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

Introducing PromQL

Prometheus was initially developed by SoundCloud in 2012; the project was accepted by the Cloud Native Computing Foundation in 2016 as the second incubated project (after Kubernetes), and version 1.0 was released shortly after. PromQL is an integral part of Prometheus, which is used to query stored data and produce dashboards and alerts.

Before we delve into the details of the language, let’s briefly look at the following ways in which Prometheus-compatible systems interact with metrics data:

  • Ingesting metrics: Prometheus-compatible systems accept a timestamp, key-value labels, and a sample value. As the details of the Prometheus Time Series Database (TSDB) are quite complicated, the following diagram shows a simplified example of how an individual sample for a metric is stored once it has been ingested:
Figure 5.1 – A simplified view of metric data stored in the TSDB

Figure 5.1 – A simplified view of metric data stored in the TSDB

  • The labels or dimensions of a metric...