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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

Using libraries to instrument efficiently

Instrumenting your application code to emit the telemetry of logs, metrics, and traces can be complex, time-consuming, and difficult to maintain. There are two main approaches to solving this problem – automatic instrumentation and manual instrumentation – with a wide selection of SDKs and libraries available to support them. Here is a brief overview of them:

  • Automatic instrumentation: Automatic instrumentation is the simplest to implement but can lack the level of control that’s often required when building an observability platform. In a very short space of time, it will provide visibility into your application and help you start answering your observability questions. Without careful configuration and design, this will lead to other problems such as performance and cost issues, and, in the worst case, render the observability platform useless.

    The approach varies depending on the programming language; for example...

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