Release notes


  • 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 zarr.blosc.list_compressors() and zarr.blosc.get_nthreads().


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 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 API docs and the Zarr storage specification version 1 for more information.


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 zarr.blosc Cython extension, so 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 zarr.blosc extension to avoid unnecessary memory copies, giving a ~10-20% performance improvement for multi-threaded compression operations.

The 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 Configuring Blosc.

Other changes

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 __getitem__ and __setitem__ machinery for slicing arrays, to properly handle getting and setting partial slices.


Thanks to Matthew Rocklin (mrocklin), Stephan Hoyer (shoyer), Francesc Alted (FrancescAlted), Anthony Scopatz (scopatz) and Martin Durant (martindurant) for contributions and comments.