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[SPARK-25479][TEST] Refactor DatasetBenchmark to use main method ## What changes were proposed in this pull request? Refactor `DatasetBenchmark` to use main method. Generate benchmark result: ```sh SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.DatasetBenchmark" ``` ## How was this patch tested? manual tests Closes #22488 from wangyum/SPARK-25479. Lead-authored-by: Yuming Wang <yumwang@ebay.com> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2018-10-04 14:58:16 -04:00
================================================================================================
Dataset Benchmark
================================================================================================
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
OpenJDK 64-Bit Server VM 1.8.0_282-b08 on Linux 5.4.0-1043-azure
Intel(R) Xeon(R) CPU E5-2673 v3 @ 2.40GHz
[SPARK-29320][TESTS] Compare `sql/core` module in JDK8/11 (Part 1) ### What changes were proposed in this pull request? This PR regenerates the `sql/core` benchmarks in JDK8/11 to compare the result. In general, we compare the ratio instead of the time. However, in this PR, the average time is compared. This PR should be considered as a rough comparison. **A. EXPECTED CASES(JDK11 is faster in general)** - [x] BloomFilterBenchmark (JDK11 is faster except one case) - [x] BuiltInDataSourceWriteBenchmark (JDK11 is faster at CSV/ORC) - [x] CSVBenchmark (JDK11 is faster except five cases) - [x] ColumnarBatchBenchmark (JDK11 is faster at `boolean`/`string` and some cases in `int`/`array`) - [x] DatasetBenchmark (JDK11 is faster with `string`, but is slower for `long` type) - [x] ExternalAppendOnlyUnsafeRowArrayBenchmark (JDK11 is faster except two cases) - [x] ExtractBenchmark (JDK11 is faster except HOUR/MINUTE/SECOND/MILLISECONDS/MICROSECONDS) - [x] HashedRelationMetricsBenchmark (JDK11 is faster) - [x] JSONBenchmark (JDK11 is much faster except eight cases) - [x] JoinBenchmark (JDK11 is faster except five cases) - [x] OrcNestedSchemaPruningBenchmark (JDK11 is faster in nine cases) - [x] PrimitiveArrayBenchmark (JDK11 is faster) - [x] SortBenchmark (JDK11 is faster except `Arrays.sort` case) - [x] UDFBenchmark (N/A, values are too small) - [x] UnsafeArrayDataBenchmark (JDK11 is faster except one case) - [x] WideTableBenchmark (JDK11 is faster except two cases) **B. CASES WE NEED TO INVESTIGATE MORE LATER** - [x] AggregateBenchmark (JDK11 is slower in general) - [x] CompressionSchemeBenchmark (JDK11 is slower in general except `string`) - [x] DataSourceReadBenchmark (JDK11 is slower in general) - [x] DateTimeBenchmark (JDK11 is slightly slower in general except `parsing`) - [x] MakeDateTimeBenchmark (JDK11 is slower except two cases) - [x] MiscBenchmark (JDK11 is slower except ten cases) - [x] OrcV2NestedSchemaPruningBenchmark (JDK11 is slower) - [x] ParquetNestedSchemaPruningBenchmark (JDK11 is slower except six cases) - [x] RangeBenchmark (JDK11 is slower except one case) `FilterPushdownBenchmark/InExpressionBenchmark/WideSchemaBenchmark` will be compared later because it took long timer. ### Why are the changes needed? According to the result, there are some difference between JDK8/JDK11. This will be a baseline for the future improvement and comparison. Also, as a reproducible environment, the following environment is used. - Instance: `r3.xlarge` - OS: `CentOS Linux release 7.5.1804 (Core)` - JDK: - `OpenJDK Runtime Environment (build 1.8.0_222-b10)` - `OpenJDK Runtime Environment 18.9 (build 11.0.4+11-LTS)` ### Does this PR introduce any user-facing change? No. ### How was this patch tested? This is a test-only PR. We need to run benchmark. Closes #26003 from dongjoon-hyun/SPARK-29320. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-10-03 11:58:25 -04:00
back-to-back map long: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
RDD 12276 12399 175 8.1 122.8 1.0X
DataFrame 2017 2094 110 49.6 20.2 6.1X
Dataset 3034 3044 15 33.0 30.3 4.0X
[SPARK-25479][TEST] Refactor DatasetBenchmark to use main method ## What changes were proposed in this pull request? Refactor `DatasetBenchmark` to use main method. Generate benchmark result: ```sh SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.DatasetBenchmark" ``` ## How was this patch tested? manual tests Closes #22488 from wangyum/SPARK-25479. Lead-authored-by: Yuming Wang <yumwang@ebay.com> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2018-10-04 14:58:16 -04:00
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
OpenJDK 64-Bit Server VM 1.8.0_282-b08 on Linux 5.4.0-1043-azure
Intel(R) Xeon(R) CPU E5-2673 v3 @ 2.40GHz
[SPARK-29320][TESTS] Compare `sql/core` module in JDK8/11 (Part 1) ### What changes were proposed in this pull request? This PR regenerates the `sql/core` benchmarks in JDK8/11 to compare the result. In general, we compare the ratio instead of the time. However, in this PR, the average time is compared. This PR should be considered as a rough comparison. **A. EXPECTED CASES(JDK11 is faster in general)** - [x] BloomFilterBenchmark (JDK11 is faster except one case) - [x] BuiltInDataSourceWriteBenchmark (JDK11 is faster at CSV/ORC) - [x] CSVBenchmark (JDK11 is faster except five cases) - [x] ColumnarBatchBenchmark (JDK11 is faster at `boolean`/`string` and some cases in `int`/`array`) - [x] DatasetBenchmark (JDK11 is faster with `string`, but is slower for `long` type) - [x] ExternalAppendOnlyUnsafeRowArrayBenchmark (JDK11 is faster except two cases) - [x] ExtractBenchmark (JDK11 is faster except HOUR/MINUTE/SECOND/MILLISECONDS/MICROSECONDS) - [x] HashedRelationMetricsBenchmark (JDK11 is faster) - [x] JSONBenchmark (JDK11 is much faster except eight cases) - [x] JoinBenchmark (JDK11 is faster except five cases) - [x] OrcNestedSchemaPruningBenchmark (JDK11 is faster in nine cases) - [x] PrimitiveArrayBenchmark (JDK11 is faster) - [x] SortBenchmark (JDK11 is faster except `Arrays.sort` case) - [x] UDFBenchmark (N/A, values are too small) - [x] UnsafeArrayDataBenchmark (JDK11 is faster except one case) - [x] WideTableBenchmark (JDK11 is faster except two cases) **B. CASES WE NEED TO INVESTIGATE MORE LATER** - [x] AggregateBenchmark (JDK11 is slower in general) - [x] CompressionSchemeBenchmark (JDK11 is slower in general except `string`) - [x] DataSourceReadBenchmark (JDK11 is slower in general) - [x] DateTimeBenchmark (JDK11 is slightly slower in general except `parsing`) - [x] MakeDateTimeBenchmark (JDK11 is slower except two cases) - [x] MiscBenchmark (JDK11 is slower except ten cases) - [x] OrcV2NestedSchemaPruningBenchmark (JDK11 is slower) - [x] ParquetNestedSchemaPruningBenchmark (JDK11 is slower except six cases) - [x] RangeBenchmark (JDK11 is slower except one case) `FilterPushdownBenchmark/InExpressionBenchmark/WideSchemaBenchmark` will be compared later because it took long timer. ### Why are the changes needed? According to the result, there are some difference between JDK8/JDK11. This will be a baseline for the future improvement and comparison. Also, as a reproducible environment, the following environment is used. - Instance: `r3.xlarge` - OS: `CentOS Linux release 7.5.1804 (Core)` - JDK: - `OpenJDK Runtime Environment (build 1.8.0_222-b10)` - `OpenJDK Runtime Environment 18.9 (build 11.0.4+11-LTS)` ### Does this PR introduce any user-facing change? No. ### How was this patch tested? This is a test-only PR. We need to run benchmark. Closes #26003 from dongjoon-hyun/SPARK-29320. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-10-03 11:58:25 -04:00
back-to-back map: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
RDD 16325 16361 50 6.1 163.2 1.0X
DataFrame 8463 8468 6 11.8 84.6 1.9X
Dataset 22525 23091 801 4.4 225.2 0.7X
[SPARK-25479][TEST] Refactor DatasetBenchmark to use main method ## What changes were proposed in this pull request? Refactor `DatasetBenchmark` to use main method. Generate benchmark result: ```sh SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.DatasetBenchmark" ``` ## How was this patch tested? manual tests Closes #22488 from wangyum/SPARK-25479. Lead-authored-by: Yuming Wang <yumwang@ebay.com> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2018-10-04 14:58:16 -04:00
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
OpenJDK 64-Bit Server VM 1.8.0_282-b08 on Linux 5.4.0-1043-azure
Intel(R) Xeon(R) CPU E5-2673 v3 @ 2.40GHz
[SPARK-29320][TESTS] Compare `sql/core` module in JDK8/11 (Part 1) ### What changes were proposed in this pull request? This PR regenerates the `sql/core` benchmarks in JDK8/11 to compare the result. In general, we compare the ratio instead of the time. However, in this PR, the average time is compared. This PR should be considered as a rough comparison. **A. EXPECTED CASES(JDK11 is faster in general)** - [x] BloomFilterBenchmark (JDK11 is faster except one case) - [x] BuiltInDataSourceWriteBenchmark (JDK11 is faster at CSV/ORC) - [x] CSVBenchmark (JDK11 is faster except five cases) - [x] ColumnarBatchBenchmark (JDK11 is faster at `boolean`/`string` and some cases in `int`/`array`) - [x] DatasetBenchmark (JDK11 is faster with `string`, but is slower for `long` type) - [x] ExternalAppendOnlyUnsafeRowArrayBenchmark (JDK11 is faster except two cases) - [x] ExtractBenchmark (JDK11 is faster except HOUR/MINUTE/SECOND/MILLISECONDS/MICROSECONDS) - [x] HashedRelationMetricsBenchmark (JDK11 is faster) - [x] JSONBenchmark (JDK11 is much faster except eight cases) - [x] JoinBenchmark (JDK11 is faster except five cases) - [x] OrcNestedSchemaPruningBenchmark (JDK11 is faster in nine cases) - [x] PrimitiveArrayBenchmark (JDK11 is faster) - [x] SortBenchmark (JDK11 is faster except `Arrays.sort` case) - [x] UDFBenchmark (N/A, values are too small) - [x] UnsafeArrayDataBenchmark (JDK11 is faster except one case) - [x] WideTableBenchmark (JDK11 is faster except two cases) **B. CASES WE NEED TO INVESTIGATE MORE LATER** - [x] AggregateBenchmark (JDK11 is slower in general) - [x] CompressionSchemeBenchmark (JDK11 is slower in general except `string`) - [x] DataSourceReadBenchmark (JDK11 is slower in general) - [x] DateTimeBenchmark (JDK11 is slightly slower in general except `parsing`) - [x] MakeDateTimeBenchmark (JDK11 is slower except two cases) - [x] MiscBenchmark (JDK11 is slower except ten cases) - [x] OrcV2NestedSchemaPruningBenchmark (JDK11 is slower) - [x] ParquetNestedSchemaPruningBenchmark (JDK11 is slower except six cases) - [x] RangeBenchmark (JDK11 is slower except one case) `FilterPushdownBenchmark/InExpressionBenchmark/WideSchemaBenchmark` will be compared later because it took long timer. ### Why are the changes needed? According to the result, there are some difference between JDK8/JDK11. This will be a baseline for the future improvement and comparison. Also, as a reproducible environment, the following environment is used. - Instance: `r3.xlarge` - OS: `CentOS Linux release 7.5.1804 (Core)` - JDK: - `OpenJDK Runtime Environment (build 1.8.0_222-b10)` - `OpenJDK Runtime Environment 18.9 (build 11.0.4+11-LTS)` ### Does this PR introduce any user-facing change? No. ### How was this patch tested? This is a test-only PR. We need to run benchmark. Closes #26003 from dongjoon-hyun/SPARK-29320. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-10-03 11:58:25 -04:00
back-to-back filter Long: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
RDD 3133 3136 3 31.9 31.3 1.0X
DataFrame 1194 1535 482 83.8 11.9 2.6X
Dataset 3146 3156 13 31.8 31.5 1.0X
[SPARK-25479][TEST] Refactor DatasetBenchmark to use main method ## What changes were proposed in this pull request? Refactor `DatasetBenchmark` to use main method. Generate benchmark result: ```sh SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.DatasetBenchmark" ``` ## How was this patch tested? manual tests Closes #22488 from wangyum/SPARK-25479. Lead-authored-by: Yuming Wang <yumwang@ebay.com> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2018-10-04 14:58:16 -04:00
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
OpenJDK 64-Bit Server VM 1.8.0_282-b08 on Linux 5.4.0-1043-azure
Intel(R) Xeon(R) CPU E5-2673 v3 @ 2.40GHz
[SPARK-29320][TESTS] Compare `sql/core` module in JDK8/11 (Part 1) ### What changes were proposed in this pull request? This PR regenerates the `sql/core` benchmarks in JDK8/11 to compare the result. In general, we compare the ratio instead of the time. However, in this PR, the average time is compared. This PR should be considered as a rough comparison. **A. EXPECTED CASES(JDK11 is faster in general)** - [x] BloomFilterBenchmark (JDK11 is faster except one case) - [x] BuiltInDataSourceWriteBenchmark (JDK11 is faster at CSV/ORC) - [x] CSVBenchmark (JDK11 is faster except five cases) - [x] ColumnarBatchBenchmark (JDK11 is faster at `boolean`/`string` and some cases in `int`/`array`) - [x] DatasetBenchmark (JDK11 is faster with `string`, but is slower for `long` type) - [x] ExternalAppendOnlyUnsafeRowArrayBenchmark (JDK11 is faster except two cases) - [x] ExtractBenchmark (JDK11 is faster except HOUR/MINUTE/SECOND/MILLISECONDS/MICROSECONDS) - [x] HashedRelationMetricsBenchmark (JDK11 is faster) - [x] JSONBenchmark (JDK11 is much faster except eight cases) - [x] JoinBenchmark (JDK11 is faster except five cases) - [x] OrcNestedSchemaPruningBenchmark (JDK11 is faster in nine cases) - [x] PrimitiveArrayBenchmark (JDK11 is faster) - [x] SortBenchmark (JDK11 is faster except `Arrays.sort` case) - [x] UDFBenchmark (N/A, values are too small) - [x] UnsafeArrayDataBenchmark (JDK11 is faster except one case) - [x] WideTableBenchmark (JDK11 is faster except two cases) **B. CASES WE NEED TO INVESTIGATE MORE LATER** - [x] AggregateBenchmark (JDK11 is slower in general) - [x] CompressionSchemeBenchmark (JDK11 is slower in general except `string`) - [x] DataSourceReadBenchmark (JDK11 is slower in general) - [x] DateTimeBenchmark (JDK11 is slightly slower in general except `parsing`) - [x] MakeDateTimeBenchmark (JDK11 is slower except two cases) - [x] MiscBenchmark (JDK11 is slower except ten cases) - [x] OrcV2NestedSchemaPruningBenchmark (JDK11 is slower) - [x] ParquetNestedSchemaPruningBenchmark (JDK11 is slower except six cases) - [x] RangeBenchmark (JDK11 is slower except one case) `FilterPushdownBenchmark/InExpressionBenchmark/WideSchemaBenchmark` will be compared later because it took long timer. ### Why are the changes needed? According to the result, there are some difference between JDK8/JDK11. This will be a baseline for the future improvement and comparison. Also, as a reproducible environment, the following environment is used. - Instance: `r3.xlarge` - OS: `CentOS Linux release 7.5.1804 (Core)` - JDK: - `OpenJDK Runtime Environment (build 1.8.0_222-b10)` - `OpenJDK Runtime Environment 18.9 (build 11.0.4+11-LTS)` ### Does this PR introduce any user-facing change? No. ### How was this patch tested? This is a test-only PR. We need to run benchmark. Closes #26003 from dongjoon-hyun/SPARK-29320. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-10-03 11:58:25 -04:00
back-to-back filter: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
RDD 5334 5352 25 18.7 53.3 1.0X
DataFrame 190 221 21 527.1 1.9 28.1X
Dataset 10536 10630 133 9.5 105.4 0.5X
[SPARK-25479][TEST] Refactor DatasetBenchmark to use main method ## What changes were proposed in this pull request? Refactor `DatasetBenchmark` to use main method. Generate benchmark result: ```sh SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.DatasetBenchmark" ``` ## How was this patch tested? manual tests Closes #22488 from wangyum/SPARK-25479. Lead-authored-by: Yuming Wang <yumwang@ebay.com> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2018-10-04 14:58:16 -04:00
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
OpenJDK 64-Bit Server VM 1.8.0_282-b08 on Linux 5.4.0-1043-azure
Intel(R) Xeon(R) CPU E5-2673 v3 @ 2.40GHz
[SPARK-29320][TESTS] Compare `sql/core` module in JDK8/11 (Part 1) ### What changes were proposed in this pull request? This PR regenerates the `sql/core` benchmarks in JDK8/11 to compare the result. In general, we compare the ratio instead of the time. However, in this PR, the average time is compared. This PR should be considered as a rough comparison. **A. EXPECTED CASES(JDK11 is faster in general)** - [x] BloomFilterBenchmark (JDK11 is faster except one case) - [x] BuiltInDataSourceWriteBenchmark (JDK11 is faster at CSV/ORC) - [x] CSVBenchmark (JDK11 is faster except five cases) - [x] ColumnarBatchBenchmark (JDK11 is faster at `boolean`/`string` and some cases in `int`/`array`) - [x] DatasetBenchmark (JDK11 is faster with `string`, but is slower for `long` type) - [x] ExternalAppendOnlyUnsafeRowArrayBenchmark (JDK11 is faster except two cases) - [x] ExtractBenchmark (JDK11 is faster except HOUR/MINUTE/SECOND/MILLISECONDS/MICROSECONDS) - [x] HashedRelationMetricsBenchmark (JDK11 is faster) - [x] JSONBenchmark (JDK11 is much faster except eight cases) - [x] JoinBenchmark (JDK11 is faster except five cases) - [x] OrcNestedSchemaPruningBenchmark (JDK11 is faster in nine cases) - [x] PrimitiveArrayBenchmark (JDK11 is faster) - [x] SortBenchmark (JDK11 is faster except `Arrays.sort` case) - [x] UDFBenchmark (N/A, values are too small) - [x] UnsafeArrayDataBenchmark (JDK11 is faster except one case) - [x] WideTableBenchmark (JDK11 is faster except two cases) **B. CASES WE NEED TO INVESTIGATE MORE LATER** - [x] AggregateBenchmark (JDK11 is slower in general) - [x] CompressionSchemeBenchmark (JDK11 is slower in general except `string`) - [x] DataSourceReadBenchmark (JDK11 is slower in general) - [x] DateTimeBenchmark (JDK11 is slightly slower in general except `parsing`) - [x] MakeDateTimeBenchmark (JDK11 is slower except two cases) - [x] MiscBenchmark (JDK11 is slower except ten cases) - [x] OrcV2NestedSchemaPruningBenchmark (JDK11 is slower) - [x] ParquetNestedSchemaPruningBenchmark (JDK11 is slower except six cases) - [x] RangeBenchmark (JDK11 is slower except one case) `FilterPushdownBenchmark/InExpressionBenchmark/WideSchemaBenchmark` will be compared later because it took long timer. ### Why are the changes needed? According to the result, there are some difference between JDK8/JDK11. This will be a baseline for the future improvement and comparison. Also, as a reproducible environment, the following environment is used. - Instance: `r3.xlarge` - OS: `CentOS Linux release 7.5.1804 (Core)` - JDK: - `OpenJDK Runtime Environment (build 1.8.0_222-b10)` - `OpenJDK Runtime Environment 18.9 (build 11.0.4+11-LTS)` ### Does this PR introduce any user-facing change? No. ### How was this patch tested? This is a test-only PR. We need to run benchmark. Closes #26003 from dongjoon-hyun/SPARK-29320. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-10-03 11:58:25 -04:00
aggregate: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
[SPARK-34950][TESTS] Update benchmark results to the ones created by GitHub Actions machines ### 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: ![Screen Shot 2021-04-03 at 9 31 23 PM](https://user-images.githubusercontent.com/6477701/113478478-f4b57b80-94c3-11eb-9047-f81ca8c59672.png) 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>
2021-04-03 16:02:56 -04:00
RDD sum 4970 5099 181 20.1 49.7 1.0X
DataFrame sum 67 81 9 1483.8 0.7 73.8X
Dataset sum using Aggregator 9474 9771 420 10.6 94.7 0.5X
Dataset complex Aggregator 13975 14701 1028 7.2 139.7 0.4X
[SPARK-25479][TEST] Refactor DatasetBenchmark to use main method ## What changes were proposed in this pull request? Refactor `DatasetBenchmark` to use main method. Generate benchmark result: ```sh SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.DatasetBenchmark" ``` ## How was this patch tested? manual tests Closes #22488 from wangyum/SPARK-25479. Lead-authored-by: Yuming Wang <yumwang@ebay.com> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2018-10-04 14:58:16 -04:00
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