ebf01ec3c1
### 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>
47 lines
3.8 KiB
Plaintext
47 lines
3.8 KiB
Plaintext
================================================================================================
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Dataset Benchmark
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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 v3 @ 2.40GHz
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back-to-back map long: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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RDD 12276 12399 175 8.1 122.8 1.0X
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DataFrame 2017 2094 110 49.6 20.2 6.1X
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Dataset 3034 3044 15 33.0 30.3 4.0X
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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 v3 @ 2.40GHz
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back-to-back map: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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RDD 16325 16361 50 6.1 163.2 1.0X
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DataFrame 8463 8468 6 11.8 84.6 1.9X
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Dataset 22525 23091 801 4.4 225.2 0.7X
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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 v3 @ 2.40GHz
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back-to-back filter Long: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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RDD 3133 3136 3 31.9 31.3 1.0X
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DataFrame 1194 1535 482 83.8 11.9 2.6X
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Dataset 3146 3156 13 31.8 31.5 1.0X
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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 v3 @ 2.40GHz
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back-to-back filter: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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RDD 5334 5352 25 18.7 53.3 1.0X
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DataFrame 190 221 21 527.1 1.9 28.1X
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Dataset 10536 10630 133 9.5 105.4 0.5X
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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 v3 @ 2.40GHz
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aggregate: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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RDD sum 4970 5099 181 20.1 49.7 1.0X
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DataFrame sum 67 81 9 1483.8 0.7 73.8X
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Dataset sum using Aggregator 9474 9771 420 10.6 94.7 0.5X
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Dataset complex Aggregator 13975 14701 1028 7.2 139.7 0.4X
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