cdc

A library for performing Content-Defined Chunking (CDC) on data streams. Implemented using generic iterators, very easy to use.

• API Documentation

Example

  let reader: BufReader<File> = BufReader::new(file);
  let byte_iter = reader.bytes().map(|b| b.unwrap());

  // Finds and iterates on the separators.
  for separator in SeparatorIter::new(byte_iter) {
    println!("Index: {}, hash: {:016x}", separator.index, separator.hash);
  }

Each module is documented via an example which you can find in the examples/ folder.

To run them, use a command like:

cargo run --example separator --release

Note: Some examples are looking for a file named myLargeFile.bin which I didn't upload to Github. Please use your own files for testing.

What's in the crate

From low level to high level:

• A RollingHash64 trait, for rolling hash with a 64 bits hash value.

• Rabin64, an implementation of the Rabin Fingerprint rolling hash with a 64 bits hash value.

• Separator, a struct which describes a place in a data stream identified as a separator.

• SeparatorIter, an adaptor which takes an Iterator<Item=u8> as input and which enumerates all the separators found.

• Chunk, a struct which describes a piece of the data stream (index and size).

• ChunkIter, an adaptor which takes an Iterator<Item=Separator> as input and which enumerates chunks.

Implementation details

• The library is not cutting any files, it only provides information on how to do it.

• You can change the default window size used by Rabin64, and how the SeparatorIter is choosing the separator.

• The design of this crate may be subject to changes sometime in the future. I am waiting for some features of Rust to mature up, specially the impl Trait feature.

Performance

There is a huge difference between the debug build and the release build in terms of performance. Remember that when you test the lib, use cargo run --release.

I may try to improve the performance of the lib at some point, but for now it is good enough for most usages.

License

Coded with ❤️ , licensed under the terms of the MIT license.