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Mastering Prometheus

Mastering Prometheus

By : Hegedus
3.7 (6)
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Mastering Prometheus

Mastering Prometheus

3.7 (6)
By: Hegedus

Overview of this book

With an increased focus on observability and reliability, establishing a scalable and reliable monitoring environment is more important than ever. Over the last decade, Prometheus has emerged as the leading open-source, time-series based monitoring software catering to this demand. This book is your guide to scaling, operating, and extending Prometheus from small on-premises workloads to multi-cloud globally distributed workloads and everything in between. Starting with an introduction to Prometheus and its role in observability, the book provides a walkthrough of its deployment. You’ll explore Prometheus’s query language and TSDB data model, followed by dynamic service discovery for monitoring targets and refining alerting through custom templates and formatting. The book then demonstrates horizontal scaling of Prometheus via sharding and federation, while equipping you with debugging techniques and strategies to fine-tune data ingestion. Advancing through the chapters, you’ll manage Prometheus at scale through CI validations and templating with Jsonnet, and integrate Prometheus with other projects such as OpenTelemetry, Thanos, VictoriaMetrics, and Mimir. By the end of this book, you’ll have practical knowledge of Prometheus and its ecosystem, which will help you discern when, why, and how to scale it to meet your ever-growing needs.
Table of Contents (21 chapters)
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Free Chapter
1
Part 1: Fundamentals of Prometheus
7
Part 2: Scaling Prometheus
11
Part 3: Extending Prometheus

Prometheus’s data model

Prometheus’s data model is – in my opinion – straightforward. It has three main data types that are all built on each other: metrics, time series, and samples.

Metrics

Metrics are the fundamental Prometheus data type. After all, Prometheus is a metrics-focused observability tool.

Every metric has – at a minimum – a name that identifies it. This name can contain letters, digits, underscores, and/or colons. To be valid, it must match the [a-zA-Z_:][a-zA-Z0-9_:]* regex.

Additionally, metrics may also include a HELP text and a TYPE field. These are optional but highly recommended to improve usability.

A full metric that’s been exposed to Prometheus may look like this:

# HELP mastering_prometheus_readers_total Number of readers of this book
# TYPE mastering_prometheus_readers_total counter
mastering_prometheus_readers_total 123467890

The HELP line just provides some arbitrary information on what...

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