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[SPARK-7007][core] Add a metric source for ExecutorAllocationManager #5589

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[SPARK-7007][core] Add a metric source for ExecutorAllocationManager #5589

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2c3540f
Add a metric source for ExecutorAllocationManager
jerryshao Apr 20, 2015
d237ba5
Address the comments
jerryshao Apr 29, 2015
c501a2c
Address the comments
jerryshao Apr 30, 2015
104d155
rebase and address the comments
jerryshao May 2, 2015
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29 changes: 29 additions & 0 deletions core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -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}

/**
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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 {
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3 changes: 3 additions & 0 deletions core/src/main/scala/org/apache/spark/SparkContext.scala
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Expand Up @@ -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 =>
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@sryza sryza Apr 29, 2015

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The model for the other metric sources seems to be that the source wraps the object. What's the reason for having the source inside of the ExecutorAllocationManager in this case?

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@jerryshao jerryshao Apr 30, 2015

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Hi @sryza , thanks a lot for your comments. The reason I put this metric source into ExecutorAllocationManager is that I don't want to change ExecutorAllocationManager a lot to expose the internal status to metric source, so I just wrote this metric source as the inner class. Do you have specific concern about this implementation?

_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
Expand Down