Kartik, Spark Workers won't start if SPARK_MASTER_IP is wrong, maybe you would have used start_slaves.sh from Master node to start all worker nodes, where Workers would have got correct SPARK_MASTER_IP initially. Later any restart from slave nodes would have failed because of wrong SPARK_MASTER_IP at worker nodes.
Check the logs of other workers running to see what SPARK_MASTER_IP it has connected, I don't think it is using a wrong Master IP. Thanks, Prabhu Joseph On Mon, Feb 15, 2016 at 12:34 PM, Kartik Mathur <kar...@bluedata.com> wrote: > Thanks Prabhu , > > I had wrongly configured spark_master_ip in worker nodes to `hostname -f` > which is the worker and not master , > > but now the question is *why the cluster was up initially for 2 days* and > workers realized of this invalid configuration after 2 days ? And why other > workers are still up even through they have the same setting ? > > Really appreciate your help > > Thanks, > Kartik > > On Sun, Feb 14, 2016 at 10:53 PM, Prabhu Joseph < > prabhujose.ga...@gmail.com> wrote: > >> Kartik, >> >> The exception stack trace >> *java.util.concurrent.RejectedExecutionException* will happen if >> SPARK_MASTER_IP in worker nodes are configured wrongly like if >> SPARK_MASTER_IP is a hostname of Master Node and workers trying to connect >> to IP of master node. Check whether SPARK_MASTER_IP in Worker nodes are >> exactly the same as what Spark Master GUI shows. >> >> >> Thanks, >> Prabhu Joseph >> >> On Mon, Feb 15, 2016 at 11:51 AM, Kartik Mathur <kar...@bluedata.com> >> wrote: >> >>> on spark 1.5.2 >>> I have a spark standalone cluster with 6 workers , I left the cluster >>> idle for 3 days and after 3 days I saw only 4 workers on the spark master >>> UI , 2 workers died with the same exception - >>> >>> Strange part is cluster was running stable for 2 days but on third day 2 >>> workers abruptly died . I am see this error in one of the affected worker . >>> No job ran for 2 days. >>> >>> >>> >>> 2016-02-14 01:12:59 ERROR Worker:75 - Connection to master failed! >>> Waiting for master to reconnect...2016-02-14 01:12:59 ERROR Worker:75 - >>> Connection to master failed! Waiting for master to reconnect...2016-02-14 >>> 01:13:10 ERROR SparkUncaughtExceptionHandler:96 - Uncaught exception in >>> thread >>> Thread[sparkWorker-akka.actor.default-dispatcher-2,5,main]java.util.concurrent.RejectedExecutionException: >>> Task java.util.concurrent.FutureTask@514b13ad rejected from >>> java.util.concurrent.ThreadPoolExecutor@17f8ec8d[Running, pool size = >>> 1, active threads = 1, queued tasks = 0, completed tasks = 3] at >>> java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2048) >>> at >>> java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:821) >>> at >>> java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1372) >>> at >>> java.util.concurrent.AbstractExecutorService.submit(AbstractExecutorService.java:110) >>> at >>> org.apache.spark.deploy.worker.Worker$$anonfun$org$apache$spark$deploy$worker$Worker$$reregisterWithMaster$1.apply$mcV$sp(Worker.scala:269) >>> at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1119) >>> at >>> org.apache.spark.deploy.worker.Worker.org$apache$spark$deploy$worker$Worker$$reregisterWithMaster(Worker.scala:234) >>> at >>> org.apache.spark.deploy.worker.Worker$$anonfun$receive$1.applyOrElse(Worker.scala:521) >>> at >>> org.apache.spark.rpc.akka.AkkaRpcEnv.org$apache$spark$rpc$akka$AkkaRpcEnv$$processMessage(AkkaRpcEnv.scala:177) >>> at >>> org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1$$anonfun$receiveWithLogging$1$$anonfun$applyOrElse$4.apply$mcV$sp(AkkaRpcEnv.scala:126) >>> at >>> org.apache.spark.rpc.akka.AkkaRpcEnv.org$apache$spark$rpc$akka$AkkaRpcEnv$$safelyCall(AkkaRpcEnv.scala:197) >>> at >>> org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1$$anonfun$receiveWithLogging$1.applyOrElse(AkkaRpcEnv.scala:125) >>> at >>> scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33) >>> at >>> scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33) >>> at >>> scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25) >>> at >>> org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:59) >>> at >>> org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:42) >>> at >>> scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118) >>> at >>> org.apache.spark.util.ActorLogReceive$$anon$1.applyOrElse(ActorLogReceive.scala:42) >>> at akka.actor.Actor$class.aroundReceive(Actor.scala:467) at >>> org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1.aroundReceive(AkkaRpcEnv.scala:92) >>> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) >>> at akka.actor.ActorCell.invoke(ActorCell.scala:487) at >>> akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238) at >>> akka.dispatch.Mailbox.run(Mailbox.scala:220) at >>> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:397) >>> 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) >>> >>> >>> >>> down votefavorite >>> <http://t.sidekickopen35.com/e1t/c/5/f18dQhb0S7lC8dDMPbW2n0x6l2B9nMJW7t5XZs4WrRx6W4XyGfn7gbDClW5vMqt056dBqBf8x44FH02?t=http%3A%2F%2Fstackoverflow.com%2Fquestions%2F35402516%2Fspark-workers-dropping-off-after-couple-of-days%23&si=5102319033384960&pi=a5b195e6-0a48-4ec8-80a6-176be5a0ebe5> >>> >>> >> >