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Quake: Adaptive Indexing for Vector Search

Talkat OSDI '25 · 2025-07-07..07-09· [machine-learning, databases]
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Quake implements a multi-level partitioning index with a practical cost model that picks splits/merges by estimated query cost, an online adaptive partition scanning recall estimator that sets per-query probes from geometric overlap, and NUMA-aware intra-query parallelism for throughput. Evaluated on a Wikipedia growth workload using the authors’ workload generator, it reports query latency reductions of about 1.5–38× and update latency reductions of 4.5–126× versus SVS, DiskANN, HNSW, and SCANN, and the system is open source.

AI-written summary · about

  1. 0:00Quake and vector search
  2. 3:26Index imbalance and recall loss
  3. 4:31Quake’s adaptive indexing
  4. 5:35Cost-driven maintenance
  5. 9:10Adaptive partition scanning
  6. 10:35Wikipedia workload evaluation
  7. 13:02Comparison with baselines
  8. 14:05Summary and release
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