Hi Robert!Thanks for reaching out. I ran into an issue and wasn't sure if this was due to a misconfiguration on my end of if this is a real bug. I have one DataStream and I'm sinking to two different kafka sinks. When the job starts, I run into this error:org.apache.flink.runtime.client.JobExecutionException: Job execution failed.at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$7.apply$mcV$sp(JobManager.scala:659)at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$7.apply(JobManager.scala:605)at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$7.apply(JobManager.scala:605)at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:41)at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:401)at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)Caused by: java.lang.UnsupportedOperationException: The accumulator 'producer-record-retry-rate' already exists and cannot be added.at org.apache.flink.api.common.functions.util.AbstractRuntimeUDFContext.addAccumulator(AbstractRuntimeUDFContext.java:121)at org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducerBase.open(FlinkKafkaProducerBase.java:204)at org.apache.flink.api.common.functions.util.FunctionUtils.openFunction(FunctionUtils.java:36)at org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.open(AbstractUdfStreamOperator.java:89)at org.apache.flink.streaming.runtime.tasks.StreamTask.openAllOperators(StreamTask.java:305)at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:227)at org.apache.flink.runtime.taskmanager.Task.run(Task.java:567)at java.lang.Thread.run(Thread.java:745)The particular accumulator the exception is complains about changes, meaning it's not always 'producer-record-retry-rate' -- most likely due to the non-deterministic ordering of the collection. Any guidance appreciated!I'm using 1.0-SNAPSHOT and my two sinks are FlinkKafkaProducer08.The flink code looks something like this:val stream: DataStream[Foo] = ...val kafkaA = new FlinkKafkaProducer08[Foo]...val kafkaB = new FlinkKafkaProducer08[Foo]...stream.addSink(kafkaA)stream..addSink(kafkaB)Thanks,DavidOn Wed, Jan 20, 2016 at 1:34 PM, Robert Metzger <email@example.com> wrote:I've now merged the pull request. DeserializationSchema.isEndOfStream() should now be evaluated correctly by the Kafka 0.9 and 0.8 connectors.Please let me know if the updated code has any issues. I'll fix the issues asap.On Wed, Jan 13, 2016 at 5:06 PM, David Kim <firstname.lastname@example.org> wrote:Thanks Robert! I'll be keeping tabs on the PR.Cheers,David--On Mon, Jan 11, 2016 at 4:04 PM, Robert Metzger <email@example.com> wrote:Hi David,In theory isEndOfStream() is absolutely the right way to go for stopping data sources in Flink.That its not working as expected is a bug. I have a pending pull request for adding a Kafka 0.9 connector, which fixes this issue as well (for all supported Kafka versions).Sorry for the inconvenience. If you want, you can check out the branch of the PR and build Flink yourself to get the fix.I hope that I can merge the connector to master this week, then, the fix will be available in 1.0-SNAPSHOT as well.Regards,Robert
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On 11.01.2016, at 21:39, David Kim <firstname.lastname@example.org> wrote:Hello all,I saw that DeserializationSchema has an API "isEndOfStream()".https://github.com/apache/flink/blob/master/flink-streaming-java/src/main/java/org/apache/flink/streaming/util/serialization/DeserializationSchema.javaCan isEndOfStream be utilized to somehow terminate a streaming flink job?I was under the impression that if we return "true" we can control when a stream can close. The use case I had in mind was controlling when unit/integration tests would terminate a flink job. We can rely on the fact that a test/spec would know how many items it expects to consume and then switch isEndOfStream to return true.Am I misunderstanding the intention for isEndOfStream?I also set a breakpoint on isEndOfStream and saw that it never was hit when using "FlinkKafkaConsumer082" to pass in a DeserializationSchema implementation.Currently testing on 1.0-SNAPSHOT.Cheers!DavidNote: this information is confidential. It is prohibited to share, post online or otherwise publicize without Braintree's prior written consent.--Note: this information is confidential. It is prohibited to share, post online or otherwise publicize without Braintree's prior written consent.