Book Image

Observability with Grafana

By : Rob Chapman, Peter Holmes
Book Image

Observability with Grafana

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)
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

Monitoring Kubernetes using Grafana

Kubernetes has been designed to be monitored, and as such, it presents multiple options for anyone wanting to monitor it or the workloads running on it using Grafana. In this section, we will focus on monitoring Kubernetes, as we have already worked with Kubernetes workloads in previous chapters using the OpenTelemetry Demo application.

The OpenTelemetry Collector introduced in Chapter 3 provides receivers, processors, and exporters to implement Kubernetes monitoring with data collection and enrichment. The following table identifies those components with a brief explanation for each of them:

OpenTelemetry Component

Description

Kubernetes Attributes Processor

The Kubernetes Attributes Processor appends Kubernetes metadata to telemetry, providing the necessary context for correlation.

Kubeletstats...