The one by default 0.07 of executor memory. I'll try increasing it and post
back the result.
Thanks
2015-03-13 18:09 GMT+01:00 Ted Yu <yuzhih...@gmail.com>:
> 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.eu...@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
>>
>>
>
