fastcdc 1.4.2

FastCDC (content defined chunking) in pure Python.

FastCDC

This package implements the "FastCDC" content defined chunking algorithm in Python with optional cython support. To learn more about content defined chunking and its applications, see the reference material linked below.

Requirements

• Python Version 3.6 and later. Tested on Linux, Mac and Windows

Installing

$ pip install fastcdc

To enable add additional support for the hash algorithms (xxhash and blake3) use

$ pip install fastcdc[hashes]

Example Usage

An example can be found in the examples directory of the source repository, which demonstrates reading files of arbitrary size into a memory-mapped buffer and passing them through the chunker (and computing the SHA256 hash digest of each chunk).

Calculate chunks with default settings:

$ fastcdc tests/SekienAkashita.jpg
hash=103159aa68bb1ea98f64248c647b8fe9a303365d80cb63974a73bba8bc3167d7 offset=0 size=22366
hash=3f2b58dc77982e763e75db76c4205aaab4e18ff8929e298ca5c58500fee5530d offset=22366 size=10491
hash=fcfb2f49ccb2640887a74fad1fb8a32368b5461a9dccc28f29ddb896b489b913 offset=32857 size=14094
hash=bd1198535cdb87c5571378db08b6e886daf810873f5d77000a54795409464138 offset=46951 size=18696
hash=d6347a2e5bf586d42f2d80559d4f4a2bf160dce8f77eede023ad2314856f3086 offset=65647 size=43819

Customize min-size, avg-size, max-size, and hash function

$ fastcdc -mi 16384 -s 32768 -ma 65536 -hf sha256 tests/SekienAkashita.jpg
hash=5a80871bad4588c7278d39707fe68b8b174b1aa54c59169d3c2c72f1e16ef46d offset=0 size=32857
hash=13f6a4c6d42df2b76c138c13e86e1379c203445055c2b5f043a5f6c291fa520d offset=32857 size=16408
hash=0fe7305ba21a5a5ca9f89962c5a6f3e29cd3e2b36f00e565858e0012e5f8df36 offset=49265 size=60201

Scan files in directory and report duplication.

$ fastcdc scan ~/Downloads
[####################################]  100%
Files:          1,332
Chunk Sizes:    min 4096 - avg 16384 - max 131072
Unique Chunks:  506,077
Total Data:     9.3 GB
Dupe Data:      873.8 MB
DeDupe Ratio:   9.36 %
Throughput:     135.2 MB/s

Show help

$ fastcdc
Usage: fastcdc [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  chunkify*  Find variable sized chunks for FILE and compute hashes.
  benchmark  Benchmark chunking performance.
  scan       Scan files in directory and report duplication.

Use from your python code

The tests also have some short examples of using the chunker, of which this code snippet is an example:

from fastcdc import fastcdc

results = list(fastcdc("tests/SekienAkashita.jpg", 16384, 32768, 65536))
assert len(results) == 3
assert results[0].offset == 0
assert results[0].length == 32857
assert results[1].offset == 32857
assert results[1].length == 16408
assert results[2].offset == 49265
assert results[2].length == 60201

Reference Material

The algorithm is as described in "FastCDC: a Fast and Efficient Content-Defined Chunking Approach for Data Deduplication"; see the paper, and presentation for details. There are some minor differences, as described below.

Differences with the FastCDC paper

The explanation below is copied from ronomon/deduplication since this codebase is little more than a translation of that implementation:

The following optimizations and variations on FastCDC are involved in the chunking algorithm:

• 31 bit integers to avoid 64 bit integers for the sake of the Javascript reference implementation.
• A right shift instead of a left shift to remove the need for an additional modulus operator, which would otherwise have been necessary to prevent overflow.
• Masks are no longer zero-padded since a right shift is used instead of a left shift.
• A more adaptive threshold based on a combination of average and minimum chunk size (rather than just average chunk size) to decide the pivot point at which to switch masks. A larger minimum chunk size now switches from the strict mask to the eager mask earlier.
• Masks use 1 bit of chunk size normalization instead of 2 bits of chunk size normalization.

The primary objective of this codebase was to have a Python implementation with a permissive license, which could be used for new projects, without concern for data parity with existing implementations.

Prior Art

This package started as Python port of the implementation by Nathan Fiedler (see the nlfiedler link below).

• nlfiedler/fastcdc-rs
  • Rust implementation on which this code is based.
• ronomon/deduplication
  • C++ and JavaScript implementation on which the rust implementation is based.
• rdedup_cdc at docs.rs
  • An alternative implementation of FastCDC to the one in this crate.
• jrobhoward/quickcdc
  • Similar but slightly earlier algorithm by some of the same researchers.

Change Log

[1.4.2] - 2020-11-25

• add binary releases to PyPI (Xie Yanbo)
• update dependencies

[1.4.1] - 2020-09-30

• fix issue with fat option in cython version
• updated dependencies

[1.4.0] - 2020-08-08

• add support for multiple path with scan command
• fix issue with building cython extension
• fix issue with fat option
• fix zero-devision error

[1.3.0] - 2020-06-26

• add new scan command to calculate deduplication ratio for directories

[1.2.0] - 2020-05-23

Added

• faster optional cython implementation
• benchmark command

[1.1.0] - 2020-05-09

Added

• high-level API
• support for streams
• support for custom hash functions

[1.0.0] - 2020-05-07

Added

• Initial release (port of nlfiedler/fastcdc-rs).