You're reading from Observability with Grafana Monitor, control, and visualize your Kubernetes and cloud platforms using the LGTM stack

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781803248004

Length 356 pages

Edition 1st Edition

Languages

Python

Tools

Grafana

Concepts

DevOps

Authors (2):

Rob Chapman

Peter Holmes

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Table of Contents (22) Chapters

Preface

1. Part 1: Get Started with Grafana and Observability

2. Chapter 1: Introducing Observability and the Grafana Stack FREE CHAPTER

3. Chapter 2: Instrumenting Applications and Infrastructure

4. Chapter 3: Setting Up a Learning Environment with Demo Applications

5. Part 2: Implement Telemetry in Grafana

6. Chapter 4: Looking at Logs with Grafana Loki

7. Chapter 5: Monitoring with Metrics Using Grafana Mimir and Prometheus

8. Chapter 6: Tracing Technicalities with Grafana Tempo

9. Chapter 7: Interrogating Infrastructure with Kubernetes, AWS, GCP, and Azure

10. Part 3: Grafana in Practice

11. Chapter 8: Displaying Data with Dashboards

12. Chapter 9: Managing Incidents Using Alerts

13. Chapter 10: Automation with Infrastructure as Code

14. Chapter 11: Architecting an Observability Platform

15. Part 4: Advanced Applications and Best Practices of Grafana

16. Chapter 12: Real User Monitoring with Grafana

17. Chapter 13: Application Performance with Grafana Pyroscope and k6

18. Chapter 14: Supporting DevOps Processes with Observability

19. Chapter 15: Troubleshooting, Implementing Best Practices, and More with Grafana

20. Index

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Tracing Technicalities with Grafana Tempo

Grafana Tempo is the third telemetry storage tool from Grafana that we’ll discuss; it provides the capability to store and query trace data. This chapter will introduce the Tempo query language (TraceQL). TraceQL can be used to select and filter traces generated by your applications to gather insights from across traces; the language is very similar to LogQL and PromQL but tailored to trace data. In this chapter, we will explore the major tracing protocols and how they can be used to output traces from applications; this will help you make informed choices on which protocol to use in an application, or which protocols to support when collecting data. We’ll then explore the architecture of Tempo to understand how it can fulfill the need for a scalable platform for tracing.

We will cover the following main topics in this chapter: