[SPARK-52228][SS][PYSPARK] Construct the benchmark purposed TWS state server with in-memory state impls and the benchmark code in python #50952
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… server with in-memory state impls and the benchmark code in python
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I'm open for suggestion w.r.t package/module path, better instruction, idea of standardization, etc. Thanks! |
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What changes were proposed in this pull request?
This PR proposes to introduce the benchmark tool which can perform performance test with state interactions between TWS state server and Python worker.
Since it requires two processes (JVM and Python) with socket connection between the two, we are not going to follow the benchmark suites we have in SQL module as of now. We leave the tool to run manually. It'd be ideal if we can make this to be standardized with existing benchmark suites as well as running automatically, but this is not an immediate goal.
Why are the changes needed?
It has been very painful to run the benchmark and look into the performance of state interactions. It required adding debug logs and running E2E queries, which is really so much work just to see the numbers.
For example, after this benchmark tool has introduced, we can verify the upcoming improvements w.r.t. state interactions - for example, we still have spots to use Arrow in state interactions, and I think this tool can show the perf benefit for the upcoming fix.
Does this PR introduce any user-facing change?
No.
How was this patch tested?
Manually tested.
./dev/make-distribution.shcd distjava -classpath "./jars/*" --add-opens=java.base/java.nio=org.apache.arrow.memory.core,ALL-UNNAMED org.apache.spark.sql.execution.python.streaming.BenchmarkTransformWithStateInPySparkStateServercd pythonpython3 pyspark/sql/streaming/benchmark/benchmark_tws_state_server.py <port that state server use> <state type> <params if required>For Python process, it is required to install libraries PySpark required first (including numpy since it's used in the benchmark).
Result will be printed out like following (NOTE: I ran the same benchmark 3 times):
https://gist.github.com/HeartSaVioR/fa4805af4d7a4dc9789c8e3437506be1
Was this patch authored or co-authored using generative AI tooling?
No.