Async Tutorial¶

Warning

This API is currently in beta, meaning the classes, methods, and behaviors described within may change before the full release. If you come across any bugs during your use of this API, please file a Jira ticket in the “Python Driver” project at https://jira.mongodb.org/browse/PYTHON.

from pymongo import AsyncMongoClient

client = AsyncMongoClient()
await client.drop_database("test-database")

This tutorial is intended as an introduction to working with MongoDB and PyMongo using the asynchronous API.

Prerequisites¶

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

>>> import pymongo

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

$ mongod

Making a Connection with AsyncMongoClient¶

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

>>> from pymongo import AsyncMongoClient
>>> client = AsyncMongoClient()

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

>>> client = AsyncMongoClient("localhost", 27017)

Or use the MongoDB URI format:

>>> client = AsyncMongoClient("mongodb://localhost:27017/")

By default, AsyncMongoClient only connects to the database on its first operation. To explicitly connect before performing an operation, use aconnect():

>>> client = await AsyncMongoClient().aconnect()

Getting a Database¶

A single instance of MongoDB can support multiple independent databases. When working with PyMongo you access databases using attribute style access on AsyncMongoClient instances:

>>> db = client.test_database

If your database name is such that using attribute style access won’t work (like test-database), you can use dictionary style access instead:

>>> db = client["test-database"]

Getting a Collection¶

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

>>> collection = db.test_collection

or (using dictionary style access):

>>> collection = db["test-collection"]

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

Documents¶

Data in MongoDB is represented (and stored) using JSON-style documents. In PyMongo we use dictionaries to represent documents. As an example, the following dictionary might be used to represent a blog post:

>>> import datetime
>>> post = {
...     "author": "Mike",
...     "text": "My first blog post!",
...     "tags": ["mongodb", "python", "pymongo"],
...     "date": datetime.datetime.now(tz=datetime.timezone.utc),
... }

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

Inserting a Document¶

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

>>> posts = db.posts
>>> post_id = (await posts.insert_one(post)).inserted_id
>>> post_id
ObjectId('...')

When a document is inserted a special key, "_id", is automatically added if the document doesn’t already contain an "_id" key. The value of "_id" must be unique across the collection. insert_one() returns an instance of InsertOneResult. For more information on "_id", see the documentation on _id.

After inserting the first document, the posts collection has actually been created on the server. We can verify this by listing all of the collections in our database:

>>> await db.list_collection_names()
['posts']

Getting a Single Document With `find_one()`¶

The most basic type of query that can be performed in MongoDB is find_one(). This method returns a single document matching a query (or None if there are no matches). It is useful when you know there is only one matching document, or are only interested in the first match. Here we use find_one() to get the first document from the posts collection:

>>> import pprint
>>> pprint.pprint(await posts.find_one())
{'_id': ObjectId('...'),
 'author': 'Mike',
 'date': datetime.datetime(...),
 'tags': ['mongodb', 'python', 'pymongo'],
 'text': 'My first blog post!'}

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

Note

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

find_one() also supports querying on specific elements that the resulting document must match. To limit our results to a document with author “Mike” we do:

>>> pprint.pprint(await posts.find_one({"author": "Mike"}))
{'_id': ObjectId('...'),
 'author': 'Mike',
 'date': datetime.datetime(...),
 'tags': ['mongodb', 'python', 'pymongo'],
 'text': 'My first blog post!'}

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

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

Querying By ObjectId¶

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

>>> post_id
ObjectId(...)
>>> pprint.pprint(await posts.find_one({"_id": post_id}))
{'_id': ObjectId('...'),
 'author': 'Mike',
 'date': datetime.datetime(...),
 'tags': ['mongodb', 'python', 'pymongo'],
 'text': 'My first blog post!'}

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

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

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

from bson.objectid import ObjectId

# The web framework gets post_id from the URL and passes it as a string
async def get(post_id):
    # Convert from string to ObjectId:
    document = await client.db.collection.find_one({'_id': ObjectId(post_id)})

Bulk Inserts¶

In order to make querying a little more interesting, let’s insert a few more documents. In addition to inserting a single document, we can also perform bulk insert operations, by passing a list as the first argument to insert_many(). This will insert each document in the list, sending only a single command to the server:

>>> new_posts = [
...     {
...         "author": "Mike",
...         "text": "Another post!",
...         "tags": ["bulk", "insert"],
...         "date": datetime.datetime(2009, 11, 12, 11, 14),
...     },
...     {
...         "author": "Eliot",
...         "title": "MongoDB is fun",
...         "text": "and pretty easy too!",
...         "date": datetime.datetime(2009, 11, 10, 10, 45),
...     },
... ]
>>> result = await posts.insert_many(new_posts)
>>> result.inserted_ids
[ObjectId('...'), ObjectId('...')]

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

The result from insert_many() now returns two ObjectId instances, one for each inserted document.

new_posts[1] has a different “shape” than the other posts - there is no "tags" field and we’ve added a new field, "title". This is what we mean when we say that MongoDB is schema-free.

Querying for More Than One Document¶

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

>>> async for post in posts.find():
...     pprint.pprint(post)
...
{'_id': ObjectId('...'),
 'author': 'Mike',
 'date': datetime.datetime(...),
 'tags': ['mongodb', 'python', 'pymongo'],
 'text': 'My first blog post!'}
{'_id': ObjectId('...'),
 'author': 'Mike',
 'date': datetime.datetime(...),
 'tags': ['bulk', 'insert'],
 'text': 'Another post!'}
{'_id': ObjectId('...'),
 'author': 'Eliot',
 'date': datetime.datetime(...),
 'text': 'and pretty easy too!',
 'title': 'MongoDB is fun'}

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

>>> async for post in posts.find({"author": "Mike"}):
...     pprint.pprint(post)
...
{'_id': ObjectId('...'),
 'author': 'Mike',
 'date': datetime.datetime(...),
 'tags': ['mongodb', 'python', 'pymongo'],
 'text': 'My first blog post!'}
{'_id': ObjectId('...'),
 'author': 'Mike',
 'date': datetime.datetime(...),
 'tags': ['bulk', 'insert'],
 'text': 'Another post!'}

Counting¶

If we just want to know how many documents match a query we can perform a count_documents() operation instead of a full query. We can get a count of all of the documents in a collection:

>>> await posts.count_documents({})
3

or just of those documents that match a specific query:

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

Range Queries¶

MongoDB supports many different types of advanced queries. As an example, lets perform a query where we limit results to posts older than a certain date, but also sort the results by author:

>>> d = datetime.datetime(2009, 11, 12, 12)
>>> async for post in posts.find({"date": {"$lt": d}}).sort("author"):
...     pprint.pprint(post)
...
{'_id': ObjectId('...'),
 'author': 'Eliot',
 'date': datetime.datetime(...),
 'text': 'and pretty easy too!',
 'title': 'MongoDB is fun'}
{'_id': ObjectId('...'),
 'author': 'Mike',
 'date': datetime.datetime(...),
 'tags': ['bulk', 'insert'],
 'text': 'Another post!'}

Here we use the special "$lt" operator to do a range query, and also call sort() to sort the results by author.

Indexing¶

Adding indexes can help accelerate certain queries and can also add additional functionality to querying and storing documents. In this example, we’ll demonstrate how to create a unique index on a key that rejects documents whose value for that key already exists in the index.

First, we’ll need to create the index:

>>> result = await db.profiles.create_index([("user_id", pymongo.ASCENDING)], unique=True)
>>> sorted(list(await db.profiles.index_information()))
['_id_', 'user_id_1']

Notice that we have two indexes now: one is the index on _id that MongoDB creates automatically, and the other is the index on user_id we just created.

Now let’s set up some user profiles:

>>> user_profiles = [{"user_id": 211, "name": "Luke"}, {"user_id": 212, "name": "Ziltoid"}]
>>> result = await db.profiles.insert_many(user_profiles)

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

>>> new_profile = {"user_id": 213, "name": "Drew"}
>>> duplicate_profile = {"user_id": 212, "name": "Tommy"}
>>> result = await db.profiles.insert_one(new_profile)  # This is fine.
>>> result = await db.profiles.insert_one(duplicate_profile)
Traceback (most recent call last):
DuplicateKeyError: E11000 duplicate key error index: test_database.profiles.$user_id_1 dup key: { : 212 }

Async Tutorial¶

Prerequisites¶

Making a Connection with AsyncMongoClient¶

Getting a Database¶

Getting a Collection¶

Documents¶

Inserting a Document¶

Getting a Single Document With `find_one()`¶

Querying By ObjectId¶

Bulk Inserts¶

Querying for More Than One Document¶

Counting¶

Range Queries¶

Indexing¶

Table of Contents

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This Page

Async Tutorial¶

Prerequisites¶

Making a Connection with AsyncMongoClient¶

Getting a Database¶

Getting a Collection¶

Documents¶

Inserting a Document¶

Getting a Single Document With find_one()¶

Querying By ObjectId¶

Bulk Inserts¶

Querying for More Than One Document¶

Counting¶

Range Queries¶

Indexing¶

Getting a Single Document With `find_one()`¶