UltraCDC

A Zig implementation of UltraCDC, a fast content-defined chunking algorithm for data deduplication.

What is this?

Content-defined chunking (CDC) splits data into variable-sized pieces based on the data itself, not arbitrary boundaries. This makes it useful for deduplication: if you change one paragraph in a document, only that chunk changes, not everything after it.

UltraCDC is a CDC algorithm from a 2022 IEEE paper that’s both fast and stable. This implementation can process data at around 2.7 GB/s, making it practical for real-world use.

Building

You’ll need Zig 0.16 or later.

zig build -Doptimize=ReleaseFast

Using the CLI

The ultracdc tool analyzes how well your files would deduplicate:

# Basic usage
zig-out/bin/ultracdc file1.dat file2.dat

# With custom chunk sizes
zig-out/bin/ultracdc --min-size 4096 --max-size 262144 backup.tar

It will show you:

• How many chunks it found
• How many are unique
• The deduplication ratio (potential storage savings)

Using as a library

const ultracdc = @import("ultracdc");

// Use default options (8KB min, 64KB normal, 128KB max)
const options = ultracdc.ChunkerOptions{};

// Find the first chunk boundary
const cutpoint = ultracdc.UltraCDC.find(options, data, data.len);

// Process the chunk
const chunk = data[0..cutpoint];

How it works

UltraCDC uses a sliding window over your data and looks at the “fingerprint” of each window using hamming distance. When it finds a fingerprint that matches a pattern, it makes a cut. The algorithm is designed to:

• Cut at the same places even if you insert or delete data elsewhere
• Avoid creating tiny or huge chunks
• Handle low-entropy data (like runs of zeros) without slowing down

Testing

zig build test

The tests cover edge cases like minimum-size data, low-entropy detection, and maximum chunk size enforcement.

Performance

zig build bench-find

Reference

The algorithm comes from:

• Zhou, Wang, Xia, and Zhang. “UltraCDC: A Fast and Stable Content-Defined Chunking Algorithm.” IEEE, 2022.