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MapReduce, mail # dev - Multi-level aggregation with combining the result of maps per node/rack


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Re: Multi-level aggregation with combining the result of maps per node/rack
Tsuyoshi OZAWA 2012-08-01, 06:48
Robert,

Thank you for your precious opinion and sharing the related JIRA tickets.

The combination consisting of reusing container (MAPREDUCE-3902) and the
coordination system in the AM is good idea to minimize implementation
cost and ensure fault tolerance. The design can also solve scheduling problem
of when to run combiner.And the current design doesn't matter security against
the intermediate data at all, so I'll consider it.

I'll create a new design note with your opinion in mind, and attach it
on a new JIRA
(MAPREDUCE-4502).

Thanks,
Tsuyoshi OZAWA

On Tue, Jul 31, 2012 at 10:46 PM, Robert Evans <[EMAIL PROTECTED]> wrote:
> Tsuyoshi,
>
>
> There has been a lot of work happening in the shuffle phase.  It is being
> made pluggable in both 1.0 and 2.0/trunk (MAPREDUCE-4049).  There is also
> some work being done to reuse containers in trunk/2.0 (MAPREDUCE-3902).
> This should have a similar, although perhaps more limited result, because
> when different map tasks run in the same container their outputs also go
> through the same combiner.  I have heard that it is showing some good
> results for both small and large jobs.  There was also some work to try
> and pull in Sailfish (No JIRA just ramblings on the mailing list), which
> moves the shuffle phase to a separate process.  I have not seen much
> happen on that front recently, but it saw some large gains on big jobs,
> but is worse on small jobs.  I think that this is something very
> interesting and I would encourage you to file a JIRA and pursue it.
>
> I don't know anything about your design, so please feel free to disregard
> my comments if they do not apply.  I would encourage you to think about
> security on this.  When you run the combiner you need to be sure that it
> runs as the user that owns the data.  This should probably not be too
> difficult if you hijack a mapper tasks that has just finished to try and
> combine the data from others on the same node.  To do this you will
> probably need some sort of a coordination system in the AM to tell that
> mapper what other mappers to try and combine data from.  It would be nice
> to coordinate this with the container reuse work, which currently just
> tells the container to run another split through.  It could be another
> option to tell it to combine with the map output from container X.
>
> Another thing to be aware of is small jobs.  It would be great to see how
> this impacts small jobs, and if it has a negative impact we should look
> for an automated way to turn this off or on.
>
> Thanks for your work,
>
> Bobby Evans
>
> On 7/30/12 8:11 PM, "Tsuyoshi OZAWA" <[EMAIL PROTECTED]> wrote:
>
>>Hi,
>>
>>We consider the shuffle cost is a main concern in MapReduce,
>>in particular, aggregation processing.
>>The shuffle costs is also expensive in Hadoop in spite of the
>>existence of combiner, because the scope of combining is limited
>>within only one MapTask.
>>
>>To solve this problem, I've implemented the prototype that
>>combines the result of multiple maps per node[1].
>>This is the first step to make hadoop faster with multi-level
>>aggregation technique like Google Dremel[2].
>>
>>I took a benchmark with the prototype.
>>We used WordCount program with in-mapper combining optimization
>>as the benchmark. The benchmark is taken under 40 nodes [3].
>>The input data set is 300GB, 500GB, 1TB, and 2TB texts which is generated
>>by default RandomTextWriter. Reducer is configured
>>as 1 on the assumption that some workload forces 1 reducer
>>like Google Dremel. The result is as follows:
>>
>>                         | 300GB | 500GB |   1TB |   2TB |
>>            Normal (sec) |  4004 |  5551 | 12177 | 27608 |
>>Combining per node (sec) |  3678 |  3844 |  7440 | 15591 |
>>
>>Note that a MapTask runs combiner per node every 3 minutes in
>>the current prototype, so the aggregation rate is very limited.
>>
>>"Normal" is the result of current hadoop, and "Combining per node"
>>is the result with my optimization.  Regardless of the 3-minutes

OZAWA Tsuyoshi