Hi Anny,

I am assuming that you perform some complex logic for processing. Can you
try to reduce your data size using RDD.sample or RDD.filter before actual
processing? That may reduce memory pressure.

Liqaun



On Wed, Oct 1, 2014 at 1:53 PM, anny9699 <anny9...@gmail.com> wrote:

> Hi Liquan,
>
> I have 8 workers, each with 15.7GB memory.
>
> What you said makes sense, but if I don't increase heap space, it keeps
> telling me "GC overhead limit exceeded".
>
> Thanks!
> Anny
>
> On Wed, Oct 1, 2014 at 1:41 PM, Liquan Pei [via Apache Spark User List] 
> <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=15543&i=0>> wrote:
>
>> Hi
>>
>> How many nodes in your cluster? It seems to me 64g does not help if each
>> of your node doesn't have that many memory.
>>
>> Liquan
>>
>> On Wed, Oct 1, 2014 at 1:37 PM, anny9699 <[hidden email]
>> <http://user/SendEmail.jtp?type=node&node=15541&i=0>> wrote:
>>
>>> Hi,
>>>
>>> After reading some previous posts about this issue, I have increased the
>>> java heap space to "-Xms64g -Xmx64g", but still met the
>>> "java.lang.OutOfMemoryError: GC overhead limit exceeded" error. Does
>>> anyone
>>> have other suggestions?
>>>
>>> I am reading a data of 200 GB and my total memory is 120 GB, so I use
>>> "MEMORY_AND_DISK_SER" and kryo serialization.
>>>
>>> Thanks a lot!
>>>
>>>
>>>
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>>
>>
>> --
>> Liquan Pei
>> Department of Physics
>> University of Massachusetts Amherst
>>
>>
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-- 
Liquan Pei
Department of Physics
University of Massachusetts Amherst

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