MessagePack for Python
Author: | INADA Naoki |
---|---|
Version: | 0.4.0 |
Date: | 2013-10-21 |
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.
Notes
Note for msgpack 2.0 support
msgpack 2.0 adds two types: bin and ext.
raw was bytes or string type like Python 2's str
.
To distinguish string and bytes, msgpack 2.0 adds bin.
It is non-string binary like Python 3's bytes
.
To use bin type for packing bytes
, pass use_bin_type=True
to
packer argument.
>>> import msgpack >>> packed = msgpack.packb([b'spam', u'egg'], use_bin_type=True) >>> msgpack.unpackb(packed, encoding='utf-8') ['spam', u'egg']
You shoud use it carefully. When you use use_bin_type=True
, packed
binary can be unpacked by unpackers supporting msgpack-2.0.
To use ext type, pass msgpack.ExtType
object to packer.
>>> import msgpack >>> packed = msgpack.packb(msgpack.ExtType(42, b'xyzzy')) >>> msgpack.unpackb(packed) ExtType(code=42, data='xyzzy')
You can use it with default
and ext_hook
. See below.
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.
Extended types
It is also possible to pack/unpack custom data types using the msgpack 2.0 feature.
>>> import msgpack >>> import array >>> def default(obj): ... if isinstance(obj, array.array) and obj.typecode == 'd': ... return msgpack.ExtType(42, obj.tostring()) ... raise TypeError("Unknown type: %r" % (obj,)) ... >>> def ext_hook(code, data): ... if code == 42: ... a = array.array('d') ... a.fromstring(data) ... return a ... return ExtType(code, data) ... >>> data = array.array('d', [1.2, 3.4]) >>> packed = msgpack.packb(data, default=default) >>> unpacked = msgpack.unpackb(packed, ext_hook=ext_hook) >>> data == unpacked True
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