f5118f81e3
### What changes were proposed in this pull request?
In the PR, I propose to replace `.collect()`, `.count()` and `.foreach(_ => ())` in SQL benchmarks and use the `NoOp` datasource. I added an implicit class to `SqlBasedBenchmark` with the `.noop()` method. It can be used in benchmark like: `ds.noop()`. The last one is unfolded to `ds.write.format("noop").mode(Overwrite).save()`.
### Why are the changes needed?
To avoid additional overhead that `collect()` (and other actions) has. For example, `.collect()` has to convert values according to external types and pull data to the driver. This can hide actual performance regressions or improvements of benchmarked operations.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Re-run all modified benchmarks using Amazon EC2.
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge (spot instance) |
| AMI | ami-06f2f779464715dc5 (ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1) |
| Java | OpenJDK8/10 |
- Run `TPCDSQueryBenchmark` using instructions from the PR #26049
```
# `spark-tpcds-datagen` needs this. (JDK8)
$ git clone https://github.com/apache/spark.git -b branch-2.4 --depth 1 spark-2.4
$ export SPARK_HOME=$PWD
$ ./build/mvn clean package -DskipTests
# Generate data. (JDK8)
$ git clone gitgithub.com:maropu/spark-tpcds-datagen.git
$ cd spark-tpcds-datagen/
$ build/mvn clean package
$ mkdir -p /data/tpcds
$ ./bin/dsdgen --output-location /data/tpcds/s1 // This need `Spark 2.4`
```
- Other benchmarks ran by the script:
```
#!/usr/bin/env python3
import os
from sparktestsupport.shellutils import run_cmd
benchmarks = [
['sql/test', 'org.apache.spark.sql.execution.benchmark.AggregateBenchmark'],
['avro/test', 'org.apache.spark.sql.execution.benchmark.AvroReadBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.BloomFilterBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.DataSourceReadBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.DateTimeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.ExtractBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.InExpressionBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.IntervalBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.JoinBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.MakeDateTimeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.MiscBenchmark'],
['hive/test', 'org.apache.spark.sql.execution.benchmark.ObjectHashAggregateExecBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.OrcNestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.OrcV2NestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.ParquetNestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.RangeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.UDFBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.WideSchemaBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.WideTableBenchmark'],
['hive/test', 'org.apache.spark.sql.hive.orc.OrcReadBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.datasources.csv.CSVBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.datasources.json.JsonBenchmark']
]
print('Set SPARK_GENERATE_BENCHMARK_FILES=1')
os.environ['SPARK_GENERATE_BENCHMARK_FILES'] = '1'
for b in benchmarks:
print("Run benchmark: %s" % b[1])
run_cmd(['build/sbt', '%s:runMain %s' % (b[0], b[1])])
```
Closes #27078 from MaxGekk/noop-in-benchmarks.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
30 lines
3.2 KiB
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30 lines
3.2 KiB
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OpenJDK 64-Bit Server VM 1.8.0_232-8u232-b09-0ubuntu1~18.04.1-b09 on Linux 4.15.0-1044-aws
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Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
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cast strings to intervals: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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prepare string w/ interval 648 721 94 1.5 648.3 1.0X
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prepare string w/o interval 562 596 49 1.8 562.3 1.2X
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1 units w/ interval 568 590 21 1.8 568.5 1.1X
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1 units w/o interval 522 538 20 1.9 521.7 1.2X
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2 units w/ interval 751 754 3 1.3 751.5 0.9X
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2 units w/o interval 716 723 6 1.4 716.1 0.9X
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3 units w/ interval 1402 1411 11 0.7 1401.6 0.5X
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3 units w/o interval 1381 1387 5 0.7 1381.2 0.5X
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4 units w/ interval 1591 1595 6 0.6 1591.2 0.4X
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4 units w/o interval 1582 1585 3 0.6 1582.3 0.4X
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5 units w/ interval 1747 1749 2 0.6 1747.3 0.4X
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5 units w/o interval 1738 1746 10 0.6 1737.7 0.4X
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6 units w/ interval 1929 1931 3 0.5 1929.1 0.3X
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6 units w/o interval 1919 1922 2 0.5 1919.0 0.3X
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7 units w/ interval 2345 2354 8 0.4 2345.0 0.3X
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7 units w/o interval 2334 2336 2 0.4 2334.1 0.3X
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8 units w/ interval 2533 2546 16 0.4 2533.0 0.3X
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8 units w/o interval 2519 2521 1 0.4 2519.4 0.3X
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9 units w/ interval 2885 2889 5 0.3 2884.5 0.2X
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9 units w/o interval 2804 2813 12 0.4 2803.9 0.2X
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10 units w/ interval 3041 3060 16 0.3 3041.3 0.2X
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10 units w/o interval 3031 3043 15 0.3 3031.2 0.2X
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11 units w/ interval 3270 3280 9 0.3 3269.9 0.2X
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11 units w/o interval 3273 3280 7 0.3 3272.6 0.2X
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