Researchbloom filterfeature storerecommender systemssketching
Sketching Feature Stores Reduce Memory And Latency
6.9
This article demonstrates using bloom-filter-backed sketching feature stores to compress and serve historical categorical features for recommender ML. Using a real click-prediction dataset (5.7 million examples, 1.762 billion extracted data points) and logistic regression, the compressed store achieved similar accuracy and throughput while fitting state in-process, reducing external lookups and operational costs.
Key Points
- 1Implements bloom-filter feature store on 5.7M examples, 1.762 billion post-extraction data points
- 2Reduces latency by avoiding external key-value lookups, removing 2–10 ms per network call
- 3Enables feasible in-process multiple lookups, lowering hosting costs and operational complexity for practitioners
Scoring Rationale
Practical benchmark and measurable latency/memory benefits drive score; limited novelty and single-source validation constrain broader adoption.
Sources
Public references used for this report.
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