Adding GC Metrics

core/src/main/scala/org/apache/spark/rapids/tool/benchmarks/Benchmark.scala

@@ -24,7 +24,7 @@ import scala.concurrent.duration.NANOSECONDS
 
 import org.apache.commons.io.output.TeeOutputStream
 
-import org.apache.spark.sql.rapids.tool.util.{RuntimeUtil, ToolsTimer}
+import org.apache.spark.sql.rapids.tool.util.{MemoryMetricsTracker, RuntimeUtil, ToolsTimer}
 
 /**
  * This code is mostly copied from org.apache.spark.benchmark.BenchmarkBase
@@ -37,7 +37,7 @@ import org.apache.spark.sql.rapids.tool.util.{RuntimeUtil, ToolsTimer}
  * This will output the average time to run each function and the rate of each function.
  */
 class Benchmark(
-    name: String,
+    name: String = "Benchmarker",
     valuesPerIteration: Long,
     minNumIters: Int,
     warmUpIterations: Int,
@@ -100,16 +100,23 @@ class Benchmark(
     val firstBest = results.head.bestMs
     // The results are going to be processor specific so it is useful to include that.
     out.println(RuntimeUtil.getJVMOSInfo.mkString("\n"))
+    out.println(s"MaxHeapMemory -> ${Runtime.getRuntime.maxMemory()} \n")
     val nameLen = Math.max(40, Math.max(name.length, benchmarks.map(_.name.length).max))
-    out.printf(s"%-${nameLen}s %14s %14s %11s %10s\n",
-      name + ":", "Best Time(ms)", "Avg Time(ms)", "Stdev(ms)", "Relative")
-    out.println("-" * (nameLen + 80))
+    out.printf(s"%-${nameLen}s %14s %14s %11s %20s %18s %18s %18s %18s %10s\n",
+      name + ":", "Best Time(ms)", "Avg Time(ms)", "Stdev(ms)","Avg GC Time(ms)",
+      "Avg GC Count", "Stdev GC Count","Max GC Time(ms)","Max GC Count", "Relative")
+    out.println("-" * (nameLen + 160))
     results.zip(benchmarks).foreach { case (result, benchmark) =>
-      out.printf(s"%-${nameLen}s %14s %14s %11s %10s\n",
+      out.printf(s"%-${nameLen}s %14s %14s %11s %20s %18s %18s %18s %18s %10s\n",
         benchmark.name,
         "%5.0f" format result.bestMs,
         "%4.0f" format result.avgMs,
         "%5.0f" format result.stdevMs,
+        "%5.1f" format result.memoryParams.avgGCTime,
+        "%5.1f" format result.memoryParams.avgGCCount,
+        "%5.0f" format result.memoryParams.stdDevGCCount,
+        "%5d" format result.memoryParams.maxGcTime,
+        "%5d" format result.memoryParams.maxGCCount,
         "%3.1fX" format (firstBest / result.bestMs))
     }
     out.println()
@@ -126,14 +133,17 @@ class Benchmark(
     }
     val minIters = if (overrideNumIters != 0) overrideNumIters else minNumIters
     val runTimes = ArrayBuffer[Long]()
-    var totalTime = 0L
+    val gcCounts = ArrayBuffer[Long]()
+    val gcTimes = ArrayBuffer[Long]()
+    //For tracking maximum GC over iterations
     for (i <- 0 until minIters) {
       val timer = new ToolsTimer(i)
+      val memoryTracker = new MemoryMetricsTracker
       f(timer)
       val runTime = timer.totalTime()
       runTimes += runTime
-      totalTime += runTime
-
+      gcCounts += memoryTracker.getTotalGCCount
+      gcTimes += memoryTracker.getTotalGCTime
       if (outputPerIteration) {
         // scalastyle:off
         println("*"*80)
@@ -148,17 +158,30 @@ class Benchmark(
     println("*"*80)
     // scalastyle:on
     assert(runTimes.nonEmpty)
-    val best = runTimes.min
-    val avg = runTimes.sum / runTimes.size
-    val stdev = if (runTimes.size > 1) {
-      math.sqrt(runTimes.map(time => (time - avg) * (time - avg)).sum / (runTimes.size - 1))
+    val bestRuntime = runTimes.min
+    val avgRuntime = runTimes.sum / runTimes.size
+    val stdevRunTime = if (runTimes.size > 1) {
+      math.sqrt(runTimes.map(time => (time - avgRuntime) *
+        (time - avgRuntime)).sum / (runTimes.size - 1))
+    } else 0
+    val maxGcCount = gcCounts.max
+    val stdevGcCount = if (gcCounts.size > 1) {
+      math.sqrt(gcCounts.map(gc => (gc - maxGcCount) *
+        (gc - maxGcCount)).sum / (gcCounts.size - 1))
     } else 0
-    Benchmark.Result(avg / 1000000.0,  best / 1000000.0, stdev / 1000000.0)
+    val avgGcCount = gcCounts.sum / minIters
+    val avgGcTime = gcTimes.sum / minIters
+    val maxGcTime = gcTimes.max
+    Benchmark.Result(avgRuntime / 1000000.0,  bestRuntime / 1000000.0, stdevRunTime / 1000000.0,
+      JVMMemoryParams(avgGcTime, avgGcCount, stdevGcCount, maxGcCount, maxGcTime))
   }
 }
 
 
 object Benchmark {
   case class Case(name: String, fn: ToolsTimer => Unit, numIters: Int)
-  case class Result(avgMs: Double, bestMs: Double, stdevMs: Double)
+  case class JVMMemoryParams( avgGCTime:Double, avgGCCount:Double,
+      stdDevGCCount: Double, maxGCCount: Long, maxGcTime:Long)
+  case class Result(avgMs: Double, bestMs: Double, stdevMs: Double,
+      memoryParams: JVMMemoryParams)
 }

core/src/main/scala/org/apache/spark/rapids/tool/benchmarks/QualificationBenchmark.scala → core/src/main/scala/org/apache/spark/rapids/tool/benchmarks/QualificationToolBenchmark.scala

@@ -27,23 +27,28 @@ import com.nvidia.spark.rapids.tool.qualification.QualificationMain.mainInternal
  * 2. Write the benchmark code in the runBenchmark method passing relevant arguments
  * 3. Write benchmarked code inside
  */
-object QualificationBenchmark extends BenchmarkBase {
+object QualificationToolBenchmark extends BenchmarkBase {
   override def runBenchmarkSuite(iterations: Int,
     warmUpIterations: Int,
     outputFormat: String,
     mainArgs: Array[String]): Unit = {
-    runBenchmark("QualificationBenchmark") {
+    runBenchmark("Benchmark_Per_SQL_Arg_Qualification") {
       val benchmarker =
         new Benchmark(
-          "QualificationBenchmark",
-          2,
+          valuesPerIteration = 2,
           output = output,
           outputPerIteration = true,
           warmUpIterations = warmUpIterations,
           minNumIters = iterations)
-      benchmarker.addCase("QualificationBenchmark") { _ =>
+      benchmarker.addCase("Enable_Per_SQL_Arg_Qualification") { _ =>
+        val (prefix,suffix) = mainArgs.splitAt(mainArgs.length - 1)
+
+        mainInternal(new QualificationArgs(prefix :+ "--per-sql" :+ suffix.head),
+          enablePB = true)
+      }
+      benchmarker.addCase("Disable_Per_SQL_Arg_Qualification") { _ =>
         mainInternal(new QualificationArgs(mainArgs),
-          printStdout = true, enablePB = true)
+          enablePB = true)
       }
       benchmarker.run()
     }

core/src/main/scala/org/apache/spark/sql/rapids/tool/util/MemoryMetricsTracker.scala

@@ -0,0 +1,32 @@
+package org.apache.spark.sql.rapids.tool.util
+
+import java.lang.management.ManagementFactory
+
+import scala.collection.convert.ImplicitConversions.`collection AsScalaIterable`
+
+/**
+ * Utility class to track memory metrics.
+ * This class is used to track memory metrics such as GC count, GC time,
+ * heap memory usage, etc.
+ *
+ */
+class MemoryMetricsTracker {
+  private val startGCMetrics = getCurrentGCMetrics
+
+  private def getCurrentGCMetrics: (Long, Long) = {
+    val gcBeans = ManagementFactory.getGarbageCollectorMXBeans
+
+    (gcBeans.map(_.getCollectionCount).sum,
+      gcBeans.map(_.getCollectionTime).sum)
+  }
+
+  def getTotalGCCount: Long = {
+    val (newGcCount:Long, _) = getCurrentGCMetrics
+    newGcCount - startGCMetrics._1
+  }
+
+  def getTotalGCTime: Long = {
+    val (_, newGcTime:Long) = getCurrentGCMetrics
+    newGcTime - startGCMetrics._2
+  }
+}

core/src/main/scala/org/apache/spark/sql/rapids/tool/util/RuntimeUtil.scala

@@ -68,7 +68,7 @@ object RuntimeUtil extends Logging {
   def getJVMOSInfo: Map[String, String] = {
     Map(
       "jvm.name" -> System.getProperty("java.vm.name"),
-      "jvm.version" -> System.getProperty("java.vm.version"),
+      "jvm.version" -> System.getProperty("java.version"),
       "os.name" -> System.getProperty("os.name"),
       "os.version" -> System.getProperty("os.version")
     )