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Description:

monk 0.13.2

An unobtrusive data modeling, manipulation and validation library.
Supports MongoDB out of the box. Can be used for any other DB (or even without one).

Installation

$ pip install monk

Dependencies
Monk is tested against the following versions of Python:

CPython 2.6, 2.7, 3.2, 3.4
PyPy 2.0

Optional dependencies:

The MongoDB extension requires pymongo.

Documentation
See the complete documentation for details.

Examples

Modeling
The schema is defined as a template using native Python data types:
# we will reuse this structure in examples below

spec = {
'title': 'Untitled',
'comments': [
{
'author': str,
'date': datetime.datetime.utcnow,
'text': str
}
],
}
You are free to design as complex a document as you need.
The manipulation and validation functions (described below) support
arbitrary nested structures.
When this “natural” pythonic approach is not sufficient, you can mix it with
a more verbose notation, e.g.:
title_spec = IsA(str, default='Untitled') | Equals(None)
There are also neat shortcuts:
spec = {
'url': nullable(str),
'status': one_of(['new', 'in progress', 'closed']),
'comments': [str],
'blob': None,
}
This could be written a bit more verbosely:
spec = {
'url': IsA(str) | Equals(None),
'status': Equals('new') | Equals('in progress') | Equals('closed'),
'comments': ListOf(IsA(str)),
'blob': Anything(),
}
It is even possible to define schemata for dictionary keys:
CATEGORIES = ['books', 'films', 'toys']
spec = {
'title': str,
optional('price'): float, # key is optional; value is mandatory
'similar_items': {
one_of(CATEGORIES): [ # suggestions grouped by category
{'url': str, 'title': str}
],
}
}

# (what if the categories should be populated dynamically?
# well, the schema is plain Python data, just copy/update on the fly.)
And, yes, you can mix notations. See FAQ.
This very short intro shows that Monk requires almost zero learning to
start and then provides very powerful tools when you need them;
you won’t have to rewrite the “intuitive” code, only augment complexity
exactly in places where it’s inevitable.

Manipulation
The schema can be used to create full documents from incomplete data:
from monk import merge_defaults

# default values are set for missing keys

>>> merge_defaults(spec, {})
{
'title': 'Untitled',
'comments': [],
}

# it's easy to override the defaults

>>> merge_defaults(spec, {'title': 'Hello'})
{
'title': 'Hello',
'comments': [],
}

# nested lists of dictionaries can be auto-filled, too.
# by the way, note the date.

>>> merge_defaults(spec, {'comments': [{'author': 'john'}]})
{
'title': 'Untitled',
'comments': [
{
'author': 'john',
'date': datetime.datetime(2013, 3, 3, 1, 8, 4, 152113),
'text': None,
}
]
}

Validation
The same schema can be used to ensure that the document has correct structure
and the values are of correct types:
from monk.validation import validate

# correct data: staying silent

>>> validate(spec, data)

# a key is missing

>>> validate(spec, {'title': 'Hello'})
Traceback (most recent call last):
...
MissingKey: 'comments'

# a key is missing in a dictionary in a nested list

>>> validate(spec, {'comments': [{'author': 'john'}]}
Traceback (most recent call last):
...
MissingKey: 'comments': #0: 'date'

# type check; also works with functions and methods (by return value)

>>> validate(spec, {'title': 123, 'comments': []})
Traceback (most recent call last):
...
ValidationError: 'title': must be str
Custom validators can be used. Behaviour can be fine-tuned.
The validate() function translates the “natural” notation to a validator
object under the hood. To improve performance you can “compile” the validator
once (using translate() function or by creating a validator instance in place)
and use it multiple times to validate different values:
>>> from monk import *
>>> translate(str) == IsA(str)
True
>>> validator = IsA(str) | IsA(int)
>>> validator('hello')
>>> validator(123)
>>> validator(5.5)
Traceback (most recent call last):
...
AllFailed: 5.5 (must be str; must be int)
The library can be also viewed as a framework for building ODMs
(object-document mappers). See the MongoDB extension and note how it reuses
mixins provided by DB-agnostic modules.
Here’s an example of the MongoDB ODM bundled with Monk:
from monk.mongo import Document

class Item(Document):
structure = dict(text=unicode, slug=unicode)
indexes = dict(text=None, slug=dict(unique=True))

# this involves manipulation (inserting missing fields)
item = Item(text=u'foo', slug=u'bar')

# this involves validation
item.save(db)

Links

Project home page (Github)
Documentation (Read the Docs)
Package distribution (PyPI)
Questions, requests, bug reports, etc.:

Issue tracker
Direct e-mail (neithere at gmail com)

Author
Originally written by Andrey Mikhaylenko since 2011.
Please feel free to submit patches, report bugs or request features:

http://github.com/neithere/monk/issues/

Licensing
Monk is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Monk is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with Monk. If not, see <http://gnu.org/licenses/>.