[GitHub] aokolnychyi commented on a change in pull request #23291: [SPARK-26203][SQL] Benchmark performance of In and InSet expressions

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aokolnychyi commented on a change in pull request #23291: [SPARK-26203][SQL] Benchmark performance of In and InSet expressions
URL: https://github.com/apache/spark/pull/23291#discussion_r240762538
 
 

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 File path: sql/core/src/test/scala/org/apache/spark/sql/InSetBenchmark.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
+
+import org.apache.spark.benchmark.Benchmark
+import org.apache.spark.sql.catalyst.expressions.In
+import org.apache.spark.sql.catalyst.expressions.InSet
+import org.apache.spark.sql.execution.benchmark.SqlBasedBenchmark
+import org.apache.spark.sql.functions.{array, struct}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types._
+
+/**
+ * A benchmark that compares the performance of [[In]] and [[InSet]] expressions.
+ *
+ * To run this benchmark:
+ * {{{
+ *   1. without sbt: bin/spark-submit --class <this class> <spark sql test jar>
+ *   2. build/sbt "sql/test:runMain <this class>"
+ *   3. generate result: SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain <this class>"
+ *      Results will be written to "benchmarks/InSetBenchmark-results.txt".
+ * }}}
+ */
+object InSetBenchmark extends SqlBasedBenchmark {
+
+  import spark.implicits._
+
+  def byteBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems bytes"
+    val values = (1 to numItems).map(v => s"CAST($v AS tinyint)")
+    val df = spark.range(1, numRows).select($"id".cast(ByteType))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def shortBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems shorts"
+    val values = (1 to numItems).map(v => s"CAST($v AS smallint)")
+    val df = spark.range(1, numRows).select($"id".cast(ShortType))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def intBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems ints"
+    val values = 1 to numItems
+    val df = spark.range(1, numRows).select($"id".cast(IntegerType))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def longBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems longs"
+    val values = (1 to numItems).map(v => s"${v}L")
+    val df = spark.range(1, numRows).toDF("id")
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def floatBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems floats"
+    val values = (1 to numItems).map(v => s"CAST($v AS float)")
+    val df = spark.range(1, numRows).select($"id".cast(FloatType))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def doubleBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems doubles"
+    val values = 1.0 to numItems by 1.0
+    val df = spark.range(1, numRows).select($"id".cast(DoubleType))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def smallDecimalBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems small decimals"
+    val values = (1 to numItems).map(v => s"CAST($v AS decimal(12, 1))")
+    val df = spark.range(1, numRows).select($"id".cast(DecimalType(12, 1)))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def largeDecimalBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems large decimals"
+    val values = (1 to numItems).map(v => s"9223372036854775812.10539$v")
+    val df = spark.range(1, numRows).select($"id".cast(DecimalType(30, 7)))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def stringBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems strings"
+    val values = (1 to numItems).map(n => s"'$n'")
+    val df = spark.range(1, numRows).select($"id".cast(StringType))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def timestampBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems timestamps"
+    val values = (1 to numItems).map(m => s"CAST('1970-01-01 01:00:$m' AS timestamp)")
+    val df = spark.range(1, numRows).select($"id".cast(TimestampType))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def dateBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems dates"
+    val values = (1 to numItems).map(n => 1970 + n).map(y => s"CAST('$y-01-01' AS date)")
+    val df = spark.range(1, numRows).select($"id".cast(TimestampType).cast(DateType))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def arrayBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems arrays"
+    val values = (1 to numItems).map(i => s"array($i)")
+    val df = spark.range(1, numRows).select(array($"id").as("id"))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def structBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {
+    val name = s"$numItems structs"
+    val values = (1 to numItems).map(i => s"struct($i)")
+    val df = spark.range(1, numRows).select(struct($"id".as("col1")).as("id"))
+    benchmark(name, df, values, numRows, minNumIters)
+  }
+
+  def benchmark(
+      name: String,
+      df: DataFrame,
+      values: Seq[Any],
+      numRows: Long,
+      minNumIters: Int): Benchmark = {
+
+    val benchmark = new Benchmark(name, numRows, minNumIters, output = output)
+
+    df.createOrReplaceTempView("t")
+
+    def testClosure(): Unit = {
+      val df = spark.sql(s"SELECT * FROM t WHERE id IN (${values mkString ","})")
+      df.queryExecution.toRdd.foreach(_ => Unit)
+    }
+
+    benchmark.addCase("In expression") { _ =>
+      withSQLConf(SQLConf.OPTIMIZER_INSET_CONVERSION_THRESHOLD.key -> values.size.toString) {
+        testClosure()
+      }
+    }
+
+    benchmark.addCase("InSet expression") { _ =>
+      withSQLConf(SQLConf.OPTIMIZER_INSET_CONVERSION_THRESHOLD.key -> "1") {
+        testClosure()
+      }
+    }
+
+    benchmark
+  }
+
+  override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
+    val largeNumRows = 10000000
+    val smallNumRows = 1000000
 
 Review comment:
   Yes, exactly, `smallNumRows` is used with data types that take longer to process.

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