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Clojure Data Analysis Cookbook - Second Edition

You're reading from   Clojure Data Analysis Cookbook - Second Edition Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784390297
Length 372 pages
Edition 2nd Edition
Languages
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Author (1):
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Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
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Table of Contents (14) Chapters Close

Preface 1. Importing Data for Analysis FREE CHAPTER 2. Cleaning and Validating Data 3. Managing Complexity with Concurrent Programming 4. Improving Performance with Parallel Programming 5. Distributed Data Processing with Cascalog 6. Working with Incanter Datasets 7. Statistical Data Analysis with Incanter 8. Working with Mathematica and R 9. Clustering, Classifying, and Working with Weka 10. Working with Unstructured and Textual Data 11. Graphing in Incanter 12. Creating Charts for the Web Index

Getting better performance with commute

The STM system we created in the first recipe of this chapter, Managing program complexity with STM, has one subtle problem: threads attempting to reference and update total-hu and total-fams contend for these two values unnecessarily. Since everything comes down to accessing these two resources, a lot of tasks are probably retried.

But they don't need to be. Both are simply updating those values with commutative functions (#(+ sum-? %)). The order in which these updates are applied doesn't matter. Since we block until all of the processing is done, we don't have to worry about the two references getting out of sync. They'll get back together eventually, before we access their values, and that's good enough for this situation.

To update references with a commutative function, instead of alter, we use commute. The alter function updates the references on the spot, while commute queues the update to happen later, when the reference...

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