7: Add a storage binding for Azure Functions in Python

Previous step: deploy a second function

You can add a storage binding for Azure Functions. A binding lets you connect your function code to resources, such as Azure storage, without writing any data access code.

A binding is defined in the function.json file and can represent both input and output. A function can use multiple input and output bindings, but only one trigger. To learn more, see Azure Functions triggers and bindings concepts.

In this section, you add a storage binding to the HttpExample function created earlier in this tutorial. The function uses this binding to write messages to storage with each request. The storage in question uses the same default storage account used by the function app. If you plan on making heavy use of storage, however, you would want to consider creating a separate account.

  1. Sync the remote settings for your Azure Functions project into your local.settings.json file by opening the Command Palette and selecting Azure Functions: Download Remote Settings.

    Open local.settings.json and check that it contains a value for AzureWebJobsStorage. That value is the connection string for the storage account.

  2. In the HttpExample folder, right-click the function.json, select Add binding:

    Add binding command in the Visual Studio Code explorer

  3. In the prompts that follow in Visual Studio Code, select or provide the following values:

    Prompt Value to provide
    Set binding direction out
    Select binding with direction out Azure Queue Storage
    The name used to identify this binding in your code msg
    The queue to which the message will be sent outqueue
    Select setting from local.settings.json (asking for the storage connection) AzureWebJobsStorage
  4. After making these selections, verify that the following binding is added to your function.json file:

          "type": "queue",
          "direction": "out",
          "name": "msg",
          "queueName": "outqueue",
          "connection": "AzureWebJobsStorage"
  5. Now that you've configured the binding, you can use it in your function code. Again, the newly defined binding appears in your code as an argument to the main function in __init__.py.

    For example, you can modify the __init__.py file in HttpExample to match the following, which shows using the msg argument to write a timestamped message with the name used in the request. The comments explain the specific changes:

    import logging
    import datetime  # MODIFICATION: added import
    import azure.functions as func
    # MODIFICATION: the added binding appears as an argument; func.Out[func.QueueMessage]
    # is the appropriate type for an output binding with "type": "queue" (in function.json).
    def main(req: func.HttpRequest, msg: func.Out[func.QueueMessage]) -> func.HttpResponse:
        logging.info('Python HTTP trigger function processed a request.')
        name = req.params.get('name')
        if not name:
                req_body = req.get_json()
            except ValueError:
                name = req_body.get('name')
        if name:
            # MODIFICATION: write the a message to the message queue, using msg.set
            msg.set(f"Request made for {name} at {datetime.datetime.now()}")
            return func.HttpResponse(f"Hello {name}!")
            return func.HttpResponse(
                 "Please pass a name on the query string or in the request body",
  6. To test these changes locally, start the debugger again in Visual Studio Code by pressing F5 or selecting the Debug > Start Debugging menu command.

    As before the Output window should show the endpoints in your project.

  7. In a browser, visit the URL http://localhost:7071/api/HttpExample?name=VS%20Code to create a request to the HttpExample endpoint, which should also write a message to the queue.

  8. To verify that the message was written to the "outqueue" queue (as named in the binding), you can use one of three methods:

    1. Sign into the Azure portal, and navigate to the resource group containing your functions project. Within that resource group, local and navigate into the storage account for the project, then navigate into Queues. On that page, navigate into "outqueue", which should display all the logged messages.

    2. Navigate and examine the queue with either the Azure Storage Explorer, which integrates with Visual Studio, as described on Connect Functions to Azure Storage using Visual Studio Code, especially the Examine the output queue section.

    3. Use the Azure CLI to query the storage queue, as described on Query the storage queue.

  9. To test in the cloud, redeploy the code by using the Deploy to Function App in the Azure: Functions explorer. If prompted, select the Function App created previously. Once deployment finishes (it takes a few minutes!), the Output window again shows the public endpoints with which you can repeat your tests.

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