[SPARK-12640][SQL] Add simple benchmarking utility class and add Parquet scan benchmarks.

[SPARK-12640][SQL] Add simple benchmarking utility class and add Parquet scan benchmarks.

We've run benchmarks ad hoc to measure the scanner performance. We will continue to invest in this
and it makes sense to get these benchmarks into code. This adds a simple benchmarking utility to do
this.

Author: Nong Li <nong@databricks.com>
Author: Nong <nongli@gmail.com>

Closes #10589 from nongli/spark-12640.

core/src/main/scala/org/apache/spark/util/Benchmark.scala

+/*
+ * 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.util
+
+import scala.collection.mutable
+
+import org.apache.commons.lang3.SystemUtils
+
+/**
+ * Utility class to benchmark components. An example of how to use this is:
+ *  val benchmark = new Benchmark("My Benchmark", valuesPerIteration)
+ *   benchmark.addCase("V1")(<function>)
+ *   benchmark.addCase("V2")(<function>)
+ *   benchmark.run
+ * This will output the average time to run each function and the rate of each function.
+ *
+ * The benchmark function takes one argument that is the iteration that's being run.
+ *
+ * If outputPerIteration is true, the timing for each run will be printed to stdout.
+ */
+private[spark] class Benchmark(
+    name: String, valuesPerIteration: Long,
+    iters: Int = 5,
+    outputPerIteration: Boolean = false) {
+  val benchmarks = mutable.ArrayBuffer.empty[Benchmark.Case]
+
+  def addCase(name: String)(f: Int => Unit): Unit = {
+    benchmarks += Benchmark.Case(name, f)
+  }
+
+  /**
+   * Runs the benchmark and outputs the results to stdout. This should be copied and added as
+   * a comment with the benchmark. Although the results vary from machine to machine, it should
+   * provide some baseline.
+   */
+  def run(): Unit = {
+    require(benchmarks.nonEmpty)
+    // scalastyle:off
+    println("Running benchmark: " + name)
+
+    val results = benchmarks.map { c =>
+      println("  Running case: " + c.name)
+      Benchmark.measure(valuesPerIteration, iters, outputPerIteration)(c.fn)
+    }
+    println
+
+    val firstRate = results.head.avgRate
+    // The results are going to be processor specific so it is useful to include that.
+    println(Benchmark.getProcessorName())
+    printf("%-24s %16s %16s %14s\n", name + ":", "Avg Time(ms)", "Avg Rate(M/s)", "Relative Rate")
+    println("-------------------------------------------------------------------------")
+    results.zip(benchmarks).foreach { r =>
+      printf("%-24s %16s %16s %14s\n",
+        r._2.name,
+        "%10.2f" format r._1.avgMs,
+        "%10.2f" format r._1.avgRate,
+        "%6.2f X" format (r._1.avgRate / firstRate))
+    }
+    println
+    // scalastyle:on
+  }
+}
+
+private[spark] object Benchmark {
+  case class Case(name: String, fn: Int => Unit)
+  case class Result(avgMs: Double, avgRate: Double)
+
+  /**
+   * This should return a user helpful processor information. Getting at this depends on the OS.
+   * This should return something like "Intel(R) Core(TM) i7-4870HQ CPU @ 2.50GHz"
+   */
+  def getProcessorName(): String = {
+    if (SystemUtils.IS_OS_MAC_OSX) {
+      Utils.executeAndGetOutput(Seq("/usr/sbin/sysctl", "-n", "machdep.cpu.brand_string"))
+    } else if (SystemUtils.IS_OS_LINUX) {
+      Utils.executeAndGetOutput(Seq("/usr/bin/grep", "-m", "1", "\"model name\"", "/proc/cpuinfo"))
+    } else {
+      System.getenv("PROCESSOR_IDENTIFIER")
+    }
+  }
+
+  /**
+   * Runs a single function `f` for iters, returning the average time the function took and
+   * the rate of the function.
+   */
+  def measure(num: Long, iters: Int, outputPerIteration: Boolean)(f: Int => Unit): Result = {
+    var totalTime = 0L
+    for (i <- 0 until iters + 1) {
+      val start = System.nanoTime()
+
+      f(i)
+
+      val end = System.nanoTime()
+      if (i != 0) totalTime += end - start
+
+      if (outputPerIteration) {
+        // scalastyle:off
+        println(s"Iteration $i took ${(end - start) / 1000} microseconds")
+        // scalastyle:on
+      }
+    }
+    Result(totalTime.toDouble / 1000000 / iters, num * iters / (totalTime.toDouble / 1000))
+  }
+}
+

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetReadBenchmark.scala

+/*
+ * 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.collection.JavaConverters._
+import scala.util.Try
+
+import org.apache.spark.sql.{SQLConf, SQLContext}
+import org.apache.spark.util.{Benchmark, Utils}
+import org.apache.spark.{SparkConf, SparkContext}
+
+/**
+ * Benchmark to measure parquet read performance.
+ * To run this:
+ *  spark-submit --class <this class> --jars <spark sql test jar>
+ */
+object ParquetReadBenchmark {
+  val conf = new SparkConf()
+  conf.set("spark.sql.parquet.compression.codec", "snappy")
+  val sc = new SparkContext("local[1]", "test-sql-context", conf)
+  val sqlContext = new SQLContext(sc)
+
+  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(sqlContext.dropTempTable)
+  }
+
+  def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
+    val (keys, values) = pairs.unzip
+    val currentValues = keys.map(key => Try(sqlContext.conf.getConfString(key)).toOption)
+    (keys, values).zipped.foreach(sqlContext.conf.setConfString)
+    try f finally {
+      keys.zip(currentValues).foreach {
+        case (key, Some(value)) => sqlContext.conf.setConfString(key, value)
+        case (key, None) => sqlContext.conf.unsetConf(key)
+      }
+    }
+  }
+
+  def intScanBenchmark(values: Int): Unit = {
+    withTempPath { dir =>
+      sqlContext.range(values).write.parquet(dir.getCanonicalPath)
+      withTempTable("tempTable") {
+        sqlContext.read.parquet(dir.getCanonicalPath).registerTempTable("tempTable")
+        val benchmark = new Benchmark("Single Int Column Scan", values)
+
+        benchmark.addCase("SQL Parquet Reader") { iter =>
+          sqlContext.sql("select sum(id) from tempTable").collect()
+        }
+
+        benchmark.addCase("SQL Parquet MR") { iter =>
+          withSQLConf(SQLConf.PARQUET_UNSAFE_ROW_RECORD_READER_ENABLED.key -> "false") {
+            sqlContext.sql("select sum(id) from tempTable").collect()
+          }
+        }
+
+        val files = SpecificParquetRecordReaderBase.listDirectory(dir).toArray
+        benchmark.addCase("ParquetReader") { num =>
+          var sum = 0L
+          files.map(_.asInstanceOf[String]).foreach { p =>
+            val reader = new UnsafeRowParquetRecordReader
+            reader.initialize(p, ("id" :: Nil).asJava)
+
+            while (reader.nextKeyValue()) {
+              val record = reader.getCurrentValue
+              if (!record.isNullAt(0)) sum += record.getInt(0)
+            }
+            reader.close()
+        }}
+
+        /*
+          Intel(R) Core(TM) i7-4870HQ CPU @ 2.50GHz
+          Single Int Column Scan:      Avg Time(ms)    Avg Rate(M/s)  Relative Rate
+          -------------------------------------------------------------------------
+          SQL Parquet Reader                 1910.0            13.72         1.00 X
+          SQL Parquet MR                     2330.0            11.25         0.82 X
+          ParquetReader                      1252.6            20.93         1.52 X
+        */
+        benchmark.run()
+      }
+    }
+  }
+
+  def intStringScanBenchmark(values: Int): Unit = {
+    withTempPath { dir =>
+      withTempTable("t1", "tempTable") {
+        sqlContext.range(values).registerTempTable("t1")
+        sqlContext.sql("select id as c1, cast(id as STRING) as c2 from t1")
+            .write.parquet(dir.getCanonicalPath)
+        sqlContext.read.parquet(dir.getCanonicalPath).registerTempTable("tempTable")
+
+        val benchmark = new Benchmark("Int and String Scan", values)
+
+        benchmark.addCase("SQL Parquet Reader") { iter =>
+          sqlContext.sql("select sum(c1), sum(length(c2)) from tempTable").collect
+        }
+
+        benchmark.addCase("SQL Parquet MR") { iter =>
+          withSQLConf(SQLConf.PARQUET_UNSAFE_ROW_RECORD_READER_ENABLED.key -> "false") {
+            sqlContext.sql("select sum(c1), sum(length(c2)) from tempTable").collect
+          }
+        }
+
+        val files = SpecificParquetRecordReaderBase.listDirectory(dir).toArray
+        benchmark.addCase("ParquetReader") { num =>
+          var sum1 = 0L
+          var sum2 = 0L
+          files.map(_.asInstanceOf[String]).foreach { p =>
+            val reader = new UnsafeRowParquetRecordReader
+            reader.initialize(p, null)
+            while (reader.nextKeyValue()) {
+              val record = reader.getCurrentValue
+              if (!record.isNullAt(0)) sum1 += record.getInt(0)
+              if (!record.isNullAt(1)) sum2 += record.getUTF8String(1).numBytes()
+            }
+            reader.close()
+          }
+        }
+
+        /*
+          Intel(R) Core(TM) i7-4870HQ CPU @ 2.50GHz
+          Int and String Scan:         Avg Time(ms)    Avg Rate(M/s)  Relative Rate
+          -------------------------------------------------------------------------
+          SQL Parquet Reader                 2245.6             7.00         1.00 X
+          SQL Parquet MR                     2914.2             5.40         0.77 X
+          ParquetReader                      1544.6            10.18         1.45 X
+        */
+        benchmark.run()
+      }
+    }
+  }
+
+  def main(args: Array[String]): Unit = {
+    intScanBenchmark(1024 * 1024 * 15)
+    intStringScanBenchmark(1024 * 1024 * 10)
+  }
+}