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Software Architecture Patterns for Serverless Systems

Software Architecture Patterns for Serverless Systems - Second Edition

By : John Gilbert
5 (15)
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Software Architecture Patterns for Serverless Systems

Software Architecture Patterns for Serverless Systems

5 (15)
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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14
Other Books You May Enjoy
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Index

Keeping data lean

Intractable volumes of data, as we have already seen, are one of the causes of data gravity. The sheer volume of data in a system can impede its evolution because the time and effort involved in reshaping data is a powerful deterrent. In the Embracing data life cycle section, we discussed how defining boundaries between the data throughout the phases of the data’s life cycle makes a big improvement as we move these groups of data into separate, leaner databases.

In the Turning the database inside out section, we saw that a large portion of a database’s size is attributable to derived (that is, duplicate) data, such as indices and materialized views. Moving this derived data into the datastores of the services that use it makes the source datastores even more lean.

But we can do more. Upstream services produce events as they create data, and these events become the source of truth in the system-wide transaction log. This frees services to pick...

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