To configure a vector database to spill over to disk in memory-constrained environments, you need to prioritize which data stays in memory and optimize disk-based access for the rest. This typically involves using hybrid storage architectures, adjusting indexing strategies, and leveraging external storage systems. The goal is to maintain acceptable query performance while minimizing memory usage by offloading less frequently accessed data to disk.
First, consider using hybrid indexing structures that partition data between memory and disk. For example, some vector databases support hierarchical indexes like HNSW (Hierarchical Navigable Small World) combined with disk-based components. In this setup, the top layers of the index (used for approximate nearest neighbor search) reside in memory for fast access, while deeper layers or less frequently queried vectors are stored on disk. Tools like FAISS-IVF or Milvus’s hybrid index allow you to configure the number of clusters or segments kept in memory, with the rest persisted to disk. You might also enable memory-mapped files (e.g., in FAISS) to let the OS cache portions of the index on disk transparently, reducing direct memory consumption.
Second, offload bulk data to external storage systems while retaining metadata or compact representations in memory. For instance, use a two-tiered storage approach: store raw vector data on disk (or in cloud storage) and keep only quantized or compressed representations (e.g., using Product Quantization) in memory. During queries, retrieve approximate results from the in-memory index and fetch full vectors from disk only for final scoring. Tools like Vespa or Weaviate support this by allowing you to configure separate storage tiers. Additionally, implement chunking and pagination for large datasets—load only the required subset of vectors into memory during queries, and use disk-based caching (e.g., SQLite, RocksDB) for intermediate results.
Finally, tune database parameters to control memory thresholds and spillover behavior. Set memory limits (e.g., in Redis with maxmemory policies) to trigger automatic offloading of older or less-used vectors to disk. Configure write-ahead logs or checkpoints to ensure durability without keeping all data in memory. For example, in Elasticsearch, you can adjust circuit breaker settings to prevent out-of-memory errors by limiting heap usage and forcing queries to rely on disk-backed indices. Use lightweight serialization formats (e.g., Protocol Buffers) to reduce disk I/O overhead when reading/writing vectors. Tools like LMDB or LevelDB can also help manage efficient disk-based key-value storage for vector metadata.