libraptorq 15.12.12

Python CFFI bindings for libRaptorQ (RaptorQ RFC6330 FEC implementation).

Python 2.X CFFI bindings for libRaptorQ - C++11 implementation of RaptorQ Forward Error Correction codes, as described in RFC6330.

Warning: as far as I know (not a lawyer), there are lots of patents around the use of this technology, which might be important for any high-profile and commercial projects, especially in US and Canada.

Quoting wikipedia on Raptor code:

Raptor codes, as with fountain codes in general, encode a given message consisting of a number of symbols, k, into a potentially limitless sequence of encoding symbols such that knowledge of any k or more encoding symbols allows the message to be recovered with some non-zero probability.

Raptor (“RAPid TORnado”) codes are the first known class of fountain codes with linear time encoding and decoding.

And RFC6330:

RaptorQ codes are a new family of codes that provide superior flexibility, support for larger source block sizes, and better coding efficiency than Raptor codes in RFC 5053.

… in most cases, a set of cardinality equal to the number of source symbols is sufficient; in rare cases, a set of cardinality slightly more than the number of source symbols is required.

In practice this means that source data block of size 1 MiB (for example) can be recovered from any 1.002 MiB of the received symbols for it (from “Application Layer Forward Error Correction for Mobile Multimedia Broadcasting Case Study” paper).

Whole input can have up to 256 “source blocks” (encoded independently).

Each block can have up to 56.403 symbols.

Each symbol can be up to 65.535 (2**16 - 1) bytes long.

This sums up to ~881 GiB max for one encoder input.

Encoded data will always be at least 4x larger in size than the original.

Contents

• Usage

  • Command-line script

  • Python module

• Installation

• Random Notes

Usage

Module includes command-line script (“rq”, when installed or as symlink in the repo), which has example code for both encoding and decoding, and can be used as a standalone tool, or for basic algorithm testing/showcase.

Can also be used from command-line via python2 -m libraptorq ... invocation (when installed as module), e.g. python2 -m libraptorq --help.

Command-line script

To encode file, with 50% extra symbols (resulting indivisible data chunks to be stored/transmitted intact or lost entirely) and 30% of total from these (K required symbols + X repair symbols) dropped (just for testing purposes) before saving them to “setup.py.enc”:

% ./rq --debug encode --repair-symbols-rate 0.5 --drop-rate 0.3 setup.py setup.py.enc
2015-12-17 16:12:13 :: DEBUG :: Encoded 1,723 B into 674 symbols\
  (needed: >431, repair rate: 50%), 198 dropped (30%), 476 left in output (7,616 B without ids)

Decode original file back from these:

% ./rq --debug decode setup.py.enc setup.py.dec
2015-12-17 16:12:13 :: DEBUG :: Decoded 1,723 B of data\
  from 431 processed symbols (6,896 B without ids, symbols total: 476)

% sha256sum -b setup.py{,.dec}
0a19b84ca98562476f79d55f19ac853ea49e567205dcc9139ba986e8572f9681 *setup.py
0a19b84ca98562476f79d55f19ac853ea49e567205dcc9139ba986e8572f9681 *setup.py.dec

No matter which chunks are dropped (get picked by random.choice), file should be recoverable from output as long as number of chunks left (in each “block”) is slightly (by ~0.02%) above K.

Output data (“setup.py.enc” in the example) for the script is JSON-encoded list of base64-encoded symbols, as well as some parameters for lib init (oti_scheme, oti_common).

See output with –help option for all the other script parameters.

Python module

To use as a python2 module:

from libraptorq import RQEncoder

data = 'some input string' * 500

with RQEncoder(data, min_subsymbol_size=4, symbol_size=16, max_memory=200) as enc:

  symbols = dict()
  oti_scheme, oti_common = enc.oti_scheme, enc.oti_common

  for block in enc:
    symbols.update(block.encode_iter(repair_rate=0))

data_encoded = oti_scheme, oti_common, symbols

oti_scheme and oti_common are two integers specifying encoder options, needed to initialize decoder, which can be hard-coded (if constant) on both ends.

block.encode_iter() can be used without options to produce max possible amount of symbols, up to block.symbols + block.max_repair. Above example only produces K symbols - min amount required.

For decoding (reverse operation):

from libraptorq import RQDecoder

oti_scheme, oti_common, symbols = data_encoded

with RQDecoder(oti_scheme, oti_common) as dec:
  for sym_id, sym in symbols.viewitems(): dec.add_symbol(sym, sym_id)

  data = dec.decode()

Note that in practice, e.g. when transmitting each symbol in a udp packet, one’d want to send something like sym_id || sym_data || checksum, and keep sending these from block.encode_iter() until other side acknowledges that it can decode a block (i.e. enough symbols received, see RQDecoder.decode_block()), then start streaming the next block in similar fashion.

See __main__.py file (cli script) for an extended example, and libRaptorQ docs for info on its API, which this module wraps around.

Installation

It’s a regular package for Python 2.7 (not 3.X).

It uses and needs CFFI (can/should be installed by pip) and libRaptorQ installed on the system.

Using pip is the best way:

% pip install libraptorq

If you don’t have it, use:

% easy_install pip
% pip install libraptorq

Alternatively (see also pip2014.com and pip install guide):

% curl https://raw.github.com/pypa/pip/master/contrib/get-pip.py | python2
% pip install libraptorq

Or, if you absolutely must:

% easy_install libraptorq

But, you really shouldn’t do that.

Current-git version can be installed like this:

% pip install 'git+https://github.com/mk-fg/python-libraptorq.git#egg=libraptorq'

Note that to install stuff in system-wide PATH and site-packages, elevated privileges are often required. Use “install –user”, ~/.pydistutils.cfg or virtualenv to do unprivileged installs into custom paths.

Alternatively, ./rq tool can be run right from the checkout tree without any installation, if that’s the only thing you need there.

Random Notes

• libRaptorQ is currently used via CFFI in “ABI Mode” to avoid any extra hassle with compilation and the need for compiler, see CFFI docs on the subject for more info on what it means.

• When decoding, libRaptorQ can raise errors for add_symbol() calls, when source block is already decoded and that extra symbol is not needed.

• libRaptorQ allows to specify “rq_type” parameter for internal data alignment size (C++ iterator element), which is hard-coded to ENC_32/DEC_32 in the module, for simplicity.

• Lack of Python 3.X compatibility is due to me not using it at all (yet?), so don’t need it, have nothing against it in principle.