PostgreSQL handles remarkable workloads on single servers, but eventually growth demands horizontal scaling. Sharding distributes data across multiple database instances, enabling virtually unlimited scale. However, sharding introduces complexity in queries, transactions, and operations that requires careful planning.
Sharding Approaches
Hash-based sharding distributes data uniformly using key hashes, providing balanced load but complicating range queries. Range-based sharding groups related data together, enabling efficient range scans but risking hotspots. Directory-based sharding offers flexibility through lookup tables but adds query overhead. Choose approaches matching your access patterns.
- Citus extends PostgreSQL with transparent sharding and distributed query execution
- Application-level sharding provides maximum control but requires significant code changes
- Vitess offers MySQL-compatible sharding with PostgreSQL support emerging
- Consider read replicas before sharding for read-heavy workloads
- Plan shard key selection carefully—changing later proves extremely difficult
Operational Complexity
Sharded databases multiply operational burden. Backups, monitoring, and maintenance apply to every shard. Cross-shard queries and transactions require careful handling. Rebalancing data when adding shards demands careful orchestration. Teams should honestly assess operational capacity before committing to sharded architectures.