Vector databases scale to millions or billions of vectors by combining distributed architectures, efficient indexing algorithms, and optimized storage strategies. These systems prioritize speed and resource efficiency while maintaining the ability to perform fast similarity searches across massive datasets.
Distributed Architecture and Sharding A core feature enabling scalability is the use of distributed systems. Vector databases partition data across multiple nodes via sharding, where each shard stores a subset of vectors. For example, a database might hash vectors into shards based on their IDs or use a range-based strategy. During queries, the system scatters requests to relevant shards, processes them in parallel, and merges results. This horizontal scaling allows the database to handle larger datasets by adding more nodes. Distributed systems also use replication for fault tolerance—each shard may have copies stored on different nodes, ensuring availability if a node fails. Tools like Apache ZooKeeper or etcd often manage cluster coordination and metadata.
Efficient Indexing with Approximate Algorithms Vector databases rely on approximate nearest neighbor (ANN) indexes to balance accuracy and speed. Algorithms like HNSW (Hierarchical Navigable Small World) organize vectors into layered graphs, enabling fast traversal during searches. IVF (Inverted File Index) groups vectors into clusters, narrowing search scope to the most relevant clusters first. These methods reduce computational complexity from O(n) to O(log n) or better. For instance, Milvus uses HNSW and IVF indexes, allowing it to search billion-scale datasets in milliseconds. Indexes are often built asynchronously or in batches to avoid blocking real-time operations, and some systems support dynamic updates to accommodate new data without full rebuilds.
Storage Optimization and Resource Management To manage memory and storage, vector databases employ techniques like compression and tiered storage. Product quantization reduces vector dimensions by splitting them into subvectors and encoding them compactly. Systems may store frequently accessed vectors in memory (using frameworks like FAISS) while offloading older data to disk. Columnar storage formats (e.g., Parquet) optimize disk reads for batch operations. Additionally, caching mechanisms store hot query results or index structures in memory to reduce latency. For example, Weaviate uses a hybrid approach where vectors are stored in-memory for fast access, while metadata resides in disk-based databases like PostgreSQL, balancing performance and cost.