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Modern Distributed Tracing in .NET

You're reading from   Modern Distributed Tracing in .NET A practical guide to observability and performance analysis for microservices

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781837636136
Length 336 pages
Edition 1st Edition
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Author (1):
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Liudmila Molkova Liudmila Molkova
Author Profile Icon Liudmila Molkova
Liudmila Molkova
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Introducing Distributed Tracing
2. Chapter 1: Observability Needs of Modern Applications FREE CHAPTER 3. Chapter 2: Native Monitoring in .NET 4. Chapter 3: The .NET Observability Ecosystem 5. Chapter 4: Low-Level Performance Analysis with Diagnostic Tools 6. Part 2: Instrumenting .NET Applications
7. Chapter 5: Configuration and Control Plane 8. Chapter 6: Tracing Your Code 9. Chapter 7: Adding Custom Metrics 10. Chapter 8: Writing Structured and Correlated Logs 11. Part 3: Observability for Common Cloud Scenarios
12. Chapter 9: Best Practices 13. Chapter 10: Tracing Network Calls 14. Chapter 11: Instrumenting Messaging Scenarios 15. Chapter 12: Instrumenting Database Calls 16. Part 4: Implementing Distributed Tracing in Your Organization
17. Chapter 13: Driving Change 18. Chapter 14: Creating Your Own Conventions 19. Chapter 15: Instrumenting Brownfield Applications 20. Assessments 21. Index 22. Other Books You May Enjoy

Chapter 7 – Adding Custom Metrics

  1. We should first decide what we need the metric for. For example, if we need it to rank memes in search results or to calculate ad hits, we should separate it from telemetry. Assuming we store the meme download counter in a database for business logic purposes, we could also stamp it on traces or events as an attribute when the counter is updated.

From a telemetry-only standpoint, metric per meme would have high cardinality as we probably have millions of memes in the system and thousands active per minute. With some additional logic (for example, if we can ignore rarely accessed memes), we might even be able to introduce a metric with a meme name as an attribute.

I would start with traces and aggregate spans by meme name in a rich query. Even if traces are sampled, I can still calculate the estimated number of downloads, compare it between memes, and see trends.

  1. Usually, both, but it depends: we need incoming HTTP request...
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