[SPARK-7007] [CORE] Add a metric source for ExecutorAllocationManager

Add a metric source to expose the internal status of ExecutorAllocationManager to better monitoring the resource usage of executors when dynamic allocation is enable. Please help to review, thanks a lot.

Author: jerryshao <saisai.shao@intel.com>

Closes #5589 from jerryshao/dynamic-allocation-source and squashes the following commits:

104d155 [jerryshao] rebase and address the comments
c501a2c [jerryshao] Address the comments
d237ba5 [jerryshao] Address the comments
2c3540f [jerryshao] Add a metric source for ExecutorAllocationManager

core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala

@@ -21,7 +21,10 @@ import java.util.concurrent.TimeUnit
 
 import scala.collection.mutable
 
+import com.codahale.metrics.{Gauge, MetricRegistry}
+
 import org.apache.spark.scheduler._
+import org.apache.spark.metrics.source.Source
 import org.apache.spark.util.{ThreadUtils, Clock, SystemClock, Utils}
 
 /**
@@ -144,6 +147,9 @@ private[spark] class ExecutorAllocationManager(
   private val executor =
     ThreadUtils.newDaemonSingleThreadScheduledExecutor("spark-dynamic-executor-allocation")
 
+  // Metric source for ExecutorAllocationManager to expose internal status to MetricsSystem.
+  val executorAllocationManagerSource = new ExecutorAllocationManagerSource
+
   /**
    * Verify that the settings specified through the config are valid.
    * If not, throw an appropriate exception.
@@ -579,6 +585,29 @@ private[spark] class ExecutorAllocationManager(
     }
   }
 
+  /**
+   * Metric source for ExecutorAllocationManager to expose its internal executor allocation
+   * status to MetricsSystem.
+   * Note: These metrics heavily rely on the internal implementation of
+   * ExecutorAllocationManager, metrics or value of metrics will be changed when internal
+   * implementation is changed, so these metrics are not stable across Spark version.
+   */
+  private[spark] class ExecutorAllocationManagerSource extends Source {
+    val sourceName = "ExecutorAllocationManager"
+    val metricRegistry = new MetricRegistry()
+
+    private def registerGauge[T](name: String, value: => T, defaultValue: T): Unit = {
+      metricRegistry.register(MetricRegistry.name("executors", name), new Gauge[T] {
+        override def getValue: T = synchronized { Option(value).getOrElse(defaultValue) }
+      })
+    }
+
+    registerGauge("numberExecutorsToAdd", numExecutorsToAdd, 0)
+    registerGauge("numberExecutorsPendingToRemove", executorsPendingToRemove.size, 0)
+    registerGauge("numberAllExecutors", executorIds.size, 0)
+    registerGauge("numberTargetExecutors", numExecutorsTarget, 0)
+    registerGauge("numberMaxNeededExecutors", maxNumExecutorsNeeded(), 0)
+  }
 }
 
 private object ExecutorAllocationManager {

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -537,6 +537,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     _taskScheduler.postStartHook()
     _env.metricsSystem.registerSource(new DAGSchedulerSource(dagScheduler))
     _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))
+    _executorAllocationManager.foreach { e =>
+      _env.metricsSystem.registerSource(e.executorAllocationManagerSource)
+    }
 
     // Make sure the context is stopped if the user forgets about it. This avoids leaving
     // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM