| Author: | INADA Naoki |
|---|---|
| Version: | 0.4.3 |
| Date: | 2015-01-07 |
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.
$ pip install msgpack-python
msgpack-python provides pure python implementation. PyPy can use this.
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.
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.
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.
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 option below.
Read the docstring for other options.
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
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.
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
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())
CPython's GC starts when growing allocated object.
This means unpacking may cause useless GC.
You can use gc.disable() when unpacking large message.
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.
MessagePack uses pytest for testing. Run test with following command:
$ py.test