#BencodePy A small Python 3 library for encoding and decoding Bencode data licensed under the GPLv2.

##Overview Although Bencoding is mainly, if not exclusively, used for BitTorrent metadata (.torrent) files, this library seeks to provide a generic means of encoding/decoding Bencode from/to Python data structures independent of torrent files.

##Docs

pip install bencodepy

from bencodepy import encode
mydata = { 'keyA': 'valueA' } #example data
bencoded_data = encode(mydata)
print (bencoded_data)
>>> b'd4:keyA6:valueAe'

######Encode Mapping

*Includes subtypes thus both dict and OrderedDict would be represented as Bencode dictionary.

From bytes...

from bencodepy import decode
mydata = b'd4:KeyA6:valueAe'
my_ordred_dict = decode(mydata)
print(my_ordred_dict)
>>> OrderedDict([(b'KeyA', b'valueA')])

Alternatively from a file...

from bencodepy import decode_from_file
my_file_path = 'c:\whatever'
my_ordred_dict = decode_from_file(my_file_path)

######Decode Mapping

Bencode dictionaries are decoded as Python OrderedDict to preserve the order of elements. This is necessary to correctly calculate certain hash values such as that of a torrent file's Info Dictionary.

Decode methods will always return an iterable. If the root element of the bencode data is not a dictionary or list, decode() will wrap the all bencode elements in a tuple. Thus input data of b'5:ItemA5:ItemB' would yield a python tuple of ('ItemA', 'ItemB').

##Performance

Hardware: Xeon 1270v3 w/ 16GB RAM OS: Windows 7 Pro Python: CPython 3.4

Method: The benchmarks measure the time taken to encode/decode objects in memory; thus disk IO is excluded. The sample data used were 5 torrent files that were multiplied (in memory) to generate a sufficient number of elements. The source code is available under tests/benchmarks in this repository.

The new encoder benchmarks have not been graphed yet. It can process 10,000 in about 1 second and 30,000 in 3. Again, this is using multiple small torrent files. Just a handful of large files (ie MB range) can take well over a second.

These benchmarks are neither scientific or rigorous. As always, YMMV.

##Roadmap

1. Determine method of distributing the optimized (cythonized) version of bencodepy.
2. Consider async file read; I may hold off until someone creates a request on the Github issue tracker.

##License Copyright © 2014, 2015 by Eric Weast

Licensed under the GPLv2