rjson
Fast JSON for Python, written in Rust directly against the CPython C API. A faster
alternative to orjson and the standard library json for
web APIs (FastAPI and others), logging and NDJSON, caches and message queues; switching is
mostly a find-and-replace (migration guide).
- Faster than orjson on 18 of the 20 cases of the reference benchmark (geomean
loads1.17×,dumps1.36×) and on 21 of 22 production-shaped workloads (1.52×); 3.5× / 14× faster thanjson. - Same output as orjson, byte for byte, including
datetime,UUID, dataclasses andEnum;dumps_strreturns astrdirectly. - Hardened for untrusted input: differential fuzzing against
json, an AddressSanitizer CI build, and stack checks for deeply nested documents. - CPython 3.10–3.14 wheels for Linux (glibc and musl), macOS and Windows. MIT licensed.
If rjson saves you CPU time, a ⭐ on GitHub helps other people find it.
Numbers behind the charts, and how they were measured
Speedup = other library's time ÷ rjson's time (higher is better). Median time per call
over 5 runs, CPython 3.13.12, orjson 3.12.0, x86_64 (Xeon), PGO build
(scripts/build_pgo.sh, as the published wheels are built). loads parses the documents'
UTF-8 bytes; dumps returns bytes in both rjson and orjson. Raw results, with each
case's per-run range: docs/img/benchmark-results.json.
| case | loads vs orjson | dumps vs orjson | loads vs json | dumps vs json |
|---|---|---|---|---|
| twitter.json | 1.20× | 1.71× | 3.23× | 12.9× |
| citm_catalog.json | 0.95× | 1.12× | 2.23× | 6.06× |
| canada.json | 1.09× | 1.10× | 4.34× | 17.7× |
| github.json | 1.33× | 1.89× | 2.98× | 18.0× |
| small dict | 1.27× | 1.27× | 6.37× | 18.5× |
| records | 1.12× | 1.34× | 2.17× | 11.8× |
| unicode strings | 1.12× | 1.05× | 1.80× | 54.0× |
| escaped strings | 1.22× | 3.19× | 5.14× | 5.38× |
| int array | 1.16× | 1.03× | 3.33× | 11.1× |
| float array | 1.26× | 0.93× | 7.53× | 18.6× |
| geomean | 1.17× | 1.36× | 3.53× | 14.2× |
The two cases below 1× are within this host's run-to-run noise (citm loads ranged
0.87–1.17×, float array dumps 0.88–1.16× over the 5 runs).
Reproduce and redraw:
benches/fetch_corpus.sh # sha256-pinned corpora -> benches/data/
scripts/build_pgo.sh python3.13 && pip install target/wheels/*cp313*.whl # or a plain build
python benches/corpus_benchmark.py --json --repeat 11 --output-json results.json
python benches/make_charts.py results.json # -> docs/img/*.svg + this table
dumps_str (returns str) is faster than orjson's dumps on 6 of 10 cases (geomean 1.13×);
it trails on twitter.json and unicode strings, because a str holding emoji must be stored
at 4 bytes per character, and is at parity on citm_catalog and the float array (1.01, 1.05). loads of the same large non-ASCII str object over and
over is 1.1–1.5× slower than orjson (twitter, citm, unicode strings): orjson keeps a UTF-8
copy attached to that string, which rjson deliberately does not (it would double the string's
memory, #10); with bytes or a new str per
call, as a server gets, rjson is faster. In plain (non-PGO) builds single cases move by up
to 1.5× with code layout (canada dumps), which PGO removes.
Methodology, per-version results and the roadmap:
docs/PERFORMANCE_REVIEW.md.
On production-shaped workloads
Identical output is checked before timing. FastAPI, logging, NDJSON and Redis/Kafka numbers for the
examples:
Faster JSON in Python services.
Numbers, method and the one slower case:
docs/SHOWCASE.md (python benches/showcase.py).
Installation
The PyPI distribution is pyrjson (the name rjson on PyPI belongs to an unrelated
project). You still import rjson.
pip install pyrjson # or: uv add pyrjson
Wheels are PGO-optimized builds for CPython 3.10–3.14 on Linux (manylinux and musllinux,
x86_64 and aarch64), macOS (arm64 and x86_64) and Windows (x86_64), with build provenance
attestations (gh attestation verify <wheel> --repo TinDang97/rjson). Elsewhere pip builds
from the sdist, which needs a Rust toolchain. The development version:
pip install "git+https://github.com/TinDang97/rjson"
# or, from a checkout:
pip install maturin && maturin develop --release
Usage
from datetime import datetime
from decimal import Decimal
from uuid import uuid4
import rjson
data = rjson.loads('{"name": "rjson", "tags": ["fast", "safe"], "stars": 1e3}')
payload = rjson.dumps(data) # b'{"name":"rjson","tags":["fast","safe"],"stars":1000.0}'
text = rjson.dumps_str(data) # the same JSON as a str
rjson.dumps({"at": datetime(2024, 5, 1, 9, 30), "id": uuid4()}) # datetime, UUID, dataclass, Enum: native
rjson.dumps({"price": Decimal("9.99")}, default=str) # convert the rest with default=
| name | kind | notes |
|---|---|---|
loads(data, *, lenient=False) |
function → object | data: str, bytes, bytearray or memoryview (any layout) |
dumps(obj, *, default=None, passthrough=0, non_str_keys=False, indent=None, sort_keys=False) |
function → bytes |
UTF-8 JSON, like orjson.dumps |
dumps_str(obj, *, default=None, passthrough=0, non_str_keys=False, indent=None, sort_keys=False) |
function → str |
like json.dumps(obj, ensure_ascii=False, separators=(",", ":")) |
dumps_bytes(...) |
function → bytes |
alias of dumps, kept for compatibility |
PASSTHROUGH_DATETIME, _UUID, _DATACLASS, _ENUM |
int flags |
for passthrough=, combine with | |
JSONDecodeError |
exception | json.JSONDecodeError itself (a ValueError) |
JSONEncodeError |
exception | subclass of both TypeError and ValueError, like orjson.JSONEncodeError |
__version__ |
str |
package version |
The wheel ships type stubs (py.typed), so mypy and pyright check calls to rjson.
lenient=True(loads): accepts everythingjson.loadsaccepts, with the same result:NaN/Infinity/-Infinity, a UTF-8 BOM on bytes and numbers overflowing toinfare parsed natively (no speed cost); lone surrogates ("\ud83d", sent by JavaScript clients that cut an emoji in half), UTF-16/UTF-32 bytes and nesting deeper than 1024 go throughjson.loads. The default stays strict, like orjson.- Types:
dict(str keys),list,tuple,str,int(any size),float,bool,None, and their subclasses (IntEnum,strenums,OrderedDict,namedtuple, …). - Native types, byte-identical to orjson:
datetime(RFC 3339:2024-05-01T09:30:00,.ffffffonly when non-zero,+HH:MMwhen aware),date,time,uuid.UUID(lowercase, hyphenated), dataclasses (as objects;_-prefixed names skipped, as in orjson) andEnummembers (their value). Subclasses ofdatetime/date/time/UUIDgo todefault=. Three orjson bugs are not copied: atzinfowhoseutcoffset()raises, or a deleted__slots__field, raise the Python error (orjson crashes);utcoffset()returningNonegives no offset, as inisoformat()(orjson writes+00:00); an offset whose seconds round up to a whole hour carries into the hour (orjson writes+00:60). passthrough=:rjson.PASSTHROUGH_*flags send those kinds todefault=(or make them raise) instead, e.g. to tag them for an exact round trip (examples/codec.py), like orjson'sOPT_PASSTHROUGH_*.non_str_keys=True: dict keys may beint(any size),float,bool,None(written exactly asjson.dumpswrites them:"1","1e-07","NaN","true","null"),Enummembers (their value), anddatetime/date/time/UUID(their native text, like orjson'sOPT_NON_STR_KEYS). Other key types raise;default=is not called for keys. Likejsonand orjson, a coerced key can duplicate a str key ({1: …, "1": …}writes"1"twice). Off by default: a non-str key raises.indent=/sort_keys=:indent=2andsort_keys=Truegive exactly the bytes of orjson'sOPT_INDENT_2andOPT_SORT_KEYS(keys sorted by code point; dataclass fields keep their order); other widths (indent=4,indent=0) lay out likejson.dumps(indent=n). Compact calls pay nothing for them; the options themselves are not yet as fast as orjson's (twitter.json:sort_keys0.78×,indent=20.48×). With either option,dumps_strraisesUnicodeEncodeErroron a lone surrogate, asdumpsdoes.default=: called with each value rjson cannot serialize; its return value is serialized in its place (and passed todefaultagain if still unsupported), as injson.dumpsand orjson. Exceptions it raises propagate unchanged. It is not called for NaN/Infinity or dict keys (seenon_str_keys=), which still raise. Supported values never reach it, so native data runs at full speed.- Errors:
loadsraisesJSONDecodeErrorwithpos/lineno/colnomatchingjson.dumps/dumps_strraiseJSONEncodeErrorfor unsupported types, non-str keys (unlessnon_str_keys=True), NaN/Infinity, and nesting deeper than 254 (which also catches circular references), so bothexcept TypeErrorandexcept ValueErrorcatch it. A lone surrogate raisesUnicodeEncodeErrorindumps;dumps_strpasses it through. - Precision: floats round-trip exactly (shortest representation, e.g.
1e+16), and integers of any size are exact in both directions.loadsaccepts nesting up to 1024.
Migrating from json or orjson
Most code migrates with a find-and-replace:
| you wrote | with rjson |
|---|---|
json.loads(s) / orjson.loads(s) |
rjson.loads(s) |
orjson.dumps(obj) |
rjson.dumps(obj) (same bytes) |
json.dumps(obj, separators=(",", ":"), ensure_ascii=False) |
rjson.dumps_str(obj) |
json.dumps(obj).encode() |
rjson.dumps(obj) |
except json.JSONDecodeError / orjson.JSONDecodeError |
except rjson.JSONDecodeError |
except TypeError / orjson.JSONEncodeError around dumps |
except rjson.JSONEncodeError (TypeError keeps working) |
json.dumps(obj, default=f) / orjson.dumps(obj, default=f) |
rjson.dumps(obj, default=f) |
orjson.dumps(datetime/UUID/dataclass/Enum) |
the same bytes |
json.dumps({1: "a", None: "b"}) / orjson.dumps(obj, option=OPT_NON_STR_KEYS) |
rjson.dumps(obj, non_str_keys=True) (key text as json writes it) |
orjson.dumps(obj, option=OPT_INDENT_2 | OPT_SORT_KEYS) |
rjson.dumps(obj, indent=2, sort_keys=True) (same bytes) |
json.dumps(obj, indent=4, sort_keys=True) |
rjson.dumps_str(obj, indent=4, sort_keys=True) (non-ASCII kept, like ensure_ascii=False) |
orjson.dumps(obj, option=OPT_PASSTHROUGH_DATETIME) |
rjson.dumps(obj, passthrough=rjson.PASSTHROUGH_DATETIME) (also _UUID, _DATACLASS, _ENUM) |
json.dumps(obj, default=lambda o: o.isoformat()) for datetimes |
rjson.dumps(obj) (the same text, except UTC offsets with a seconds part, which are rounded to the minute) |
What does not carry over yet, and how to handle it:
| feature | status | workaround |
|---|---|---|
| NaN / Infinity | by design | dumps raises (json writes NaN, orjson null) |
| floats below 1e-4 | by design | 1e-7, same as orjson; json writes 1e-07 (same value, different bytes) |
FastAPI
from fastapi import FastAPI
from fastapi.responses import JSONResponse
import rjson
class RJSONResponse(JSONResponse):
def render(self, content) -> bytes:
return rjson.dumps(content)
app = FastAPI()
@app.get("/items")
def items() -> RJSONResponse:
return RJSONResponse([{"id": 1, "name": "widget"}])
Return RJSONResponse(...) directly for dicts and lists. Measured on whole requests
through the app, against stock FastAPI's own fast path (a return type, serialized by
Pydantic's dump_json): a 50-row page is 1.25–1.4× faster, 1.8–1.9× with UUID/datetime/Enum
fields, and a 1,000-row export 3.0–3.2× faster; against an endpoint without a return type,
9–74× (numbers). Don't set it as
default_response_class: endpoints that return Pydantic models already go through
dump_json. examples/fastapi_app.py adds a fallback for
Decimal, Pydantic models and dataclasses, and rjson-parsed request bodies.
Logging, NDJSON, Redis and Kafka
examples/json_logging.py: alogging.Formatterwriting one JSON object per line (2.2–2.5 µs per record, 2.4–2.7× faster than the same formatter onjson), plus NDJSON read/write (writing 12.5× faster, reading 2.5–2.8×).examples/codec.py: a versioned bytes codec for Redis/Kafka with typed round trips, andvalue_serializer/value_deserializercallables. Round trips of JSON-native payloads are 4.1–4.6× faster than the same codec onjson, and faster thanpickle.docs/ASYNC.md: aiohttp, httpx, asyncpg,redis.asyncioand aiokafka one-liners.
Production use
The full report, with every number and how it was measured: docs/PRODUCTION_READINESS.md. In short, rjson is ready for services whose payloads are JSON-native (dicts, lists, strings, numbers), and not yet a drop-in for code that relies on orjson's options.
FAQ
Is it faster than orjson in real services, not just benchmarks?
On our production-shaped suite (benches/production_benchmark.py: REST pages, request
bodies, NDJSON logs, 100 MB files, cache blobs) rjson is faster on most shapes (geomean
dumps 0.67×, loads 0.83× of orjson's time, PGO wheels) and uses 30–37% less peak
memory on large loads. The report lists the few shapes where it is not.
Is it safe?
It checks the exact type of every value, reserves the worst-case output size before
writing, and version-gates every CPython internal it uses, with self-tests at import.
Untrusted input is bounded: nesting limits plus a thread-stack check (deep documents raise
RecursionError instead of overflowing a small thread stack), and CPython's integer digit
limit. CI runs the tests, differential fuzzing against json and thread/re-entrancy
stress tests, also on an AddressSanitizer build. It is still 0.x: pin the version.
Report vulnerabilities privately: SECURITY.md.
Does it work with asyncio / uvloop?
Yes. Calls are synchronous and hold the GIL, so a large payload blocks the event loop, and
asyncio.to_thread does not help. docs/ASYNC.md shows what to do instead.
Is it thread-safe?
Yes, on regular (GIL) CPython builds, including calls re-entered from default= and
threads switching mid-call (stress-tested). Threads do not parallelize JSON work.
Free-threaded builds (3.13t/3.14t) and subinterpreters are not supported yet. In threads
with small stacks (musl/Alpine's 128 KiB default, threading.stack_size), very deep
documents raise RecursionError: about 975 nested arrays or 620 nested objects fit in
128 KiB, the full 1024 levels in 256 KiB.
Why does dumps return bytes?
It is what you send over the network or write to a file, and it avoids a copy. Use
dumps_str when you need a str.
Should I compress, or switch to msgpack / Arrow to go faster?
Compress in the transport (HTTP middleware, Kafka compression.type), and only payloads
of a few KB and up: compression costs as much CPU as serialization. Binary formats aren't
faster when the result is Python dicts, because creating objects dominates. Arrow and
Polars win when data stays columnar. Numbers:
Transfer size.
Why is dumps_str sometimes slower than dumps?
A str containing emoji must store every character in 4 bytes, and CJK text in 2, while
UTF-8 bytes stay compact.
Why does my previously accepted request now fail with a 422?
By default loads rejects a UTF-8 BOM, NaN/Infinity literals and escaped lone
surrogates, like orjson does (the stdlib accepts them). Pass lenient=True to accept
exactly what json.loads accepts.
Compatibility
- CPython 3.10–3.14 on Linux x86_64/aarch64, macOS arm64 and Windows (CI). x86_64 builds target x86-64-v2 and select AVX2/AVX-512 kernels at runtime.
- Not supported: PyPy, GraalPy, free-threaded (no-GIL) builds, subinterpreters.
Status
Experimental. The API may change before 1.0; pin the version you test against.
Why it is faster than orjson
Both are Rust on the CPython C API, cache dict keys and format floats with zmij.
Internals rjson relies on are version-gated and self-tested at import (CLAUDE.md);
details in docs/PERFORMANCE_REVIEW.md.
Contributing
Bug reports and pull requests are welcome. CONTRIBUTING.md covers the development setup, the hard rules for code that touches the C API, and how to benchmark. Security issues: SECURITY.md. Changes: CHANGELOG.md.
uv venv .venv -p 3.13 && . .venv/bin/activate
uv pip install maturin orjson pytest
maturin develop --release && python -m pytest tests -q
| path | contents |
|---|---|
src/parser.rs, src/lemire.rs |
loads |
src/ser.rs |
dumps / dumps_str |
src/entry.rs, src/compat.rs |
C-API entry points, version-portable helpers |
rjson.pyi |
type stubs |
tests/ |
pytest suites |
examples/ |
FastAPI, logging/NDJSON and Redis/Kafka integrations (tested) |
benches/ |
corpus_benchmark.py (reference), showcase.py (vs orjson on production shapes), production_benchmark.py (production workloads), examples_benchmark.py (the examples on rjson vs json vs orjson), perf_gate.py, make_charts.py / make_showcase_charts.py / make_demo_svg.py (README charts, demo), demo.py |
docs/ |
showcase, performance review, production readiness report, async guide |
Troubleshooting source builds
- Linker errors (
symbol(s) not found for architecture arm64): Python and Rust must target the same architecture. Check withpython3 -c "import platform; print(platform.machine())"; on Apple Silicon use an arm64 Python such as/opt/homebrew/bin/python3, thencargo cleanand rebuild. - Missing Python headers: install your Python's development package (e.g.
python3-dev).
Roadmap
- Streaming decoder/encoder for async I/O; free-threading and subinterpreter support (docs/ASYNC.md)
- Performance: NEON kernels for aarch64
License
MIT © 2025 Tin Dang. src/lemire.rs is adapted from
fast-float (MIT OR Apache-2.0).
1 maintainer
Metadata
1 maintainer
Release files for pyrjson 0.2.0
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