Book Image

JavaScript Concurrency

By : Adam Boduch
Book Image

JavaScript Concurrency

By: Adam Boduch

Overview of this book

Concurrent programming may sound abstract and complex, but it helps to deliver a better user experience. With single threaded JavaScript, applications lack dynamism. This means that when JavaScript code is running, nothing else can happen. The DOM can’t update, which means the UI freezes. In a world where users expect speed and responsiveness – in all senses of the word – this is something no developer can afford. Fortunately, JavaScript has evolved to adopt concurrent capabilities – one of the reasons why it is still at the forefront of modern web development. This book helps you dive into concurrent JavaScript, and demonstrates how to apply its core principles and key techniques and tools to a range of complex development challenges. Built around the three core principles of concurrency – parallelism, synchronization, and conservation – you’ll learn everything you need to unlock a more efficient and dynamic JavaScript, to lay the foundations of even better user experiences. Throughout the book you’ll learn how to put these principles into action by using a range of development approaches. Covering everything from JavaScript promises, web workers, generators and functional programming techniques, everything you learn will have a real impact on the performance of your applications. You’ll also learn how to move between client and server, for a more frictionless and fully realized approach to development. With further guidance on concurrent programming with Node.js, JavaScript Concurrency is committed to making you a better web developer. The best developers know that great design is about more than the UI – with concurrency, you can be confident every your project will be expertly designed to guarantee its dynamism and power.
Table of Contents (17 chapters)
JavaScript Concurrency
About the Author
About the Reviewer

Lazy workers

It's time for us to look at web workers from a different angle. The fundamental reason we're using workers in the first place is that we want to compute more than we have in the past in the same amount of time. Doing this, as we now know, involves messaging intricacies, divide and conquer strategies so to speak. We have to get data into and out of the worker, usually as an array.

Generators help us compute lazily. That is, we don't want to compute something or allocate data in memory until we really need it. Do web workers make this difficult or impossible to achieve? Or can we leverage generators to compute lazily and in parallel?

In this section, we'll explore ideas related to using generators in web workers. First, we'll look at the overhead issues associated with web workers. Then, we'll write some code that uses generators to pass data in and out of workers. Finally, we'll see if we can lazily pass data through a chain of generators, all residing in web workers.

Reducing overhead...