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From Nigel Daley <nda...@yahoo-inc.com>
Subject Re: JavaSpaces (Blitz?) and hadoop - comparison?
Date Fri, 02 Mar 2007 03:56:42 GMT
One more difference...

Being that JavaSpaces is a Jini service, its host/port can be  
dynamically discovered (and rediscovered else where if it fails) at  
run time by clients.
OTOH, Hadoop servers and clients are currently pre-configured with  
necessary host/ports.


On Mar 1, 2007, at 6:00 AM, Dan Creswell wrote:

> Tom White wrote:
>> Good question. JavaSpaces is actually very general, so I would ask  
>> how
>> the Replicated Worker Pattern, which is fits nicely with JavaSpaces
>> (http://today.java.net/pub/a/today/2005/04/21/farm.html,
>> https://computeserver.dev.java.net/) compares with Hadoop and
>> MapReduce.
>> My take is that at a high level JavaSpaces RWP is good for
>> distributing jobs that don't operate on large datasets whereas Hadoop
>> (MapReduce) is good for operating on very large datasets. JavaSpaces
>> doesn't really have mechanisms for distributing large quantities of
>> data in the way that HDFS does. On the other hand, JavaSpaces is good
> Indeed JavaSpaces doesn't provide support for shipping around large
> quantities of data however I wouldn't usually pass this kind of data
> through the JavaSpace, I pass a reference to those chunks of data.
> This reference can be to some filesystem or another, an http or ftp  
> URI etc.
> In loose terms I'd use the JavaSpace to co-ordinate the MapReduce  
> effort
> whilst having some other infrastructure element handle the
> access/distribution of data (which I guess could be HDFS?)
> I'm not sure if, under the covers, Hadoop doesn't have a similar
> division of responsibility for co-ordination and data-distribution?
>> for sharing modest sized data objects - with MapReduce you are  
>> sharing
>> data, so you have to think carefully how to encode the data that the
>> map and reduce tasks operate on. JavaSpaces in general allows you a
>> richer computational model (compared to MapReduce) - but this
>> generality comes at the price of being able to perform well for
>> certain classes of application.
> Based on what I say above, I'd refine this statement and say if you  
> want
> to use JavaSpaces alone to solve the entire problem it may not perform
> well but if you use JavaSpaces as part of a complete solution it will
> perform pretty well.
>> Put another way: JavaSpaces RWP is a good fit for writing a  
>> program to
>> calculate if a large number is prime (since the subtasks don't  
>> need to
>> use much intermediate data), whereas Hadoop MapReduce is a good fit
>> for counting web server access hits by host (since the object is to
>> analyse a large set of data).
>> (I think they're both great pieces of technology BTW.)
> Me too!
> Dan.
>> Tom
>> On 01/03/07, Dan Creswell <dan.creswell@lonecrusader.co.uk> wrote:
>>> Hi,
>>> I'm the author of Blitz as it happens :)
>>> Blitz has various different modes of operation.  It can be  
>>> persistent
>>> but also can operate in "memory-only" configuration.
>>> The basic difference would be that Blitz is just a core element  
>>> around
>>> which you could build a MapReduce implementation (in fact I have  
>>> done in
>>> the past) etc. Hadoop by contrast has a core of it's own based on  
>>> a GFS
>>> equivalent and also includes the framework for MapReduce etc.
>>> In conclusion, Blitz provides the base of a stack and Hadoop has  
>>> that
>>> base (in another form) plus additional layers (MapReduce etc).
>>> Hope that helps,
>>> Dan.
>>> Tomi N/A wrote:
>>>> I just came across a technology which sounded interesting, but  
>>>> doesn't
>>>> seem very wide spread called JavaSpaces. An implementation is Blitz
>>>> (http://www.dancres.org/blitz/).
>>>>> From what I see, it seems to be a distributed computation and
>>>> persistence engine so it made me wonder if anyone on this list  
>>>> would
>>>> know anything about it and, maybe, compare the two technologies.  
>>>> Well?
>>>> :)
>>>> Cheers,
>>>> t.n.a.

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