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Observability with Grafana

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

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781803248004
Length 356 pages
Edition 1st Edition
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Authors (2):
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Rob Chapman Rob Chapman
Author Profile Icon Rob Chapman
Rob Chapman
Peter Holmes Peter Holmes
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Peter Holmes
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Toc

Table of Contents (22) Chapters Close

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 21. Other Books You May Enjoy

Introducing PromQL

Prometheus was initially developed by SoundCloud in 2012; the project was accepted by the Cloud Native Computing Foundation in 2016 as the second incubated project (after Kubernetes), and version 1.0 was released shortly after. PromQL is an integral part of Prometheus, which is used to query stored data and produce dashboards and alerts.

Before we delve into the details of the language, let’s briefly look at the following ways in which Prometheus-compatible systems interact with metrics data:

  • Ingesting metrics: Prometheus-compatible systems accept a timestamp, key-value labels, and a sample value. As the details of the Prometheus Time Series Database (TSDB) are quite complicated, the following diagram shows a simplified example of how an individual sample for a metric is stored once it has been ingested:
Figure 5.1 – A simplified view of metric data stored in the TSDB

Figure 5.1 – A simplified view of metric data stored in the TSDB

  • The labels or dimensions of a metric...
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