Mongothon

Mongothon is a MongoDB object-document mapping API for Python, loosely based on the awesome mongoose.js library.

Installation

Install via easy_install:

easy_install mongothon

Or, via pip:

pip install mongothon

Getting Started

Mongothon allows you to couple reusable schemas (based on the Schemer API) with Model classes which can be used in your application to perform IO with your associated Mongo collection.

Example

Define the Mongo document structure and constraints in a Schema:

from mongothon import Schema

car_schema = Schema({
    "make":         {"type": basestring, "required": True},
    "model":        {"type": basestring, "required": True},
    "num_wheels":   {"type": int,        "default": 4, "validates": gte(0)}
    "color":        {"type": basestring, "validates": one_of("red", "green", "blue")}
})

Generate a reusable model class from the Schema and pymongo collection:

Car = create_model(car_schema, db['car'])

Find, modify and save a document:

car = Car.find_by_id(some_id)
car['color'] = "green"
car.save()

Create a new document:

car = new Car({
    "make":     "Ford",
    "model":    "F-150",
    "color":    "red"
})
car.save()

Remove a document

car.remove()

Validate a document

car = new Car({
    "make":         "Ford",
    "model":        "F-150",
    "num_wheels":   -1
    "color":        "red"
})

try:
    car.validate()
except ValidationException:
    # num_wheels should be >= 0

API Reference

Schemas

Schemas in Mongothon are based almost completely on the Schema class provided by the Schemer library. Take a look at the Schemer docs for details of how to describe your document's structure, validation rules and defaults.

For convenience, Mongothon offers it's own Schema subclass which includes standard Schemer functionality but adds support for Mongo "_id" fields.

Models

Where Schemas are used to declare the structure and constraints of a Mongo document, Models allow those Schemas to be used in interacting with the database to enforce that document structure.

Creating a model class

To create a new model class from an existing schema, use the create_model method:

Order = create_model(order_schema, db['orders'])

The second argument which must be provided to create_model is the PyMongo collection object associated with the underlying MongoDB collection to be associated with the model.

Class methods

Model classes provide a number of class methods which can be used to interact with the underlying collection as a whole.

Finding documents

Model classes can be used to find individual documents by ID:

order = Order.find_by_id(some_id)  # returns an instance of Order
                                   # or throws NotFoundException

or using a search condition:

order = Order.find_one({'total_due': {'$gte': '10'}})  # returns an instance of Order

Selections of documents can also be retrieved using search criteria:

order = Order.find({'total_due': {'$gte': '10'}})  # returns a cursor containing Order instances

Updating documents

Mongothon two mechanisms to run updates against documents.

Model.update (static method)

The class method version of update is essentially a proxy for the underlying Pymongo collection object's update method and can be called as such.

Order.update({'total_due': {'$gte': 700}}, {'$unset': {'line_items': 1}})

model.update_instance (instance method)

The instance method update_instance makes it easy to run an update statement against the current model document by defaulting the query used to {'_id': self['_id']}.

order = Order.find_by_id(some_id)
order.update_instance({'$unset': {'line_items': 1}})

Note

model.update (instance method) will delegate to python's dictionary API:

order = Order.find_by_id(some_id)
order.update({'line_items': 1})
print order['line_items']  # 1

Counting items

Order.count()

Custom class methods

You can dynamically add custom class methods to your model by using the model's class_method decorator function. These are useful for adding custom finder methods to your model:

@BlogPost.class_method
def find_by_author(cls, author):
    return cls.find({"author": author})

posts = BlogPost.find_by_author("Jeff Atwood")

Instance methods

Instances of models allow documents to be easily created, manipulated, save and deleted.

Creating documents

Create a new instance of a model by passing the document as a Python dict into the constructor:

order = Order({
    "line_items": [
        {"item_name": "iPhone 5", "price": 200},
        {"item_name": "Mac Mini", "price": 500}
    ],
    "total_due": 700
})

Validating documents

You can validate a document against its Schema by simply calling validate on the document instance:

order.validate()  # raises a ValidationException is the document is invalid

Saving documents

In order to persist document changes to the DB, the model can be saved:

order.save()

Saving an existing, previously loaded document will cause it to be updated. Saving a new document will cause it to be inserted. In all cases, saving a document results in schema defaults being applied where appropriate and the document being validated before it is saved to the database. In the event of a validation failure save() will raise a ValidationException.

Deleting documents

A document may be removed from the underlying collection by calling the remove() method on the associated model instance:

order = Order.find_by_id(some_id)
order.remove()  # document is removed from the DB

Reload

You can easily reload a model instance from the database by calling the reload method on an instance:

order = Order.find_by_id(some_id)
...
order.reload()

Custom instance methods

Custom instance methods can be added to a model using the model's instance_method decorator. This comes in handy when you want to wrap up common operations on a document:

@Order.instance_method
def add_line_item(self, name, price):
    self.line_items.append({'item_name': name, 'price': price})

order = Order.find_by_id(some_id)
order.add_line_item("iPad Mini", 300)
order.save()

"Scopes" (beta)

Scopes are a dynamic way of attaching reusable sets of query options to a model which can then be chained together dynamically in order to run actual queries against the model's underlying collection.

For example:

@Order.scope
def before(date):
    return {"created_date": {"$lt": date}}

@Order.scope
def single_item():
    return {"items": {"$size": 1}}

# Obtains a list of orders which were created before 20120101 which have a single line item.
orders = Order.before(datetime(2012, 1, 1)).single_item()

Combining queries with nested criteria

When dealing with multiple chained scopes, Mongothon uses a "deep merge, last query wins" approach to combine multiple query dicts into a single query dicts. This ensures that queries with nested query elements may be combined just as easily as simple key-value queries.

Examples:

@Order.scope
def item_priced_lt(price):
    return {"items": {
        "$elemMatch": {
            "price": {"$lt": price}
        }
    }}

@Order.scope
def item_priced_gt(price):
    return {"items": {
        "$elemMatch": {
            "price": {"$gt": price}
        }
    }}

@Order.scope
def item_named(name:
    return {"items": {
        "$elemMatch": {
            "name": name
        }
    }}

orders = Order.item_named('iPhone').item_priced_lt(500).item_priced_gt(200)

# Resultant query:
#   {"items": {
#       "$elemMatch": {
#           "name": "iPhone",
#           "price": {"$gt": 200, "$lt": 500}
#       }
#   }}

Other notes:

• If you have multiple queries specifying a list of values (e.g. as part of an $in statement) for the same field, Mongothon will combine the two lists for you. {'tags': {'$in': ['red', 'blue']} + {'tags': {'$in': ['green', 'blue']} => {'tags': {'$in': ['red', 'blue', 'green']}
• Even with deep merging, if you attempt to combine two queries which specify different values for matching a field, the last scope in the chain will win.

Implementing scope functions

A "scope" function is simply a function which returns up to three return values:

• A query dict
• A projection dict
• An options dict, containing a list of kwargs suitable for passing to PyMongo's find method.

A scope is registered with a given model by using the model's scope decorator.

Some example scopes:

@BlogPost.scope
def author(name):
    """A scope which restricts the query to only blog posts by the given author"""
    return {"name": name}

@BlogPost.scope
def id_only():
    """Only return the ID from the query"""
    return {}, {"_id": 1}

@BlogPost.scope
def by_created_date():
    """Sorts the query results by created date"""
    return {}, {}, {"sort": ["created_date", 1]}

Using scopes

Scope functions, once registered to a given model, can be called on the model class to dynamically build up a query context in a chainable manner.

Once the query context has been built up, it will executed as soon as the caller attempts to access the results.

# Finds all BlogPosts with a given author, only returning their IDs
posts = BlogPost.author("bob").id_only()

# The actual query is only executed against Mongo when we attempt access
first = posts[0]

The builder API which allows scopes to be chained together in this manner implements the Python iterator protocol as well:

for post in BlogPost.author("bob").id_only():
    # Do something

You can call any pymongo Cursor method via the scope builder:

num_posts_by_bob = BlogPost.author("bob").count()

ten_posts_by_bob = BlogPost.author("bob").limit(10)

Furthermore, scopes can be further refined even after you have performed access on them:

posts = BlogPost.author('bob')
print "Bob has written a total of {} posts".format(posts.count())

gardening_posts = posts.tagged('gardening')
print "{} of these are about gardening".format(gardening_posts.count())

Events

Mongothon Models emit events at various points in the lifecycle of a model instance. You can register one or more handler functions for a given event against the model class. These functions are then invoked at the point a model instance emits the event.

To register a function to receive an event, use the on model class method, either by calling it directly passing your handler function, or as a decorator:

def log_save(blog_post):
    logging.info('Blog post {} was saved!'.format(blog_post['_id']))

# Register the handler function
BlogPost.on('did_save', log_save)

...

@BlogPost.on('did_save')
def log_save(blog_post):
    logging.info('Blog post {} was saved!'.format(blog_post['_id']))

Implementing handler functions

A valid event handler function should always expect to receive:

• the model instance from which the event is being emitted as it's first argument
• any other specific arguments associated with the given event (see below for a list of standard events and their additional arguments).

@BlogPost.on('did_remove')
def log_remove(blog_post, *args, **kwargs):
    logging.info('Blog post {} was removed!'.format(blog_post['_id']))

@BlogPost.on('did_update')
def log_update(blog_post, document, *args, **kwargs):
    logging.info('Blog post {} was updated using document'.format(blog_post['_id'], document))

When emitting custom events (see below for more details), this allows essentially any arguments to be passed to all handlers registered for that event.

@BlogPost.on('archived')
def log_archived(blog_post, archived_by):
    logging.info('Blog post {} was archived by {}'.format(blog_post['_id'], archived_by))

...

def archive_blog_post(post_id, user_email):
    blog_post = BlogPost.find_by_id(post_id)
    blog_post['archived'] = True
    blog_post.save()
    blog_post.emit('archived', archived_by=user_email)

Standard events

Every Mongothon model emits the following events as part of its lifecycle:

Working copy event arguments

'will_validate', 'did_validate' and 'will_save' events include a working argument which is a working copy of the model instance. To properly understand what this argument is, it is useful to think about the steps Mongothon goes through when saving a Mongothon Model instance:

1. A working (deep) copy of the model instance is created.
2. Any schema default values are applied to the working copy, without affecting the primary object instance.
3. The working copy is validated against the model's schema.
4. If validation passes, an attempt is made to save the working copy to the underlying database collection.
5. If the database save operation succeeds, the working copy is merged back into the primary object instance so that it reflects the document in the collection.

So for these events which receive the working argument, depending on the model's schema it is possible that this object may contain different values to the primary model instance.

Also note that if you want to implement any universal "pre-save" updates to the model just before it is saved (e.g. updating a 'modified' timestamp), you can do this simply by manipulating the working copy.

Emitting custom events

As well as the standard set of events listed above which are emitted by models, it's also possible to use the Model event bus for any custom events you want to emit.

To emit a custom event, just invoke emit on a give model instance passing a string to identify the type of event, along with any custom arguments which are relevant to that event.

(Note that you don't need to pass the model instance itself as an argument to emit).

post = BlogPost.find_by_id(post_id)
post.emit('loaded', datetime.utcnow())

Generally speaking, rather than emitting events directly from your model-consuming code, a better pattern is to implement an @instance_method on your Model which wraps up some operation and emit an event from within that method.

To handle a custom event, just register a handler function in the same way you would for a standard event:

BlogPost.on('loaded')
def log_load(post, loaded_time):
    logging.debug('Loaded post {} at {}'.format(post['_id'], loaded_time))

Model State

Mongothon models provide a few handy methods which let you determine the document's current persistence state:

post = BlogPost()
assert post.is_new()

post.save()
assert not post.is_new()
assert post.is_persisted()

post.remove()
assert not post.is_new()
assert not post.is_persisted()
assert port.is_deleted()

Developing and Contributing

To run Mongothon's tests, simply run python setup.py nosetests at the command line.

All contributions submitted as GitHub pull requests are warmly received.