opthash

Rust implementations of Elastic Hashing and Funnel Hashing from Optimal Bounds for Open Addressing Without Reordering (Farach-Colton, Krapivin, Kuszmaul, 2025).

Both are open-addressing hash maps that achieve optimal expected probe complexity without reordering elements after insertion.

Data Structures

• ElasticHashMap<K, V> — Multi-level table with geometrically halving levels. Keys are placed via batch-based insertion across levels using stride-based probing.
• FunnelHashMap<K, V> — Multi-level bucketed table with a 3/4-ratio geometric progression and a special overflow array (primary + fallback) for keys that don't fit in any level.

Both support insert, get, get_mut, contains_key, remove, and clear. Maps start with zero allocation (new()) and grow dynamically on demand. Advanced tuning is available through ElasticOptions, FunnelOptions, and with_options(...).

Benchmarks

Current Criterion throughput results on Apple M1 (aarch64, NEON SIMD), normalized so std::HashMap is the 1.0x baseline:

Core workloads:

Secondary workloads:

Regenerate the benchmark chart:

cargo bench --bench throughput
uv venv
uv pip install -r requirements.txt
uv run scripts/generate_speedup_chart.py

Criterion also generates an interactive HTML report at target/criterion/report/index.html.

Usage

use opthash::{ElasticHashMap, ElasticOptions, FunnelHashMap};

let mut map = FunnelHashMap::new();
map.insert("key", 42);
assert_eq!(map.get("key"), Some(&42));

let tuned = ElasticHashMap::<u64, u64>::with_options(ElasticOptions {
    capacity: 1024,
    reserve_fraction: 0.10,
    probe_scale: 12.0,
});
assert_eq!(tuned.len(), 0);