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Performance Tuning

This section covers performance optimization techniques for PyFlink applications.

Key Factors

Several factors affect PyFlink application performance:

Parallelism: Number of parallel instances for operators
Memory Configuration: Heap and off-heap memory settings
State Backend: Choice of state storage backend
Network Buffers: Network buffer configuration
Checkpointing: Checkpoint interval and timeout settings

Parallelism Configuration

.. code-block:: python

from pyflink.datastream import StreamExecutionEnvironment from pyflink.table import StreamTableEnvironment, EnvironmentSettings

Create execution environment

env = StreamExecutionEnvironment.get_execution_environment()

Set global parallelism

env.set_parallelism(4)

Set parallelism for specific operators

ds = env.from_collection([1, 2, 3, 4, 5]) ds = ds.map(lambda x: x * 2).set_parallelism(2) ds = ds.filter(lambda x: x > 5).set_parallelism(1)