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https://issues.apache.org/jira/browse/FLINK-4341?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15414301#comment-15414301
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Scott Kidder commented on FLINK-4341:
-------------------------------------

I also noticed that when checkpointing is enabled and I'm using a parallelism 
of 2 the processing speed is extremely slow compared to that of Flink 18995c8. 
I disabled checkpointing altogether and the speed returned to previous levels.

I'm currently building Flink from source to pull in hotfixes added to the 
release-1.1 branch since commit 45f7825. I'll update this issue with my 
findings.

> Checkpoint state size grows unbounded when task parallelism not uniform
> -----------------------------------------------------------------------
>
>                 Key: FLINK-4341
>                 URL: https://issues.apache.org/jira/browse/FLINK-4341
>             Project: Flink
>          Issue Type: Bug
>          Components: Core
>    Affects Versions: 1.1.0
>            Reporter: Scott Kidder
>
> This issue was first encountered with Flink release 1.1.0 (commit 45f7825). I 
> was previously using a 1.1.0 snapshot (commit 18995c8) which performed as 
> expected.  This issue was introduced somewhere between those commits.
> I've got a Flink application that uses the Kinesis Stream Consumer to read 
> from a Kinesis stream with 2 shards. I've got 2 task managers with 2 slots 
> each, providing a total of 4 slots.  When running the application with a 
> parallelism of 4, the Kinesis consumer uses 2 slots (one per Kinesis shard) 
> and 4 slots for subsequent tasks that process the Kinesis stream data. I use 
> an in-memory store for checkpoint data.
> Yesterday I upgraded to Flink 1.1.0 (45f7825) and noticed that checkpoint 
> states were growing unbounded when running with a parallelism of 4, 
> checkpoint interval of 10 seconds:
> {code}
> ID  State Size
> 1   11.3 MB
> 2    20.9 MB
> 3   30.6 MB
> 4   41.4 MB
> 5   52.6 MB
> 6   62.5 MB
> 7   71.5 MB
> 8   83.3 MB
> 9   93.5 MB
> {code}
> The first 4 checkpoints generally succeed, but then fail with an exception 
> like the following:
> {code}
> java.lang.RuntimeException: Error triggering a checkpoint as the result of 
> receiving checkpoint barrier at 
> org.apache.flink.streaming.runtime.tasks.StreamTask$2.onEvent(StreamTask.java:768)
>  at 
> org.apache.flink.streaming.runtime.tasks.StreamTask$2.onEvent(StreamTask.java:758)
>  at 
> org.apache.flink.streaming.runtime.io.BarrierBuffer.processBarrier(BarrierBuffer.java:203)
>  at 
> org.apache.flink.streaming.runtime.io.BarrierBuffer.getNextNonBlocked(BarrierBuffer.java:129)
>  at 
> org.apache.flink.streaming.runtime.io.StreamInputProcessor.processInput(StreamInputProcessor.java:183)
>  at 
> org.apache.flink.streaming.runtime.tasks.OneInputStreamTask.run(OneInputStreamTask.java:66)
>  at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:266)
>  at org.apache.flink.runtime.taskmanager.Task.run(Task.java:584) at 
> java.lang.Thread.run(Thread.java:745) Caused by: java.io.IOException: Size of 
> the state is larger than the maximum permitted memory-backed state. 
> Size=12105407 , maxSize=5242880 . Consider using a different state backend, 
> like the File System State backend. at 
> org.apache.flink.runtime.state.memory.MemoryStateBackend.checkSize(MemoryStateBackend.java:146)
>  at 
> org.apache.flink.runtime.state.memory.MemoryStateBackend$MemoryCheckpointOutputStream.closeAndGetBytes(MemoryStateBackend.java:200)
>  at 
> org.apache.flink.runtime.state.memory.MemoryStateBackend$MemoryCheckpointOutputStream.closeAndGetHandle(MemoryStateBackend.java:190)
>  at 
> org.apache.flink.runtime.state.AbstractStateBackend$CheckpointStateOutputView.closeAndGetHandle(AbstractStateBackend.java:447)
>  at 
> org.apache.flink.streaming.runtime.operators.windowing.WindowOperator.snapshotOperatorState(WindowOperator.java:879)
>  at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.performCheckpoint(StreamTask.java:598)
>  at 
> org.apache.flink.streaming.runtime.tasks.StreamTask$2.onEvent(StreamTask.java:762)
>  ... 8 more
> {code}
> Or:
> {code}
> 2016-08-09 17:44:43,626 INFO  
> org.apache.flink.streaming.runtime.tasks.StreamTask           - Restoring 
> checkpointed state to task Fold: property_id, player -> 10-minute 
> Sliding-Window Percentile Aggregation -> Sink: InfluxDB (2/4)
> 2016-08-09 17:44:51,236 ERROR akka.remote.EndpointWriter            - 
> Transient association error (association remains live) 
> akka.remote.OversizedPayloadException: Discarding oversized payload sent to 
> Actor[akka.tcp://flink@10.55.2.212:6123/user/jobmanager#510517238]: max 
> allowed size 10485760 bytes, actual size of encoded class 
> org.apache.flink.runtime.messages.checkpoint.AcknowledgeCheckpoint was 
> 10891825 bytes.
> {code}
> This can be fixed by simply submitting the job with a parallelism of 2. I 
> suspect there was a regression introduced relating to assumptions about the 
> number of sub-tasks associated with a job stage (e.g. assuming 4 instead of a 
> value ranging from 1-4). This is currently preventing me from using all 
> available Task Manager slots.



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