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Cloud-Native Observability with OpenTelemetry

Cloud-Native Observability with OpenTelemetry

By : Boten
4.8 (8)
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Cloud-Native Observability with OpenTelemetry

Cloud-Native Observability with OpenTelemetry

4.8 (8)
By: 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)
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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

Concepts of sampling across signals

A method often used in the domain of research, the process of sampling selects a subset of data points across a larger dataset to reduce the amount of data to be analyzed. This can be done because either analyzing the entire dataset would be impossible, or unnecessary to achieve the research goal, or because it would be impractical to do so. For example, if we wanted to record how many doors on average each car in a store parking lot has, it may be possible to go through the entire parking lot and record the data in its entirety. However, if the parking lot contains 20,000 cars, it may be best to select a sample of those cars, say 2,000, and analyze that instead. There are many sampling methods used to ensure that a representational subset of the data is selected, to ensure the meaning of the data is not lost because of the sampling.

Methods for sampling can be grouped as either of the following:

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