Production-grade data structures and algorithms — zero external dependencies.

A collection of space-efficient probabilistic data structures and graph algorithms for general-purpose use.

Modules

• [bloom] - Bloom filter for approximate set membership
• [count_min] - Count-Min sketch for frequency estimation
• [graph] - Graph algorithms (BFS, Dijkstra, A*)
• [heap] - Bounded heap for K-nearest neighbor tracking
• [trie] - Trie prefix tree and Rabin-Karp string matching

Usage

use resq_dsa::bloom::BloomFilter;
use resq_dsa::count_min::CountMinSketch;
use resq_dsa::graph::Graph;

// Bloom filter for deduplication
let mut bf = BloomFilter::new(1000, 0.01);
bf.add("drone-001");
assert!(bf.has("drone-001"));

// Count-Min for frequency tracking
let mut cms = CountMinSketch::new(0.01, 0.01);
cms.increment("sensor-reading", 5);

// Graph for pathfinding
let mut g = Graph::<&str>::new();
g.add_edge("base", "waypoint-1", 100);
g.add_edge("waypoint-1", "target", 50);
let (path, cost) = g.dijkstra(&"base", &"target").unwrap();
assert_eq!(path, vec!["base", "waypoint-1", "target"]);