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Clojure High Performance Programming, Second Edition

You're reading from   Clojure High Performance Programming, Second Edition Become an expert at writing fast and high performant code in Clojure 1.7.0

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781785283642
Length 198 pages
Edition 2nd Edition
Languages
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Author (1):
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Shantanu Kumar Shantanu Kumar
Author Profile Icon Shantanu Kumar
Shantanu Kumar
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Table of Contents (10) Chapters Close

Preface 1. Performance by Design FREE CHAPTER 2. Clojure Abstractions 3. Leaning on Java 4. Host Performance 5. Concurrency 6. Measuring Performance 7. Performance Optimization 8. Application Performance Index

Transducers

Clojure 1.7 introduced a new abstraction called transducers for "composable algorithmic transformations", commonly used to apply a series of transformations over collections. The idea of transducers follows from the reducing function, which accepts arguments of the form (result, input) and returns result. A reducing function is what we typically use with reduce. A transducer accepts a reducing function, wraps/composes over its functionality to provide something extra, and returns another reducing function.

The functions in clojure.core that deal with collections have acquired an arity-1 variant, which returns a transducer, namely map, cat, mapcat, filter, remove, take, take-while, take-nth, drop, drop-while, replace, partition-by, partition-all, keep, keep-indexed, dedupe and random-sample.

Consider the following few examples, all of which do the same thing:

user=> (reduce ((filter odd?) +) [1 2 3 4 5])
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user=> (transduce (filter odd?) + [1 2 3 4 5])
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user=&gt...
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