Overview
Sharding distributes vector data across multiple nodes, enabling horizontal scaling to handle datasets too large for a single machine.
How It Works
- Partition Data: Divide vectors across shards
- Distribute: Each shard on different node
- Parallel Queries: Query all shards simultaneously
- Merge Results: Combine top-k from each shard
Sharding Strategies
Hash-Based
- Deterministic distribution
- Even load balancing
- Simple implementation
Range-Based
- By vector ID ranges
- Ordered access patterns
- Can create hotspots
Semantic Sharding
- Group similar vectors
- Query routing optimization
- Complex implementation
Benefits
- Horizontal Scaling: Add nodes for capacity
- Parallel Processing: Faster queries
- Load Distribution: Spread traffic
- Fault Tolerance: Replica shards
Considerations
- Query Latency: Network overhead
- Result Merging: Top-k accuracy
- Rebalancing: Adding/removing shards
- Cross-Shard Queries: Complexity
Vector Database Support
- Weaviate: Native sharding
- Milvus: Distributed architecture
- Qdrant: Sharding + replication
- Pinecone: Automatic sharding
- Elasticsearch: Index sharding
Best Practices
- Plan shard count for growth
- Monitor shard sizes
- Use replication for availability
- Consider query patterns
- Test rebalancing procedures
Pricing
Feature of distributed vector databases, affects infrastructure costs.
