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>
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54 lines
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Nested Schema Pruning Benchmark For Parquet
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================================================================================================
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OpenJDK 64-Bit Server VM 11.0.5+10-post-Ubuntu-0ubuntu1.118.04 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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Selection: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Top-level column 150 218 36 6.6 150.5 1.0X
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Nested column 294 334 39 3.4 293.8 0.5X
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Nested column in array 994 1024 34 1.0 994.3 0.2X
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OpenJDK 64-Bit Server VM 11.0.5+10-post-Ubuntu-0ubuntu1.118.04 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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Limiting: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Top-level column 161 183 13 6.2 160.9 1.0X
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Nested column 296 332 43 3.4 295.6 0.5X
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Nested column in array 1048 1089 48 1.0 1047.9 0.2X
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OpenJDK 64-Bit Server VM 11.0.5+10-post-Ubuntu-0ubuntu1.118.04 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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Repartitioning: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Top-level column 393 411 16 2.5 393.1 1.0X
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Nested column 550 563 9 1.8 549.9 0.7X
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Nested column in array 1468 1494 20 0.7 1467.5 0.3X
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OpenJDK 64-Bit Server VM 11.0.5+10-post-Ubuntu-0ubuntu1.118.04 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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Repartitioning by exprs: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Top-level column 404 419 15 2.5 404.2 1.0X
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Nested column 2833 2875 24 0.4 2832.8 0.1X
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Nested column in array 3340 3396 53 0.3 3340.3 0.1X
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OpenJDK 64-Bit Server VM 11.0.5+10-post-Ubuntu-0ubuntu1.118.04 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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Sample: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Top-level column 142 159 12 7.0 142.3 1.0X
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Nested column 308 318 8 3.2 307.8 0.5X
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Nested column in array 1119 1154 32 0.9 1118.5 0.1X
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OpenJDK 64-Bit Server VM 11.0.5+10-post-Ubuntu-0ubuntu1.118.04 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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Sorting: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
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Top-level column 338 342 2 3.0 338.0 1.0X
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Nested column 1873 1971 129 0.5 1873.1 0.2X
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Nested column in array 2708 2760 50 0.4 2707.9 0.1X
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