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Observability with Grafana

Observability with Grafana

By : Rob Chapman, Peter Holmes
4 (4)
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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

Common log formats

Log files are a standard component of computer systems and an essential tool for software developers and operators – in our example, Diego and Ophelia, respectively. Logs support performance and capacity monitoring in infrastructure, bug detection in software, root cause analysis, user behavior tracking, and more. There is no perfect recipe for logs and as such, it does not matter what your logs look like, though following certain guidelines will help your future self when you need to analyze logs. In this section, we will learn about different log formats and how the data can be used. Log formats are the definition of what a log file looks like and should explain how the data can be interpreted.

Log formats usually identify if they are structured or unstructured, the data types used in them, and if any encoding or delimitation is being used. We’ll explore structure first and then look at example log formats in more detail in the following sections...

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