{
  "slug": "broom-filter",
  "term": "Broom filter",
  "aliases": [
    "Broom filters"
  ],
  "definition": "A broom filter is an adaptive approximate-membership structure that refines fingerprints using remote collision information while retaining compact local state.",
  "note": "Correction: an exact dictionary check triggers a reverse lookup and fingerprint extension. The guarantee assumes suitable hashing and valid set updates. [broom-paper]\n\nDeletion: correction history survives deletion and reinsertion until rehashing; the design retains quotient and adaptivity bits but drops the remainder. Local-space optimality excludes remote state and is not a measured latency claim. [broom-paper]",
  "citations": [
    {
      "id": "broom-paper",
      "title": "Bender et al. Bloom Filters, Adaptivity, and the Dictionary Problem. FOCS 2018. DOI: 10.1109/FOCS.2018.00026. arXiv v3, sections 2-4.",
      "url": "https://arxiv.org/abs/1711.01616v3",
      "accessed": "2026-11-12"
    },
    {
      "id": "hesela-guarantees",
      "title": "Hesela: fresh challenges, repeated queries and correction history",
      "url": "https://hesela.com/analyses/bloom-filters-lsm-read-cost/#fresh-versus-repeated",
      "accessed": "2026-11-12"
    }
  ],
  "related": [
    "bloom-filter",
    "adaptive-quotient-filter",
    "adversarial-resilient-filter"
  ],
  "status": "research",
  "advantages": [],
  "limitations": [],
  "supersedes": [],
  "supersededBy": [],
  "firstSeen": null,
  "lastSeen": null,
  "updated": "2026-11-12",
  "provenance": [
    "Primary-source synthesis; no filter implementation, security evaluation or storage benchmark executed."
  ]
}