orjson 2.0.1

Fast, correct Python JSON library

orjson

orjson is a fast, correct JSON library for Python. It benchmarks as the fastest Python library for JSON and has comprehensive unit, integration, and interoperability tests.

Its serialization performance is 2x to 3x the nearest other library and 4.5x to 11.5x the standard library. Its deserialization performance is 0.95x to 1.1x the nearest other library and 1.2x to 3x the standard library.

It differs in behavior from other Python JSON libraries in supporting datetimes, not supporting subclasses without a default hook, serializing UTF-8 to bytes rather than escaped ASCII (e.g., "好" rather than "\\u597d") by default, having strict UTF-8 conformance, having strict JSON conformance on NaN/Infinity/-Infinity, having an option for strict JSON conformance on 53-bit integers, not supporting pretty printing, and not supporting all standard library options.

It supports CPython 3.6 and 3.7 and distributes wheels for Linux, macOS, and Windows. The repository and issue tracker is github.com/ijl/orjson.

Usage

Install

To install a wheel from PyPI:

pip install --upgrade orjson

To build a release wheel from source, assuming a Rust nightly toolchain and Python environment:

git clone https://github.com/ijl/orjson.git && cd orjson
pip install --upgrade pyo3-pack
pyo3-pack build --release --strip --interpreter python3.7

There is no runtime dependency other than a manylinux environment (i.e., deploying this does not require Rust or non-libc type libraries.)

Serialize

def dumps(obj: Any, default: Optional[Callable[[Any], Any]], option: Optional[int]) -> bytes: ...

dumps() serializes Python objects to JSON.

It natively serializes str, dict, list, tuple, int, float, datetime.datetime, datetime.date, datetime.time, and None instances. It supports arbitrary types through default. It does not serialize subclasses of supported types natively, but default may be used.

It accepts options via an option keyword argument. These include:

• orjson.OPT_STRICT_INTEGER for enforcing a 53-bit limit on integers. The limit is otherwise 64 bits, the same as the Python standard library.
• orjson.OPT_NAIVE_UTC for assuming datetime.datetime objects without a tzinfo are UTC.

To specify multiple options, mask them together, e.g., option=orjson.OPT_STRICT_INTEGER | orjson.OPT_NAIVE_UTC.

It raises JSONEncodeError on an unsupported type. This exception message describes the invalid object.

It raises JSONEncodeError on a str that contains invalid UTF-8.

It raises JSONEncodeError on an integer that exceeds 64 bits by default or, with OPT_STRICT_INTEGER, 53 bits.

It raises JSONEncodeError if a dict has a key of a type other than str.

It raises JSONEncodeError if the output of default recurses to handling by default more than five levels deep.

It raises JSONEncodeError if a tzinfo on a datetime object is incorrect.

JSONEncodeError is a subclass of TypeError. This is for compatibility with the standard library.

import orjson

try:
    val = orjson.dumps(...)
except orjson.JSONEncodeError:
    raise

To serialize arbitrary types, specify default as a callable that returns a supported type. default may be a function, lambda, or callable class instance.

>>> import orjson, numpy
>>> def default(obj):
        if isinstance(obj, numpy.ndarray):
            return obj.tolist()
>>> orjson.dumps(numpy.random.rand(2, 2), default=default)
b'[[0.08423896597867486,0.854121264944197],[0.8452845446981371,0.19227780743524303]]'

If the default callable does not return an object, and an exception was raised within the default function, an exception describing this is raised. If no object is returned by the default callable but also no exception was raised, it falls through to raising JSONEncodeError on an unsupported type.

The default callable may return an object that itself must be handled by default up to five levels deep before an exception is raised.

Deserialize

def loads(obj: Union[bytes, str]) -> Union[dict, list, int, float, str, None]: ...

loads() deserializes JSON to Python objects.

It raises JSONDecodeError if given an invalid type or invalid JSON. This includes if the input contains NaN, Infinity, or -Infinity, which the standard library allows, but is not valid JSON.

JSONDecodeError is a subclass of ValueError. This is for compatibility with the standard library.

import orjson

try:
    val = orjson.loads(...)
except orjson.JSONDecodeError:
    raise

Comparison

There are slight differences in output between libraries. The differences are not an issue for interoperability. Note orjson returns bytes. Its output is slightly smaller as well.

>>> import orjson, ujson, rapidjson, json
>>> data = {'bool': True, '🐈':'哈哈', 'int': 9223372036854775807, 'float': 1.337e+40}
>>> orjson.dumps(data)
b'{"bool":true,"\xf0\x9f\x90\x88":"\xe5\x93\x88\xe5\x93\x88","int":9223372036854775807,"float":1.337e40}'
>>> ujson.dumps(data)
'{"bool":true,"\\ud83d\\udc08":"\\u54c8\\u54c8","int":9223372036854775807,"float":1.337000000000000e+40}'
>>> rapidjson.dumps(data)
'{"bool":true,"\\uD83D\\uDC08":"\\u54C8\\u54C8","int":9223372036854775807,"float":1.337e+40}'
>>> json.dumps(data)
'{"bool": true, "\\ud83d\\udc08": "\\u54c8\\u54c8", "int": 9223372036854775807, "float": 1.337e+40}'

datetime

orjson serializes datetime.datetime objects to RFC 3339 format, a subset of ISO 8601.

datetime.datetime objects must have tzinfo set. For UTC timezones, datetime.timezone.utc is sufficient. For other timezones, tzinfo must be a timezone object from the pendulum, pytz, or dateutil libraries. For applications in which naive datetimes are known to be UTC, tzinfo may be omitted if orjson.OPT_NAIVE_UTC if specified. This does not affect datetimes with a tzinfo set.

>>> import orjson, datetime, pendulum
>>> orjson.dumps(
    datetime.datetime.fromtimestamp(4123518902), option=orjson.OPT_NAIVE_UTC
)
b'"2100-09-01T21:55:02+00:00"'
>>> orjson.dumps(
    datetime.datetime.fromtimestamp(4123518902).replace(tzinfo=datetime.timezone.utc)
)
b'"2100-09-01T21:55:02+00:00"'
>>> orjson.dumps(
    datetime.datetime(2018, 12, 1, 2, 3, 4, 9, tzinfo=pendulum.timezone('Australia/Adelaide'))
)
b'"2018-12-01T02:03:04.9+10:30"'

datetime.time objects must not have a tzinfo. datetime.date objects will always serialize.

>>> import orjson, datetime
>>> orjson.dumps(datetime.date(1900, 1, 2))
b'"1900-01-02"'
>>> orjson.dumps(datetime.time(12, 0, 15, 291290))
b'"12:00:15.291290"'

Errors with tzinfo result in JSONEncodeError being raised.

It is faster to have orjson serialize datetime objects than to do so before calling dumps(). If using an unsupported type such as pendulum.datetime, use default.

int

JSON only requires that implementations accept integers with 53-bit precision. orjson will, by default, serialize 64-bit integers. This is compatible with the Python standard library and other non-browser implementations. For transmitting JSON to a web browser or other strict implementations, dumps() can be configured to raise a JSONEncodeError on values exceeding the 53-bit range.

>>> import orjson
>>> orjson.dumps(9007199254740992)
b'9007199254740992'
>>> orjson.dumps(9007199254740992, option=orjson.OPT_STRICT_INTEGER)
JSONEncodeError: Integer exceeds 53-bit max
>>> orjson.dumps(-9007199254740992, option=orjson.OPT_STRICT_INTEGER)
JSONEncodeError: Integer exceeds 53-bit max

UTF-8

orjson raises an exception on invalid UTF-8. This is necessary because Python 3 str objects may contain UTF-16 surrogates. The standard library's json module accepts invalid UTF-8.

>>> import orjson, ujson, rapidjson, json
>>> orjson.dumps('\ud800')
JSONEncodeError: str is not valid UTF-8: surrogates not allowed
>>> ujson.dumps('\ud800')
UnicodeEncodeError: 'utf-8' codec ...
>>> rapidjson.dumps('\ud800')
UnicodeEncodeError: 'utf-8' codec ...
>>> json.dumps('\ud800')
'"\\ud800"'

>>> import orjson, ujson, rapidjson, json
>>> orjson.loads('"\\ud800"')
JSONDecodeError: unexpected end of hex escape at line 1 column 8: line 1 column 1 (char 0)
>>> ujson.loads('"\\ud800"')
''
>>> rapidjson.loads('"\\ud800"')
ValueError: Parse error at offset 1: The surrogate pair in string is invalid.
>>> json.loads('"\\ud800"')
'\ud800'

Testing

The library has comprehensive tests. There are unit tests against the roundtrip, jsonchecker, and fixtures files of the nativejson-benchmark repository. It is tested to not crash against the Big List of Naughty Strings. It is tested to not leak memory. It is tested to be correct against input from the PyJFuzz JSON fuzzer. It is tested to not crash against and not accept invalid UTF-8. There are integration tests exercising the library's use in web servers (gunicorn using multiprocess/forked workers) and when multithreaded. It also uses some tests from the ultrajson library.

Performance

Serialization and deserialization performance of orjson is better than ultrajson, rapidjson, simplejson, or json. The benchmarks are done on fixtures of real data:

• twitter.json, 631.5KiB, results of a search on Twitter for "一", containing CJK strings, dictionaries of strings and arrays of dictionaries, indented.

• github.json, 55.8KiB, a GitHub activity feed, containing dictionaries of strings and arrays of dictionaries, not indented.

• citm_catalog.json, 1.7MiB, concert data, containing nested dictionaries of strings and arrays of integers, indented.

• canada.json, 2.2MiB, coordinates of the Canadian border in GeoJSON format, containing floats and arrays, indented.

twitter.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.49	2034.9	1
ujson	1.42	703.7	2.89
rapidjson	1.57	638.5	3.19
simplejson	2.79	358	5.69
json	2.7	370.3	5.5

twitter.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	2.4	416.4	1
ujson	2.25	444.4	0.94
rapidjson	3.04	328.4	1.27
simplejson	2.49	401.8	1.04
json	2.82	354.3	1.17

github.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.05	18299.4	1
ujson	0.14	7126.2	2.57
rapidjson	0.16	6370.2	2.87
simplejson	0.31	3274.2	5.59
json	0.27	3713	4.93

github.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.21	4873.7	1
ujson	0.22	4493.4	1.08
rapidjson	0.27	3721.3	1.31
simplejson	0.22	4593.9	1.06
json	0.24	4142.5	1.18

citm_catalog.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.81	1223.7	1
ujson	2.66	376	3.27
rapidjson	2.45	408.7	3.01
simplejson	11	91	13.53
json	6.03	165.8	7.42

citm_catalog.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	4.6	217	1
ujson	4.49	222.8	0.98
rapidjson	5.68	175.8	1.24
simplejson	6.06	165.2	1.32
json	5.99	166.8	1.3

canada.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	4.29	232.4	1
ujson	8.71	115.7	2.03
rapidjson	43.69	22.9	10.18
simplejson	66.56	15	15.51
json	49.27	19.9	11.48

canada.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	8.63	116.4	1
ujson	8.55	116.9	0.99
rapidjson	29.37	34.1	3.4
simplejson	26.56	37.6	3.08
json	27.56	36.3	3.19

This was measured using Python 3.7.2 on Linux with orjson 2.0.0, ujson 1.35, python-rapidson 0.6.3, and simplejson 3.16.0.

The results can be reproduced using the pybench and graph scripts.

License

orjson is dual licensed under the Apache 2.0 and MIT licenses. It contains tests from the hyperjson and ultrajson libraries. It is implemented using the serde_json and pyo3 libraries.