Re: [Haskell-cafe] Haskell and Big Data

Hi, There is the Holumbus http://holumbus.fh-wedel.de/trac packages. It seems to have a DFS and support for analytics (map/reduce at least). Regards J-C On Sat, Dec 21, 2013 at 2:50 PM, Alexander Kjeldaas < alexander.kjeldaas@gmail.com> wrote:

In the HPCC documentation it is hard to cut through the buzzword jungle. Is there an efficient storage solution lurking there?

I searched for haskell packages related to the big data storage layer, and the only thing I've found that could support efficient erasure code-based storage is this 3 years old binding to libhdfs. There is only one commit in github:

https://github.com/kim/hdfs-haskell

Somewhat related are these bindings to zfec, from 2008, and part of the Tahoe LAFS project.

http://hackage.haskell.org/package/fec

Alexander

On Fri, Dec 20, 2013 at 8:24 AM, Carter Schonwald < carter.schonwald@gmail.com> wrote:

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Cloud Haskell is a substrate that could be used to build such a layer. I'm sure the cloud Haskell people would love such experimenration.

On Friday, December 20, 2013, He-chien Tsai wrote:

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What I meant is that split the data into several parts,send each splited data to different computers, train them seperately, finally send the results back and combine those results. I didn't mean to use Cloud Haskell.

2013/12/20 上午5:40 於 "jean-christophe mincke" < jeanchristophe.mincke@gmail.com> 寫道：

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He-Chien Tsai,

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its training result is designed for composable

Yes it is indeed composable (parallel function of that lib) but

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Moreover using Cloud Haskell (for instance) implies that: 1. training functions should be (serializable) clojures, which can only be defined as module level (not as local -let/where - bindings). 2. train is a typeclass function and is not serializable.

So the idea behind HLearn are interesting but I do not see how it could be run on a cluster... But, unfortunately, I am not an Haskell expert.

What do you think?

Regards

J-C

On Thu, Dec 19, 2013 at 6:15 PM, He-chien Tsai wrote:

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have you took a look at hlearn and statistics packages? it's even

easy to parallellize hlearn on cluster because it's training result is designed for composable, which means you can create two model , train them seperately and finally combine them. you can also use other database such as redis or cassandra,which has haskell binding, as backend. for

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I personally prefer python for data science because it has much more

mature packages and is more interactive and more effective (not kidding. you can create compiled C for core datas and algorithms by python-like cython and call it from python, and exploit gpus for accelerating by

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2013/12/18 下午3:41 於 "jean-christophe mincke" <

jeanchristophe.mincke@gmail.com> 寫道：

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Hello Cafe,

Big Data is a bit trendy these days.

Does anybody know about plans to develop an Haskell eco-system in

parallelizing it on a cluster changes all the type because running on a cluster implies IO. parallellizing on clusters, hdph is also good. theano) than haskell and scala, spark also has a unfinish python binding. that domain?

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I.e tools such as Storm or Spark (possibly on top of Cloud Haskell) or, at least, bindings to tools which exist in other languages.

Thank you

Regards

J-C

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