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    3. SPFresh

    SPFresh

    Incremental in-place update system for billion-scale vector search from Microsoft Research. Maintains 2.41x lower P99.9 latency than baselines while supporting efficient vector updates with minimal resource overhead.

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    About this tool

    Overview

    SPFresh is a system that supports in-place vector updates, with LIRE (Lightweight Incremental REbalancing) at its core - a protocol to split vector partitions and reassign vectors in nearby partitions to adapt to data distribution shift.

    Key Performance Characteristics

    • Provides superior query latency and accuracy to solutions based on global rebuild
    • Uses only 1% of DRAM compared to state-of-the-art
    • Requires less than 10% cores at peak for billion-scale vector index with 1% daily update rate
    • Maintains 2.41x lower P99.9 latency than baselines
    • Keeps high accuracy in dynamic scenarios
    • Achieves 5.30x lower memory usage than baselines

    Technical Approach - LIRE Protocol

    LIRE achieves low-overhead vector updates by only reassigning vectors at the boundary between partitions, where in a high-quality vector index the amount of such vectors are deemed small.

    Problem Addressed

    Existing systems maintain a secondary index to accumulate updates, which are merged by the main index through global rebuilding. This approach has:

    • High fluctuations of search latency and accuracy
    • Substantial resource requirements
    • Extremely time-consuming rebuilds

    SPFresh eliminates the need for global index rebuilds through incremental in-place updates.

    Research

    Published in October 2024 by USTC, Microsoft Research Asia, and Harvard University.

    Applications

    Designed for applications requiring efficient vector index updates:

    • Information retrieval
    • Question answering systems
    • Recommendation systems
    Surveys

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    Information

    Websitewww.microsoft.com
    PublishedMar 8, 2026

    Categories

    1 Item
    Research Papers & Surveys

    Tags

    3 Items
    #Ann#Research#Dynamic Updates

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