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From David Rowe <davidr...@gmail.com>
Subject Re: Issues with partitionBy: FetchFailed
Date Mon, 22 Sep 2014 11:11:52 GMT
Yep, this is what I was seeing. I'll experiment tomorrow with a version
prior to the changeset in that ticket.

On Mon, Sep 22, 2014 at 8:29 PM, Andrew Ash <andrew@andrewash.com> wrote:

> 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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