Skip to main content

Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy

Project description

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 3.6, 3.7, 3.8, 3.9, and 3.10. It distributes x86_64/amd64 and aarch64/armv8 wheels for Linux and macOS. It distributes x86_64/amd64 wheels for Windows. orjson does not support PyPy. 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.

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

Usage

Install

To install a wheel from PyPI:

pip install --upgrade "pip>=19.3" # manylinux2014 support
pip install --upgrade orjson

Notice that Linux environments with a pip version shipped in 2018 or earlier must first upgrade pip to support manylinux2014 wheels.

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, dataclasses.dataclass, typing.TypedDict, datetime.datetime, datetime.date, datetime.time, uuid.UUID, numpy.ndarray, and None 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.

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 0.06 0.07 1.0
ujson 0.18 0.19 2.8
rapidjson 0.22
simplejson 0.35 1.49 21.4
json 0.36 1.19 17.2

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 0.88 1.73 1.0
ujson 3.73 4.52 2.6
rapidjson 3.54
simplejson 11.77 72.06 41.6
json 6.71 55.22 31.9

rapidjson is blank because it does not support pretty printing. 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 0.5 0.92 1
ujson 1.61 2.48 2.7
rapidjson 2.17 2.89 3.2
simplejson 3.56 5.13 5.6
json 3.59 4.59 5

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

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. 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 512 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.

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}]}'

Users may wish to control how dataclass instances are serialized, e.g., to not serialize an attribute or to change the name of an attribute when serialized. orjson may implement support using the metadata mapping on field attributes, e.g., field(metadata={"json_serialize": False}), if use cases are clear.

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 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.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"'

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.

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.

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, se 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. The same behavior is observed in rapidjson, simplejson, and json. ujson 1.35 was inaccurate in both serialization and deserialization, i.e., it modifies the data, and the recent 2.0 release is accurate.

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.int64, numpy.int32, numpy.int8, numpy.uint64, numpy.uint32, and numpy.uint8 instances. Arrays may have a dtype of numpy.bool, numpy.float32, numpy.float64, numpy.int32, numpy.int64, numpy.uint32, numpy.uint64, numpy.uintp, or numpy.intp. 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.

If an array is not a contiguous C array or contains an supported datatype, orjson falls through to default. In default, obj.tolist() can be specified. If an array is malformed, which is not expected, 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 38 0
rapidjson 6 0
simplejson 13 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

alt text alt text alt text alt text alt text alt text alt text alt text

twitter.json serialization

Library Median latency (milliseconds) Operations per second Relative (latency)
orjson 0.59 1698.8 1
ujson 2.14 464.3 3.64
rapidjson 2.39 418.5 4.06
simplejson 3.15 316.9 5.36
json 3.56 281.2 6.06

twitter.json deserialization

Library Median latency (milliseconds) Operations per second Relative (latency)
orjson 2.28 439.3 1
ujson 2.89 345.9 1.27
rapidjson 3.85 259.6 1.69
simplejson 3.66 272.1 1.61
json 4.05 246.7 1.78

github.json serialization

Library Median latency (milliseconds) Operations per second Relative (latency)
orjson 0.07 15265.2 1
ujson 0.22 4556.7 3.35
rapidjson 0.26 3808.9 4.02
simplejson 0.37 2690.4 5.68
json 0.35 2847.8 5.36

github.json deserialization

Library Median latency (milliseconds) Operations per second Relative (latency)
orjson 0.18 5610.1 1
ujson 0.28 3540.7 1.58
rapidjson 0.33 3031.5 1.85
simplejson 0.29 3385.6 1.65
json 0.29 3402.1 1.65

citm_catalog.json serialization

Library Median latency (milliseconds) Operations per second Relative (latency)
orjson 0.99 1008.5 1
ujson 3.69 270.7 3.72
rapidjson 3.55 281.4 3.58
simplejson 11.76 85.1 11.85
json 6.89 145.1 6.95

citm_catalog.json deserialization

Library Median latency (milliseconds) Operations per second Relative (latency)
orjson 4.53 220.5 1
ujson 5.67 176.5 1.25
rapidjson 7.51 133.3 1.66
simplejson 7.54 132.7 1.66
json 7.8 128.2 1.72

canada.json serialization

Library Median latency (milliseconds) Operations per second Relative (latency)
orjson 4.72 198.9 1
ujson 17.76 56.3 3.77
rapidjson 61.83 16.2 13.11
simplejson 80.6 12.4 17.09
json 52.38 18.8 11.11

canada.json deserialization

Library Median latency (milliseconds) Operations per second Relative (latency)
orjson 10.28 97.4 1
ujson 16.49 60.5 1.6
rapidjson 37.92 26.4 3.69
simplejson 37.7 26.5 3.67
json 37.87 27.6 3.68

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 13.5 2.5
ujson 14 4.1
rapidjson 14.7 6.5
simplejson 13.2 2.5
json 12.9 2.3

github.json

Library import, read() RSS (MiB) loads() increase in RSS (MiB)
orjson 13.1 0.3
ujson 13.5 0.3
rapidjson 14 0.7
simplejson 12.6 0.3
json 12.3 0.1

citm_catalog.json

Library import, read() RSS (MiB) loads() increase in RSS (MiB)
orjson 14.6 7.9
ujson 15.1 11.1
rapidjson 15.8 36
simplejson 14.3 27.4
json 14 27.2

canada.json

Library import, read() RSS (MiB) loads() increase in RSS (MiB)
orjson 17.1 15.7
ujson 17.6 17.4
rapidjson 18.3 17.9
simplejson 16.9 19.6
json 16.5 19.4

Reproducing

The above was measured using Python 3.8.3 on Linux (x86_64) with orjson 3.3.0, ujson 3.0.0, python-rapidson 0.9.1, and simplejson 3.17.2.

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. pip added support for manylinux2014 in 2019.

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.

Will it support PyPy?

If someone implements it well.

Packaging

To package orjson requires Rust on the nightly channel and the maturin build tool. maturin can be installed from PyPI or packaged as well. This is the simplest and recommended way of installing from source, assuming rustup is available from a package manager:

rustup default nightly
pip wheel --no-binary=orjson orjson

This is an example of building a wheel using the repository as source, rustup installed from upstream, and a pinned version of Rust:

pip install maturin
curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain nightly-2021-05-25 --profile minimal -y
maturin build --no-sdist --release --strip --manylinux off
ls -1 target/wheels

Problems with the Rust nightly channel may require pinning a version. nightly-2021-05-25 is known to be ok.

orjson is tested for amd64 and aarch64 on Linux, macOS, and Windows. It may not work on 32-bit targets. It has recommended RUSTFLAGS specified in .cargo/config so it is recommended to either not set RUSTFLAGS or include these options.

There are no runtime dependencies other than libc.

orjson's tests are included in the source distribution on PyPI. It is necessarily to install dependencies from PyPI specified in test/requirements.txt. These require a C compiler. The tests do not make network requests.

The tests should be run as part of the build. It can be run like this:

pip install -r test/requirements.txt
pytest -q test

License

orjson was written by ijl <ijl@mailbox.org>, copyright 2018 - 2021, licensed under both the Apache 2 and MIT licenses.

Project details


Release history Release notifications | RSS feed

This version

3.5.3

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

orjson-3.5.3.tar.gz (742.3 kB view details)

Uploaded Source

Built Distributions

orjson-3.5.3-cp310-cp310-manylinux_2_24_x86_64.whl (229.7 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.24+ x86-64

orjson-3.5.3-cp310-cp310-manylinux_2_24_aarch64.whl (211.5 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.24+ ARM64

orjson-3.5.3-cp39-none-win_amd64.whl (180.4 kB view details)

Uploaded CPython 3.9Windows x86-64

orjson-3.5.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (229.6 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

orjson-3.5.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (211.7 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

orjson-3.5.3-cp39-cp39-macosx_10_9_universal2.whl (429.8 kB view details)

Uploaded CPython 3.9macOS 10.9+ universal2 (ARM64, x86-64)

orjson-3.5.3-cp39-cp39-macosx_10_7_x86_64.whl (227.1 kB view details)

Uploaded CPython 3.9macOS 10.7+ x86-64

orjson-3.5.3-cp38-none-win_amd64.whl (180.4 kB view details)

Uploaded CPython 3.8Windows x86-64

orjson-3.5.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (229.6 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

orjson-3.5.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (211.7 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

orjson-3.5.3-cp38-cp38-macosx_10_9_universal2.whl (429.8 kB view details)

Uploaded CPython 3.8macOS 10.9+ universal2 (ARM64, x86-64)

orjson-3.5.3-cp38-cp38-macosx_10_7_x86_64.whl (227.1 kB view details)

Uploaded CPython 3.8macOS 10.7+ x86-64

orjson-3.5.3-cp37-none-win_amd64.whl (180.4 kB view details)

Uploaded CPython 3.7Windows x86-64

orjson-3.5.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (229.7 kB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

orjson-3.5.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (211.8 kB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ ARM64

orjson-3.5.3-cp37-cp37m-macosx_10_9_universal2.whl (429.8 kB view details)

Uploaded CPython 3.7mmacOS 10.9+ universal2 (ARM64, x86-64)

orjson-3.5.3-cp37-cp37m-macosx_10_7_x86_64.whl (227.2 kB view details)

Uploaded CPython 3.7mmacOS 10.7+ x86-64

orjson-3.5.3-cp36-none-win_amd64.whl (180.4 kB view details)

Uploaded CPython 3.6Windows x86-64

orjson-3.5.3-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (229.7 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ x86-64

orjson-3.5.3-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (211.8 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ ARM64

orjson-3.5.3-cp36-cp36m-macosx_10_9_universal2.whl (429.8 kB view details)

Uploaded CPython 3.6mmacOS 10.9+ universal2 (ARM64, x86-64)

orjson-3.5.3-cp36-cp36m-macosx_10_7_x86_64.whl (227.2 kB view details)

Uploaded CPython 3.6mmacOS 10.7+ x86-64

File details

Details for the file orjson-3.5.3.tar.gz.

File metadata

  • Download URL: orjson-3.5.3.tar.gz
  • Upload date:
  • Size: 742.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for orjson-3.5.3.tar.gz
Algorithm Hash digest
SHA256 8818f651ef7ed55f7c0ee34fa51f3de0988dd35386e8cefd0c2e1f32ff9f1966
MD5 57447e1da4e5c39835e34e69d1862689
BLAKE2b-256 3dfa53dba2273db6fa955bcfc250391c95dce0a27ec1f56cbec2e2a86a3107d6

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp310-cp310-manylinux_2_24_x86_64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp310-cp310-manylinux_2_24_x86_64.whl
Algorithm Hash digest
SHA256 9c9a6a544713204b832ffcebd61a2a12764ed56531b52926c7b7ce4a40198fe3
MD5 10aa87920d2e11e49b8e9321436eac01
BLAKE2b-256 62731420f895fefdaec24a2c38209e28449d57d6738b0264693616ee46533e5d

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp310-cp310-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp310-cp310-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 055e47e93a4096352e025f1830c3ab094b4101a628f81b702178cbfd76b6744e
MD5 64424c11e9f023073e4b8430c000588c
BLAKE2b-256 9e6a9dab899363a399b0e912454abe023cc702cfa4636712ad2bcadeab1fbcb0

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp39-none-win_amd64.whl.

File metadata

  • Download URL: orjson-3.5.3-cp39-none-win_amd64.whl
  • Upload date:
  • Size: 180.4 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.9.5

File hashes

Hashes for orjson-3.5.3-cp39-none-win_amd64.whl
Algorithm Hash digest
SHA256 111ebdbca5fe51d4b22d155861ec8d35ce48f62d92717ed5828566b13a284c1a
MD5 1afb81f1154d124422251e220ed3d886
BLAKE2b-256 97203ae407fb351f8b9a17cb2a343848ae7099e79b517f3aea9aaf86d8b5422e

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 45b249d9d7ef6f241bca0a09cde57c99d019a0ca73df9bffb25c768b0f806b6d
MD5 fd0aac9533af903a1f2427d08e9440ea
BLAKE2b-256 2d11787bb9aaab96787747cef1e993093ebee1b7bc270e54864ccf0f40c2def5

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0eeb1dd42a4613d7032146e4693f44b334c150eae193a91a14789ac89c1d7455
MD5 5d7a32d43adb9d3299e85bfb0cdde5d1
BLAKE2b-256 712973770d775c683b80cad06a657538b3f99a655e3626a0692ec82cb4c78989

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

  • Download URL: orjson-3.5.3-cp39-cp39-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 429.8 kB
  • Tags: CPython 3.9, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.9.5

File hashes

Hashes for orjson-3.5.3-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 d61edb73c5a7287e776dc000c056d59e1cc8d548cc672977b74e74c0164be3ef
MD5 127d05c8ff815f098baf0a393b37a755
BLAKE2b-256 3dde21e2b93db6bc90944e4d417208e148ce9ce6622a0bc0c922fb62277f8433

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp39-cp39-macosx_10_7_x86_64.whl.

File metadata

  • Download URL: orjson-3.5.3-cp39-cp39-macosx_10_7_x86_64.whl
  • Upload date:
  • Size: 227.1 kB
  • Tags: CPython 3.9, macOS 10.7+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.9.5

File hashes

Hashes for orjson-3.5.3-cp39-cp39-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 6186755180e53436ebac3e0ce1590b27f218727f888c6e3f4c8fdabcb3ef840e
MD5 5d258f8b557e085091ec12e8d5dfbbf0
BLAKE2b-256 d1d39c8981bc62aab51778b1ea7e279d762129e8002ec7d075ade4f74b3e97d0

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp38-none-win_amd64.whl.

File metadata

  • Download URL: orjson-3.5.3-cp38-none-win_amd64.whl
  • Upload date:
  • Size: 180.4 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for orjson-3.5.3-cp38-none-win_amd64.whl
Algorithm Hash digest
SHA256 dcf711f6e4f5ee33206d51436eb9a2322a4338fd9081729c662e37d062f51c9d
MD5 7eb1371ba6c1b25cd6655ccbda3e63ad
BLAKE2b-256 733c22efc162e704a963d0feddef6faffed38a4d99bea4915b31378c7c6085ca

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 27fa08fe5d2b9913b3ac8728960971544f255778e120849add596d67a7720f1f
MD5 430afcbf5e4ad17c2c5e16aeace235a9
BLAKE2b-256 9c61286416e6a2d9e8c2542b0e2a5c31f5afeb90d706ceabfdf102e96595aa2a

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 dbe2b73de6febbcfd8b8ee9629e11d33f88f54bf675cacced7bfee84684fec93
MD5 1773a8b3dabf728f2a6097ad2d5fface
BLAKE2b-256 7dc057be6824fc898c222a52d351922cd3233573b75f919e3642c425de134e88

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

  • Download URL: orjson-3.5.3-cp38-cp38-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 429.8 kB
  • Tags: CPython 3.8, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for orjson-3.5.3-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 91c31999cbd4650459ef5160f5cf248cb4a7f1e24407f90cd9c58d113d335561
MD5 0e3436691bc5db8f62dcd36c57bebfe2
BLAKE2b-256 5c2be530b43f99cd7e699f1fbff948b8d216ad832534c06bac3a3904287d3e1b

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp38-cp38-macosx_10_7_x86_64.whl.

File metadata

  • Download URL: orjson-3.5.3-cp38-cp38-macosx_10_7_x86_64.whl
  • Upload date:
  • Size: 227.1 kB
  • Tags: CPython 3.8, macOS 10.7+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for orjson-3.5.3-cp38-cp38-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 e0e74f47a3aafc6751d6dc238e34b38ae9a77a2373b98a722c428d832c919617
MD5 c29d6f4457ff07d904a9385648e823bb
BLAKE2b-256 f3e371e84e6ec21adf3241378f69b3e42afcfbd5f20c69c261a250ccc0368b21

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp37-none-win_amd64.whl.

File metadata

  • Download URL: orjson-3.5.3-cp37-none-win_amd64.whl
  • Upload date:
  • Size: 180.4 kB
  • Tags: CPython 3.7, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.7.9

File hashes

Hashes for orjson-3.5.3-cp37-none-win_amd64.whl
Algorithm Hash digest
SHA256 0c70bee40f215ede3949b34f1ae6b5260e108c00c914a7c62741ce6f8de2e27c
MD5 d38726e399a01e91360519d8e890b3bd
BLAKE2b-256 4eaecd5f74b9db02035128f2c1bfe6a1a79f2f9126d5c9130f97739d70131670

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b427ad034625ed522b683c1333ab2de83c25c1787fee47968a27f72fa2b55dca
MD5 27524bfb285f944d2df4dfb478529a1d
BLAKE2b-256 051164846f7c16470887737bb14ed5339918518c9ca84b4caba3590b929410cc

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ed823902b9e8c5130e0c67d317eab9ec200e45d26b96510efb7ae39f732ef24c
MD5 83b862c6fc23d653a486d0fc43200c1c
BLAKE2b-256 18d1e7f706ffb4015d920cc973acd42d7eabb1b14767c4e4f7f25be357515ef6

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp37-cp37m-macosx_10_9_universal2.whl.

File metadata

  • Download URL: orjson-3.5.3-cp37-cp37m-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 429.8 kB
  • Tags: CPython 3.7m, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.7.10

File hashes

Hashes for orjson-3.5.3-cp37-cp37m-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 b3b7ffdca6408b268aed9492e8558ac80f2e3bb362b992c2e7ecbbeb49b2a51e
MD5 250823584c24b33a88d476e29e89b55b
BLAKE2b-256 df8d80a09aeca7230da8032dc6519284e50ee1af5d9a3af7e2002db97eb135ae

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp37-cp37m-macosx_10_7_x86_64.whl.

File metadata

  • Download URL: orjson-3.5.3-cp37-cp37m-macosx_10_7_x86_64.whl
  • Upload date:
  • Size: 227.2 kB
  • Tags: CPython 3.7m, macOS 10.7+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.7.10

File hashes

Hashes for orjson-3.5.3-cp37-cp37m-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 4c80de99cb9617fe023201b543b8ed4b02dd8b52fbf7dd9b399d3b9d5f352398
MD5 f161dacf1949e5ad5abb421f4f5f14bd
BLAKE2b-256 0961cbd2b43ee792ee74a04a396be3925d4aceada0743c7da7e9344d704e74b6

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp36-none-win_amd64.whl.

File metadata

  • Download URL: orjson-3.5.3-cp36-none-win_amd64.whl
  • Upload date:
  • Size: 180.4 kB
  • Tags: CPython 3.6, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.6.8

File hashes

Hashes for orjson-3.5.3-cp36-none-win_amd64.whl
Algorithm Hash digest
SHA256 7e65fc393a77b5db391f28c7ccfcdc844f9dd0624e42dcf17d36fc20ddd3f3a0
MD5 e1be0a89c016ba9e9aacefceeacf64a0
BLAKE2b-256 d33508149ad097afdc4bc0058e46f627940243edaf878c05ef30dde309f464ae

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b2add8eeb14746f961330330ab5ce3dd09c858fb634eeeb26ceac14443e82830
MD5 525a6620fd961944ea9373d6174dffed
BLAKE2b-256 8d983cb0a3f2a33018785cbbc9c7d986393307e0e562184ef883ef49884215d1

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for orjson-3.5.3-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f697b8e3dceb787c173184cd4ec8331c27e0af7cc75d43759abcb5d2464d1ade
MD5 6667c41151d8244e3c87331d0a6ebc66
BLAKE2b-256 99c50bf7b9ccd038fb7269748dcbe0cabdacb6b0e63057c3e3f9d6dd7cfabc69

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp36-cp36m-macosx_10_9_universal2.whl.

File metadata

  • Download URL: orjson-3.5.3-cp36-cp36m-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 429.8 kB
  • Tags: CPython 3.6m, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.6.13

File hashes

Hashes for orjson-3.5.3-cp36-cp36m-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 eb0cfe56687ac915e83dcfa1aa100e68883b42fe8eecae7275dc05da8cf96faa
MD5 2665108d5923cc9ffcb1a7e5fe6a652c
BLAKE2b-256 e2b4ee58fcd7f17a46a34013bc362e5de2b230d2b60929b2ab2221ffc468818a

See more details on using hashes here.

File details

Details for the file orjson-3.5.3-cp36-cp36m-macosx_10_7_x86_64.whl.

File metadata

  • Download URL: orjson-3.5.3-cp36-cp36m-macosx_10_7_x86_64.whl
  • Upload date:
  • Size: 227.2 kB
  • Tags: CPython 3.6m, macOS 10.7+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.6.13

File hashes

Hashes for orjson-3.5.3-cp36-cp36m-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 f22e2b3a1686a0f90aca920a522033b326cb2f945c8ed8fd8effa9f302672627
MD5 dd5d3b4c57b1dddcfec323ac6fd3b878
BLAKE2b-256 db79a954e5cdb0bedfe2f63aa564d5cd78ad7611aecea5eaa44fbe58e6cd03e7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page