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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

Scrape jitter

Scrape jitter is the most common cause of oversized TSDB blocks that I have observed. Recall from Chapter 3 how – from the third scrape onwards – timestamp values are stored in the TSDB as they only store the delta of the delta of the sample timestamp. So long as this delta of the delta is 0, the TSDB’s compaction process can save a lot of space by effectively dropping the timestamp value from stored samples that all occur at a consistent delta. With millions of samples, this can add up to gigabytes of storage space in every TSDB block. However, when the delta of the delta is not consistent, this is referred to as scrape jitter.

Scrape jitter is a way to say that scrapes do not occur at consistent intervals. In Prometheus, this often means that they are off by just a few milliseconds. By default, Prometheus will automatically adjust timestamps that are within a 2ms tolerance.

Configuring timestamp adjustments

Whether or not timestamps are...

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