deepdiff-rs

onix is a Rust rewrite of Python DeepDiff's core: byte-compatible output, 37-4245x faster, with ignore_order support included. Install it as deepdiff-rs, a drop-in DeepDiff class for Python, or run the diff engine as the onix command-line tool.

deepdiff-rs reads live Python objects (or JSON) and produces the exact same report DeepDiff does at verbose_level=2, so it slots into code that already parses DeepDiff output while running dramatically faster on large or deeply nested inputs.

Status (September 2026): deepdiff-rs 0.x is live on PyPI (Python 3.9+, wheels for Linux x86_64/aarch64, macOS arm64/x86_64, and Windows x64, plus an sdist); the onix CLI builds from source, and nothing is on crates.io yet. Ordered and ignore_order diffing are complete, differentially tested against real DeepDiff 9.1.0, and benchmarked. It is 0.x, not stable or 1.0: the API may still change before 1.0.

Table of contents

• Install
• Quickstart
• Diffing tables
• Known limitations
• Performance
• Reference
• Layout
• Contributing
• License

Install

Python (the deepdiff-rs package, import name deepdiff_rs):

pip install deepdiff-rs

From source:

cd crates/onix-py
uv tool install maturin              # the build tool (skip if already installed)
uv sync --group test                 # creates .venv, installs pytest, pinned deepdiff, and pyarrow/polars/duckdb for the table-diff tests
uv run --group test maturin develop --release

CLI (the onix binary), from a clean clone:

cargo install --path crates/onix-cli

Library crate (onix-core), a path dependency only (it sets publish = false):

[dependencies]
onix-core = { path = "crates/onix-core" }

Quickstart

The drop-in DeepDiff class, on live Python objects:

from deepdiff_rs import DeepDiff

diff = DeepDiff({"a": 1}, {"a": 2})
if diff:
    print(diff.to_json())   # byte-compatible with DeepDiff(...).to_json() at verbose_level=2
    print(diff.to_dict())   # the same report as a native Python dict

{"values_changed":{"root['a']":{"new_value":2,"old_value":1}}}
{'values_changed': {"root['a']": {'new_value': 2, 'old_value': 1}}}

diff_json, the fast path when you already have JSON text (it parses, diffs, and serializes entirely in Rust, with no Python-object conversion):

from deepdiff_rs import diff_json

print(diff_json('{"a": 1}', '{"a": 2}'))

{"values_changed":{"root['a']":{"new_value":2,"old_value":1}}}

The onix CLI, diffing two JSON files (compact JSON to stdout, {} for no differences):

$ echo '{"a": 1}' > left.json
$ echo '{"a": 2}' > right.json
$ onix diff left.json right.json
{"values_changed":{"root['a']":{"new_value":2,"old_value":1}}}

Pass --ignore-order to compare every list by value instead of by position, mirroring DeepDiff(..., ignore_order=True).

Diffing tables

diff_tables compares two tables the way DeepDiff compares two objects. It takes any object implementing the Arrow PyCapsule interface — a pyarrow Table or RecordBatch, a polars DataFrame, a DuckDB relation — and imports it with no Python round trip. The two tables are matched on a required, non-empty set of key columns (the table's primary key).

This version reports the schema diff: which columns were added, removed, or changed type. Row-level results (which rows were added or removed and which cells changed) arrive in a later version; the rows_added, rows_removed, cells_changed, and duplicate_keys members exist now but raise NotImplementedError.

import pyarrow as pa
from deepdiff_rs import diff_tables

left = pa.table({
    "id": pa.array([1, 2], pa.int64()),
    "amount": pa.array([1, 2], pa.int32()),
})
right = pa.table({
    "id": pa.array([1, 2], pa.int64()),
    "amount": pa.array([1, 2], pa.int64()),
    "note": pa.array(["a", "b"], pa.string()),
})

diff = diff_tables(left, right, key=["id"])
print(diff.summary())
for change in diff.schema:
    print(change["column"], change["change"], change["left_type"], change["right_type"])

{'columns_added': 1, 'columns_removed': 0, 'columns_type_changed': 1}
amount type_changed Int32 Int64
note added None Utf8

Type comparison uses the full logical Arrow type (timestamp unit and timezone, decimal precision and scale, and so on), but physical encodings that carry the same logical type compare equal — a dictionary-encoded string equals a plain string, polars' Utf8View equals pyarrow's Utf8, the list variants normalize together, and a map compares equal however a library spells it — so the same table read through pyarrow, polars, or DuckDB reports no spurious type changes. The full normalization rules are documented on normalized_type (and map_entries) in crates/onix-arrow/src/schema.rs; nullability is ignored but reported in each record. Column names must be unique on each side; a repeated name raises ValueError. diff.schema_arrow is the same result as an Arrow table: it implements __arrow_c_stream__, so polars.DataFrame(diff.schema_arrow) and pandas consume it directly, and diff.schema_arrow.to_pyarrow() returns a pyarrow.Table.

pyarrow is optional: install it with pip install deepdiff-rs[arrow]. It is needed only for to_pyarrow() and for passing pyarrow objects in — importing deepdiff_rs and diffing polars or DuckDB tables need it not at all. Passing an object that implements neither Arrow protocol raises TypeError; calling to_pyarrow() without pyarrow installed raises ImportError naming the extra.

Known limitations

• Only the core diff is implemented: exclude_paths, significant_digits, custom operators, verbose_level != 2, and delta/patch are not (yet) supported.
• Supported value types are None, bool, int, float, str, dict (with str keys), list, tuple, set, frozenset, datetime.datetime, and datetime.date; a set/frozenset member may be any of these except a list, dict or set, matching Python's own hashability rule, transitively through whatever the member nests. ints must fit in i64/u64, floats must be finite, and anything else — time, timedelta, a non-str dict key, a custom object, an arbitrary-precision int, or a non-finite float — raises TypeError/ValueError naming the exact path it was found at. The Datetimes and Sets bullets below cover the deliberate divergences for those two types. See crates/onix-py/src/convert.rs and tests/golden/README.md.
• A subclass of a supported type (a tuple, set or frozenset subclass including namedtuple, a datetime/date subclass such as pandas' Timestamp) raises TypeError rather than being diffed as its base type, because DeepDiff reports each value's own type name. A type_changes entry's old_type/new_type are type names in to_dict(), where DeepDiff returns the type objects. Both are described in tests/golden/README.md.
• Datetimes compare by instant, with a naive value read as UTC, matching DeepDiff. A changed pair is reported normalized to UTC (to_json() renders ...+00:00, to_dict() returns UTC-aware datetimes); everywhere else a datetime keeps its raw value. Three deliberate departures: to_json() renders a date as YYYY-MM-DD where DeepDiff's own to_json() raises TypeError (a documented superset); a zoneinfo/pytz tzinfo comes back from to_dict() as a fixed-offset datetime.timezone carrying the offset it was in force at, not the original zone object; and a set holding both a naive and an aware value at one instant reports both as members, where DeepDiff's own digest cache can report only one (see crates/onix-py/src/convert.rs). Comparing two datetimes whose UTC form would leave year 1..=9999 raises ValueError naming the path, where DeepDiff raises OverflowError; under ignore_order DeepDiff's hasher normalizes every datetime and so raises for such a value even when it is only added, removed, or shuffled, where onix hashes by instant and reports it normally (see tests/golden/README.md). truncate_datetime, time and timedelta are not supported. The normalized-versus-raw split is documented in tests/golden/README.md.
• Sets are diffed deterministically, where DeepDiff's own answers depend on the order the running process happens to iterate a set in (hash order, and PYTHONHASHSEED-dependent for str members) or on how its digest cache/computation handles a tuple, frozenset, or calendar member independently of Python's own ==. Each consequence — entry order, which member of an equality class is reported, set-versus-sequence coercion, and a tuple/frozenset member's own (positional, not order-/repetition-insensitive) matching rule — is shown with both tools' output in tests/golden/README.md's "Set iteration order" section. A report holding a frozenset value also serializes to JSON here, where DeepDiff's own to_json() raises TypeError — a superset, not a difference in the findings.
• A str containing a lone (unpaired) surrogate code point (e.g. '\udc80', legal in Python but not encodable as UTF-8) raises ValueError naming the exact path on either side, before the two values are ever compared — including a pair DeepDiff would call equal and report as no change, since DeepDiff's scalar equality is plain Python == and never hits the encoding problem; DeepDiff does report a plain change for a differing pair, and crashes with an unhandled UnicodeEncodeError if such a string is ever hashed (a set/frozenset member). See tests/golden/README.md's "Known DeepDiff quirks" section.
• A str inside a tuple or frozenset set item is rendered with Python's repr(), which escapes every non-printable character; onix escapes those below U+0100 (the complete set in that range) and passes higher non-printable code points through literally, since escaping them would mean carrying a Unicode category table. Exact for all of ASCII and all printable text. See crates/onix-core/src/path.rs.
• Adversarially deep input raises MaxDepthError instead of crashing: the default max_depth is 512 and the hard ceiling is MAX_DEPTH_CEILING (20,000). See crates/onix-py/src/guard.rs.
• ignore_order pairing is O(N^2) in unpaired elements per side and carries a polynomial cost in both time and memory with input depth; it has no max_passes/max_diffs cutoff, so bound the size and depth of untrusted input yourself. See crates/onix-core/src/ignore_order/mod.rs.
• A values_changed between two multi-line strings runs a difflib-style O(N*M) line diff on the default path with no opt-out, worst when changes are spread evenly through the text (about 35 s for a heavily edited 1 MB string and growing quadratically, so a few megabytes is minutes), so bound the size of untrusted strings yourself. See crates/onix-core/src/unified_diff.rs.
• In diff_tables, a column name containing an embedded NUL byte (\0) arrives truncated at the NUL through the Arrow C Data Interface, and the report shows the truncated name; such names are rare in practice.
• In diff_tables, a list of structs named exactly key/value with a nullable key and a real map are not distinguished, so a migration between the two is not reported as a type change (polars exports both as the same Arrow type); see map_entries in crates/onix-arrow/src/schema.rs.
• In diff_tables, DuckDB labels a TIMESTAMP WITH TIME ZONE column with the connection's session time zone when it exports to Arrow (a UTC session as Timestamp(µs, "UTC"), an America/New_York session as Timestamp(µs, "America/New_York")), so on a non-UTC machine such a column can be reported as a type change against a UTC column from another library. Run SET TimeZone='UTC' on the DuckDB connection first for a deterministic, machine-independent result.
• diff_tables refuses a column whose Arrow type is nested deeper than MAX_NESTING_DEPTH (128) with a MaxDepthError, because comparing arbitrarily deep nesting would overflow the native stack; 128 is far beyond any real schema. Importing a schema nested many thousands of levels deep is also slow regardless, a cost of the Arrow C Data Interface itself.
• Output is byte-identical to DeepDiff except for the cases listed above and two path-rendering quirks; tests/golden/README.md enumerates every accepted exception, including integers past 2^53 (the limit of exact f64 representation) inside ordered scalar lists and ignore_order pairing among naive datetimes, which DeepDiff ranks using the process's local timezone while onix reads a naive value as UTC everywhere.

Performance

Two committed, regenerable reports back the numbers below; every figure here is copied verbatim from them.

The Python bindings against real deepdiff on live Python objects, the number a real caller pays (source: crates/onix-py/benchmarks/bench_bindings.py, macOS 26.5.1, Apple M5 Max, median of 11 isolated subprocess runs per side, run on 2026-09-04):

The engine's own diff-only time and peak resident memory against pinned deepdiff 9.1.0 (source: perf/RESULTS.md, same machine, median over tier-appropriate runs, diff time excluding process startup and JSON parsing on both sides):

Both reports carry their full methodology, fairness rules, and the reproduce command. perf/RESULTS.md is an upper bound (JSON parsed straight into the engine, no Python-object conversion); the bindings table is the product-surface number. Regenerate them with perf/run_bench.sh and crates/onix-py/benchmarks/bench_bindings.py (see CONTRIBUTING.md).

Reference

Python API. The public surface is DeepDiff, diff_json, MaxDepthError, and MAX_DEPTH_CEILING.

• DeepDiff(t1, t2, ignore_order=False, max_depth=None): diffs two live Python objects of supported value types — None, bool, int, float, str, dict (with str keys), list, tuple, set, frozenset, datetime.datetime, and datetime.date (see Known limitations for the exact restrictions and exclusions); .to_json() returns the DeepDiff-compatible JSON string, .to_dict() the same report as a dict — with Python types preserved, so a value the diff found in a tuple, set or frozenset comes back as one and a datetime/date comes back as a real datetime/date — and the instance is falsy when there is no difference. The set_item_added/set_item_removed categories are lists of path strings, each ending in the item itself (root['a'][2], root['x'], root[(1, 2)]).
• diff_json(a, b, ignore_order=False, max_depth=None) -> str: diffs two JSON strings entirely in Rust and returns the report as a JSON string.
• MaxDepthError (a ValueError subclass) is raised when input exceeds max_depth; MAX_DEPTH_CEILING (20,000) is the hard upper bound on max_depth.
• diff_tables(left, right, key=[...]) -> TableDiff: diffs two Arrow tables (see Diffing tables). TableDiff.schema is the list of changed columns, .schema_arrow the same as an Arrow table, .summary() the change counts, .to_json() both as JSON; .rows_added(), .rows_removed(), .cells_changed(), and .duplicate_keys() raise NotImplementedError until a later version.

CLI. onix diff <a.json> <b.json> [--max-depth N] [--ignore-order] [--timing] reads both files as JSON and prints a compact, single-line DeepDiff-compatible report to stdout ({} when there is no difference).

• --max-depth N overrides the recursion-depth bound (default: the ONIX_MAX_DEPTH environment variable if set, else 512).
• --ignore-order compares every list by hash-based matching instead of by position, mirroring DeepDiff(..., ignore_order=True).
• --timing prints one line of JSON ({"parse_ns": N, "diff_ns": N}) to stderr.

Exit codes:

Layout

crates/onix-core   # the diff engine (library, no I/O)
crates/onix-cli    # the `onix` binary (thin CLI over the core)
crates/onix-arrow  # Arrow table diffing (schema diff; rows in a later version)
crates/onix-py     # PyO3 bindings, published as `deepdiff-rs`
scripts/           # gen_goldens.py: regenerates tests/golden/ from real DeepDiff
tests/golden       # DeepDiff-generated expected outputs (the compatibility corpus)
perf/              # cross-language benchmark harness and RESULTS.md

Contributing

Issues and pull requests are welcome. Open an issue to report a bug, a DeepDiff divergence (include both inputs and the report each engine produces), or a question. Building from source, the quality gates, the golden corpus, benchmarking, mutation testing, and publishing are all in CONTRIBUTING.md.

License

MIT: see LICENSE.

onix reimplements algorithms from CPython's difflib (PSF License) and reproduces the behavior of DeepDiff (MIT); their notices and license texts are in THIRD-PARTY-NOTICES.md.