# Cribra: Probably yes. Definitely no

> A from-scratch Bloom filter: k hashes by double hashing over a packed bit array, optimal m/k from a target error rate, and false-positive + cardinality estimators. 12 tests led by the two defining properties - no false negatives, and an empirical false-positive rate that tracks theory.

Cribra is an interactive Bloom filter that can tell you a value is definitely absent or probably present, using far less memory than storing the set itself. The signature technique applies k hash functions via double hashing over a packed bit array, derives the optimal m and k from a target error rate, and adds false-positive and cardinality estimators, with tests led by its two defining properties - no false negatives, and an empirical false-positive rate that tracks theory. It is a from-scratch, dependency-light build you can download and run locally.

## About the app

Probabilistic data structures

## What to take away

- A from-scratch Bloom filter: k hashes by double hashing over a packed bit array, optimal m/k from a target error rate, and false-positive + cardinality estimators. 12 tests led by the two defining properties - no false negatives, and an empirical false-positive rate that tracks theory.
- Download: Cribra (ZIP)

## Tags

Free software, Cribra

## Read the full write-up

/downloads/cribra.zip

## We build this for clients

https://dfieldsolutions.com/en/services/full-stack

## More from the lab

- https://dfieldsolutions.com/en/lab/sylex.md — Sylex: Make sense of the slashes
- https://dfieldsolutions.com/en/lab/kindra.md — Kindra: Miss a beat, not the rhythm
- https://dfieldsolutions.com/en/lab/voxara.md — Voxara: Argue better. Lose gracefully
- https://dfieldsolutions.com/en/lab/wraithward.md — Wraithward: Spot the bait before you bite

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Source: https://dfieldsolutions.com/en/lab/cribra
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