I am really sorry for the ridiculously late response. I will describe
briefly our 1st year and our current approach.
1st year approach.
During the first year, we used infinispan MR to implement our operators.
Most of our operators were Map-only (for example project,filter) and for
these we did
On 02/19/2014 03:22 PM, Dan Berindei wrote:
I forgot to ask about this... we already have the entries stored as
key,value pairs, so we expect the data to be already in the cache. That
means there is no ordering in the inputs, and the mapper can't rely on
sequential inputs to be related. Would
into a current pull request which
addresses some (all?) of these issues
https://github.com/infinispan/infinispan/pull/2300
Tristan
On 14/02/2014 16:10, Evangelos Vazaios wrote:
Hello everyone,
I started using the MapReduce implementation of Infinispan and I came
across some possible
On 02/18/2014 01:40 PM, Dan Berindei wrote:
On Tue, Feb 18, 2014 at 12:21 PM, Evangelos Vazaios vag...@gmail.comwrote:
Hi Radim,
Since Hadoop is the most popular implementation of MapReduce I will give
a brief overview of how it works and then I'll provide with an example
where
On 02/18/2014 04:39 PM, Dan Berindei wrote:
On Tue, Feb 18, 2014 at 2:17 PM, Evangelos Vazaios vag...@gmail.com wrote:
On 02/18/2014 01:40 PM, Dan Berindei wrote:
On Tue, Feb 18, 2014 at 12:21 PM, Evangelos Vazaios vag...@gmail.com
wrote:
Hi Radim,
Since Hadoop is the most popular
On 02/18/2014 05:36 PM, Vladimir Blagojevic wrote:
On 2/18/2014, 4:59 AM, Dan Berindei wrote:
The limitation we have now is that in the reduce phase, the entire
list of values for one intermediate key must be in memory at once. I
think Hadoop only loads a block of intermediate values in
Hello everyone,
I started using the MapReduce implementation of Infinispan and I came
across some possible limitations. Thus, I want to make some suggestions
about the MapReduce (MR) implementation of Infinispan.
Depending on the algorithm, there might be some memory problems,
especially for
