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 Tempo and the TraceQL query language

Tempo and TraceQL are the newest of the tools and query languages we will explore in depth in this book. Like LogQL, TraceQL was built using PromQL as an inspiration and offers developers and operators a familiar set of filtering, aggregation, and mathematical tools that aid in the observability flow between metrics, logs, and traces.

Let’s have a quick look at how Tempo sees trace data:

  • Trace collection: Introduced in Chapter 2, a trace (or distributed trace) is a collection of data that represents a request propagating through a system. Traces are often collected from multiple applications. Spans are sent by each application to some form of collection architecture and, ultimately, to Tempo for storage and querying.
  • Trace fields: The following diagram introduces a simplified structure of a trace, similar to the simplified structure of logs, seen in Chapter 4, and traces, seen in Chapter 5:
Figure 6.1 – A simplified view of a trace containing four spans ...