opthash
opthash provides Rust hash maps and sets based on Elastic Hashing and Funnel Hashing, two open-addressing algorithms introduced in Optimal Bounds for Open Addressing Without Reordering. It turns the paper's fixed-table constructions into familiar collection APIs that can be used in real programs.
[!IMPORTANT] opthash is experimental. It is intended for evaluating and improving these algorithms, not as a drop-in performance replacement for
std::HashMaporhashbrown. Benchmark and test it against your own workload before adopting it in production.
Features
ElasticHashMap,ElasticHashSet,FunnelHashMap, andFunnelHashSet- Familiar map, set, entry, iterator, and collection APIs
- Paper-faithful placement during normal fixed-size operation
- Configurable spare capacity, hashers, and allocators
no_stdsupport withalloc- Optional Python bindings with typed map and set APIs
Quick start
Add opthash to a Rust project:
cargo add opthash
Both algorithms expose the same core collection APIs:
use opthash::{ElasticHashMap, FunnelHashSet};
let mut scores = ElasticHashMap::new();
scores.insert("Ada", 42);
scores.entry("Linus").or_insert(37);
assert_eq!(scores.get("Ada"), Some(&42));
let mut visited = FunnelHashSet::new();
visited.insert("Paris");
assert!(visited.contains("Paris"));
The default configuration uses foldhash and keeps one-eighth of the table
available as spare capacity. Most users should start with these defaults.
Advanced users can tune the reserve with ReserveFraction, provide another
BuildHasher, or use a custom allocator.
To use opthash without std, disable default features and provide a hasher:
[dependencies]
opthash = { version = "0.10", default-features = false }
Choosing an algorithm
Elastic and Funnel use different placement strategies but share the same core API. Choose the construction you want to explore; switching between them is straightforward. For current performance results, see the benchmark guide.
Compared with the paper
opthash preserves the paper's finite geometry, placement rules, and probe order during normal operation between table rebuilds. A usable dynamic collection also needs behavior outside the paper's model:
| Topic | Paper | opthash |
|---|---|---|
| Table lifetime | Fixed size | Grows and rebuilds as needed |
| Updates | Insertions only | Also replaces, deletes, clears, and cleans up tombstones |
| Placement | Uses the prescribed candidates | Follows them normally; rare exhaustion can trigger a broader correctness fallback |
| Randomness | Analyzes ideal random choices | Uses concrete deterministic mixing |
| Bounds | Analyzes probe complexity in a fixed-size, insertion-only model | The paper's bounds do not cover deletion, growth, Elastic misses, or wall-clock performance |
These differences preserve normal paper-faithful traces while making the maps usable as general collections. They also mean the paper's theoretical bounds should not be read as guarantees for every library operation. See the paper source used by this project for the exact construction.
Python
Python bindings expose the same four map and set families:
pip install opthash
from opthash import FunnelHashMap
scores = FunnelHashMap({"Ada": 42})
scores["Linus"] = 37
assert scores["Ada"] == 42
Python 3.10 or newer is supported.
Project resources
License
Licensed under the Apache License 2.0.
1 maintainer
Release files for opthash 0.10.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| opthash-0.10.4.tar.gz | 125.2 kB | Details |
Built distributions (wheels)
Total release size: 14.6 MB
Release files / opthash-0.10.4.tar.gz
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