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

Configuring the metrics pipeline

The metrics signal was designed to be conceptually similar to the tracing signal. The metrics pipeline consists of the following:

  • A MeterProvider to determine how metrics should be generated and provide access to a meter.
  • The meter is used to create instruments, which are used to record measurements.
  • Views allow the application developer to filter and process metrics produced by the software development kit (SDK).
  • A MetricReader, which collects metrics being recorded.
  • The MetricExporter provides a mechanism to translate metrics into an output format for various protocols.

There are quite a few components, and a picture always helps me grasp concepts more quickly. The following figure shows us the different elements in the pipeline:

Figure 5.1 – Metrics pipeline

MeterProvider can be associated with a resource to identify the source of metrics produced. We'll see shortly how we can reuse the LocalMachineResourceDetector...