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Posted to user@spark.apache.org by Dmitry Goldenberg <dg...@gmail.com> on 2017/02/03 18:10:09 UTC
NoNodeAvailableException (None of the configured nodes are available)
error when trying to push data to Elastic from a Spark job
Hi,
Any reason why we might be getting this error? The code seems to work fine
in the non-distributed mode but the same code when run from a Spark job is
not able to get to Elastic.
Spark version: 2.0.1 built for Hadoop 2.4, Scala 2.11
Elastic version: 2.3.1
I've verified the Elastic hosts and the cluster name.
The spot in the code where this happens is:
ClusterHealthResponse clusterHealthResponse = client.admin().cluster()
.prepareHealth()
.setWaitForGreenStatus()
.setTimeout(TimeValue.*timeValueSeconds*(10))
.get();
Stack trace:
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org
<http://org.apache.spark.scheduler.dagscheduler.org/>$apache$spark$
scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1454)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$
abortStage$1.apply(DAGScheduler.scala:1442)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$
abortStage$1.apply(DAGScheduler.scala:1441)
at scala.collection.mutable.ResizableArray$class.foreach(
ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(
ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(
DAGScheduler.scala:1441)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$
handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$
handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(
DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.
doOnReceive(DAGScheduler.scala:1667)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.
onReceive(DAGScheduler.scala:1622)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.
onReceive(DAGScheduler.scala:1611)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(
DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1890)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1903)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1916)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1930)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.
apply(RDD.scala:902)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.
apply(RDD.scala:900)
at org.apache.spark.rdd.RDDOperationScope$.withScope(
RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(
RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:900)
at org.apache.spark.api.java.JavaRDDLike$class.
foreachPartition(JavaRDDLike.scala:218)
at org.apache.spark.api.java.AbstractJavaRDDLike.
foreachPartition(JavaRDDLike.scala:45)
at com.myco.MyDriver$3.call(com.myco.MyDriver.java:214)
at com.myco.MyDriver$3.call(KafkaSparkStreamingDriver.java:201)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$
anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:272)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$
anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:272)
at org.apache.spark.streaming.dstream.DStream$$anonfun$
foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
at org.apache.spark.streaming.dstream.DStream$$anonfun$
foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
at org.apache.spark.streaming.dstream.ForEachDStream$$
anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.ForEachDStream$$
anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.ForEachDStream$$
anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.DStream.
createRDDWithLocalProperties(DStream.scala:415)
at org.apache.spark.streaming.dstream.ForEachDStream$$
anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$
anonfun$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$
anonfun$1.apply(ForEachDStream.scala:50)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at org.apache.spark.streaming.scheduler.JobScheduler$
JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:247)
at org.apache.spark.streaming.scheduler.JobScheduler$
JobHandler$$anonfun$run$1.apply(JobScheduler.scala:247)
at org.apache.spark.streaming.scheduler.JobScheduler$
JobHandler$$anonfun$run$1.apply(JobScheduler.scala:247)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.scheduler.JobScheduler$
JobHandler.run(JobScheduler.scala:246)
at java.util.concurrent.ThreadPoolExecutor.runWorker(
ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(
ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: NoNodeAvailableException[None of the configured nodes are
available: [{#transport#-1}{XX.XXX.XXX.XX}{XX.XXX.XXX.XX:9300}]]
at org.elasticsearch.client.transport.TransportClientNodesService.
ensureNodesAreAvailable(TransportClientNodesService.java:290)
at org.elasticsearch.client.transport.TransportClientNodesService.
execute(TransportClientNodesService.java:207)
at org.elasticsearch.client.transport.support.
TransportProxyClient.execute(TransportProxyClient.java:55)
at org.elasticsearch.client.transport.TransportClient.
doExecute(TransportClient.java:288)
at org.elasticsearch.client.support.AbstractClient.
execute(AbstractClient.java:359)
at org.elasticsearch.client.support.AbstractClient$
ClusterAdmin.execute(AbstractClient.java:853)
at org.elasticsearch.action.ActionRequestBuilder.execute(
ActionRequestBuilder.java:86)
at org.elasticsearch.action.ActionRequestBuilder.execute(
ActionRequestBuilder.java:56)
at org.elasticsearch.action.ActionRequestBuilder.get(
ActionRequestBuilder.java:64)
at com.myco.MyDriver.work()
at org.apache.spark.api.java.JavaRDDLike$$anonfun$
foreachPartition$1.apply(JavaRDDLike.scala:218)
at org.apache.spark.api.java.JavaRDDLike$$anonfun$
foreachPartition$1.apply(JavaRDDLike.scala:218)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$
anonfun$apply$28.apply(RDD.scala:902)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$
anonfun$apply$28.apply(RDD.scala:902)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(
SparkContext.scala:1916)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(
SparkContext.scala:1916)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.
scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(
Executor.scala:274)
Re: NoNodeAvailableException (None of the configured nodes are
available) error when trying to push data to Elastic from a Spark job
Posted by Jacek Laskowski <ja...@japila.pl>.
Hi,
I'd say the error says it all :
Caused by: NoNodeAvailableException[None of the configured nodes are
available: [{#transport#-1}{XX.XXX.XXX.XX}{XX.XXX.XXX.XX:9300}]]
Jacek
On 3 Feb 2017 7:58 p.m., "Anastasios Zouzias" <zo...@gmail.com> wrote:
Hi there,
Are you sure that the cluster nodes where the executors run have network
connectivity to the elastic cluster?
Speaking of which, why don't you use: https://github.com/
elastic/elasticsearch-hadoop#apache-spark ?
Cheers,
Anastasios
On Fri, Feb 3, 2017 at 7:10 PM, Dmitry Goldenberg <dg...@gmail.com>
wrote:
> Hi,
>
> Any reason why we might be getting this error? The code seems to work
> fine in the non-distributed mode but the same code when run from a Spark
> job is not able to get to Elastic.
>
> Spark version: 2.0.1 built for Hadoop 2.4, Scala 2.11
> Elastic version: 2.3.1
>
> I've verified the Elastic hosts and the cluster name.
>
> The spot in the code where this happens is:
>
> ClusterHealthResponse clusterHealthResponse = client.admin().cluster()
>
> .prepareHealth()
>
> .setWaitForGreenStatus()
>
> .setTimeout(TimeValue.*timeValueSeconds*(10))
>
> .get();
>
>
> Stack trace:
>
>
> Driver stacktrace:
>
> at org.apache.spark.scheduler.DAGScheduler.org
> <http://org.apache.spark.scheduler.dagscheduler.org/>$apache$spark$
> scheduler$DAGScheduler$$failJobAndIndependentStages(DAGSched
> uler.scala:1454)
>
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$
> 1.apply(DAGScheduler.scala:1442)
>
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$
> 1.apply(DAGScheduler.scala:1441)
>
> at scala.collection.mutable.ResizableArray$class.foreach(Resiza
> bleArray.scala:59)
>
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.sca
> la:48)
>
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGSchedu
> ler.scala:1441)
>
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskS
> etFailed$1.apply(DAGScheduler.scala:811)
>
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskS
> etFailed$1.apply(DAGScheduler.scala:811)
>
> at scala.Option.foreach(Option.scala:257)
>
> at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(
> DAGScheduler.scala:811)
>
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOn
> Receive(DAGScheduler.scala:1667)
>
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onRe
> ceive(DAGScheduler.scala:1622)
>
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onRe
> ceive(DAGScheduler.scala:1611)
>
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.
> scala:632)
>
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1890)
>
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1903)
>
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1916)
>
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1930)
>
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(R
> DD.scala:902)
>
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(R
> DD.scala:900)
>
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperati
> onScope.scala:151)
>
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperati
> onScope.scala:112)
>
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
>
> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:900)
>
> at org.apache.spark.api.java.JavaRDDLike$class.foreachPartition
> (JavaRDDLike.scala:218)
>
> at org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartiti
> on(JavaRDDLike.scala:45)
>
> at com.myco.MyDriver$3.call(com.myco.MyDriver.java:214)
>
> at com.myco.MyDriver$3.call(KafkaSparkStreamingDriver.java:201)
>
> at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun
> $foreachRDD$1.apply(JavaDStreamLike.scala:272)
>
> at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun
> $foreachRDD$1.apply(JavaDStreamLike.scala:272)
>
> at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachR
> DD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
>
> at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachR
> DD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1
> $$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1
> $$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1
> $$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
>
> at org.apache.spark.streaming.dstream.DStream.createRDDWithLoca
> lProperties(DStream.scala:415)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1
> .apply$mcV$sp(ForEachDStream.scala:50)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1
> .apply(ForEachDStream.scala:50)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1
> .apply(ForEachDStream.scala:50)
>
> at scala.util.Try$.apply(Try.scala:192)
>
> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
>
> at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler
> $$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:247)
>
> at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler
> $$anonfun$run$1.apply(JobScheduler.scala:247)
>
> at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler
> $$anonfun$run$1.apply(JobScheduler.scala:247)
>
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
>
> at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler
> .run(JobScheduler.scala:246)
>
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPool
> Executor.java:1142)
>
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoo
> lExecutor.java:617)
>
> at java.lang.Thread.run(Thread.java:745)
>
> Caused by: NoNodeAvailableException[None of the configured nodes are
> available: [{#transport#-1}{XX.XXX.XXX.XX}{XX.XXX.XXX.XX:9300}]]
>
> at org.elasticsearch.client.transport.TransportClientNodesServi
> ce.ensureNodesAreAvailable(TransportClientNodesService.java:290)
>
> at org.elasticsearch.client.transport.TransportClientNodesServi
> ce.execute(TransportClientNodesService.java:207)
>
> at org.elasticsearch.client.transport.support.TransportProxyCli
> ent.execute(TransportProxyClient.java:55)
>
> at org.elasticsearch.client.transport.TransportClient.doExecute
> (TransportClient.java:288)
>
> at org.elasticsearch.client.support.AbstractClient.execute(Abst
> ractClient.java:359)
>
> at org.elasticsearch.client.support.AbstractClient$ClusterAdmin
> .execute(AbstractClient.java:853)
>
> at org.elasticsearch.action.ActionRequestBuilder.execute(Action
> RequestBuilder.java:86)
>
> at org.elasticsearch.action.ActionRequestBuilder.execute(Action
> RequestBuilder.java:56)
>
> at org.elasticsearch.action.ActionRequestBuilder.get(ActionRequ
> estBuilder.java:64)
>
> at com.myco.MyDriver.work()
>
> at org.apache.spark.api.java.JavaRDDLike$$anonfun$foreachPartit
> ion$1.apply(JavaRDDLike.scala:218)
>
> at org.apache.spark.api.java.JavaRDDLike$$anonfun$foreachPartit
> ion$1.apply(JavaRDDLike.scala:218)
>
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfu
> n$apply$28.apply(RDD.scala:902)
>
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfu
> n$apply$28.apply(RDD.scala:902)
>
> at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkC
> ontext.scala:1916)
>
> at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkC
> ontext.scala:1916)
>
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.sca
> la:70)
>
> at org.apache.spark.scheduler.Task.run(Task.scala:86)
>
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.s
> cala:274)
>
--
-- Anastasios Zouzias
<az...@zurich.ibm.com>
Re: NoNodeAvailableException (None of the configured nodes are
available) error when trying to push data to Elastic from a Spark job
Posted by Anastasios Zouzias <zo...@gmail.com>.
Hi there,
Are you sure that the cluster nodes where the executors run have network
connectivity to the elastic cluster?
Speaking of which, why don't you use:
https://github.com/elastic/elasticsearch-hadoop#apache-spark ?
Cheers,
Anastasios
On Fri, Feb 3, 2017 at 7:10 PM, Dmitry Goldenberg <dg...@gmail.com>
wrote:
> Hi,
>
> Any reason why we might be getting this error? The code seems to work
> fine in the non-distributed mode but the same code when run from a Spark
> job is not able to get to Elastic.
>
> Spark version: 2.0.1 built for Hadoop 2.4, Scala 2.11
> Elastic version: 2.3.1
>
> I've verified the Elastic hosts and the cluster name.
>
> The spot in the code where this happens is:
>
> ClusterHealthResponse clusterHealthResponse = client.admin().cluster()
>
> .prepareHealth()
>
> .setWaitForGreenStatus()
>
> .setTimeout(TimeValue.*timeValueSeconds*(10))
>
> .get();
>
>
> Stack trace:
>
>
> Driver stacktrace:
>
> at org.apache.spark.scheduler.DAGScheduler.org
> <http://org.apache.spark.scheduler.dagscheduler.org/>$apache$spark$
> scheduler$DAGScheduler$$failJobAndIndependentStages(DAGSched
> uler.scala:1454)
>
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$
> 1.apply(DAGScheduler.scala:1442)
>
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$
> 1.apply(DAGScheduler.scala:1441)
>
> at scala.collection.mutable.ResizableArray$class.foreach(Resiza
> bleArray.scala:59)
>
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.
> scala:48)
>
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGSchedu
> ler.scala:1441)
>
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskS
> etFailed$1.apply(DAGScheduler.scala:811)
>
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskS
> etFailed$1.apply(DAGScheduler.scala:811)
>
> at scala.Option.foreach(Option.scala:257)
>
> at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(
> DAGScheduler.scala:811)
>
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOn
> Receive(DAGScheduler.scala:1667)
>
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onRe
> ceive(DAGScheduler.scala:1622)
>
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onRe
> ceive(DAGScheduler.scala:1611)
>
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.
> scala:632)
>
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1890)
>
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1903)
>
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1916)
>
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1930)
>
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(
> RDD.scala:902)
>
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(
> RDD.scala:900)
>
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperati
> onScope.scala:151)
>
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperati
> onScope.scala:112)
>
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
>
> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:900)
>
> at org.apache.spark.api.java.JavaRDDLike$class.foreachPartition
> (JavaRDDLike.scala:218)
>
> at org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartiti
> on(JavaRDDLike.scala:45)
>
> at com.myco.MyDriver$3.call(com.myco.MyDriver.java:214)
>
> at com.myco.MyDriver$3.call(KafkaSparkStreamingDriver.java:201)
>
> at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun
> $foreachRDD$1.apply(JavaDStreamLike.scala:272)
>
> at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun
> $foreachRDD$1.apply(JavaDStreamLike.scala:272)
>
> at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachR
> DD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
>
> at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachR
> DD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$
> 1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$
> 1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$
> 1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
>
> at org.apache.spark.streaming.dstream.DStream.createRDDWithLoca
> lProperties(DStream.scala:415)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$
> 1.apply$mcV$sp(ForEachDStream.scala:50)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$
> 1.apply(ForEachDStream.scala:50)
>
> at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$
> 1.apply(ForEachDStream.scala:50)
>
> at scala.util.Try$.apply(Try.scala:192)
>
> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
>
> at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler
> $$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:247)
>
> at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler
> $$anonfun$run$1.apply(JobScheduler.scala:247)
>
> at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler
> $$anonfun$run$1.apply(JobScheduler.scala:247)
>
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
>
> at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler
> .run(JobScheduler.scala:246)
>
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPool
> Executor.java:1142)
>
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoo
> lExecutor.java:617)
>
> at java.lang.Thread.run(Thread.java:745)
>
> Caused by: NoNodeAvailableException[None of the configured nodes are
> available: [{#transport#-1}{XX.XXX.XXX.XX}{XX.XXX.XXX.XX:9300}]]
>
> at org.elasticsearch.client.transport.TransportClientNodesServi
> ce.ensureNodesAreAvailable(TransportClientNodesService.java:290)
>
> at org.elasticsearch.client.transport.TransportClientNodesServi
> ce.execute(TransportClientNodesService.java:207)
>
> at org.elasticsearch.client.transport.support.TransportProxyCli
> ent.execute(TransportProxyClient.java:55)
>
> at org.elasticsearch.client.transport.TransportClient.doExecute
> (TransportClient.java:288)
>
> at org.elasticsearch.client.support.AbstractClient.execute(
> AbstractClient.java:359)
>
> at org.elasticsearch.client.support.AbstractClient$ClusterAdmin
> .execute(AbstractClient.java:853)
>
> at org.elasticsearch.action.ActionRequestBuilder.execute(Action
> RequestBuilder.java:86)
>
> at org.elasticsearch.action.ActionRequestBuilder.execute(Action
> RequestBuilder.java:56)
>
> at org.elasticsearch.action.ActionRequestBuilder.get(ActionRequ
> estBuilder.java:64)
>
> at com.myco.MyDriver.work()
>
> at org.apache.spark.api.java.JavaRDDLike$$anonfun$foreachPartit
> ion$1.apply(JavaRDDLike.scala:218)
>
> at org.apache.spark.api.java.JavaRDDLike$$anonfun$foreachPartit
> ion$1.apply(JavaRDDLike.scala:218)
>
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfu
> n$apply$28.apply(RDD.scala:902)
>
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfu
> n$apply$28.apply(RDD.scala:902)
>
> at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkC
> ontext.scala:1916)
>
> at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkC
> ontext.scala:1916)
>
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.sca
> la:70)
>
> at org.apache.spark.scheduler.Task.run(Task.scala:86)
>
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.
> scala:274)
>
--
-- Anastasios Zouzias
<az...@zurich.ibm.com>