# Vector Database Sharding

> Distributing vector data across multiple nodes for horizontal scaling. Enables handling billions of vectors by partitioning data and parallelizing queries.

- **URL:** https://weaviate.io/blog/scaling-and-weaviate
- **Category:** Concepts & Definitions
- **Tags:** Sharding, scalability, Distributed
- **Updated:** 2026-03-11 12:14
- **Canonical page:** https://vectordb.ever.works/zh/items/vector-database-sharding

## Details

## Overview

Sharding distributes vector data across multiple nodes, enabling horizontal scaling to handle datasets too large for a single machine.

## How It Works

1. **Partition Data**: Divide vectors across shards
2. **Distribute**: Each shard on different node
3. **Parallel Queries**: Query all shards simultaneously
4. **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.

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