# FastCDC
[](https://github.com/titusz/fastcdc-py/actions?query=workflow%3ATests)
[](https://pypi.python.org/pypi/fastcdc/)
[](https://pepy.tech/project/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](https://www.python.org/) Version 3.7 and later. Tested on Linux, Mac and
Windows
## Installing
```shell
$ pip install fastcdc
```
To enable add additional support for the hash algorithms
([xxhash](https://github.com/Cyan4973/xxHash) and
[blake3](https://github.com/BLAKE3-team/BLAKE3/)) use
```shell
$ pip install fastcdc[hashes]
```
## Usage
### Calculate chunks with default settings:
```shell
$ 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
```shell
$ 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.
```shell
$ 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
```shell
$ 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:
```python
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](https://www.usenix.org/system/files/conference/atc16/atc16-paper-xia.pdf),
and
[presentation](https://www.usenix.org/sites/default/files/conference/protected-files/atc16_slides_xia.pdf)
for details. There are some minor differences, as described below.
### Differences with the FastCDC paper
The explanation below is copied from
[ronomon/deduplication](https://github.com/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](https://github.com/nlfiedler/fastcdc-rs)
+ Rust implementation on which this code is based.
* [ronomon/deduplication](https://github.com/ronomon/deduplication)
+ C++ and JavaScript implementation on which the rust implementation is based.
* [rdedup_cdc at docs.rs](https://docs.rs/crate/rdedup-cdc/0.1.0/source/src/fastcdc.rs)
+ An alternative implementation of FastCDC to the one in this crate.
* [jrobhoward/quickcdc](https://github.com/jrobhoward/quickcdc)
+ Similar but slightly earlier algorithm by some of the same researchers.
## Change Log
## [1.7.0] - 2024-06-27
- Performance improvement [@dw](https://github.com/dw)
- Fixed issue with inputs smaller than min_size [@grote](https://github.com/grote)
## [1.6.0] - 2024-05-09
- added python 3.12 support
- removed python 3.7 support
- updated dependencies
## [1.5.0] - 2023-03-13
- added python 3.10/3.11 support
- removed python 3.6 support
- update dependencies
## [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](https://github.com/nlfiedler/fastcdc-rs)).
Raw data
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"description": "# FastCDC\n\n[](https://github.com/titusz/fastcdc-py/actions?query=workflow%3ATests)\n[](https://pypi.python.org/pypi/fastcdc/)\n[](https://pepy.tech/project/fastcdc)\n\nThis package implements the \"FastCDC\" content defined chunking algorithm in\nPython with optional cython support. To learn more about content\ndefined chunking and its applications, see the reference material linked below.\n\n\n## Requirements\n\n* [Python](https://www.python.org/) Version 3.7 and later. Tested on Linux, Mac and\nWindows\n\n## Installing\n\n```shell\n$ pip install fastcdc\n```\n\nTo enable add additional support for the hash algorithms\n([xxhash](https://github.com/Cyan4973/xxHash) and\n[blake3](https://github.com/BLAKE3-team/BLAKE3/)) use\n\n```shell\n$ pip install fastcdc[hashes]\n```\n\n## Usage\n\n### Calculate chunks with default settings:\n```shell\n$ fastcdc tests/SekienAkashita.jpg\nhash=103159aa68bb1ea98f64248c647b8fe9a303365d80cb63974a73bba8bc3167d7 offset=0 size=22366\nhash=3f2b58dc77982e763e75db76c4205aaab4e18ff8929e298ca5c58500fee5530d offset=22366 size=10491\nhash=fcfb2f49ccb2640887a74fad1fb8a32368b5461a9dccc28f29ddb896b489b913 offset=32857 size=14094\nhash=bd1198535cdb87c5571378db08b6e886daf810873f5d77000a54795409464138 offset=46951 size=18696\nhash=d6347a2e5bf586d42f2d80559d4f4a2bf160dce8f77eede023ad2314856f3086 offset=65647 size=43819\n```\n\n### Customize min-size, avg-size, max-size, and hash function\n```shell\n$ fastcdc -mi 16384 -s 32768 -ma 65536 -hf sha256 tests/SekienAkashita.jpg\nhash=5a80871bad4588c7278d39707fe68b8b174b1aa54c59169d3c2c72f1e16ef46d offset=0 size=32857\nhash=13f6a4c6d42df2b76c138c13e86e1379c203445055c2b5f043a5f6c291fa520d offset=32857 size=16408\nhash=0fe7305ba21a5a5ca9f89962c5a6f3e29cd3e2b36f00e565858e0012e5f8df36 offset=49265 size=60201\n```\n\n### Scan files in directory and report duplication.\n```shell\n$ fastcdc scan ~/Downloads\n[####################################] 100%\nFiles: 1,332\nChunk Sizes: min 4096 - avg 16384 - max 131072\nUnique Chunks: 506,077\nTotal Data: 9.3 GB\nDupe Data: 873.8 MB\nDeDupe Ratio: 9.36 %\nThroughput: 135.2 MB/s\n```\n\n### Show help\n\n```shell\n$ fastcdc\nUsage: fastcdc [OPTIONS] COMMAND [ARGS]...\n\nOptions:\n --version Show the version and exit.\n --help Show this message and exit.\n\nCommands:\n chunkify* Find variable sized chunks for FILE and compute hashes.\n benchmark Benchmark chunking performance.\n scan Scan files in directory and report duplication.\n```\n\n### Use from your python code\nThe tests also have some short examples of using the chunker, of which this\ncode snippet is an example:\n\n```python\nfrom fastcdc import fastcdc\n\nresults = list(fastcdc(\"tests/SekienAkashita.jpg\", 16384, 32768, 65536))\nassert len(results) == 3\nassert results[0].offset == 0\nassert results[0].length == 32857\nassert results[1].offset == 32857\nassert results[1].length == 16408\nassert results[2].offset == 49265\nassert results[2].length == 60201\n```\n\n## Reference Material\n\nThe algorithm is as described in \"FastCDC: a Fast and Efficient Content-Defined\nChunking Approach for Data Deduplication\"; see the\n[paper](https://www.usenix.org/system/files/conference/atc16/atc16-paper-xia.pdf),\nand\n[presentation](https://www.usenix.org/sites/default/files/conference/protected-files/atc16_slides_xia.pdf)\nfor details. There are some minor differences, as described below.\n\n### Differences with the FastCDC paper\n\nThe explanation below is copied from\n[ronomon/deduplication](https://github.com/ronomon/deduplication) since this\ncodebase is little more than a translation of that implementation:\n\n> The following optimizations and variations on FastCDC are involved in the chunking algorithm:\n> * 31 bit integers to avoid 64-bit integers for the sake of the Javascript reference implementation.\n> * 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.\n> * Masks are no longer zero-padded since a right shift is used instead of a left shift.\n> * 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. 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