layered-yaml-attrdict-config (lya)
----------------------------------

YAML-based configuration module.

A set of classes I've created over time to make configuration files more
readable and easier to use in the code.

Basic syntax
^^^^^^^^^^^^

Idea is the same as with ``yaml.safe_load()`` (``yaml.load()`` was used
before 14.06.5, see #2 for rationale behind the change) to load YAML
configuration file like this one:

::

    core:
      connection:
        # twisted endpoint syntax, see twisted.internet.endpoints.html#clientFromString
        endpoint: tcp:host=example.com:port=6667
        nickname: testbot
        reconnect:
          maxDelay: 30
      xattr_emulation: /tmp/xattr.db

But when you use resulting nested-dicts in code, consider the difference
between ``config['core']['connection']['reconnect']['maxDelay']`` and
``config.core.connection.reconnect.maxDelay``.

Python dicts support only the first syntax, this module supports both.
Assigning values through attributes is also possible.

Recursive updates (inheritance)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

I find it useful to have default parameters specified in the same format
as any configurable overrides to them - simple yaml file.

So consider this use-case:

::

    import lya
    cfg = lya.AttrDict.from_yaml('default.yaml')
    for path in sys.argv[1:]: cfg.update_yaml(path)
    cfg.dump(sys.stdout)

(there is also ``AttrDict.update_dict`` method for recursive updates
from dict)

With default configuration file from the previous section shipped along
with the package as "default.yaml", you can have simple override like:

::

    core:
      connection:
        endpoint: ssl:host=some.local.host:port=6697

And above code will result in the following config (which will be dumped
as nicely-formatted yaml, as presented below):

::

    core:
      connection:
        endpoint: ssl:host=some.local.host:port=6697
        nickname: testbot
        reconnect:
          maxDelay: 30
      xattr_emulation: /tmp/xattr.db

Rebase
^^^^^^

Similar to the above, but reversed, so result presented above can be
produced by taking some arbitrary configuration (AttrDict) and rebasing
it on top of some other (base) config:

::

    import lya
    base = lya.AttrDict.from_yaml('default.yaml')
    for path in sys.argv[1:]:
      cfg.rebase(base)
      print 'Config:', path
      cfg.dump(sys.stdout)

Useful to fill-in default values for similar configuration parts (e.g.
configuration for each module or component).

Key ordering
^^^^^^^^^^^^

Keys in python dictionaries are unordered and by default, yaml module
loses any ordering of keys in yaml dicts as well.

Strictly speaking, this is correct processing of YAML, but for most
cases it is inconvenient when instead of clear section like this one:

::

    processing_order:
      receive_test:
        name: '#bot-central'
        server: testserver
      important_filter: '^important:'
      announce: '#important-news'
      debug_filter: '\(debug message\)'
      feedback: botmaster

...you have to resort to putting all the keys that need ordering into a
list just to preserve ordering.

Especially annoying if you have to access these sections by key
afterwards (and they should be unique) or you need to override some of
the sections later, so list wrapper becomes completely artificial as it
have to be converted into OrderedDict anyway.

YAML files, parsed from ``AttrDict.from_yaml`` and
``AttrDict.update_yaml`` methods have key ordering preserved, and
AttrDict objects are based on OrderedDict objects, which provide all the
features of dict and preserve ordering during the iteration like lists
do.

There's no downside to it - both ordered dicts and lists can be used as
usual, if that's more desirable.

Flattening
^^^^^^^^^^

Sometimes it's useful to have nested configuration (like presented
above) to be represented as flat list of key-value pairs.

Example usage can be storage of the configuration tree in a simple k-v
database (like berkdb) or comparison of configuration objects - ordered
flat lists can be easily processed by the "diff" command, tested for
equality or hashed.

That is easy to do via ``AttrDict.flatten`` method, producing (from
config above) a list like this one:

::

    [ (('core', 'connection', 'endpoint'), 'ssl:host=some.local.host:port=6697'),
      (('core', 'connection', 'nickname'), 'testbot'),
      (('core', 'connection', 'reconnect', 'maxDelay'), 30),
      (('core', 'xattr_emulation'), '/tmp/xattr.db') ]

Resulting list contains 2-value tuples - key tuple, containing the full
path of the value and the value object itself.

Installation
------------

It's a regular package for Python 2.6+ and Python 3.0+.

Using `pip <http://pip-installer.org/>`__ is the best way (see also
`pip2014 <http://pip2014.com/>`__ basic usage essentials):

::

    % pip install layered-yaml-attrdict-config

If you don't have it, use:

::

    % easy_install pip
    % pip install layered-yaml-attrdict-config

Alternatively (`see
also <http://www.pip-installer.org/en/latest/installing.html>`__):

::

    % curl https://raw.github.com/pypa/pip/master/contrib/get-pip.py | python
    % pip install layered-yaml-attrdict-config

Or, if you absolutely must:

::

    % easy_install layered-yaml-attrdict-config

But, you really shouldn't do that.

Current-git version can be installed like this:

::

    % pip install 'git+https://github.com/mk-fg/layered-yaml-attrdict-config.git#egg=layered-yaml-attrdict-config'

Module uses `PyYAML <http://pyyaml.org/>`__ for processing of the actual
YAML files, but can work without it, as long as you use any methods with
"yaml" in their name, i.e. creating and using AttrDict objects like a
regular dicts.

Example
-------

::

    import sys, lya

    if len(sys.argv) == 1:
      print('Usage: {} [ config.yaml ... ]', file=sys.stderr)
      sys.exit(1)

    cfg = lya.AttrDict.from_yaml(sys.argv[1])
    for path in sys.argv[2:]: cfg.update_yaml(path)

    cfg.dump(sys.stdout)

