Quickstart: Build a Python application using an Azure Cosmos DB SQL API account


In this quickstart, you create and manage an Azure Cosmos DB SQL API account from the Azure portal, and from Visual Studio Code with a Python app cloned from GitHub. Azure Cosmos DB is a multi-model database service that lets you quickly create and query document, table, key-value, and graph databases with multiple-region distribution and horizontal scale capabilities.


Important update on Python 2.x Support

New releases of this SDK won't support Python 2.x starting January 1st, 2022. Please check the CHANGELOG for more information.

Create a database account

  1. From the Azure portal menu or the Home page, select Create a resource.

  2. On the New page, search for and select Azure Cosmos DB.

  3. On the Azure Cosmos DB page, select Create.

  4. In the Create Azure Cosmos DB Account page, enter the basic settings for the new Azure Cosmos account.

    Setting Value Description
    Subscription Subscription name Select the Azure subscription that you want to use for this Azure Cosmos account.
    Resource Group Resource group name Select a resource group, or select Create new, then enter a unique name for the new resource group.
    Account Name A unique name Enter a name to identify your Azure Cosmos account. Because documents.azure.cn is appended to the name that you provide to create your URI, use a unique name.

    The name can only contain lowercase letters, numbers, and the hyphen (-) character. It must be between 3-44 characters in length.
    API The type of account to create Select Core (SQL) to create a document database and query by using SQL syntax.

    The API determines the type of account to create. Azure Cosmos DB provides five APIs: Core (SQL) and MongoDB for document data, Gremlin for graph data, Azure Table, and Cassandra. Currently, you must create a separate account for each API.

    Learn more about the SQL API.
    Location The region closest to your users Select a geographic location to host your Azure Cosmos DB account. Use the location that is closest to your users to give them the fastest access to the data.
    Capacity mode Provisioned throughput or Serverless Select Provisioned throughput to create an account in provisioned throughput mode. Select Serverless to create an account in serverless mode.
    Apply Free Tier Discount Apply or Do not apply With Azure Cosmos DB free tier, you will get the first 1000 RU/s and 25 GB of storage for free in an account. Learn more about free tier.


    You can have up to one free tier Azure Cosmos DB account per Azure subscription and must opt-in when creating the account. If you do not see the option to apply the free tier discount, this means another account in the subscription has already been enabled with free tier.

    The new account page for Azure Cosmos DB

  5. In the Global Distribution tab, configure the following details. You can leave the default values for this quickstart:

    Setting Value Description
    Geo-Redundancy Disable Enable or disable multiple-region distribution on your account by pairing your region with a pair region. You can add more regions to your account later.
    Multi-region Writes Disable Multi-region writes capability allows you to take advantage of the provisioned throughput for your databases and containers across China.


    The following options are not available if you select Serverless as the Capacity mode:

    • Apply Free Tier Discount
    • Geo-redundancy
    • Multi-region Writes
  6. Optionally you can configure more details in the following tabs:

    • Networking - Configure access from a virtual network.
    • Backup Policy - Configure either periodic or continuous backup policy.
    • Encryption - Use either service-managed key or a customer-managed key.
    • Tags - Tags are name/value pairs that enable you to categorize resources and view consolidated billing by applying the same tag to multiple resources and resource groups.
  7. Select Review + create.

  8. Review the account settings, and then select Create. It takes a few minutes to create the account. Wait for the portal page to display Your deployment is complete.

    The Azure portal Notifications pane

  9. Select Go to resource to go to the Azure Cosmos DB account page.

    The Azure Cosmos DB account page

Add a container

You can now use the Data Explorer tool in the Azure portal to create a database and container.

  1. Select Data Explorer > New Container.

    The Add Container area is displayed on the far right, you may need to scroll right to see it.

    The Azure portal Data Explorer, Add Container pane

  2. In the Add container page, enter the settings for the new container.

    Setting Suggested value Description
    Database ID ToDoList Enter Tasks as the name for the new database. Database names must contain from 1 through 255 characters, and they cannot contain /, \\, #, ?, or a trailing space. Check the Share throughput across containers option, it allows you to share the throughput provisioned on the database across all the containers within the database. This option also helps with cost savings.
    Database throughput You can provision Autoscale or Manual throughput. Manual throughput allows you to scale RU/s yourself whereas autoscale throughput allows the system to scale RU/s based on usage. Select Manual for this example.

    Leave the throughput at 400 request units per second (RU/s). If you want to reduce latency, you can scale up the throughput later by estimating the required RU/s with the capacity calculator.

    Note: This setting is not available when creating a new container in a serverless account.
    Container ID Items Enter Items as the name for your new container. Container IDs have the same character requirements as database names.
    Partition key /category The sample described in this article uses /category as the partition key.

    Don't add Unique keys or turn on Analytical store for this example. Unique keys let you add a layer of data integrity to the database by ensuring the uniqueness of one or more values per partition key. For more information, see Unique keys in Azure Cosmos DB. Analytical store is used to enable large-scale analytics against operational data without any impact to your transactional workloads.

    Select OK. The Data Explorer displays the new database and container.

Add sample data

You can now add data to your new container using Data Explorer.

  1. From the Data Explorer, expand the Tasks database, expand the Items container. Select Items, and then select New Item.

    Create new documents in Data Explorer in the Azure portal

  2. Now add a document to the container with the following structure.

        "id": "1",
        "category": "personal",
        "name": "groceries",
        "description": "Pick up apples and strawberries.",
        "isComplete": false
  3. Once you've added the json to the Documents tab, select Save.

    Copy in json data and select Save in Data Explorer in the Azure portal

  4. Create and save one more document where you insert a unique value for the id property, and change the other properties as you see fit. Your new documents can have any structure you want as Azure Cosmos DB doesn't impose any schema on your data.

Query your data

You can use queries in Data Explorer to retrieve and filter your data.

  1. At the top of the Items tab in Data Explorer, review the default query SELECT * FROM c. This query retrieves and displays all documents from the container ordered by ID.

    Default query in Data Explorer is SELECT * FROM c

  2. To change the query, select Edit Filter, replace the default query with ORDER BY c._ts DESC, and then select Apply Filter.

    Change the default query by adding ORDER BY c._ts DESC and clicking Apply Filter

    The modified query displays the documents in descending order based on their time stamp, so now your second document is listed first.

    Changed query to ORDER BY c._ts DESC and clicking Apply Filter

If you're familiar with SQL syntax, you can enter any supported SQL queries in the query predicate box. You can also use Data Explorer to create stored procedures, UDFs, and triggers for server-side business logic.

Data Explorer provides easy Azure portal access to all of the built-in programmatic data access features available in the APIs. You also use the portal to scale throughput, get keys and connection strings, and review metrics and SLAs for your Azure Cosmos DB account.

Clone the sample application

Now let's clone a SQL API app from GitHub, set the connection string, and run it. This quickstart uses version 4 of the Python SDK.

  1. Open a command prompt, create a new folder named git-samples, then close the command prompt.

    md git-samples

    If you are using a bash prompt, you should instead use the following command:

    mkdir "git-samples"
  2. Open a git terminal window, such as git bash, and use the cd command to change to the new folder to install the sample app.

    cd "git-samples"
  3. Run the following command to clone the sample repository. This command creates a copy of the sample app on your computer.

    git clone https://github.com/Azure-Samples/azure-cosmos-db-python-getting-started.git

Update your connection string

Now go back to the Azure portal to get your connection string information and copy it into the app.

  1. In your Azure Cosmos DB account in the Azure portal, select Keys from the left navigation. Use the copy buttons on the right side of the screen to copy the URI and Primary Key into the cosmos_get_started.py file in the next step.

    Get an access key and URI in the Keys settings in the Azure portal

  2. In Visual Studio Code, open the cosmos_get_started.py file in \git-samples\azure-cosmos-db-python-getting-started.

  3. Copy your URI value from the portal (using the copy button) and make it the value of the endpoint variable in cosmos_get_started.py.

    endpoint = 'https://FILLME.documents.azure.cn',

  4. Then copy your PRIMARY KEY value from the portal and make it the value of the key in cosmos_get_started.py. You've now updated your app with all the info it needs to communicate with Azure Cosmos DB.

    key = 'FILLME'

  5. Save the cosmos_get_started.py file.

Review the code

This step is optional. Learn about the database resources created in code, or skip ahead to Update your connection string.

The following snippets are all taken from the cosmos_get_started.py file.

  • The CosmosClient is initialized. Make sure to update the "endpoint" and "key" values as described in the Update your connection string section.

    client = CosmosClient(endpoint, key)
  • A new database is created.

    database_name = 'AzureSampleFamilyDatabase'
    database = client.create_database_if_not_exists(id=database_name)
  • A new container is created, with 400 RU/s of provisioned throughput. We choose lastName as the partition key, which allows us to do efficient queries that filter on this property.

    container_name = 'FamilyContainer'
    container = database.create_container_if_not_exists(
  • Some items are added to the container. Containers are a collection of items (JSON documents) that can have varied schema. The helper methods get_[name]_family_item return representations of a family that are stored in Azure Cosmos DB as JSON documents.

    for family_item in family_items_to_create:
  • Point reads (key value lookups) are performed using the read_item method. We print out the RU charge of each operation.

    for family in family_items_to_create:
        item_response = container.read_item(item=family['id'], partition_key=family['lastName'])
        request_charge = container.client_connection.last_response_headers['x-ms-request-charge']
        print('Read item with id {0}. Operation consumed {1} request units'.format(item_response['id'], (request_charge)))
  • A query is performed using SQL query syntax. Because we're using partition key values of lastName in the WHERE clause, Azure Cosmos DB will efficiently route this query to the relevant partitions, improving performance.

    query = "SELECT * FROM c WHERE c.lastName IN ('Wakefield', 'Andersen')"
    items = list(container.query_items(
    request_charge = container.client_connection.last_response_headers['x-ms-request-charge']
    print('Query returned {0} items. Operation consumed {1} request units'.format(len(items), request_charge))

Run the app

  1. In Visual Studio Code, select View > Command Palette.

  2. At the prompt, enter Python: Select Interpreter and then select the version of Python to use.

    The Footer in Visual Studio Code is updated to indicate the interpreter selected.

  3. Select View > Integrated Terminal to open the Visual Studio Code integrated terminal.

  4. In the integrated terminal window, ensure you are in the azure-cosmos-db-python-getting-started folder. If not, run the following command to switch to the sample folder.

    cd "\git-samples\azure-cosmos-db-python-getting-started"`
  5. Run the following command to install the azure-cosmos package.

    pip install --pre azure-cosmos

    If you get an error about access being denied when attempting to install azure-cosmos, you'll need to run VS Code as an administrator.

  6. Run the following command to run the sample and create and store new documents in Azure Cosmos DB.

    python cosmos_get_started.py
  7. To confirm the new items were created and saved, in the Azure portal, select Data Explorer > AzureSampleFamilyDatabase > Items. View the items that were created. For example, here is a sample JSON document for the Andersen family:

        "id": "Andersen-1569479288379",
        "lastName": "Andersen",
        "district": "WA5",
        "parents": [
                "familyName": null,
                "firstName": "Thomas"
                "familyName": null,
                "firstName": "Mary Kay"
        "children": null,
        "address": {
            "state": "WA",
            "county": "King",
            "city": "Seattle"
        "registered": true,
        "_rid": "8K5qAIYtZXeBhB4AAAAAAA==",
        "_self": "dbs/8K5qAA==/colls/8K5qAIYtZXc=/docs/8K5qAIYtZXeBhB4AAAAAAA==/",
        "_etag": "\"a3004d78-0000-0800-0000-5d8c5a780000\"",
        "_attachments": "attachments/",
        "_ts": 1569479288

Review SLAs in the Azure portal

The Azure portal monitors your Cosmos DB account throughput, storage, availability, latency, and consistency. Charts for metrics associated with an Azure Cosmos DB Service Level Agreement (SLA) show the SLA value compared to actual performance. This suite of metrics makes monitoring your SLAs transparent.

To review metrics and SLAs:

  1. Select Metrics in your Cosmos DB account's navigation menu.

  2. Select a tab such as Latency, and select a timeframe on the right. Compare the Actual and SLA lines on the charts.

    Azure Cosmos DB metrics suite

  3. Review the metrics on the other tabs.

Clean up resources

When you're done with your app and Azure Cosmos DB account, you can delete the Azure resources you created so you don't incur more charges. To delete the resources:

  1. In the Azure portal Search bar, search for and select Resource groups.

  2. From the list, select the resource group you created for this quickstart.

    Select the resource group to delete

  3. On the resource group Overview page, select Delete resource group.

    Delete the resource group

  4. In the next window, enter the name of the resource group to delete, and then select Delete.

Next steps

In this quickstart, you've learned how to create an Azure Cosmos DB account, create a container using the Data Explorer, and run a Python app in Visual Studio Code. You can now import additional data to your Azure Cosmos DB account.

Trying to do capacity planning for a migration to Azure Cosmos DB? You can use information about your existing database cluster for capacity planning.