执行模式
This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.

Execution Mode #

The Python API supports different runtime execution modes from which you can choose depending on the requirements of your use case and the characteristics of your job. The Python runtime execution mode defines how to execute your customized Python functions.

Prior to release-1.15, there is the only execution mode called PROCESS execution mode. The PROCESS mode means that the Python user-defined functions will be executed in separate Python processes.

In release-1.15, it has introduced a new execution mode called THREAD execution mode. The THREAD mode means that the Python user-defined functions will be executed in the same process as Java Operator, It should be noted that multiple Python user-defined functions running in the same JVM are still affected by GIL.

When can/should I use THREAD execution mode? #

The purpose of the introduction of THREAD mode is to overcome the overhead of serialization/deserialization and network communication caused in PROCESS mode. So if performance is not your concern, or the computing logic of your customized Python functions is the performance bottleneck of the job, PROCESS mode will be the best choice as PROCESS mode provides the best isolation compared to THREAD mode.

Configuring Python execution mode #

The execution mode can be configured via the python.execution-mode setting. There are two possible values:

  • PROCESS: The Python user-defined functions will be executed in separate Python process. (default)
  • THREAD: The Python user-defined functions will be executed in the same process as Java operator.

You could specify the Python execution mode using Python Table API as following:

# Specify `PROCESS` mode
table_env.get_config().set("python.execution-mode", "process")

# Specify `THREAD` mode
table_env.get_config().set("python.execution-mode", "thread")

Currently, it still doesn’t support to execute Python UDFs in THREAD execution mode in all places. It will fall back to PROCESS execution mode in these cases. So it may happen that you configure a job to execute in THREAD execution mode, however, it’s actually executed in PROCESS execution mode.
THREAD execution mode is only supported in Python 3.7+.

Execution Behavior #

This section provides an overview of the execution behavior of THREAD execution mode and contrasts they with PROCESS execution mode. For more details, please refer to the FLIP that introduced this feature: FLIP-206.

PROCESS Execution Mode #

In PROCESS execution mode, the Python user-defined functions will be executed in separate Python Worker process. The Java operator process communicates with the Python worker process using various Grpc services.

Process Execution Mode

THREAD Execution Mode #

In THREAD execution mode, the Python user-defined functions will be executed in the same process as Java operators. PyFlink takes use of third part library PEMJA to embed Python in Java Application.

Embedded Execution Mode