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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

Using Thanos to mitigate Prometheus shortcomings at scale

When you start to scale Prometheus, you quickly bump into the problem of cross-shard visibility. Indeed, Grafana can help, as you can add multiple datastore sources in the same dashboard panel, but this becomes harder to maintain, especially if multiple teams have different needs. Keeping track of which shard has which metric might not be trivial when there aren't clearly defined boundaries - while this might not be a problem when you have a shard per team as each team might only care about their own metrics, issues arise when there are several shards maintained by a single team and exposed as a service to the organization.

Additionally, it is common practice to run two identical Prometheus instances to prevent single points of failure (SPOF) in the alerting path - known as HA (or high-availability) pairs. This complicates...

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