blosc2 2.7.1

Python wrapper for the C-Blosc2 library

A Python wrapper for the extremely fast Blosc2 compression library

Author:

The Blosc development team

Contact:

[e-mail]

Github:

https://github.com/Blosc/python-blosc2

Actions:

PyPi:

NumFOCUS:

Code of Conduct:

What it is

C-Blosc2 is the new major version of C-Blosc, and is backward compatible with both the C-Blosc1 API and its in-memory format. Python-Blosc2 is a Python package that wraps C-Blosc2, the newest version of the Blosc compressor.

Currently Python-Blosc2 already reproduces the API of Python-Blosc, so it can be used as a drop-in replacement. However, there are a few exceptions for a full compatibility.

In addition, Python-Blosc2 aims to leverage the full C-Blosc2 functionality to support super-chunks (SChunk), multi-dimensional arrays (NDArray), metadata, serialization and other bells and whistles introduced in C-Blosc2.

Note: Python-Blosc2 is meant to be backward compatible with Python-Blosc data. That means that it can read data generated with Python-Blosc, but the opposite is not true (i.e. there is no forward compatibility).

SChunk: a 64-bit compressed store

A SChunk is a simple data container that handles setting, expanding and getting data and metadata. Contrarily to chunks, a super-chunk can update and resize the data that it contains, supports user metadata, and it does not have the 2 GB storage limitation.

Additionally, you can convert a SChunk into a contiguous, serialized buffer (aka cframe) and vice-versa; as a bonus, the serialization/deserialization process also works with NumPy arrays and PyTorch/TensorFlow tensors at a blazing speed:

while reaching excellent compression ratios:

Also, if you are a Mac M1/M2 owner, make you a favor and use its native arm64 arch (yes, we are distributing Mac arm64 wheels too; you are welcome ;-):

Read more about SChunk features in our blog entry at: https://www.blosc.org/posts/python-blosc2-improvements

NDArray: an N-Dimensional store

One of the latest and more exciting additions in Python-Blosc2 is the NDArray object. It can write and read n-dimensional datasets in an extremely efficient way thanks to a n-dim 2-level partitioning, allowing to slice and dice arbitrary large and compressed data in a more fine-grained way:

To wet you appetite, here it is how the NDArray object performs on getting slices orthogonal to the different axis of a 4-dim dataset:

We have blogged about this: https://www.blosc.org/posts/blosc2-ndim-intro

We also have a ~2 min explanatory video on why slicing in a pineapple-style (aka double partition) is useful:

Installing

Blosc is now offering Python wheels for the main OS (Win, Mac and Linux) and platforms. You can install binary packages from PyPi using pip:

pip install blosc2

Documentation

The documentation is here:

https://blosc.org/python-blosc2/python-blosc2.html

Also, some examples are available on:

https://github.com/Blosc/python-blosc2/tree/main/examples

Building from sources

python-blosc2 comes with the C-Blosc2 sources with it and can be built in-place:

git clone https://github.com/Blosc/python-blosc2/
cd python-blosc2
git submodule update --init --recursive
python -m pip install -r requirements-build.txt
python setup.py build_ext --inplace

That’s all. You can proceed with testing section now.

Testing

After compiling, you can quickly check that the package is sane by running the tests:

python -m pip install -r requirements-tests.txt
python -m pytest  (add -v for verbose mode)

Benchmarking

If curious, you may want to run a small benchmark that compares a plain NumPy array copy against compression through different compressors in your Blosc build:

PYTHONPATH=. python bench/pack_compress.py

License

The software is licenses under a 3-Clause BSD license. A copy of the python-blosc2 license can be found in LICENSE.txt.

Mailing list

Discussion about this module is welcome in the Blosc list:

[e-mail]

https://groups.google.es/group/blosc

Twitter

Please follow @Blosc2 to get informed about the latest developments.

Citing Blosc

You can cite our work on the different libraries under the Blosc umbrella as:

@ONLINE{blosc,
  author = {{Blosc Development Team}},
  title = "{A fast, compressed and persistent data store library}",
  year = {2009-2023},
  note = {https://blosc.org}
}

---

Enjoy!