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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

Best practices and approaches

In this chapter, we have provided an overview of several popular cloud infrastructures. Let’s now discuss some of the best practices that should be considered when implementing observability on any application or system:

  • Performance: The process of retrieving telemetry data can potentially incur a performance overhead. For example, with a remote Grafana data source, the telemetry data is fetched at query time over a great distance. This can introduce latency when compared to data stored closer to the Grafana query engine using one of the Grafana Cloud data sources, such as Loki, Mimir, and Tempo. Where performance is important and there is an option to ship telemetry into Grafana, that could be the best choice. Alternatively, several data sources have caching options to improve query speed; improvements in query speed can also be made using specific configurations. Take the time to understand your data and ensure you are using it in an optimal...
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