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From "Scott Kidder (JIRA)" <j...@apache.org>
Subject [jira] [Created] (FLINK-4341) Checkpoint state size grows unbounded when task parallelism not uniform
Date Tue, 09 Aug 2016 20:20:20 GMT
Scott Kidder created FLINK-4341:

             Summary: 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:

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

The first 4 checkpoints generally succeed, but then fail with an exception like the following:

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


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@]: max allowed
size 10485760 bytes, actual size of encoded class org.apache.flink.runtime.messages.checkpoint.AcknowledgeCheckpoint
was 10891825 bytes.

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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