[GitHub] dongjoon-hyun commented on a change in pull request #23862: [SPARK-26958][SQL][TEST] Add NestedSchemaPruningBenchmark

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dongjoon-hyun commented on a change in pull request #23862: [SPARK-26958][SQL][TEST] Add NestedSchemaPruningBenchmark
URL: https://github.com/apache/spark/pull/23862#discussion_r259073632
 
 

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 File path: sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/NestedSchemaPruningBenchmark.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
+
+import org.apache.spark.benchmark.Benchmark
+import org.apache.spark.sql.internal.SQLConf
+
+/**
+ * Synthetic benchmark for nested schema pruning performance.
+ * To run this benchmark:
+ * {{{
+ *   1. without sbt:
+ *      bin/spark-submit --class <this class> --jars <spark core test jar> <sql core 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/NestedSchemaPruningBenchmark-results.txt".
+ * }}}
+ */
+object NestedSchemaPruningBenchmark extends SqlBasedBenchmark {
+
+  import spark.implicits._
+
+  private val N = 1000000
+  private val numIters = 10
+
+  // We use `col1 BIGINT, col2 STRUCT<_1: BIGINT, _2: STRING>` as a test schema.
+  // col1 and col2._1 is used for comparision. col2._2 mimics the burden for the other columns
+  private val df = spark
+    .range(N * 10)
+    .sample(false, 0.1)
+    .map(x => (x, (x, s"$x" * 100)))
+    .toDF("col1", "col2")
+
+  private def addCase(benchmark: Benchmark, name: String, sql: String): Unit = {
+    benchmark.addCase(name) { _ =>
+      spark.sql(sql).write.format("noop").save()
+    }
+  }
+
+  private def selectBenchmark(numRows: Int, numIters: Int): Unit = {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      Seq(1, 2).foreach { i =>
+        df.write.parquet(path + s"/$i")
+        spark.read.parquet(path + s"/$i").createOrReplaceTempView(s"t$i")
+      }
+
+      val benchmark = new Benchmark(s"Selection", numRows, numIters, output = output)
+
+      addCase(benchmark, "Top-level column", "SELECT col1 FROM (SELECT col1 FROM t1)")
+      addCase(benchmark, "Nested column", "SELECT col2._1 FROM (SELECT col2 FROM t2)")
+
+      benchmark.run()
+    }
+  }
+
+  private def limitBenchmark(numRows: Int, numIters: Int): Unit = {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      Seq(1, 2).foreach { i =>
+        df.write.parquet(path + s"/$i")
+        spark.read.parquet(path + s"/$i").createOrReplaceTempView(s"t$i")
+      }
+
+      val benchmark = new Benchmark(s"Limiting", numRows, numIters, output = output)
+
+      addCase(benchmark, "Top-level column",
+        s"SELECT col1 FROM (SELECT col1 FROM t1 LIMIT ${Int.MaxValue})")
+      addCase(benchmark, "Nested column",
+        s"SELECT col2._1 FROM (SELECT col2 FROM t2 LIMIT ${Int.MaxValue})")
+
+      benchmark.run()
+    }
+  }
+
+  private def repartitionBenchmark(numRows: Int, numIters: Int): Unit = {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      Seq(1, 2).foreach { i =>
+        df.write.parquet(path + s"/$i")
+        spark.read.parquet(path + s"/$i").createOrReplaceTempView(s"t$i")
+      }
+
+      val benchmark = new Benchmark(s"Repartitioning", numRows, numIters, output = output)
+
+      addCase(benchmark, "Top-level column",
+        s"SELECT col1 FROM (SELECT /*+ REPARTITION(1) */ col1 FROM t1)")
+      addCase(benchmark, "Nested column",
+        s"SELECT col2._1 FROM (SELECT /*+ REPARTITION(1) */ col2 FROM t2)")
+
+      benchmark.run()
+    }
+  }
+
+  private def repartitionByExprBenchmark(numRows: Int, numIters: Int): Unit = {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      Seq(1, 2).foreach { i =>
+        df.write.parquet(path + s"/$i")
+        spark.read.parquet(path + s"/$i").createOrReplaceTempView(s"t$i")
+      }
+
+      val benchmark = new Benchmark(s"Repartitioning by exprs", numRows, numIters, output = output)
+
+      addCase(benchmark, "Top-level column",
+        s"SELECT col1 FROM (SELECT col1 FROM t1 DISTRIBUTE BY col1)")
+      addCase(benchmark, "Nested column",
+        s"SELECT col2._1 FROM (SELECT col2 FROM t2 DISTRIBUTE BY col2._1)")
+
+      benchmark.run()
+    }
+  }
+
+  private def sortBenchmark(numRows: Int, numIters: Int): Unit = {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      Seq(1, 2).foreach { i =>
+        df.write.parquet(path + s"/$i")
+        spark.read.parquet(path + s"/$i").createOrReplaceTempView(s"t$i")
+      }
+
+      val benchmark = new Benchmark(s"Sorting", numRows, numIters, output = output)
+
+      addCase(benchmark, "Top-level column", "SELECT col1 FROM t1 ORDER BY col1")
+      addCase(benchmark, "Nested column", "SELECT col2._1 FROM t2 ORDER BY col2._1")
+
+      benchmark.run()
+    }
+  }
+
+  override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
+    runBenchmark(s"Nested Schema Pruning Benchmark") {
+      withSQLConf (SQLConf.NESTED_SCHEMA_PRUNING_ENABLED.key -> "true") {
+        selectBenchmark (N, numIters)
+        limitBenchmark (N, numIters)
+        repartitionBenchmark (N, numIters)
+        repartitionByExprBenchmark (N, numIters)
+        sortBenchmark (N, numIters)
 
 Review comment:
   All benchmarks share the same structure for now, but it will vary according to the upcoming PRs (like additional SQL statement and configurations).

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