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Mastering Prometheus

You're reading from   Mastering Prometheus Gain expert tips to monitoring your infrastructure, applications, and services

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805125662
Length 310 pages
Edition 1st Edition
Concepts
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Author (1):
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William Hegedus William Hegedus
Author Profile Icon William Hegedus
William Hegedus
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Fundamentals of Prometheus FREE CHAPTER
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

Understanding remote write and remote read

Prometheus is purposely limited in the functionality it endeavors to implement. Notably, its storage is tied to a local filesystem, and there is no clustering or replication support for Prometheus instances. When running Prometheus at a sufficient scale, you will inevitably encounter the desire to scale Prometheus beyond these limitations. Rather than constrain you to only using Prometheus, Prometheus has built-in functionality to integrate with other metrics storage systems that are more featureful (but also more complex).

Prometheus does this by exposing two remote storage APIs—one for reading data from a storage system and one for writing data to it. Both APIs are exposed via HTTP, use protocol buffers (protobufs) for communication, and use “snappy” compression for both requests and responses.

Remote write is certainly the more popular of the two since it involves sending data to a larger metrics system, which...

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