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
>>>
>>>
>>>
