Book Image

Software Architecture Patterns for Serverless Systems - Second Edition

By : John Gilbert
Book Image

Software Architecture Patterns for Serverless Systems - Second Edition

By: John Gilbert

Overview of this book

Organizations undergoing digital transformation rely on IT professionals to design systems to keep up with the rate of change while maintaining stability. With this edition, enriched with more real-world examples, you’ll be perfectly equipped to architect the future for unparalleled innovation. This book guides through the architectural patterns that power enterprise-grade software systems while exploring key architectural elements (such as events-driven microservices, and micro frontends) and learning how to implement anti-fragile systems. First, you'll divide up a system and define boundaries so that your teams can work autonomously and accelerate innovation. You'll cover the low-level event and data patterns that support the entire architecture while getting up and running with the different autonomous service design patterns. This edition is tailored with several new topics on security, observability, and multi-regional deployment. It focuses on best practices for security, reliability, testability, observability, and performance. You'll be exploring the methodologies of continuous experimentation, deployment, and delivery before delving into some final thoughts on how to start making progress. By the end of this book, you'll be able to architect your own event-driven, serverless systems that are ready to adapt and change.
Table of Contents (16 chapters)
14
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15
Index

Processing event streams

At first glance, stream processing might seem like it is some extreme paradigm shift. And truth be told, if you have to do it all from scratch, it is not for the fainthearted. For example, we need a process that consumes from a stream and invokes the business logic. We need one of these for each shard. We need logic to increase or decrease the number of these processes with the number of shards. We need to restart these processes when they fall over. Finally, we need to keep track of the offset so that we read from the right position on the next call.

This can add up to a decent cloud bill, not to mention all the development elbow grease required. Of course, we can implement this processing as a container sidecar. But serverless, function-as-a-service offerings, such as AWS Lambda, provide this capability for just the cost of invoking the functions to execute the business logic. It takes care of all the heavy lifting.

Still, stream processing is different...