Rensa

High-performance MinHash in Rust with Python bindings. On a reference full benchmark suite, Rensa is 608.52x faster than datasketch and 11.92x faster than FastSketch, with near-identical results.

What is Rensa?

Rensa (Swedish for "clean") computes MinHash signatures for similarity estimation and deduplication. If you need to find near-duplicates in large datasets, Rensa does what datasketch does, much faster.

It ships two MinHash variants:

  • R-MinHash: Rensa's own variant. Very close to datasketch output, fastest option.
  • C-MinHash: Based on the C-MinHash paper. Slightly different results, backed by formal variance proofs.

Open In Colab   Thanks mlabonne for the Colab notebook!

Performance

Numbers below come from a reference full benchmark run (benchmarks/full_benchmark.py) over 7 datasets and 2 thread lanes (threads=1,8), 128 permutations, threshold 0.8, and 8 bands.

Deduplication speed: full benchmark suite

Comparison Average speedup
Rensa vs Datasketch 608.52x faster
Rensa vs FastSketch 11.92x faster
Accuracy vs Datasketch Value
Mean Jaccard of kept sets 0.987219
Mean duplicate-flag mismatch rate 0.010717

These accuracy metrics are from the same full benchmark run as the speed numbers above.

How R-MinHash works

MinHash estimates Jaccard similarity between sets. Apply k random hash functions to a set, keep the minimum value from each. Two sets sharing many elements will produce similar minimums, and the fraction of matching slots estimates the Jaccard index.

Standard implementations generate each permutation as (a * hash(x) + b) mod p, where p is a large prime (typically the Mersenne prime 2^61 - 1). Mathematically clean, but modular reduction is still more expensive than a simple bit shift on modern CPUs.

R-MinHash replaces the modular reduction with multiply-shift hashing:

signature[i] = min { (a[i] * hash(x) + b[i]) >> 32 }  for all x in set

Instead of reducing mod a prime, take the upper 32 bits of the 64-bit multiply-add. This is a proven universal hash family (Dietzfelbinger et al., 1997). In R-MinHash, it is used as a practical approximation that keeps deduplication results very close to datasketch in benchmarks while making the hot path cheaper.

This choice has a useful side effect: since the output is naturally 32 bits, signatures are stored as u32 — 4 bytes per slot instead of 8. For 128 permutations, that's 512 bytes per signature. Half the memory, and twice as many signature slots fit in a cache line.

Performance engineering

On top of the algorithm, Rensa applies several low-level optimizations.

Input elements are hashed with a fast non-cryptographic hash that mirrors rustc_hash::FxHasher semantics while avoiding trait dispatch in the hot path. MinHash needs uniform distribution, not collision resistance, so there's no reason to pay for a cryptographic hash function.

Elements are hashed in groups of 32. Permutations are applied to each batch in chunks of 16, using a fixed-size temporary array ([u32; 16]) that is register-friendly. This gives the compiler room to optimize tight loops and keeps working data cache-local.

The (a, b) permutation pairs are deterministic, derived from a seed via Xoshiro256++. They are initialized at construction and reused across updates to avoid recomputing setup state on every incremental update. Permutation tables are shared process-wide per (num_perm, seed), so constructing or cloning many RMinHash objects with the same parameters costs O(1) in num_perm.

Token extraction reads compact ASCII str data inline (the common case for tokenized text) instead of round-tripping through PyUnicode_AsUTF8AndSize. For batch sketching over lists of ASCII/bytes tokens, worker threads read list items and string payloads directly from CPython object memory (safe because the calling thread holds the GIL and never runs Python during the build), so extraction itself is parallelized instead of bottlenecking on one producer thread; rows containing other token types fall back to the GIL thread. One-shot LSH deduplication groups rows by raw band hashes with intrusive chains (no per-bucket allocations), refines fold windows by exact hash-pair equality, and reuses the same scans' collision counts for its recall-rescue pass, with band scans fanned out across threads when a Rayon pool is available.

The global allocator is MiMalloc, which handles the batch-allocate-then-free pattern better than the system default. Rensa disables MiMalloc's eager arena commit at module load (unless overridden via MIMALLOC_ARENA_EAGER_COMMIT), which keeps peak RSS flat when many threads allocate short-lived sketch buffers.

C-MinHash

Rensa also includes C-MinHash, based on the C-MinHash paper. It uses a two-stage scheme (sigma then pi) that reduces the need for k independent permutations by deriving the k slots from a small parameter set. In this implementation, that means sigma_a/sigma_b and pi_c/pi_d, with precomputed pi terms for speed. The paper proves tighter variance bounds than standard MinHash. In practice, both variants produce similar results and R-MinHash is usually a bit faster. Use R-MinHash unless you have a specific reason not to.

Installation

uv add rensa

Works on Linux, macOS, and Windows. Python >= 3.8.

Usage

Computing similarity

from rensa import RMinHash

m1 = RMinHash(num_perm=128, seed=42)
m1.update("the quick brown fox jumps over the lazy dog".split())

m2 = RMinHash(num_perm=128, seed=42)
m2.update("the quick brown fox jumps over the lazy cat".split())

print(m1.jaccard(m2))  # ~0.78

CMinHash has the same interface. Just swap the class name.

Deduplicating a dataset

from datasets import load_dataset
from rensa import RMinHash, RMinHashLSH

dataset = load_dataset("gretelai/synthetic_text_to_sql")["train"]

# Build MinHash signatures
minhashes = {}
for idx, row in enumerate(dataset):
    m = RMinHash(num_perm=128, seed=42)
    m.update(row["sql"].split())
    minhashes[idx] = m

# Index into LSH
lsh = RMinHashLSH(threshold=0.8, num_perm=128, num_bands=16)
for doc_id, mh in minhashes.items():
    lsh.insert(doc_id, mh)

# Find and remove duplicates
to_remove = set()
for doc_id, mh in minhashes.items():
    if doc_id in to_remove:
        continue
    for candidate in lsh.query(mh):
        if candidate != doc_id and candidate not in to_remove:
            if mh.jaccard(minhashes[candidate]) >= 0.85:
                to_remove.add(max(doc_id, candidate))

print(f"Removed {len(to_remove)} duplicates from {len(dataset)} rows")

Batch APIs

For large batches, build and query in bulk to reduce Python call overhead:

from rensa import RMinHash, RMinHashLSH, RMinHashDeduplicator

token_sets = [
    "select id from users".split(),
    "select name from users".split(),
    "select id from users".split(),
]
keys = [f"doc-{idx}" for idx in range(len(token_sets))]

minhashes = RMinHash.from_token_sets(token_sets, num_perm=128, seed=42)
digests = RMinHash.digests_from_token_sets(token_sets, num_perm=128, seed=42)

lsh = RMinHashLSH(threshold=0.8, num_perm=128, num_bands=8)
lsh.insert_pairs(enumerate(minhashes))
candidates_per_doc = lsh.query_all(minhashes)

dedup = RMinHashDeduplicator(threshold=0.8, num_perm=128, use_lsh=True, num_bands=8)
added_flags = dedup.add_pairs(zip(keys, minhashes))
is_dup_flags = dedup.is_duplicate_pairs(zip(keys, minhashes))
duplicate_sets = dedup.get_duplicate_sets(minhashes)

CMinHash supports the same batch constructors, plus digests64_from_token_sets(...).

For expert throughput paths (when you already have hashed tokens or byte tokens):

from rensa import CMinHash, RMinHash

token_sets = [
    "select id from users".split(),
    "select name from users".split(),
]
token_hash_sets = RMinHash.hash_token_sets(token_sets)

r_matrix = RMinHash.digest_matrix_from_token_hash_sets(
    token_hash_sets, num_perm=128, seed=42
)
byte_matrix = RMinHash.digest_matrix_from_token_byte_sets(
    [[b"alpha", b"beta"], [b"gamma", b"delta"]],
    num_perm=128,
    seed=42,
)
c_digests64 = CMinHash.digests64_from_token_hash_sets(
    token_hash_sets, num_perm=128, seed=42
)

Streaming deduplication

For continuous data streams, use the built-in deduplicator:

from rensa import RMinHash, RMinHashDeduplicator

dedup = RMinHashDeduplicator(threshold=0.8, num_perm=128, use_lsh=True, num_bands=16)

for doc in document_stream:
    mh = RMinHash(num_perm=128, seed=42)
    mh.update(doc["text"].split())

    if not dedup.is_duplicate(doc["id"], mh):
        dedup.add(doc["id"], mh)
        # process unique document

API

RMinHash / CMinHash

Method Description
__init__(num_perm, seed) Create a MinHash with num_perm permutations
update(items) Add items (list of strings, bytes, or iterables)
jaccard(other) Estimate Jaccard similarity (requires matching num_perm)
digest() Return the signature as a list of integers
from_token_sets(...) Build many MinHash objects from token iterables
digests_from_token_sets(...) Compute many digests in one call
hash_token_sets(...) Hash token sets to reusable u64 token hashes
digest_matrix_from_token_sets(...) Build compact row-major digest matrix
digest_matrix_from_token_hash_sets(...) Build compact digest matrix from pre-hashed u64 tokens
digest_matrix_from_token_byte_sets(...) Build compact digest matrix from bytes-like tokens
digests64_from_token_sets(...) CMinHash only, returns u64-precision digests
digests64_from_token_hash_sets(...) CMinHash only, uses pre-hashed u64 tokens

RMinHashLSH

Method Description
__init__(threshold, num_perm, num_bands) Create an LSH index. num_bands must divide num_perm evenly
insert(key, minhash) Add a document to the index
query(minhash) Return candidate similar document keys
remove(key) Remove a document from the index
insert_pairs(entries) Insert many (key, minhash) pairs
insert_many(minhashes, start_key=0) Insert many minhashes with sequential keys
query_all(minhashes) Query many minhashes in one call
query_duplicate_flags(minhashes) Return len(query(minhash)) > 1 flags for many minhashes

RMinHashDeduplicator / CMinHashDeduplicator

Method Description
RMinHashDeduplicator(threshold, num_perm, use_lsh, num_bands=None, seed=42) R-MinHash streaming deduplicator
CMinHashDeduplicator(threshold, num_perm=None, seed=42) C-MinHash streaming deduplicator
add(key, minhash) -> bool Add if unique, returns whether it was added
is_duplicate(key, minhash) -> bool Check without adding
get_duplicates(minhash) -> list[str] Find keys of similar stored items
remove(key) / clear() Manage stored items
add_pairs(entries) -> list[bool] Batch add (key, minhash) or (key, token_set) pairs
is_duplicate_pairs(entries) -> list[bool] Batch duplicate checks for minhash or token-set pairs
get_duplicate_sets(minhashes) -> list[list[str]] Batch duplicate candidate lookup (minhash or token-set inputs)

Running Benchmarks

git clone https://github.com/beowolx/rensa.git && cd rensa
uv venv && uv sync --group bench --no-install-project
uv run maturin develop --release
uv run python benchmarks/simple_benchmark.py

Run the full cross-library benchmark (single-thread + multi-thread lanes):

uv run python benchmarks/full_benchmark.py

benchmarks/ now contains three scripts:

  • benchmarks/simple_benchmark.py: single-thread quick comparison across Datasketch, FastSketch, R-MinHash, and C-MinHash.
  • benchmarks/full_benchmark.py: fair per-run process-isolated benchmark (all engines per subprocess, randomized order) across Datasketch, FastSketch, and Rensa on the full dataset preset suite.
  • benchmarks/perf_memory_benchmark.py: Rensa-only time and peak-memory (VmHWM/VmRSS, Linux) benchmark on a deterministic synthetic corpus with injected duplicates, per thread lane and per phase (sketch / dedup / classic update path), with duplicate-flag precision/recall as an accuracy guardrail. Use --label to tag runs and compare JSON outputs across code changes.

simple_benchmark.py times rensa_c, but excludes it from accuracy comparisons because CMinHashDeduplicator.add_pairs uses streaming add-if-unique semantics rather than batch query-all semantics.

Contributing

Contributions welcome, just open a PR or issue.

License

MIT

Project links

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Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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Avatar for beowolx from gravatar.com beowolx

Credits

Author: Luis Cardoso

GitHub Statistics

Data verified by PyPI on Jul 23, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.8

Classifiers

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