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

Chapter 12: Sampling

One of the challenges of telemetry, in general, is managing the quantity of data that can be produced by instrumentation. This can be problematic at the time of generation if the tools producing telemetry consume too many resources. It can also be costly to transfer the data across various points of the network. And, of course, the more data is produced, the more storage it consumes, and the more resources are required to sift through it at the time of analysis. The last topic we'll discuss in this book focuses on how we can reduce the amount of data produced by instrumentation while retaining the value and fidelity of the data. To achieve this, we will be looking at sampling. Although primarily a concern of tracing, sampling has an impact across metrics and logs as well, which we'll learn about throughout this chapter. We'll look at the following areas:

  • Concepts of sampling, including sampling strategies, across the different signals of...