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From Ted Yu <yuzhih...@gmail.com>
Subject Re: Hanging tasks in spark 1.2.1 while working with 1.1.1
Date Fri, 13 Mar 2015 17:09:18 GMT
Might be related: what's the value for spark.yarn.executor.memoryOverhead ?

See SPARK-6085

Cheers

On Fri, Mar 13, 2015 at 9:45 AM, Eugen Cepoi <cepoi.eugen@gmail.com> wrote:

> Hi,
>
> I have a job that hangs after upgrading to spark 1.2.1 from 1.1.1. Strange
> thing, the exact same code does work (after upgrade) in the spark-shell.
> But this information might be misleading as it works with 1.1.1...
>
>
> *The job takes as input two data sets:*
>  - rdd A of +170gb (with less it is hard to reproduce) and more than 11K
> partitions
>  - rdd B of +100mb and 32 partitions
>
> I run it via EMR over YARN and use 4*m3.xlarge computing nodes. I am not
> sure the executor config is relevant here. Anyway I tried with multiple
> small executors with fewer ram and the inverse.
>
>
> *The job basically does this:*
> A.flatMap(...).union(B).keyBy(f).reduceByKey(..., 32).map(...).save
>
> After the flatMap rdd A size is much smaller similar to B.
>
> *Configs I used to run this job:*
>
> storage.memoryFraction: 0
> shuffle.memoryFraction: 0.5
>
> akka.timeout 500
> akka.frameSize 40
>
> // this one defines also the memory used by yarn master, but not sure if
> it needs to be important
> driver.memory 5g
> excutor.memory 4250m
>
> I have 7 executors with 2 cores.
>
> *What happens:*
> The job produces two stages: keyBy and save. The keyBy stage runs fine and
> produces a shuffle write of ~150mb. The save stage where the suffle read
> occurs hangs. Greater the initial dataset is more tasks hang.
>
> I did run it for much larger datasets with same config/cluster but without
> doing the union and it worked fine.
>
> *Some more infos and logs:*
>
> Amongst 4 nodes 1 finished all his tasks and the "running" ones are on the
> 3 other nodes. But not sure this is a good information (one node that
> completed all his work vs the others) as with some smaller dataset I manage
> to get only one hanging task.
>
> Here are the last parts of the executor logs that show some timeouts.
>
> *An executor from node ip-10-182-98-220*
>
> 15/03/13 15:43:10 INFO storage.ShuffleBlockFetcherIterator: Started 6 remote fetches
in 66 ms
> 15/03/13 15:58:44 WARN server.TransportChannelHandler: Exception in connection from /10.181.48.153:56806
> java.io.IOException: Connection timed out
>
>
> *An executor from node ip-10-181-103-186*
>
> 15/03/13 15:43:22 INFO storage.ShuffleBlockFetcherIterator: Started 6 remote fetches
in 20 ms
> 15/03/13 15:58:41 WARN server.TransportChannelHandler: Exception in connection from /10.182.98.220:38784
> java.io.IOException: Connection timed out
>
> *An executor from node ip-10-181-48-153* (all the logs bellow belong this node)
>
> 15/03/13 15:43:24 INFO executor.Executor: Finished task 26.0 in stage 1.0 (TID 13860).
802 bytes result sent to driver
> 15/03/13 15:58:43 WARN server.TransportChannelHandler: Exception in connection from /10.181.103.186:46381
> java.io.IOException: Connection timed out
>
> *Followed by many *
>
> 15/03/13 15:58:43 ERROR server.TransportRequestHandler: Error sending result ChunkFetchSuccess{streamChunkId=StreamChunkId{streamId=2064203432016,
chunkIndex=405}, buffer=FileSegmentManagedBuffer{file=/mnt/var/lib/hadoop/tmp/nm-local-dir/usercache/hadoop/appcache/application_1426256247374_0002/spark-1659efcd-c6b6-4a12-894d-e869486d3d00/35/shuffle_0_9885_0.data,
offset=8631, length=571}} to /10.181.103.186:46381; closing connection
> java.nio.channels.ClosedChannelException
>
> *with last one being*
>
> 15/03/13 15:58:43 ERROR server.TransportRequestHandler: Error sending result RpcResponse{requestId=7377187355282895939,
response=[B@6fcd0014} to /10.181.103.186:46381; closing connection
> java.nio.channels.ClosedChannelException
>
>
> The executors from the node that finished his tasks doesn't show anything
> special.
>
> Note that I don't cache anything thus reduced the storage.memoryFraction
> to 0.
> I see some of those, but don't think they are related.
>
> 15/03/13 15:43:15 INFO storage.MemoryStore: Memory use = 0.0 B (blocks) + 0.0 B (scratch
space shared across 0 thread(s)) = 0.0 B. Storage limit = 0.0 B.
>
>
> Sorry for the long email with maybe misleading infos, but I hope it might
> help to track down what happens and why it was working with spark 1.1.1.
>
> Thanks,
> Eugen
>
>

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