854a0f752e
### 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>
60 lines
5.4 KiB
Plaintext
60 lines
5.4 KiB
Plaintext
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Benchmark to measure CSV read/write performance
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================================================================================================
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OpenJDK 64-Bit Server VM 11.0.4+11-LTS on Linux 3.10.0-862.3.2.el7.x86_64
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Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
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Parsing quoted values: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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One quoted string 56894 57106 184 0.0 1137889.9 1.0X
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OpenJDK 64-Bit Server VM 11.0.4+11-LTS on Linux 3.10.0-862.3.2.el7.x86_64
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Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
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Wide rows with 1000 columns: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Select 1000 columns 220825 222234 2018 0.0 220825.5 1.0X
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Select 100 columns 50507 50723 278 0.0 50506.6 4.4X
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Select one column 38629 38642 16 0.0 38628.6 5.7X
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count() 8549 8597 51 0.1 8549.2 25.8X
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Select 100 columns, one bad input field 68309 68474 182 0.0 68309.2 3.2X
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Select 100 columns, corrupt record field 74551 74701 136 0.0 74551.5 3.0X
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OpenJDK 64-Bit Server VM 11.0.4+11-LTS on Linux 3.10.0-862.3.2.el7.x86_64
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Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
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Count a dataset with 10 columns: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Select 10 columns + count() 27745 28050 276 0.4 2774.5 1.0X
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Select 1 column + count() 19989 20315 319 0.5 1998.9 1.4X
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count() 6091 6109 25 1.6 609.1 4.6X
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OpenJDK 64-Bit Server VM 11.0.4+11-LTS on Linux 3.10.0-862.3.2.el7.x86_64
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Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
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Write dates and timestamps: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Create a dataset of timestamps 2235 2301 59 4.5 223.5 1.0X
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to_csv(timestamp) 16033 16205 153 0.6 1603.3 0.1X
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write timestamps to files 13556 13685 167 0.7 1355.6 0.2X
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Create a dataset of dates 2262 2290 44 4.4 226.2 1.0X
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to_csv(date) 11122 11160 33 0.9 1112.2 0.2X
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write dates to files 8436 8486 76 1.2 843.6 0.3X
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OpenJDK 64-Bit Server VM 11.0.4+11-LTS on Linux 3.10.0-862.3.2.el7.x86_64
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Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
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Read dates and timestamps: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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read timestamp text from files 2617 2644 26 3.8 261.7 1.0X
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read timestamps from files 53245 53381 149 0.2 5324.5 0.0X
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infer timestamps from files 103797 104026 257 0.1 10379.7 0.0X
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read date text from files 2371 2378 7 4.2 237.1 1.1X
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read date from files 41808 41929 177 0.2 4180.8 0.1X
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infer date from files 35069 35336 458 0.3 3506.9 0.1X
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timestamp strings 3104 3127 21 3.2 310.4 0.8X
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parse timestamps from Dataset[String] 61888 62132 342 0.2 6188.8 0.0X
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infer timestamps from Dataset[String] 112494 114609 1949 0.1 11249.4 0.0X
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date strings 3558 3603 41 2.8 355.8 0.7X
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parse dates from Dataset[String] 45871 46000 120 0.2 4587.1 0.1X
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from_csv(timestamp) 56975 57035 53 0.2 5697.5 0.0X
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from_csv(date) 43711 43795 74 0.2 4371.1 0.1X
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