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MapReduce, mail # user - Chaning Multiple Reducers: Reduce -> Reduce -> Reduce


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Jim Twensky 2012-10-05, 16:31
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Harsh J 2012-10-05, 17:18
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Jim Twensky 2012-10-05, 17:43
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Harsh J 2012-10-05, 17:54
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Jim Twensky 2012-10-05, 18:02
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Bertrand Dechoux 2012-10-08, 10:39
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Fabio Pitzolu 2012-10-08, 10:44
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Bertrand Dechoux 2012-10-08, 10:51
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Re: Chaning Multiple Reducers: Reduce -> Reduce -> Reduce
Jim Twensky 2012-10-08, 19:09
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 <[EMAIL PROTECTED]> 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.
>
>
> JMHO
>
> -Mike
>
> On Oct 8, 2012, at 6:35 AM, Edward J. Yoon <[EMAIL PROTECTED]> 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 <[EMAIL PROTECTED]> 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.
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Michael Segel 2012-10-08, 19:19