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

Summary


In this chapter, I gave an overview of what the MapReduce pattern looked like in a general sense and demonstrated how MapReduce works with some example code. From there, we reviewed the MapReduce pattern as applied to serverless architectures. We stepped through the details of implementing this pattern by parsing 1.5 GB of email data and counting the unique occurrences of From and To email addresses. I showed that a serverless system could be built using this pattern to perform our task in less than a minute, on average.

We covered some of the limitations of this pattern when implemented on a serverless platform. Finally, we discussed alternative solutions for general data analysis problems using serverless platforms such as AWS Athena and managed systems such as EMR, as well as ways to use a centralized data store such as Redis in a serverless MapReduce system.