Generate redundant blocks of information such that if some of the blocks are lost then the original data can be recovered from the remaining blocks. This package includes command-line tools, C API, Python API, and Haskell API.
This package implements an "erasure code", or "forward error correction code".
You may use this package under the GNU General Public License, version 2 or, at your option, any later version. You may use this package under the Transitive Grace Period Public Licence, version 1.0 or, at your option, any later version. (You may choose to use this package under the terms of either licence, at your option.) See the file COPYING.GPL for the terms of the GNU General Public License, version 2. See the file COPYING.TGPPL.rst for the terms of the Transitive Grace Period Public Licence, version 1.0.
The most widely known example of an erasure code is the RAID-5 algorithm which makes it so that in the event of the loss of any one hard drive, the stored data can be completely recovered. The algorithm in the zfec package has a similar effect, but instead of recovering from the loss of only a single element, it can be parameterized to choose in advance the number of elements whose loss it can tolerate.
This package is largely based on the old "fec" library by Luigi Rizzo et al., which is a mature and optimized implementation of erasure coding. The zfec package makes several changes from the original "fec" package, including addition of the Python API, refactoring of the C API to support zero-copy operation, a few clean-ups and optimizations of the core code itself, and the addition of a command-line tool named "zfec".
This package is managed with the "setuptools" package management tool. To
build and install the package directly into your system, just run python
./setup.py install. If you prefer to keep the package limited to a
specific directory so that you can manage it yourself (perhaps by using the
"GNU stow") tool, then give it these arguments: python ./setup.py install
--single-version-externally-managed
--record=${specificdirectory}/zfec-install.log
--prefix=${specificdirectory}
To run the self-tests, execute python ./setup.py test (or if you have
Twisted Python installed, you can run trial zfec for nicer output and
test options.) This will run the tests of the C API, the Python API, and the
command-line tools.
To run the tests of the Haskell API: runhaskell haskell/test/FECTest.hs
Note that in order to run the Haskell API tests you must have installed the library first due to the fact that the interpreter cannot process FEC.hs as it takes a reference to an FFI function.
The source is currently available via darcs on the web with the command:
darcs get https://tahoe-lafs.org/source/zfec/trunk
More information on darcs is available at http://darcs.net
Please post about zfec to the Tahoe-LAFS mailing list and contribute patches:
<https://tahoe-lafs.org/cgi-bin/mailman/listinfo/tahoe-dev>
This package performs two operations, encoding and decoding. Encoding takes some input data and expands its size by producing extra "check blocks", also called "secondary blocks". Decoding takes some data -- any combination of blocks of the original data (called "primary blocks") and "secondary blocks", and produces the original data.
The encoding is parameterized by two integers, k and m. m is the total number of blocks produced, and k is how many of those blocks are necessary to reconstruct the original data. m is required to be at least 1 and at most 256, and k is required to be at least 1 and at most m.
(Note that when k == m then there is no point in doing erasure coding -- it degenerates to the equivalent of the Unix "split" utility which simply splits the input into successive segments. Similarly, when k == 1 it degenerates to the equivalent of the unix "cp" utility -- each block is a complete copy of the input data.)
Note that each "primary block" is a segment of the original data, so its size is 1/k'th of the size of original data, and each "secondary block" is of the same size, so the total space used by all the blocks is m/k times the size of the original data (plus some padding to fill out the last primary block to be the same size as all the others). In addition to the data contained in the blocks themselves there are also a few pieces of metadata which are necessary for later reconstruction. Those pieces are: 1. the value of K, 2. the value of M, 3. the sharenum of each block, 4. the number of bytes of padding that were used. The "zfec" command-line tool compresses these pieces of data and prepends them to the beginning of each share, so each the sharefile produced by the "zfec" command-line tool is between one and four bytes larger than the share data alone.
The decoding step requires as input k of the blocks which were produced by the encoding step. The decoding step produces as output the data that was earlier input to the encoding step.
The bin/ directory contains two Unix-style, command-line tools "zfec" and
"zunfec". Execute zfec --help or zunfec --help for usage
instructions.
To run the benchmarks, execute the included bench/bench_zfec.py script with optional --k= and --m= arguments.
On my Athlon 64 2.4 GHz workstation (running Linux), the "zfec" command-line tool encoded a 160 MB file with m=100, k=94 (about 6% redundancy) in 3.9 seconds, where the "par2" tool encoded the file with about 6% redundancy in 27 seconds. zfec encoded the same file with m=12, k=6 (100% redundancy) in 4.1 seconds, where par2 encoded it with about 100% redundancy in 7 minutes and 56 seconds.
The underlying C library in benchmark mode encoded from a file at about 4.9 million bytes per second and decoded at about 5.8 million bytes per second.
On Peter's fancy Intel Mac laptop (2.16 GHz Core Duo), it encoded from a file at about 6.2 million bytes per second.
On my even fancier Intel Mac laptop (2.33 GHz Core Duo), it encoded from a file at about 6.8 million bytes per second.
On my old PowerPC G4 867 MHz Mac laptop, it encoded from a file at about 1.3 million bytes per second.
Here is a paper analyzing the performance of various erasure codes and their implementations, including zfec:
http://www.usenix.org/events/fast09/tech/full_papers/plank/plank.pdf
Zfec shows good performance on different machines and with different values of K and M. It also has a nice small memory footprint.
Each block is associated with "blocknum". The blocknum of each primary block is its index (starting from zero), so the 0'th block is the first primary block, which is the first few bytes of the file, the 1'st block is the next primary block, which is the next few bytes of the file, and so on. The last primary block has blocknum k-1. The blocknum of each secondary block is an arbitrary integer between k and 255 inclusive. (When using the Python API, if you don't specify which secondary blocks you want when invoking encode(), then it will by default provide the blocks with ids from k to m-1 inclusive.)
C API
fec_encode() takes as input an array of k pointers, where each pointer points to a memory buffer containing the input data (i.e., the i'th buffer contains the i'th primary block). There is also a second parameter which is an array of the blocknums of the secondary blocks which are to be produced. (Each element in that array is required to be the blocknum of a secondary block, i.e. it is required to be >= k and < m.)
The output from fec_encode() is the requested set of secondary blocks which are written into output buffers provided by the caller.
Note that this fec_encode() is a "low-level" API in that it requires the input data to be provided in a set of memory buffers of exactly the right sizes. If you are starting instead with a single buffer containing all of the data then please see easyfec.py's "class Encoder" as an example of how to split a single large buffer into the appropriate set of input buffers for fec_encode(). If you are starting with a file on disk, then please see filefec.py's encode_file_stringy_easyfec() for an example of how to read the data from a file and pass it to "class Encoder". The Python interface provides these higher-level operations, as does the Haskell interface. If you implement functions to do these higher-level tasks in other languages, please send a patch to [email protected] so that your API can be included in future releases of zfec.
fec_decode() takes as input an array of k pointers, where each pointer points to a buffer containing a block. There is also a separate input parameter which is an array of blocknums, indicating the blocknum of each of the blocks which is being passed in.
The output from fec_decode() is the set of primary blocks which were missing from the input and had to be reconstructed. These reconstructed blocks are written into output buffers provided by the caller.
Python API
encode() and decode() take as input a sequence of k buffers, where a "sequence" is any object that implements the Python sequence protocol (such as a list or tuple) and a "buffer" is any object that implements the Python buffer protocol (such as a string or array). The contents that are required to be present in these buffers are the same as for the C API.
encode() also takes a list of desired blocknums. Unlike the C API, the Python API accepts blocknums of primary blocks as well as secondary blocks in its list of desired blocknums. encode() returns a list of buffer objects which contain the blocks requested. For each requested block which is a primary block, the resulting list contains a reference to the apppropriate primary block from the input list. For each requested block which is a secondary block, the list contains a newly created string object containing that block.
decode() also takes a list of integers indicating the blocknums of the blocks being passed int. decode() returns a list of buffer objects which contain all of the primary blocks of the original data (in order). For each primary block which was present in the input list, then the result list simply contains a reference to the object that was passed in the input list. For each primary block which was not present in the input, the result list contains a newly created string object containing that primary block.
Beware of a "gotcha" that can result from the combination of mutable data and the fact that the Python API returns references to inputs when possible.
Returning references to its inputs is efficient since it avoids making an unnecessary copy of the data, but if the object which was passed as input is mutable and if that object is mutated after the call to zfec returns, then the result from zfec -- which is just a reference to that same object -- will also be mutated. This subtlety is the price you pay for avoiding data copying. If you don't want to have to worry about this then you can simply use immutable objects (e.g. Python strings) to hold the data that you pass to zfec.
Haskell API
The Haskell code is fully Haddocked, to generate the documentation, run
runhaskell Setup.lhs haddock.
The filefec.py module has a utility function for efficiently reading a file and encoding it piece by piece. This module is used by the "zfec" and "zunfec" command-line tools from the bin/ directory.
A C compiler is required. To use the Python API or the command-line tools a Python interpreter is also required. We have tested it with Python v2.4, v2.5, v2.6, and v2.7. For the Haskell interface, GHC >= 6.8.1 is required.
Thanks to the author of the original fec lib, Luigi Rizzo, and the folks that contributed to it: Phil Karn, Robert Morelos-Zaragoza, Hari Thirumoorthy, and Dan Rubenstein. Thanks to the Mnet hackers who wrote an earlier Python wrapper, especially Myers Carpenter and Hauke Johannknecht. Thanks to Brian Warner and Amber O'Whielacronx for help with the API, documentation, debugging, compression, and unit tests. Thanks to Adam Langley for improving the C API and contributing the Haskell API. Thanks to the creators of GCC (starting with Richard M. Stallman) and Valgrind (starting with Julian Seward) for a pair of excellent tools. Thanks to my coworkers at Allmydata -- http://allmydata.com -- Fabrice Grinda, Peter Secor, Rob Kinninmont, Brian Warner, Zandr Milewski, Justin Boreta, Mark Meras for sponsoring this work and releasing it under a Free Software licence. Thanks to Jack Lloyd, Samuel Neves, and David-Sarah Hopwood.
Note: a Unix-style tool like "zfec" does only one thing -- in this case erasure coding -- and leaves other tasks to other tools. Other Unix-style tools that go well with zfec include GNU tar for archiving multiple files and directories into one file, lzip for compression, and GNU Privacy Guard for encryption or b2sum for integrity. It is important to do things in order: first archive, then compress, then either encrypt or integrity-check, then erasure code. Note that if GNU Privacy Guard is used for privacy, then it will also ensure integrity, so the use of b2sum is unnecessary in that case. Note also that you also need to do integrity checking (such as with b2sum) on the blocks that result from the erasure coding in addition to doing it on the file contents! (There are two different subtle failure modes -- see "more than one file can match an immutable file cap" on the Hack Tahoe-LAFS! Hall of Fame.)
The Tahoe-LAFS project uses zfec as part of a complete distributed filesystem with integrated encryption, integrity, remote distribution of the blocks, directory structure, backup of changed files or directories, access control, immutable files and directories, proof-of-retrievability, and repair of damaged files and directories.
fecpp is an alternative to zfec. It implements a bitwise-compatible algorithm to zfec and is BSD-licensed.
Enjoy!
Zooko Wilcox-O'Hearn
2013-05-15
Boulder, Colorado