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How to partition a file to smaller size for performing KNN in hadoop mapreduce

+3 votes
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In KNN like algorithm we need to load model Data into cache for predicting the records.

Here is the example for KNN.

So if the model will be a large file say1 or 2 GB we will be able to load them into Distributed cache.

The one way is to split/partition the model Result into some files and perform the distance calculation for all records in that file and then find the min ditance and max occurance of classlabel and predict the outcome.

How can we parttion the file and perform the operation on these partition ?

ie 1 record  parttition1,partition2,.... 2nd record  parttition1,partition2,...

This is what came to my thought. Is there any further way. Any pointers would help me.

posted Jan 15, 2015 by Deepak Dasgupta

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have you considered implementing using something like spark? That could be much easier than raw map-reduce
commented Jan 15, 2015 by Alok Sharma

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Here I am writing minimum,maximum and mean of wordcount.

My input file :

high low medium high low high low large small medium

Actual output is :

high - 3------maximum

low - 3--------maximum

large - 1------minimum

small - 1------minimum

but i am not getting above output ...can anyone please help me?

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