Hi Zhijiang,
Thanks for the explanation and the workaround suggested.
While this can work for the example stated above, we have more complex use
cases where we would have to re-tune the above parameters. FYI, we ran into
same problems when we did a simple groupBy on the skewed dataset.
Thanks,
Akshay
On Fri, Nov 23, 2018 at 8:29 AM zhijiang <wangzhijiang...@aliyun.com> wrote:
> Hi Akshay,
>
> Sorrry I have not thought of a proper way to handle single large record in
> distributed task managers in flink. But I can give some hints for adjusting
> the related memories for work around OOM issue.
> Large fraction of memories in task manager are managed by flink for
> efficiency, and these memories are long live persistent in JVM not recycled
> by gc. You can check the parameter "taskmanager.memory.fraction" for this
> and the default value is 0.7 if you have not changed, that means 7GB * 0.7
> are used by framework.
>
> I am not sure what is the flink version you used. If I rememberd
> correctly, before release-1.5 the network buffers also uses heap memories
> by default, so you should also minus this part of memory from total task
> manager memory.
>
> If not considering network buffer used by framework, you only leave 7GB *
> 0.3 temporaray memories for other parts. The temporaray memories in
> serializer will exceed twice as current size every time if not covering the
> record size, that means one serializer may need 2GB overhead memories for
> your 1GB record. You have 2 slots per task manager for running two tasks,
> so the total overhead memories may need 4GB almost. So you can decrease
> the "taskmanager.memory.fraction" in low fraction or increase the total
> task manager to cover this overhead memories, or set one slot for each task
> manager.
>
> Best,
> Zhijiang
>
> ------------------------------------------------------------------
> 发件人：Akshay Mendole <akshaymend...@gmail.com>
> 发送时间：2018年11月23日(星期五) 02:54
> 收件人：trohrmann <trohrm...@apache.org>
> 抄 送：zhijiang <wangzhijiang...@aliyun.com>; user <user@flink.apache.org>;
> Shreesha Madogaran <msshree...@gmail.com>
> 主 题：Re: OutOfMemoryError while doing join operation in flink
>
> Hi,
> Thanks for your reply. I tried running a simple "group by" on just one
> dataset where few keys are repeatedly occurring (in order of millions) and
> did not include any joins. I wanted to see if this issue is specific to
> join. But as I was expecting, I ran into the same issue. I am giving 7GBs
> to each task manager with 2 slots per task manager. From what I understood
> so far, such cases where individual records somewhere in the pipeline
> become so large that they should be handled in distributed manner instead
> of handling them by a simple data structure in single JVM. I am guessing
> there is no way to do this in Flink today.
> Could you please confirm this?
> Thanks,
> Akshay
>
>
> On Thu, Nov 22, 2018 at 9:28 PM Till Rohrmann <trohrm...@apache.org>
> wrote:
> Hi Akshay,
>
> Flink currently does not support to automatically distribute hot keys
> across different JVMs. What you can do is to adapt the parallelism/number
> of partitions manually if you encounter that one partition contains a lot
> of hot keys. This might mitigate the problem by partitioning the hot keys
> into different partitions.
>
> Apart from that, the problem seems to be as Zhijiang indicated that your
> join result is quite large. One record is 1 GB large. Try to decrease it or
> give more memory to your TMs.
>
> Cheers,
> Till
>
> On Thu, Nov 22, 2018 at 1:08 PM Akshay Mendole <akshaymend...@gmail.com>
> wrote:
> Hi Zhijiang,
> Thanks for the quick reply. My concern is more towards
> how flink perform joins of two *skewed *datasets. Pig
> <https://wiki.apache.org/pig/PigSkewedJoinSpec> and spark
> <https://wiki.apache.org/pig/PigSkewedJoinSpec> seems to support the join
> of skewed datasets. The record size that you are mentioning about in your
> reply is after join operation takes place which is definitely going to be
> huge enough not to fit in jvm task manager task slot in my use case. We
> want to know if there is a way in flink to handle such skewed keys by
> distributing their values across different jvms. Let me know if you need
> more clarity on the issue.
> Thanks,
> Akshay
>
> On Thu, Nov 22, 2018 at 2:38 PM zhijiang <wangzhijiang...@aliyun.com>
> wrote:
> Hi Akshay,
>
> You encountered an existing issue for serializing large records to cause
> OOM.
>
> Every subpartition would create a separate serializer before, and each
> serializer would maintain an internal bytes array for storing intermediate
> serialization results. The key point is that these overhead internal bytes
> array are not managed by framework, and their size would exceed with the
> record size dynamically. If your job has many subpartitions with large
> records, it may probably cause OOM issue.
>
> I already improved this issue to some extent by sharing only one
> serializer for all subpartitions [1], that means we only have one bytes
> array overhead at most. This issue is covered in release-1.7.
> Currently the best option may reduce your record size if possible or you
> can increase the heap size of task manager container.
>
> [1] https://issues.apache.org/jira/browse/FLINK-9913
>
> Best,
> Zhijiang
> ------------------------------------------------------------------
> 发件人：Akshay Mendole <akshaymend...@gmail.com>
> 发送时间：2018年11月22日(星期四) 13:43
> 收件人：user <user@flink.apache.org>
> 主 题：OutOfMemoryError while doing join operation in flink
>
> Hi,
> We are converting one of our pig pipelines to flink using apache beam.
> The pig pipeline reads two different data sets (R1 & R2) from hdfs,
> enriches them, joins them and dumps back to hdfs. The data set R1 is
> skewed. In a sense, it has few keys with lot of records. When we converted
> the pig pipeline to apache beam and ran it using flink on a production yarn
> cluster, we got the following error
>
> 2018-11-21 16:52:25,307 ERROR
> org.apache.flink.runtime.operators.BatchTask - Error in
> task code: GroupReduce (GroupReduce at CoGBK/GBK) (25/100)
> java.lang.RuntimeException: Emitting the record caused an I/O exception:
> Failed to serialize element. Serialized size (> 1136656562 bytes) exceeds
> JVM heap space
> at
> org.apache.flink.runtime.operators.shipping.OutputCollector.collect(OutputCollector.java:69)
> at
> org.apache.flink.runtime.operators.util.metrics.CountingCollector.collect(CountingCollector.java:35)
> at
> org.apache.beam.runners.flink.translation.functions.SortingFlinkCombineRunner.combine(SortingFlinkCombineRunner.java:140)
> at
> org.apache.beam.runners.flink.translation.functions.FlinkReduceFunction.reduce(FlinkReduceFunction.java:85)
> at
> org.apache.flink.api.java.operators.translation.PlanUnwrappingReduceGroupOperator$TupleUnwrappingNonCombinableGroupReducer.reduce(PlanUnwrappingReduceGroupOperator.java:111)
> at
> org.apache.flink.runtime.operators.GroupReduceDriver.run(GroupReduceDriver.java:131)
> at
> org.apache.flink.runtime.operators.BatchTask.run(BatchTask.java:503)
> at
> org.apache.flink.runtime.operators.BatchTask.invoke(BatchTask.java:368)
> at org.apache.flink.runtime.taskmanager.Task.run(Task.java:711)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.io.IOException: Failed to serialize element. Serialized
> size (> 1136656562 bytes) exceeds JVM heap space
> at
> org.apache.flink.core.memory.DataOutputSerializer.resize(DataOutputSerializer.java:323)
> at
> org.apache.flink.core.memory.DataOutputSerializer.write(DataOutputSerializer.java:149)
> at
> org.apache.beam.runners.flink.translation.wrappers.DataOutputViewWrapper.write(DataOutputViewWrapper.java:48)
> at java.io.DataOutputStream.write(DataOutputStream.java:107)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
> at
> java.io.ObjectOutputStream.writeNonProxyDesc(ObjectOutputStream.java:1286)
> at
> java.io.ObjectOutputStream.writeClassDesc(ObjectOutputStream.java:1231)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1427)
> at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1577)
> at
> java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:351)
> at
> org.apache.beam.sdk.coders.SerializableCoder.encode(SerializableCoder.java:170)
> at
> org.apache.beam.sdk.coders.SerializableCoder.encode(SerializableCoder.java:50)
> at org.apache.beam.sdk.coders.Coder.encode(Coder.java:136)
> at
> org.apache.beam.sdk.transforms.join.UnionCoder.encode(UnionCoder.java:71)
> at
> org.apache.beam.sdk.transforms.join.UnionCoder.encode(UnionCoder.java:58)
> at
> org.apache.beam.sdk.transforms.join.UnionCoder.encode(UnionCoder.java:32)
> at
> org.apache.beam.sdk.coders.IterableLikeCoder.encode(IterableLikeCoder.java:98)
> at
> org.apache.beam.sdk.coders.IterableLikeCoder.encode(IterableLikeCoder.java:60)
> at org.apache.beam.sdk.coders.Coder.encode(Coder.java:136)
> at org.apache.beam.sdk.coders.KvCoder.encode(KvCoder.java:71)
> at org.apache.beam.sdk.coders.KvCoder.encode(KvCoder.java:36)
> at
> org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.encode(WindowedValue.java:529)
> at
> org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.encode(WindowedValue.java:520)
> at
> org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.encode(WindowedValue.java:480)
> at
> org.apache.beam.runners.flink.translation.types.CoderTypeSerializer.serialize(CoderTypeSerializer.java:83)
> at
> org.apache.flink.runtime.plugable.SerializationDelegate.write(SerializationDelegate.java:54)
> at
> org.apache.flink.runtime.io.network.api.serialization.SpanningRecordSerializer.addRecord(SpanningRecordSerializer.java:88)
> at
> org.apache.flink.runtime.io.network.api.writer.RecordWriter.sendToTarget(RecordWriter.java:131)
> at
> org.apache.flink.runtime.io.network.api.writer.RecordWriter.emit(RecordWriter.java:107)
> at
> org.apache.flink.runtime.operators.shipping.OutputCollector.collect(OutputCollector.java:65)
> ... 9 more
> Caused by: java.lang.OutOfMemoryError: Java heap space
> at
> org.apache.flink.core.memory.DataOutputSerializer.resize(DataOutputSerializer.java:305)
> at
> org.apache.flink.core.memory.DataOutputSerializer.write(DataOutputSerializer.java:149)
> at
> org.apache.beam.runners.flink.translation.wrappers.DataOutputViewWrapper.write(DataOutputViewWrapper.java:48)
> at java.io.DataOutputStream.write(DataOutputStream.java:107)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
> at
> java.io.ObjectOutputStream.writeNonProxyDesc(ObjectOutputStream.java:1286)
> at
> java.io.ObjectOutputStream.writeClassDesc(ObjectOutputStream.java:1231)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1427)
> at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1577)
> at
> java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:351)
> at
> org.apache.beam.sdk.coders.SerializableCoder.encode(SerializableCoder.java:170)
> at
> org.apache.beam.sdk.coders.SerializableCoder.encode(SerializableCoder.java:50)
> at org.apache.beam.sdk.coders.Coder.encode(Coder.java:136)
> at
> org.apache.beam.sdk.transforms.join.UnionCoder.encode(UnionCoder.java:71)
> at
> org.apache.beam.sdk.transforms.join.UnionCoder.encode(UnionCoder.java:58)
> at
> org.apache.beam.sdk.transforms.join.UnionCoder.encode(UnionCoder.java:32)
> at
> org.apache.beam.sdk.coders.IterableLikeCoder.encode(IterableLikeCoder.java:98)
> at
> org.apache.beam.sdk.coders.IterableLikeCoder.encode(IterableLikeCoder.java:60)
> at org.apache.beam.sdk.coders.Coder.encode(Coder.java:136)
> at org.apache.beam.sdk.coders.KvCoder.encode(KvCoder.java:71)
> at org.apache.beam.sdk.coders.KvCoder.encode(KvCoder.java:36)
> at
> org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.encode(WindowedValue.java:529)
> at
> org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.encode(WindowedValue.java:520)
> at
> org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.encode(WindowedValue.java:480)
> at
> org.apache.beam.runners.flink.translation.types.CoderTypeSerializer.serialize(CoderTypeSerializer.java:83)
> at
> org.apache.flink.runtime.plugable.SerializationDelegate.write(SerializationDelegate.java:54)
> at
> org.apache.flink.runtime.io.network.api.serialization.SpanningRecordSerializer.addRecord(SpanningRecordSerializer.java:88)
> at
> org.apache.flink.runtime.io.network.api.writer.RecordWriter.sendToTarget(RecordWriter.java:131)
> at
> org.apache.flink.runtime.io.network.api.writer.RecordWriter.emit(RecordWriter.java:107)
> at
> org.apache.flink.runtime.operators.shipping.OutputCollector.collect(OutputCollector.java:65)
>
>
> From the exception view in flink job manager dashboard, we could see that
> this is happening at a join operation.
> *When I say R1 dataset is skewed, there are some keys with number of
> occurrences as high as 8,000,000 , while most of the keys occur just once.*
> *Dataset R2 has records with keys occurring at most once.*
> Also, if we exclude such keys which has high number of occurrences, the
> pipeline runs absolutely fine which proves it is happening due these few
> keys only.
>
> Hadoop version : 2.7.1
> Beam verision : 2.8.0
> Flink Runner version : 2.8.0
>
> Let me know what more information should I fetch and post here in order
> for you to help me resolve this.
>
> Thanks,
> Akshay
>
>
>
>
