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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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1
Part 1: Fundamentals of Prometheus
7
Part 2: Scaling Prometheus
11
Part 3: Extending Prometheus

Defining SLOs with Prometheus data

Prometheus metrics are great for SLIs since they are generally specific enough to pick out individual aspects of a service’s performance but still generic enough to be aggregated into an overarching view of how that service is performing. Once you understand the patterns of defining SLOs with Prometheus, the most challenging part is really just identifying which of your hundreds of thousands (or millions) of metrics are best suited to being the SLIs for SLOs.

Once you have your SLI(s) picked out, the SLOs in Prometheus are typically defined in recording rules. Since SLIs will normally need to be aggregated across multiple instances or dimensions, it’s simpler to precompute their values instead of running their queries over long time ranges. I also recommend keeping the naming of your SLO recording rules consistent so that it is easy and intuitive to query for SLO metrics across services, for example, using the same resultant series...

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