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Clojure for Data Science

You're reading from  Clojure for Data Science

Product type Book
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Pages 608 pages
Edition 1st Edition
Languages
Author (1):
Henry Garner Henry Garner
Profile icon Henry Garner

Table of Contents (18) Chapters

Clojure for Data Science
Credits
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
1. Statistics 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:

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. To make use...

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