[SPARK-53893][TESTS] Regenerate benchmark results after upgrading to Scala 2.13.17 by dongjoon-hyun · Pull Request #52600 · apache/spark

dongjoon-hyun · 2025-10-14T02:10:54Z

What changes were proposed in this pull request?

This PR aims to regenerate benchmark results after upgrading to Scala 2.13.17.

Why are the changes needed?

Since last update, we change important libraries, not only Scala, but also Hadoop, ORC, ZSTD libraries. This PR aims to make the benchmark result up-to-date as a way to detect any performance regression.

Does this PR introduce any user-facing change?

No.

How was this patch tested?

Manual review.

Was this patch authored or co-authored using generative AI tooling?

No.

…Scala 2.13.17

dongjoon-hyun · 2025-10-14T02:37:48Z

 -----------------------------------------------------------------------------------------------------------------------------
-Compression 1024 array values in 1 threads 44503 0.0 43112.9 1.0X
-Compression 1024 array values single-threaded 32 33 0 0.0 31315.11.4X
+Compression 1024 array values in 2 threads 23251 0.0 22108.7 1.0X


The number of thread seems to be changed by GitHub Action side.
spark/core/src/test/scala/org/apache/spark/io/LZFBenchmark.scala
Lines 69 to 77 in 7d028c6
defgetNThreads:Int= {
varnThreads=Runtime.getRuntime.availableProcessors
valjmx=ManagementFactory.getOperatingSystemMXBean
if (jmx !=null) {
valloadAverage= jmx.getSystemLoadAverage.toInt
if (nThreads >1&& loadAverage >=1) nThreads =Math.max(1, nThreads - loadAverage)
}
nThreads
}

dongjoon-hyun · 2025-10-14T02:51:52Z

-Decompression 10000 times from level 3 with buffer pool 540541 0 0.0 54016.91.1X
+Decompression 10000 times from level 1 without buffer pool 1661670 0.116645.6 1.0X
+Decompression 10000 times from level 2 without buffer pool 1661660 0.116558.6 1.0X
+Decompression 10000 times from level 3 without buffer pool 166167 0 0.116629.4 1.0X


The ZSTD decompression speed is improved in Java 17 benchmark. Since Java 21 doesn't show any change, it could be some transient result.

dongjoon-hyun · 2025-10-14T03:14:20Z

-UTF-32 5629556403153 0.25629.5 1.0X
-UTF-16 5064450653 130.2 5064.4 1.1X
-UTF-8 305993061928 0.3 3059.9 1.8X
+UTF-32 3351733545 41 0.33351.7 1.0X


Here, UTF-32 performance is improved, but Java 21 result is the same. This could be a transient one.

dongjoon-hyun · 2025-10-14T03:15:04Z

Thank you, @LuciferYang and @yaooqinn .

dongjoon-hyun · 2025-10-14T03:16:06Z

At the first glance, there is no outstanding regression .Let me merge this since this is only a result of regeneration.

…Scala 2.13.17 ### What changes were proposed in this pull request? This PR aims to regenerate benchmark results after upgrading to Scala 2.13.17. ### Why are the changes needed? Since last update, we change important libraries, not only Scala, but also Hadoop, ORC, ZSTD libraries. This PR aims to make the benchmark result up-to-date as a way to detect any performance regression. - apache#52509 - apache#51127 - apache#52478 - apache#52591 ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Manual review. ### Was this patch authored or co-authored using generative AI tooling? No. Closesapache#52600 from dongjoon-hyun/SPARK-53893. Lead-authored-by: Dongjoon Hyun <dongjoon@apache.org> Co-authored-by: dongjoon-hyun <dongjoon-hyun@users.noreply.github.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

### What changes were proposed in this pull request? This PR aims to regenerate benchmark results to check the intermediate status as a part of Apache Spark 4.2.0 preparation. Please note that `V2FunctionBenchmark` is excluded because it's broken due to `NumericEvalContext.evalMode()` error currently. It's good to identify this kind of bug as early as possible via this PR. - [SPARK-55519 `V2FunctionBenchmark` is broken](https://issues.apache.org/jira/browse/SPARK-55519) ### Why are the changes needed? Apache Spark 4.2.0 introduced many improvements on top of the key dependency differences from Spark 4.1.0: - #53396 - #53582 - #53347 - #54233 - #54292 We updated the benchmark result 4 months ago. So, it's time to make them up-to-date with our actual code and the current infra. - #52600 ``` - OpenJDK 64-Bit Server VM 17.0.16+8-LTS on Linux 6.11.0-1018-azure + OpenJDK 64-Bit Server VM 17.0.18+8-LTS on Linux 6.14.0-1017-azure ``` ``` - OpenJDK 64-Bit Server VM 21.0.8+9-LTS on Linux 6.11.0-1018-azure + OpenJDK 64-Bit Server VM 21.0.10+7-LTS on Linux 6.14.0-1017-azure ``` ### Does this PR introduce _any_ user-facing change? No. This is a change on benchmark result files. ### How was this patch tested? Manual review. ### Was this patch authored or co-authored using generative AI tooling? No. Closes#54313 from dongjoon-hyun/SPARK-55520. Lead-authored-by: Dongjoon Hyun <dongjoon@apache.org> Co-authored-by: dongjoon-hyun <dongjoon-hyun@users.noreply.github.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

### What changes were proposed in this pull request? This PR aims to regenerate benchmark results to check the intermediate status as a part of Apache Spark 4.2.0 preparation. Please note that `V2FunctionBenchmark` is excluded because it's broken due to `NumericEvalContext.evalMode()` error currently. It's good to identify this kind of bug as early as possible via this PR. - [SPARK-55519 `V2FunctionBenchmark` is broken](https://issues.apache.org/jira/browse/SPARK-55519) ### Why are the changes needed? Apache Spark 4.2.0 introduced many improvements on top of the key dependency differences from Spark 4.1.0: - apache#53396 - apache#53582 - apache#53347 - apache#54233 - apache#54292 We updated the benchmark result 4 months ago. So, it's time to make them up-to-date with our actual code and the current infra. - apache#52600 ``` - OpenJDK 64-Bit Server VM 17.0.16+8-LTS on Linux 6.11.0-1018-azure + OpenJDK 64-Bit Server VM 17.0.18+8-LTS on Linux 6.14.0-1017-azure ``` ``` - OpenJDK 64-Bit Server VM 21.0.8+9-LTS on Linux 6.11.0-1018-azure + OpenJDK 64-Bit Server VM 21.0.10+7-LTS on Linux 6.14.0-1017-azure ``` ### Does this PR introduce _any_ user-facing change? No. This is a change on benchmark result files. ### How was this patch tested? Manual review. ### Was this patch authored or co-authored using generative AI tooling? No. Closesapache#54313 from dongjoon-hyun/SPARK-55520. Lead-authored-by: Dongjoon Hyun <dongjoon@apache.org> Co-authored-by: dongjoon-hyun <dongjoon-hyun@users.noreply.github.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

[SPARK-53893][TESTS] Regenerate benchmark results after upgrading to …
7b7ce19
…Scala 2.13.17

github-actionsBot added SQL MLLIB CORE AVRO labels Oct 14, 2025

dongjoon-hyun commented Oct 14, 2025
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LuciferYang approved these changes Oct 14, 2025
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yaooqinn approved these changes Oct 14, 2025
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dongjoon-hyun commented Oct 14, 2025
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dongjoon-hyun closed this in 8898ec9Oct 14, 2025

dongjoon-hyun deleted the SPARK-53893 branch October 14, 2025 03:18

dongjoon-hyun mentioned this pull request Feb 13, 2026
[SPARK-55520][TESTS] Regenerate benchmark results #54313
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[SPARK-53893][TESTS] Regenerate benchmark results after upgrading to Scala 2.13.17 - #52600

[SPARK-53893][TESTS] Regenerate benchmark results after upgrading to Scala 2.13.17#52600
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	defgetNThreads:Int= {
	varnThreads=Runtime.getRuntime.availableProcessors
	valjmx=ManagementFactory.getOperatingSystemMXBean
	if (jmx !=null) {
	valloadAverage= jmx.getSystemLoadAverage.toInt
	if (nThreads >1&& loadAverage >=1) nThreads =Math.max(1, nThreads - loadAverage)
	}
	nThreads
	}

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