Sanjay, I got job working on 25920 partitions on 18TB. its just failing.
Hi Viral, yes we have enough spcae on cluster and could not find any such
kind of log.
On Thu, Apr 25, 2013 at 3:34 PM, Viral Bajaria <[EMAIL PROTECTED]>wrote:
> How about running it via sub-queries where each query runs over a subset
> of the data and has a better chance of finishing. I fear that the amount of
> data to shuffle might be too big and you might be running out of
> scratch/temp space. Did you verify that the job does not fail due to out of
> disk space before the shuffle/reduce can kick in ?
> On Thu, Apr 25, 2013 at 3:10 PM, Sanjay Subramanian <
> [EMAIL PROTECTED]> wrote:
>> That’s a lot of partitions for one Hive Job ! Not sure if that itself
>> is the root of the issues….There have been quite a few discussions on max
>> 1000-ish number of partitions as good…
>> Is your use case conducive too using Combiners (though they cannot be
>> guaranteed to be called)
>> From: Srinivas Surasani <[EMAIL PROTECTED]>
>> Reply-To: "[EMAIL PROTECTED]" <[EMAIL PROTECTED]>
>> Date: Thursday, April 25, 2013 2:33 PM
>> To: "[EMAIL PROTECTED]" <[EMAIL PROTECTED]>
>> Subject: map tasks are taking ever when running job on 24 TB
>> I'm running hive job on 24TB dataset (on 34560 partitions ). here about
>> 500 to 1000 mappers are getting succeded (total of 80000) and rest mappaers
>> are taking for ever ( their status stays at 0% all times ). Is there any
>> limitations on number of partitions/dataset ? are there any paraemeters to
>> set here?
>> Same job is suceeding on 18TB (25920 partitions ).
>> I already set below in my hive query.
>> set mapreduce.jobtracker.split.metainfo.maxsize=-1;
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