Book Image

The Complete Rust Programming Reference Guide

By : Rahul Sharma, Vesa Kaihlavirta, Claus Matzinger
Book Image

The Complete Rust Programming Reference Guide

By: Rahul Sharma, Vesa Kaihlavirta, Claus Matzinger

Overview of this book

Rust is a powerful language with a rare combination of safety, speed, and zero-cost abstractions. This Learning Path is filled with clear and simple explanations of its features along with real-world examples, demonstrating how you can build robust, scalable, and reliable programs. You’ll get started with an introduction to Rust data structures, algorithms, and essential language constructs. Next, you will understand how to store data using linked lists, arrays, stacks, and queues. You’ll also learn to implement sorting and searching algorithms, such as Brute Force algorithms, Greedy algorithms, Dynamic Programming, and Backtracking. As you progress, you’ll pick up on using Rust for systems programming, network programming, and the web. You’ll then move on to discover a variety of techniques, right from writing memory-safe code, to building idiomatic Rust libraries, and even advanced macros. By the end of this Learning Path, you’ll be able to implement Rust for enterprise projects, writing better tests and documentation, designing for performance, and creating idiomatic Rust code. This Learning Path includes content from the following Packt products: • Mastering Rust - Second Edition by Rahul Sharma and Vesa Kaihlavirta • Hands-On Data Structures and Algorithms with Rust by Claus Matzinger
Table of Contents (29 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Benchmarks


When business needs change and your program gets a requirement to perform more efficiently, the first step to take is to find out the areas that are slow in the program. How can you tell where the bottlenecks are? You can tell by measuring individual parts of your program on various expected ranges or on a magnitude of inputs. This is known as benchmarking your code. Benchmarking is usually done at the very last stage of development (but does not have to be) to provide insights on areas where there are performance pitfalls in code.

There are various ways to perform benchmark tests for a program. The trivial way is to use the Unix tool time to measure the execution time of your program after your changes. But that doesn't provide precise micro-level insights. Rust provides us with a built-in micro benchmarking framework. By micro benchmarking, we mean that it can be used to benchmark individual parts of the code in isolation and remains unbiased from external factors. However, it...