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Posted to user@spark.apache.org by Walid LEZZAR <wa...@gmail.com> on 2016/01/07 10:59:43 UTC
[Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Hi,
We have been using spark streaming for a little while now.
Until now, we were running our spark streaming jobs in spark 1.5.1 and it
was working well. Yesterday, we upgraded to spark 1.6.0 without any changes
in the code. But our streaming jobs are not working any more. We are
getting an "AbstractMethodError". Please, find the stack trace at the end
of the mail. Can we have some hints on what this error means ? (we are
using spark to connect to kafka)
The stack trace :
16/01/07 10:44:39 INFO ZkState: Starting curator service
16/01/07 10:44:39 INFO CuratorFrameworkImpl: Starting
16/01/07 10:44:39 INFO ZooKeeper: Initiating client connection,
connectString=localhost:2181 sessionTimeout=120000
watcher=org.apache.curator.ConnectionState@2e9fa23a
16/01/07 10:44:39 INFO ClientCnxn: Opening socket connection to server
localhost/127.0.0.1:2181. Will not attempt to authenticate using SASL
(unknown error)
16/01/07 10:44:39 INFO ClientCnxn: Socket connection established to
localhost/127.0.0.1:2181, initiating session
16/01/07 10:44:39 INFO ClientCnxn: Session establishment complete on server
localhost/127.0.0.1:2181, sessionid = 0x1521b6d262e0035, negotiated timeout
= 60000
16/01/07 10:44:39 INFO ConnectionStateManager: State change: CONNECTED
16/01/07 10:44:40 INFO PartitionManager: Read partition information from:
/spark-kafka-consumer/StreamingArchiver/lbc.job.multiposting.input/partition_0
--> null
16/01/07 10:44:40 INFO JobScheduler: Added jobs for time 1452159880000 ms
16/01/07 10:44:40 INFO JobScheduler: Starting job streaming job
1452159880000 ms.0 from job set of time 1452159880000 ms
16/01/07 10:44:40 ERROR Utils: uncaught error in thread
StreamingListenerBus, stopping SparkContext
ERROR Utils: uncaught error in thread StreamingListenerBus, stopping
SparkContext
java.lang.AbstractMethodError
at
org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:47)
at
org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:26)
at
org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
at
org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(AsynchronousListenerBus.scala:64)
at org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1180)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1.run(AsynchronousListenerBus.scala:63)
16/01/07 10:44:40 INFO JobScheduler: Finished job streaming job
1452159880000 ms.0 from job set of time 1452159880000 ms
16/01/07 10:44:40 INFO JobScheduler: Total delay: 0.074 s for time
1452159880000 ms (execution: 0.032 s)
16/01/07 10:44:40 ERROR JobScheduler: Error running job streaming job
1452159880000 ms.0
java.lang.IllegalStateException: SparkContext has been shutdown
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
at
org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:225)
at
org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:46)
at
fr.leboncoin.morpheus.jobs.streaming.StreamingArchiver.lambda$run$ade930b4$1(StreamingArchiver.java:103)
at
org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
at
org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at
org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at
org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at
org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at
org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
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)
Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Jacek Laskowski <ja...@japila.pl>.
What a "find-a-cause" battle! You beat me to it! :)
Pozdrawiam,
Jacek
Jacek Laskowski | https://medium.com/@jaceklaskowski/
Mastering Apache Spark
==> https://jaceklaskowski.gitbooks.io/mastering-apache-spark/
Follow me at https://twitter.com/jaceklaskowski
On Thu, Jan 7, 2016 at 11:26 AM, Ted Yu <yu...@gmail.com> wrote:
> Looks like the following is related:
>
> SPARK-10900 [STREAMING] Add output operation events to StreamingListener
>
> Note the following change to StreamingListener.scala :
> + def onOutputOperationStarted(
> + outputOperationStarted: StreamingListenerOutputOperationStarted) { }
>
> Do you have custom listener registered ?
>
> Cheers
>
> On Thu, Jan 7, 2016 at 1:59 AM, Walid LEZZAR <wa...@gmail.com> wrote:
>>
>> Hi,
>>
>> We have been using spark streaming for a little while now.
>>
>> Until now, we were running our spark streaming jobs in spark 1.5.1 and it
>> was working well. Yesterday, we upgraded to spark 1.6.0 without any changes
>> in the code. But our streaming jobs are not working any more. We are getting
>> an "AbstractMethodError". Please, find the stack trace at the end of the
>> mail. Can we have some hints on what this error means ? (we are using spark
>> to connect to kafka)
>>
>> The stack trace :
>> 16/01/07 10:44:39 INFO ZkState: Starting curator service
>> 16/01/07 10:44:39 INFO CuratorFrameworkImpl: Starting
>> 16/01/07 10:44:39 INFO ZooKeeper: Initiating client connection,
>> connectString=localhost:2181 sessionTimeout=120000
>> watcher=org.apache.curator.ConnectionState@2e9fa23a
>> 16/01/07 10:44:39 INFO ClientCnxn: Opening socket connection to server
>> localhost/127.0.0.1:2181. Will not attempt to authenticate using SASL
>> (unknown error)
>> 16/01/07 10:44:39 INFO ClientCnxn: Socket connection established to
>> localhost/127.0.0.1:2181, initiating session
>> 16/01/07 10:44:39 INFO ClientCnxn: Session establishment complete on
>> server localhost/127.0.0.1:2181, sessionid = 0x1521b6d262e0035, negotiated
>> timeout = 60000
>> 16/01/07 10:44:39 INFO ConnectionStateManager: State change: CONNECTED
>> 16/01/07 10:44:40 INFO PartitionManager: Read partition information from:
>> /spark-kafka-consumer/StreamingArchiver/lbc.job.multiposting.input/partition_0
>> --> null
>> 16/01/07 10:44:40 INFO JobScheduler: Added jobs for time 1452159880000 ms
>> 16/01/07 10:44:40 INFO JobScheduler: Starting job streaming job
>> 1452159880000 ms.0 from job set of time 1452159880000 ms
>> 16/01/07 10:44:40 ERROR Utils: uncaught error in thread
>> StreamingListenerBus, stopping SparkContext
>>
>> ERROR Utils: uncaught error in thread StreamingListenerBus, stopping
>> SparkContext
>> java.lang.AbstractMethodError
>> at
>> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:47)
>> at
>> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:26)
>> at
>> org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
>> at
>> org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
>> at
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
>> at
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
>> at
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
>> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
>> at
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(AsynchronousListenerBus.scala:64)
>> at
>> org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1180)
>> at
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1.run(AsynchronousListenerBus.scala:63)
>> 16/01/07 10:44:40 INFO JobScheduler: Finished job streaming job
>> 1452159880000 ms.0 from job set of time 1452159880000 ms
>> 16/01/07 10:44:40 INFO JobScheduler: Total delay: 0.074 s for time
>> 1452159880000 ms (execution: 0.032 s)
>> 16/01/07 10:44:40 ERROR JobScheduler: Error running job streaming job
>> 1452159880000 ms.0
>> java.lang.IllegalStateException: SparkContext has been shutdown
>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
>> at
>> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
>> at
>> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
>> at
>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
>> at
>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
>> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
>> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
>> at
>> org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:225)
>> at
>> org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:46)
>> at
>> fr.leboncoin.morpheus.jobs.streaming.StreamingArchiver.lambda$run$ade930b4$1(StreamingArchiver.java:103)
>> at
>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
>> at
>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
>> at
>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
>> at
>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
>> at
>> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
>> at
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
>> at scala.util.Try$.apply(Try.scala:161)
>> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
>> at
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
>> at
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
>> at
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
>> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
>> at
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
>> 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)
>
>
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Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Ted Yu <yu...@gmail.com>.
Looks like the following is related:
SPARK-10900 [STREAMING] Add output operation events to StreamingListener
Note the following change to StreamingListener.scala :
+ def onOutputOperationStarted(
+ outputOperationStarted: StreamingListenerOutputOperationStarted) { }
Do you have custom listener registered ?
Cheers
On Thu, Jan 7, 2016 at 1:59 AM, Walid LEZZAR <wa...@gmail.com> wrote:
> Hi,
>
> We have been using spark streaming for a little while now.
>
> Until now, we were running our spark streaming jobs in spark 1.5.1 and it
> was working well. Yesterday, we upgraded to spark 1.6.0 without any changes
> in the code. But our streaming jobs are not working any more. We are
> getting an "AbstractMethodError". Please, find the stack trace at the end
> of the mail. Can we have some hints on what this error means ? (we are
> using spark to connect to kafka)
>
> The stack trace :
> 16/01/07 10:44:39 INFO ZkState: Starting curator service
> 16/01/07 10:44:39 INFO CuratorFrameworkImpl: Starting
> 16/01/07 10:44:39 INFO ZooKeeper: Initiating client connection,
> connectString=localhost:2181 sessionTimeout=120000
> watcher=org.apache.curator.ConnectionState@2e9fa23a
> 16/01/07 10:44:39 INFO ClientCnxn: Opening socket connection to server
> localhost/127.0.0.1:2181. Will not attempt to authenticate using SASL
> (unknown error)
> 16/01/07 10:44:39 INFO ClientCnxn: Socket connection established to
> localhost/127.0.0.1:2181, initiating session
> 16/01/07 10:44:39 INFO ClientCnxn: Session establishment complete on
> server localhost/127.0.0.1:2181, sessionid = 0x1521b6d262e0035,
> negotiated timeout = 60000
> 16/01/07 10:44:39 INFO ConnectionStateManager: State change: CONNECTED
> 16/01/07 10:44:40 INFO PartitionManager: Read partition information from:
> /spark-kafka-consumer/StreamingArchiver/lbc.job.multiposting.input/partition_0
> --> null
> 16/01/07 10:44:40 INFO JobScheduler: Added jobs for time 1452159880000 ms
> 16/01/07 10:44:40 INFO JobScheduler: Starting job streaming job
> 1452159880000 ms.0 from job set of time 1452159880000 ms
> 16/01/07 10:44:40 ERROR Utils: uncaught error in thread
> StreamingListenerBus, stopping SparkContext
>
> ERROR Utils: uncaught error in thread StreamingListenerBus, stopping
> SparkContext
> java.lang.AbstractMethodError
> at
> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:47)
> at
> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:26)
> at
> org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
> at
> org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(AsynchronousListenerBus.scala:64)
> at org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1180)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1.run(AsynchronousListenerBus.scala:63)
> 16/01/07 10:44:40 INFO JobScheduler: Finished job streaming job
> 1452159880000 ms.0 from job set of time 1452159880000 ms
> 16/01/07 10:44:40 INFO JobScheduler: Total delay: 0.074 s for time
> 1452159880000 ms (execution: 0.032 s)
> 16/01/07 10:44:40 ERROR JobScheduler: Error running job streaming job
> 1452159880000 ms.0
> java.lang.IllegalStateException: SparkContext has been shutdown
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
> at
> org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:225)
> at
> org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:46)
> at
> fr.leboncoin.morpheus.jobs.streaming.StreamingArchiver.lambda$run$ade930b4$1(StreamingArchiver.java:103)
> at
> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
> at
> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> at scala.util.Try$.apply(Try.scala:161)
> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
> 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)
>
Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Dibyendu Bhattacharya <di...@gmail.com>.
Right .. if you are using github version, just modify the ReceiverLauncher
and add that . I will fix it for Spark 1.6 and release new version in
spark-packages for spark 1.6
Dibyendu
On Thu, Jan 7, 2016 at 4:14 PM, Ted Yu <yu...@gmail.com> wrote:
> I cloned git@github.com:dibbhatt/kafka-spark-consumer.git a moment ago.
>
> In ./src/main/java/consumer/kafka/ReceiverLauncher.java , I see:
> jsc.addStreamingListener(new StreamingListener() {
>
> There is no onOutputOperationStarted method implementation.
>
> Looks like it should be added for Spark 1.6.0
>
> Cheers
>
> On Thu, Jan 7, 2016 at 2:39 AM, Dibyendu Bhattacharya <
> dibyendu.bhattachary@gmail.com> wrote:
>
>> You are using low level spark kafka consumer . I am the author of the
>> same.
>>
>> Are you using the spark-packages version ? if yes which one ?
>>
>> Regards,
>> Dibyendu
>>
>> On Thu, Jan 7, 2016 at 4:07 PM, Jacek Laskowski <ja...@japila.pl> wrote:
>>
>>> Hi,
>>>
>>> Do you perhaps use custom StreamingListener?
>>> `StreamingListenerBus.scala:47` calls
>>> `StreamingListener.onOutputOperationStarted` that was added in
>>> [SPARK-10900] [STREAMING] Add output operation events to
>>> StreamingListener [1]
>>>
>>> The other guess could be that at runtime you still use Spark < 1.6.
>>>
>>> [1] https://issues.apache.org/jira/browse/SPARK-10900
>>>
>>> Pozdrawiam,
>>> Jacek
>>>
>>> Jacek Laskowski | https://medium.com/@jaceklaskowski/
>>> Mastering Apache Spark
>>> ==> https://jaceklaskowski.gitbooks.io/mastering-apache-spark/
>>> Follow me at https://twitter.com/jaceklaskowski
>>>
>>>
>>>
>>> On Thu, Jan 7, 2016 at 10:59 AM, Walid LEZZAR <wa...@gmail.com>
>>> wrote:
>>> > Hi,
>>> >
>>> > We have been using spark streaming for a little while now.
>>> >
>>> > Until now, we were running our spark streaming jobs in spark 1.5.1 and
>>> it
>>> > was working well. Yesterday, we upgraded to spark 1.6.0 without any
>>> changes
>>> > in the code. But our streaming jobs are not working any more. We are
>>> getting
>>> > an "AbstractMethodError". Please, find the stack trace at the end of
>>> the
>>> > mail. Can we have some hints on what this error means ? (we are using
>>> spark
>>> > to connect to kafka)
>>> >
>>> > The stack trace :
>>> > 16/01/07 10:44:39 INFO ZkState: Starting curator service
>>> > 16/01/07 10:44:39 INFO CuratorFrameworkImpl: Starting
>>> > 16/01/07 10:44:39 INFO ZooKeeper: Initiating client connection,
>>> > connectString=localhost:2181 sessionTimeout=120000
>>> > watcher=org.apache.curator.ConnectionState@2e9fa23a
>>> > 16/01/07 10:44:39 INFO ClientCnxn: Opening socket connection to server
>>> > localhost/127.0.0.1:2181. Will not attempt to authenticate using SASL
>>> > (unknown error)
>>> > 16/01/07 10:44:39 INFO ClientCnxn: Socket connection established to
>>> > localhost/127.0.0.1:2181, initiating session
>>> > 16/01/07 10:44:39 INFO ClientCnxn: Session establishment complete on
>>> server
>>> > localhost/127.0.0.1:2181, sessionid = 0x1521b6d262e0035, negotiated
>>> timeout
>>> > = 60000
>>> > 16/01/07 10:44:39 INFO ConnectionStateManager: State change: CONNECTED
>>> > 16/01/07 10:44:40 INFO PartitionManager: Read partition information
>>> from:
>>> >
>>> /spark-kafka-consumer/StreamingArchiver/lbc.job.multiposting.input/partition_0
>>> > --> null
>>> > 16/01/07 10:44:40 INFO JobScheduler: Added jobs for time 1452159880000
>>> ms
>>> > 16/01/07 10:44:40 INFO JobScheduler: Starting job streaming job
>>> > 1452159880000 ms.0 from job set of time 1452159880000 ms
>>> > 16/01/07 10:44:40 ERROR Utils: uncaught error in thread
>>> > StreamingListenerBus, stopping SparkContext
>>> >
>>> > ERROR Utils: uncaught error in thread StreamingListenerBus, stopping
>>> > SparkContext
>>> > java.lang.AbstractMethodError
>>> > at
>>> >
>>> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:47)
>>> > at
>>> >
>>> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:26)
>>> > at
>>> > org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
>>> > at
>>> >
>>> org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
>>> > at
>>> >
>>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
>>> > at
>>> >
>>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
>>> > at
>>> >
>>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
>>> > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
>>> > at
>>> >
>>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(AsynchronousListenerBus.scala:64)
>>> > at
>>> org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1180)
>>> > at
>>> >
>>> org.apache.spark.util.AsynchronousListenerBus$$anon$1.run(AsynchronousListenerBus.scala:63)
>>> > 16/01/07 10:44:40 INFO JobScheduler: Finished job streaming job
>>> > 1452159880000 ms.0 from job set of time 1452159880000 ms
>>> > 16/01/07 10:44:40 INFO JobScheduler: Total delay: 0.074 s for time
>>> > 1452159880000 ms (execution: 0.032 s)
>>> > 16/01/07 10:44:40 ERROR JobScheduler: Error running job streaming job
>>> > 1452159880000 ms.0
>>> > java.lang.IllegalStateException: SparkContext has been shutdown
>>> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
>>> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
>>> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
>>> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
>>> > at
>>> >
>>> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
>>> > at
>>> >
>>> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
>>> > at
>>> >
>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
>>> > at
>>> >
>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
>>> > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
>>> > at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
>>> > at
>>> >
>>> org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:225)
>>> > at
>>> >
>>> org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:46)
>>> > at
>>> >
>>> fr.leboncoin.morpheus.jobs.streaming.StreamingArchiver.lambda$run$ade930b4$1(StreamingArchiver.java:103)
>>> > at
>>> >
>>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
>>> > at
>>> >
>>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
>>> > at
>>> >
>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
>>> > at scala.util.Try$.apply(Try.scala:161)
>>> > at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
>>> > at
>>> >
>>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
>>> > at
>>> >
>>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
>>> > at
>>> >
>>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
>>> > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
>>> > at
>>> >
>>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
>>> > 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)
>>>
>>> ---------------------------------------------------------------------
>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>> For additional commands, e-mail: user-help@spark.apache.org
>>>
>>>
>>
>
Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Ted Yu <yu...@gmail.com>.
I cloned git@github.com:dibbhatt/kafka-spark-consumer.git a moment ago.
In ./src/main/java/consumer/kafka/ReceiverLauncher.java , I see:
jsc.addStreamingListener(new StreamingListener() {
There is no onOutputOperationStarted method implementation.
Looks like it should be added for Spark 1.6.0
Cheers
On Thu, Jan 7, 2016 at 2:39 AM, Dibyendu Bhattacharya <
dibyendu.bhattachary@gmail.com> wrote:
> You are using low level spark kafka consumer . I am the author of the
> same.
>
> Are you using the spark-packages version ? if yes which one ?
>
> Regards,
> Dibyendu
>
> On Thu, Jan 7, 2016 at 4:07 PM, Jacek Laskowski <ja...@japila.pl> wrote:
>
>> Hi,
>>
>> Do you perhaps use custom StreamingListener?
>> `StreamingListenerBus.scala:47` calls
>> `StreamingListener.onOutputOperationStarted` that was added in
>> [SPARK-10900] [STREAMING] Add output operation events to
>> StreamingListener [1]
>>
>> The other guess could be that at runtime you still use Spark < 1.6.
>>
>> [1] https://issues.apache.org/jira/browse/SPARK-10900
>>
>> Pozdrawiam,
>> Jacek
>>
>> Jacek Laskowski | https://medium.com/@jaceklaskowski/
>> Mastering Apache Spark
>> ==> https://jaceklaskowski.gitbooks.io/mastering-apache-spark/
>> Follow me at https://twitter.com/jaceklaskowski
>>
>>
>>
>> On Thu, Jan 7, 2016 at 10:59 AM, Walid LEZZAR <wa...@gmail.com> wrote:
>> > Hi,
>> >
>> > We have been using spark streaming for a little while now.
>> >
>> > Until now, we were running our spark streaming jobs in spark 1.5.1 and
>> it
>> > was working well. Yesterday, we upgraded to spark 1.6.0 without any
>> changes
>> > in the code. But our streaming jobs are not working any more. We are
>> getting
>> > an "AbstractMethodError". Please, find the stack trace at the end of the
>> > mail. Can we have some hints on what this error means ? (we are using
>> spark
>> > to connect to kafka)
>> >
>> > The stack trace :
>> > 16/01/07 10:44:39 INFO ZkState: Starting curator service
>> > 16/01/07 10:44:39 INFO CuratorFrameworkImpl: Starting
>> > 16/01/07 10:44:39 INFO ZooKeeper: Initiating client connection,
>> > connectString=localhost:2181 sessionTimeout=120000
>> > watcher=org.apache.curator.ConnectionState@2e9fa23a
>> > 16/01/07 10:44:39 INFO ClientCnxn: Opening socket connection to server
>> > localhost/127.0.0.1:2181. Will not attempt to authenticate using SASL
>> > (unknown error)
>> > 16/01/07 10:44:39 INFO ClientCnxn: Socket connection established to
>> > localhost/127.0.0.1:2181, initiating session
>> > 16/01/07 10:44:39 INFO ClientCnxn: Session establishment complete on
>> server
>> > localhost/127.0.0.1:2181, sessionid = 0x1521b6d262e0035, negotiated
>> timeout
>> > = 60000
>> > 16/01/07 10:44:39 INFO ConnectionStateManager: State change: CONNECTED
>> > 16/01/07 10:44:40 INFO PartitionManager: Read partition information
>> from:
>> >
>> /spark-kafka-consumer/StreamingArchiver/lbc.job.multiposting.input/partition_0
>> > --> null
>> > 16/01/07 10:44:40 INFO JobScheduler: Added jobs for time 1452159880000
>> ms
>> > 16/01/07 10:44:40 INFO JobScheduler: Starting job streaming job
>> > 1452159880000 ms.0 from job set of time 1452159880000 ms
>> > 16/01/07 10:44:40 ERROR Utils: uncaught error in thread
>> > StreamingListenerBus, stopping SparkContext
>> >
>> > ERROR Utils: uncaught error in thread StreamingListenerBus, stopping
>> > SparkContext
>> > java.lang.AbstractMethodError
>> > at
>> >
>> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:47)
>> > at
>> >
>> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:26)
>> > at
>> > org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
>> > at
>> >
>> org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
>> > at
>> >
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
>> > at
>> >
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
>> > at
>> >
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
>> > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
>> > at
>> >
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(AsynchronousListenerBus.scala:64)
>> > at
>> org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1180)
>> > at
>> >
>> org.apache.spark.util.AsynchronousListenerBus$$anon$1.run(AsynchronousListenerBus.scala:63)
>> > 16/01/07 10:44:40 INFO JobScheduler: Finished job streaming job
>> > 1452159880000 ms.0 from job set of time 1452159880000 ms
>> > 16/01/07 10:44:40 INFO JobScheduler: Total delay: 0.074 s for time
>> > 1452159880000 ms (execution: 0.032 s)
>> > 16/01/07 10:44:40 ERROR JobScheduler: Error running job streaming job
>> > 1452159880000 ms.0
>> > java.lang.IllegalStateException: SparkContext has been shutdown
>> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
>> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
>> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
>> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
>> > at
>> >
>> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
>> > at
>> >
>> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
>> > at
>> >
>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
>> > at
>> >
>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
>> > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
>> > at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
>> > at
>> >
>> org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:225)
>> > at
>> >
>> org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:46)
>> > at
>> >
>> fr.leboncoin.morpheus.jobs.streaming.StreamingArchiver.lambda$run$ade930b4$1(StreamingArchiver.java:103)
>> > at
>> >
>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
>> > at
>> >
>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
>> > at
>> >
>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
>> > at
>> >
>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
>> > at
>> >
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
>> > at
>> >
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
>> > at
>> >
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
>> > at
>> >
>> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
>> > at
>> >
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
>> > at
>> >
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
>> > at
>> >
>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
>> > at scala.util.Try$.apply(Try.scala:161)
>> > at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
>> > at
>> >
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
>> > at
>> >
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
>> > at
>> >
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
>> > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
>> > at
>> >
>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
>> > 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)
>>
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>> For additional commands, e-mail: user-help@spark.apache.org
>>
>>
>
Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Jacek Laskowski <ja...@japila.pl>.
On Thu, Jan 7, 2016 at 12:10 PM, Dibyendu Bhattacharya
<di...@gmail.com> wrote:
> Some discussion is there in https://github.com/dibbhatt/kafka-spark-consumer
> and some is mentioned in https://issues.apache.org/jira/browse/SPARK-11045
>
> Let me know if those answer your question .
Thanks a lot, Dibyendu. That gives me lots of sources to improve in this area.
Jacek
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Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Dibyendu Bhattacharya <di...@gmail.com>.
Some discussion is there in https://github.com/dibbhatt/kafka-spark-consumer
and some is mentioned in https://issues.apache.org/jira/browse/SPARK-11045
Let me know if those answer your question .
In short, Direct Stream is good choice if you need exact once semantics and
message ordering , but many use case does not need such requirement of
exact-once and message ordering . If you use Direct Stream the RDD
processing parallelism is limited to Kafka partition and you need to store
offset details to external store as checkpoint location is not reliable if
you modify driver code .
Whereas in Receiver based mode , you need to enable WAL for no data loss .
But Spark Receiver based consumer from KafkaUtils which uses Kafka High
Level API has serious issues , and thus if at all you need to switch to
receiver based mode , this low level consumer is a better choice.
Performance wise I have not published any number yet , but from internal
testing and benchmarking I did ( and validated by folks who uses this
consumer ), it perform much better than any existing consumer in Spark .
Regards,
Dibyendu
On Thu, Jan 7, 2016 at 4:28 PM, Jacek Laskowski <ja...@japila.pl> wrote:
> On Thu, Jan 7, 2016 at 11:39 AM, Dibyendu Bhattacharya
> <di...@gmail.com> wrote:
> > You are using low level spark kafka consumer . I am the author of the
> same.
>
> If I may ask, what are the differences between this and the direct
> version shipped with spark? I've just started toying with it, and
> would appreciate some guidance. Thanks.
>
> Jacek
>
Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Jacek Laskowski <ja...@japila.pl>.
On Thu, Jan 7, 2016 at 11:39 AM, Dibyendu Bhattacharya
<di...@gmail.com> wrote:
> You are using low level spark kafka consumer . I am the author of the same.
If I may ask, what are the differences between this and the direct
version shipped with spark? I've just started toying with it, and
would appreciate some guidance. Thanks.
Jacek
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Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Dibyendu Bhattacharya <di...@gmail.com>.
You are using low level spark kafka consumer . I am the author of the same.
Are you using the spark-packages version ? if yes which one ?
Regards,
Dibyendu
On Thu, Jan 7, 2016 at 4:07 PM, Jacek Laskowski <ja...@japila.pl> wrote:
> Hi,
>
> Do you perhaps use custom StreamingListener?
> `StreamingListenerBus.scala:47` calls
> `StreamingListener.onOutputOperationStarted` that was added in
> [SPARK-10900] [STREAMING] Add output operation events to
> StreamingListener [1]
>
> The other guess could be that at runtime you still use Spark < 1.6.
>
> [1] https://issues.apache.org/jira/browse/SPARK-10900
>
> Pozdrawiam,
> Jacek
>
> Jacek Laskowski | https://medium.com/@jaceklaskowski/
> Mastering Apache Spark
> ==> https://jaceklaskowski.gitbooks.io/mastering-apache-spark/
> Follow me at https://twitter.com/jaceklaskowski
>
>
> On Thu, Jan 7, 2016 at 10:59 AM, Walid LEZZAR <wa...@gmail.com> wrote:
> > Hi,
> >
> > We have been using spark streaming for a little while now.
> >
> > Until now, we were running our spark streaming jobs in spark 1.5.1 and it
> > was working well. Yesterday, we upgraded to spark 1.6.0 without any
> changes
> > in the code. But our streaming jobs are not working any more. We are
> getting
> > an "AbstractMethodError". Please, find the stack trace at the end of the
> > mail. Can we have some hints on what this error means ? (we are using
> spark
> > to connect to kafka)
> >
> > The stack trace :
> > 16/01/07 10:44:39 INFO ZkState: Starting curator service
> > 16/01/07 10:44:39 INFO CuratorFrameworkImpl: Starting
> > 16/01/07 10:44:39 INFO ZooKeeper: Initiating client connection,
> > connectString=localhost:2181 sessionTimeout=120000
> > watcher=org.apache.curator.ConnectionState@2e9fa23a
> > 16/01/07 10:44:39 INFO ClientCnxn: Opening socket connection to server
> > localhost/127.0.0.1:2181. Will not attempt to authenticate using SASL
> > (unknown error)
> > 16/01/07 10:44:39 INFO ClientCnxn: Socket connection established to
> > localhost/127.0.0.1:2181, initiating session
> > 16/01/07 10:44:39 INFO ClientCnxn: Session establishment complete on
> server
> > localhost/127.0.0.1:2181, sessionid = 0x1521b6d262e0035, negotiated
> timeout
> > = 60000
> > 16/01/07 10:44:39 INFO ConnectionStateManager: State change: CONNECTED
> > 16/01/07 10:44:40 INFO PartitionManager: Read partition information from:
> >
> /spark-kafka-consumer/StreamingArchiver/lbc.job.multiposting.input/partition_0
> > --> null
> > 16/01/07 10:44:40 INFO JobScheduler: Added jobs for time 1452159880000 ms
> > 16/01/07 10:44:40 INFO JobScheduler: Starting job streaming job
> > 1452159880000 ms.0 from job set of time 1452159880000 ms
> > 16/01/07 10:44:40 ERROR Utils: uncaught error in thread
> > StreamingListenerBus, stopping SparkContext
> >
> > ERROR Utils: uncaught error in thread StreamingListenerBus, stopping
> > SparkContext
> > java.lang.AbstractMethodError
> > at
> >
> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:47)
> > at
> >
> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:26)
> > at
> > org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
> > at
> >
> org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
> > at
> >
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
> > at
> >
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
> > at
> >
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
> > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
> > at
> >
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(AsynchronousListenerBus.scala:64)
> > at
> org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1180)
> > at
> >
> org.apache.spark.util.AsynchronousListenerBus$$anon$1.run(AsynchronousListenerBus.scala:63)
> > 16/01/07 10:44:40 INFO JobScheduler: Finished job streaming job
> > 1452159880000 ms.0 from job set of time 1452159880000 ms
> > 16/01/07 10:44:40 INFO JobScheduler: Total delay: 0.074 s for time
> > 1452159880000 ms (execution: 0.032 s)
> > 16/01/07 10:44:40 ERROR JobScheduler: Error running job streaming job
> > 1452159880000 ms.0
> > java.lang.IllegalStateException: SparkContext has been shutdown
> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
> > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
> > at
> > org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
> > at
> > org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
> > at
> >
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> > at
> >
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
> > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
> > at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
> > at
> >
> org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:225)
> > at
> >
> org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:46)
> > at
> >
> fr.leboncoin.morpheus.jobs.streaming.StreamingArchiver.lambda$run$ade930b4$1(StreamingArchiver.java:103)
> > at
> >
> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
> > at
> >
> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
> > at
> >
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> > at
> >
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> > at
> >
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
> > at
> >
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> > at
> >
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> > at
> >
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
> > at
> >
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
> > at
> >
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> > at
> >
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> > at scala.util.Try$.apply(Try.scala:161)
> > at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
> > at
> >
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
> > at
> >
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
> > at
> >
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
> > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
> > at
> >
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
> > 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)
>
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>
>
Re: [Spark 1.6] Spark Streaming - java.lang.AbstractMethodError
Posted by Jacek Laskowski <ja...@japila.pl>.
Hi,
Do you perhaps use custom StreamingListener?
`StreamingListenerBus.scala:47` calls
`StreamingListener.onOutputOperationStarted` that was added in
[SPARK-10900] [STREAMING] Add output operation events to
StreamingListener [1]
The other guess could be that at runtime you still use Spark < 1.6.
[1] https://issues.apache.org/jira/browse/SPARK-10900
Pozdrawiam,
Jacek
Jacek Laskowski | https://medium.com/@jaceklaskowski/
Mastering Apache Spark
==> https://jaceklaskowski.gitbooks.io/mastering-apache-spark/
Follow me at https://twitter.com/jaceklaskowski
On Thu, Jan 7, 2016 at 10:59 AM, Walid LEZZAR <wa...@gmail.com> wrote:
> Hi,
>
> We have been using spark streaming for a little while now.
>
> Until now, we were running our spark streaming jobs in spark 1.5.1 and it
> was working well. Yesterday, we upgraded to spark 1.6.0 without any changes
> in the code. But our streaming jobs are not working any more. We are getting
> an "AbstractMethodError". Please, find the stack trace at the end of the
> mail. Can we have some hints on what this error means ? (we are using spark
> to connect to kafka)
>
> The stack trace :
> 16/01/07 10:44:39 INFO ZkState: Starting curator service
> 16/01/07 10:44:39 INFO CuratorFrameworkImpl: Starting
> 16/01/07 10:44:39 INFO ZooKeeper: Initiating client connection,
> connectString=localhost:2181 sessionTimeout=120000
> watcher=org.apache.curator.ConnectionState@2e9fa23a
> 16/01/07 10:44:39 INFO ClientCnxn: Opening socket connection to server
> localhost/127.0.0.1:2181. Will not attempt to authenticate using SASL
> (unknown error)
> 16/01/07 10:44:39 INFO ClientCnxn: Socket connection established to
> localhost/127.0.0.1:2181, initiating session
> 16/01/07 10:44:39 INFO ClientCnxn: Session establishment complete on server
> localhost/127.0.0.1:2181, sessionid = 0x1521b6d262e0035, negotiated timeout
> = 60000
> 16/01/07 10:44:39 INFO ConnectionStateManager: State change: CONNECTED
> 16/01/07 10:44:40 INFO PartitionManager: Read partition information from:
> /spark-kafka-consumer/StreamingArchiver/lbc.job.multiposting.input/partition_0
> --> null
> 16/01/07 10:44:40 INFO JobScheduler: Added jobs for time 1452159880000 ms
> 16/01/07 10:44:40 INFO JobScheduler: Starting job streaming job
> 1452159880000 ms.0 from job set of time 1452159880000 ms
> 16/01/07 10:44:40 ERROR Utils: uncaught error in thread
> StreamingListenerBus, stopping SparkContext
>
> ERROR Utils: uncaught error in thread StreamingListenerBus, stopping
> SparkContext
> java.lang.AbstractMethodError
> at
> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:47)
> at
> org.apache.spark.streaming.scheduler.StreamingListenerBus.onPostEvent(StreamingListenerBus.scala:26)
> at
> org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
> at
> org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(AsynchronousListenerBus.scala:64)
> at org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1180)
> at
> org.apache.spark.util.AsynchronousListenerBus$$anon$1.run(AsynchronousListenerBus.scala:63)
> 16/01/07 10:44:40 INFO JobScheduler: Finished job streaming job
> 1452159880000 ms.0 from job set of time 1452159880000 ms
> 16/01/07 10:44:40 INFO JobScheduler: Total delay: 0.074 s for time
> 1452159880000 ms (execution: 0.032 s)
> 16/01/07 10:44:40 ERROR JobScheduler: Error running job streaming job
> 1452159880000 ms.0
> java.lang.IllegalStateException: SparkContext has been shutdown
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
> at
> org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:225)
> at
> org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:46)
> at
> fr.leboncoin.morpheus.jobs.streaming.StreamingArchiver.lambda$run$ade930b4$1(StreamingArchiver.java:103)
> at
> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
> at
> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$3.apply(JavaDStreamLike.scala:335)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> at scala.util.Try$.apply(Try.scala:161)
> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
> 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)
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