Using a document database rather than a full DBMS gets more common these days. This pattern shows how to use MongoKit, a document mapper library, to integrate with MongoDB.

This pattern requires a running MongoDB server and the MongoKit library installed.

There are two very common ways to use MongoKit. I will outline each of them here:

## 6.1. Declarative¶

The default behavior of MongoKit is the declarative one that is based on common ideas from Django or the SQLAlchemy declarative extension.

Here an example app.py module for your application:

from flask import Flask
from mongokit import Connection, Document

# configuration
MONGODB_HOST = 'localhost'
MONGODB_PORT = 27017

# create the little application object
app.config.from_object(__name__)

# connect to the database
connection = Connection(app.config['MONGODB_HOST'],
app.config['MONGODB_PORT'])


To define your models, just subclass the Document class that is imported from MongoKit. If you’ve seen the SQLAlchemy pattern you may wonder why we do not have a session and even do not define a init_db function here. On the one hand, MongoKit does not have something like a session. This sometimes makes it more to type but also makes it blazingly fast. On the other hand, MongoDB is schemaless. This means you can modify the data structure from one insert query to the next without any problem. MongoKit is just schemaless too, but implements some validation to ensure data integrity.

Here is an example document (put this also into app.py, e.g.):

from mongokit import ValidationError

def max_length(length):
def validate(value):
if len(value) <= length:
return True
# must have %s in error format string to have mongokit place key in there
raise ValidationError('%s must be at most {} characters long'.format(length))
return validate

class User(Document):
structure = {
'name': unicode,
'email': unicode,
}
validators = {
'name': max_length(50),
'email': max_length(120)
}
use_dot_notation = True
def __repr__(self):
return '<User %r>' % (self.name)

# register the User document with our current connection
connection.register([User])


This example shows you how to define your schema (named structure), a validator for the maximum character length and uses a special MongoKit feature called use_dot_notation. Per default MongoKit behaves like a python dictionary but with use_dot_notation set to True you can use your documents like you use models in nearly any other ORM by using dots to separate between attributes.

You can insert entries into the database like this:

>>> from yourapplication.database import connection
>>> from yourapplication.models import User
>>> collection = connection['test'].users
>>> user = collection.User()
>>> user.save()


Note that MongoKit is kinda strict with used column types, you must not use a common str type for either name or email but unicode.

Querying is simple as well:

>>> list(collection.User.find())


## 6.2. PyMongo Compatibility Layer¶

If you just want to use PyMongo, you can do that with MongoKit as well. You may use this process if you need the best performance to get. Note that this example does not show how to couple it with Flask, see the above MongoKit code for examples:

from MongoKit import Connection

connection = Connection()


To insert data you can use the insert method. We have to get a collection first, this is somewhat the same as a table in the SQL world.

>>> collection = connection['test'].users
>>> collection.insert(user)


MongoKit will automatically commit for us.

To query your database, you use the collection directly:

>>> list(collection.find())

>>> r = collection.find_one({'name': u'admin'})