===================== Announcing carray 0.3 ===================== What's new ==========
A lot of stuff. The most outstanding feature in this version is the introduction of a `ctable` object. A `ctable` is similar to a structured array in NumPy, but instead of storing the data row-wise, it uses a column-wise arrangement. This allows for much better performance for very wide tables, which is one of the scenarios where a `ctable` makes more sense. Of course, as `ctable` is based on `carray` objects, it inherits all its niceties (like on-the-flight compression and fast iterators). Also, the `carray` object itself has received many improvements, like new constructors (arange(), fromiter(), zeros(), ones(), fill()), iterators (where(), wheretrue()) or resize mehtods (resize(), trim()). Most of these also work with the new `ctable`. Besides, Numexpr is supported now (but it is optional) in order to carry out stunningly fast queries on `ctable` objects. For example, doing a query on a table with one million rows and one thousand columns can be up to 2x faster than using a plain structured array, and up to 20x faster than using SQLite (using the ":memory:" backend and indexing). See 'bench/ctable-query.py' for details. Finally, binaries for Windows (both 32-bit and 64-bit) are provided. For more detailed info, see the release notes in: https://github.com/FrancescAlted/carray/wiki/Release-0.3 What it is ========== carray is a container for numerical data that can be compressed in-memory. The compresion process is carried out internally by Blosc, a high-performance compressor that is optimized for binary data. Having data compressed in-memory can reduce the stress of the memory subsystem. The net result is that carray operations may be faster than using a traditional ndarray object from NumPy. carray also supports fully 64-bit addressing (both in UNIX and Windows). Below, a carray with 1 trillion of rows has been created (7.3 TB total), filled with zeros, modified some positions, and finally, summed-up:: >>> %time b = ca.zeros(1e12) CPU times: user 54.76 s, sys: 0.03 s, total: 54.79 s Wall time: 55.23 s >>> %time b[[1, 1e9, 1e10, 1e11, 1e12-1]] = (1,2,3,4,5) CPU times: user 2.08 s, sys: 0.00 s, total: 2.08 s Wall time: 2.09 s >>> b carray((1000000000000,), float64) nbytes: 7450.58 GB; cbytes: 2.27 GB; ratio: 3275.35 cparams := cparams(clevel=5, shuffle=True) [0.0, 1.0, 0.0, ..., 0.0, 0.0, 5.0] >>> %time b.sum() CPU times: user 10.08 s, sys: 0.00 s, total: 10.08 s Wall time: 10.15 s 15.0 ['%time' is a magic function provided by the IPyhton shell] Please note that the example above is provided for demonstration purposes only. Do not try to run this at home unless you have more than 3 GB of RAM available, or you will get into trouble. Resources ========= Visit the main carray site repository at: http://github.com/FrancescAlted/carray You can download a source package from: http://carray.pytables.org/downloads Manual: http://carray.pytables.org/manual Home of Blosc compressor: http://blosc.pytables.org User's mail list: car...@googlegroups.com http://groups.google.com/group/carray Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- Enjoy! -- Francesc Alted ------------------------------------------------------------------------------ Forrester recently released a report on the Return on Investment (ROI) of Google Apps. They found a 300% ROI, 38%-56% cost savings, and break-even within 7 months. Over 3 million businesses have gone Google with Google Apps: an online email calendar, and document program that's accessible from your browser. Read the Forrester report: http://p.sf.net/sfu/googleapps-sfnew _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
