Python bindings for the Rust blake3 crate

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blake3-py Actions Status PyPI version

Python bindings for the official Rust implementation of BLAKE3, based on PyO3. These bindings expose all the features of BLAKE3, including extendable output, keying, and multithreading.

Caution: This is a brand new library. Please expect some build issues on platforms not covered by CI testing. If you're using this for anything important, please test your code against known-good outputs. See also the soundness concerns below.

Example

from blake3 import blake3, KEY_LEN, OUT_LEN

# Hash some input all at once.
hash1 = blake3(b"foobarbaz").digest()

# Hash the same input incrementally.
hasher = blake3()
hasher.update(b"foo")
hasher.update(b"bar")
hasher.update(b"baz")
hash2 = hasher.digest()
assert hash1 == hash2

# Hexadecimal output.
print("The hash of 'hello world' is", blake3(b"hello world").hexdigest())

# Use the keyed hashing mode, which takes a 32-byte key.
zero_key = b"\0" * KEY_LEN
message = b"a message to authenticate"
mac = blake3(message, key=zero_key)

# Use the key derivation mode, which takes a context string. Context
# strings should be hardcoded, globally unique, and application-specific.
example_context = "blake3-py 2020-03-04 11:13:10 example context"
key_material = b"some super secret key material"
derived_key = blake3(key_material, context=example_context)

# Extendable output. The default OUT_LEN is 32 bytes.
extended = blake3(b"foo").digest(length=100)
assert extended[:OUT_LEN] == blake3(b"foo").digest()
assert extended[75:100] == blake3(b"foo").digest(length=25, seek=75)

# Hash a large input with multithreading. Note that this can be slower
# for short inputs, and you should benchmark it for your use case on
# your platform. As a rule of thumb, don't use multithreading for inputs
# shorter than 1 MB.
large_input = bytearray(1_000_000)
hash3 = blake3(large_input, multithreading=True)

Installation

pip install blake3

As usual with Pip, you might need to use sudo or the --user flag with the command above, depending on how you installed Python on your system.

There are binary wheels available on PyPI for most environments. But if you're building the source distribution, or if a binary wheel isn't available for your environment, you'll need to install the Rust toolchain.

Thread Safety and Soundness

This wrapper is not currently thread-safe. Like the hash implementations in the Python standard library, we release the GIL during update, to avoid blocking the entire process. However, that means that calling the update method on the same object from multiple threads at the same time is undefined behavior. We could solve this by putting a Mutex inside the wrapper type, but I'd like to get some expert advice about the best practice here first.

A deeper problem is that another thread might mutate a bytearray while we're hashing it, and while our Rust code is treating it as a &[u8]. That violates Rust's aliasing guarantees and is also technically undefined behavior. However, the only possible way to solve this while still supporting bytearray would be to retain the GIL. Again, I'm in need of expert advice.

These concerns are more theoretical than practical, however. If you're racing to update a hasher, or racing to hash a buffer while it's being written to, the result is inherently nondeterministic. That's almost certainly a bug in your program, whether or not it's technically sound.

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0.4.1

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