Implement Redis Caching and Multi-layer Rate Limiting

[anuja12mishra]

Feature Request: Redis Caching & Rate Limiting for Blogic Backend

📌 Background

Currently, the Blogic backend interacts directly with MongoDB for every request.
There is no protection against high-frequency automated requests (DoS/DDoS) or abuse of the AI generation feature.

🎯 Goals

• Improve response times for blog posts and comments using Redis caching.
• Secure the API with global and AI-specific rate limiting.
• Reduce database load and infrastructure costs.

🛠 Proposed Features

1. Redis Caching System

• Cache all blogs and single blog details.
• Cache comments for each blog post.
• Implement automated cache invalidation on create, update, and delete operations to ensure data consistency.

2. Multi-layer Rate Limiting

• Global API Limiter: Prevent DoS/DDoS attacks with a reasonable per-IP limit.
• AI-Specific Limiter: Restrict high-cost Gemini AI calls (e.g., 5 requests per hour).

✅ Implementation Checklist

• Set up Redis connection and configuration in the backend.
• Create middleware for apiLimiter and aiLimiter using express-rate-limit.
• Create a reusable cacheMiddleware for GET requests.
• Integrate caching into Blog and Comment routes.
• Add cache-clearing logic to Blog and Comment controllers.

📈 Expected Impact

• Faster page loads for the frontend.
• Decreased Mongoose/MongoDB resource consumption.
• Enhanced security and cost control for AI features.

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🔒 Priority

High – This feature directly impacts performance, scalability, and security.

🧩 Labels

enhancement backend performance security redis rate-limiting