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Cloud-Native Observability with OpenTelemetry

You're reading from  Cloud-Native Observability with OpenTelemetry

Product type Book
Published in May 2022
Publisher Packt
ISBN-13 9781801077705
Pages 386 pages
Edition 1st Edition
Languages
Author (1):
Alex Boten Alex Boten
Profile icon Alex Boten

Table of Contents (17) Chapters

Preface 1. Section 1: The Basics
2. Chapter 1: The History and Concepts of Observability 3. Chapter 2: OpenTelemetry Signals – Traces, Metrics, and Logs 4. Chapter 3: Auto-Instrumentation 5. Section 2: Instrumenting an Application
6. Chapter 4: Distributed Tracing – Tracing Code Execution 7. Chapter 5: Metrics – Recording Measurements 8. Chapter 6: Logging – Capturing Events 9. Chapter 7: Instrumentation Libraries 10. Section 3: Using Telemetry Data
11. Chapter 8: OpenTelemetry Collector 12. Chapter 9: Deploying the Collector 13. Chapter 10: Configuring Backends 14. Chapter 11: Diagnosing Problems 15. Chapter 12: Sampling 16. Other Books You May Enjoy

Concepts of sampling across signals

A method often used in the domain of research, the process of sampling selects a subset of data points across a larger dataset to reduce the amount of data to be analyzed. This can be done because either analyzing the entire dataset would be impossible, or unnecessary to achieve the research goal, or because it would be impractical to do so. For example, if we wanted to record how many doors on average each car in a store parking lot has, it may be possible to go through the entire parking lot and record the data in its entirety. However, if the parking lot contains 20,000 cars, it may be best to select a sample of those cars, say 2,000, and analyze that instead. There are many sampling methods used to ensure that a representational subset of the data is selected, to ensure the meaning of the data is not lost because of the sampling.

Methods for sampling can be grouped as either of the following:

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