Architected and developed a high-performance NFT marketplace backend system capable of processing thousands of NFT collections with real-time metadata normalization, rarity computation, and dynamic ranking algorithms. Built with Node.js for scalable async processing and MongoDB for flexible document storage, the system handles complex metadata ingestion from multiple blockchain networks. Implemented intelligent rarity scoring algorithms that analyze trait combinations, compute statistical distributions, and generate real-time rankings. The backend includes robust worker queues for batch processing, WebSocket connections for live updates, and RESTful APIs for frontend integration. Designed for horizontal scaling to handle peak marketplace traffic and collection launches.
Tech Used
Process Followed
Started with requirements analysis for NFT metadata processing and marketplace functionality. Designed scalable architecture with microservices for ingestion, processing, and API layers. Implemented Node.js workers for async metadata normalization from multiple blockchain sources. Built MongoDB schemas optimized for NFT collection queries and trait filtering. Developed rarity computation algorithms analyzing trait frequency and statistical distributions. Created real-time ranking system using Redis for caching and WebSocket for live updates. Implemented Bull queue for background job processing and rate limiting. Built RESTful APIs with Express for frontend integration. Integrated IPFS for decentralized metadata storage. Conducted load testing and optimization for high-traffic scenarios. Deployed with Docker containers and orchestrated with Kubernetes for auto-scaling.
Delivered in Time
5 months
