Metadata-Version: 1.0
Name: facturedata
Version: 0.2.02
Summary: Factories that create fixtures
Home-page: http://pypi.python.org/pypi/facturedata/
Author: Gordon McCreight
Author-email: gordon@mccreight.com
License: LICENSE.txt
Description: =======
        Facture
        =======
        
        --------
        Overview
        --------
        
        Facture is FAC-tories that generate partition-isolated fix-TURE data, ideal for
        elaborating scenarios.
        
        ------------
        Installation
        ------------
        
        ``pip install facturedata``
        
        --------------------------------------------------
        How is this different from factories and fixtures?
        --------------------------------------------------
        
        Originally this tool was created to generate the test data for a database
        that had very high overhead per-insert.  Using traditional data factories and
        individual transactions with rollback would not work; batching the inserts was
        necessary.
        
        Fixtures alone could solve this problem, but it's really hard to enumerate and
        manage the joins in a large number of scenarios in fixtures effectively.  The
        primary advantage that factories have is their ease of use in testing
        individual scenarios without needing to understand other scenarios or manage a
        corpus of shared data.  Carefully paritioning inserted data so the data is
        unambiguously owned by a scenario can provide the same benefit as factories,
        where it becomes easy to understand the data associated with individual
        scenarios.
        
        ----------
        How to Use
        ----------
        
        There is a fair amount of configuration that goes into setting facture up, but
        here is a quick inline example data group that shows how foreign keys work::
        
            'group': 'facture_group_shawshank_redemption',
            'offset': 100,
            'data': [
                ['actors a_mf', {'attrs': {'first_name': 'Morgan', 'last_name': 'Freeman'}}],
                ['actors a_tr', {'attrs': {'first_name': 'Tim', 'last_name': 'Robbins'}}],
                ['films f', {'attrs': {'name': 'Shawshank Redemption', 'year': '1994'}}],
                ['roles r1', {'refs': {'actor_id': '.a_mf.id', 'film_id': '.f.id'}}],
                ['roles r1', {'refs': {'actor_id': '.a_tr.id', 'film_id': '.f.id'}}]
            ]
        
        For a deeper dive I recommend that you look at this example:
        https://github.com/gmccreight/facture/tree/master/tests/examples/sql_inject_target
        
        Copy the ``factureconf.py`` there to the directory you will be running ``facture``
        in.  Look at the ``facture_json`` target section in the ``original.sql`` file
        and copy it into a file of your own choosing.  Update the ``conf_targets``
        section of your newly created ``factureconf.py`` file to point to the file
        where you put the ``facture_json``.
        
        Run::
        
            facture
        
        Your target file should now be filled in with some generated data.  You're off
        to the races!
        
        -------------------
        Additional benefits
        -------------------
        
        * "compile"-time factory configuration consistency checks.  Checks for many
          typos, join issues, etc.
        
        * Materializes the output into your version-controlled files, unrolling the
          complexity.  Because the fixtures that we generate are automatically created,
          we can put a lot of effort into making the generated data comprehensible (and
          lineage-providing).  This means we annotate all generated data with the column names, 
          and the scenario name.  It's also extremely easy to see what the effects of
          any change you make is, since it's all laid out for you.
        
        * Code as configuration.  The configuration files are written in Python, so you have
          the full power of the language.
        
        * Easy to reason about each scenario in complete isolation because of data partitioning
        
        * Plays well with others... can be introduced into existing fixtures.  Integrates
          with how you do things, doesn't try to take over completely.  Can target
          files or sections within files.
        
Platform: UNKNOWN
