在本博客的《Spark Metrics配置详解》文章中介绍了Spark Metrics的配置，其中我们就介绍了Spark监控支持Ganglia Sink。

Ganglia是UC Berkeley发起的一个开源集群监视项目，主要是用来监控系统性能，如：cpu 、mem、硬盘利用率， I/O负载、网络流量情况等，通过曲线很容易见到每个节点的工作状态，对合理调整、分配系统资源，提高系统整体性能起到重要作用。

由于Licene的限制，没有放到默认的build里面，如果需要使用，需要自己编译。在使用Maven编译Spark的时候，我们可以加上-Pspark-ganglia-lgpl选项来将Ganglia相关的类打包进spark-assembly-x.x.x-hadoopx.x.x.jar中，命令如下：

[iteblog@iteblog spark]$ ./make-distribution.sh --tgz -Phadoop-2.4 -Pyarn -DskipTests 
　　-Dhadoop.version=2.4.0 -Pspark-ganglia-lgpl

　　如果你使用的是SBT来编译，可以加上SPARK_GANGLIA_LGPL=true，完整命令如下：

[iteblog@iteblog spark]$ SPARK_HADOOP_VERSION=2.4.0 SPARK_YARN=true 
　　SPARK_GANGLIA_LGPL=true sbt/sbt assembly

　　或者你在提交作业的时候，单独将Ganglia相关依赖加入到--jars选项中：

--jars lib/spark-ganglia-lgpl_2.10-x.x.x.jar  ...

　　依赖弄好之后，我们需要在$SPARK_HOME/conf/metrics.properties文件中加入一下配置：

*.sink.ganglia.class=org.apache.spark.metrics.sink.GangliaSink
*.sink.ganglia.host=www.iteblog.com
*.sink.ganglia.port=8080
*.sink.ganglia.period=10
*.sink.ganglia.unit=seconds
*.sink.ganglia.ttl=1
*.sink.ganglia.mode=multicast

host和port这个就是你Ganglia监控的地址，其中mode支持'unicast'(单播) 和 'multicast'(多播)两种模式。

15/06/11 23:35:14 ERROR MetricsSystem: Sink class org.apache.spark.metrics.sink.GangliaSink cannot be instantialized 
java.lang.ClassNotFoundException: org.apache.spark.metrics.sink.GangliaSink 
        at java.net.URLClassLoader$1.run(URLClassLoader.java:366) 
        at java.net.URLClassLoader$1.run(URLClassLoader.java:355) 
        at java.security.AccessController.doPrivileged(Native Method) 
        at java.net.URLClassLoader.findClass(URLClassLoader.java:354) 
        at java.lang.ClassLoader.loadClass(ClassLoader.java:425) 
        at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308) 
        at java.lang.ClassLoader.loadClass(ClassLoader.java:358) 
        at java.lang.Class.forName0(Native Method) 
        at java.lang.Class.forName(Class.java:191) 
        at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSinks$1.apply(MetricsSystem.scala:138) 
        at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSinks$1.apply(MetricsSystem.scala:134) 
        at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:98) 
        at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:98) 
        at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:226) 
        at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:39) 
        at scala.collection.mutable.HashMap.foreach(HashMap.scala:98) 
        at org.apache.spark.metrics.MetricsSystem.registerSinks(MetricsSystem.scala:134) 
        at org.apache.spark.metrics.MetricsSystem.<init>(MetricsSystem.scala:84) 
        at org.apache.spark.metrics.MetricsSystem$.createMetricsSystem(MetricsSystem.scala:171) 
        at org.apache.spark.deploy.worker.Worker.<init>(Worker.scala:106) 
        at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) 
        at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57) 
        at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) 
        at java.lang.reflect.Constructor.newInstance(Constructor.java:526) 
        at akka.util.Reflect$.instantiate(Reflect.scala:65) 
        at akka.actor.Props.newActor(Props.scala:337) 
        at akka.actor.ActorCell.newActor(ActorCell.scala:534) 
        at akka.actor.ActorCell.create(ActorCell.scala:560) 
        at akka.actor.ActorCell.invokeAll$1(ActorCell.scala:425) 
        at akka.actor.ActorCell.systemInvoke(ActorCell.scala:447) 
        at akka.dispatch.Mailbox.processAllSystemMessages(Mailbox.scala:262) 
        at akka.dispatch.Mailbox.run(Mailbox.scala:218) 
        at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386) 
        at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) 
        at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) 
        at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) 
        at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) 

　　配置弄好之后，启动你的Spark集群，然后去/ganglia-web监控页面查看是否弄好了，类似下面的信息：

如果想及时了解Spark、Hadoop或者HBase相关的文章，欢迎关注微信公众号：iteblog_hadoop

　　除了上图的master.apps 和master.workers 监控，Ganglia里面还显示如下的信息：

{
    "version": "3.0.0", 
    "gauges": {
        "jvm.PS-MarkSweep.count": {
            "value": 0
        }, 
        "jvm.PS-MarkSweep.time": {
            "value": 0
        }, 
        "jvm.PS-Scavenge.count": {
            "value": 186
        }, 
        "jvm.PS-Scavenge.time": {
            "value": 375
        }, 
        "jvm.heap.committed": {
            "value": 536412160
        }, 
        "jvm.heap.init": {
            "value": 536870912
        }, 
        "jvm.heap.max": {
            "value": 536412160
        }, 
        "jvm.heap.usage": {
            "value": 0.315636349481712
        }, 
        "jvm.heap.used": {
            "value": 169311176
        }, 
        "jvm.non-heap.committed": {
            "value": 37879808
        }, 
        "jvm.non-heap.init": {
            "value": 24313856
        }, 
        "jvm.non-heap.max": {
            "value": 184549376
        }, 
        "jvm.non-heap.usage": {
            "value": 0.19970542734319513
        }, 
        "jvm.non-heap.used": {
            "value": 36855512
        }, 
        "jvm.pools.Code-Cache.usage": {
            "value": 0.031689961751302086
        }, 
        "jvm.pools.PS-Eden-Space.usage": {
            "value": 0.9052384254331968
        }, 
        "jvm.pools.PS-Old-Gen.usage": {
            "value": 0.02212668565200476
        }, 
        "jvm.pools.PS-Perm-Gen.usage": {
            "value": 0.26271122694015503
        }, 
        "jvm.pools.PS-Survivor-Space.usage": {
            "value": 0.5714285714285714
        }, 
        "jvm.total.committed": {
            "value": 574291968
        }, 
        "jvm.total.init": {
            "value": 561184768
        }, 
        "jvm.total.max": {
            "value": 720961536
        }, 
        "jvm.total.used": {
            "value": 206166688
        }, 
        "master.apps": {
            "value": 0
        }, 
        "master.waitingApps": {
            "value": 0
        }, 
        "master.workers": {
            "value": 0
        }
    }, 
    "counters": { }, 
    "histograms": { }, 
    "meters": { }, 
    "timers": { }
}