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Learn Grafana 10.x

Learn Grafana 10.x

By : Salituro
3 (3)
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Learn Grafana 10.x

Learn Grafana 10.x

3 (3)
By: Salituro

Overview of this book

Get ready to unlock the full potential of the open-source Grafana observability platform, ideal for analyzing and monitoring time-series data with this updated second edition. This beginners guide will help you get up to speed with Grafana’s latest features for querying, visualizing, and exploring logs and metrics, no matter where they are stored. Starting with the basics, this book demonstrates how to quickly install and set up a Grafana server using Docker. You’ll then be introduced to the main components of the Grafana interface before learning how to analyze and visualize data from sources such as InfluxDB, Telegraf, Prometheus, Logstash, and Elasticsearch. The book extensively covers key panel visualizations in Grafana, including Time Series, Stat, Table, Bar Gauge, and Text, and guides you in using Python to pipeline data, transformations to facilitate analytics, and templating to build dynamic dashboards. Exploring real-time data streaming with Telegraf, Promtail, and Loki, you’ll work with observability features like alerting rules and integration with PagerDuty and Slack. As you progress, the book addresses the administrative aspects of Grafana, from configuring users and organizations to implementing user authentication with Okta and LDAP, as well as organizing dashboards into folders, and more. By the end of this book, you’ll have gained all the knowledge you need to start building interactive dashboards.
Table of Contents (23 chapters)
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1
Part 1 – Getting Started with Grafana
5
Part 2 – Real-World Grafana
16
Part 3 – Managing Grafana

Detecting trends with aggregations

As we continue up the stack, let’s now examine some server performance metrics. How about an obvious web server metric? Enter prometheus_http_requests_total to get an idea of how many requests have been served so far:

Figure 4.15 – Prometheus HTTP requests

Figure 4.15 – Prometheus HTTP requests

Well, this is a bit of a mess. You can’t see all 22 of the time series—they’re all stacked on top of each other—and there’s the ominous warning Selected metric is a counter. As we saw in the previous section, it’s no problem to apply filters—say, to filter the 200 codes—but then we’d still have a stack of nearly 20 individual series.

Applying aggregations to our query data

If only there were some way to combine all the individual data series into one. It turns out there is, and it’s called an aggregation. We can tell Prometheus to apply an aggregation function (in this case, sum...

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