A Python Document-Object Mapper for working with MongoDB
Project description
- Info:
MongoEngine is an ORM-like layer on top of PyMongo.
- Maintainer:
Ross Lawley (http://github.com/rozza)
About
MongoEngine is a Python Object-Document Mapper for working with MongoDB. Documentation available at http://mongoengine-odm.rtfd.org - there is currently a tutorial, a user guide and an API reference.
Installation
If you have setuptools you can use easy_install -U mongoengine. Otherwise, you can download the source from GitHub and run python setup.py install.
Dependencies
pymongo 1.1+
sphinx (optional - for documentation generation)
Examples
Some simple examples of what MongoEngine code looks like:
class BlogPost(Document):
title = StringField(required=True, max_length=200)
posted = DateTimeField(default=datetime.datetime.now)
tags = ListField(StringField(max_length=50))
class TextPost(BlogPost):
content = StringField(required=True)
class LinkPost(BlogPost):
url = StringField(required=True)
# Create a text-based post
>>> post1 = TextPost(title='Using MongoEngine', content='See the tutorial')
>>> post1.tags = ['mongodb', 'mongoengine']
>>> post1.save()
# Create a link-based post
>>> post2 = LinkPost(title='MongoEngine Docs', url='hmarr.com/mongoengine')
>>> post2.tags = ['mongoengine', 'documentation']
>>> post2.save()
# Iterate over all posts using the BlogPost superclass
>>> for post in BlogPost.objects:
... print '===', post.title, '==='
... if isinstance(post, TextPost):
... print post.content
... elif isinstance(post, LinkPost):
... print 'Link:', post.url
... print
...
=== Using MongoEngine ===
See the tutorial
=== MongoEngine Docs ===
Link: hmarr.com/mongoengine
>>> len(BlogPost.objects)
2
>>> len(HtmlPost.objects)
1
>>> len(LinkPost.objects)
1
# Find tagged posts
>>> len(BlogPost.objects(tags='mongoengine'))
2
>>> len(BlogPost.objects(tags='mongodb'))
1
Tests
To run the test suite, ensure you are running a local instance of MongoDB on the standard port, and run python setup.py test.
Community
Contributing
The source is available on GitHub - to contribute to the project, fork it on GitHub and send a pull request, all contributions and suggestions are welcome!
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