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Mastering Clojure

Mastering Clojure

By : Wali
3.5 (2)
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Mastering Clojure

Mastering Clojure

3.5 (2)
By: Wali

Overview of this book

Clojure is a general-purpose language from the Lisp family with an emphasis on functional programming. It has some interesting concepts and features such as immutability, gradual typing, thread-safe concurrency primitives, and macro-based metaprogramming, which makes it a great choice to create modern, performant, and scalable applications. Mastering Clojure gives you an insight into the nitty-gritty details and more advanced features of the Clojure programming language to create more scalable, maintainable, and elegant applications. You’ll start off by learning the details of sequences, concurrency primitives, and macros. Packed with a lot of examples, you’ll get a walkthrough on orchestrating concurrency and parallelism, which will help you understand Clojure reducers, and we’ll walk through composing transducers so you know about functional composition and process transformation inside out. We also explain how reducers and transducers can be used to handle data in a more performant manner. Later on, we describe how Clojure also supports other programming paradigms such as pure functional programming and logic programming. Furthermore, you’ll level up your skills by taking advantage of Clojure's powerful macro system. Parallel, asynchronous, and reactive programming techniques are also described in detail. Lastly, we’ll show you how to test and troubleshoot your code to speed up your development cycles and allow you to deploy the code faster.
Table of Contents (14 chapters)
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12
A. References
13
Index

Using fold to parallelize collections

A collection that implements the CollReduce protocol is still sequential by nature. Using the reduce function with CollReduce does have a certain amount of performance gain, but it still processes elements in a collection in a sequential order. The most obvious way to improve the performance of a computation that is performed over a collection is parallelization. Such computations can be parallelized if we ignore the ordering of elements in a given collection to produce the result of the computation. In the reducers library, this is implemented based on the fork/join model of parallelization from the java.util.concurrent namespace. The fork/join model essentially partitions a collection over which a computation has to be performed into two halves and processes each partition in parallel. This halving of the collection is done in a recursive manner. The granularity of the partitions affects the overall performance of a computation modeled using fork...

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Mastering Clojure
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