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Pragmatic Microservices with C# and Azure

You're reading from   Pragmatic Microservices with C# and Azure Build, deploy, and scale microservices efficiently to meet modern software demands

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835088296
Length 508 pages
Edition 1st Edition
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Author (1):
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Christian Nagel Christian Nagel
Author Profile Icon Christian Nagel
Christian Nagel
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Creating Microservices with .NET FREE CHAPTER
2. Chapter 1: Introduction to .NET Aspire and Microservices 3. Chapter 2: Minimal APIs – Creating REST Services 4. Chapter 3: Writing Data to Relational and NoSQL Databases 5. Chapter 4: Creating Libraries for Client Applications 6. Part 2: Hosting and Deploying
7. Chapter 5: Containerization of Microservices 8. Chapter 6: Microsoft Azure for Hosting Applications 9. Chapter 7: Flexible Configurations 10. Chapter 8: CI/CD – Publishing with GitHub Actions 11. Chapter 9: Authentication and Authorization with Services and Clients 12. Part 3: Troubleshooting and Scaling
13. Chapter 10: All About Testing the Solution 14. Chapter 11: Logging and Monitoring 15. Chapter 12: Scaling Services 16. Part 4: More communication options
17. Chapter 13: Real-Time Messaging with SignalR 18. Chapter 14: gRPC for Binary Communication 19. Chapter 15: Asynchronous Communication with Messages and Events 20. Chapter 16: Running Applications On-Premises and in the Cloud 21. Index 22. Other Books You May Enjoy

Logging and Monitoring

With a microservices solution, many services can interact with each other. When one service fails, the complete solution should not break. In the previous chapter, we covered different kinds of tests to find issues early. Here, we’ll look at finding issues in production as early as possible – probably before a user sees a problem.

To find issues when the application is running and see how the application runs successfully, the solution needs to be enhanced to offer telemetry data. With logging, we see what’s going on; based on different log levels, we can differentiate between informational logs and errors. With metrics data, we can monitor counters, such as memory and CPU consumption, and the number of HTTP requests. We will also write custom counters to see the number of games played and the number of game moves needed for a win. Distributed tracing gives information on how services interact. Who is making calls to this service? Where...

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