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MapReduce >> mail # user >> Distributing Keys across Reducers


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RE: Distributing Keys across Reducers
These numbers are with everything I laid out below.  The job was running acceptably until a couple of days ago when a change increased the output of the Map phase by about 30%.  I don't think there is anything special about those additional keys that would force them all into the same reducer.

Dave Shine
Sr. Software Engineer
321.939.5093 direct |  407.314.0122 mobile
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-----Original Message-----
From: Harsh J [mailto:[EMAIL PROTECTED]]
Sent: Friday, July 20, 2012 11:56 AM
To: [EMAIL PROTECTED]
Cc: [EMAIL PROTECTED]
Subject: Re: Distributing Keys across Reducers

Does applying a combiner make any difference? Or are these numbers with the combiner included?

On Fri, Jul 20, 2012 at 8:46 PM, Dave Shine <[EMAIL PROTECTED]> wrote:
> Thanks John.
>
> The key is my own WritableComparable object, and I have created custom Comparator, Partitioner, and KeyValueGroupingComparator.  However, they are all pretty generic.  The Key class is has two properties, a boolean and a string.  I'm grouping on just the string, but comparing on both properties to ensure that the reducer receives the "true" values before the "false" values.
>
> My partitioner does the basic hash of just the string portion of the key class.  I'm hoping to find some guidance on how to make that partitioner smarter and avoid this problem.
>
> Dave Shine
> Sr. Software Engineer
> 321.939.5093 direct |  407.314.0122 mobile CI Boost(tm) Clients  
> Outperform Online(tm)  www.ciboost.com
>
>
> -----Original Message-----
> From: John Armstrong [mailto:[EMAIL PROTECTED]]
> Sent: Friday, July 20, 2012 10:20 AM
> To: [EMAIL PROTECTED]
> Subject: Re: Distributing Keys across Reducers
>
> On 07/20/2012 09:20 AM, Dave Shine wrote:
>> 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?
>
> I don't know about blog posts or white papers, but the canonical answer here is usually using a different Partitioner.
>
> The default one takes the .hash() of each Mapper output key and reduces it modulo the number of Reducers you've specified (43, here).  So the first place I'd look is to see if there's some reason you're getting so many more outputs with one key-hash-mod-43 than the others.
>
> A common answer here is that one key alone has a huge number of outputs, in which case it's hard to do anything better with it.  Another case is that your key class' hash function is bad at telling apart a certain class of keys that occur with some regularity.  Since 43 is an odd prime, I would not expect a moderately evenly distributed hash to suddenly get spikes at certain values mod-43.
>
> So if you want to (and can) rejigger your hashes to spread things more evenly, great.  If not, you're down to writing your own partitioner.
> It's slightly different depending on which API you're using, but either way you basically have to write a function called getPartition that takes a mapper output record (key and value) and the number of reducers and returns the index (from 0 to numReducers-1) of the reducer that should handle that record.  And unless you REALLY know what you're doing, the function should probably only depend on the key.
>
> Good luck.
>
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--
Harsh J
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB