niliushall opened a new pull request, #29439:
URL: https://github.com/apache/flink/pull/29439
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## What is the purpose of the change
Add Kafka source and sink support to the PyFlink DataFrame API
(FLINK-40198). The new
`pf.read_kafka` and `DataFrame.write_kafka` methods follow the existing
DataFrame I/O
conventions (`read_json`, `read_parquet`,...), so Kafka can be used without
falling back to
`read_generic` / `write_generic` with raw connector options.
## Brief change log
- Add `pf.read_kafka(...)`: builds Kafka source options (topic or topic
list, topic pattern,
`group-id`, startup mode incl. `timestamp` and `specific-offsets`, bounded
mode,
topic-partition discovery interval, key/value formats, Kafka `properties`)
and creates the
DataFrame through the existing reader path, including `computed_columns`
and `watermark`.
- Add `DataFrame.write_kafka(...)`: builds Kafka sink options (topic,
key/value formats and
format options, `value.fields-include`, `sink.delivery-guarantee`,
parallelism, Kafka
`properties`) and submits through the existing writer path, including
`statement_set=`
staging like the other writers.
- Add shared Kafka option helpers (option building, `properties.` prefix
normalization,
format-option alias merging, argument validation).
- Export `read_kafka` in `pyflink.dataframe`.
- Add tests covering option building, argument validation and descriptor
generation.
## Verifying this change
This change added tests and can be verified as follows:
- `flink-python/pyflink/dataframe/tests/test_kafka_io.py` covers source/sink
option building
(topic list, topic pattern, specific-offsets conversion, `properties.`
prefixing,
`value_format` aliases), argument validation, and the generated
source/sink descriptors for
both `read_kafka` and `write_kafka`.
- Run with `python -m pytest pyflink/dataframe/tests/test_kafka_io.py` in a
pyflink
development environment.
## 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)`: yes (new
`read_kafka` / `DataFrame.write_kafka` annotated with `@PublicEvolving`)
- The serializers: no
- The runtime per-record code paths (performance sensitive): no
- 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? yes
- If yes, how is the feature documented? not documented
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