A Return-on-Investment driven, hands-on approach
Welcome to Cloud Observability in Action, your hands-on guide to applying observability in the context of cloud native environments.
Observability is the capability to continuously generate and discover actionable insights based on signals from the system under observation with the goal to influence the system.
In this book you will learn about the basic signal types (logs, metrics, traces, profiles), telemetry including agents, back-end and front-end destinations, and goood practices around dashboarding, alerting, and SLOs/SLIs.
Some chapters of the book are now available via the Manning MEAP Program and you can find code snippets we use throughout the book via the site you’re on, currently.
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In the context of this book we focus on cloud native environments such as Kubernetes and serverless offerings (such as FaaS like AWS Lambda). We mainly use open source observability tooling (Grafana, Prometheus, Jaeger) so that you can try out everything without license costs. While it is important that we use open source tooling to show the concepts in action, they are universally applicable (that is, using any of the commerical offerings). In this chapter we have a look at an end-to-end example and define terminology, from sources to agents to destinations.
In this chapter we review different signal types most often used, how to instrument and collect each, and discuss the costs and benefits of doing that. With observability you want to take an Return-On-Investment (ROI) driven approach. In other words, you need to understand the costs of each signal type and what it enables you to do.
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