Book Image

Cloud-Native Observability with OpenTelemetry

By : Alex Boten
Book Image

Cloud-Native Observability with OpenTelemetry

By: Alex Boten

Overview of this book

Cloud-Native Observability with OpenTelemetry is a guide to helping you look for answers to questions about your applications. This book teaches you how to produce telemetry from your applications using an open standard to retain control of data. OpenTelemetry provides the tools necessary for you to gain visibility into the performance of your services. It allows you to instrument your application code through vendor-neutral APIs, libraries and tools. By reading Cloud-Native Observability with OpenTelemetry, you’ll learn about the concepts and signals of OpenTelemetry - traces, metrics, and logs. You’ll practice producing telemetry for these signals by configuring and instrumenting a distributed cloud-native application using the OpenTelemetry API. The book also guides you through deploying the collector, as well as telemetry backends necessary to help you understand what to do with the data once it's emitted. You’ll look at various examples of how to identify application performance issues through telemetry. By analyzing telemetry, you’ll also be able to better understand how an observable application can improve the software development life cycle. By the end of this book, you’ll be well-versed with OpenTelemetry, be able to instrument services using the OpenTelemetry API to produce distributed traces, metrics and logs, and more.
Table of Contents (17 chapters)
1
Section 1: The Basics
3
Chapter 2: OpenTelemetry Signals – Traces, Metrics, and Logs
5
Section 2: Instrumenting an Application
10
Section 3: Using Telemetry Data

Using telemetry first to answer questions

These experiments are a great way to gain familiarity with telemetry. Still, it feels like cheating to know what caused a change before referring to the telemetry to investigate a problem. A more common way to use telemetry is to look at it when a problem occurs without intentionally causing it. Usually, this happens when deploying new code in a system.

Code changes are deployed to many services in a distributed system several times a day. This makes it challenging to figure out which change is responsible for a regression. The complexity of identifying problematic code is compounded by the updates being deployed by different teams. Update the image configuration for the shopper, grocery-store, and legacy-inventory services in the Docker Compose configuration to use the following:

docker-compose.yml

  shopper:
    image: codeboten/shopper:chapter11-example1
...
  grocery-store:
  &...