.for_format("csv")
    ...         .option("field-delimiter", ",")
    ...         .build())
    ...     .build())
    >>> t.select("a + 1, b, c")\
    ...     .execute_insert("batch_sink").wait()
    >>> # If the job runs in local mode, you can exec following code in Python shell to see the result:
    >>> with open(os.path.join(sink_path, os.listdir(sink_path)[0]), 'r') as f:
    ...     print(f.read())

Setup
=====

To get an overview of what options the Python Shell provides, please use

.. code-block:: bash

    pyflink-shell.sh --help

Local
-----

To use the shell with an integrated Flink cluster just execute:

.. code-block:: bash

    pyflink-shell.sh local

Remote
------

To use it with a running cluster, please start the Python shell with the keyword ``remote``
and supply the host and port of the JobManager with:

.. code-block:: bash

    pyflink-shell.sh remote <hostname> <portnumber>

Yarn Python Shell cluster
-------------------------

The shell can deploy a Flink cluster to YARN, which is used exclusively by the
shell.
The shell deploys a new Flink cluster on YARN and connects the
cluster. You can also specify options for YARN cluster such as memory for
JobManager, name of YARN application, etc.

For example, to start a Yarn cluster for the Python Shell with two TaskManagers
use the following:

.. code-block:: bash

    pyflink-shell.sh yarn -n 2

For all other options, see the full reference at the bottom.

Yarn Session
------------

If you have previously deployed a Flink cluster using the Flink Yarn Session,
the Python shell can connect with it using the following command:

.. code-block:: bash

    pyflink-shell.sh yarn

Full Reference
==============

.. code-block:: text

    Flink Python Shell
    Usage: pyflink-shell.sh [local|remote|yarn] [options] <args>...

    Command: local [options]
    Starts Flink Python shell with a local Flink cluster
    usage:
         -h,--help   Show the help message with descriptions of all options.
    Command: remote [options] <host> <port>
    Starts Flink Python shell connecting to a remote cluster
      <host>
            Remote host name as string
      <port>
            Remote port as integer

    usage:
         -h,--help   Show the help message with descriptions of all options.
    Command: yarn [options]
    Starts Flink Python shell connecting to a yarn cluster
    usage:
         -h,--help                       Show the help message with descriptions of
                                         all options.
         -jm,--jobManagerMemory <arg>    Memory for JobManager Container with
                                         optional unit (default: MB)
         -nm,--name <arg>                Set a custom name for the application on
                                         YARN
         -qu,--queue <arg>               Specify YARN queue.
         -s,--slots <arg>                Number of slots per TaskManager
         -tm,--taskManagerMemory <arg>   Memory per TaskManager Container with
                                         optional unit (default: MB)
    -h | --help
          Prints this usage text