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)
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Leveraging change data capture

Let’s change gears a little bit. Up to this point in this chapter, we have primarily focused on the downstream consume and query side of the CPCQ flow. Now, we will look into the upstream command and publish side of the CPCQ flow and how it intersects with the system-wide Event Sourcing pattern that we introduced in Chapter 4, Trusting Facts and Eventual Consistency.

In the Turning the database inside out section, we learned that splitting data out into multiple databases creates the need for a system-wide transaction log. We fulfill this need with the Event Sourcing pattern and the event lake. Each autonomous subsystem plays its role by providing an event hub service to collect events and an event lake service to store them. Individual autonomous services play their part by publishing events to the hub as their state changes.

Each individual database still has its own transaction log, and we leverage it as a powerful tool. Change Data...