[GitHub] spark pull request #21859: [SPARK-24900][SQL]Speed up sort when the dataset ...

[dongjoon-hyun <gi...@git.apache.org>]

Github user dongjoon-hyun commented on a diff in the pull request:

    https://github.com/apache/spark/pull/21859#discussion_r227592853
  
    --- Diff: sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/SmallDataSortBenchmark.scala ---
    @@ -0,0 +1,105 @@
    +/*
    + * 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 java.io.File
    +
    +import org.apache.spark.SparkConf
    +import org.apache.spark.sql.SparkSession
    +import org.apache.spark.util.{Benchmark, Utils}
    +
    +/**
    + * The benchmarks aims to measure performance of
    + * [SPARK-24900][SQL]speed up sort when the dataset is small
    + */
    +object SmallDataSortBenchmark {
    +
    +  val conf = new SparkConf()
    +
    +  val spark = SparkSession.builder
    +    .master("local[1]")
    +    .appName("speed up sort when the dataset is small")
    +    .config(conf)
    +    .getOrCreate()
    +
    +  import spark.implicits._
    +
    +  def withTempPath(f: File => Unit): Unit = {
    +    val path = Utils.createTempDir()
    +    path.delete()
    +    try f(path) finally Utils.deleteRecursively(path)
    +  }
    +
    +  def run(rowsNum: Int): Unit = {
    +    val factor = 1000
    +    val key = rowsNum / 2
    +    val benchmark = new Benchmark("small data sort", rowsNum * factor)
    +    withTempPath { path =>
    +      // scalastyle:off println
    +      benchmark.out.println("Preparing data for benchmarking ...")
    +      // scalastyle:on println
    +
    +      val list = (0 to factor).toList
    +
    +      spark.sparkContext.range(0, rowsNum, 1)
    +        .flatMap(num => {
    +          list.map(x => (num, x))
    +        })
    +        .toDF("key", "value")
    +        .write
    +        .option("encoding", "UTF-8")
    +        .json(path.getAbsolutePath)
    +
    +      val dataset = spark.read.json(path.getAbsolutePath)
    +
    +      dataset.createOrReplaceTempView("src")
    +
    +      benchmark.addCase("with optimization", 10) { _ =>
    +        // 334 * 3 > 1000, the optimization works
    +        spark.conf.set("spark.sql.shuffle.partitions", dataset.rdd.getNumPartitions)
    +        spark.conf.set("spark.sql.execution.rangeExchange.sampleSizePerPartition", "334")
    +        val result = spark.
    +          sql(s"select * from src where key = $key order by value").collectAsList().size()
    +
    +      }
    +
    +      benchmark.addCase("without optimization", 10) { _ =>
    +        // 333 * 3 < 1000, the optimization doesn't work
    +        spark.conf.set("spark.sql.shuffle.partitions", dataset.rdd.getNumPartitions)
    +        spark.conf.set("spark.sql.execution.rangeExchange.sampleSizePerPartition", "333")
    +        val result = spark.
    +          sql(s"select * from src where key = $key order by value").collectAsList().size()
    +
    +      }
    +
    +      /*
    +       * Java HotSpot(TM) 64-Bit Server VM 1.8.0_91-b14 on Mac OS X 10.13.6
    +       * Intel(R) Core(TM) i5-5257U CPU @ 2.70GHz
    +       *
    +       * small data sort:           Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
    +       * ----------------------------------------------------------------------------------
    +       * with optimization             54077 / 57989          1.8         540.8       1.0X
    +       * without optimization        111780 / 115001          0.9        1117.8       0.5X
    --- End diff --
    
    For the benchmark, we usually put the baseline result at the first line. Could you switch line 71~78 and 80~87?


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