orjson 2.0.11

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 is more correct than the standard json library or third-party libraries.

Its serialization performance is 2.5x to 9.5x the nearest other library and 4x to 12x the standard library. Its deserialization performance is 1.2x to 1.3x the nearest other library and 1.4x to 2x 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.

orjson supports CPython 3.5, 3.6, 3.7, and 3.8. It distributes wheels for Linux, macOS, and Windows. The manylinux1 wheel differs from PEP 513 in requiring glibc 2.18, released 2013, or later. orjson does not currently support PyPy.

orjson is licensed under both the Apache 2.0 and MIT licenses. The repository and issue tracker is github.com/ijl/orjson, and patches may be submitted there.

Usage

Install

To install a wheel from PyPI:

pip install --upgrade orjson

To build from source requires Rust on the nightly channel. Package a wheel from a PEP 517 source distribution using pip:

pip wheel --no-binary=orjson orjson

There are no runtime dependencies other than libc. orjson is compatible with systems using glibc earlier than 2.18 if compiled on such a system. Tooling does not currently support musl libc.

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, bool, typing.TypedDict, 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 on circular references.

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.

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]) -> Any: ...

loads() deserializes JSON to Python objects. It deserializes to dict, list, int, float, str, bool, and None objects.

Either bytes or str input are accepted. If the input exists as bytes (was read directly from a source), it is recommended to pass bytes. This has lower memory usage and lower latency.

orjson maintains a cache of map keys for the duration of the process. This causes a net reduction in memory usage by avoiding duplicate strings. The keys must be at most 64 chars to be cached and 512 entries are stored.

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 json.JSONDecodeError and ValueError. This is for compatibility with the standard library.

datetime

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

datetime.datetime objects serialize with or without a tzinfo. For a full RFC 3339 representation, tzinfo must be present or orjson.OPT_NAIVE_UTC must be specified (e.g., for timestamps stored in a database in UTC and deserialized by the database adapter without a tzinfo). If a tzinfo is not present, a timezone offset is not serialized.

tzinfo, if specified, must be a timezone object that is either datetime.timezone.utc or from the pendulum, pytz, or dateutil/arrow libraries.

>>> import orjson, datetime, pendulum
>>> orjson.dumps(
    datetime.datetime(2018, 12, 1, 2, 3, 4, 9, tzinfo=pendulum.timezone('Australia/Adelaide'))
)
b'"2018-12-01T02:03:04.000009+10:30"'
>>> orjson.dumps(
    datetime.datetime.fromtimestamp(4123518902).replace(tzinfo=datetime.timezone.utc)
)
b'"2100-09-01T21:55:02+00:00"'
>>> orjson.dumps(
    datetime.datetime.fromtimestamp(4123518902)
)
b'"2100-09-01T21:55:02"'

orjson.OPT_NAIVE_UTC, if specified, only applies to objects that do not have a tzinfo.

>>> 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(2018, 12, 1, 2, 3, 4, 9, tzinfo=pendulum.timezone('Australia/Adelaide')),
    option=orjson.OPT_NAIVE_UTC
)
b'"2018-12-01T02:03:04.000009+10:30"'

datetime.time objects must not have a tzinfo.

>>> import orjson, datetime
>>> orjson.dumps(datetime.time(12, 0, 15, 290))
b'"12:00:15.000290"'

datetime.date objects will always serialize.

>>> import orjson, datetime
>>> orjson.dumps(datetime.date(1900, 1, 2))
b'"1900-01-02"'

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 range
>>> orjson.dumps(-9007199254740992, option=orjson.OPT_STRICT_INTEGER)
JSONEncodeError: Integer exceeds 53-bit range

float

orjson serializes and deserializes floats with no loss of precision and consistent rounding. The same behavior is observed in rapidjson, simplejson, and json. ujson is inaccurate in both serialization and deserialization, i.e., it modifies the data.

orjson.dumps() serializes Nan, Infinity, and -Infinity, which are not compliant JSON, as null:

>>> import orjson
>>> orjson.dumps([float("NaN"), float("Infinity"), float("-Infinity")])
b'[null,null,null]'
>>> ujson.dumps([float("NaN"), float("Infinity"), float("-Infinity")])
OverflowError: Invalid Inf value when encoding double
>>> rapidjson.dumps([float("NaN"), float("Infinity"), float("-Infinity")])
'[NaN,Infinity,-Infinity]'
>>> json.dumps([float("NaN"), float("Infinity"), float("-Infinity")])
'[NaN, Infinity, -Infinity]'

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 deserializes and serializes 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 tests against fixtures in the JSONTestSuite and nativejson-benchmark repositories. 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.

Latency

twitter.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.76	1308.1	1
ujson	2.14	468	2.8
rapidjson	2.14	467.5	2.8
simplejson	3.45	289.1	4.52
json	3.6	277.9	4.71

twitter.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	2.8	357.9	1
ujson	3.37	296.1	1.21
rapidjson	4.48	222.9	1.6
simplejson	3.71	269.3	1.33
json	4.13	242.3	1.48

github.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.08	12430.9	1
ujson	0.21	4774.2	2.6
rapidjson	0.23	4350.4	2.87
simplejson	0.44	2290.5	5.42
json	0.36	2786.8	4.46

github.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.25	3939.5	1
ujson	0.33	3053.2	1.29
rapidjson	0.39	2589.7	1.52
simplejson	0.28	3549.6	1.11
json	0.36	2767.7	1.43

citm_catalog.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	1.17	856	1
ujson	3.65	273	3.11
rapidjson	3.45	274.1	2.94
simplejson	14.67	68.2	12.5
json	8.19	122.2	6.98

citm_catalog.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	5.62	178	1
ujson	6.76	148.3	1.2
rapidjson	7.65	129	1.36
simplejson	9.05	110.4	1.61
json	8.24	120.8	1.47

canada.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	7.58	138.6	1
ujson
rapidjson	73.02	13.7	9.64
simplejson	101.21	9.8	13.36
json	91.45	11.5	12.07

canada.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	22.89	43.8	1
ujson
rapidjson	47.18	21.3	2.06
simplejson	44.41	22.5	1.94
json	43.72	22.9	1.91

If a row is blank, the library did not serialize and deserialize the fixture without modifying it, e.g., returning different values for floating point numbers.

Memory

orjson's memory usage when deserializing is similar to or lower than the standard library and other third-party libraries.

This measures, in the first column, RSS after importing a library and reading the fixture, and in the second column, increases in RSS after repeatedly calling loads() on the fixture.

twitter.json

Library	import, read() RSS (MiB)	loads() increase in RSS (MiB)
orjson	12.8	2.7
ujson	12.8	4.6
rapidjson	14.3	6.4
simplejson	13	2.8
json	12.3	2.5

github.json

Library	import, read() RSS (MiB)	loads() increase in RSS (MiB)
orjson	12.3	0.3
ujson	12.4	0.5
rapidjson	13.8	0.5
simplejson	12.4	0.3
json	11.7	0.3

citm_catalog.json

Library	import, read() RSS (MiB)	loads() increase in RSS (MiB)
orjson	13.9	8.3
ujson	13.8	12
rapidjson	15.5	20.3
simplejson	14.1	21.8
json	13.4	20.2

canada.json

Library	import, read() RSS (MiB)	loads() increase in RSS (MiB)
orjson	16.5	17.5
ujson
rapidjson	17.8	19.8
simplejson	16.5	21.3
json	16.1	21.3

Reproducing

The above was measured using Python 3.7.4 on Linux with orjson 2.0.10, ujson 1.35, python-rapidson 0.8.0, and simplejson 3.16.0.

The latency results can be reproduced using the pybench and graph scripts. The memory results can be reproduced using the pymem script.