Microservices Architecture: Java Spring Boot + Python Flask

## Why Hybrid Microservices?

When building McIntosh Digital Solutions, I needed a system that could handle both complex business logic and AI/ML processing efficiently. The solution? A hybrid approach using Java Spring Boot for business logic and Python Flask for AI services.

Architecture Overview

┌─────────────────┐     ┌─────────────────┐
│  React Frontend │────▶│  API Gateway    │
└─────────────────┘     └────────┬────────┘
                                 │
                    ┌────────────┼────────────┐
                    │            │            │
              ┌─────▼─────┐ ┌────▼────┐ ┌────▼────┐
              │  Spring   │ │  Flask  │ │  Auth   │
              │  Boot     │ │  AI     │ │  Service│
              │  Service  │ │  Service│ │         │
              └─────┬─────┘ └────┬────┘ └────┬────┘
                    │            │            │
              ┌─────▼────────────▼────────────▼─────┐
              │           PostgreSQL / Redis        │
              └─────────────────────────────────────┘

Spring Boot for Business Logic

Java Spring Boot handles all core business operations:

@RestController
@RequestMapping("/api/projects")
public class ProjectController {
    
    @Autowired
    private ProjectService projectService;
    
    @PostMapping
    public ResponseEntity<Project> createProject(
        @Valid @RequestBody ProjectDTO dto
    ) {
        return ResponseEntity.ok(
            projectService.create(dto)
        );
    }
}

Flask for AI Processing

Python Flask handles all AI/ML operations where Python's ecosystem shines:

from flask import Flask, request, jsonify

app = Flask(__name__) classifier = pipeline("sentiment-analysis")

@app.route("/api/ai/analyze", methods=["POST"]) def analyze(): text = request.json.get("text") result = classifier(text) return jsonify(result) ```

Inter-Service Communication

Services communicate via REST and message queues for async operations.

Results

• 60 FPS animations on the frontend
• Sub-100ms API response times
• Scalable AI processing
• Clean separation of concerns