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

Runtime hooks and monkey patching

In Python, unlike in Java, where a single archive contains everything that's needed to support auto-instrumentation, the implementation relies on several separate components that must be discussed to help us fully understand how auto-instrumentation works.

Instrumenting libraries

Instrumentation libraries in Python rely on one of two mechanisms to instrument third-party libraries:

  • Event hooks are exposed by the libraries being instrumented, allowing the instrumenting libraries to register and produce telemetry as events occur.
  • Any intercepting calls to libraries are instrumented and are replaced at runtime via a technique known as monkey patching (https://en.wikipedia.org/wiki/Monkey_patch). The instrumenting library receives the original call, produces telemetry data, and then calls the underlying library.

Monkey patching is like bytecode injection in that the applications make calls to libraries without suspecting that...