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

Alerting on work metrics

We set out to turn all our observability data into actionable information so that teams can experiment with the confidence that they can fail forward fast. But we need to avoid alert fatigue. If we over-alert, then the alerts become noise and the team will start to ignore them. As a result, their confidence will go down, their lead times will go up, and innovation will stagnate.

Instead, we need to zero in on the high-value metrics and only wake the team up in the middle of the night when these Key Performance Indicators (KPIs) go sideways. However, it is hard to determine which of the many metrics are the key metrics. So, let’s start with some examples.

Netflix provides the classic example (https://netflixtechblog.com/sps-the-pulse-of-netflix-streaming-ae4db0e05f8a). They have identified one metric as the most important indicator of a significant problem with their system. This one metric is the rate at which users press the Play button. They...