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Mastering Prometheus

You're reading from  Mastering Prometheus

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781805125662
Pages 310 pages
Edition 1st Edition
Languages
Concepts
Author (1):
William Hegedus William Hegedus
Profile icon William Hegedus

Table of Contents (21) Chapters

Preface 1. Part 1: Fundamentals of Prometheus
2. Chapter 1: Observability, Monitoring, and Prometheus 3. Chapter 2: Deploying Prometheus 4. Chapter 3: The Prometheus Data Model and PromQL 5. Chapter 4: Using Service Discovery 6. Chapter 5: Effective Alerting with Prometheus 7. Part 2: Scaling Prometheus
8. Chapter 6: Advancing Prometheus: Sharding, Federation, and High Availability 9. Chapter 7: Optimizing and Debugging Prometheus 10. Chapter 8: Enabling Systems Monitoring with the Node Exporter 11. Part 3: Extending Prometheus
12. Chapter 9: Utilizing Remote Storage Systems with Prometheus 13. Chapter 10: Extending Prometheus Globally with Thanos 14. Chapter 11: Jsonnet and Monitoring Mixins 15. Chapter 12: Utilizing Continuous Integration (CI) Pipelines with Prometheus 16. Chapter 13: Defining and Alerting on SLOs 17. Chapter 14: Integrating Prometheus with OpenTelemetry 18. Chapter 15: Beyond Prometheus 19. Index 20. Other Books You May Enjoy

Prometheus’ limitations

When you first start using it, Prometheus may seem like it can do anything. It’s a hammer and everything looks like a nail. After a while, though, reality sets in and the cracks start to show. Queries start to get slower. Memory usage begins to creep up. You’re waiting longer and longer for WAL replays to finish when Prometheus starts up. Where did I go wrong? What do I need to do?

Rest assured, you did nothing wrong and you’re not alone. Prometheus, like any other technology, has limitations. Some of them are specific to Prometheus and others are limitations of time series databases in general. So, what are they? Well, the two we need to care about the most are cardinality and long-term storage.

Cardinality

Cardinality refers to the measure of unique values of a dataset. High cardinality indicates that there is a large number of unique values, whereas low cardinality is a small number of unique values.

Examples of high...

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