Book Image

Learn Grafana 7.0

By : Eric Salituro
Book Image

Learn Grafana 7.0

By: Eric Salituro

Overview of this book

Grafana is an open-source analytical platform used to analyze and monitoring time-series data. This beginner's guide will help you get to grips with Grafana's new features for querying, visualizing, and exploring metrics and logs no matter where they are stored. The book begins by showing you how to install and set up the Grafana server. You'll explore the working mechanism of various components of the Grafana interface along with its security features, and learn how to visualize and monitor data using, InfluxDB, Prometheus, Logstash, and Elasticsearch. This Grafana book covers the advanced features of the Graph panel and shows you how Stat, Table, Bar Gauge, and Text are used. You'll build dynamic dashboards to perform end-to-end analytics and label and organize dashboards into folders to make them easier to find. As you progress, the book delves into the administrative aspects of Grafana by creating alerts, setting permissions for teams, and implementing user authentication. Along with exploring Grafana's multi-cloud monitoring support, you'll also learn about Grafana Loki, which is a backend logger for users running Prometheus and Kubernetes. By the end of this book, you'll have gained all the knowledge you need to start building interactive dashboards.
Table of Contents (19 chapters)
1
Getting Started with Grafana
5
Real-World Grafana
13
Managing Grafana

Understanding the data source limitations

After seeing how powerful even relatively simple PromQL queries can be, it is tempting to think you can query and graph virtually any metric in your data source. Unfortunately, there are limitations to certain kinds of calculations, either imposed by the nature of the data or by the data source application.

It is important to remember that when you create a graph, you are entering into a trust relationship between you and your audience (which might even be you). When you place a pixel on a graph that isn't explicitly represented by a corresponding data point, you are asking your audience to accept that what you are doing is, in essence, reconstructing a signal from the underlying data.

Therefore, you have an obligation to respect the integrity of the data and not abuse that trust by manipulating the data to say things that aren't true or lead the viewer to draw erroneous conclusions.

Throughout this book...