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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

Understanding LogQL

Grafana developed LogQL as the query language for Loki using the Prometheus Query Language (PromQL) for inspiration. It was designed with developers (Diego) and operators (Ophelia) in mind (you can refer to Chapter 1 for an introduction to these personas), providing familiar filtering and aggregation mechanisms. Loki does not index the log content. Log events are grouped into log streams and indexed with labels (the log metadata). Executing a LogQL query in Loki invokes a type of distributed filtering against log streams to aggregate the log data.

Let’s explore the Grafana explorer UI for LogQL, where you will be executing most of your LogQL queries.

LogQL query builder

We took a brief look at the Grafana explorer UI in Figure 3.16 in Chapter 3. For our examples, we will mostly work with raw LogQL in the Code editor. The following screenshot shows LogQL typed directly into the query builder code editor:

Figure 4.3 – LogQL query builder Code editor

Figure 4.3 –...

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