Book Image

Clojure High Performance Programming

By : Shantanu Kumar
Book Image

Clojure High Performance Programming

By: Shantanu Kumar

Overview of this book

<p>Clojure is a young, dynamic, functional programming language that runs on the Java Virtual Machine. It is built with performance, pragmatism, and simplicity in mind. Like most general purpose languages, Clojure’s features have different performance characteristics that one should know in order to write high performance code.<br /><br />Clojure High Performance Programming is a practical, to-the-point guide that shows you how to evaluate the performance implications of different Clojure abstractions, learn about their underpinnings, and apply the right approach for optimum performance in real-world programs.<br /><br />This book discusses the Clojure language in the light of performance factors that you can exploit in your own code.</p> <p>You will also learn about hardware and JVM internals that also impact Clojure’s performance. Key features include performance vocabulary, performance analysis, optimization techniques, and how to apply these to your programs. You will also find detailed information on Clojure's concurrency, state-management, and parallelization primitives.</p> <p>This book is your key to writing high performance Clojure code using the right abstraction, in the right place, using the right technique.</p>
Table of Contents (15 chapters)
Clojure High Performance Programming
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Data sizing


The cost of abstractions in terms of data size plays an important role. For example, whether or not a data element can fit into a processor cache line depends directly upon its size. On a Linux system, we can find out the cache line size and other parameters by inspecting the values in the files under /sys/devices/system/cpu/cpu0/cache/. Refer to Chapter 4, Host Performance, where we discussed how to compute the size of primitives, objects, and data elements.

Another concern we generally find with data sizing is how much data we are holding at a time in the heap. As we noted in earlier chapters, GC has direct consequences on the application's performance. While processing data, often we do not really need all the data we hold on to. Consider the example of generating a summary report of sold items for a certain period (months) of time. After the subperiod (month wise), summary data is computed. We do not need the item details anymore, hence it's better to remove the unwanted data...