- The comparison operators are < (less than), > (greater than), == (equals), != (differs), => (greater than or equal to), and <= (less than or equal to).
- When the time series you want to enrich are on the right-hand side of the PromQL expression.
- topk already sorts its results.
- While the rate() function provides the per-second average rate of change over the specified interval by using the first and last values in the range scaled to fit the range window, the irate() function uses the last two values in the range for the calculation, which produces the instant rate of change.
- Metrics of type info have their names ending in _info and are regular gauges with one possible value, 1. This special kind of metric was designed to be a place where labels whose values might change over time are stored, such as versions (for example...
Hands-On Infrastructure Monitoring with Prometheus
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Hands-On Infrastructure Monitoring with Prometheus
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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.
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Hands-On Infrastructure Monitoring with Prometheus
Table of Contents (21 chapters)
Preface
Monitoring Fundamentals
An Overview of the Prometheus Ecosystem
Setting Up a Test Environment
Section 2: Getting Started with Prometheus
Prometheus Metrics Fundamentals
Running a Prometheus Server
Exporters and Integrations
Prometheus Query Language - PromQL
Troubleshooting and Validation
Section 3: Dashboards and Alerts
Defining Alerting and Recording Rules
Discovering and Creating Grafana Dashboards
Understanding and Extending Alertmanager
Section 4: Scalability, Resilience, and Maintainability
Choosing the Right Service Discovery
Scaling and Federating Prometheus
Integrating Long-Term Storage with Prometheus
Assessments
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