The question is not how to sequence all. Cascading could indeed help in that case.
But how to skip the map phase and do the split/local sort directly at the end of the reduce so that the next reduce need only to do a merge on the sorted files obtained from the previous reduce. This is basically a performance optimization (avoid unnecessary network/disk transfers). Cascading is not equipped to do it, it will only compile the flow into a sequence of map-reduce.
Isn't also of some help using Cascading (http://www.cascading.org/) ?
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2012/10/8 Bertrand Dechoux <email@example.com>Have you looked at graph processing for Hadoop? Like Hama (http://hama.apache.org/) or Giraph (http://incubator.apache.org/giraph/).
I can't say for sure it would help you but it seems to be in the same problem domain.
With regard to the chaining reducer issue this is indeed a general implementation decision of Hadoop 1.
From a purely functional point of view, regardless of performance, I guess it could be shown that a map/reduce/map can be done with a reduce only and that a sequence of map can be done with a single map. Of course, with Hadoop the picture is bit more complex due to the sort phase.
map -> sort -> reduce : operations in map/reduce can not generally be transferred due to the sort 'blocking' them when they are related to the sort key
reduce -> map : all operations can be performed in the reduce
map -> sort -> reduce -> map -> sort -> reduce -> map -> sort -> reduce
can generally be implemented as
map -> sort -> reduce -> sort -> reduce -> sort -> reduce
if you are willing to let the possibility of having different scaling options for maps and reduces
And that's what you are asking. But with hadoop 1 the map phase is not an option (even though you could use the identify but that's not a wise option with regards to performance like you said). The picture might be changing with Hadoop 2/YARN. I can't provide the details but it may be worth it to look at it.
Bertrand--On Fri, Oct 5, 2012 at 8:02 PM, Jim Twensky <firstname.lastname@example.org> wrote:
The hidden map operation which is applied to the reduced partition at
one stage can generate keys that are outside of the range covered by
that particular reducer. I still need to have the many-to-many
communication from reduce step k to reduce step k+1. Otherwise, I
think the ChainReducer would do the job and apply multiple maps to
each isolated partition produced by the reducer.
On Fri, Oct 5, 2012 at 12:54 PM, Harsh J <email@example.com> wrote:
> Would it then be right to assume that the keys produced by the reduced
> partition at one stage would be isolated to its partition alone and
> not occur in any of the other partition outputs? I'm guessing not,
> based on the nature of your data?
> I'm trying to understand why shuffling is good to be avoided here, and
> if it can be in some ways, given the data. As I see it, you need
> re-sort based on the new key per partition, but not the shuffle? Or am
> I wrong?
> On Fri, Oct 5, 2012 at 11:13 PM, Jim Twensky <firstname.lastname@example.org> wrote:
>> Hi Harsh,
>> Yes, there is actually a "hidden" map stage, that generates new
>> <key,value> pairs based on the last reduce output but I can create
>> those records during the reduce step instead and get rid of the
>> intermediate map computation completely. The idea is to apply the map
>> function to each output of the reduce inside the reduce class and emit
>> the result as the output of the reducer.
>> On Fri, Oct 5, 2012 at 12:18 PM, Harsh J <email@example.com> wrote:
>>> Hey Jim,
>>> Are you looking to re-sort or re-partition your data by a different
>>> key or key combo after each output from reduce?
>>> On Fri, Oct 5, 2012 at 10:01 PM, Jim Twensky <firstname.lastname@example.org> wrote:
>>>> 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
>>> Harsh J
> Harsh J