Database Scaling

Database scaling is a spectrum: start simple (read replicas), add complexity only as needed (sharding). The golden rule: sharding is a last resort because of the massive complexity it adds. Most systems can run on a well-tuned single primary with read replicas for years. Why Single Databases Fail at Scale Your startup is growing. You started with one PostgreSQL database handling everything. Then this happens: 100 users: Everything is fast. Life is good. 10,000 users: Queries start slowing down. You add indexes. 100,000 users: Writes are bottlenecked. One write per ms = 1000 writes/sec max. 1,000,000 users: Database is the bottleneck. Reads are slow, writes are queued. The Three Walls CPU wall: Complex queries eat CPU. More users = more queries. Memory wall: Hot data doesn't fit in RAM anymore. Disk reads are 100x slower. Storage wall: Disk is full. Can't store more data on one machine. The Solution Spectrum Always start simple and scale only when you have a measured problem. Premature optimization is the root of all evil. Python Implementation Java Implementation JavaScript/TypeScript Implementation