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

Summary

Understanding the different options for sampling provides us with the ability to manage the amount of data produced by our applications. Knowing the trade-offs of different sampling strategies and some of the methods available helps decrease the level of noise in a busy environment.

The OpenTelemetry configuration and samplers available to configure sampling at the application level can help reduce the load and cost upfront in systems via head sampling. Configuring tail sampling at collection time provides the added benefit of making a more informed decision on what to keep or discard. This benefit comes at the added cost of having to run a collection point with sufficient resources to buffer the data until a decision can be reached.

Ultimately, the decisions made when configuring sampling will impact what data is available to observe what is happening in a system. Sample too little and you may miss important events. Sample too much and the cost of producing telemetry...