hadoop support

hi guys, I dont see much activity for shadoop. is there an actively
supported scala library for hadoop out there right now ?

Re: hadoop support

I would certainly look at GridGain (and it's Scalar DSL) that includes
both compute and in-memory data grid before looking at Hadoop:
http://www.gridgain.com

On Jul 15, 9:29 am, glob157 wrote:
> hi guys, I dont see much activity for shadoop.  is there an actively
> supported scala library for hadoop out there right now ?

Re: Re: hadoop support

thanks nikita (who coincidentally happens to be the creator of gridgain)  :)  

I am interested in your product and I  sent you a private message .  In particular, I am interested in

Using gridgain with mongo .

Re: hadoop support

On Fri, Jul 15, 2011 at 9:29 AM, glob157 <jayunit100 [at] gmail [dot] com> wrote:
hi guys, I dont see much activity for shadoop.  is there an actively
supported scala library for hadoop out there right now ?

If you're open to a different--and apparently very nice--solution to similar kinds of problems, check out http://www.spark-project.org/release-0.3.html -- if it's just plain old Hadoop you're after, I can't suggest anything Scala-specific.
-0xe1a 

Re: hadoop support

Also check out SMR, which *does* run on Hadoop and which Spark is directly inspired by: https://github.com/dlwh/smr/ (note: never used it, don't know anything beyond having come across that cute code example before)

Re: hadoop support

On Fri, Jul 15, 2011 at 8:49 PM, Yang Zhang wrote:
> Also check out SMR, which *does* run on Hadoop and which Spark is directly
> inspired by: https://github.com/dlwh/smr/ (note: never used it, don't know
> anything beyond having come across that cute code example before)

As the original author: SMR is dead. You can resurrect it if you want,
but for most all machine learning stuff (which is what I do), the
Spark approach is way better than Hadoop.

I've toyed with resurrecting SMR, but it would not be based on Hadoop,
and would instead be geared towards implementing distributed
collections (rather than parallel collections)

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