This repository is a comprehensive, task-driven implementation of all core and advanced concepts in the Spring AOP module of the Spring Boot Mastery Learning Plan.
It focuses on building true hands-on muscle memory through incremental coding tasks, ensuring practical mastery of AOP principles such as advices, join points, pointcuts, proxying, and real-world cross-cutting concerns.
All tasks were assigned by ChatGPT, and each implementation was fully coded by the author (Chandra Sekhar) based on the structured Spring AOP learning plan.
This repository is solely a record of learning and implementation work; it holds no other functional purpose.
Repository Name: spring-aop-muscle-memory
Language: Java 21 Framework: Spring Boot 3.x Build Tool: Maven IDE Used: IntelliJ IDEA Author: Chandra Sekhar
This repository covers 6 major topics from the official SPRING AOP.txt learning plan, each broken down into structured, incremental hands-on tasks.
| Topic No. | Topic Name | Description |
|---|---|---|
| 1 | Introduction to AOP | Demonstrated manual logging repetition to show why AOP is needed. |
| 2 | AOP Concepts in Spring | Implemented all five advice types (@Before, @After, @AfterReturning, @AfterThrowing, @Around). |
| 3 | Spring AOP Architecture | Showed JDK Dynamic Proxy vs CGLIB Proxy and how Spring chooses between them. |
| 4 | Defining and Applying Aspects | Created, combined, and reused aspects; implemented multiple pointcuts and JoinPoint-based contextual logging. |
| 5 | Pointcut Expressions & Reusability | Demonstrated all major designators (execution, within, args, this, target), wildcard patterns, named and reusable pointcuts. |
| 6 | Common Real-World Use Cases of AOP | Built realistic cross-cutting implementations for Logging, Performance Monitoring, Security, and Exception Handling. |
The goal of this repository is to develop instinctive fluency in Spring AOP by mastering:
-
Advice types (
@Before,@After,@AfterReturning,@AfterThrowing,@Around) -
Pointcut expressions and designators
-
Proxy mechanisms (JDK and CGLIB)
-
Combining and reusing pointcuts
-
JoinPoint context usage
-
Real-world cross-cutting logic:
- Logging & Auditing
- Performance Profiling
- Security & Access Control
- Exception Handling & Recovery
Each task is self-contained and runnable independently for focused practice.
spring-aop-muscle-memory/
├── src/
│ ├── main/
│ │ ├── java/
│ │ │ ├── topic1_introduction/
│ │ │ ├── topic2_aop_concepts/
│ │ │ ├── topic3_architecture/
│ │ │ ├── topic4_aspect_definition/
│ │ │ ├── topic5_pointcuts/
│ │ │ └── topic6_usecases/
│ │ └── resources/
│ │ └── application.properties
├── pom.xml
└── .gitignore
Each topic contains multiple task packages (e.g., task1, task2, etc.), each with its own service, aspect, and runner class.
Each task includes its own main() or AppRunner class.
- Open the project.
- Navigate to the desired task (e.g.
topic2_aop_concepts/task3/AppRunner.java). - Right-click → Run ‘AppRunner.main()’.
Each commit follows the standard convention:
feat: implement <topic>-<task> – <short description>
Examples:
feat: implement task2.3 – demonstrate @AfterReturning advice
feat: implement task5.2 – demonstrate wildcard pointcut expressions
feat: implement task6.2 – add performance monitoring using @Around advice
- Java 21+
- Maven 3.9+
- IntelliJ IDEA or VS Code
- Spring Boot 3.x dependencies (managed via Maven)
- Practical mastery of Spring AOP architecture and proxy mechanisms.
- Hands-on understanding of advice execution flow and ordering.
- Ability to write reusable and maintainable aspects.
- Confidence in implementing cross-cutting features for logging, performance, and security.
- Strong grasp of pointcut syntax, patterns, and designators.
→ Proceed to the Spring Data JPA Module following the same task-driven structure. Each new module continues to build on this foundation.
Author: Chandra Sekhar Learning Journey: Spring Boot Mastery Plan Mentorship & Tasks Provided By: ChatGPT (OpenAI)