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Hands-On Infrastructure Monitoring with Prometheus

Hands-On Infrastructure Monitoring with Prometheus

By : Joel Bastos, Pedro Araújo
3.1 (7)
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Hands-On Infrastructure Monitoring with Prometheus

Hands-On Infrastructure Monitoring with Prometheus

3.1 (7)
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)
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1
Section 1: Introduction
5
Section 2: Getting Started with Prometheus
11
Section 3: Dashboards and Alerts
15
Section 4: Scalability, Resilience, and Maintainability

Understanding how rule evaluation works

Prometheus allows the periodic evaluation of PromQL expressions and the storage of the time series generated by them; these are called rules. There are two types of rules, as we'll see in this chapter. These rules are recording and alerting rules. They share the same evaluation engine, but have some variation in purpose, which we'll go into next.

The recording rules' evaluation results are saved into the Prometheus database as samples for the time series specified in the configuration. This type of rule can help take the load off of heavy dashboards by pre-computing expensive queries, aggregating raw data into a time series that can then be exported to external systems (such as higher-level Prometheus instances through federation, as described in Chapter 13, Scaling and Federating Prometheus), and can help to create compound...

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