ormsgpack 1.2.3

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

ormsgpack

ormsgpack is a fast msgpack library for Python. It is a fork/reboot of orjson It serializes faster than msgpack-python and deserializes a bit slower (right now). It supports serialization of: dataclass, datetime, numpy, pydantic and UUID instances natively.

Its features and drawbacks compared to other Python msgpack libraries:

• serializes dataclass instances natively.
• serializes datetime, date, and time instances to RFC 3339 format, e.g., "1970-01-01T00:00:00+00:00"
• serializes numpy.ndarray instances natively and faster.
• serializes pydantic.BaseModel instances natively (disregards the configuration ATM).
• serializes arbitrary types using a default hook

ormsgpack supports CPython 3.6, 3.7, 3.8, 3.9, and 3.10. ormsgpack does not support PyPy. Releases follow semantic versioning and serializing a new object type without an opt-in flag is considered a breaking change.

ormsgpack is licensed under both the Apache 2.0 and MIT licenses. The repository and issue tracker is github.com/aviramha/ormsgpack, and patches may be submitted there. There is a CHANGELOG available in the repository.

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

Usage

Install

To install a wheel from PyPI:

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

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 ormsgpack, datetime, numpy
>>> data = {
    "type": "job",
    "created_at": datetime.datetime(1970, 1, 1),
    "status": "🆗",
    "payload": numpy.array([[1, 2], [3, 4]]),
}
>>> ormsgpack.packb(data, option=ormsgpack.OPT_NAIVE_UTC | ormsgpack.OPT_SERIALIZE_NUMPY)
b'\x84\xa4type\xa3job\xaacreated_at\xb91970-01-01T00:00:00+00:00\xa6status\xa4\xf0\x9f\x86\x97\xa7payload\x92\x92\x01\x02\x92\x03\x04'
>>> ormsgpack.unpackb(_)
{'type': 'job', 'created_at': '1970-01-01T00:00:00+00:00', 'status': '🆗', 'payload': [[1, 2], [3, 4]]}

Serialize

def packb(
    __obj: Any,
    default: Optional[Callable[[Any], Any]] = ...,
    option: Optional[int] = ...,
) -> bytes: ...

packb() serializes Python objects to msgpack.

It natively serializes bytes, 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 ormsgpack.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 MsgpackEncodeError 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 MsgpackEncodeError on a str that contains invalid UTF-8.

It raises MsgpackEncodeError if a dict has a key of a type other than str or bytes, unless OPT_NON_STR_KEYS is specified.

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

It raises MsgpackEncodeError on circular references.

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

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

>>> ormsgpack.packb(decimal.Decimal("0.0842389659712649442845"))
MsgpackEncodeError: Type is not JSON serializable: decimal.Decimal
>>> ormsgpack.packb(decimal.Decimal("0.0842389659712649442845"), default=default)
b'\xb80.0842389659712649442845'
>>> ormsgpack.packb({1, 2}, default=default)
ormsgpack.MsgpackEncodeError: Type is not msgpack 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 ormsgpack, json, rapidjson
>>>
def default(obj):
    if isinstance(obj, decimal.Decimal):
        return str(obj)

>>> ormsgpack.unpackb(ormsgpack.packb({"set":{1, 2}}, default=default))
{'set': None}

option

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

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 ormsgpack, datetime
>>> ormsgpack.unpackb(ormsgpack.packb(
        datetime.datetime(1970, 1, 1, 0, 0, 0),
    ))
"1970-01-01T00:00:00"
>>> ormsgpack.unpackb(ormsgpack.packb(
        datetime.datetime(1970, 1, 1, 0, 0, 0),
        option=ormsgpack.OPT_NAIVE_UTC,
    ))
"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.

>>> import ormsgpack, datetime, uuid
>>> ormsgpack.packb(
        {uuid.UUID("7202d115-7ff3-4c81-a7c1-2a1f067b1ece"): [1, 2, 3]},
        option=ormsgpack.OPT_NON_STR_KEYS,
    )
>>> ormsgpack.packb(
        {datetime.datetime(1970, 1, 1, 0, 0, 0): [1, 2, 3]},
        option=ormsgpack.OPT_NON_STR_KEYS | ormsgpack.OPT_NAIVE_UTC,
    )

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.

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., {"1970-01-01T00:00:00+00:00": true, datetime.datetime(1970, 1, 1, 0, 0, 0): false}. The last key to be inserted to the dict will be serialized last and a msgpack deserializer will presumably take the last occurrence of a key (in the above, false). The first value will be lost.

OPT_OMIT_MICROSECONDS

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

>>> import ormsgpack, datetime
>>> ormsgpack.packb(
        datetime.datetime(1970, 1, 1, 0, 0, 0, 1),
    )
>>> ormsgpack.packb(
        datetime.datetime(1970, 1, 1, 0, 0, 0, 1),
        option=ormsgpack.OPT_OMIT_MICROSECONDS,
    )

OPT_PASSTHROUGH_BIG_INT

Enables passthrough of big (Python) ints. By setting this option, one can set a default function for ints larger than 63 bits, smaller ints are still serialized efficiently.

>>> import ormsgpack
>>> ormsgpack.packb(
        2**65,
    )
TypeError: Integer exceeds 64-bit range
>>> ormsgpack.unpackb(
        ormsgpack.packb(
            2**65,
            option=ormsgpack.OPT_PASSTHROUGH_BIG_INT,
            default=lambda _: {"type": "bigint", "value": str(_) }
        )
    )
{'type': 'bigint', 'value': '36893488147419103232'}

OPT_PASSTHROUGH_DATACLASS

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

>>> import ormsgpack, 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

>>> ormsgpack.packb(User("3b1", "asd", "zxc"))
b'\x83\xa2id\xa33b1\xa4name\xa3asd\xa8password\xa3zxc'
>>> ormsgpack.packb(User("3b1", "asd", "zxc"), option=ormsgpack.OPT_PASSTHROUGH_DATACLASS)
TypeError: Type is not msgpack serializable: User
>>> ormsgpack.packb(
        User("3b1", "asd", "zxc"),
        option=ormsgpack.OPT_PASSTHROUGH_DATACLASS,
        default=default,
    )
b'\x82\xa2id\xa33b1\xa4name\xa3asd'

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

>>> ormsgpack.packb({"created_at": datetime.datetime(1970, 1, 1)})
b'\x81\xaacreated_at\xb31970-01-01T00:00:00'
>>> ormsgpack.packb({"created_at": datetime.datetime(1970, 1, 1)}, option=ormsgpack.OPT_PASSTHROUGH_DATETIME)
TypeError: Type is not msgpack serializable: datetime.datetime
>>> ormsgpack.packb(
        {"created_at": datetime.datetime(1970, 1, 1)},
        option=ormsgpack.OPT_PASSTHROUGH_DATETIME,
        default=default,
    )
b'\x81\xaacreated_at\xbdThu, 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 ormsgpack
>>>
class Secret(str):
    pass

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

>>> ormsgpack.packb(Secret("zxc"))
b'\xa3zxc'
>>> ormsgpack.packb(Secret("zxc"), option=ormsgpack.OPT_PASSTHROUGH_SUBCLASS)
TypeError: Type is not msgpack serializable: Secret
>>> ormsgpack.packb(Secret("zxc"), option=ormsgpack.OPT_PASSTHROUGH_SUBCLASS, default=default)
b'\xa6******'

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

OPT_PASSTHROUGH_TUPLE

Passthrough tuples to default.

>>> import ormsgpack
>>> ormsgpack.unpackb(
        ormsgpack.packb(
            (9193, "test", 42),
        )
    )
[9193, 'test', 42]
>>> ormsgpack.unpackb(
        ormsgpack.packb(
            (9193, "test", 42),
            option=ormsgpack.OPT_PASSTHROUGH_TUPLE,
            default=lambda _: {"type": "tuple", "value": list(_)}
        )
    )
{'type': 'tuple', 'value': [9193, 'test', 42]}

OPT_SERIALIZE_NUMPY

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

OPT_SERIALIZE_PYDANTIC

Serialize pydantic.BaseModel instances. Right now it ignores the config (str transformations), support might be added later.

OPT_UTC_Z

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

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

Deserialize

def unpackb(__obj: Union[bytes, bytearray, memoryview], / , option=None) -> Any: ...

unpackb() deserializes msgpack to Python objects. It deserializes to dict, list, int, float, str, bool, bytes and None objects.

bytes, bytearray, memoryview input are accepted.

ormsgpack 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 MsgpackDecodeError if given an invalid type or invalid msgpack.

MsgpackDecodeError is a subclass of ValueError.

option

unpackb() supports the OPT_NON_STR_KEYS option, that is similar to original msgpack's strict_map_keys=False. Be aware that this option is considered unsafe and disabled by default in msgpack due to possibility of HashDoS.

Types

dataclass

ormsgpack 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__.

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

>>> import dataclasses, ormsgpack, 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]

>>> ormsgpack.packb(Object(1, "a", [Member(1, True), Member(2)]))
b'\x83\xa2id\x01\xa4name\xa1a\xa7members\x92\x82\xa2id\x01\xa6active\xc3\x82\xa2id\x02\xa6active\xc2'

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. ormsgpack may implement support using the metadata mapping on field attributes, e.g., field(metadata={"json_serialize": False}), if use cases are clear.

Performance

--------------------------------------------------------------------------------- benchmark 'dataclass': 2 tests --------------------------------------------------------------------------------
Name (time in ms)                 Min                 Max                Mean            StdDev              Median               IQR            Outliers       OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_dataclass_ormsgpack       3.4248 (1.0)        7.7949 (1.0)        3.6266 (1.0)      0.3293 (1.0)        3.5815 (1.0)      0.0310 (1.0)          4;34  275.7434 (1.0)         240           1
test_dataclass_msgpack       140.2774 (40.96)    143.6087 (18.42)    141.3847 (38.99)    1.0038 (3.05)     141.1823 (39.42)    0.7304 (23.60)         2;1    7.0729 (0.03)          8           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

datetime

ormsgpack 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 ormsgpack, datetime, zoneinfo
>>> ormsgpack.packb(
    datetime.datetime(2018, 12, 1, 2, 3, 4, 9, tzinfo=zoneinfo.ZoneInfo('Australia/Adelaide'))
)
>>> ormsgpack.unpackb(_)
"2018-12-01T02:03:04.000009+10:30"
>>> ormsgpack.packb(
    datetime.datetime.fromtimestamp(4123518902).replace(tzinfo=datetime.timezone.utc)
)
>>> ormsgpack.unpackb(_)
"2100-09-01T21:55:02+00:00"
>>> ormsgpack.packb(
    datetime.datetime.fromtimestamp(4123518902)
)
>>> ormsgpack.unpackb(_)
"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 ormsgpack, datetime
>>> ormsgpack.packb(datetime.time(12, 0, 15, 290))
>>> ormsgpack.unpackb(_)
"12:00:15.000290"

datetime.date objects will always serialize.

>>> import ormsgpack, datetime
>>> ormsgpack.packb(datetime.date(1900, 1, 2))
>>> ormsgpack.unpackb(_)
"1900-01-02"

Errors with tzinfo result in MsgpackEncodeError being raised.

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

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

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

To assume datetimes without timezone are UTC, se the option ormsgpack.OPT_NAIVE_UTC.

enum

ormsgpack serializes enums natively. Options apply to their values.

>>> import enum, datetime, ormsgpack
>>>
class DatetimeEnum(enum.Enum):
    EPOCH = datetime.datetime(1970, 1, 1, 0, 0, 0)
>>> ormsgpack.packb(DatetimeEnum.EPOCH)
>>> ormsgpack.unpackb(_)
"1970-01-01T00:00:00"
>>> ormsgpack.packb(DatetimeEnum.EPOCH, option=ormsgpack.OPT_NAIVE_UTC)
>>> ormsgpack.unpackb(_)
"1970-01-01T00:00:00+00:00"

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

>>> import enum, ormsgpack
>>>
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)

>>> ormsgpack.packb(CustomEnum.ONE, default=default)
>>> ormsgpack.unpackb(_)
1

float

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

int

ormsgpack 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).

numpy

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

>>> import ormsgpack, numpy
>>> ormsgpack.packb(
        numpy.array([[1, 2, 3], [4, 5, 6]]),
        option=ormsgpack.OPT_SERIALIZE_NUMPY,
)
>>> ormsgpack.unpackb(_)
[[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, ormsgpack falls through to default. In default, obj.tolist() can be specified. If an array is malformed, which is not expected, ormsgpack.MsgpackEncodeError is raised.

Performance

---------------------------------------------------------------------------------- benchmark 'numpy float64': 2 tests ---------------------------------------------------------------------------------
Name (time in ms)                      Min                 Max                Mean             StdDev              Median                IQR            Outliers      OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_numpy_ormsgpack[float64]      77.9625 (1.0)       85.2507 (1.0)       79.0326 (1.0)       1.9043 (1.0)       78.5505 (1.0)       0.7408 (1.0)           1;1  12.6530 (1.0)          13           1
test_numpy_msgpack[float64]       511.5176 (6.56)     606.9395 (7.12)     559.0017 (7.07)     44.0661 (23.14)    572.5499 (7.29)     81.2972 (109.75)        3;0   1.7889 (0.14)          5           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


------------------------------------------------------------------------------------- benchmark 'numpy int32': 2 tests -------------------------------------------------------------------------------------
Name (time in ms)                      Min                   Max                  Mean             StdDev                Median                IQR            Outliers     OPS            Rounds  Iterations
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_numpy_ormsgpack[int32]       197.8751 (1.0)        210.3111 (1.0)        201.1033 (1.0)       5.1886 (1.0)        198.8518 (1.0)       3.8297 (1.0)           1;1  4.9726 (1.0)           5           1
test_numpy_msgpack[int32]       1,363.8515 (6.89)     1,505.4747 (7.16)     1,428.2127 (7.10)     53.4176 (10.30)    1,425.3516 (7.17)     72.8064 (19.01)         2;0  0.7002 (0.14)          5           1
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------- benchmark 'numpy int8': 2 tests ---------------------------------------------------------------------------------
Name (time in ms)                   Min                 Max                Mean            StdDev              Median                IQR            Outliers     OPS            Rounds  Iterations
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_numpy_ormsgpack[int8]     107.8013 (1.0)      113.7336 (1.0)      109.0364 (1.0)      1.7805 (1.0)      108.3574 (1.0)       0.4066 (1.0)           1;2  9.1712 (1.0)          10           1
test_numpy_msgpack[int8]       685.4149 (6.36)     703.2958 (6.18)     693.2396 (6.36)     7.9572 (4.47)     691.5435 (6.38)     14.4142 (35.45)         1;0  1.4425 (0.16)          5           1
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


------------------------------------------------------------------------------------- benchmark 'numpy npbool': 2 tests --------------------------------------------------------------------------------------
Name (time in ms)                       Min                   Max                  Mean             StdDev                Median                IQR            Outliers      OPS            Rounds  Iterations
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_numpy_ormsgpack[npbool]        87.9005 (1.0)         89.5460 (1.0)         88.7928 (1.0)       0.5098 (1.0)         88.8508 (1.0)       0.6609 (1.0)           4;0  11.2622 (1.0)          12           1
test_numpy_msgpack[npbool]       1,095.0599 (12.46)    1,176.3442 (13.14)    1,120.5916 (12.62)    32.9993 (64.73)    1,110.4216 (12.50)    38.4189 (58.13)         1;0   0.8924 (0.08)          5           1
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


--------------------------------------------------------------------------------- benchmark 'numpy uint8': 2 tests ---------------------------------------------------------------------------------
Name (time in ms)                    Min                 Max                Mean             StdDev              Median                IQR            Outliers     OPS            Rounds  Iterations
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_numpy_ormsgpack[uint8]     133.1743 (1.0)      134.7246 (1.0)      134.2793 (1.0)       0.4946 (1.0)      134.3120 (1.0)       0.4492 (1.0)           1;1  7.4472 (1.0)           8           1
test_numpy_msgpack[uint8]       727.1393 (5.46)     824.8247 (6.12)     775.7032 (5.78)     34.9887 (70.73)    775.9595 (5.78)     36.2824 (80.78)         2;0  1.2892 (0.17)          5           1
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

uuid

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

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

Pydantic

ormsgpack serializes pydantic.BaseModel instances natively. Currently it ignores pydantic.BaseModel.Config.

Performance

-------------------------------------------------------------------------------- benchmark 'pydantic': 2 tests ---------------------------------------------------------------------------------
Name (time in ms)                Min                 Max                Mean            StdDev              Median               IQR            Outliers       OPS            Rounds  Iterations
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_pydantic_ormsgpack       4.3918 (1.0)       12.6521 (1.0)        4.8550 (1.0)      1.1455 (3.98)       4.6101 (1.0)      0.0662 (1.0)         11;24  205.9727 (1.0)         204           1
test_pydantic_msgpack       124.5500 (28.36)    125.5427 (9.92)     125.0582 (25.76)    0.2877 (1.0)      125.0855 (27.13)    0.2543 (3.84)          2;0    7.9963 (0.04)          8           1
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Latency

Graphs

Data

----------------------------------------------------------------------------- benchmark 'canada packb': 2 tests ------------------------------------------------------------------------------
Name (time in ms)                   Min                Max              Mean            StdDev            Median               IQR            Outliers       OPS            Rounds  Iterations
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_ormsgpack_packb[canada]     3.5302 (1.0)       3.8939 (1.0)      3.7319 (1.0)      0.0563 (1.0)      3.7395 (1.0)      0.0484 (1.0)         56;22  267.9571 (1.0)         241           1
test_msgpack_packb[canada]       8.8642 (2.51)     14.0432 (3.61)     9.3660 (2.51)     0.5649 (10.03)    9.2983 (2.49)     0.0982 (2.03)         3;11  106.7691 (0.40)        106           1
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


------------------------------------------------------------------------------- benchmark 'canada unpackb': 2 tests --------------------------------------------------------------------------------
Name (time in ms)                      Min                Max               Mean             StdDev             Median                IQR            Outliers      OPS            Rounds  Iterations
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_msgpack_unpackb[canada]       10.1176 (1.0)      62.0466 (1.18)     33.4806 (1.0)      18.8279 (1.0)      46.6582 (1.0)      38.5921 (1.02)         30;0  29.8680 (1.0)          67           1
test_ormsgpack_unpackb[canada]     11.3992 (1.13)     52.6587 (1.0)      34.1842 (1.02)     18.9461 (1.01)     47.6456 (1.02)     37.8024 (1.0)           8;0  29.2533 (0.98)         20           1
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


----------------------------------------------------------------------------- benchmark 'citm_catalog packb': 2 tests -----------------------------------------------------------------------------
Name (time in ms)                         Min               Max              Mean            StdDev            Median               IQR            Outliers       OPS            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_ormsgpack_packb[citm_catalog]     1.8024 (1.0)      2.1259 (1.0)      1.9487 (1.0)      0.0346 (1.0)      1.9525 (1.0)      0.0219 (1.0)         79;60  513.1650 (1.0)         454           1
test_msgpack_packb[citm_catalog]       3.4195 (1.90)     3.8128 (1.79)     3.6928 (1.90)     0.0535 (1.55)     3.7009 (1.90)     0.0250 (1.14)        47;49  270.7958 (0.53)        257           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


------------------------------------------------------------------------------ benchmark 'citm_catalog unpackb': 2 tests ------------------------------------------------------------------------------
Name (time in ms)                           Min                Max               Mean             StdDev            Median               IQR            Outliers      OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_ormsgpack_unpackb[citm_catalog]     5.6986 (1.0)      46.1843 (1.0)      14.2491 (1.0)      15.9791 (1.0)      6.1051 (1.0)      0.3074 (1.0)           5;5  70.1798 (1.0)          23           1
test_msgpack_unpackb[citm_catalog]       7.2600 (1.27)     56.6642 (1.23)     16.4095 (1.15)     16.3257 (1.02)     7.7364 (1.27)     0.4944 (1.61)        28;29  60.9404 (0.87)        125           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


----------------------------------------------------------------------------------- benchmark 'github packb': 2 tests -----------------------------------------------------------------------------------
Name (time in us)                     Min                 Max                Mean            StdDev              Median               IQR            Outliers  OPS (Kops/s)            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_ormsgpack_packb[github]      73.0000 (1.0)      215.9000 (1.0)       80.4826 (1.0)      4.8889 (1.0)       80.3000 (1.0)      1.1000 (1.83)     866;1118       12.4250 (1.0)        6196           1
test_msgpack_packb[github]       103.8000 (1.42)     220.8000 (1.02)     112.8049 (1.40)     4.9686 (1.02)     113.0000 (1.41)     0.6000 (1.0)     1306;1560        8.8649 (0.71)       7028           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


----------------------------------------------------------------------------------- benchmark 'github unpackb': 2 tests -----------------------------------------------------------------------------------
Name (time in us)                       Min                 Max                Mean            StdDev              Median               IQR            Outliers  OPS (Kops/s)            Rounds  Iterations
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_ormsgpack_unpackb[github]     201.3000 (1.0)      318.5000 (1.0)      219.0861 (1.0)      6.7340 (1.0)      219.1000 (1.0)      1.2000 (1.0)       483;721        4.5644 (1.0)        3488           1
test_msgpack_unpackb[github]       289.8000 (1.44)     436.0000 (1.37)     314.9631 (1.44)     9.4130 (1.40)     315.1000 (1.44)     2.3000 (1.92)      341;557        3.1750 (0.70)       2477           1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

--------------------------------------------------------------------------------------- benchmark 'twitter packb': 2 tests ---------------------------------------------------------------------------------------
Name (time in us)                        Min                   Max                  Mean             StdDev                Median                IQR            Outliers         OPS            Rounds  Iterations
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_ormsgpack_packb[twitter]       820.7000 (1.0)      2,945.2000 (2.03)       889.3791 (1.0)      78.4139 (2.43)       884.2000 (1.0)      12.5250 (1.0)          4;76  1,124.3799 (1.0)         809           1
test_msgpack_packb[twitter]       1,209.3000 (1.47)     1,451.2000 (1.0)      1,301.3615 (1.46)     32.2147 (1.0)      1,306.7000 (1.48)     14.1000 (1.13)      118;138    768.4260 (0.68)        592           1
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


------------------------------------------------------------------------------ benchmark 'twitter unpackb': 2 tests -----------------------------------------------------------------------------
Name (time in ms)                      Min                Max              Mean            StdDev            Median               IQR            Outliers       OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_ormsgpack_unpackb[twitter]     2.7097 (1.0)      41.1530 (1.0)      3.2721 (1.0)      3.5860 (1.03)     2.8868 (1.0)      0.0614 (1.32)         4;38  305.6098 (1.0)         314           1
test_msgpack_unpackb[twitter]       3.8079 (1.41)     42.0617 (1.02)     4.4459 (1.36)     3.4893 (1.0)      4.1097 (1.42)     0.0465 (1.0)          2;54  224.9267 (0.74)        228           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Reproducing

The above was measured using Python 3.7.9 on Azure Linux VM (x86_64) with ormsgpack 0.2.1 and msgpack 1.0.2.

The latency results can be reproduced using ./scripts/benchmark.sh and graphs using pytest --benchmark-histogram benchmarks/bench_*.

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.

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 support PyPy?

If someone implements it well.

Packaging

To package ormsgpack 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=ormsgpack ormsgpack

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
export RUSTFLAGS="-C target-cpu=k8"
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.

ormsgpack 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.

License

orjson was written by ijl <[e-mail]>, copyright 2018 - 2021, licensed under both the Apache 2 and MIT licenses.

ormsgpack was forked from orjson and is maintained by Aviram Hassan <[e-mail]>, licensed same as orjson.