Building a Modern AI-Ready Backend: Web MVC, JNI, WebFlux, and Python RPC
I designed and implemented a modular backend architecture that supports traditional Spring MVC services, native AI acceleration through JNI, reactive APIs using WebFlux, and remote Python-based inference. Through this project, I explored multiple approaches to bridging classical Java backend engineering with contemporary AI workloads.
Demo Video
Here is the complete demonstration integrating all four components.
1. Java Web MVC Core Library
The module focuses on building a clean and extensible Java MVC backend library.
- Implemented using a standard, well-structured Spring MVC architecture
- Abstracted the service, controller, and repository layers for rapid development
- Built reusable components to support future AI modules and reactive extensions
- Optimized for fast iteration and deployment in low-traffic environments
2. JNI Bridge for AI Integration
To connect Java with native AI modules, I developed a Java Native Interface (JNI) layer.
- Bridges Java services with C++ native AI components
- Enables efficient data exchange between the JVM and the native runtime
- Provides high-performance access to low-level AI model execution
- Serves as a critical foundation for extending Java applications with advanced AI capabilities
3. Reactive API with Spring WebFlux
Next, I added a non-blocking reactive API layer using Spring WebFlux.
- Handles high concurrency with minimal resource usage
- Ideal for streaming AI responses or batch prediction workloads
- Fully asynchronous and scalable
- This module brings modern reactive architecture into the system
4. Python RPC Model Service
Finally, I created a Python RPC inference server.
- Exposes AI model inference through a lightweight RPC protocol
- Decouples AI lifecycle from JVM, making the system more flexible
- Allows Java services to call Python algorithms remotely
- This design is highly suitable for real AI deployment in production environments
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