Hi David, you are right. I'll fix the issue in this pull request: https://github.com/apache/flink/pull/1541
I guess everything in your topology runs with a parallelism of 1? Running it with a parallelism higher than 1 will also work around the issue (because then the two Sinks are not executed in one Task). On Fri, Jan 22, 2016 at 4:56 PM, David Kim <david....@braintreepayments.com> wrote: > Hi Robert, > > Thanks for the workaround. Unfortunately I think I found a bug in the code > that controls the metrics logic. > > Should Boolean.getBoolean be Boolean.valueOf instead? > > > https://github.com/apache/flink/blob/81320c1c7ee98b9a663998df51cc4d5aa73d9b2a/flink-streaming-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaProducerBase.java#L192 > > Thanks! > David > > On Fri, Jan 22, 2016 at 2:17 AM, Robert Metzger <rmetz...@apache.org> > wrote: > >> Hi David, >> >> thank you for reporting the issue. I'll look into it. In the meantime, >> you can set "flink.disable-metrics" to "true" in the properties. This way, >> you disable the metrics. >> I'll probably have to introduce something like a client id to >> differentiate between the producers. >> >> Robert >> >> On Thu, Jan 21, 2016 at 11:51 PM, David Kim < >> david....@braintreepayments.com> wrote: >> >>> 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, >>> David >>> >>> On Wed, Jan 20, 2016 at 1:34 PM, Robert Metzger <rmetz...@apache.org> >>> 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 < >>>> david....@braintreepayments.com> wrote: >>>> >>>>> Thanks Robert! I'll be keeping tabs on the PR. >>>>> >>>>> Cheers, >>>>> David >>>>> >>>>> On Mon, Jan 11, 2016 at 4:04 PM, Robert Metzger <metrob...@gmail.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 >>>>>> >>>>>> >>>>>> >>>>>> Sent from my iPhone >>>>>> >>>>>> On 11.01.2016, at 21:39, David Kim <david....@braintreepayments.com> >>>>>> 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.java >>>>>> >>>>>> Can *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! >>>>>> David >>>>>> >>>>>> >>>>> >>>>> >>>>> -- >>>>> Note: 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. >>> >> >> > > > -- > Note: this information is confidential. It is prohibited to share, post > online or otherwise publicize without Braintree's prior written consent. >
