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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

The reducers library

The count operation we implemented previously is a sequential algorithm. Each line is processed one at a time until the sequence is exhausted. But there is nothing about the operation that demands that it must be done in this way.

We could split the number of lines into two sequences (ideally of roughly equal length) and reduce over each sequence independently. When we're done, we would just add together the total number of lines from each sequence to get the total number of lines in the file:

The reducers library

If each Reduce ran on its own processing unit, then the two count operations would run in parallel. All the other things being equal, the algorithm would run twice as fast. This is one of the aims of the clojure.core.reducers library—to bring the benefit of parallelism to algorithms implemented on a single machine by taking advantage of multiple cores.

Parallel folds with reducers

The parallel implementation of reduce implemented by the reducers library is called fold...

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