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MongoLite is a fork of MongoKit by the same author. It aims to come back to simplicity by stripping a lot of features and replacing them with best practices.
The goal of MongoLite is to stick as much as possible to the pymongo api. MongoLite always choose speed over syntaxic sugar, this is why you won't see validation or dot notation features in this project.
MongoLite is perfect for who wants a thing layer on top of pymongo and don't care about validation stuff.
A mongolite is a beautiful stone
- the basics
- quering documents
- updating documents
- referencing other documents
- json support
- inheritance and polymorphism
- indexes
- best practices
Your data is clean
"Tools change, not data". In order to follow this "credo", just like MongoKit, MongoLite won't add any information into your data saved into the database. So if you need to use other mongo tools or ODMs in other languages, your data won't be polluted by MongoLite's stuff.
- schema less feature
- inheritance and polymorphisme support
- skeleton generation (your object is automaticaly filled by the correct fields)
- nested and complex schema declaration
- default values features
- inherited queries (this is huge !)
- random query support (which returns a random document from the database)
- json helpers
- GridFS support
A Document declaration look like that:
>>> from mongolite import Document, Connection
>>> import datetime
>>> connection = Connection()
>>> @connection.register
... class BlogPost(Document):
... __database__ = 'tutorial'
... __collection__ = 'blogposts'
... skeleton = {
... 'title':unicode,
... 'body':unicode,
... 'author':unicode,
... 'date_creation':datetime.datetime,
... }
... optional = {
... 'tags': [unicode],
... 'rank':int,
... }
... default_values = {'rank':0, 'date_creation':datetime.datetime.utcnow}
...
>>> blogpost = con.BlogPost() # this use the db "test" from `__database__` and the collection "example" from `__collection__`
>>> blogpost # the skeleton is automatically generated (based on the skeleton attribute)
{'body': None, 'title': None, 'date_creation': datetime.datetime(...), 'author': None}
>>> blogpost['title'] = u'my title'
>>> blogpost['body'] = u'a body'
>>> blogpost['author'] = u'me'
>>> blogpost['tags'] = ['about me', 'first post']
>>> blogpost
{'body': u'a body', 'title': u'my title', 'date_creation': datetime.datetime(...), 'rank': 0, 'author': u'me', 'tags': [u'about me', u'first post']}
>>> blogpost.save()
Note that, while fields in skeleton
should be present in the document, fields in optional
attribut are not generated by default. It aims to be for documentation only...
To access those fields, you can use the following convention:
for fields in skeleton:
>>> title = blogpost['title']
for fields in optional:
>>> tags = blogpost.get('tags', [])
MongoLite is written on top of pymongo. All the pymongo's API is accessible and the results are wrapped into Document objects:
>>> blogpost = con.BlogPost.find_one() # this is a blogpost object
However, if you need more performances, you can use the pymongo layer directly:
>>> blogpost = con.test.example.find_one() # this is a dict
Suggestion and patches are really welcome. If you find mistakes in the documentation (english is not my primary langage) feel free to contact me. You can find me (namlook) on twitter