similar-rs

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Rust-powered text diffing for Python: bindings to the similar crate by Armin Ronacher. It also ships a drop-in replacement for the standard library's difflib that runs 2-4x faster on real file diffs and ~200x on accurate whole-file similarity — on tiny inputs it is a wash. It also adds six diff algorithms stdlib does not have.

Bar chart comparing stdlib difflib and similar-rs on three workloads: about the same on a tiny diff, 4x faster on a real file diff, about 200x faster on accurate whole-file similarity.

Real CPython sources, one Apple M4 — per-pair tables in benchmarks/results.md.

Install

pip install similar-rs

Wheels: Linux (x86_64, aarch64), macOS (arm64, x86_64) and Windows (AMD64). They are abi3 — one wheel per platform, CPython 3.9+. No system dependencies. An sdist is published too; building from it needs a Rust toolchain.

Usage

import similar

print(similar.unified_diff("a\nb\n", "a\nc\n", header=("old", "new")))

TextDiff computes the diff once and exposes the opcodes and a similarity ratio:

d = similar.TextDiff("a\nb\n", "a\nc\n", granularity="lines")
d.ops()    # [("equal", (0, 1), (0, 1)), ("replace", (1, 2), (1, 2))]
d.ratio()  # 0.5

granularity is lines, words or chars. algorithm is one of myers (default), raw-myers, patience, lcs, hunt or histogram; both TextDiff and unified_diff accept it.

difflib drop-in

Change one import line — SequenceMatcher, unified_diff and get_close_matches then run on Rust:

from similar import difflib  # instead of: import difflib

difflib.SequenceMatcher(None, "kitten", "sitting").ratio()  # 0.6153...
"".join(difflib.unified_diff(["a\n", "b\n"], ["a\n", "c\n"], "old", "new"))

difflib compatibility

API Status
SequenceMatcher.ratio, quick_ratio, real_quick_ratio Rust
SequenceMatcher.get_opcodes, get_matching_blocks, get_grouped_opcodes Rust
unified_diff, get_close_matches Rust
Differ, HtmlDiff, ndiff, context_diff, restore, diff_bytes stdlib passthrough
SequenceMatcher.find_longest_match stdlib passthrough

Known differences:

  • isjunk and autojunk are ignored; passing an isjunk raises a RuntimeWarning.
  • Opcodes are a valid edit script, but not byte-for-byte the one stdlib produces — it is a different algorithm, finding a different (as a rule, no smaller) set of matches. ratio() and get_opcodes() can therefore differ from stdlib's at either autojunk setting, not just autojunk=True.
  • get_close_matches can return a different set for the same reason: the underlying ratios differ. The tie order among equal ratios matches stdlib.
  • find_longest_match runs stdlib's algorithm with autojunk=False, so its result can differ from a default stdlib matcher's (autojunk=True) — and it may name a block that get_matching_blocks() of the same object (Rust opcodes) does not contain.
  • Inputs must be str or sequences of str.
  • The stdlib instance attributes b2j, bjunk, bpopular, opcodes and matching_blocks are absent; the caches are private.
  • Strings holding lone surrogates (as diff_bytes produces) cannot cross into Rust, so they take a slow pure-Python fallback via stdlib difflib.

Limitations

No isjunk/autojunk, str only (no bytes), and no grapheme or Unicode-word granularity yet. Open an issue if you need any of these.

Benchmarks

The same three workloads as the chart above, measured on real CPython sources (v3.9.0 vs v3.13.0 releases):

workload stdlib similar-rs speedup
two-line diff (call overhead) 3.7 us 2.9 us 1.3x
real file diff (argparse.py, 2,669 lines) 3.7 ms 0.9 ms 4.0x
accurate similarity of two ~35 KB files (autojunk=False) 23.8 s 120 ms 198.1x

Why the floor is a few times, not more: stdlib difflib is not naive — it hashes lines once and diffs the hashes through C-backed dicts, so a Rust port removes the interpreter, not the algorithm. Across five real file pairs the win is 1.1-4.0x, growing with how much of the file changed.

Why the ceiling is ~200x: stdlib's autojunk heuristic trades accuracy for speed, and on char-level input it discards most of the alphabet. Turn it off for an accurate answer and stdlib exceeded a 30 s cap on four of the five pairs, while similar-rs stays under 0.7 s. The heuristic also has bad days of its own: on the difflib.py pair stdlib's default ran 712 ms against our 3 ms. With autojunk on, the two libraries price the answer differently — our default ratio is coarser than stdlib's on some inputs; algorithm="raw-myers" returns a better match set than accurate stdlib at a fraction of its cost.

Crossing into Rust costs a fixed ~2 us per call, which only shows on inputs too small to contain work — that is the first row. For whole texts, prefer the native entry point (similar.unified_diff): the line splitting and formatting happen in Rust too.

When to use it:

you are doing what you get
diffs in a hot loop (services, CI, batch pipelines) typically 2-4x for a one-line import change
accurate similarity of long texts (dedup, fuzzy matching) the 200x class: feasible where stdlib times out
git-style diffs (patience, histogram) algorithms stdlib does not have at any speed

benchmarks/results.md has the per-pair tables and the exact method; python benchmarks/bench.py reproduces all of it, charts included. One machine, one benchmark — try it on yours.

Contributing

Issues and PRs welcome. Build from a clone:

uv venv && uv pip install maturin pytest hypothesis
maturin develop
pytest

License

Apache-2.0 (see LICENSE). The difflib-compatible formatting code in python/similar/difflib.py is derived from CPython's difflib and is covered by the PSF License Version 2; see the notice at the end of LICENSE. The wheel statically links the Rust crates it is built from; their licenses are collected in LICENSE-THIRD-PARTY, which ships inside the wheel.

Project links

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Credits

Author: Maksim Burtsev

GitHub Statistics

Data verified by PyPI on Aug 28, 2026
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License expression

Apache-2.0
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Requires

Python >=3.9

Provides Extra

bench test

Tags

diff difflib sequencematcher unified-diff myers patience text-diff text-comparison string-similarity fuzzy-matching get-close-matches diff-algorithm rust pyo3

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