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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

Introducing the components of observability systems

Observability systems consist of many components involved in producing, consuming, transforming, storing, and using data. Over the course of this chapter, we will split these components into four distinct systems to be clear about which aspect of observability platforms we are discussing. The different aspects of automation will be of interest to different audiences. The systems we will discuss are as follows:

  1. Data production systems: These are the systems that generate data. The applications, infrastructure, and even components of the data collection system will produce data. Let’s look at the key features:
    • These systems are managed by developers such as Diego, or by operations experts such as Ophelia (refer to Chapter 1 for details on these personas).
    • These systems are tested as part of the application- or component-testing process. If a data schema is in use, this can be validated using a tool such as JSON Schema.
  2. ...

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