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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
; {" K2 c( h- { -Dhadoop.version=2.4.0 -Pspark-ganglia-lgpl
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3 w, N' y+ G4 c* W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：5 c- |6 Q7 |7 V! {1 m
[iteblog@iteblog spark]$ SPARK_HADOOP_VERSION=2.4.0 SPARK_YARN=true
7 f6 d; _" @! J. J: B% f 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： [size=1em][size=1em]--jars lib/spark-ganglia-lgpl_2.10-x.x.x.jar ...
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依赖弄好之后，我们需要在$SPARK_HOME/conf/metrics.properties文件中加入一下配置：
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依赖弄好之后，我们需要在$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 - Y6 [# B& B2 }
+ W4 G3 r ~! M+ O) e7 G, Jhost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。 如果你出现类似下面的异常信息： 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$1.run(URLClassLoader.java:355) at java.security.AccessController.doPrivileged(Native Method) at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
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at java.lang.ClassLoader.loadClass(ClassLoader.java:425) at sun.misc.Launcher$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)
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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) ) I( b( u+ M" r" w! _, b
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$WorkQueue.runTask(ForkJoinPool.java:1339) at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) 请查看你的Spark包是否将org.apache.spark.metrics.sink.GangliaSink打包进去了；或者仔细看下你的配置文件，请尽量拷贝我这里提供的。 除了上图的master.apps 和master.workers 监控，Ganglia里面还显示如下的信息： { "version": "3.0.0", "gauges": { "jvm.PS-MarkSweep.count": { "value": 0 }, "jvm.PS-MarkSweep.time": { "value": 0 }, "jvm.PS-Scavenge.c ount": { "value": 186 }, "jvm.PS-Scavenge.time": { "value": 375 }, "jvm.heap.committed": { "value": 536412160 }, "jvm.heap.init": { "value": 536870912 }, "jvm.heap.max": { "value": 536412160 }, "jvm.heap.usage": { "value": 0.315636349481712 }, "jvm.heap.used": { "value": 169311176 }, "jvm.non-heap.committed": { "value": 37879808 }, "jvm.non-heap.init": { "value": 24313856 }, "jvm.non-heap.max": { "value": 184549376 }, "jvm.non-heap.usage": { "value": 0.19970542734319513 }, "jvm.non-heap.used": { "value": 36855512 + E2 m1 x% y$ v8 s' P$ F
}, "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": { } % \$ I U7 S" P, i3 O. [! [
}
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发表于 2017-8-31 22:11:56