ZS is a compressed, read-only file format for efficiently distributing, querying, and archiving arbitrarily large record-oriented datasets.
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README.rst

ZS is a simple, read-only, binary file format designed for distributing, querying, and archiving arbitrarily large record-oriented datasets (up to tens of terabytes and beyond). It allows the data to be stored in compressed form, while still supporting very fast queries for either specific entries, or for all entries in a specified range of values (e.g., prefix searches), and allows highly-CPU-parallel decompression. It also places an emphasis on data integrity -- all data is protected by 64-bit CRC checksums -- and on discoverability -- every ZS file includes arbitrarily detailed structured metadata stored directly inside it.

Basically you can think of ZS as a turbo-charged replacement for storing data in line-based text file formats. It was originally developed to provide a better way to work with the massive Google N-grams, but is potentially useful for data sets of any size.

https://travis-ci.org/njsmith/zs.png?branch=master https://coveralls.io/repos/njsmith/zs/badge.png?branch=master
Documentation:
http://zs.readthedocs.org/
Installation:

You need either Python 2.7, or else Python 3.3 or greater.

Because zs includes a C extension, you'll also need a C compiler and Python headers. On Ubuntu or Debian, for example, you get these with:

sudo apt-get install build-essential python-dev

Once you have the ability to build C extensions, then on Python 3 you should be able to just run:

pip install zs

On Python 2.7, things are slightly more complicated: here, zs requires the backports.lzma package, which in turn requires the liblzma library. On Ubuntu or Debian, for example, something like this should work:

sudo apt-get install liblzma-dev
pip install backports.lzma
pip install zs

zs also requires the following packages: six, docopt, requests. However, these are all pure-Python packages which pip will install for you automatically when you run pip install zs.

Downloads:
http://pypi.python.org/pypi/zs/
Code and bug tracker:
https://github.com/njsmith/zs
Contact:
Nathaniel J. Smith <nathaniel.smith@ed.ac.uk>
Citation:

If you use this software (or the ZS format in general) in work that leads to a scientific publication, and feel that a citation would be appropriate, then here is a possible citation:

Smith, N. J. (submitted). ZS: A file format for efficiently distributing, using, and archiving record-oriented data sets of any size. Retrieved from http://vorpus.org/papers/draft/zs-paper.pdf

In addition, if you wish to document exactly which version of this software you used, then each official release has its own DOI which you can find on the change history page.

It may make sense to cite either or both of these at the same time -- the paper gives a general introduction to the ZS format and why it is useful, the version-specific DOIs link directly to archived snapshots of a specific version of this specific implementation of the ZS format. FWIW, I personally benefit more from citations to the paper.

Developer dependencies (only needed for hacking on source):
  • Cython: needed to build from checkout
  • nose: needed to run tests
  • nose-cov: because we use multiprocessing, we need this package to get useful test coverage information
  • nginx: needed to run HTTP tests
License:
2-clause BSD, see LICENSE.txt for details.