MongoDB ORM for Python? [closed]

Another option is MongoEngine. The ORM for MongoEngine is very similar to the ORM used by Django.

Example (from the tutorial):

class Post(Document):    title = StringField(max_length=120, required=True)    author = ReferenceField(User)class TextPost(Post):    content = StringField()class ImagePost(Post):    image_path = StringField()class LinkPost(Post):    link_url = StringField()

Not being satisfied with either MongoKit or MongoEngine, I decided to write my own object-oriented interface for Python.

I delegated all queries directly to pymongo, so the query syntax there is the same. Mostly, it's just an object-wrapper around the results, with some other helpers like database connection pooling, DBRef support, and other convenience methods to make your life easier.

It's called Minimongo and it's available from github. Happy hacking!

Example:

from minimongo import Model, MongoCollection class MyObject(Model):     model = MongoCollection(database='test', collection='my_collection')m = MyObject()m.x = 1m.field = 'value'm.other = {'list': True}m.save()x = MyObject({'x': 1, 'y': 2}).save()objs = MyObject.find({'x': 1})for o in objs:     print o

You want MongoKit. It is one layer of abstraction higher than PyMongo. Not sure if you're using Django, but there's also django-mongokit integration.

Example from this blog post. Note that instances of Computer can then reference make/model directly once the structure is defined ( e.g. atari.make, c64.model, ... ). No need for dictionaries:

import datetime from mongokit import Documentclass Computer(Document):    structure = {       'make': unicode,       'model': unicode,       'purchase_date': datetime.datetime,       'cpu_ghz': float,     }    validators = {       'cpu_ghz': lambda x: x > 0,       'make': lambda x: x.strip(),     }    default_values = {       'purchase_date': datetime.datetime.utcnow,     }    use_dot_notation = True    indexes = [       {'fields': ['make']},     ]