Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/21409#discussion_r190207562
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetWriteBenchmark.scala
---
@@ -0,0 +1,128 @@
+/*
+ * 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.datasources.parquet
+
+import java.io.File
+
+import scala.util.Try
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.util.{Benchmark, Utils}
+
+/**
+ * Benchmark to measure parquet write performance.
+ * To run this:
+ * spark-submit --class <this class> --jars <spark sql test jar>
+ */
+object ParquetWriteBenchmark {
+ val conf = new SparkConf()
+ conf.set("spark.sql.parquet.compression.codec", "snappy")
+
+ val spark = SparkSession.builder
+ .master("local[1]")
+ .appName("parquet-write-benchmark")
+ .config(conf)
+ .getOrCreate()
+
+ // Set default configs. Individual cases will change them if necessary.
+ spark.conf.set(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, "true")
+
+ def withTempPath(f: File => Unit): Unit = {
+ val path = Utils.createTempDir()
+ path.delete()
+ try f(path) finally Utils.deleteRecursively(path)
+ }
+
+ def withTempTable(tableNames: String*)(f: => Unit): Unit = {
+ try f finally tableNames.foreach(spark.catalog.dropTempView)
+ }
+
+ def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
+ val (keys, values) = pairs.unzip
+ val currentValues = keys.map(key => Try(spark.conf.get(key)).toOption)
+ (keys, values).zipped.foreach(spark.conf.set)
+ try f finally {
+ keys.zip(currentValues).foreach {
+ case (key, Some(value)) => spark.conf.set(key, value)
+ case (key, None) => spark.conf.unset(key)
+ }
+ }
+ }
+
+ def runSQL(name: String, sql: String, values: Int): Unit = {
+ withTempTable("t1") {
+ spark.range(values).createOrReplaceTempView("t1")
+ val benchmark = new Benchmark(name, values)
+ benchmark.addCase("Parquet Writer") { _ =>
+ withTempPath { dir =>
+ spark.sql("select cast(id as INT) as id from
t1").write.parquet(dir.getCanonicalPath)
--- End diff --
shouldn't we use `sql` here?
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