[Swift-devel] Comparing Swift and Hadoop

[Ioan Raicu]

Hi Tim,
I always thought of MapReduce being a subset of workflow systems. Can you give me an example of an application that can be implemented in MapReduce, but not a workflow system such as Swift? I can't think of any off the top of my head. 

Ioan

-- 
=================================================================
Ioan Raicu, Ph.D.
Assistant Professor
=================================================================
Computer Science Department
Illinois Institute of Technology
10 W. 31st Street Chicago, IL 60616 =================================================================
Cel:   1-847-722-0876
Email: iraicu at cs.iit.edu
Web:   http://www.cs.iit.edu/~iraicu/
=================================================================
=================================================================



On May 13, 2012, at 1:09 PM, Tim Armstrong <tim.g.armstrong at gmail.com> wrote:

> I've worked on both Swift and Hadoop implementations and my tendency is to say that there isn't actually any deep similarity beyond them both supporting  distributed data processing/computation.  They both make fundamentally different assumptions about the clusters they run on and the applications they're supporting.
> 
> Swift is mainly designed for time-shared clusters with reliable shared file systems. Hadoop assumes that it will be running on unreliable commodity machines with no shared file system, and will be running continuously on all machines on the cluster.  Swift is designed for orchestrating existing executables with their own file formats, so mostly remains agnostic to the contents of the files it is processing.  Hadoop needs to have some understanding of the contents of the files it is processing, to be able to segment them into records and perform key comparisons so it can do a distributed sort, etc.  It provides its own file formats (including compression, serialization, etc) that users can use, although is extensible to custom file formats. 
> Hadoop implements its own distributed file-system with software redundancy, Swift uses an existing cluster filesystem or node-local file systems.  For bulk data processing, this means Hadoop will generally be able to deliver more disk bandwidth and has a bunch of other implications.
> Hadoop has a record-oriented view of the world, i.e. it is built around the idea that you are processing a record at at time, rather than a file at a time as in Swift
> As a result, Hadoop includes a bunch of functionality to do with file formats, compression, serialization etc: Swift is B.Y.O. file format
> Hadoop's distributed sort is a core part of the MapReduce (and something that a lot of effort has gone into implementing and optimizing), Swift doesn't have built-in support for anything similar
> Swift lets you construct arbitrary dataflow graphs between tasks, so in some ways is less restrictive than the map-reduce pattern (although it doesn't directly support some things that the map-reduce pattern does, so I wouldn't say that it is strictly more general)
> I'd say that some applications might fit in both paradigms, but that neither supports a superset of the applications that the other supports.  Performance would depend to a large extent on the application.  Swift might actually be quicker to start up a job and dispatch tasks (Hadoop is notoriously slow on that front), but otherwise I'd say it just depends on the application, how you implement the application, the cluster, etc. I'm not sure that there is a fair comparison between the two systems since they're just very different: most of the results would be predictable just be looking at the design of the system (e.g. if the application needs to do a big distributed sort, Hadoop is much better) .  If the application is embarrassingly parallel (like it sounds like your application is), then you could probably implement it in either, but I'm not sure that it would actually stress the differences between the systems if data sizes are small and runtime is mostly dominated by computation.
> I think the Cloudera Hadoop distribution is well documented reasonably easy to set up and run, provided that you're not on a time-shared cluster.  Apache Hadoop is more of a pain to get working.
> 
> - Tim
> 
> 
> On Sun, May 13, 2012 at 9:27 AM, Ketan Maheshwari <ketancmaheshwari at gmail.com> wrote:
> Hi,
> 
> We are working on a project from GE Energy corporation which runs independent MonteCarlo simulations in order to find device reliability leading to a power grid wise device replacement decisions. The computation is repeated MC simulations done in parallel.
> 
> Currently, this is running under Hadoop setup on Cornell Redcloud and EC2 (10 nodes). Looking at the computation, it struck me this is a good Swift candidate. And since the performance numbers etc are already extracted for Hadoop, it might also be nice to have a comparison between Swift and Hadoop.
> 
> However, some reality check before diving in: has it been done before? Do we know how Swift fares against map-reduce? Are they even comparable? I have faced this question twice here: Why use Swift when you have Hadoop?
> 
> I could see Hadoop needs quite a bit of setup effort before getting it to run. Could we quantify usability and compare the two?
> 
> Any ideas and inputs are welcome.
> 
> Regards,
> -- 
> Ketan
> 
> 
> 
> _______________________________________________
> Swift-devel mailing list
> Swift-devel at ci.uchicago.edu
> https://lists.ci.uchicago.edu/cgi-bin/mailman/listinfo/swift-devel
> 
> 
> _______________________________________________
> Swift-devel mailing list
> Swift-devel at ci.uchicago.edu
> https://lists.ci.uchicago.edu/cgi-bin/mailman/listinfo/swift-devel
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.mcs.anl.gov/pipermail/swift-devel/attachments/20120513/d3ababa9/attachment.html>