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Life & fun · August 21, 2025

SIMD-optimized Bloom filters in Mojo for large-scale systems

SIMD-Optimized Bloom Filters in Mojo for Large-Scale Systems - axiomhq/mojo-bloomfilter

SIMD-optimized Bloom filters in Mojo for large-scale systems launched on August 21, 2025 with 6 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.

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What it does

A Bloom filter is a space-efficient probabilistic data structure designed to test whether an element is a member of a set. It can tell you if an item is definitely not in the set or might be in the set. The key properties: Common use cases include web crawlers (detecting already-visited URLs), databases (avoiding unnecessary disk lookups), and distributed systems (reducing network calls). git clone https://github.com/seif/mojo-bloomfilter.git cd mojo-bloomfilter Use Standard: from standard import StandardBloomFilter var bf = StandardBloomFilter.create_for_fpr( 10000 , 0.01 ) bf.add(data) if bf.contains(data): print ( " maybe there " ) Split block (SIMD): from splitblock import…from github.com

Does a similar job

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  • BloomSearch – Keyword search with hierarchical Bloom filters2025 · github.com · ▲66

    Hey HN! I got nerd-sniped by Bloom Filters this weekend, specifically for searching datasets with high "cardinality" (number of unique items). They're an _amazing_ data structure that, at a fixed size, tracks potential set membership. That means unlike normal b-tree indexes, they don't grow with the number of unique items in the dataset. This makes them great for "needle in a haystack" search (logs, document) as implementations like VictoriaMetrics and Bing's BitFunnel show. I've used them in the past, but they've never been center-stage in my projects. I wanted high cardinality keyword…

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