- The bundled Blosc library has been upgraded to version 1.10.0. The ‘zstd’ internal compression library is now available within Blosc. See the tutorial section on Compression for an example.
- When using the Blosc compressor, the default internal compression library is now ‘lz4’.
- The default number of internal threads for the Blosc compressor has been increased to a maximum of 8 (previously 4).
- Added convenience functions
This release includes a complete re-organization of the code base. The major version number has been bumped to indicate that there have been backwards-incompatible changes to the API and the on-disk storage format. However, Zarr is still in an early stage of development, so please do not take the version number as an indicator of maturity.
The main motivation for re-organizing the code was to create an
abstraction layer between the core array logic and data storage (#21). In this release, any
object that implements the
MutableMapping interface can be used as
an array store. See the tutorial sections on Persistent arrays
and Storage alternatives, the Zarr storage specification version 1, and the
zarr.storage module documentation for more information.
Please note also that the file organization and file name conventions
used when storing a Zarr array in a directory on the file system have
changed. Persistent Zarr arrays created using previous versions of the
software will not be compatible with this version. See the
zarr.storage API docs and the Zarr storage specification version 1 for more
An abstraction layer has also been created between the core array
logic and the code for compressing and decompressing array
chunks. This release still bundles the c-blosc library and uses Blosc
as the default compressor, however other compressors including zlib,
BZ2 and LZMA are also now supported via the Python standard
library. New compressors can also be dynamically registered for use
with Zarr. See the tutorial sections on Compression and
Configuring Blosc, the Zarr storage specification version 1, and the
zarr.compressors module documentation for more information.
The synchronization code has also been refactored to create a layer of
abstraction, enabling Zarr arrays to be used in parallel computations
with a number of alternative synchronization methods. For more
information see the tutorial section on Parallel computing and synchronization and the
zarr.sync module documentation.
Changes to the Blosc extension¶
NumPy is no longer a build dependency for the
extension, so setup.py will run even if NumPy is not already
installed, and should automatically install NumPy as a runtime
dependency. Manual installation of NumPy prior to installing Zarr is
still recommended, however, as the automatic installation of NumPy may
fail or be sub-optimal on some platforms.
Some optimizations have been made within the
extension to avoid unnecessary memory copies, giving a ~10-20%
performance improvement for multi-threaded compression operations.
zarr.blosc extension now automatically detects whether it
is running within a single-threaded or multi-threaded program and
adapts its internal behaviour accordingly (#27). There is no need for
the user to make any API calls to switch Blosc between contextual and
non-contextual (global lock) mode. See also the tutorial section on
The internal code for managing chunks has been rewritten to be more efficient. Now no state is maintained for chunks outside of the array store, meaning that chunks do not carry any extra memory overhead not accounted for by the store. This negates the need for the “lazy” option present in the previous release, and this has been removed.
The memory layout within chunks can now be set as either “C” (row-major) or “F” (column-major), which can help to provide better compression for some data (#7). See the tutorial section on Changing memory layout for more information.
A bug has been fixed within the
machinery for slicing arrays, to properly handle getting and setting
Thanks to Matthew Rocklin (mrocklin), Stephan Hoyer (shoyer), Francesc Alted (FrancescAlted), Anthony Scopatz (scopatz) and Martin Durant (martindurant) for contributions and comments.