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From Andrew Ash <and...@andrewash.com>
Subject Re: Issues with partitionBy: FetchFailed
Date Mon, 22 Sep 2014 10:29:34 GMT
Hi David and Saisai,

Are the exceptions you two are observing similar to the first one at
https://issues.apache.org/jira/browse/SPARK-3633 ?  Copied below:

14/09/19 12:10:38 WARN TaskSetManager: Lost task 51.0 in stage 2.1
(TID 552, c1705.halxg.cloudera.com): FetchFailed(BlockManagerId(1,
c1706.halxg.cloudera.com, 49612, 0), shuffleId=3, mapId=75,
reduceId=120)


I'm seeing the same using Spark SQL on 1.1.0 -- I think there may have been
a regression in 1.1 because the same SQL query works on the same cluster
when back on 1.0.2

Thanks!
Andrew

On Sun, Sep 21, 2014 at 5:15 AM, David Rowe <davidrowe@gmail.com> wrote:

> Hi,
>
> I've seen this problem before, and I'm not convinced it's GC.
>
> When spark shuffles it writes a lot of small files to store the data to be
> sent to other executors (AFAICT). According to what I've read around the
> place the intention is that these files be stored in disk buffers, and
> since sync() is never called, they exist only in memory. The problem is
> when you have a lot of shuffle data, and the executors are configured to
> use, say 90% of your memory, one of those is going to be written to disk -
> either the JVM will be swapped out, or the files will be written out of
> cache.
>
> So, when these nodes are timing out, it's not a GC problem, it's that the
> machine is actually thrashing.
>
> I've had some success with this problem by reducing the amount of memory
> that the executors are configured to use from say 90% to 60%. I don't know
> the internals of the code, but I'm sure this number is related to the
> fraction of your data that's going to be shuffled to other nodes. In any
> case, it's not something that I can estimate in my own jobs very well - I
> usually have to find the right number by trial and error.
>
> Perhaps somebody who knows the internals a bit better can shed some light.
>
> Cheers
>
> Dave
>
> On Sun, Sep 21, 2014 at 9:54 PM, Shao, Saisai <saisai.shao@intel.com>
> wrote:
>
>>  Hi,
>>
>>
>>
>> I’ve also met this problem before, I think you can try to set
>> “spark.core.connection.ack.wait.timeout” to a large value to avoid ack
>> timeout, default is 60 seconds.
>>
>>
>>
>> Sometimes because of GC pause or some other reasons, acknowledged message
>> will be timeout, which will lead to this exception, you can try setting a
>> large value of this configuration.
>>
>>
>>
>> Thanks
>>
>> Jerry
>>
>>
>>
>> *From:* Julien Carme [mailto:julien.carme@gmail.com]
>> *Sent:* Sunday, September 21, 2014 7:43 PM
>> *To:* user@spark.apache.org
>> *Subject:* Issues with partitionBy: FetchFailed
>>
>>
>>
>> Hello,
>>
>> I am facing an issue with partitionBy, it is not clear whether it is a
>> problem with my code or with my spark setup. I am using Spark 1.1,
>> standalone, and my other spark projects work fine.
>>
>> So I have to repartition a relatively large file (about 70 million
>> lines). Here is a minimal version of what is not working fine:
>>
>> myRDD = sc.textFile("...").map { line => (extractKey(line),line) }
>>
>> myRepartitionedRDD = myRDD.partitionBy(new HashPartitioner(100))
>>
>> myRepartitionedRDD.saveAsTextFile(...)
>>
>> It runs quite some time, until I get some errors and it retries. Errors
>> are:
>>
>> FetchFailed(BlockManagerId(3,myWorker2, 52082,0),
>> shuffleId=1,mapId=1,reduceId=5)
>>
>> Logs are not much more infomrative. I get:
>>
>> Java.io.IOException : sendMessageReliability failed because ack was not
>> received within 60 sec
>>
>>
>>
>> I get similar errors with all my workers.
>>
>> Do you have some kind of explanation for this behaviour? What could be
>> wrong?
>>
>> Thanks,
>>
>>
>>
>>
>>
>
>

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