Implement database sharding for horizontal scalability. Use when scaling large databases, distributing data across multiple servers, or designing sharded architectures.

Database Sharding Table of Contents [Overview]( overview) [When to Use]( when to use) [Quick Start]( quick start) [Reference Guides]( reference guides) [Best Practices]( best practices) Overview Implement horizontal data partitioning across multiple database servers. Covers sharding strategies, consistent hashing, shard key selection, and cross shard querying patterns. When to Use Database size exceeds single server capacity Read/write throughput needs horizontal scaling Geographic data distribution requirements Multi tenant data isolation Cost optimization through distributed architecture Load balancing across database instances Quick Start Minimal working example: Reference Guides Detailed implementations in the references/ directory: Guide Contents [Range Based Sharding](references/range based sharding.md) Range Based Sharding [Hash Based Sharding](references/hash based sharding.md) Hash Based Sharding [Directory Based Sharding](references/directory based sharding.md) Directory Based Sharding Best Practices ✅ DO Follow established patterns and conventions Write clean, maintainable code Add appropriate documentation Test thoroughly before deploying ❌ DON'T Skip testing or validation Ignore error handling Hard code configuration values

database-sharding