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Posted to commits@spark.apache.org by we...@apache.org on 2018/05/30 01:31:52 UTC
spark git commit: [SPARK-24365][SQL] Add Data Source write benchmark
Repository: spark
Updated Branches:
refs/heads/master 900bc1f7d -> f48938800
[SPARK-24365][SQL] Add Data Source write benchmark
## What changes were proposed in this pull request?
Add Data Source write benchmark. So that it would be easier to measure the writer performance.
Author: Gengliang Wang <ge...@databricks.com>
Closes #21409 from gengliangwang/parquetWriteBenchmark.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/f4893880
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/f4893880
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/f4893880
Branch: refs/heads/master
Commit: f48938800e6dc3880441f160dd93856b9f86874e
Parents: 900bc1f
Author: Gengliang Wang <ge...@databricks.com>
Authored: Wed May 30 09:32:33 2018 +0800
Committer: Wenchen Fan <we...@databricks.com>
Committed: Wed May 30 09:32:33 2018 +0800
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.../benchmark/DataSourceWriteBenchmark.scala | 149 +++++++++++++++++++
1 file changed, 149 insertions(+)
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http://git-wip-us.apache.org/repos/asf/spark/blob/f4893880/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/DataSourceWriteBenchmark.scala
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diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/DataSourceWriteBenchmark.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/DataSourceWriteBenchmark.scala
new file mode 100644
index 0000000..2d2cdeb
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/DataSourceWriteBenchmark.scala
@@ -0,0 +1,149 @@
+/*
+ * 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
+
+import org.apache.spark.SparkConf
+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 {
+ 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")
+
+ 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
+
+ def withTempTable(tableNames: String*)(f: => Unit): Unit = {
+ try f finally tableNames.foreach(spark.catalog.dropTempView)
+ }
+
+ def withTable(tableNames: String*)(f: => Unit): Unit = {
+ try f finally {
+ tableNames.foreach { name =>
+ spark.sql(s"DROP TABLE IF EXISTS $name")
+ }
+ }
+ }
+
+ def writeNumeric(table: String, format: String, benchmark: Benchmark, dataType: String): Unit = {
+ spark.sql(s"create table $table(id $dataType) using $format")
+ benchmark.addCase(s"Output Single $dataType Column") { _ =>
+ spark.sql(s"INSERT OVERWRITE TABLE $table SELECT CAST(id AS $dataType) AS c1 FROM $tempTable")
+ }
+ }
+
+ def writeIntString(table: String, format: String, benchmark: Benchmark): Unit = {
+ spark.sql(s"CREATE TABLE $table(c1 INT, c2 STRING) USING $format")
+ benchmark.addCase("Output Int and String Column") { _ =>
+ spark.sql(s"INSERT OVERWRITE TABLE $table SELECT CAST(id AS INT) AS " +
+ s"c1, CAST(id AS STRING) AS c2 FROM $tempTable")
+ }
+ }
+
+ def writePartition(table: String, format: String, benchmark: Benchmark): Unit = {
+ spark.sql(s"CREATE TABLE $table(p INT, id INT) USING $format PARTITIONED BY (p)")
+ benchmark.addCase("Output Partitions") { _ =>
+ spark.sql(s"INSERT OVERWRITE TABLE $table SELECT CAST(id AS INT) AS id," +
+ s" CAST(id % 2 AS INT) AS p FROM $tempTable")
+ }
+ }
+
+ def writeBucket(table: String, format: String, benchmark: Benchmark): Unit = {
+ spark.sql(s"CREATE TABLE $table(c1 INT, c2 INT) USING $format CLUSTERED BY (c2) INTO 2 BUCKETS")
+ benchmark.addCase("Output Buckets") { _ =>
+ spark.sql(s"INSERT OVERWRITE TABLE $table SELECT CAST(id AS INT) AS " +
+ s"c1, CAST(id AS INT) AS c2 FROM $tempTable")
+ }
+ }
+
+ def main(args: Array[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()
+ }
+ }
+ }
+ }
+}
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