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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

Tips, tricks, and best practices

In this section, we will look at a few best practices for filtering and cardinality. We will then look at the LogQL Analyzer and LogCLI, which are tools that can help you when you are working with Grafana Loki log data.

Here are some best practices to keep in mind:

  • Filter first: Loki stores the raw log in object storage as compressed chunks. Because of this, it is important, from a speed point of view, to filter early. Processing complex parsing on smaller datasets will increase the response time.
  • Cardinality: High cardinality in Loki can be very detrimental. It is important to design your Loki labels well. Anything that has a lot of variable data in it is a bad idea as that will multiply the number of log streams and therefore storage chunks by that factor. Thinking of them as a locator rather than a content descriptor helps. You can always extract labels from log lines with the range of parsers available. Some examples of good labels...

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