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 other third-party libraries. It serializes dataclass, datetime, numpy, and UUID instances natively.

Its features and drawbacks compared to other Python JSON libraries:

serializes dataclass instances 40-50x as fast as other libraries
serializes datetime, date, and time instances to RFC 3339 format, e.g., "1970-01-01T00:00:00+00:00"
serializes numpy.ndarray instances 4-12x as fast with 0.3x the memory usage of other libraries
pretty prints 10x to 20x as fast as the standard library
serializes to bytes rather than str, i.e., is not a drop-in replacement
serializes str without escaping unicode to ASCII, e.g., "好" rather than "\\u597d"
serializes float 10x as fast and deserializes twice as fast as other libraries
serializes subclasses of str, int, list, and dict natively, requiring default to specify how to serialize others
serializes arbitrary types using a default hook
has strict UTF-8 conformance, more correct than the standard library
has strict JSON conformance in not supporting Nan/Infinity/-Infinity
has an option for strict JSON conformance on 53-bit integers with default support for 64-bit
does not provide load() or dump() functions for reading from/writing to file-like objects

orjson supports CPython 3.8, 3.9, 3.10, 3.11, 3.12, 3.13, and 3.14.

It distributes amd64/x86_64, aarch64/armv8, arm7, POWER/ppc64le, and s390x wheels for Linux, amd64 and aarch64 wheels for macOS, and amd64 and i686/x86 wheels for Windows.

orjson does not and will not support PyPy, embedded Python builds for Android/iOS, or PEP 554 subinterpreters.

Releases follow semantic versioning and serializing a new object type without an opt-in flag is considered a breaking change.

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. There is a CHANGELOG available in the repository.

Usage
1. Install
2. Quickstart
3. Migrating
4. Serialize
  1. default
  2. option
  3. Fragment
5. Deserialize
Types
1. dataclass
2. datetime
3. enum
4. float
5. int
6. numpy
7. str
8. uuid
Testing
Performance
Questions
Packaging
License

Usage

Install

To install a wheel from PyPI, install the orjson package.

In requirements.in or requirements.txt format, specify:

orjson>=3.10,<4

In Poetry, specify:

orjson = "^3"

To build a wheel, see packaging.

Quickstart

This is an example of serializing, with options specified, and deserializing:

>>> import orjson, datetime, numpy
>>> data = {
    "type": "job",
    "created_at": datetime.datetime(1970, 1, 1),
    "status": "🆗",
    "payload": numpy.array([[1, 2], [3, 4]]),
}
>>> orjson.dumps(data, option=orjson.OPT_NAIVE_UTC | orjson.OPT_SERIALIZE_NUMPY)
b'{"type":"job","created_at":"1970-01-01T00:00:00+00:00","status":"\xf0\x9f\x86\x97","payload":[[1,2],[3,4]]}'
>>> orjson.loads(_)
{'type': 'job', 'created_at': '1970-01-01T00:00:00+00:00', 'status': '🆗', 'payload': [[1, 2], [3, 4]]}

Migrating

orjson version 3 serializes more types than version 2. Subclasses of str, int, dict, and list are now serialized. This is faster and more similar to the standard library. It can be disabled with orjson.OPT_PASSTHROUGH_SUBCLASS.dataclasses.dataclass instances are now serialized by default and cannot be customized in a default function unless option=orjson.OPT_PASSTHROUGH_DATACLASS is specified. uuid.UUID instances are serialized by default. For any type that is now serialized, implementations in a default function and options enabling them can be removed but do not need to be. There was no change in deserialization.

To migrate from the standard library, the largest difference is that orjson.dumps returns bytes and json.dumps returns a str. Users with dict objects using non-str keys should specify option=orjson.OPT_NON_STR_KEYS. sort_keys is replaced by option=orjson.OPT_SORT_KEYS. indent is replaced by option=orjson.OPT_INDENT_2 and other levels of indentation are not supported.

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, None, dataclasses.dataclass, typing.TypedDict, datetime.datetime, datetime.date, datetime.time, uuid.UUID, numpy.ndarray, and orjson.Fragment instances. It supports arbitrary types through default. It serializes subclasses of str, int, dict, list, dataclasses.dataclass, and enum.Enum. It does not serialize subclasses of tuple to avoid serializing namedtuple objects as arrays. To avoid serializing subclasses, specify the option orjson.OPT_PASSTHROUGH_SUBCLASS.

The output is a bytes object containing UTF-8.

The global interpreter lock (GIL) is held for the duration of the call.

It raises JSONEncodeError on an unsupported type. This exception message describes the invalid object with the error message Type is not JSON serializable: .... To fix this, specify default.

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, unless OPT_NON_STR_KEYS is specified.

It raises JSONEncodeError if the output of default recurses to handling by default more than 254 levels deep.

It raises JSONEncodeError on circular references.

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

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

If the failure was caused by an exception in default then JSONEncodeError chains the original exception as __cause__.

default

To serialize a subclass or arbitrary types, specify default as a callable that returns a supported type. default may be a function, lambda, or callable class instance. To specify that a type was not handled by default, raise an exception such as TypeError.

>>> import orjson, decimal
>>>
def default(obj):
    if isinstance(obj, decimal.Decimal):
        return str(obj)
    raise TypeError

>>> orjson.dumps(decimal.Decimal("0.0842389659712649442845"))
JSONEncodeError: Type is not JSON serializable: decimal.Decimal
>>> orjson.dumps(decimal.Decimal("0.0842389659712649442845"), default=default)
b'"0.0842389659712649442845"'
>>> orjson.dumps({1, 2}, default=default)
orjson.JSONEncodeError: Type is not JSON serializable: set

The default callable may return an object that itself must be handled by default up to 254 times before an exception is raised.

It is important that default raise an exception if a type cannot be handled. Python otherwise implicitly returns None, which appears to the caller like a legitimate value and is serialized:

>>> import orjson, json, rapidjson
>>>
def default(obj):
    if isinstance(obj, decimal.Decimal):
        return str(obj)

>>> orjson.dumps({"set":{1, 2}}, default=default)
b'{"set":null}'
>>> json.dumps({"set":{1, 2}}, default=default)
'{"set":null}'
>>> rapidjson.dumps({"set":{1, 2}}, default=default)
'{"set":null}'

option

To modify how data is serialized, specify option. Each option is an integer constant in orjson. To specify multiple options, mask them together, e.g., option=orjson.OPT_STRICT_INTEGER | orjson.OPT_NAIVE_UTC.

OPT_APPEND_NEWLINE

Append \n to the output. This is a convenience and optimization for the pattern of dumps(...) + "\n". bytes objects are immutable and this pattern copies the original contents.

>>> import orjson
>>> orjson.dumps([])
b"[]"
>>> orjson.dumps([], option=orjson.OPT_APPEND_NEWLINE)
b"[]\n"

OPT_INDENT_2

Pretty-print output with an indent of two spaces. This is equivalent to indent=2 in the standard library. Pretty printing is slower and the output larger. orjson is the fastest compared library at pretty printing and has much less of a slowdown to pretty print than the standard library does. This option is compatible with all other options.

>>> import orjson
>>> orjson.dumps({"a": "b", "c": {"d": True}, "e": [1, 2]})
b'{"a":"b","c":{"d":true},"e":[1,2]}'
>>> orjson.dumps(
    {"a": "b", "c": {"d": True}, "e": [1, 2]},
    option=orjson.OPT_INDENT_2
)
b'{\n  "a": "b",\n  "c": {\n    "d": true\n  },\n  "e": [\n    1,\n    2\n  ]\n}'

If displayed, the indentation and linebreaks appear like this:

{
  "a": "b",
  "c": {
    "d": true
  },
  "e": [
    1,
    2
  ]
}

This measures serializing the github.json fixture as compact (52KiB) or pretty (64KiB):

Library	compact (ms)	pretty (ms)	vs. orjson
orjson	0.03	0.04	1
ujson	0.18	0.19	4.6
rapidjson	0.1	0.12	2.9
simplejson	0.25	0.89	21.4
json	0.18	0.71	17

This measures serializing the citm_catalog.json fixture, more of a worst case due to the amount of nesting and newlines, as compact (489KiB) or pretty (1.1MiB):

Library	compact (ms)	pretty (ms)	vs. orjson
orjson	0.59	0.71	1
ujson	2.9	3.59	5
rapidjson	1.81	2.8	3.9
simplejson	10.43	42.13	59.1
json	4.16	33.42	46.9

This can be reproduced using the pyindent script.

OPT_NAIVE_UTC

Serialize datetime.datetime objects without a tzinfo as UTC. This has no effect on datetime.datetime objects that have tzinfo set.

>>> import orjson, datetime
>>> orjson.dumps(
        datetime.datetime(1970, 1, 1, 0, 0, 0),
    )
b'"1970-01-01T00:00:00"'
>>> orjson.dumps(
        datetime.datetime(1970, 1, 1, 0, 0, 0),
        option=orjson.OPT_NAIVE_UTC,
    )
b'"1970-01-01T00:00:00+00:00"'

OPT_NON_STR_KEYS

Serialize dict keys of type other than str. This allows dict keys to be one of str, int, float, bool, None, datetime.datetime, datetime.date, datetime.time, enum.Enum, and uuid.UUID. For comparison, the standard library serializes str, int, float, bool or None by default. orjson benchmarks as being faster at serializing non-str keys than other libraries. This option is slower for str keys than the default.

>>> import orjson, datetime, uuid
>>> orjson.dumps(
        {uuid.UUID("7202d115-7ff3-4c81-a7c1-2a1f067b1ece"): [1, 2, 3]},
        option=orjson.OPT_NON_STR_KEYS,
    )
b'{"7202d115-7ff3-4c81-a7c1-2a1f067b1ece":[1,2,3]}'
>>> orjson.dumps(
        {datetime.datetime(1970, 1, 1, 0, 0, 0): [1, 2, 3]},
        option=orjson.OPT_NON_STR_KEYS | orjson.OPT_NAIVE_UTC,
    )
b'{"1970-01-01T00:00:00+00:00":[1,2,3]}'

These types are generally serialized how they would be as values, e.g., datetime.datetime is still an RFC 3339 string and respects options affecting it. The exception is that int serialization does not respect OPT_STRICT_INTEGER.

This option has the risk of creating duplicate keys. This is because non-str objects may serialize to the same str as an existing key, e.g., {"1": true, 1: false}. The last key to be inserted to the dict will be serialized last and a JSON deserializer will presumably take the last occurrence of a key (in the above, false). The first value will be lost.

This option is compatible with orjson.OPT_SORT_KEYS. If sorting is used, note the sort is unstable and will be unpredictable for duplicate keys.

>>> import orjson, datetime
>>> orjson.dumps(
    {"other": 1, datetime.date(1970, 1, 5): 2, datetime.date(1970, 1, 3): 3},
    option=orjson.OPT_NON_STR_KEYS | orjson.OPT_SORT_KEYS
)
b'{"1970-01-03":3,"1970-01-05":2,"other":1}'

This measures serializing 589KiB of JSON comprising a list of 100 dict in which each dict has both 365 randomly-sorted int keys representing epoch timestamps as well as one str key and the value for each key is a single integer. In "str keys", the keys were converted to str before serialization, and orjson still specifes option=orjson.OPT_NON_STR_KEYS (which is always somewhat slower).

Library	str keys (ms)	int keys (ms)	int keys sorted (ms)
orjson	1.53	2.16	4.29
ujson	3.07	5.65
rapidjson	4.29
simplejson	11.24	14.50	21.86
json	7.17	8.49

ujson is blank for sorting because it segfaults. json is blank because it raises TypeError on attempting to sort before converting all keys to str. rapidjson is blank because it does not support non-str keys. This can be reproduced using the pynonstr script.

OPT_OMIT_MICROSECONDS

Do not serialize the microsecond field on datetime.datetime and datetime.time instances.

>>> import orjson, datetime
>>> orjson.dumps(
        datetime.datetime(1970, 1, 1, 0, 0, 0, 1),
    )
b'"1970-01-01T00:00:00.000001"'
>>> orjson.dumps(
        datetime.datetime(1970, 1, 1, 0, 0, 0, 1),
        option=orjson.OPT_OMIT_MICROSECONDS,
    )
b'"1970-01-01T00:00:00"'

OPT_PASSTHROUGH_DATACLASS

Passthrough dataclasses.dataclass instances to default. This allows customizing their output but is much slower.

>>> import orjson, dataclasses
>>>
@dataclasses.dataclass
class User:
    id: str
    name: str
    password: str

def default(obj):
    if isinstance(obj, User):
        return {"id": obj.id, "name": obj.name}
    raise TypeError

>>> orjson.dumps(User("3b1", "asd", "zxc"))
b'{"id":"3b1","name":"asd","password":"zxc"}'
>>> orjson.dumps(User("3b1", "asd", "zxc"), option=orjson.OPT_PASSTHROUGH_DATACLASS)
TypeError: Type is not JSON serializable: User
>>> orjson.dumps(
        User("3b1", "asd", "zxc"),
        option=orjson.OPT_PASSTHROUGH_DATACLASS,
        default=default,
    )
b'{"id":"3b1","name":"asd"}'

OPT_PASSTHROUGH_DATETIME

Passthrough datetime.datetime, datetime.date, and datetime.time instances to default. This allows serializing datetimes to a custom format, e.g., HTTP dates:

>>> import orjson, datetime
>>>
def default(obj):
    if isinstance(obj, datetime.datetime):
        return obj.strftime("%a, %d %b %Y %H:%M:%S GMT")
    raise TypeError

>>> orjson.dumps({"created_at": datetime.datetime(1970, 1, 1)})
b'{"created_at":"1970-01-01T00:00:00"}'
>>> orjson.dumps({"created_at": datetime.datetime(1970, 1, 1)}, option=orjson.OPT_PASSTHROUGH_DATETIME)
TypeError: Type is not JSON serializable: datetime.datetime
>>> orjson.dumps(
        {"created_at": datetime.datetime(1970, 1, 1)},
        option=orjson.OPT_PASSTHROUGH_DATETIME,
        default=default,
    )
b'{"created_at":"Thu, 01 Jan 1970 00:00:00 GMT"}'

This does not affect datetimes in dict keys if using OPT_NON_STR_KEYS.

OPT_PASSTHROUGH_SUBCLASS

Passthrough subclasses of builtin types to default.

>>> import orjson
>>>
class Secret(str):
    pass

def default(obj):
    if isinstance(obj, Secret):
        return "******"
    raise TypeError

>>> orjson.dumps(Secret("zxc"))
b'"zxc"'
>>> orjson.dumps(Secret("zxc"), option=orjson.OPT_PASSTHROUGH_SUBCLASS)
TypeError: Type is not JSON serializable: Secret
>>> orjson.dumps(Secret("zxc"), option=orjson.OPT_PASSTHROUGH_SUBCLASS, default=default)
b'"******"'

This does not affect serializing subclasses as dict keys if using OPT_NON_STR_KEYS.

OPT_SERIALIZE_DATACLASS

This is deprecated and has no effect in version 3. In version 2 this was required to serialize dataclasses.dataclass instances. For more, see dataclass.

OPT_SERIALIZE_NUMPY

Serialize numpy.ndarray instances. For more, see numpy.

OPT_SERIALIZE_UUID

This is deprecated and has no effect in version 3. In version 2 this was required to serialize uuid.UUID instances. For more, see UUID.

OPT_SORT_KEYS

Serialize dict keys in sorted order. The default is to serialize in an unspecified order. This is equivalent to sort_keys=True in the standard library.

This can be used to ensure the order is deterministic for hashing or tests. It has a substantial performance penalty and is not recommended in general.

>>> import orjson
>>> orjson.dumps({"b": 1, "c": 2, "a": 3})
b'{"b":1,"c":2,"a":3}'
>>> orjson.dumps({"b": 1, "c": 2, "a": 3}, option=orjson.OPT_SORT_KEYS)
b'{"a":3,"b":1,"c":2}'

This measures serializing the twitter.json fixture unsorted and sorted:

Library	unsorted (ms)	sorted (ms)	vs. orjson
orjson	0.32	0.54	1
ujson	1.6	2.07	3.8
rapidjson	1.12	1.65	3.1
simplejson	2.25	3.13	5.8
json	1.78	2.32	4.3

The benchmark can be reproduced using the pysort script.

The sorting is not collation/locale-aware:

>>> import orjson
>>> orjson.dumps({"a": 1, "ä": 2, "A": 3}, option=orjson.OPT_SORT_KEYS)
b'{"A":3,"a":1,"\xc3\xa4":2}'

This is the same sorting behavior as the standard library, rapidjson, simplejson, and ujson.

dataclass also serialize as maps but this has no effect on them.

OPT_STRICT_INTEGER

Enforce 53-bit limit on integers. The limit is otherwise 64 bits, the same as the Python standard library. For more, see int.

OPT_UTC_Z

Serialize a UTC timezone on datetime.datetime instances as Z instead of +00:00.

>>> import orjson, datetime, zoneinfo
>>> orjson.dumps(
        datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=zoneinfo.ZoneInfo("UTC")),
    )
b'"1970-01-01T00:00:00+00:00"'
>>> orjson.dumps(
        datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=zoneinfo.ZoneInfo("UTC")),
        option=orjson.OPT_UTC_Z
    )
b'"1970-01-01T00:00:00Z"'

Fragment

orjson.Fragment includes already-serialized JSON in a document. This is an efficient way to include JSON blobs from a cache, JSONB field, or separately serialized object without first deserializing to Python objects via loads().

>>> import orjson
>>> orjson.dumps({"key": "zxc", "data": orjson.Fragment(b'{"a": "b", "c": 1}')})
b'{"key":"zxc","data":{"a": "b", "c": 1}}'

It does no reformatting: orjson.OPT_INDENT_2 will not affect a compact blob nor will a pretty-printed JSON blob be rewritten as compact.

The input must be bytes or str and given as a positional argument.

This raises orjson.JSONEncodeError if a str is given and the input is not valid UTF-8. It otherwise does no validation and it is possible to write invalid JSON. This does not escape characters. The implementation is tested to not crash if given invalid strings or invalid JSON.

This is similar to RawJSON in rapidjson.

Deserialize

def loads(__obj: Union[bytes, bytearray, memoryview, str]) -> Any: ...

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

bytes, bytearray, memoryview, and str input are accepted. If the input exists as a memoryview, bytearray, or bytes object, it is recommended to pass these directly rather than creating an unnecessary str object. That is, orjson.loads(b"{}") instead of orjson.loads(b"{}".decode("utf-8")). This has lower memory usage and lower latency.

The input must be valid UTF-8.

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 bytes to be cached and 2048 entries are stored.

The global interpreter lock (GIL) is held for the duration of the call.

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.

It raises JSONDecodeError if a combination of array or object recurses 1024 levels deep.

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

Types

dataclass

orjson serializes instances of dataclasses.dataclass natively. It serializes instances 40-50x as fast as other libraries and avoids a severe slowdown seen in other libraries compared to serializing dict.

It is supported to pass all variants of dataclasses, including dataclasses using __slots__, frozen dataclasses, those with optional or default attributes, and subclasses. There is a performance benefit to not using __slots__.

Library	dict (ms)	dataclass (ms)	vs. orjson
orjson	1.40	1.60	1
ujson
rapidjson	3.64	68.48	42
simplejson	14.21	92.18	57
json	13.28	94.90	59

This measures serializing 555KiB of JSON, orjson natively and other libraries using default to serialize the output of dataclasses.asdict(). This can be reproduced using the pydataclass script.

Dataclasses are serialized as maps, with every attribute serialized and in the order given on class definition:

>>> import dataclasses, orjson, typing

@dataclasses.dataclass
class Member:
    id: int
    active: bool = dataclasses.field(default=False)

@dataclasses.dataclass
class Object:
    id: int
    name: str
    members: typing.List[Member]

>>> orjson.dumps(Object(1, "a", [Member(1, True), Member(2)]))
b'{"id":1,"name":"a","members":[{"id":1,"active":true},{"id":2,"active":false}]}'

datetime

orjson serializes datetime.datetime objects to RFC 3339 format, e.g., "1970-01-01T00:00:00+00:00". This is a subset of ISO 8601 and is compatible with isoformat() in the standard library.

>>> import orjson, datetime, zoneinfo
>>> orjson.dumps(
    datetime.datetime(2018, 12, 1, 2, 3, 4, 9, tzinfo=zoneinfo.ZoneInfo("Australia/Adelaide"))
)
b'"2018-12-01T02:03:04.000009+10:30"'
>>> orjson.dumps(
    datetime.datetime(2100, 9, 1, 21, 55, 2).replace(tzinfo=zoneinfo.ZoneInfo("UTC"))
)
b'"2100-09-01T21:55:02+00:00"'
>>> orjson.dumps(
    datetime.datetime(2100, 9, 1, 21, 55, 2)
)
b'"2100-09-01T21:55:02"'

datetime.datetime supports instances with a tzinfo that is None, datetime.timezone.utc, a timezone instance from the python3.9+ zoneinfo module, or a timezone instance from the third-party pendulum, pytz, or dateutil/arrow libraries.

It is fastest to use the standard library's zoneinfo.ZoneInfo for timezones.

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.

To disable serialization of datetime objects specify the option orjson.OPT_PASSTHROUGH_DATETIME.

To use "Z" suffix instead of "+00:00" to indicate UTC ("Zulu") time, use the option orjson.OPT_UTC_Z.

To assume datetimes without timezone are UTC, use the option orjson.OPT_NAIVE_UTC.

enum

orjson serializes enums natively. Options apply to their values.

>>> import enum, datetime, orjson
>>>
class DatetimeEnum(enum.Enum):
    EPOCH = datetime.datetime(1970, 1, 1, 0, 0, 0)
>>> orjson.dumps(DatetimeEnum.EPOCH)
b'"1970-01-01T00:00:00"'
>>> orjson.dumps(DatetimeEnum.EPOCH, option=orjson.OPT_NAIVE_UTC)
b'"1970-01-01T00:00:00+00:00"'

Enums with members that are not supported types can be serialized using default:

>>> import enum, orjson
>>>
class Custom:
    def __init__(self, val):
        self.val = val

def default(obj):
    if isinstance(obj, Custom):
        return obj.val
    raise TypeError

class CustomEnum(enum.Enum):
    ONE = Custom(1)

>>> orjson.dumps(CustomEnum.ONE, default=default)
b'1'

float

orjson serializes and deserializes double precision floats with no loss of precision and consistent rounding.

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

>>> import orjson, ujson, rapidjson, json
>>> 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]'

int

orjson serializes and deserializes 64-bit integers by default. The range supported is a signed 64-bit integer's minimum (-9223372036854775807) to an unsigned 64-bit integer's maximum (18446744073709551615). This is widely compatible, but there are implementations that only support 53-bits for integers, e.g., web browsers. For those 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

numpy

orjson natively serializes numpy.ndarray and individual numpy.float64, numpy.float32, numpy.float16 (numpy.half), numpy.int64, numpy.int32, numpy.int16, numpy.int8, numpy.uint64, numpy.uint32, numpy.uint16, numpy.uint8, numpy.uintp, numpy.intp, numpy.datetime64, and numpy.bool instances.

orjson is compatible with both numpy v1 and v2.

orjson is faster than all compared libraries at serializing numpy instances. Serializing numpy data requires specifying option=orjson.OPT_SERIALIZE_NUMPY.

>>> import orjson, numpy
>>> orjson.dumps(
        numpy.array([[1, 2, 3], [4, 5, 6]]),
        option=orjson.OPT_SERIALIZE_NUMPY,
)
b'[[1,2,3],[4,5,6]]'

The array must be a contiguous C array (C_CONTIGUOUS) and one of the supported datatypes.

Note a difference between serializing numpy.float32 using ndarray.tolist() or orjson.dumps(..., option=orjson.OPT_SERIALIZE_NUMPY): tolist() converts to a double before serializing and orjson's native path does not. This can result in different rounding.

numpy.datetime64 instances are serialized as RFC 3339 strings and datetime options affect them.

>>> import orjson, numpy
>>> orjson.dumps(
        numpy.datetime64("2021-01-01T00:00:00.172"),
        option=orjson.OPT_SERIALIZE_NUMPY,
)
b'"2021-01-01T00:00:00.172000"'
>>> orjson.dumps(
        numpy.datetime64("2021-01-01T00:00:00.172"),
        option=(
            orjson.OPT_SERIALIZE_NUMPY |
            orjson.OPT_NAIVE_UTC |
            orjson.OPT_OMIT_MICROSECONDS
        ),
)
b'"2021-01-01T00:00:00+00:00"'

If an array is not a contiguous C array, contains an unsupported datatype, or contains a numpy.datetime64 using an unsupported representation (e.g., picoseconds), orjson falls through to default. In default, obj.tolist() can be specified.

If an array is not in the native endianness, e.g., an array of big-endian values on a little-endian system, orjson.JSONEncodeError is raised.

If an array is malformed, orjson.JSONEncodeError is raised.

This measures serializing 92MiB of JSON from an numpy.ndarray with dimensions of (50000, 100) and numpy.float64 values:

Library	Latency (ms)	RSS diff (MiB)	vs. orjson
orjson	194	99	1.0
ujson
rapidjson	3,048	309	15.7
simplejson	3,023	297	15.6
json	3,133	297	16.1

This measures serializing 100MiB of JSON from an numpy.ndarray with dimensions of (100000, 100) and numpy.int32 values:

Library	Latency (ms)	RSS diff (MiB)	vs. orjson
orjson	178	115	1.0
ujson
rapidjson	1,512	551	8.5
simplejson	1,606	504	9.0
json	1,506	503	8.4

This measures serializing 105MiB of JSON from an numpy.ndarray with dimensions of (100000, 200) and numpy.bool values:

Library	Latency (ms)	RSS diff (MiB)	vs. orjson
orjson	157	120	1.0
ujson
rapidjson	710	327	4.5
simplejson	931	398	5.9
json	996	400	6.3

In these benchmarks, orjson serializes natively, ujson is blank because it does not support a default parameter, and the other libraries serialize ndarray.tolist() via default. The RSS column measures peak memory usage during serialization. This can be reproduced using the pynumpy script.

orjson does not have an installation or compilation dependency on numpy. The implementation is independent, reading numpy.ndarray using PyArrayInterface.

str

orjson is strict about UTF-8 conformance. This is stricter than the standard library's json module, which will serialize and deserialize UTF-16 surrogates, e.g., "\ud800", that are invalid UTF-8.

If orjson.dumps() is given a str that does not contain valid UTF-8, orjson.JSONEncodeError is raised. If loads() receives invalid UTF-8, orjson.JSONDecodeError is raised.

orjson and rapidjson are the only compared JSON libraries to consistently error on bad input.

>>> 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"'
>>> 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'

To make a best effort at deserializing bad input, first decode bytes using the replace or lossy argument for errors:

>>> import orjson
>>> orjson.loads(b'"\xed\xa0\x80"')
JSONDecodeError: str is not valid UTF-8: surrogates not allowed
>>> orjson.loads(b'"\xed\xa0\x80"'.decode("utf-8", "replace"))
'���'

uuid

orjson serializes uuid.UUID instances to RFC 4122 format, e.g., "f81d4fae-7dec-11d0-a765-00a0c91e6bf6".

>>> import orjson, uuid
>>> orjson.dumps(uuid.UUID('f81d4fae-7dec-11d0-a765-00a0c91e6bf6'))
b'"f81d4fae-7dec-11d0-a765-00a0c91e6bf6"'
>>> orjson.dumps(uuid.uuid5(uuid.NAMESPACE_DNS, "python.org"))
b'"886313e1-3b8a-5372-9b90-0c9aee199e5d"'

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 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.

orjson is the most correct of the compared libraries. This graph shows how each library handles a combined 342 JSON fixtures from the JSONTestSuite and nativejson-benchmark tests:

Library	Invalid JSON documents not rejected	Valid JSON documents not deserialized
orjson	0	0
ujson	31	0
rapidjson	6	0
simplejson	10	0
json	17	0

This shows that all libraries deserialize valid JSON but only orjson correctly rejects the given invalid JSON fixtures. Errors are largely due to accepting invalid strings and numbers.

The graph above can be reproduced using the pycorrectness script.

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	0.1	8377	1
ujson	0.9	1088	7.3
rapidjson	0.8	1228	6.8
simplejson	1.9	531	15.6
json	1.4	744	11.3

twitter.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.6	1811	1
ujson	1.2	814	2.1
rapidjson	2.1	476	3.8
simplejson	1.6	626	3
json	1.8	557	3.3

github.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.01	104424	1
ujson	0.09	10594	9.8
rapidjson	0.07	13667	7.6
simplejson	0.2	5051	20.6
json	0.14	7133	14.6

github.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.05	20069	1
ujson	0.11	8913	2.3
rapidjson	0.13	8077	2.6
simplejson	0.11	9342	2.1
json	0.11	9291	2.2

citm_catalog.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	0.3	3757	1
ujson	1.7	598	6.3
rapidjson	1.3	768	4.9
simplejson	8.3	120	31.1
json	3	331	11.3

citm_catalog.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	1.4	730	1
ujson	2.6	384	1.9
rapidjson	4	246	3
simplejson	3.7	271	2.7
json	3.7	267	2.7

canada.json serialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	2.4	410	1
ujson	9.6	104	3.9
rapidjson	28.7	34	11.8
simplejson	49.3	20	20.3
json	30.6	32	12.6

canada.json deserialization

Library	Median latency (milliseconds)	Operations per second	Relative (latency)
orjson	3	336	1
ujson	7.1	141	2.4
rapidjson	20.1	49	6.7
simplejson	16.8	59	5.6
json	18.2	55	6.1

Memory

orjson as of 3.7.0 has higher baseline memory usage than other libraries due to a persistent buffer used for parsing. Incremental memory usage when deserializing is similar to 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	15.7	3.4
ujson	16.4	3.4
rapidjson	16.6	4.4
simplejson	14.5	1.8
json	13.9	1.8

github.json

Library	import, read() RSS (MiB)	loads() increase in RSS (MiB)
orjson	15.2	0.4
ujson	15.4	0.4
rapidjson	15.7	0.5
simplejson	13.7	0.2
json	13.3	0.1

citm_catalog.json

Library	import, read() RSS (MiB)	loads() increase in RSS (MiB)
orjson	16.8	10.1
ujson	17.3	10.2
rapidjson	17.6	28.7
simplejson	15.8	30.1
json	14.8	20.5

canada.json

Library	import, read() RSS (MiB)	loads() increase in RSS (MiB)
orjson	17.2	22.1
ujson	17.4	18.3
rapidjson	18	23.5
simplejson	15.7	21.4
json	15.4	20.4

Reproducing

The above was measured using Python 3.11.9 on Linux (amd64) with orjson 3.10.6, ujson 5.10.0, python-rapidson 1.18, and simplejson 3.19.2.

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

Questions

Why can't I install it from PyPI?

Probably pip needs to be upgraded to version 20.3 or later to support the latest manylinux_x_y or universal2 wheel formats.

"Cargo, the Rust package manager, is not installed or is not on PATH."

This happens when there are no binary wheels (like manylinux) for your platform on PyPI. You can install Rust through rustup or a package manager and then it will compile.

Will it deserialize to dataclasses, UUIDs, decimals, etc or support object_hook?

No. This requires a schema specifying what types are expected and how to handle errors etc. This is addressed by data validation libraries a level above this.

Will it serialize to `str`?

No. bytes is the correct type for a serialized blob.

Packaging

To package orjson requires at least Rust 1.72 and the maturin build tool. The recommended build command is:

maturin build --release --strip

It benefits from also having a C build environment to compile a faster deserialization backend. See this project's manylinux_2_28 builds for an example using clang and LTO.

The project's own CI tests against nightly-2024-09-25 and stable 1.72. It is prudent to pin the nightly version because that channel can introduce breaking changes.

orjson is tested for amd64 on Linux and cross-compiles for aarch64, arm7, ppc64le, and s390x. It is tested for either aarch64 or amd64 on macOS and cross-compiles for the other, depending on version. For Windows it is tested on amd64 and i686.

There are no runtime dependencies other than libc.

The source distribution on PyPI contains all dependencies' source and can be built without network access. The file can be downloaded from https://files.pythonhosted.org/packages/source/o/orjson/orjson-${version}.tar.gz.

orjson's tests are included in the source distribution on PyPI. The requirements to run the tests are specified in test/requirements.txt. The tests should be run as part of the build. It can be run with pytest -q test.

Name		Name	Last commit message	Last commit date
Latest commit History 682 Commits
.github/workflows		.github/workflows
bench		bench
ci		ci
data		data
doc		doc
include		include
integration		integration
pysrc/orjson		pysrc/orjson
script		script
src		src
test		test
.gitattributes		.gitattributes
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE-APACHE		LICENSE-APACHE
LICENSE-MIT		LICENSE-MIT
README.md		README.md
build.rs		build.rs
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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orjson

Usage

Install

Quickstart

Migrating

Serialize

default

option

OPT_APPEND_NEWLINE

OPT_INDENT_2

OPT_NAIVE_UTC

OPT_NON_STR_KEYS

OPT_OMIT_MICROSECONDS

OPT_PASSTHROUGH_DATACLASS

OPT_PASSTHROUGH_DATETIME

OPT_PASSTHROUGH_SUBCLASS

OPT_SERIALIZE_DATACLASS

OPT_SERIALIZE_NUMPY

OPT_SERIALIZE_UUID

OPT_SORT_KEYS

OPT_STRICT_INTEGER

OPT_UTC_Z

Fragment

Deserialize

Types

dataclass

datetime

enum

float

int

numpy

str

uuid

Testing

Performance

Latency

twitter.json serialization

twitter.json deserialization

github.json serialization

github.json deserialization

citm_catalog.json serialization

citm_catalog.json deserialization

canada.json serialization

canada.json deserialization

Memory

twitter.json

github.json

citm_catalog.json

canada.json

Reproducing

Questions

Why can't I install it from PyPI?

"Cargo, the Rust package manager, is not installed or is not on PATH."

Will it deserialize to dataclasses, UUIDs, decimals, etc or support object_hook?

Will it serialize to str?

Packaging

License

About

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Used by 133k

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Will it serialize to `str`?

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