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From Michael Segel <michael_se...@hotmail.com>
Subject Re: Chaning Multiple Reducers: Reduce -> Reduce -> Reduce
Date Mon, 08 Oct 2012 19:19:00 GMT
Well I was thinking ... 

Map -> Combiner -> Reducer -> Identity Mapper -> combiner -> reducer ->
Identity Mapper -> combiner -> reducer...

May make things easier. 



On Oct 8, 2012, at 2:09 PM, Jim Twensky <jim.twensky@gmail.com> wrote:

> Thank you for the comments. Some similar frameworks I looked at
> include Haloop, Twister, Hama, Giraph and Cascading. I am also doing
> large scale graph processing so I assumed one of them could serve the
> purpose. Here is a summary of what I found out about them that is
> relevant:
> 1) Haloop and Twister: They cache static data among a chain of
> MapReduce jobs. The main contribution is to reduce the intermediate
> data shipped from mappers to reducers. Still, the output of each
> reduce goes to the file system.
> 2) Cascading: A higher level API to create MapReduce workflows.
> Anything you can do with Cascading can be done practically by more
> programing effort and using Hadoop only. Bypassing map and running a
> chain of sort->reduce->sort->reduce jobs is not possible. Please
> correct me if I'm wrong.
> 3) Giraph: Built on the BSP model and is very similar to Pregel. I
> couldn't find a detailed overview of their architecture but my
> understanding is that your data needs to fit in distributed memory,
> which is also true for Pregel.
> 4) Hama: Also follows the BSP model. I don't know how the intermediate
> data is serialized and passed to the next set of nodes and whether it
> is possible to do a performance optimization similar to what I am
> asking for. If anyone who used Hama can point a few articles about how
> the framework actually works and handles the messages passed between
> vertices, I'd really appreciate that.
> Conclusion: None of the above tools can bypass the map step or do a
> similar performance optimization. Of course Giraph and Hama are built
> on a different model - not really MapReduce - so it is not very
> accurate to say that they don't have the required functionality.
> If I'm missing anything and.or if there are folks who used Giraph or
> Hama and think that they might serve the purpose, I'd be glad to hear
> more.
> Jim
> On Mon, Oct 8, 2012 at 6:52 AM, Michael Segel <michael_segel@hotmail.com> wrote:
>> I don't believe that Hama would suffice.
>> In terms of M/R where you want to chain reducers...
>> Can you chain combiners? (I don't think so, but you never know)
>> If not, you end up with a series of M/R jobs and the Mappers are just identity mappers.
>> Or you could use HBase, with a small caveat... you have to be careful not to use
speculative execution and that if a task fails, that the results of the task won't be affected
if they are run a second time. Meaning that they will just overwrite the data in a column
with a second cell and that you don't care about the number of versions.
>> Note: HBase doesn't have transactions, so you would have to think about how to tag
cells so that if a task dies, upon restart, you can remove the affected cells.  Along with
some post job synchronization...
>> Again HBase may work, but there may also be additional problems that could impact
your results. It will have to be evaluated on a case by case basis.
>> -Mike
>> On Oct 8, 2012, at 6:35 AM, Edward J. Yoon <edwardyoon@apache.org> wrote:
>>>> call context.write() in my mapper class)? If not, are there any other
>>>> MR platforms that can do this? I've been searching around and couldn't
>>> You can use Hama BSP[1] instead of Map/Reduce.
>>> No stable release yet but I confirmed that large graph with billions
>>> of nodes and edges can be crunched in few minutes[2].
>>> 1. http://hama.apache.org
>>> 2. http://wiki.apache.org/hama/Benchmarks
>>> On Sat, Oct 6, 2012 at 1:31 AM, Jim Twensky <jim.twensky@gmail.com> wrote:
>>>> Hi,
>>>> I have a complex Hadoop job that iterates over  large graph data
>>>> multiple times until some convergence condition is met. I know that
>>>> the map output goes to the local disk of each particular mapper first,
>>>> and then fetched by the reducers before the reduce tasks start. I can
>>>> see that this is an overhead, and it theory we can ship the data
>>>> directly from mappers to reducers, without serializing on the local
>>>> disk first. I understand that this step is necessary for fault
>>>> tolerance and it is an essential building block of MapReduce.
>>>> In my application, the map process consists of identity mappers which
>>>> read the input from HDFS and ship it to reducers. Essentially, what I
>>>> am doing is applying chains of reduce jobs until the algorithm
>>>> converges. My question is, can I bypass the serialization of the local
>>>> data and ship it from mappers to reducers immediately (as soon as I
>>>> call context.write() in my mapper class)? If not, are there any other
>>>> MR platforms that can do this? I've been searching around and couldn't
>>>> see anything similar to what I need. Hadoop On Line is a prototype and
>>>> has some similar functionality but it hasn't been updated for a while.
>>>> Note: I know about ChainMapper and ChainReducer classes but I don't
>>>> want to chain multiple mappers in the same local node. I want to chain
>>>> multiple reduce functions globally so the data flow looks like: Map ->
>>>> Reduce -> Reduce -> Reduce, which means each reduce operation is
>>>> followed by a shuffle and sort essentially bypassing the map
>>>> operation.
>>> --
>>> Best Regards, Edward J. Yoon
>>> @eddieyoon

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