Mind that age! This blog post is 10 years old! Most likely, its content is outdated. Especially if it's technical.

Here I'm going to explain how to combine Django and MongoDB using MongoKit and django-mongokit.

MongoDB is a document store built for high speed and high concurrency with a very good redundancy story. It's an alternative to relational databases (e.g. MySQL) that is what Django is tightly coupled with in it's ORM (Object Relation Mapping) and what it's called now is ODM (Object Document Mapping) in lack of a better acronym. That's where MongoKit comes in. It's written in Python and it connects to the MongoDB database using a library called pymongo and it turns data from the MongoDB and turns it into instances of classes you have defined. MongoKit has nothing to do with Django. That's where django-mongokit comes in. Written by yours truly.

So we start by defining a MongoKit subclass:

import datetime from mongokit import Document class 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' ]}, ]

All of these class attributes are features of MongoKit. Their names are so obvious that it needs no explanation. Perhaps the one about 'use_dot_notation'; it makes it possible to access data in the structure with a dot on the instance rather that the normal dictionary lookup method. Now let's work with this class on the shell. Important: to actually try this you have to have MongoDB and pymongo installed and up and running MongoDB:

>>> from mongokit import Connection >>> conn = Connection () >>> from mymodels import Computer >>> conn . register ([ Computer ]) >>> database = conn . mydb # will be created if it didn't exist >>> collection = database . mycollection # equivalent of a SQL table >>> instance = collection . Computer () >>> instance . make = u "Apple" >>> instance . model = u "G5" >>> instance . cpu_hrz = 2.66 >>> instance . save () >>> >>> type ( instance ) < class ' mymodels . Computer '> >>> instance { 'model' : u 'G5' , 'make' : u 'Apple' , '_id' : ObjectId ( '4b9244989d40b334b4000000' ), 'cpu_ghz' : None , 'purchase_date' : datetime . datetime ( 2010 , 3 , 6 , 12 , 3 , 8 , 281905 )} >>>

As you can see it's pretty easy to work with and it just feels so pythonic and obvious. What you get is a something that works just like a normal base class with some extra sugar plus the fact that it can save the data persistently and does so efficiently and redundantly (assuming you do some work on your MongoDB set it up with replication and/or sharding). Now let's look at retrieval which, as per the design principles of MongoKit, follows the basic interface of pymongo. To learn about querying you can skim the MongoKit documentation but really the thing to read is the pymongo documentation which MongoKit layers thinly:

>>> from mongokit import Connection >>> conn = Connection () >>> from mymodels import Computer >>> conn . register ([ Computer ]) >>> database = conn . mydb >>> collection = database . mycollection >>> instances = collection . Computer . find () >>> type ( instances ) < class ' mongokit . generators . MongoDocumentCursor '> >>> list ( instances )[ 0 ] { u 'cpu_ghz' : None , u 'model' : u 'G5' , u '_id' : ObjectId ( '4b9244989d40b334b4000000' ), u 'purchase_date' : datetime . datetime ( 2010 , 3 , 6 , 12 , 3 , 8 , 281000 ), u 'make' : u 'Apple' } >>> instances = collection . Computer . find () . count () 1 >>> collection . Computer . one () == list ( collection . Computer . find ())[ 0 ] True

The query methods one() and find() can take search parameters which limits what you get back. These are quite similar to how Django's default Manager has a method called objects.get() and objects.filter() which should make you feel familiar.

So, what would it take to be able to do this MongoKit business in a running Django so that you can write Django views and templates that interface with your Mongo "documents". Answer: use django-mongokit. django-mongokit is a thin wrapper around MongoKit that makes it just slightly more convenient to use MongoKit in a Django environment. The primary tasks django-mongokit takes care of are: (1) the connection and (2) giving your classes a _meta class attribute. Especially important regarding the connection is that django-mongokit takes care of setting up and destroying a test database for you for running your tests. And since it's all in one place you don't have to worry about creating various connections to MongoKit in your views or management commands. Let's first define the database in your settings.py file:

DATABASES = { 'default' : { 'ENGINE' : 'sqlite3' , 'NAME' : 'example-sqlite3.db' , }, 'mongodb' : { 'ENGINE' : 'django_mongokit.mongodb' , 'NAME' : 'mydb' , }, }

Then, with that in place all you need to get a connection are these lines:

>>> from django_mongokit import get_database >>> database = get_database ()

The reason it's a function an not an instance is because the database is going to be different based on if you're running tests or running in production/development mode. Had we imported a database instance instead of a function to get a database instance, the code would need to know what database you want when the python files are imported which is something that happens before we even know what you're doing with the imported code. django-mongokit also gives you the connection instances which you'll need to register your own models:

>>> from django_mongokit import connection >>> connection . register ([ Computer ])

But I recommend that a best practice is to always register your models right after you have defined them. This brings us to the DjangoDocument class so let's get straight into it this time in your models.py file inside a Django app you've just created:

import datetime from django_mongokit import connection from django_mongokit.document import DjangoDocument class Computer ( DjangoDocument ): # notice difference from above class Meta : verbose_name_plural = "Computerz" 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' ]}, ] connection . register ([ Computer ])

That's now all you need to get on with your code. The DjangoDocument class offers a few more gems that makes your life easier such as handling signals and registering itself in a global variable (import django_mongokit.document.model_names and inspect). See the django-mongokit README file for more information.

So, what's so great about this setup? It's by personal taste but for me it's simplicity and purity. I like the thin layer MongoKit adds on top of pure pymongo that becomes oh so practical such as helping you make sure you only store what you said you would and it's easier to work with class instances you can see the definition of than it is to work with dictionaries and lists.

And here's one of MongoKit's best selling points for me: the few times you need speed, speed and more speed it's possible to go straight to the source without doing any wrapping. This is equivalent of how you sometimes in Django run raw SQL queries which, let's be honest, does happen quite frequently when the project becomes non-trivial. Django's ORM has the ability to turn the output of the raw SQL output into objects and with MongoKit when you go straight into MongoDB you get pure Python dictionaries which you can use to create instances with. Here's an example where you can't query what you're looking for but you might be trolling through thousands of documents:

>>> from some.thridparty import my_kind_of_cpu >>> computers = [] >>> for item in collection . find (): ... # can't use dot notation when it's not a document ... cpu = item [ 'cpu_ghz' ] ... if my_kind_of_cpu ( cpu ): ... computers . append ( collection . Computer ( item )) ...

A use case for this is when you want to store different types of documents in the same collection and by a value extracted from a raw query you only turn selected few results into mapped instances. More about that in a later post maybe.

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