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MessagePack serializer implementation for Python
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README.rst

MessagePack for Python

Author: INADA Naoki
Version: 0.3.0
Date: 2012-12-07
https://secure.travis-ci.org/msgpack/msgpack-python.png

What's this

MessagePack is a fast, compact binary serialization format, suitable for similar data to JSON. This package provides CPython bindings for reading and writing MessagePack data.

Install

You can use pip or easy_install to install msgpack:

$ easy_install msgpack-python
  or
$ pip install msgpack-python

PyPy

msgpack-python provides pure python implementation. PyPy can use this.

Windows

When you can't use binary distribution, you need to install Visual Studio or Windows SDK on Windows. (NOTE: Visual C++ Express 2010 doesn't support amd64. Windows SDK is recommanded way to build amd64 msgpack without any fee.)

Without extension, using pure python implementation on CPython runs slowly.

Note for msgpack 0.2.x users

The msgpack 0.3 have some incompatible changes.

The default value of use_list keyword argument is True from 0.3. You should pass the argument explicitly for backward compatibility.

Unpacker.unpack() and some unpack methods now raises OutOfData instead of StopIteration. StopIteration is used for iterator protocol only.

How to use

One-shot pack & unpack

Use packb for packing and unpackb for unpacking. msgpack provides dumps and loads as alias for compatibility with json and pickle.

pack and dump packs to file-like object. unpack and load unpacks from file-like object.

>>> import msgpack
>>> msgpack.packb([1, 2, 3])
'\x93\x01\x02\x03'
>>> msgpack.unpackb(_)
[1, 2, 3]

unpack unpacks msgpack's array to Python's list, but can unpack to tuple:

>>> msgpack.unpackb(b'\x93\x01\x02\x03', use_list=False)
(1, 2, 3)

You should always pass the use_list keyword argument. See performance issues relating to use_list_ below.

Read the docstring for other options.

Streaming unpacking

Unpacker is a "streaming unpacker". It unpacks multiple objects from one stream (or from bytes provided through its feed method).

import msgpack
from io import BytesIO

buf = BytesIO()
for i in range(100):
   buf.write(msgpack.packb(range(i)))

buf.seek(0)

unpacker = msgpack.Unpacker(buf)
for unpacked in unpacker:
    print unpacked

Packing/unpacking of custom data type

It is also possible to pack/unpack custom data types. Here is an example for datetime.datetime.

import datetime

import msgpack

useful_dict = {
    "id": 1,
    "created": datetime.datetime.now(),
}

def decode_datetime(obj):
    if b'__datetime__' in obj:
        obj = datetime.datetime.strptime(obj["as_str"], "%Y%m%dT%H:%M:%S.%f")
    return obj

def encode_datetime(obj):
    if isinstance(obj, datetime.datetime):
        return {'__datetime__': True, 'as_str': obj.strftime("%Y%m%dT%H:%M:%S.%f")}
    return obj


packed_dict = msgpack.packb(useful_dict, default=encode_datetime)
this_dict_again = msgpack.unpackb(packed_dict, object_hook=decode_datetime)

Unpacker's object_hook callback receives a dict; the object_pairs_hook callback may instead be used to receive a list of key-value pairs.

Advanced unpacking control

As an alternative to iteration, Unpacker objects provide unpack, skip, read_array_header and read_map_header methods. The former two read an entire message from the stream, respectively deserialising and returning the result, or ignoring it. The latter two methods return the number of elements in the upcoming container, so that each element in an array, or key-value pair in a map, can be unpacked or skipped individually.

Each of these methods may optionally write the packed data it reads to a callback function:

from io import BytesIO

def distribute(unpacker, get_worker):
    nelems = unpacker.read_map_header()
    for i in range(nelems):
        # Select a worker for the given key
        key = unpacker.unpack()
        worker = get_worker(key)

        # Send the value as a packed message to worker
        bytestream = BytesIO()
        unpacker.skip(bytestream.write)
        worker.send(bytestream.getvalue())

Note about performance

GC

CPython's GC starts when growing allocated object. This means unpacking may cause useless GC. You can use gc.disable() when unpacking large message.

use_list option

List is the default sequence type of Python. But tuple is lighter than list. You can use use_list=False while unpacking when performance is important.

Python's dict can't use list as key and MessagePack allows array for key of mapping. use_list=False allows unpacking such message. Another way to unpacking such object is using object_pairs_hook.

Test

MessagePack uses pytest for testing. Run test with following command:

$ py.test
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