[Numpy-discussion] [ANN] PyTables 2.2 released: enter the multi-core age

Francesc Alted faltet@pytables....
Thu Jul 1 14:10:42 CDT 2010

 Announcing PyTables 2.2 (final)

I'm happy to announce PyTables 2.2 (final).  After 18 months of
continuous development and testing, this is, by far, the most powerful
and well-tested release ever.  I hope you like it too.

What's new

The main new features in 2.2 series are:

  * A new compressor called Blosc, designed to read/write data to/from
    memory at speeds that can be faster than a system `memcpy()` call.
    With it, many internal PyTables operations that are currently
    bounded by CPU or I/O bandwith are speed-up.  Some benchmarks:

    And a demonstration on how Blosc can improve PyTables performance:

  * Support for HDF5 hard links, soft links and external links (kind of
    mounting external filesystems).  A new tutorial about its usage has
    been added to the 'Tutorials' chapter of User's Manual.  See:

  * A new `tables.Expr` module (based on Numexpr) that allows to do
    persistent, on-disk computations on many algebraic operations.
    For a brief look on its performance, see:

  * Suport for 'fancy' indexing (i.e., à la NumPy) in all the data
    containers in PyTables.  Backported from the implementation in the
    h5py project.  Thanks to Andrew Collette for his fine work on this!

  * Binaries for both Windows 32-bit and 64-bit are provided now.

As always, a large amount of bugs have been addressed and squashed too.

In case you want to know more in detail what has changed in this
version, have a look at:

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:

For an on-line version of the manual, visit:

What it is?

PyTables is a library for managing hierarchical datasets and designed to
efficiently cope with extremely large amounts of data with support for
full 64-bit file addressing.  PyTables runs on top of the HDF5 library
and NumPy package for achieving maximum throughput and convenient use.


About PyTables:


About the HDF5 library:


About NumPy:



Thanks to many users who provided feature improvements, patches, bug
reports, support and suggestions.  See the ``THANKS`` file in the
distribution package for a (incomplete) list of contributors.  Most
specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
makers.  Without them, PyTables simply would not exist.

Share your experience

Let us know of any bugs, suggestions, gripes, kudos, etc. you may


  **Enjoy data!**

  -- The PyTables Team

Francesc Alted

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