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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 1.8.0_282-b08 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() 2468 2609 192 40.5 24.7 1.0X
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make_date(2019, 9, 16) 2260 2298 44 44.2 22.6 1.1X
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make_date(*, *, *) 4163 4247 74 24.0 41.6 0.6X
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OpenJDK 64-Bit Server VM 1.8.0_282-b08 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() 3667 3722 54 0.3 3667.0 1.0X
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make_timestamp(2019, 1, 2, 3, 4, 50.123456) 44 44 0 22.9 43.6 84.0X
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make_timestamp(2019, 1, 2, 3, 4, 60.000000) 41 42 1 24.3 41.1 89.1X
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make_timestamp(2019, 12, 31, 23, 59, 60.00) 41 43 3 24.6 40.7 90.0X
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make_timestamp(*, *, *, 3, 4, 50.123456) 244 247 2 4.1 244.4 15.0X
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make_timestamp(*, *, *, *, *, 0) 241 248 7 4.2 240.6 15.2X
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make_timestamp(*, *, *, *, *, 60.0) 239 245 6 4.2 239.5 15.3X
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make_timestamp(2019, 1, 2, *, *, *) 3894 3944 58 0.3 3893.6 0.9X
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make_timestamp(*, *, *, *, *, *) 3952 3976 38 0.3 3951.8 0.9X
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