set the ip of the mesos master on a slave - apache-spark

I have three mesos slave nodes and one master on 10.14.56.157, 10.14.56.159 and 10.14.56.160 and 10.14.56.156 respectively. The names of the machines are worker1, worker2, worker3 and master.
I managed to set up the mesos cluster correctly (I believe). The web UI on 10.0.0.4:5050 shows all the three slaves. Then I'm running a spark shell on the cluster. Everything initially works fine: shell starts, UI shows a new framework started etc. Then I'm trying to run a simple test:
val numbers = sc.parallelize(1 to 1000000, 1000)
which works fine and then
numbers.count
Of course this is when spark actually does some work. So it starts the tasks, sends it to slaves (I can see it in the logs) but then none of the tasks completes (status: LOST). Spark retries the tasks up to 4 times and eventually gives up. I looked into the logs on the slave machines (the sandbox link in the UI) and I get the following output:
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0227 13:47:59.842319 17015 fetcher.cpp:76] Fetching URI '/home/user01/spark-1.2.1-bin-hadoop1.tgz'
I0227 13:47:59.842658 17015 fetcher.cpp:179] Copying resource from '/home/user01/spark-1.2.1-bin-hadoop1.tgz' to '/tmp/mesos/slaves/20150226-160235-2620919306-5050-14323-1/frameworks/20150227-132220-2620919306-5050-30420-0001/executors/20150226-160235-2620919306-5050-14323-1/runs/1978f267-cb47-4a6c-bd1f-69e99c00ae13'
I0227 13:48:09.896682 17015 fetcher.cpp:64] Extracted resource '/tmp/mesos/slaves/20150226-160235-2620919306-5050-14323-1/frameworks/20150227-132220-2620919306-5050-30420-0001/executors/20150226-160235-2620919306-5050-14323-1/runs/1978f267-cb47-4a6c-bd1f-69e99c00ae13/spark-1.2.1-bin-hadoop1.tgz' into '/tmp/mesos/slaves/20150226-160235-2620919306-5050-14323-1/frameworks/20150227-132220-2620919306-5050-30420-0001/executors/20150226-160235-2620919306-5050-14323-1/runs/1978f267-cb47-4a6c-bd1f-69e99c00ae13'
Spark assembly has been built with Hive, including Datanucleus jars on classpath
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/02/27 13:48:11 INFO MesosExecutorBackend: Registered signal handlers for [TERM, HUP, INT]
I0227 13:48:11.493357 17124 exec.cpp:132] Version: 0.20.1
I0227 13:48:11.496057 17142 exec.cpp:206] Executor registered on slave 20150226-160235-2620919306-5050-14323-1
15/02/27 13:48:11 INFO MesosExecutorBackend: Registered with Mesos as executor ID 20150226-160235-2620919306-5050-14323-1 with 1 cpus
15/02/27 13:48:11 INFO Executor: Starting executor ID 20150226-160235-2620919306-5050-14323-1 on host 10.14.56.160
15/02/27 13:48:11 INFO SecurityManager: Changing view acls to: user01
15/02/27 13:48:11 INFO SecurityManager: Changing modify acls to: user01
15/02/27 13:48:11 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(user01); users with modify permissions: Set(user01)
15/02/27 13:48:12 INFO Slf4jLogger: Slf4jLogger started
15/02/27 13:48:12 INFO Remoting: Starting remoting
15/02/27 13:48:12 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkExecutor#10.14.56.160:42869]
15/02/27 13:48:12 INFO Utils: Successfully started service 'sparkExecutor' on port 42869.
15/02/27 13:48:12 INFO AkkaUtils: Connecting to MapOutputTracker: akka.tcp://sparkDriver#master:48886/user/MapOutputTracker
15/02/27 13:48:12 WARN Remoting: Tried to associate with unreachable remote address [akka.tcp://sparkDriver#master:48886]. Address is now gated for 5000 ms, all messages to this address will be delivered to dead letters. Reason: master: Name or service not known
akka.actor.ActorNotFound: Actor not found for: ActorSelection[Anchor(akka.tcp://sparkDriver#master:48886/), Path(/user/MapOutputTracker)]
at akka.actor.ActorSelection$$anonfun$resolveOne$1.apply(ActorSelection.scala:65)
at akka.actor.ActorSelection$$anonfun$resolveOne$1.apply(ActorSelection.scala:63)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at akka.dispatch.BatchingExecutor$Batch$$anonfun$run$1.processBatch$1(BatchingExecutor.scala:67)
at akka.dispatch.BatchingExecutor$Batch$$anonfun$run$1.apply$mcV$sp(BatchingExecutor.scala:82)
at akka.dispatch.BatchingExecutor$Batch$$anonfun$run$1.apply(BatchingExecutor.scala:59)
at akka.dispatch.BatchingExecutor$Batch$$anonfun$run$1.apply(BatchingExecutor.scala:59)
at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
at akka.dispatch.BatchingExecutor$Batch.run(BatchingExecutor.scala:58)
at akka.dispatch.ExecutionContexts$sameThreadExecutionContext$.unbatchedExecute(Future.scala:74)
at akka.dispatch.BatchingExecutor$class.execute(BatchingExecutor.scala:110)
at akka.dispatch.ExecutionContexts$sameThreadExecutionContext$.execute(Future.scala:73)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at akka.pattern.PromiseActorRef.$bang(AskSupport.scala:267)
at akka.actor.EmptyLocalActorRef.specialHandle(ActorRef.scala:508)
at akka.actor.DeadLetterActorRef.specialHandle(ActorRef.scala:541)
at akka.actor.DeadLetterActorRef.$bang(ActorRef.scala:531)
at akka.remote.RemoteActorRefProvider$RemoteDeadLetterActorRef.$bang(RemoteActorRefProvider.scala:87)
at akka.remote.EndpointWriter.postStop(Endpoint.scala:561)
at akka.actor.Actor$class.aroundPostStop(Actor.scala:475)
at akka.remote.EndpointActor.aroundPostStop(Endpoint.scala:415)
at akka.actor.dungeon.FaultHandling$class.akka$actor$dungeon$FaultHandling$$finishTerminate(FaultHandling.scala:210)
at akka.actor.dungeon.FaultHandling$class.terminate(FaultHandling.scala:172)
at akka.actor.ActorCell.terminate(ActorCell.scala:369)
at akka.actor.ActorCell.invokeAll$1(ActorCell.scala:462)
at akka.actor.ActorCell.systemInvoke(ActorCell.scala:478)
at akka.dispatch.Mailbox.processAllSystemMessages(Mailbox.scala:263)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
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)
Exception in thread "Thread-1" I0227 13:48:12.364940 17142 exec.cpp:413] Deactivating the executor libprocess
The line where the error occurs says:
Tried to associate with unreachable remote address [akka.tcp://sparkDriver#master:48886]
It seems to me that the slave cannot resolve the name master to the master's IP. Is that correct? If so how to change it to the actual IP. If not, how to fix it? Thanks!

What happens if you type ping master on one of the slave machines? If that fails, that's your problem, and you could fix it by adding a line to each slave's /etc/hosts file pointing master to the correct IP.
You could also try setting spark.driver.host to its IP when launching the spark driver, to change what "host" it advertises itself as.

Related

spark-submit: unable to get driver status

I'm running a job on a test Spark standalone in cluster mode, but I'm finding myself unable to monitor the status of the driver.
Here is a minimal example using spark-2.4.3 (master and one worker running on the same node, started running sbin/start-all.sh on a freshly unarchived installation using the default conf, no conf/slaves set), executing spark-submit from the node itself:
$ spark-submit --master spark://ip-172-31-15-245:7077 --deploy-mode cluster \
--class org.apache.spark.examples.SparkPi \
/home/ubuntu/spark/examples/jars/spark-examples_2.11-2.4.3.jar 100
log4j:WARN No appenders could be found for logger (org.apache.hadoop.util.NativeCodeLoader).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
19/06/27 09:08:28 INFO SecurityManager: Changing view acls to: ubuntu
19/06/27 09:08:28 INFO SecurityManager: Changing modify acls to: ubuntu
19/06/27 09:08:28 INFO SecurityManager: Changing view acls groups to:
19/06/27 09:08:28 INFO SecurityManager: Changing modify acls groups to:
19/06/27 09:08:28 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(ubuntu); groups with view permissions: Set(); users with modify permissions: Set(ubuntu); groups with modify permissions: Set()
19/06/27 09:08:28 INFO Utils: Successfully started service 'driverClient' on port 36067.
19/06/27 09:08:28 INFO TransportClientFactory: Successfully created connection to ip-172-31-15-245/172.31.15.245:7077 after 29 ms (0 ms spent in bootstraps)
19/06/27 09:08:28 INFO ClientEndpoint: Driver successfully submitted as driver-20190627090828-0008
19/06/27 09:08:28 INFO ClientEndpoint: ... waiting before polling master for driver state
19/06/27 09:08:33 INFO ClientEndpoint: ... polling master for driver state
19/06/27 09:08:33 INFO ClientEndpoint: State of driver-20190627090828-0008 is RUNNING
19/06/27 09:08:33 INFO ClientEndpoint: Driver running on 172.31.15.245:41057 (worker-20190627083412-172.31.15.245-41057)
19/06/27 09:08:33 INFO ShutdownHookManager: Shutdown hook called
19/06/27 09:08:33 INFO ShutdownHookManager: Deleting directory /tmp/spark-34082661-f0de-4c56-92b7-648ea24fa59c
> spark-submit --master spark://ip-172-31-15-245:7077 --status driver-20190627090828-0008
19/06/27 09:09:27 WARN RestSubmissionClient: Unable to connect to server spark://ip-172-31-15-245:7077.
Exception in thread "main" org.apache.spark.deploy.rest.SubmitRestConnectionException: Unable to connect to server
at org.apache.spark.deploy.rest.RestSubmissionClient$$anonfun$requestSubmissionStatus$3.apply(RestSubmissionClient.scala:165)
at org.apache.spark.deploy.rest.RestSubmissionClient$$anonfun$requestSubmissionStatus$3.apply(RestSubmissionClient.scala:148)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
at org.apache.spark.deploy.rest.RestSubmissionClient.requestSubmissionStatus(RestSubmissionClient.scala:148)
at org.apache.spark.deploy.SparkSubmit.requestStatus(SparkSubmit.scala:111)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:88)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:924)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:933)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: org.apache.spark.deploy.rest.SubmitRestConnectionException: No response from server
at org.apache.spark.deploy.rest.RestSubmissionClient.readResponse(RestSubmissionClient.scala:285)
at org.apache.spark.deploy.rest.RestSubmissionClient.org$apache$spark$deploy$rest$RestSubmissionClient$$get(RestSubmissionClient.scala:195)
at org.apache.spark.deploy.rest.RestSubmissionClient$$anonfun$requestSubmissionStatus$3.apply(RestSubmissionClient.scala:152)
... 11 more
Caused by: java.util.concurrent.TimeoutException: Futures timed out after [10 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:223)
at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:227)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190)
at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:190)
at org.apache.spark.deploy.rest.RestSubmissionClient.readResponse(RestSubmissionClient.scala:278)
... 13 more
Spark is in good health (I'm able to run other jobs after the one above), the driver-20190627090828-0008 appears as "FINISHED" in the web UI.
Is there something I am missing?
UPDATE:
on the master log all I get is
19/07/01 09:40:24 INFO master.Master: 172.31.15.245:42308 got disassociated, removing it.

Can't connect slaves to master in Spark

Using 4 instances on Compute Engine, each running spark set up with Cloudera Manager. I have no problems starting the master and connecting in my local browser, and it connects as spark://instance-1:7077. When I start the start-slave on the remaining instances I get no errors, until I look in the log:
16/05/02 13:10:18 INFO worker.Worker: Started daemon with process name: 12612#instance-2.c.cluster1-1294.internal
16/05/02 13:10:18 INFO worker.Worker: Registered signal handlers for [TERM, HUP, INT]
16/05/02 13:10:18 INFO spark.SecurityManager: Changing view acls to: root
16/05/02 13:10:18 INFO spark.SecurityManager: Changing modify acls to: root
16/05/02 13:10:18 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with mod$
16/05/02 13:10:19 INFO util.Utils: Successfully started service 'sparkWorker' on port 60270.
16/05/02 13:10:19 INFO worker.Worker: Starting Spark worker 10.142.0.3:60270 with 2 cores, 6.3 GB RAM
16/05/02 13:10:19 INFO worker.Worker: Running Spark version 1.6.0
16/05/02 13:10:19 INFO worker.Worker: Spark home: /opt/cloudera/parcels/CDH-5.7.0-1.cdh5.7.0.p0.45/lib/spark
16/05/02 13:10:19 ERROR worker.Worker: Failed to create work directory /opt/cloudera/parcels/CDH-5.7.0-1.cdh5.7.0.p0.45/lib/spark/work
If i use mkdir to create 'work' then it throws and error and says the directory already exists:
mkdir: cannot create directory ‘work’: File exists
The file does exist and when using ls to find it it is highlighted in red with a black background. Any help would be appreciated.
Maybe this is the permission issue,
Try this,
$sudo chown -R your_userName:your_groupName /opt/cloudera/parcels/CDH-5.7.0-1.cdh5.7.0.p0.45/lib/spark
Now change the Mode of the above path
$sudo chmod 777 /opt/cloudera/parcels/CDH-5.7.0-1.cdh5.7.0.p0.45/lib/spark
Also all the slaves must have ssh to each other and can able to talk one another.
And Copy all the Configuration file of spark to the slave nodes also.

Spark Cluster, failed to connect to master. (WARN Worker: Failed to connect to master)

I have a spark cluster with 2 nodes, master(172.17.0.229) and slave(172.17.0.228). I have edited spark-env.sh, added SPARK_MASTER_IP=127.17.0.229 and slaves, added 172.17.0.228.
I am starting my master node using start-master.sh and slave node using start-slaves.sh.
I can see the webUI with a master node with no worker, but the log of worker node is as:
Spark Command: /usr/lib/jvm/java-7-oracle/jre/bin/java -cp /usr/local/src/spark-1.5.2-bin-hadoop2.6/sbin/../conf/:/usr/local/src/spark-1.5.2-bin-hadoop$
========================================
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/12/18 14:17:25 INFO Worker: Registered signal handlers for [TERM, HUP, INT]
15/12/18 14:17:26 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/12/18 14:17:26 INFO SecurityManager: Changing view acls to: ujjwal
15/12/18 14:17:26 INFO SecurityManager: Changing modify acls to: ujjwal
15/12/18 14:17:26 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(ujjwal); users wit$
15/12/18 14:17:27 INFO Slf4jLogger: Slf4jLogger started
15/12/18 14:17:27 INFO Remoting: Starting remoting
15/12/18 14:17:27 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkWorker#172.17.0.228:47599]
15/12/18 14:17:27 INFO Utils: Successfully started service 'sparkWorker' on port 47599.
15/12/18 14:17:27 INFO Worker: Starting Spark worker 172.17.0.228:47599 with 2 cores, 2.7 GB RAM
15/12/18 14:17:27 INFO Worker: Running Spark version 1.5.2
15/12/18 14:17:27 INFO Worker: Spark home: /usr/local/src/spark-1.5.2-bin-hadoop2.6
15/12/18 14:17:27 INFO Utils: Successfully started service 'WorkerUI' on port 8081.
15/12/18 14:17:27 INFO WorkerWebUI: Started WorkerWebUI at http://172.17.0.228:8081
15/12/18 14:17:27 INFO Worker: Connecting to master 127.17.0.229:7077...
15/12/18 14:17:27 WARN ReliableDeliverySupervisor: Association with remote system [akka.tcp://sparkMaster#127.17.0.229:7077] has failed, address is now$
15/12/18 14:17:27 WARN Worker: Failed to connect to master 127.17.0.229:7077
akka.actor.ActorNotFound: Actor not found for: ActorSelection[Anchor(akka.tcp://sparkMaster#127.17.0.229:7077/), Path(/user/Master)]
at akka.actor.ActorSelection$$anonfun$resolveOne$1.apply(ActorSelection.scala:65)
at akka.actor.ActorSelection$$anonfun$resolveOne$1.apply(ActorSelection.scala:63)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at akka.dispatch.BatchingExecutor$AbstractBatch.processBatch(BatchingExecutor.scala:55)
at akka.dispatch.BatchingExecutor$Batch.run(BatchingExecutor.scala:73)
at akka.dispatch.ExecutionContexts$sameThreadExecutionContext$.unbatchedExecute(Future.scala:74)
at akka.dispatch.BatchingExecutor$class.execute(BatchingExecutor.scala:120)
at akka.dispatch.ExecutionContexts$sameThreadExecutionContext$.execute(Future.scala:73)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at akka.pattern.PromiseActorRef.$bang(AskSupport.scala:266)
at akka.actor.EmptyLocalActorRef.specialHandle(ActorRef.scala:533)
at akka.actor.DeadLetterActorRef.specialHandle(ActorRef.scala:569)
at akka.actor.DeadLetterActorRef.$bang(ActorRef.scala:559)
at akka.remote.RemoteActorRefProvider$RemoteDeadLetterActorRef.$bang(RemoteActorRefProvider.scala:87)
at akka.remote.EndpointWriter.postStop(Endpoint.scala:557)
at akka.actor.Actor$class.aroundPostStop(Actor.scala:477)
at akka.remote.EndpointActor.aroundPostStop(Endpoint.scala:411)
at akka.actor.dungeon.FaultHandling$class.akka$actor$dungeon$FaultHandling$$finishTerminate(FaultHandling.scala:210)
at akka.actor.dungeon.FaultHandling$class.terminate(FaultHandling.scala:172)
at akka.actor.ActorCell.terminate(ActorCell.scala:369)
at akka.actor.ActorCell.invokeAll$1(ActorCell.scala:462)
Thanks for your suggestion.
Generally, checking the IP that your worker is trying to connect to against the reported spark://...:7077 address on the web UI at 172.17.0.229 port 8080 will help identify whether the address is correct.
In this particular case, it looks like you have a typo; change
SPARK_MASTER_IP=127.17.0.229
to read:
SPARK_MASTER_IP=172.17.0.229
(you seem to have 127/172 inverted).
My issue was a version mismatch between the spark java library I was using (2.0.0) and the version of the spark cluster (2.2.1)

Spark worker can not connect to Master

While starting the worker node I get the following error :
Spark Command: /usr/lib/jvm/default-java/bin/java -cp /home/ubuntu/spark-1.5.1-bin-hadoop2.6/sbin/../conf/:/home/ubuntu/spark-1.5.1-bin-hadoop2.6/lib/spark-assembly-1.5.1-hadoop2.6.0.jar:/home/ubuntu/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar:/home/ubuntu/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/home/ubuntu/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar -Xms1g -Xmx1g -XX:MaxPermSize=256m org.apache.spark.deploy.worker.Worker --webui-port 8081 spark://ip-1-70-44-5:7077
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/10/16 19:19:10 INFO Worker: Registered signal handlers for [TERM, HUP, INT]
15/10/16 19:19:11 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/10/16 19:19:11 INFO SecurityManager: Changing view acls to: ubuntu
15/10/16 19:19:11 INFO SecurityManager: Changing modify acls to: ubuntu
15/10/16 19:19:11 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(ubuntu); users with modify permissions: Set(ubuntu)
15/10/16 19:19:12 INFO Slf4jLogger: Slf4jLogger started
15/10/16 19:19:12 INFO Remoting: Starting remoting
15/10/16 19:19:12 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkWorker#1.70.44.4:55126]
15/10/16 19:19:12 INFO Utils: Successfully started service 'sparkWorker' on port 55126.
15/10/16 19:19:12 INFO Worker: Starting Spark worker 1.70.44.4:55126 with 2 cores, 2.9 GB RAM
15/10/16 19:19:12 INFO Worker: Running Spark version 1.5.1
15/10/16 19:19:12 INFO Worker: Spark home: /home/ubuntu/spark-1.5.1-bin-hadoop2.6
15/10/16 19:19:12 INFO Utils: Successfully started service 'WorkerUI' on port 8081.
15/10/16 19:19:12 INFO WorkerWebUI: Started WorkerWebUI at http://1.70.44.4:8081
15/10/16 19:19:12 INFO Worker: Connecting to master ip-1-70-44-5:7077...
15/10/16 19:19:24 INFO Worker: Retrying connection to master (attempt # 1)
15/10/16 19:19:24 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[sparkWorker-akka.actor.default-dispatcher-5,5,main]
java.util.concurrent.RejectedExecutionException: Task java.util.concurrent.FutureTask#1c5651e9 rejected from java.util.concurrent.ThreadPoolExecutor#671ba687[Running, pool size = 1, active threads = 0, queued tasks = 0, completed tasks = 0]
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$$anonfun$org$apache$spark$deploy$worker$Worker$$tryRegisterAllMasters$1.apply(Worker.scala:211)
at org.apache.spark.deploy.worker.Worker$$anonfun$org$apache$spark$deploy$worker$Worker$$tryRegisterAllMasters$1.apply(Worker.scala:210)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
at org.apache.spark.deploy.worker.Worker.org$apache$spark$deploy$worker$Worker$$tryRegisterAllMasters(Worker.scala:210)
at org.apache.spark.deploy.worker.Worker$$anonfun$org$apache$spark$deploy$worker$Worker$$reregisterWithMaster$1.apply$mcV$sp(Worker.scala:288)
at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1119)
at org.apache.spark.deploy.worker.Worker.org$apache$spark$deploy$worker$Worker$$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)
15/10/16 19:19:24 INFO ShutdownHookManager: Shutdown hook called
I have added the hostnames to the conf/slaves file. I dont know which enviroment variables to set in spark-env.sh so right not its not being used.
Any pointers to the solution ?
Also, if I should use spark-env.sh then which enviroment vvariables should I run ?
setup details :
2 ubuntu14 machines having 2 cores each.
Please advise.
thanks
So, after some tinkering around I found that slave was not able to communicate with Master on the given port. I changed the security access rules and enabled all TCP traffic on all ports . This solved the problem.
To check if the port is open :
telnet master.ip master.port
The default port is 7077.
My spark-env.sh :
export SPARK_WORKER_INSTANCES=2
export SPARK_MASTER_IP=<ip address>
I'm afraid your hostname may be invalid to Spark, and you hava to change your spark-env.sh.
You can set the variable SPARK_MASTER_IP to be the real ip of master, instead of its hostname.
e.g.
export SPARK_MASTER_IP=1.70.44.5
INSTEAD OF
export SPARK_MASTER_IP=ip-1-70-44-5

SPARK + Standalone Cluster: Cannot start worker from another machine

I've been setting up a Spark standalone cluster setup following this link. I have 2 machines; The first one (ubuntu0) serve as both the master and a worker, and the second one (ubuntu1) is just a worker. Password-less ssh has been properly configured for both machines already and was tested by doing SSH manually on both sides.
Now when I tried to ./start-all.ssh, both master and worker on the master machine (ubuntu0) were started properly. This is signified by (1)WebUI being accessible (localhost:8081 on my part) and (2) Worker registered/displayed on the WebUI.
However, the other worker on the second machine (ubuntu1), was not started. The error displayed was:
ubuntu1: ssh: connect to host ubuntu1 port 22: Connection timed out
Now this is quite weird already given that I've properly configured the ssh to be password-less on both sides. Given this, I accessed the second machine and tried to start the worker manually using these commands:
./spark-class org.apache.spark.deploy.worker.Worker spark://ubuntu0:7707
./spark-class org.apache.spark.deploy.worker.Worker spark://<ip>:7707
However, below is the result:
14/05/23 13:49:08 INFO Utils: Using Spark's default log4j profile:
org/apache/spark/log4j-defaults.properties
14/05/23 13:49:08 WARN Utils: Your hostname, ubuntu1 resolves to a loopback address:
127.0.1.1; using 192.168.122.1 instead (on interface virbr0)
14/05/23 13:49:08 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
14/05/23 13:49:09 INFO Slf4jLogger: Slf4jLogger started
14/05/23 13:49:09 INFO Remoting: Starting remoting
14/05/23 13:49:09 INFO Remoting: Remoting started; listening on addresses :
[akka.tcp://sparkWorker#ubuntu1.local:42739]
14/05/23 13:49:09 INFO Worker: Starting Spark worker ubuntu1.local:42739 with 8 cores,
4.8 GB RAM
14/05/23 13:49:09 INFO Worker: Spark home: /home/ubuntu1/jaysonp/spark/spark-0.9.1
14/05/23 13:49:09 INFO WorkerWebUI: Started Worker web UI at http://ubuntu1.local:8081
14/05/23 13:49:09 INFO Worker: Connecting to master spark://ubuntu0:7707...
14/05/23 13:49:29 INFO Worker: Connecting to master spark://ubuntu0:7707...
14/05/23 13:49:49 INFO Worker: Connecting to master spark://ubuntu0:7707...
14/05/23 13:50:09 ERROR Worker: All masters are unresponsive! Giving up.
Below are the contents of my master and slave\worker spark-env.ssh:
SPARK_MASTER_IP=192.168.3.222
STANDALONE_SPARK_MASTER_HOST=`hostname -f`
How should I resolve this? Thanks in advance!
For those who are still encountering error(s) when it comes to starting workers on different machines, I just want to share that using IP addresses in conf/slaves worked for me.
Hope this helps!
I have add similar issues today running spark 1.5.1 on RHEL 6.7.
I have 2 machines, their hostname being
- master.domain.com
- slave.domain.com
I installed a standalone version of spark (pre-build against hadoop 2.6) and installed my Oracle jdk-8u66.
Spark download:
wget http://d3kbcqa49mib13.cloudfront.net/spark-1.5.1-bin-hadoop2.6.tgz
Java download
wget --no-cookies --no-check-certificate --header "Cookie: gpw_e24=http%3A%2F%2Fwww.oracle.com%2F; oraclelicense=accept-securebackup-cookie" "http://download.oracle.com/otn-pub/java/jdk/8u66-b17/jdk-8u66-linux-x64.tar.gz"
after spark and java are unpacked in my home directory I did the following:
on 'master.domain.com' I ran:
./sbin/start-master.sh
The webUI become available at http://master.domain.com:8080 (no slave running)
on 'slave.domain.com' I did try:
./sbin/start-slave.sh spark://master.domain.com:7077 FAILED AS FOLLOW
Spark Command: /root/java/bin/java -cp /root/spark-1.5.1-bin-hadoop2.6/sbin/../conf/:/root/spark-1.5.1-bin-hadoop2.6/lib/spark-assembly-1.5.1-hadoop2.6.0.jar:/root/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/root/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/root/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar -Xms1g -Xmx1g org.apache.spark.deploy.worker.Worker --webui-port 8081 spark://master.domain.com:7077
========================================
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/11/06 11:03:51 INFO Worker: Registered signal handlers for [TERM, HUP, INT]
15/11/06 11:03:51 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/11/06 11:03:51 INFO SecurityManager: Changing view acls to: root
15/11/06 11:03:51 INFO SecurityManager: Changing modify acls to: root
15/11/06 11:03:51 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
15/11/06 11:03:52 INFO Slf4jLogger: Slf4jLogger started
15/11/06 11:03:52 INFO Remoting: Starting remoting
15/11/06 11:03:52 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkWorker#10.80.70.38:50573]
15/11/06 11:03:52 INFO Utils: Successfully started service 'sparkWorker' on port 50573.
15/11/06 11:03:52 INFO Worker: Starting Spark worker 10.80.70.38:50573 with 8 cores, 6.7 GB RAM
15/11/06 11:03:52 INFO Worker: Running Spark version 1.5.1
15/11/06 11:03:52 INFO Worker: Spark home: /root/spark-1.5.1-bin-hadoop2.6
15/11/06 11:03:53 INFO Utils: Successfully started service 'WorkerUI' on port 8081.
15/11/06 11:03:53 INFO WorkerWebUI: Started WorkerWebUI at http://10.80.70.38:8081
15/11/06 11:03:53 INFO Worker: Connecting to master master.domain.com:7077...
15/11/06 11:04:05 INFO Worker: Retrying connection to master (attempt # 1)
15/11/06 11:04:05 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[sparkWorker-akka.actor.default-dispatcher-4,5,main]
java.util.concurrent.RejectedExecutionException: Task java.util.concurrent.FutureTask#48711bf5 rejected from java.util.concurrent.ThreadPoolExecutor#14db705b[Running, pool size = 1, active threads = 0, queued tasks = 0, completed tasks = 1]
at java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2047)
at java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:823)
at java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1369)
at java.util.concurrent.AbstractExecutorService.submit(AbstractExecutorService.java:112)
at org.apache.spark.deploy.worker.Worker$$anonfun$org$apache$spark$deploy$worker$Worker$$tryRegisterAllMasters$1.apply(Worker.scala:211)
at org.apache.spark.deploy.worker.Worker$$anonfun$org$apache$spark$deploy$worker$Worker$$tryRegisterAllMasters$1.apply(Worker.scala:210)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
at org.apache.spark.deploy.worker.Worker.org$apache$spark$deploy$worker$Worker$$tryRegisterAllMasters(Worker.scala:210)
at org.apache.spark.deploy.worker.Worker$$anonfun$org$apache$spark$deploy$worker$Worker$$reregisterWithMaster$1.apply$mcV$sp(Worker.scala:288)
at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1119)
at org.apache.spark.deploy.worker.Worker.org$apache$spark$deploy$worker$Worker$$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)
15/11/06 11:04:05 INFO ShutdownHookManager: Shutdown hook called
start-slave spark://<master-IP>:7077 also FAILED as above.
start-slave spark://master:7077 WORKED and the worker shows in the master webUI
Spark Command: /root/java/bin/java -cp /root/spark-1.5.1-bin-hadoop2.6/sbin/../conf/:/root/spark-1.5.1-bin-hadoop2.6/lib/spark-assembly-1.5.1-hadoop2.6.0.jar:/root/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/root/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/root/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar -Xms1g -Xmx1g org.apache.spark.deploy.worker.Worker --webui-port 8081 spark://master:7077
========================================
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/11/06 11:08:15 INFO Worker: Registered signal handlers for [TERM, HUP, INT]
15/11/06 11:08:15 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/11/06 11:08:15 INFO SecurityManager: Changing view acls to: root
15/11/06 11:08:15 INFO SecurityManager: Changing modify acls to: root
15/11/06 11:08:15 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
15/11/06 11:08:16 INFO Slf4jLogger: Slf4jLogger started
15/11/06 11:08:16 INFO Remoting: Starting remoting
15/11/06 11:08:17 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkWorker#10.80.70.38:40780]
15/11/06 11:08:17 INFO Utils: Successfully started service 'sparkWorker' on port 40780.
15/11/06 11:08:17 INFO Worker: Starting Spark worker 10.80.70.38:40780 with 8 cores, 6.7 GB RAM
15/11/06 11:08:17 INFO Worker: Running Spark version 1.5.1
15/11/06 11:08:17 INFO Worker: Spark home: /root/spark-1.5.1-bin-hadoop2.6
15/11/06 11:08:17 INFO Utils: Successfully started service 'WorkerUI' on port 8081.
15/11/06 11:08:17 INFO WorkerWebUI: Started WorkerWebUI at http://10.80.70.38:8081
15/11/06 11:08:17 INFO Worker: Connecting to master master:7077...
15/11/06 11:08:17 INFO Worker: Successfully registered with master spark://master:7077
Note: I haven't added any extra config in conf/spark-env.sh
Note2: when looking in the master webUI, the spark master URL at the top is actually the one that worked for me, so I'd say in doubts just use that one.
I hope this helps ;)
Using hostname in /cong/slaves worked well for me.
Here are some steps I would take it,
Checked SSH public key
scp /etc/spark/conf.dist/spark-env.sh to your workers
My part of setting in spark-env.sh
export STANDALONE_SPARK_MASTER_HOST=hostname
export SPARK_MASTER_IP=$STANDALONE_SPARK_MASTER_HOST
I guess you missed something in your configuration, that's what I learned from your log.
Check your /etc/hosts, make sure ubuntu1 in your master's host list and its Ip is match the slave's IP address.
Add export SPARK_LOCAL_IP='ubuntu1' in the spark-env.sh file of your slave

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