When the dataset size exceeds available RAM, what approaches can be used to still perform vector search (e.g., disk-based indexes, streaming data from disk, or hierarchical indexing)?

When working with vector datasets that exceed available RAM, three practical approaches can enable efficient vector search: disk-based indexes, streaming data in batches, and hierarchical indexing. Each method balances memory usage and computational efficiency differently, allowing developers to handle large-scale data without requiring excessive hardware resources.

Disk-based indexes store the vector index on disk rather than in RAM, using memory-mapping techniques to access portions of the index as needed. For example, libraries like FAISS (Facebook AI Similarity Search) support memory-mapped indexes, where only the parts of the index required for a query are loaded into RAM. This reduces memory pressure but increases latency due to disk I/O. To mitigate performance issues, developers can optimize disk access patterns (e.g., sequential reads) or use SSDs for faster read speeds. Disk-based indexes work well for scenarios where latency is acceptable, such as batch processing or offline tasks. However, they may struggle with real-time applications if disk access becomes a bottleneck.

Streaming data from disk involves processing vectors in smaller chunks instead of loading the entire dataset. For instance, you can split the dataset into shards stored on disk and load one shard at a time into RAM for searching. Tools like memory-mapped arrays (e.g., NumPy’s memmap) allow partial loading of vectors without copying the entire file into memory. Another approach is to use a database or file format that supports streaming, such as HDF5 or Parquet. This method requires careful management of data chunks to avoid redundant disk reads and ensure that relevant vectors are available during searches. While this approach minimizes RAM usage, it may require trade-offs in search speed, especially if the query needs to scan multiple shards.

Hierarchical indexing combines coarse-grained and fine-grained search layers to reduce computational overhead. For example, a two-stage process might use a fast, approximate index (like IVF in FAISS) to narrow down candidate vectors stored on disk, followed by a precise search on the smaller subset loaded into RAM. Another variation is partitioning the dataset into clusters and storing cluster centroids in RAM, while keeping detailed vectors on disk. During a query, only the most relevant clusters are retrieved from disk for comparison. This approach leverages the strengths of both in-memory and disk-based operations, balancing speed and memory usage. However, it requires tuning parameters like cluster size and approximation levels to maintain accuracy and performance.

Developers should choose the approach based on their specific constraints: disk-based indexes for simplicity, streaming for flexibility in data size, and hierarchical indexing for a balance of speed and scalability. Combining methods (e.g., hierarchical indexing with memory-mapped files) can further optimize performance for large-scale vector search tasks.