Book Image

Hands-On Infrastructure Monitoring with Prometheus

By : Joel Bastos, Pedro Araújo
2 (1)
Book Image

Hands-On Infrastructure Monitoring with Prometheus

2 (1)
By: Joel Bastos, Pedro Araújo

Overview of this book

Prometheus is an open source monitoring system. It provides a modern time series database, a robust query language, several metric visualization possibilities, and a reliable alerting solution for traditional and cloud-native infrastructure. This book covers the fundamental concepts of monitoring and explores Prometheus architecture, its data model, and how metric aggregation works. Multiple test environments are included to help explore different configuration scenarios, such as the use of various exporters and integrations. You’ll delve into PromQL, supported by several examples, and then apply that knowledge to alerting and recording rules, as well as how to test them. After that, alert routing with Alertmanager and creating visualizations with Grafana is thoroughly covered. In addition, this book covers several service discovery mechanisms and even provides an example of how to create your own. Finally, you’ll learn about Prometheus federation, cross-sharding aggregation, and also long-term storage with the help of Thanos. By the end of this book, you’ll be able to implement and scale Prometheus as a full monitoring system on-premises, in cloud environments, in standalone instances, or using container orchestration with Kubernetes.
Table of Contents (21 chapters)
Free Chapter
1
Section 1: Introduction
5
Section 2: Getting Started with Prometheus
11
Section 3: Dashboards and Alerts
15
Section 4: Scalability, Resilience, and Maintainability

Summary

In this chapter, we had the opportunity to observe a different way to produce a derivative time series. Recording rules help improve monitoring system stability and performance when recurrent heavy queries are required by pre-computing them into new time series that are comparatively cheap to consult. Alerting rules bring the power and flexibility of PromQL to alerts; they enable triggering alerts for complex and dynamic thresholds as well as targeting multiple instances or even different applications using a single alert rule. Having a good grasp on how delays are introduced in alerts will now help you tailor them to your needs, but remember, a little delay is better than noisy alerts. Finally, we explored how to create unit tests for our rules and validate them even before a Prometheus server is running.

The next chapter will step into another component of monitoring...