Quickstart: Use Azure Cosmos DB for MongoDB vCore with MongoDB driver for Python

In this quickstart, you deploy a basic Azure Cosmos DB for MongoDB application using Python. Azure Cosmos DB for MongoDB vCore is a schemaless data store allowing applications to store unstructured documents in the cloud with MongoDB libraries. You learn how to create documents and perform basic tasks within your Azure Cosmos DB resource using Python.

Library source code | Package (PyPI) | Azure Developer CLI

• Azure Developer CLI

• Docker Desktop

• An Azure subscription

  • If you don't have an Azure subscription, create a Trial before you begin.

• Python 3.12

Use the Azure Developer CLI (azd) to create an Azure Cosmos DB for MongoDB vCore cluster and deploy a containerized sample application. The sample application uses the client library to manage, create, read, and query sample data.

1. Open a terminal in an empty directory.

2. If you're not already authenticated, authenticate to the Azure Developer CLI using azd auth login. Follow the steps specified by the tool to authenticate to the CLI using your preferred Azure credentials.

   azd auth login
   

3. Use azd init to initialize the project.

   azd init --template cosmos-db-mongodb-vcore-python-quickstart
   

4. During initialization, configure a unique environment name.

5. Deploy the cluster using azd up. The Bicep templates also deploy a sample web application.

   azd up
   

6. During the provisioning process, select your subscription, desired location, and target resource group. Wait for the provisioning process to complete. The process can take approximately ten minutes.

7. Once the provisioning of your Azure resources is done, a URL to the running web application is included in the output.

   Deploying services (azd deploy)
   
     (✓) Done: Deploying service web
   - Endpoint: <https://[container-app-sub-domain].azurecontainerapps.io>
   
   SUCCESS: Your application was provisioned and deployed to Azure in 5 minutes 0 seconds.
   

8. Use the URL in the console to navigate to your web application in the browser. Observe the output of the running app.

The client library is available through PyPi, as the pymongo package.

1. Open a terminal and navigate to the /src folder.

   cd ./src
   

2. If not already installed, install the pymongo package using pip install.

   pip install pymongo
   

3. If not already installed, install the azure.identity package using pip install.

   pip install azure.identity
   

4. Open and review the src/requirements.txt file to validate that both package entries exist.

Import the following namespaces into your application code:

from azure.identity import DefaultAzureCredential

from pymongo import MongoClient
from pymongo.auth_oidc import OIDCCallback, OIDCCallbackContext, OIDCCallbackResult

• Authenticate the client
• Get a database
• Get a collection
• Create a document
• Get a document
• Query documents

The sample code in the template uses a database named cosmicworks and collection named products. The products collection contains details such as name, category, quantity, and a unique identifier for each product. The collection uses the /category property as a shard key.

While Microsoft Entra authentication for Azure Cosmos DB for MongoDB vCore can use well known TokenCredential types, you must implement a custom token handler. This sample implementation can be used to create a MongoClient with support for standard Microsoft Entra authentication of many identity types.

1. First, define a class named AzureIdentityTokenCallback that defines a fetch function taking in the OIDCCallbackContext parameter and returning a OIDCCallbackResult.

   class AzureIdentityTokenCallback(OIDCCallback):
       def __init__(self, credential):
           self.credential = credential
   
       def fetch(self, context: OIDCCallbackContext) -> OIDCCallbackResult:
           token = self.credential.get_token(
               "https://ossrdbms-aad.database.chinacloudapi.cn/.default").token
           return OIDCCallbackResult(access_token=token)
   

2. Use your custom handler class passing in a new instance of the DefaultAzureCredential type

   credential = DefaultAzureCredential()
   
   authProperties = {"OIDC_CALLBACK": AzureIdentityTokenCallback(credential)}
   

3. Build an instance of MongoClient using your cluster name, and the known best practice configuration options for Azure Cosmos DB for MongoDB vCore. Also, configure your custom authentication mechanism.

   clusterName = "<azure-cosmos-db-mongodb-vcore-cluster-name>"
   
   client = MongoClient(
       f"mongodb+srv://{clusterName}.global.mongocluster.cosmos.azure.com/",
       connectTimeoutMS=120000,
       tls=True,
       retryWrites=True,
       authMechanism="MONGODB-OIDC",
       authMechanismProperties=authProperties
   )
   

This sample creates an instance of the Database type using the get_database function of the MongoClient type.

database = client.get_database("<database-name>")

This sample creates an instance of the Collection type using the get_collection function of the Database type.

collection = database.get_collection("<collection-name>")

Create a document in the collection using collection.update_one. This method "upserts" the item effectively replacing the item if it already exists.

new_document = {
    "_id": "aaaaaaaa-0000-1111-2222-bbbbbbbbbbbb",
    "category": "gear-surf-surfboards",
    "name": "Yamba Surfboard",
    "quantity": 12,
    "sale": False,
}

filter = {
    "_id": "aaaaaaaa-0000-1111-2222-bbbbbbbbbbbb",
    "category": "gear-surf-surfboards"
}

payload = {
    "$set": new_document
}

result = collection.update_one(filter, payload, upsert=True);

Perform a point read operation by using both the unique identifier (id) and shard key fields. Use collection.find_one to efficiently retrieve the specific item.

filter = {
    "_id": "bbbbbbbb-1111-2222-3333-cccccccccccc",
    "category": "gear-surf-surfboards"
}

existing_document = collection.find_one(filter)

Perform a query over multiple items in a container using collection.find. This query finds all items within a specified category (shard key).

filter = {
    "category": "gear-surf-surfboards"
}

matched_documents = collection.find(filter)

for document in matched_documents:
    # Do something with each item

Delete a document by sending a filter for the unique identifier of the document. Use delete_one to remove the document from the collection.

filter = {
    '_id': id
}

result = collection.delete_one(filter)

Use the Visual Studio Code extension for Azure Cosmos DB to explore your MongoDB vCore data. You can perform core database operations including, but not limited to:

• Performing queries using a scrapbook or the query editor
• Modifying, updating, creating, and deleting documents
• Importing bulk data from other sources
• Managing databases and collections

For more information, see How-to use Visual Studio Code extension to explore Azure Cosmos DB for MongoDB vCore data.

When you no longer need the sample application or resources, remove the corresponding deployment and all resources.

azd down --force

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