A 2026 research paper on streaming approximate nearest neighbor search with in-place graph index updates. The approach enables real-time index modifications without expensive rebuilds, crucial for dynamic datasets.
Published in February 2026 (arXiv:2502.13826), this paper addresses a critical challenge in graph-based vector indexes: efficiently updating the index as data streams in without costly rebuilds.
Graph-based indexes (HNSW, DiskANN) achieve excellent search performance but struggle with updates:
Naive Insertion: Simply adding nodes can degrade graph quality over time
Periodic Rebuilds: Expensive and cause service interruptions
Incremental Updates: Complex to maintain graph quality
Deletions: Particularly challenging due to singly-linked graph structure
The paper presents algorithms for modifying graph indexes directly:
Methods to add nodes while preserving graph navigability and search quality
Techniques to remove nodes and repair graph connectivity without expensive global updates
Algorithms ensuring the graph structure remains optimal as it evolves
Strategies for efficiently processing batches of insertions/deletions
Real-Time Freshness: Index reflects current data without delays
No Downtime: Avoid service interruptions from rebuilds
Lower Cost: Incremental updates cheaper than full rebuilds
Scalability: Practical for continuous data streams
Related to but distinct from FreshDiskANN:
Many production vector search applications have dynamic data:
In-place update techniques make graph-based indexes practical for these scenarios without sacrificing the search quality that makes them attractive.
Published as arXiv preprint arXiv:2502.13826 (2026) with algorithmic details and performance analysis.
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