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authorNong Li <nong@databricks.com>2016-01-06 19:20:43 -0800
committerReynold Xin <rxin@databricks.com>2016-01-06 19:20:43 -0800
commita74d743cc7c52a78fa023fdd0d06847b7d48bf78 (patch)
tree8dc42001d0384e80c4cae03f9661f4272c07722c /core
parentac56cf605b61803c26e0004b43c703cca7e02d61 (diff)
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[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.
Diffstat (limited to 'core')
-rw-r--r--core/src/main/scala/org/apache/spark/util/Benchmark.scala120
1 files changed, 120 insertions, 0 deletions
diff --git a/core/src/main/scala/org/apache/spark/util/Benchmark.scala b/core/src/main/scala/org/apache/spark/util/Benchmark.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.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))
+ }
+}
+