Python API Tutorial

In this guide we will start from scratch and go from setting up a Flink Python project to running a Python Table API program.

Setting up a Python Project

Firstly, you can fire up your favorite IDE and create a Python project and then you need to install the PyFlink package. You can install the latest PyFlink from PyPI via this command: pip install apache-flink. If you want to build and install PyFlink from source code, please see Build PyFlink for more details about this.

The first step in a Flink Python Table API program is to create a BatchTableEnvironment (or StreamTableEnvironment if you are writing a streaming job). It is the main entry point for Python Table API jobs.

exec_env = ExecutionEnvironment.get_execution_environment()
exec_env.set_parallelism(1)
t_config = TableConfig()
t_env = BatchTableEnvironment.create(exec_env, t_config)

The ExecutionEnvironment (or StreamExecutionEnvironment if you are writing a streaming job) can be used to set execution parameters, such as the restart strategy, default parallelism, etc.

The TableConfig can be used by setting the parameters such as the built-in catalog name, the threshold where generating code, etc.

Next we will create a source table and a sink table.

t_env.connect(FileSystem().path('/tmp/input')) \
    .with_format(OldCsv()
                 .line_delimiter(' ')
                 .field('word', DataTypes.STRING())) \
    .with_schema(Schema()
                 .field('word', DataTypes.STRING())) \
    .create_temporary_table('mySource')

t_env.connect(FileSystem().path('/tmp/output')) \
    .with_format(OldCsv()
                 .field_delimiter('\t')
                 .field('word', DataTypes.STRING())
                 .field('count', DataTypes.BIGINT())) \
    .with_schema(Schema()
                 .field('word', DataTypes.STRING())
                 .field('count', DataTypes.BIGINT())) \
    .create_temporary_table('mySink')

This registers a table named mySource and a table named mySink in the ExecutionEnvironment. The table mySource has only one column: word. It represents the words read from file /tmp/input. The table mySink has two columns: word and count. It writes data to file /tmp/output, with \t as the field delimiter.

Then we need to create a job which reads input from table mySource, preforms some operations and writes the results to table mySink.

t_env.from_path('mySource') \
    .group_by('word') \
    .select('word, count(1)') \
    .insert_into('mySink')

The last thing is to start the actual Flink Python Table API job. All operations, such as creating sources, transformations and sinks only build up a graph of internal operations. Only when t_env.execute(job_name) is called, this graph of operations will be thrown on a cluster or executed on your local machine.

t_env.execute("tutorial_job")

The complete code so far is as follows:

from pyflink.dataset import ExecutionEnvironment
from pyflink.table import TableConfig, DataTypes, BatchTableEnvironment
from pyflink.table.descriptors import Schema, OldCsv, FileSystem

exec_env = ExecutionEnvironment.get_execution_environment()
exec_env.set_parallelism(1)
t_config = TableConfig()
t_env = BatchTableEnvironment.create(exec_env, t_config)

t_env.connect(FileSystem().path('/tmp/input')) \
    .with_format(OldCsv()
                 .line_delimiter(' ')
                 .field('word', DataTypes.STRING())) \
    .with_schema(Schema()
                 .field('word', DataTypes.STRING())) \
    .create_temporary_table('mySource')

t_env.connect(FileSystem().path('/tmp/output')) \
    .with_format(OldCsv()
                 .field_delimiter('\t')
                 .field('word', DataTypes.STRING())
                 .field('count', DataTypes.BIGINT())) \
    .with_schema(Schema()
                 .field('word', DataTypes.STRING())
                 .field('count', DataTypes.BIGINT())) \
    .create_temporary_table('mySink')

t_env.from_path('mySource') \
    .group_by('word') \
    .select('word, count(1)') \
    .insert_into('mySink')

t_env.execute("tutorial_job")

You can run this example in your IDE or on the command line (suppose the job script file is WordCount.py):

$ python WordCount.py

The command builds and runs the Python Table API program in a local mini cluster. You can also submit the Python Table API program to a remote cluster, you can refer Job Submission Examples for more details.

This should get you started with writing your own Flink Python Table API programs. To learn more about the Python Table API, you can refer Flink Python Table API Docs for more details.