{
  "id": 14509603,
  "title": "Part 6: Quotient Filters - the Theory",
  "url": "https://urgent.news/2026/10/14/part-6-quotient-filters-the-theory",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-14T18:30:33.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/fkereki/part-6-quotient-filters-the-theory-228a"
  },
  "original_language": "en",
  "account": "Quotient Filters (QFs) offer a solution to the limitations of Bloom Filters when it comes to removing added values. Unlike Bloom Filters, QFs can support removals without incurring significant memory overhead. The main innovation behind QFs lies in their structure, which computes a single hash per item and splits it into two parts: a quotient and a remainder. The quotient determines a slot in the filter, while the remainder is stored as a fingerprint within that slot. This approach allows QFs to retain enough information to support removals, using memory close to that of a Bloom Filter.",
  "summary": "In the previous articles of this series, we saw Bloom Filters, with restrictions on removing added values. Here we'll see Quotient Filters, which free the caller from those limits. Quotient Filters Bloom Filters answer one question cheaply: \"Have I possibly seen this value before?\" An SBF cannot support removals, and a CBF can, albeit at a cost: it requires more memory, and to ensure a removal…",
  "key_points": [
    "Quotient Filters (QFs) address Bloom Filters' removal limitations",
    "QFs compute a single hash per item, split into quotient and remainder",
    "Quotient determines slot, remainder stored as fingerprint within slot"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}