Flink comes with an integrated interactive Python Shell. It can be used in a local setup as well as in a cluster setup.
To use the shell with an integrated Flink cluster, you can simply install PyFlink with PyPi and execute the shell directly:
# install PyFlink
$ python -m pip install apache-flink==1.9.*
# execute the shell
$ pyflink-shell.sh local
To run the shell on a cluster, please see the Setup section below.
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.
The example below is a simple program in the Python shell:
>>> 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.connect(FileSystem().path(sink_path))\
... .with_format(OldCsv()
... .field_delimiter(',')
... .field("a", DataTypes.BIGINT())
... .field("b", DataTypes.STRING())
... .field("c", DataTypes.STRING()))\
... .with_schema(Schema()
... .field("a", DataTypes.BIGINT())
... .field("b", DataTypes.STRING())
... .field("c", DataTypes.STRING()))\
... .register_table_sink("stream_sink")
>>> t.select("a + 1, b, c")\
... .insert_into("stream_sink")
>>> st_env.execute("stream_job")
>>> # If the job runs in local mode, you can exec following code in Python shell to see the result:
>>> with open(sink_path, 'r') as f:
... print(f.read())
>>> 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'])
>>> bt_env.connect(FileSystem().path(sink_path))\
... .with_format(OldCsv()
... .field_delimiter(',')
... .field("a", DataTypes.BIGINT())
... .field("b", DataTypes.STRING())
... .field("c", DataTypes.STRING()))\
... .with_schema(Schema()
... .field("a", DataTypes.BIGINT())
... .field("b", DataTypes.STRING())
... .field("c", DataTypes.STRING()))\
... .register_table_sink("batch_sink")
>>> t.select("a + 1, b, c")\
... .insert_into("batch_sink")
>>> bt_env.execute("batch_job")
>>> # If the job runs in local mode, you can exec following code in Python shell to see the result:
>>> with open(sink_path, 'r') as f:
... print(f.read())
To get an overview of what options the Python Shell provides, please use
pyflink-shell.sh --help
To use the shell with an integrated Flink cluster just execute:
pyflink-shell.sh local
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:
pyflink-shell.sh remote <hostname> <portnumber>
The shell can deploy a Flink cluster to YARN, which is used exclusively by the
shell. The number of YARN containers can be controlled by the parameter -n <arg>
.
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:
pyflink-shell.sh yarn -n 2
For all other options, see the full reference at the bottom.
If you have previously deployed a Flink cluster using the Flink Yarn Session, the Python shell can connect with it using the following command:
pyflink-shell.sh yarn
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)
-n,--container <arg> Number of YARN container to allocate
(=Number of Task Managers)
-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