In distributed vector databases, data is typically partitioned using strategies that balance load and optimize search performance. Common approaches include vector-aware sharding (like clustering similar vectors using algorithms such as k-means or FAISS's IVF), hash/range-based partitioning for even distribution, and attribute-based sharding for filtering (e.g., geospatial or user-specific attributes). Clustering-based methods group vectors into shards based on proximity in the embedding space, allowing queries to target specific clusters first. Hash-based partitioning spreads data evenly but ignores semantic relationships, which can scatter related vectors across shards. Attribute-based sharding combines metadata with vector data, enabling hybrid queries (e.g., filtering by region before vector search). For example, a database might shard product embeddings by category (attribute) and then cluster similar products within each category.
Searching across shards introduces challenges in accuracy, latency, and resource coordination. When a query targets nearest neighbors, each shard returns local top results, but the global nearest might reside in another shard. To mitigate this, systems often query multiple shards (e.g., probing the nearest clusters in IVF), but this increases network overhead and latency. For high-dimensional data, distance calculations become computationally expensive, especially when aggregating results from multiple shards. Additionally, shard-specific indexing (like per-shard HNSW graphs) can lead to inconsistent rankings, requiring post-processing (e.g., reranking merged results). For example, a query might retrieve 50 candidates from 5 shards but spend extra time sorting them globally.
Further challenges include handling dynamic data updates and balancing shard sizes. Adding or removing vectors may necessitate re-clustering, which is resource-intensive. Load imbalances can occur if certain shards (e.g., popular product categories) grow faster, leading to hotspots. Approximate algorithms trade accuracy for speed, but cross-shard aggregation amplifies approximation errors. Consistency is another concern: if a shard updates its vectors during a query, results may become inconsistent. Systems often use eventual consistency or versioning to address this. For instance, a real-time recommendation system might prioritize low latency by querying the most recent shard snapshot while background processes handle rebalancing.