questions.md

💡 Design Considerations for Scaling and High Performance

Handling 1 Billion to 100 Billion Tap Records

With a significantly larger dataset, like 1 billion or 100 billion tap records, the design would pivot towards a robust, scalable, and efficient system capable of managing and querying large datasets. Key changes would include:

• Database Integration: Moving from .CSV files to a scalable database solution like PostgreSQL.
• ORM and API Layer: Utilizing Prisma as an ORM and GraphQL for efficient data querying.
• Scalability Strategies: Implementing horizontal scaling, caching, and microservices architecture.
• Continuous Optimization: Regular monitoring and optimization for database and application performance.

Achieving Sub 50ms Response Times

To consistently achieve sub 50ms response times, significant adjustments would focus on:

• In-Memory Data Stores: Using Redis or Memcached for caching.
• Database and Code Optimization: Database performance tuning and code optimization to reduce execution time.
• Efficient Data Processing: Utilizing parallel processing and optimized data models.
• Infrastructure Enhancements: Adopting microservices, serverless architectures, and edge computing for reduced latency.