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[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
================================================================================================
Nested Schema Pruning Benchmark For Parquet
================================================================================================
[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) Platinum 8171M CPU @ 2.60GHz
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
Selection: 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
Top-level column 56 62 5 17.8 56.1 1.0X
Nested column 291 306 9 3.4 291.0 0.2X
Nested column in array 1126 1166 24 0.9 1126.1 0.0X
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
[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) Platinum 8171M CPU @ 2.60GHz
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
Limiting: 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
Top-level column 372 399 28 2.7 371.9 1.0X
Nested column 686 724 27 1.5 686.0 0.5X
Nested column in array 1892 1973 72 0.5 1892.0 0.2X
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
[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) Platinum 8171M CPU @ 2.60GHz
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
Repartitioning: 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
Top-level column 351 361 6 2.9 350.6 1.0X
Nested column 661 677 8 1.5 660.6 0.5X
Nested column in array 1886 1935 41 0.5 1886.3 0.2X
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
[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) Platinum 8171M CPU @ 2.60GHz
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
Repartitioning by exprs: 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
Top-level column 322 343 15 3.1 322.2 1.0X
Nested column 686 712 23 1.5 686.4 0.5X
Nested column in array 1813 1918 67 0.6 1813.1 0.2X
[SPARK-26975][SQL] Support nested-column pruning over limit/sample/repartition ## What changes were proposed in this pull request? As [SPARK-26958](https://github.com/apache/spark/pull/23862/files) benchmark shows, nested-column pruning has limitations. This PR aims to remove the limitations on `limit/repartition/sample`. Here, repartition means `Repartition`, not `RepartitionByExpression`. **PREPARATION** ```scala scala> spark.range(100).map(x => (x, (x, s"$x" * 100))).toDF("col1", "col2").write.mode("overwrite").save("/tmp/p") scala> sql("set spark.sql.optimizer.nestedSchemaPruning.enabled=true") scala> spark.read.parquet("/tmp/p").createOrReplaceTempView("t") ``` **BEFORE** ```scala scala> sql("SELECT col2._1 FROM (SELECT col2 FROM t LIMIT 1000000)").explain == Physical Plan == CollectLimit 1000000 +- *(1) Project [col2#22._1 AS _1#28L] +- *(1) FileScan parquet [col2#22] Batched: false, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/tmp/p], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<col2:struct<_1:bigint>> scala> sql("SELECT col2._1 FROM (SELECT /*+ REPARTITION(1) */ col2 FROM t)").explain == Physical Plan == *(2) Project [col2#22._1 AS _1#33L] +- Exchange RoundRobinPartitioning(1) +- *(1) Project [col2#22] +- *(1) FileScan parquet [col2#22] Batched: false, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/tmp/p], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<col2:struct<_1:bigint,_2:string>> ``` **AFTER** ```scala scala> sql("SELECT col2._1 FROM (SELECT /*+ REPARTITION(1) */ col2 FROM t)").explain == Physical Plan == Exchange RoundRobinPartitioning(1) +- *(1) Project [col2#5._1 AS _1#11L] +- *(1) FileScan parquet [col2#5] Batched: false, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/tmp/p], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<col2:struct<_1:bigint>> ``` This supercedes https://github.com/apache/spark/pull/23542 and https://github.com/apache/spark/pull/23873 . ## How was this patch tested? Pass the Jenkins with a newly added test suite. Closes #23964 from dongjoon-hyun/SPARK-26975-ALIAS. Lead-authored-by: Dongjoon Hyun <dhyun@apple.com> Co-authored-by: DB Tsai <d_tsai@apple.com> Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com> Co-authored-by: Takeshi Yamamuro <yamamuro@apache.org> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-19 23:24:22 -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) Platinum 8171M CPU @ 2.60GHz
[SPARK-26975][SQL] Support nested-column pruning over limit/sample/repartition ## What changes were proposed in this pull request? As [SPARK-26958](https://github.com/apache/spark/pull/23862/files) benchmark shows, nested-column pruning has limitations. This PR aims to remove the limitations on `limit/repartition/sample`. Here, repartition means `Repartition`, not `RepartitionByExpression`. **PREPARATION** ```scala scala> spark.range(100).map(x => (x, (x, s"$x" * 100))).toDF("col1", "col2").write.mode("overwrite").save("/tmp/p") scala> sql("set spark.sql.optimizer.nestedSchemaPruning.enabled=true") scala> spark.read.parquet("/tmp/p").createOrReplaceTempView("t") ``` **BEFORE** ```scala scala> sql("SELECT col2._1 FROM (SELECT col2 FROM t LIMIT 1000000)").explain == Physical Plan == CollectLimit 1000000 +- *(1) Project [col2#22._1 AS _1#28L] +- *(1) FileScan parquet [col2#22] Batched: false, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/tmp/p], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<col2:struct<_1:bigint>> scala> sql("SELECT col2._1 FROM (SELECT /*+ REPARTITION(1) */ col2 FROM t)").explain == Physical Plan == *(2) Project [col2#22._1 AS _1#33L] +- Exchange RoundRobinPartitioning(1) +- *(1) Project [col2#22] +- *(1) FileScan parquet [col2#22] Batched: false, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/tmp/p], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<col2:struct<_1:bigint,_2:string>> ``` **AFTER** ```scala scala> sql("SELECT col2._1 FROM (SELECT /*+ REPARTITION(1) */ col2 FROM t)").explain == Physical Plan == Exchange RoundRobinPartitioning(1) +- *(1) Project [col2#5._1 AS _1#11L] +- *(1) FileScan parquet [col2#5] Batched: false, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/tmp/p], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<col2:struct<_1:bigint>> ``` This supercedes https://github.com/apache/spark/pull/23542 and https://github.com/apache/spark/pull/23873 . ## How was this patch tested? Pass the Jenkins with a newly added test suite. Closes #23964 from dongjoon-hyun/SPARK-26975-ALIAS. Lead-authored-by: Dongjoon Hyun <dhyun@apple.com> Co-authored-by: DB Tsai <d_tsai@apple.com> Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com> Co-authored-by: Takeshi Yamamuro <yamamuro@apache.org> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-19 23:24:22 -04:00
Sample: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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[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
Top-level column 81 92 8 12.3 81.2 1.0X
Nested column 331 393 42 3.0 330.6 0.2X
Nested column in array 1209 1324 70 0.8 1209.5 0.1X
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
[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) Platinum 8171M CPU @ 2.60GHz
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
Sorting: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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[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
Top-level column 475 482 4 2.1 475.1 1.0X
Nested column 843 883 17 1.2 843.2 0.6X
Nested column in array 1895 1936 33 0.5 1895.1 0.3X
[SPARK-27043][SQL] Add ORC nested schema pruning benchmarks ## What changes were proposed in this pull request? We have benchmark of nested schema pruning, but only for Parquet. This adds similar benchmark for ORC. This is used with nested schema pruning of ORC. ## How was this patch tested? Added test. Closes #23955 from viirya/orc-nested-schema-pruning-benchmark. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-03-05 14:12:57 -05:00
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