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MapReduce, mail # user - chaining (the output of) jobs/ reducers


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Adrian CAPDEFIER 2013-09-12, 13:36
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Vinod Kumar Vavilapalli 2013-09-13, 04:26
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Adrian CAPDEFIER 2013-09-17, 13:23
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Adrian CAPDEFIER 2013-09-12, 16:35
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Bryan Beaudreault 2013-09-12, 17:38
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Re: chaining (the output of) jobs/ reducers
Adrian CAPDEFIER 2013-09-12, 19:02
Thanks Bryan.

Yes, I am using hadoop + hdfs.

If I understand your point, hadoop tries to start the mapping processes on
nodes where the data is local and if that's not possible, then it is hdfs
that replicates the data to the mapper nodes?

I expected to have to set up this in the code and I completely ignored
HDFS; I guess it's a case of not seeing the forest from all the trees!
On Thu, Sep 12, 2013 at 6:38 PM, Bryan Beaudreault <[EMAIL PROTECTED]
> wrote:

> It really comes down to the following:
>
> In Job A set mapred.output.dir to some directory X.
> In Job B set mapred.input.dir to the same directory X.
>
> For Job A, do context.write() as normally, and each reducer will create an
> output file in mapred.output.dir.  Then in Job B each of those will
> correspond to a mapper.
>
> Of course you need to make sure your input and output formats, as well as
> input and output keys/values, match up between the two jobs as well.
>
> If you are using HDFS, which it seems you are, the directories specified
> can be HDFS directories.  In that case, with a replication factor of 3,
> each of these output files will exist on 3 nodes.  Hadoop and HDFS will do
> the work to ensure that the mappers in the second job do as good a job as
> possible to be data or rack-local.
>
>
> On Thu, Sep 12, 2013 at 12:35 PM, Adrian CAPDEFIER <[EMAIL PROTECTED]
> > wrote:
>
>> Thank you, Chris. I will look at Cascading and Pig, but for starters I'd
>> prefer to keep, if possible, everything as close to the hadoop libraries.
>>
>> I am sure I am overlooking something basic as repartitioning is a fairly
>> common operation in MPP environments.
>>
>>
>> On Thu, Sep 12, 2013 at 2:39 PM, Chris Curtin <[EMAIL PROTECTED]>wrote:
>>
>>> If you want to stay in Java look at Cascading. Pig is also helpful. I
>>> think there are other (Spring integration maybe?) but I'm not familiar with
>>> them enough to make a recommendation.
>>>
>>> Note that with Cascading and Pig you don't write 'map reduce' you write
>>> logic and they map it to the various mapper/reducer steps automatically.
>>>
>>> Hope this helps,
>>>
>>> Chris
>>>
>>>
>>> On Thu, Sep 12, 2013 at 9:36 AM, Adrian CAPDEFIER <
>>> [EMAIL PROTECTED]> wrote:
>>>
>>>> Howdy,
>>>>
>>>> My application requires 2 distinct processing steps (reducers) to be
>>>> performed on the input data. The first operation generates changes the key
>>>> values and, records that had different keys in step 1 can end up having the
>>>> same key in step 2.
>>>>
>>>> The heavy lifting of the operation is in step1 and step2 only combines
>>>> records where keys were changed.
>>>>
>>>> In short the overview is:
>>>> Sequential file -> Step 1 -> Step 2 -> Output.
>>>>
>>>>
>>>> To implement this in hadoop, it seems that I need to create a separate
>>>> job for each step.
>>>>
>>>> Now I assumed, there would some sort of job management under hadoop to
>>>> link Job 1 and 2, but the only thing I could find was related to job
>>>> scheduling and nothing on how to synchronize the input/output of the linked
>>>> jobs.
>>>>
>>>>
>>>>
>>>> The only crude solution that I can think of is to use a temporary file
>>>> under HDFS, but even so I'm not sure if this will work.
>>>>
>>>> The overview of the process would be:
>>>> Sequential Input (lines) => Job A[Mapper (key1, value1) => ChainReducer
>>>> (key2, value2)] => Temporary file => Job B[Mapper (key2, value2) => Reducer
>>>> (key2, value 3)] => output.
>>>>
>>>> Is there a better way to pass the output from Job A as input to Job B
>>>> (e.g. using network streams or some built in java classes that don't do
>>>> disk i/o)?
>>>>
>>>>
>>>>
>>>> The temporary file solution will work in a single node configuration,
>>>> but I'm not sure about an MPP config.
>>>>
>>>> Let's say Job A runs on nodes 0 and 1 and job B runs on nodes 2 and 3
>>>> or both jobs run on all 4 nodes - will HDFS be able to redistribute
>>>> automagically the records between nodes or does this need to be coded
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Bryan Beaudreault 2013-09-12, 19:49
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Venkata K Pisupat 2013-09-12, 20:07
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Shahab Yunus 2013-09-12, 17:33