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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

Writing great alerts using SLIs and SLOs

An SLI is a measurement that is used to indicate a current service level. An example could be the number of errors over a 15-minute period.

It is best practice to keep the number of SLIs small; three to five SLIs for a service is a good rule of thumb to follow. This reduces confusion and allows teams to focus on what is critical for their service. SLIs can also be thought of as a fractal concept; while a service team can have indicators for a component of a larger system, the system can also be tracked by a small number of SLIs – for example, the number of services that are failing their SLOs. By keeping the number of SLIs tracked relatively small, the potential for spurious alerts is reduced, and the impact of continuously monitoring services is kept small. This means more services can be monitored without scaling the tools used and increasing operating costs.

The patterns we discussed earlier of RED, USE, golden signals, and core...

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