Announcing BLZ 0.6 series ========================= What it is ---------- BLZ is a chunked container for numerical data. Chunking allows for efficient enlarging/shrinking of data container. In addition, it can also be compressed for reducing memory/disk needs. The compression process is carried out internally by Blosc, a high-performance compressor that is optimized for binary data. The main objects in BLZ are `barray` and `btable`. `barray` is meant for storing multidimensional homogeneous datasets efficiently. `barray` objects provide the foundations for building `btable` objects, where each column is made of a single `barray`. Facilities are provided for iterating, filtering and querying `btables` in an efficient way. You can find more info about `barray` and `btable` in the tutorial: http://blz.pydata.org/blz-manual/tutorial.html BLZ can use numexpr internally so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too) either from memory or from disk. In the future, it is planned to use Numba as the computational kernel and to provide better Blaze (http://blaze.pydata.org) integration. What's new ---------- BLZ has been branched off from the Blaze project (http://blaze.pydata.org). BLZ was meant as a persistent format and library for I/O in Blaze. BLZ in Blaze is based on previous carray 0.5 and this is why this new version is labeled 0.6. BLZ supports completely transparent storage on-disk in addition to memory. That means that *everything* that can be done with the in-memory container can be done using the disk as well. The advantages of a disk-based container is that the addressable space is much larger than just your available memory. Also, as BLZ is based on a chunked and compressed data layout based on the super-fast Blosc compression library, the data access speed is very good. The format chosen for the persistence layer is based on the 'bloscpack' library and described in the "Persistent format for BLZ" chapter of the user manual ('docs/source/persistence-format.rst'). More about Bloscpack here: https://github.com/esc/bloscpack You may want to know more about BLZ in this blog entry: http://continuum.io/blog/blz-format In this version, support for Blosc 1.3 has been added, that meaning that a new `cname` parameter has been added to the `bparams` class, so that you can select you preferred compressor from 'blosclz', 'lz4', 'lz4hc', 'snappy' and 'zlib'. Also, many bugs have been fixed, providing a much smoother experience. CAVEAT: The BLZ/bloscpack format is still evolving, so don't trust on forward compatibility of the format, at least until 1.0, where the internal format will be declared frozen. Resources --------- Visit the main BLZ site repository at: http://github.com/ContinuumIO/blz Read the online docs at: http://blz.pydata.org/blz-manual/index.html Home of Blosc compressor: http://www.blosc.org User's mail list: blaze-dev@continuum.io ---- Enjoy! Francesc Alted Continuum Analytics, Inc.