On Tue, Jan 3, 2012 at 1:49 AM, Vladimir Blagojevic
<vblagoje@redhat.com> wrote:
Hi Ondra,
On 12-01-02 9:40 AM, Ondra Nevelik wrote:
> Hi all,
> I was supposed to write an arbitrary app with Infinispan so I wanted to rewrite one of my programs that is implemented using Hadoop MapReduce to have a comparison of performance between the two.
>
> However the way MapReduce is done in Infinispan right now greatly limits the number of problems that can be solved with it - there is a reduce phase on local data on each of the compute nodes to decrease the amount of data transferred. There is a "global" reduce after that. This means that the types of input keys/values has to be the same as output types and that differs from the original MapReduce concept.
This is simply not true!
>
> A possible solution would be to use a "combiner function" (see [1]) instead of the local reduce phase so that the amount of data transferred could still be reduced(if applicable)(e.g. the WordCount example would still use the reduce function as the reducer) but it will be possible to have different input and output types. As I went briefly through the code of classes from mapreduce package I think there even won't be much work needed.
>
> What do you think? Is this idea worth implementing?
Possibly. I have not looked into combiner function. Sanne has mentioned
it before and he might have further comments!
Regards,
Vladimir