Add Executor instrumentation

core/src/main/scala/spark/executor/ExecutorInstrumentation.scala

+package spark.executor
+
+import com.codahale.metrics.{Gauge, MetricRegistry}
+
+import spark.metrics.source.Source
+
+class ExecutorInstrumentation(val executor: Option[Executor]) extends Source{
+  val metricRegistry = new MetricRegistry()
+  val sourceName = "executor"
+  
+  // Gauge for executor thread pool's actively executing task counts
+  metricRegistry.register(MetricRegistry.name("threadpool", "active_task", "number"), 
+    new Gauge[Int] {
+      override def getValue: Int = executor.map(_.threadPool.getActiveCount()).getOrElse(0)
+  })
+  
+  // Gauge for executor thread pool's approximate total number of tasks that have been completed
+  metricRegistry.register(MetricRegistry.name("threadpool", "complete_task", "count"),
+    new Gauge[Long] {
+      override def getValue: Long = executor.map(_.threadPool.getCompletedTaskCount()).getOrElse(0)
+  })
+  
+  // Gauge for executor thread pool's current number of threads
+  metricRegistry.register(MetricRegistry.name("threadpool", "current_pool", "size"), 
+    new Gauge[Int] {
+      override def getValue: Int = executor.map(_.threadPool.getPoolSize()).getOrElse(0)
+  })
+  
+  // Gauge got executor thread pool's largest number of threads that have ever simultaneously been in th pool
+  metricRegistry.register(MetricRegistry.name("threadpool", "max_pool", "size"), 
+    new Gauge[Int] {
+      override def getValue: Int = executor.map(_.threadPool.getMaximumPoolSize()).getOrElse(0)
+    })
+
+}
\ No newline at end of file

core/src/main/scala/spark/executor/MesosExecutorBackend.scala

@@ -24,6 +24,7 @@ import spark.TaskState.TaskState
 import com.google.protobuf.ByteString
 import spark.{Utils, Logging}
 import spark.TaskState
+import spark.metrics.MetricsSystem
 
 private[spark] class MesosExecutorBackend
   extends MesosExecutor
@@ -32,6 +33,9 @@ private[spark] class MesosExecutorBackend
 
   var executor: Executor = null
   var driver: ExecutorDriver = null
+  
+  val executorInstrumentation = new ExecutorInstrumentation(Option(executor))
+  MesosExecutorBackend.metricsSystem.start()
 
   override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) {
     val mesosTaskId = TaskID.newBuilder().setValue(taskId.toString).build()
@@ -79,13 +83,17 @@ private[spark] class MesosExecutorBackend
 
   override def frameworkMessage(d: ExecutorDriver, data: Array[Byte]) {}
 
-  override def shutdown(d: ExecutorDriver) {}
+  override def shutdown(d: ExecutorDriver) {
+    MesosExecutorBackend.metricsSystem.stop()
+  }
 }
 
 /**
  * Entry point for Mesos executor.
  */
 private[spark] object MesosExecutorBackend {
+  private val metricsSystem = MetricsSystem.createMetricsSystem("executor")
+  
   def main(args: Array[String]) {
     MesosNativeLibrary.load()
     // Create a new Executor and start it running

core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala

@@ -24,6 +24,7 @@ import spark.util.AkkaUtils
 import akka.actor.{ActorRef, Actor, Props, Terminated}
 import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected}
 import java.util.concurrent.{TimeUnit, ThreadPoolExecutor, SynchronousQueue}
+import spark.metrics.MetricsSystem
 import spark.scheduler.cluster._
 import spark.scheduler.cluster.RegisteredExecutor
 import spark.scheduler.cluster.LaunchTask
@@ -45,6 +46,8 @@ private[spark] class StandaloneExecutorBackend(
 
   var executor: Executor = null
   var driver: ActorRef = null
+  
+  val executorInstrumentation = new ExecutorInstrumentation(Option(executor))
 
   override def preStart() {
     logInfo("Connecting to driver: " + driverUrl)
@@ -52,6 +55,9 @@ private[spark] class StandaloneExecutorBackend(
     driver ! RegisterExecutor(executorId, hostPort, cores)
     context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent])
     context.watch(driver) // Doesn't work with remote actors, but useful for testing
+    
+    StandaloneExecutorBackend.metricsSystem.registerSource(executorInstrumentation)
+    StandaloneExecutorBackend.metricsSystem.start()
   }
 
   override def receive = {
@@ -81,9 +87,15 @@ private[spark] class StandaloneExecutorBackend(
   override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) {
     driver ! StatusUpdate(executorId, taskId, state, data)
   }
+  
+  override def postStop() {
+    StandaloneExecutorBackend.metricsSystem.stop()
+  }
 }
 
 private[spark] object StandaloneExecutorBackend {
+  private val metricsSystem = MetricsSystem.createMetricsSystem("executor")
+  
   def run(driverUrl: String, executorId: String, hostname: String, cores: Int) {
     SparkHadoopUtil.runAsUser(run0, Tuple4[Any, Any, Any, Any] (driverUrl, executorId, hostname, cores))
   }