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### What changes were proposed in this pull request? https://github.com/apache/spark/pull/32015 added a way to run benchmarks much more easily in the same GitHub Actions build. This PR updates the benchmark results by using the way. **NOTE** that looks like GitHub Actions use four types of CPU given my observations: - Intel(R) Xeon(R) Platinum 8171M CPU 2.60GHz - Intel(R) Xeon(R) CPU E5-2673 v4 2.30GHz - Intel(R) Xeon(R) CPU E5-2673 v3 2.40GHz - Intel(R) Xeon(R) Platinum 8272CL CPU 2.60GHz Given my quick research, seems like they perform roughly similarly:  I couldn't find enough information about Intel(R) Xeon(R) Platinum 8272CL CPU 2.60GHz but the performance seems roughly similar given the numbers. So shouldn't be a big deal especially given that this way is much easier, encourages contributors to run more and guarantee the same number of cores and same memory with the same softwares. ### Why are the changes needed? To have a base line of the benchmarks accordingly. ### Does this PR introduce _any_ user-facing change? No, dev-only. ### How was this patch tested? It was generated from: - [Run benchmarks: * (JDK 11)](https://github.com/HyukjinKwon/spark/actions/runs/713575465) - [Run benchmarks: * (JDK 8)](https://github.com/HyukjinKwon/spark/actions/runs/713154337) Closes #32044 from HyukjinKwon/SPARK-34950. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: Max Gekk <max.gekk@gmail.com>
23 lines
2.1 KiB
Plaintext
23 lines
2.1 KiB
Plaintext
OpenJDK 64-Bit Server VM 11.0.10+9-LTS on Linux 5.4.0-1043-azure
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Intel(R) Xeon(R) CPU E5-2673 v4 @ 2.30GHz
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make_date(): Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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prepare make_date() 4106 4124 28 24.4 41.1 1.0X
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make_date(2019, 9, 16) 3151 3190 40 31.7 31.5 1.3X
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make_date(*, *, *) 4929 4967 44 20.3 49.3 0.8X
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OpenJDK 64-Bit Server VM 11.0.10+9-LTS on Linux 5.4.0-1043-azure
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Intel(R) Xeon(R) CPU E5-2673 v4 @ 2.30GHz
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make_timestamp(): Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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prepare make_timestamp() 4039 4077 46 0.2 4038.9 1.0X
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make_timestamp(2019, 1, 2, 3, 4, 50.123456) 54 58 6 18.7 53.5 75.5X
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make_timestamp(2019, 1, 2, 3, 4, 60.000000) 51 57 6 19.8 50.6 79.9X
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make_timestamp(2019, 12, 31, 23, 59, 60.00) 50 51 1 19.9 50.4 80.2X
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make_timestamp(*, *, *, 3, 4, 50.123456) 294 309 17 3.4 294.4 13.7X
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make_timestamp(*, *, *, *, *, 0) 264 291 28 3.8 264.0 15.3X
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make_timestamp(*, *, *, *, *, 60.0) 272 293 26 3.7 272.1 14.8X
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make_timestamp(2019, 1, 2, *, *, *) 4472 4498 24 0.2 4471.6 0.9X
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make_timestamp(*, *, *, *, *, *) 4306 4324 29 0.2 4305.9 0.9X
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