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[SPARK-24777][SQL] Add write benchmark for AVRO #22451

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[SPARK-24777][SQL] Add write benchmark for AVRO #22451

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avro write benchmark
gengliangwang Sep 17, 2018
34af59d
update comments
gengliangwang Sep 18, 2018
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40 changes: 40 additions & 0 deletions ...nal/avro/src/test/scala/org/apache/spark/sql/execution/benchmark/AvroWriteBenchmark.scala
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

package org.apache.spark.sql.execution.benchmark

/**
* Benchmark to measure Avro data sources write performance.
* Usage:
* 1. with spark-submit: bin/spark-submit --class <this class> <spark sql test jar>
* 2. with sbt: build/sbt "avro/test:runMain <this class>"
*/
object AvroWriteBenchmark extends DataSourceWriteBenchmark {
def main(args: Array[String]): Unit = {
/*
Intel(R) Core(TM) i7-6920HQ CPU @ 2.90GHz
Avro writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 2481 / 2499 6.3 157.8 1.0X
Output Single Double Column 2705 / 2710 5.8 172.0 0.9X
Output Int and String Column 5539 / 5639 2.8 352.2 0.4X
Output Partitions 4613 / 5004 3.4 293.3 0.5X
Output Buckets 5554 / 5561 2.8 353.1 0.4X
*/
runBenchmark("Avro")
}
}
79 changes: 79 additions & 0 deletions ...test/scala/org/apache/spark/sql/execution/benchmark/BuiltInDataSourceWriteBenchmark.scala
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql.execution.benchmark

/**
* Benchmark to measure built-in data sources write performance.
* By default it measures 4 data source format: Parquet, ORC, JSON, CSV. Run it with spark-submit:
* spark-submit --class <this class> <spark sql test jar>
* Or with sbt:
* build/sbt "sql/test:runMain <this class>"
*
* To measure specified formats, run it with arguments:
* spark-submit --class <this class> <spark sql test jar> format1 [format2] [...]
* Or with sbt:
* build/sbt "sql/test:runMain <this class> format1 [format2] [...]"
*/
object BuiltInDataSourceWriteBenchmark extends DataSourceWriteBenchmark {
def main(args: Array[String]): Unit = {
val formats: Seq[String] = if (args.isEmpty) {
Seq("Parquet", "ORC", "JSON", "CSV")
} else {
args
}

spark.conf.set("spark.sql.parquet.compression.codec", "snappy")
spark.conf.set("spark.sql.orc.compression.codec", "snappy")
/*
Intel(R) Core(TM) i7-6920HQ CPU @ 2.90GHz
Parquet writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 1815 / 1932 8.7 115.4 1.0X
Output Single Double Column 1877 / 1878 8.4 119.3 1.0X
Output Int and String Column 6265 / 6543 2.5 398.3 0.3X
Output Partitions 4067 / 4457 3.9 258.6 0.4X
Output Buckets 5608 / 5820 2.8 356.6 0.3X

ORC writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 1201 / 1239 13.1 76.3 1.0X
Output Single Double Column 1542 / 1600 10.2 98.0 0.8X
Output Int and String Column 6495 / 6580 2.4 412.9 0.2X
Output Partitions 3648 / 3842 4.3 231.9 0.3X
Output Buckets 5022 / 5145 3.1 319.3 0.2X

JSON writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 1988 / 2093 7.9 126.4 1.0X
Output Single Double Column 2854 / 2911 5.5 181.4 0.7X
Output Int and String Column 6467 / 6653 2.4 411.1 0.3X
Output Partitions 4548 / 5055 3.5 289.1 0.4X
Output Buckets 5664 / 5765 2.8 360.1 0.4X

CSV writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 3025 / 3190 5.2 192.3 1.0X
Output Single Double Column 3575 / 3634 4.4 227.3 0.8X
Output Int and String Column 7313 / 7399 2.2 464.9 0.4X
Output Partitions 5105 / 5190 3.1 324.6 0.6X
Output Buckets 6986 / 6992 2.3 444.1 0.4X
*/
formats.foreach { format =>
runBenchmark(format)
}
}
}
75 changes: 12 additions & 63 deletions ...re/src/test/scala/org/apache/spark/sql/execution/benchmark/DataSourceWriteBenchmark.scala
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Expand Up @@ -21,25 +21,14 @@ import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.util.Benchmark

/**
* Benchmark to measure data source write performance.
* By default it measures 4 data source format: Parquet, ORC, JSON, CSV:
* spark-submit --class <this class> <spark sql test jar>
* To measure specified formats, run it with arguments:
* spark-submit --class <this class> <spark sql test jar> format1 [format2] [...]
*/
object DataSourceWriteBenchmark {
trait DataSourceWriteBenchmark {
val conf = new SparkConf()
.setAppName("DataSourceWriteBenchmark")
.setIfMissing("spark.master", "local[1]")
.set("spark.sql.parquet.compression.codec", "snappy")
.set("spark.sql.orc.compression.codec", "snappy")
.set(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, "true")

val spark = SparkSession.builder.config(conf).getOrCreate()

// Set default configs. Individual cases will change them if necessary.
spark.conf.set(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, "true")

val tempTable = "temp"
val numRows = 1024 * 1024 * 15

Expand Down Expand Up @@ -86,64 +75,24 @@ object DataSourceWriteBenchmark {
}
}

def main(args: Array[String]): Unit = {
def runBenchmark(format: String): Unit = {
val tableInt = "tableInt"
val tableDouble = "tableDouble"
val tableIntString = "tableIntString"
val tablePartition = "tablePartition"
val tableBucket = "tableBucket"
val formats: Seq[String] = if (args.isEmpty) {
Seq("Parquet", "ORC", "JSON", "CSV")
} else {
args
}
/*
Intel(R) Core(TM) i7-6920HQ CPU @ 2.90GHz
Parquet writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 1815 / 1932 8.7 115.4 1.0X
Output Single Double Column 1877 / 1878 8.4 119.3 1.0X
Output Int and String Column 6265 / 6543 2.5 398.3 0.3X
Output Partitions 4067 / 4457 3.9 258.6 0.4X
Output Buckets 5608 / 5820 2.8 356.6 0.3X

ORC writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 1201 / 1239 13.1 76.3 1.0X
Output Single Double Column 1542 / 1600 10.2 98.0 0.8X
Output Int and String Column 6495 / 6580 2.4 412.9 0.2X
Output Partitions 3648 / 3842 4.3 231.9 0.3X
Output Buckets 5022 / 5145 3.1 319.3 0.2X

JSON writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 1988 / 2093 7.9 126.4 1.0X
Output Single Double Column 2854 / 2911 5.5 181.4 0.7X
Output Int and String Column 6467 / 6653 2.4 411.1 0.3X
Output Partitions 4548 / 5055 3.5 289.1 0.4X
Output Buckets 5664 / 5765 2.8 360.1 0.4X

CSV writer benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Output Single Int Column 3025 / 3190 5.2 192.3 1.0X
Output Single Double Column 3575 / 3634 4.4 227.3 0.8X
Output Int and String Column 7313 / 7399 2.2 464.9 0.4X
Output Partitions 5105 / 5190 3.1 324.6 0.6X
Output Buckets 6986 / 6992 2.3 444.1 0.4X
*/
withTempTable(tempTable) {
spark.range(numRows).createOrReplaceTempView(tempTable)
formats.foreach { format =>
withTable(tableInt, tableDouble, tableIntString, tablePartition, tableBucket) {
val benchmark = new Benchmark(s"$format writer benchmark", numRows)
writeNumeric(tableInt, format, benchmark, "Int")
writeNumeric(tableDouble, format, benchmark, "Double")
writeIntString(tableIntString, format, benchmark)
writePartition(tablePartition, format, benchmark)
writeBucket(tableBucket, format, benchmark)
benchmark.run()
}
withTable(tableInt, tableDouble, tableIntString, tablePartition, tableBucket) {
val benchmark = new Benchmark(s"$format writer benchmark", numRows)
writeNumeric(tableInt, format, benchmark, "Int")
writeNumeric(tableDouble, format, benchmark, "Double")
writeIntString(tableIntString, format, benchmark)
writePartition(tablePartition, format, benchmark)
writeBucket(tableBucket, format, benchmark)
benchmark.run()
}
}
}
}