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Mastering Spring Boot 3.0

You're reading from   Mastering Spring Boot 3.0 A comprehensive guide to building scalable and efficient backend systems with Java and Spring

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781803230788
Length 256 pages
Edition 1st Edition
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Author (1):
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Ahmet Meric Ahmet Meric
Author Profile Icon Ahmet Meric
Ahmet Meric
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Architectural Foundations
2. Chapter 1: Introduction to Advanced Spring Boot Concepts FREE CHAPTER 3. Part 2: Architectural Patterns and Reactive Programming
4. Chapter 2: Key Architectural Patterns in Microservices – DDD, CQRS, and Event Sourcing 5. Chapter 3: Reactive REST Development and Asynchronous Systems 6. Part 3: Data Management, Testing, and Security
7. Chapter 4: Spring Data: SQL, NoSQL, Cache Abstraction, and Batch Processing 8. Chapter 5: Securing Your Spring Boot Applications 9. Chapter 6: Advanced Testing Strategies 10. Part 4: Deployment, Scalability, and Productivity
11. Chapter 7: Spring Boot 3.0 Features for Containerization and Orchestration 12. Chapter 8: Exploring Event-Driven Systems with Kafka 13. Chapter 9: Enhancing Productivity and Development Simplification 14. Index 15. Other Books You May Enjoy

Monitoring event-driven systems

In the dynamic world of event-driven systems, where applications communicate through a constant flow of messages, monitoring plays a crucial role in ensuring everything runs smoothly. Just as a busy airport needs air traffic control to keep planes moving safely and efficiently, an EDA relies on monitoring to maintain the health and performance of its components. This oversight is vital for spotting when things go wrong and understanding the overall system behavior under various loads and conditions. It enables developers and operations teams to make informed decisions, optimize performance, and prevent issues before they impact users.

For applications built with Kafka and Spring Boot, a robust set of monitoring tools and techniques is essential for keeping an eye on the system’s pulse. At its core, Kafka is designed to handle high volumes of data, making monitoring aspects such as message throughput, broker health, and consumer lag imperative...

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