Book Image

Hands-On Infrastructure Monitoring with Prometheus

By : Joel Bastos, Pedro Araújo
Book Image

Hands-On Infrastructure Monitoring with Prometheus

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

A tour of the four core metric types

Prometheus metrics are divided into four main types: counters, gauges, histograms, and summaries. It is essential to understand them in depth, as most functions provided by Prometheus only work correctly with a given data type. So, to that end, here is an overview of each.

Counter

This is a strictly cumulative metric whose value can only increase. The only exception for this rule is when the metric is reset, which brings it back to zero.

This is one of the most useful metric types because even if a scrape fails, you won't lose the cumulative increase in the data, which will be available on the next scrape. To be clear, in the case of a failed scrape, granularity would be lost as fewer...