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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

Federating Prometheus

The elusive “single pane of glass.” Everybody wants it. Every software vendor purports to sell it. The dream is to have one place you can go to see all of your monitoring data in just that single location. Sharding Prometheus may seem antithetical to that but through federation, we can still achieve it.

What is federation? Federation is the process of joining together metrics from multiple sources into a central location. It is useful for aggregating your metrics into a centralized Prometheus instance. They may be a 1:1 match of the metrics present in the “lower” Prometheus instances, but they can also have PromQL query functions applied to them to perform series aggregation and consolidation before storage in the “higher” Prometheus instance(s).

Should I federate?

If you’re federating because you’ve sharded your Prometheus instances, you’ve probably already run into the issue where one Prometheus...

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