hadoop-mapreduce-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Robert Evans <ev...@yahoo-inc.com>
Subject Re: Multi-level aggregation with combining the result of maps per node/rack
Date Tue, 31 Jul 2012 13:46:27 GMT

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" <ozawa.tsuyoshi@gmail.com> wrote:

>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
>restriction, the prototype is 1.7 times faster than normal hadoop
>in 2TB case.  Another benchmark also shows that the shuffle costs
>is cut down by 50%.
>I want to know from you guys, do you think is it a useful feature?
>If yes, I will work for contributing it.
>It is also welcome to tell me the benchmark that you want me to do
>with my prototype.
>[1] The idea is also described in Hadoop wiki:
>    http://wiki.apache.org/hadoop/HadoopResearchProjects
>[2] Dremel paper is available at:
>    http://research.google.com/pubs/pub36632.html
>[3] The specification of each nodes is as follows:
>    CPU Core(TM)2 Duo CPU E7400 2.80GHz x 2
>    Memory 8 GB
>    Network 1 GbE

View raw message