[FLINK-39725][python] Apply the Python TimestampAssigner in from_source

[wilmerdooley]

What is the purpose of the change

When StreamExecutionEnvironment.from_source is called in PyFlink with a WatermarkStrategy that carries a custom Python TimestampAssigner, the assigner was never invoked. The strategy was passed straight to the Java source operator, which runs timestamp extraction and watermark generation on the JVM side and so ignores the Python assigner entirely.

Brief change log

• flink-python/pyflink/datastream/stream_execution_environment.py: in from_source, when the WatermarkStrategy carries a Python TimestampAssigner (watermark_strategy._timestamp_assigner is not None), create the source with WatermarkStrategy.no_watermarks() and then apply the original strategy downstream via DataStream.assign_timestamps_and_watermarks, so the Python TimestampAssigner is executed. The existing path (no Python assigner) is unchanged.

Verifying this change

This change added a unit test: test_from_source_with_timestamp_assigner in flink-python/pyflink/datastream/tests/test_stream_execution_environment.py runs a NumberSequenceSource with a WatermarkStrategy.for_monotonous_timestamps() and a custom TimestampAssigner, then reads ctx.timestamp() in a ProcessFunction and asserts the timestamps the Python assigner produced (value * 1000). The test fails on the pre-fix code, where the Python assigner is ignored and the timestamp is not set.

Does this pull request potentially affect one of the following parts:

• Dependencies (does it add or upgrade a dependency): no
• The public API, i.e., is any changed class annotated with @Public(Evolving): no (the change is in PyFlink Python stream_execution_environment.py; no Java @Public(Evolving) class is touched. It does change the observable behavior of the public PyFlink from_source method, as described above, without changing its signature.)
• The serializers: no
• The runtime per-record code paths (performance sensitive): yes (only when a Python TimestampAssigner is supplied: the fix then routes the stream through a downstream assign_timestamps_and_watermarks operator so the assigner runs per record, which is the intended behavior and the existing PyFlink mechanism. The default path, with no Python assigner, is unchanged.)
• Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: no
• The S3 file system connector: no

Documentation

• Does this pull request introduce a new feature? no
• If yes, how is the feature documented? not applicable

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

Generated-by: Claude Code

JIRA: https://issues.apache.org/jira/browse/FLINK-39725

[flinkbot]

CI report:

• 1d86e78 Azure: FAILURE

Bot commands

The @flinkbot bot supports the following commands:

• @flinkbot run azure re-run the last Azure build