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
