Re: Running K-Means on a cluster setup

Wed, 22 Jan 2014 14:13:26 -0800

I think Mayur pointed to that code because it includes the relevant initialization code you were asking about. Running on a cluster doesn't require much change: pass the spark:// address of the master instead of "local" and add any jars containing your code. You could set the jars manually, but the linked code uses 
JavaSparkContext.jarOfClass(JavaKMeans.class)
 to get the right jar filename.

-Ewen

January 22, 2014 2:02 PM
please understand that the code from your link is completely useless to me. It's like someone is trying to solve a differential equation and you tell them what's the formula for the area of the circle. 

i can do that with my code too (kmeans code). the idea is that i want to run it on a cluster ...


On Wednesday, January 22, 2014 5:31 PM, Mayur Rustagi <[email protected]> wrote:
I am sorry that is not a tutorial. You can take this source code: 


Sync and Build this project: 
You should be able to call JavaKMeans class, Reynold may be able to shed some details on how to use it. 
If you reach some where and get stuck post it back and I can try and help. I hope this helps.

Regards
Mayur






On Wed, Jan 22, 2014 at 8:35 PM, Kal El <[email protected]> wrote:



January 22, 2014 7:19 AM
I am sorry that is not a tutorial. You can take this source code: 


Sync and Build this project: 
You should be able to call JavaKMeans class, Reynold may be able to shed some details on how to use it. 
If you reach some where and get stuck post it back and I can try and help. I hope this helps.

Regards
Mayur







January 22, 2014 7:05 AM
@Mayur: I do not see any tutorial about how to run mlib on a cluster, just some basic presentation non related with actual running the algorithm

@Ognen: Thanks, I have figured that out :)) that's why I need some tutorials


On Wednesday, January 22, 2014 4:59 PM, Mayur Rustagi <[email protected]> wrote:



On Wed, Jan 22, 2014 at 8:20 PM, Ognen Duzlevski <[email protected]> wrote:



January 22, 2014 6:58 AM




January 22, 2014 6:50 AM
Hello,

I have found that you generally need two separate pools of knowledge to be successful in this game :). One is to have enough knowledge of network topologies, systems, java, scala and whatever else to actually set up the whole system (esp. if your requirements are different than running on a local machine or in the ec2 cluster supported by the scripts that come with spark).

The other is actual knowledge of the API and how it works and how to express and solve your problems using the primitives offered by spark.

There is also a third: since you can supply any function to a spark primitive, you generally need to know scala or java (or python?) to actually solve your problem.

I am not sure this list is viewed as appropriate place to offer advice on how to actually solve these problems. Not that I would mind seeing various solutions to various problems :) and also optimizations.

For example, I am trying to do rudimentary retention analysis. I am a total beginner in the whole map/reduce way of solving problems. I have come up with a solution that is pretty slow but implemented in 5 or 6 lines of code for the simplest problem. However, my files are 20 GB in size each, all json strings. Figuring out what the limiting factor is (network bandwidth is my suspicion since I am accessing things via S3 is my guess) is somewhat of a black magic to me at this point. I think for most of this stuff you will have to read the code. The bigger question after that is optimizing your solutions to be faster :). I would love to see practical tutorials on doing such things and I am willing to put my attempts at solving problems out there to eventually get cannibalized, ridiculed and reimplemented properly :).

Sorry for this long winded email, it did not really answer your question anyway :)
Ognen



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