Book Image

Serverless Design Patterns and Best Practices

By : Brian Zambrano
Book Image

Serverless Design Patterns and Best Practices

By: Brian Zambrano

Overview of this book

Serverless applications handle many problems that developers face when running systems and servers. The serverless pay-per-invocation model can also result in drastic cost savings, contributing to its popularity. While it's simple to create a basic serverless application, it's critical to structure your software correctly to ensure it continues to succeed as it grows. Serverless Design Patterns and Best Practices presents patterns that can be adapted to run in a serverless environment. You will learn how to develop applications that are scalable, fault tolerant, and well-tested. The book begins with an introduction to the different design pattern categories available for serverless applications. You will learn thetrade-offs between GraphQL and REST and how they fare regarding overall application design in a serverless ecosystem. The book will also show you how to migrate an existing API to a serverless backend using AWS API Gateway. You will learn how to build event-driven applications using queuing and streaming systems, such as AWS Simple Queuing Service (SQS) and AWS Kinesis. Patterns for data-intensive serverless application are also explained, including the lambda architecture and MapReduce. This book will equip you with the knowledge and skills you need to develop scalable and resilient serverless applications confidently.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
Index

Chapter 8. The MapReduce Pattern

MapReduce is a common data processing pattern made famous by Google and now implemented in various systems and frameworks, most notably Apache Hadoop. Nowadays, this pattern is familiar and easy to understand at its core, but running large-scale systems such as Hadoop comes with its own set of challenges and cost of ownership. In this chapter, we'll show how this pattern can be implemented on your own using serverless technologies.

Implementing big data applications in a serverless environment may seem counter-intuitive due to the computing limitations of FaaS. Certain types of problems fit very well into a serverless ecosystem, especially considering we practically have unlimited file storage with distributed filesystems such as AWS S3. Additionally, MapReduce's magic is not so much in the application of an algorithm, but in the distribution of computing power such that computation is performed in parallel.

In this chapter, we will discuss the application...