Hey Niels, Flink currently restarts the complete job if you have a restart strategy configured: https://ci.apache.org/projects/flink/flink-docs-release-1.4/dev/restart_strategies.html.
I agree that only restarting the required parts of the pipeline is an important optimization. Flink has not implemented this (fully) yet but it's on the agenda [1] and work has already started [2]. In this particular case, everything is just slow and we don't need the restart at all if you give the consumer a higher max timeout. Please report back when you have more info :-) – Ufuk [1] https://cwiki.apache.org/confluence/display/FLINK/FLIP-1+%3A+Fine+Grained+Recovery+from+Task+Failures [2] https://issues.apache.org/jira/browse/FLINK-4256 On Thu, Oct 12, 2017 at 10:17 AM, Niels Basjes <ni...@basjes.nl> wrote: > Hi, > > I'm currently doing some tests to see it this info helps. > I was running a different high CPU task on one of the nodes outside Yarn, so > I took that one out of the cluster to see if that helps. > > What I do find strange that in this kind of error scenario the entire job > fails. > I would have expected something similar as with 'good old' MapReduce: The > missing task is simply resubmitted and ran again. > Why doesn't that happen? > > > Niels > > On Wed, Oct 11, 2017 at 8:49 AM, Ufuk Celebi <u...@apache.org> wrote: >> >> Hey Niels, >> >> any update on this? >> >> – Ufuk >> >> >> On Mon, Oct 9, 2017 at 10:16 PM, Ufuk Celebi <u...@apache.org> wrote: >> > Hey Niels, >> > >> > thanks for the detailed report. I don't think that it is related to >> > the Hadoop or Scala version. I think the following happens: >> > >> > - Occasionally, one of your tasks seems to be extremely slow in >> > registering its produced intermediate result (the data shuffled >> > between TaskManagers) >> > - Another task is already requesting to consume data from this task >> > but cannot find it (after multiple retries) and it fails the complete >> > job (your stack trace) >> > >> > That happens only occasionally probably due to load in your cluster. >> > The slow down could have multiple reasons... >> > - Is your Hadoop cluster resource constrained and the tasks are slow to >> > deploy? >> > - Is your application JAR very large and needs a lot of time >> > downloading? >> > >> > We have two options at this point: >> > 1) You can increase the maximum retries via the config option: >> > "taskmanager.network.request-backoff.max" The default is 10000 >> > (milliseconds) and specifies what the maximum request back off is [1]. >> > Increasing this to 30000 would give you two extra retries with pretty >> > long delays (see [1]). >> > >> > 2) To be sure that this is really what is happening we could increase >> > the log level of certain classes and check whether they have >> > registered their results or not. If you want to do this, I'm more than >> > happy to provide you with some classes to enable DEBUG logging for. >> > >> > What do you think? >> > >> > – Ufuk >> > >> > DETAILS >> > ======= >> > >> > - The TaskManagers produce and consume intermediate results >> > - When a TaskManager wants to consume a result, it directly queries >> > the producing TaskManager for it >> > - An intermediate result becomes ready for consumption during initial >> > task setup (state DEPLOYING) >> > - When a TaskManager is slow to register its intermediate result and >> > the consumer requests the result before it is ready, it can happen >> > that a requested partition is "not found" >> > >> > This is what is also happening here. We retry to request the >> > intermediate result multiple times with timed backoff [1] and only >> > fail the request (your stack trace) if the partition is still not >> > ready although we expect it to be ready (that is there was no failure >> > at the producing task). >> > >> > [1] Starting by default at 100 millis and going up to 10_000 millis by >> > doubling that time (100, 200, 400, 800, 1600, 3200, 6400, 10000) >> > >> > >> > On Mon, Oct 9, 2017 at 10:51 AM, Niels Basjes <ni...@basjes.nl> wrote: >> >> Hi, >> >> >> >> I'm having some trouble running a java based Flink job in a >> >> yarn-session. >> >> >> >> The job itself consists of reading a set of files resulting in a >> >> DataStream >> >> (I use DataStream because in the future I intend to change the file >> >> with a >> >> Kafka feed), then does some parsing and eventually writes the data into >> >> HBase. >> >> >> >> Most of the time running this works fine yet sometimes it fails with >> >> this >> >> exception: >> >> >> >> >> >> org.apache.flink.runtime.io.network.partition.PartitionNotFoundException: >> >> Partition >> >> 794b5ce385c296b7943fa4c1f072d6b9@13aa7ef02a5d9e0898204ec8ce283363 >> >> not found. >> >> at >> >> >> >> org.apache.flink.runtime.io.network.partition.consumer.RemoteInputChannel.failPartitionRequest(RemoteInputChannel.java:203) >> >> at >> >> >> >> org.apache.flink.runtime.io.network.partition.consumer.RemoteInputChannel.retriggerSubpartitionRequest(RemoteInputChannel.java:128) >> >> at >> >> >> >> org.apache.flink.runtime.io.network.partition.consumer.SingleInputGate.retriggerPartitionRequest(SingleInputGate.java:345) >> >> at >> >> >> >> org.apache.flink.runtime.taskmanager.Task.onPartitionStateUpdate(Task.java:1286) >> >> at >> >> org.apache.flink.runtime.taskmanager.Task$2.apply(Task.java:1123) >> >> at >> >> org.apache.flink.runtime.taskmanager.Task$2.apply(Task.java:1118) >> >> at >> >> >> >> org.apache.flink.runtime.concurrent.impl.FlinkFuture$5.onComplete(FlinkFuture.java:272) >> >> at akka.dispatch.OnComplete.internal(Future.scala:248) >> >> at akka.dispatch.OnComplete.internal(Future.scala:245) >> >> at akka.dispatch.japi$CallbackBridge.apply(Future.scala:175) >> >> at akka.dispatch.japi$CallbackBridge.apply(Future.scala:172) >> >> at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32) >> >> at >> >> >> >> akka.dispatch.BatchingExecutor$AbstractBatch.processBatch(BatchingExecutor.scala:55) >> >> at >> >> >> >> akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply$mcV$sp(BatchingExecutor.scala:91) >> >> at >> >> >> >> akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply(BatchingExecutor.scala:91) >> >> at >> >> >> >> akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply(BatchingExecutor.scala:91) >> >> at >> >> scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72) >> >> at >> >> >> >> akka.dispatch.BatchingExecutor$BlockableBatch.run(BatchingExecutor.scala:90) >> >> at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:40) >> >> at >> >> >> >> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:397) >> >> at >> >> scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) >> >> at >> >> >> >> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) >> >> at >> >> scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) >> >> at >> >> >> >> scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) >> >> >> >> I went through all logs at the Hadoop side of all the related >> >> containers and >> >> other than this exception I did not see any warning/error that might >> >> explain >> >> what is going on here. >> >> >> >> Now the "Most of the time running this works fine" makes this hard to >> >> troubleshoot. When I run the same job again it may run perfectly that >> >> time. >> >> >> >> I'm using flink-1.3.2-bin-hadoop27-scala_2.11.tgz and I double checked >> >> my >> >> pom.xml and I use the same version for Flink / Scala in there. >> >> >> >> The command used to start the yarn-session on my experimental cluster >> >> (no >> >> security, no other users): >> >> >> >> /usr/local/flink-1.3.2/bin/yarn-session.sh \ >> >> --container 180 \ >> >> --name "Flink on Yarn Experiments" \ >> >> --slots 1 \ >> >> --jobManagerMemory 4000 \ >> >> --taskManagerMemory 4000 \ >> >> --streaming \ >> >> --detached >> >> >> >> Two relevant fragments from my application pom.xml: >> >> >> >> <flink.version>1.3.2</flink.version> >> >> <flink.scala.version>2.11</flink.scala.version> >> >> >> >> >> >> >> >> <dependency> >> >> <groupId>org.apache.flink</groupId> >> >> <artifactId>flink-java</artifactId> >> >> <version>${flink.version}</version> >> >> </dependency> >> >> >> >> <dependency> >> >> <groupId>org.apache.flink</groupId> >> >> <artifactId>flink-streaming-java_${flink.scala.version}</artifactId> >> >> <version>${flink.version}</version> >> >> </dependency> >> >> >> >> <dependency> >> >> <groupId>org.apache.flink</groupId> >> >> <artifactId>flink-clients_${flink.scala.version}</artifactId> >> >> <version>${flink.version}</version> >> >> </dependency> >> >> >> >> <dependency> >> >> <groupId>org.apache.flink</groupId> >> >> <artifactId>flink-hbase_${flink.scala.version}</artifactId> >> >> <version>${flink.version}</version> >> >> </dependency> >> >> >> >> >> >> I could really use some suggestions where to look for the root cause of >> >> this. >> >> Is this something in my application? My Hadoop cluster? Or is this a >> >> problem >> >> in Flink 1.3.2? >> >> >> >> Thanks. >> >> >> >> -- >> >> Best regards / Met vriendelijke groeten, >> >> >> >> Niels Basjes > > > > > -- > Best regards / Met vriendelijke groeten, > > Niels Basjes
