[flink-runner] Improve Datastream for batch performances

[jto]

Context

Flink will drop support for the dataset API in 2.0 which should be released by EOY so it quite important for Beam to support Datastream well.

The PR

This PR improves the performances of Batch jobs executed with --useDatastreamForBatch by porting the following performance optimizations already present in FlinkBatchTransformTranslators but lacking in FlinkStreamingTransformTranslators.

• Limit the max size of source splits. Similar to Reduce the maximum size of input splits in Flink to better distribute work #28045
• Pre-combine before shuffle (both reduce by key and GBK)
• Disable bundling in batch mode (except for pre-combine). Lower the default bundle size since the new behavior puts pressure on the heap.

It also implements the following optimizations:

• Use a "lazy" split enumerator to distributes split dynamically rather than eagerly. This new enumerator greatly reduces skew as each slot is able to pull new splits to consume only when it has finished its work.
• Set the default maxParallelism to parallelism as the total number of key groups is equal to maxParallelism. Again this reduces skew.
• Make ToKeyedWorkItem part of DoFnOperator which reduces the size of the job graph and avoid unnecessary inter-task communication.
• Force a common slot-sharing group on every bounded IOs. This emulate the behavior of the Dataset API which again improves performances especially when data is being shuffled several times while partitioning keys are unchanged (for example of the job does GBK -> map -> CombinePerKey). Add a flag to control this feature (defaults to active).
• Use a custom class for keys which guarantee a good distribution of data even when the number of keys is not >> parallelism.
• Other minor optimizations removing repeated serde work.

Benchmarks

The patched version was tested against a few of Spotify's production batch workflows. All settings were left unchanged except for the followings:

• passed --useDatastreamForBatch=true
• set jobmanager.scheduler: default (otherwise datastream default to adaptive scheduler).

		Beam 2.56 - dataset	Beam 2.56 - datastream		Beam 2.56 - datastream patched
job	# workers	execution time	execution time	% diff	execution time	% diff
Job 1	350	2:19:00	fails after 4h29min	-	1:43:00	-25.90%
Job 2	160	0:23:00	0:35:00	52.17%	0:22:36	-1.74%
Job 3	200	0:53:08	1:34:39	78.14%	failed	-
Job 4	160	2:31:20	4:27:00	76.43%	2:19:35	-7.76%
Job 5	1	0:43:00	not tested	-	0:38:00	-11.63%
Job 6	300	3:01:00	not tested	-	2:55:00

Note

Job 3 fails with a stackoverflow exception because of a bug in versions of Kryo < 3.0. I believe this is because the job uses taskmanager.runtime.large-record-handler: true and it should be fixed in Flink 2.0 since Kryo is upgraded to a more recent version.

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[damccorm]

@kennknowles would you mind taking a look at this one?

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[damccorm]

R: @kennknowles

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[kennknowles]

To test this thoroughly, let us add some of the postcommits by touching trigger files. In #32648 you can see how I edited the JSON files (including some new ones) and I think these are all the Flink-specific postcommit jobs.

[jto]

@kennknowles done. I also rebased master but some of the tests seem to be quite flaky now. There are test failing on things I did not touch (direct runner) and the Flink tests that are failing here are not failing on my machine... Any idea how I could make them work ?

[kennknowles]

I opened jto#236 with some more trigger files. The "PVR" trigger files stands for "Portable Validates Runner" that isn't as directly impacted. I think the non-portable ValidatesRunner tests should test that the runner still complies with the model and passes the basic tests.

[jto]

Thanks! I just merged it.
Sorry for the slow response. I was on vacation :)

[jto]

Hey there!

I rebased master into my branch and a few tests are failing however:

In beam_PreCommit_Java (Run Java PreCommit)

• tests are failing in :runners:spark:3:test. I did not change anything in the spark runner and according to the report those tests are also failing in other builds. I guess my PR is not the cause of those failures.
• One of the tests in :runners:flink:1.17:test is failing but it succeeds on my machine. It looks like something may not be deterministic. I already fixed a couple of determinism issues in tests. I'll try to fix this one too.

In beam_PostCommit_Java_ValidatesRunner_Flink (Run Flink ValidatesRunner)

• One test in :runners:flink:1.19:validatesRunnerBatchWithDataStream is failing. This one is a "real" failure. I'll investigate it too.

PostCommit Go VR Flink / beam_PostCommit_Go_VR_Flink (Run Go Flink ValidatesRunner)

Logs are truncated. I don't know if there's an actual failure or what it might be...

[jto]

Hey @kennknowles!
I tried really hard to make the failing test (org.apache.beam.sdk.transforms.ParDoTest$LifecycleTests) work but in the end I think it's impossible.
The behaviour of the runner is correct (it fails when it's supposed to) but Flink will either fail with the expected exception or just fail with a generic error (TaskNotRunningException: Task is not running, but in state FAILED).
The expected error is there in the logs but the "final" error may or may not be the expected one. The behaviour is non-deterministic.
In the end I just added this class into sickbay and that seems to have fixed almost all the problems.

The Python PostCommits are failing but the error is:

2024-11-18T11:39:33.3464029Z Execution failed for task ':sdks:python:container:py312:docker'.                                                                                                              │
│2024-11-18T11:39:33.3464463Z > Process 'command 'docker'' finished with non-zero exit value 1

which I think is unrelated to those changes and I could not find anything in the logs suggesting otherwise.