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Incremental IVF Index Maintenance for Streaming Vector Search

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arxiv 2411.00970 v1 pith:6H6KVS7T submitted 2024-11-01 cs.DB cs.AIcs.LG

Jason Mohoney , Anil Pacaci , Shihabur Rahman Chowdhury , Umar Farooq Minhas , Jeffery Pound , Cedric Renggli , Nima Reyhani , Ihab F. Ilyas
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Theodoros Rekatsinas Shivaram Venkataraman
This is my paper · ORCID
classification cs.DBcs.AIcs.LG
keywords indexsearchmaintenancevectorada-ivfindexesapplicationsdata
verification ladder T0 review T1 audit T2 compute T3 formal
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The prevalence of vector similarity search in modern machine learning applications and the continuously changing nature of data processed by these applications necessitate efficient and effective index maintenance techniques for vector search indexes. Designed primarily for static workloads, existing vector search indexes degrade in search quality and performance as the underlying data is updated unless costly index reconstruction is performed. To address this, we introduce Ada-IVF, an incremental indexing methodology for Inverted File (IVF) indexes. Ada-IVF consists of 1) an adaptive maintenance policy that decides which index partitions are problematic for performance and should be repartitioned and 2) a local re-clustering mechanism that determines how to repartition them. Compared with state-of-the-art dynamic IVF index maintenance strategies, Ada-IVF achieves an average of 2x and up to 5x higher update throughput across a range of benchmark workloads.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When to Repair a Graph ANN Index: A Matched-Budget Negative Result, and the Interpolated-Baseline Trap That Hid It

    cs.DB 2026-07 unverdicted novelty 6.0 of 10

    Signal-triggered local repair in graph ANN indexes improves minimum recall@10 by 0.014-0.050 under bursty churn versus fixed-cadence repair at matched budget on SIFT-128 and Fashion-MNIST-784.

  2. CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search

    cs.DB 2025-08 conditional novelty 6.0 of 10

    CRouting prunes unpromising neighbors in graph-based ANNS by estimating their distance with a fixed angle from the dataset's angle distribution, cutting distance computations by up to 41.5% and boosting QPS by up to 1.48x.

  3. Quake: Adaptive Indexing for Vector Search

    cs.IR 2025-06 conditional novelty 6.0 of 10

    Quake adaptively splits, merges, and scans partitions per query to keep vector search fast and accurate when data and access patterns change.

  4. Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

    cs.IR 2026-07 conditional novelty 3.5 of 10

    Temporalized full-text BM25 is the strongest LongEval-Sci 2026 Task 1 submission, while uncalibrated temporal overlays and citation features remain fragile or unproven.

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