A Python Document-Object Mapper for working with MongoDB
Project description
- Info:
MongoEngine is an ORM-like layer on top of PyMongo.
About
MongoEngine is a Python Object-Document Mapper for working with MongoDB. Documentation available at http://hmarr.com/mongoengine/ - 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!
Project details
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