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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 explored metrics in detail. We saw all the operators available in PromQL and wrote two queries using the language. With that foundation of querying knowledge, we looked at the tools available to collect data and the various protocols with which applications can share data. We then looked at the architecture for Prometheu, and saw how Mimir takes the concepts of Prometheus and turns them into a highly scalable data processing tool, able to meet the needs of organizations of any size. Our final exploration was of Exemplars, giving us a concrete data example to add context to the aggregated data seen in metrics.

The next chapter will explore how traces work in Grafana Tempo, which will show you how powerful the use of exemplars and logging trace and span information can be to create a truly observable system for your organization’s customers.

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