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RE: Distributing Keys across Reducers
Yes, that is a possibility, but it will take some significant rearchitecture.  I was assuming that was what I was going to have to do until I saw the key distribution problem and though I might be able to buy some relief by addressing that.

The job runs once per day, starting at 1:00AM EDT.  I have changed it to use a fewer number of reducers just to see how that effects the distribution.

Dave Shine
Sr. Software Engineer
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-----Original Message-----
From: Tim Broberg [mailto:[EMAIL PROTECTED]]
Sent: Friday, July 20, 2012 1:03 PM
Subject: RE: Distributing Keys across Reducers

Just a thought, but can you deal with the problem with increased granularity by simply making the jobs smaller?

If you have enough jobs, when one takes twice as long there will be plenty of other small jobs to employ the other nodes, right?

    - Tim.

From: David Rosenstrauch [[EMAIL PROTECTED]]
Sent: Friday, July 20, 2012 7:45 AM
Subject: Re: Distributing Keys across Reducers

On 07/20/2012 09:20 AM, Dave Shine wrote:
> I have a job that is emitting over 3 billion rows from the map to the reduce.  The job is configured with 43 reduce tasks.  A perfectly even distribution would amount to about 70 million rows per reduce task.  However I actually got around 60 million for most of the tasks, one task got over 100 million, and one task got almost 350 million.  This uneven distribution caused the job to run exceedingly long.
> I believe this is referred to as a "key skew problem", which I know is heavily dependent on the actual data being processed.  Can anyone point me to any blog posts, white papers, etc. that might give me some options on how to deal with this issue?

Hadoop lets you override the default partitioner and replace it with your own.  This lets you write a custom partitioning scheme which distributes your data more evenly.



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