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Observability with Grafana

Observability with Grafana

By : Rob Chapman, Peter Holmes
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Observability with Grafana

Observability with Grafana

4 (4)
By: Rob Chapman, Peter Holmes

Overview of this book

To overcome application monitoring and observability challenges, Grafana Labs offers a modern, highly scalable, cost-effective Loki, Grafana, Tempo, and Mimir (LGTM) stack along with Prometheus for the collection, visualization, and storage of telemetry data. Beginning with an overview of observability concepts, this book teaches you how to instrument code and monitor systems in practice using standard protocols and Grafana libraries. As you progress, you’ll create a free Grafana cloud instance and deploy a demo application to a Kubernetes cluster to delve into the implementation of the LGTM stack. You’ll learn how to connect Grafana Cloud to AWS, GCP, and Azure to collect infrastructure data, build interactive dashboards, make use of service level indicators and objectives to produce great alerts, and leverage the AI & ML capabilities to keep your systems healthy. You’ll also explore real user monitoring with Faro and performance monitoring with Pyroscope and k6. Advanced concepts like architecting a Grafana installation, using automation and infrastructure as code tools for DevOps processes, troubleshooting strategies, and best practices to avoid common pitfalls will also be covered. After reading this book, you’ll be able to use the Grafana stack to deliver amazing operational results for the systems your organization uses.
Table of Contents (22 chapters)
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1
Part 1: Get Started with Grafana and Observability
5
Part 2: Implement Telemetry in Grafana
10
Part 3: Grafana in Practice
15
Part 4: Advanced Applications and Best Practices of Grafana

Summary

In this chapter, we have taken a look at Loki, exploring the log ingest format and the importance of log labels. We then started looking at the comprehensive features of LogQL, the query language of Loki, and how we can select log streams and then filter, parse, format, and transform log lines. These techniques will be invaluable when working with Loki to build dashboards in Chapter 8. Then, we looked at the Loki architecture to get an understanding of what’s going on behind the scenes. We also explained how our data is stored and how Loki can be scaled to increase performance. Lastly, we reviewed some tips and best practices that can help you improve your experience with Loki.

In the next chapter, we’ll move on from logs to explore metrics and Prometheus, where Loki took its original inspiration from.

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