Tutorial

    Before we start, make sure that you have the PyMongo distribution. In the Python shell, the followingshould run without raising an exception:

    This tutorial also assumes that a MongoDB instance is running on thedefault host and port. Assuming you have downloaded and installed MongoDB, youcan start it like so:

    1. $ mongod

    Making a Connection with MongoClient

    The first step when working with PyMongo is to create a to the running mongodinstance. Doing so is easy:

    1. >>> from pymongo import MongoClient
    2. >>> client = MongoClient()

    The above code will connect on the default host and port. We can alsospecify the host and port explicitly, as follows:

    1. >>> client = MongoClient('localhost', 27017)

    Or use the MongoDB URI format:

    1. >>> client = MongoClient('mongodb://localhost:27017/')

    Getting a Database

    A single instance of MongoDB can support multiple independentdatabases. Whenworking with PyMongo you access databases using attribute style accesson instances:

    1. >>> db = client.test_database

    If your database name is such that using attribute style access won’twork (like test-database), you can use dictionary style accessinstead:

    1. >>> db = client['test-database']

    Getting a Collection

    A collection is agroup of documents stored in MongoDB, and can be thought of as roughlythe equivalent of a table in a relational database. Getting acollection in PyMongo works the same as getting a database:

    1. >>> collection = db.test_collection

    or (using dictionary style access):

    1. >>> collection = db['test-collection']

    An important note about collections (and databases) in MongoDB is thatthey are created lazily - none of the above commands have actuallyperformed any operations on the MongoDB server. Collections anddatabases are created when the first document is inserted into them.

    Data in MongoDB is represented (and stored) using JSON-styledocuments. In PyMongo we use dictionaries to represent documents. Asan example, the following dictionary might be used to represent a blogpost:

    Note that documents can contain native Python types (like instances) which will be automaticallyconverted to and from the appropriate BSON types.

    Inserting a Document

    To insert a document into a collection we can use the method:

    1. >>> posts = db.posts
    2. >>> post_id = posts.insert_one(post).inserted_id
    3. >>> post_id
    4. ObjectId('...')

    When a document is inserted a special key, "_id", is automaticallyadded if the document doesn’t already contain an "_id" key. The valueof "_id" must be unique across thecollection. insert_one() returns aninstance of . For more informationon "_id", see the documentation on _id.

    1. >>> db.list_collection_names()
    2. [u'posts']

    Getting a Single Document With find_one()

    The most basic type of query that can be performed in MongoDB is. This method returns asingle document matching a query (or if there are nomatches). It is useful when you know there is only one matchingdocument, or are only interested in the first match. Here we usefind_one() to get the firstdocument from the posts collection:

    1. >>> import pprint
    2. >>> pprint.pprint(posts.find_one())
    3. {u'_id': ObjectId('...'),
    4.  u'author': u'Mike',
    5.  u'date': datetime.datetime(...),
    6.  u'tags': [u'mongodb', u'python', u'pymongo'],
    7.  u'text': u'My first blog post!'}

    The result is a dictionary matching the one that we inserted previously.

    Note

    The returned document contains an "_id", which wasautomatically added on insert.

    also supports queryingon specific elements that the resulting document must match. To limitour results to a document with author “Mike” we do:

    1. >>> pprint.pprint(posts.find_one({"author": "Mike"}))
    2. {u'_id': ObjectId('...'),
    3.  u'author': u'Mike',
    4.  u'date': datetime.datetime(...),
    5.  u'tags': [u'mongodb', u'python', u'pymongo'],

    If we try with a different author, like “Eliot”, we’ll get no result:

    1. >>> posts.find_one({"author": "Eliot"})
    2. >>>

    Querying By ObjectId

    We can also find a post by its _id, which in our example is an ObjectId:

    1. >>> post_id
    2. ObjectId(...)
    3. >>> pprint.pprint(posts.find_one({"_id": post_id}))
    4. {u'_id': ObjectId('...'),
    5.  u'author': u'Mike',
    6.  u'date': datetime.datetime(...),
    7.  u'tags': [u'mongodb', u'python', u'pymongo'],
    8.  u'text': u'My first blog post!'}

    Note that an ObjectId is not the same as its string representation:

    1. >>> post_id_as_str = str(post_id)
    2. >>> posts.find_one({"_id": post_id_as_str}) # No result
    3. >>>

    A common task in web applications is to get an ObjectId from therequest URL and find the matching document. It’s necessary in thiscase to convert the ObjectId from a string before passing it tofind_one:

    1. from bson.objectid import ObjectId
    2.  
    3. # The web framework gets post_id from the URL and passes it as a string
    4. def get(post_id):
    5.     # Convert from string to ObjectId:
    6.     document = client.db.collection.find_one({'_id': ObjectId(post_id)})

    See also

    When I query for a document by ObjectId in my web application I get no result

    You probably noticed that the regular Python strings we stored earlier lookdifferent when retrieved from the server (e.g. u’Mike’ instead of ‘Mike’).A short explanation is in order.

    MongoDB stores data in . BSON strings areUTF-8 encoded so PyMongo must ensure that any strings it stores contain onlyvalid UTF-8 data. Regular strings (<type ‘str’>) are validated and storedunaltered. Unicode strings (<type ‘unicode’>) are encoded UTF-8 first. Thereason our example string is represented in the Python shell as u’Mike’ insteadof ‘Mike’ is that PyMongo decodes each BSON string to a Python unicode string,not a regular str.

    You can read more about Python unicode strings here.

    Bulk Inserts

    There are a couple of interesting things to note about this example:

    Querying for More Than One Document

    To get more than a single document as the result of a query we use themethod. find() returns a instance, which allows us to iterateover all matching documents. For example, we can iterate over everydocument in the posts collection:

    1. >>> for post in posts.find():
    2. ...   pprint.pprint(post)
    3. ...
    4. {u'_id': ObjectId('...'),
    5.  u'author': u'Mike',
    6.  u'date': datetime.datetime(...),
    7.  u'tags': [u'mongodb', u'python', u'pymongo'],
    8.  u'text': u'My first blog post!'}
    9. {u'_id': ObjectId('...'),
    10.  u'author': u'Mike',
    11.  u'date': datetime.datetime(...),
    12.  u'tags': [u'bulk', u'insert'],
    13.  u'text': u'Another post!'}
    14. {u'_id': ObjectId('...'),
    15.  u'author': u'Eliot',
    16.  u'date': datetime.datetime(...),
    17.  u'text': u'and pretty easy too!',
    18.  u'title': u'MongoDB is fun'}

    Just like we did with find_one(),we can pass a document to to limit the returned results. Here, we get only those documents whoseauthor is “Mike”:

    1. >>> for post in posts.find({"author": "Mike"}):
    2. ...   pprint.pprint(post)
    3. {u'_id': ObjectId('...'),
    4.  u'author': u'Mike',
    5.  u'date': datetime.datetime(...),
    6.  u'tags': [u'mongodb', u'python', u'pymongo'],
    7.  u'text': u'My first blog post!'}
    8. {u'_id': ObjectId('...'),
    9.  u'author': u'Mike',
    10.  u'date': datetime.datetime(...),
    11.  u'tags': [u'bulk', u'insert'],
    12.  u'text': u'Another post!'}

    Counting

    If we just want to know how many documents match a query we canperform a count_documents() operationinstead of a full query. We can get a count of all of the documentsin a collection:

    1. >>> posts.count_documents({})
    2. 3

    or just of those documents that match a specific query:

    1. >>> posts.count_documents({"author": "Mike"})
    2. 2

    MongoDB supports many different types of . As anexample, lets perform a query where we limit results to posts olderthan a certain date, but also sort the results by author:

    1. >>> d = datetime.datetime(2009, 11, 12, 12)
    2. >>> for post in posts.find({"date": {"$lt": d}}).sort("author"):
    3. ...   pprint.pprint(post)
    4. ...
    5. {u'_id': ObjectId('...'),
    6.  u'author': u'Eliot',
    7.  u'date': datetime.datetime(...),
    8.  u'text': u'and pretty easy too!',
    9.  u'title': u'MongoDB is fun'}
    10. {u'_id': ObjectId('...'),
    11.  u'author': u'Mike',
    12.  u'date': datetime.datetime(...),
    13.  u'tags': [u'bulk', u'insert'],
    14.  u'text': u'Another post!'}

    Here we use the special "$lt" operator to do a range query, andalso call sort() to sort the resultsby author.

    Indexing

    Adding indexes can help accelerate certain queries and can also add additionalfunctionality to querying and storing documents. In this example, we’lldemonstrate how to create a on a key that rejectsdocuments whose value for that key already exists in the index.

    First, we’ll need to create the index:

    1. >>> result = db.profiles.create_index([('user_id', pymongo.ASCENDING)],
    2. ...                                   unique=True)
    3. >>> sorted(list(db.profiles.index_information()))
    4. [u'_id_', u'user_id_1']

    Notice that we have two indexes now: one is the index on _id that MongoDBcreates automatically, and the other is the index on user_id we justcreated.

    Now let’s set up some user profiles:

    1. >>> user_profiles = [
    2. ...     {'user_id': 211, 'name': 'Luke'},
    3. ...     {'user_id': 212, 'name': 'Ziltoid'}]
    4. >>> result = db.profiles.insert_many(user_profiles)

    The index prevents us from inserting a document whose user_id is already inthe collection:

    1. >>> new_profile = {'user_id': 213, 'name': 'Drew'}
    2. >>> duplicate_profile = {'user_id': 212, 'name': 'Tommy'}
    3. >>> result = db.profiles.insert_one(new_profile)  # This is fine.
    4. >>> result = db.profiles.insert_one(duplicate_profile)
    5. Traceback (most recent call last):

    See also

    The MongoDB documentation on indexes