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

Alternate Implementations


Our example application is quite robust and can handle quite a bit of load and traffic with few to no changes. As easy as this pattern is to understand, implement, and run, it's not a silver bullet. You will likely require different implementations of this Messaging Pattern in your scenarios. We'll review a few alternative applications of the same pattern, which uses a queue as a message broker between disparate systems.

Using the Fan-out and Messaging Patterns together

Earlier, during the explanation of our system architecture, I briefly discussed the possibility of fanning out messages from the stream listener to multiple queues. A design such as this would be useful when there are different types of workload to be performed from a single data producer. The following example architecture shows a system made up of an individual Twitter stream data producer that fans out messages to multiple queues based on the payload:

For this example, assume we're interested in...