You really don't want to run a single reducer unless you know that you don't
have a lot of mappers.
As long as the output data types and structure are the same as the input, you
can run your code as the combiner, and then run it again as the reducer.
Problem solved with one or two lines of code.
If your input and output don't match, then you can use the existing code as a
combiner, and then write a new reducer. It could as easily be an identity
reducer too. (Don't know the exact problem.)
So here's a silly question. Why wouldn't you want to run a combiner?
On Jul 31, 2012, at 12:08 AM, Jay Vyas <jayunit...@gmail.com> wrote:
> Its not clear to me that you need custom input formats....
>
> 1) Getmerge might work or
>
> 2) Simply run a SINGLE reducer job (have mappers output static final int
> key=1, or specify numReducers=1).
>
> In this case, only one reducer will be called, and it will read through all
> the values.
>
> On Tue, Jul 31, 2012 at 12:30 AM, Bejoy KS <bejoy.had...@gmail.com> wrote:
>
>> Hi
>>
>> Why not use 'hadoop fs -getMerge <outputFolderInHdfs>
>> <targetFileNameInLfs>' while copying files out of hdfs for the end users to
>> consume. This will merge all the files in 'outputFolderInHdfs' into one
>> file and put it in lfs.
>>
>> Regards
>> Bejoy KS
>>
>> Sent from handheld, please excuse typos.
>>
>> -----Original Message-----
>> From: Michael Segel <michael_se...@hotmail.com>
>> Date: Mon, 30 Jul 2012 21:08:22
>> To: <common-user@hadoop.apache.org>
>> Reply-To: common-user@hadoop.apache.org
>> Subject: Re: Merge Reducers Output
>>
>> Why not use a combiner?
>>
>> On Jul 30, 2012, at 7:59 PM, Mike S wrote:
>>
>>> Liked asked several times, I need to merge my reducers output files.
>>> Imagine I have many reducers which will generate 200 files. Now to
>>> merge them together, I have written another map reduce job where each
>>> mapper read a complete file in full in memory, and output that and
>>> then only one reducer has to merge them together. To do so, I had to
>>> write a custom fileinputreader that reads the complete file into
>>> memory and then another custom fileoutputfileformat to append the each
>>> reducer item bytes together. this how my mapper and reducers looks
>>> like
>>>
>>> public static class MapClass extends Mapper<NullWritable,
>>> BytesWritable, IntWritable, BytesWritable>
>>> {
>>> @Override
>>> public void map(NullWritable key, BytesWritable value,
>> Context
>>> context) throws IOException, InterruptedException
>>> {
>>> context.write(key, value);
>>> }
>>> }
>>>
>>> public static class Reduce extends Reducer<NullWritable,
>>> BytesWritable, NullWritable, BytesWritable>
>>> {
>>> @Override
>>> public void reduce(NullWritable key,
>> Iterable<BytesWritable> values,
>>> Context context) throws IOException, InterruptedException
>>> {
>>> for (BytesWritable value : values)
>>> {
>>> context.write(NullWritable.get(), value);
>>> }
>>> }
>>> }
>>>
>>> I still have to have one reducers and that is a bottle neck. Please
>>> note that I must do this merging as the users of my MR job are outside
>>> my hadoop environment and the result as one file.
>>>
>>> Is there better way to merge reducers output files?
>>>
>>
>>
>
>
> --
> Jay Vyas
> MMSB/UCHC
