Vector search manages memory usage through a combination of efficient data structures, compression techniques, and storage optimization strategies. At its core, vector search involves storing and querying high-dimensional vectors, which can consume significant memory due to their size. To address this, systems often employ methods like dimensionality reduction, quantization, and approximate nearest neighbor (ANN) algorithms. For example, using product quantization, a vector is split into subvectors, each represented by a smaller codebook entry, drastically reducing memory footprint without losing critical information. Similarly, techniques like hierarchical navigable small world (HNSW) graphs optimize memory by organizing vectors into layers of interconnected nodes, balancing search speed and storage efficiency.
Another key approach involves indexing strategies that prioritize memory efficiency. For instance, inverted file (IVF) indexing partitions vectors into clusters, allowing the system to store only cluster centroids and pointers to associated vectors. This reduces redundant storage and limits the scope of searches to relevant clusters. Additionally, memory-mapped files or on-disk storage can offload less frequently accessed data to disk while keeping hot data in RAM. However, this requires careful tuning to avoid performance penalties. Developers might also use compressed vector formats, such as 8-bit integers instead of 32-bit floats, which cut memory usage by 75% with minimal accuracy loss. Tools like FAISS or Annoy implement many of these optimizations out of the box, letting developers trade off between memory, speed, and precision based on their use case.
Finally, memory management in vector search often involves balancing real-time requirements with resource constraints. For example, in-memory databases like Redis or specialized vector databases (e.g., Milvus) use tiered storage: recent or high-priority vectors reside in faster, more expensive memory, while older data moves to cheaper, slower storage. Sharding—splitting data across multiple machines—also helps distribute memory load horizontally. For large-scale systems, hybrid approaches combine techniques like quantization for individual vectors and graph-based indexing for efficient search. A practical example is an e-commerce recommendation engine storing millions of product embeddings: using product quantization reduces memory per vector, while HNSW ensures fast lookups. Developers must evaluate their specific needs, such as query latency tolerance and dataset size, to choose the right mix of strategies.