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Tutorial: Python function with Azure Blob Storage as input

  • Artikkeli

In this tutorial, you learn how to configure a Python function with Azure Blob storage as input by completing the following tasks:

  • Use Visual Studio Code to create a Python function project.
  • Change codes to add storage blob input function binding.
  • Use Visual Studio Code to run the function locally.
  • Use the Azure CLI to create a connection between Azure Function and Storage Blob with Service Connector.
  • Use Visual Studio to deploy your function.

An overview of the function project components in this tutorial:

Project Component Selection / Solution
Source Service Azure Function
Target Service Azure Storage Blob
Function Binding HTTP trigger, Storage Blob as Input
Local Project Auth Type Connection String
Cloud Function Auth Type System-Assigned Managed Identity

Warning

Microsoft recommends that you use the most secure authentication flow available. The authentication flow described in this procedure requires a very high degree of trust in the application, and carries risks that are not present in other flows. You should only use this flow when other more secure flows, such as managed identities, aren't viable.

Prerequisites

Create a Python function project

Follow the tutorial to create a local Azure Functions project, and provide the following information at the prompts:

Prompt Selection
Select a language Choose Python. (v1 programming language model)
Select a Python interpreter to create a virtual environment Choose your preferred Python interpreter. If an option isn't shown, type in the full path to your Python binary.
Select a template for your project's first function Choose HTTP trigger.
Provide a function name Enter BlobStorageInputFunc.
Authorization level Choose Anonymous, which lets anyone call your function endpoint. 

You have created a Python function project with an HTTP trigger.

Add a Blob Storage input binding

Binding attributes are defined in the function.json file for a given function. To create a binding, right-click (Ctrl+click on macOS) the function.json file in your function folder and choose Add binding... . Follow the prompts to define the following binding properties for the new binding:

Prompt Value Description
Select binding direction in The binding is an input binding.
Select binding with direction... Azure Blob Storage The binding is an Azure Storage blob binding.
The name used to identify this binding in your code inputBlob Name that identifies the binding parameter referenced in your code.
The path within your storage account from which the blob will be read testcontainer/test.txt The blob path your function read as input. Prepare a file named test.txt, with a Hello, World! as the file content. Create a container named testcontainer , and upload the file to the container.
Select setting from "local.setting.json" Create new local app settings Select the Storage Account your function reads as input. Visual Studio Code retrieves its connection string for local project connection.

To check the binding was added successfully,

  1. Open the BlobStorageInputFunc/function.json file, check that a new binding with type: blob and direction: in was added into this file.
  2. Open the local.settings.json file, check that a new key-value pair <your-storage-account-name>_STORAGE: <your-storage-account-connection-string> that contains your storage account connection string was added into this file.

After the binding is added, update your function codes to consume the binding by replacing BlobStorageInputFunc/__init__.py with the Python file here.

import logging
import azure.functions as func

def main(req: func.HttpRequest, inputBlob: bytes) -> func.HttpResponse:
    logging.info('Python HTTP trigger function processed a request.')
    return func.HttpResponse('The triggered function executed successfully. And read blob content: {}'.format(inputBlob))

Run the function locally

Follow the tutorial to run the function locally and verify the blob input.

  1. Select the storage account you used when creating the Azure Function resource if you're prompted to connect to Storage. It is for Azure Function runtime's internal use, and isn't necessarily the same with the one you use for input.
  2. To start the function locally, press <kbd>F5 </kbd> or select the Run and Debug icon in the left-hand side Activity bar.
  3. To verify the function can read the blob, right click Execute Function Now... on the function in the Visual Studio Code WORKSPACE and check the function response. The response message should contain the content in your blob file.

Create a connection using Service Connector

You just ran the project and verified the function locally, and your local project connects to your storage blob using a connection string.

Now you'll learn how to configure the connection between the Azure Function and Azure Blob Storage, so that your function can read the blob after being deployed to the cloud. In the cloud environment, we demonstrate how to authenticate using a system-assigned managed identity.

  1. Open the function.json file in your local project, change the value of the connection property in bindings to be MyBlobInputConnection.
  2. Run the following Azure CLI command to create a connection between your Azure Function and your Azure Storage.
az functionapp connection create storage-blob --source-id "<your-function-resource-id>" --target-id "<your-storage-blob-resource-id>" --system-identity --customized-keys AZURE_STORAGEBLOB_RESOURCEENDPOINT=MyBlobInputConnection__serviceUri
  • --source-id format: /subscriptions/{subscription}/resourceGroups/{source_resource_group}/providers/Microsoft.Web/sites/{site}
  • --target-id format: /subscriptions/{subscription}/resourceGroups/{target_resource_group}/providers/Microsoft.Storage/storageAccounts/{account}/blobServices/default

You have created a connection between Azure Function and Azure Blob Storage using Service Connector, with a system-assigned managed identity.

Service Connector configured a MyBlobInputConnection__serviceUri variable in the function's app settings used by the function binding runtime to connect to the storage, so that the function can read data from the blob storage. You can learn more about how Service Connector helps Azure Functions connect to services.

Deploy your function to Azure

Now you can deploy your function to Azure and verify the storage blob input binding works.

  1. Follow the tutorial to deploy your function to Azure.
  2. To verify the function can read the blob, right click Execute Function Now... on the function in the Visual Studio Code RESOURCES view and check the function response. The response message should contain the content in your blob file.

Troubleshoot

If there are any errors related with storage host, such as No such host is known (<account-name>.blob.core.windows.net:443), you need to check whether the connection string you use to connect to Azure Storage contains the blob endpoint or not. If it doesn't, go to Azure Storage in the Azure portal, copy the connection string from the Access keys blade, and replace the values.

If the error happens when you start the project locally, check the local.settings.json file.

If the error happens when you deploy your function to cloud (in this case, Function deployment usually fails on Syncing triggers ), check your function's App Settings.

Clean up resources

If you're not going to continue to use this project, delete the Function App resource you created earlier.

  1. In the Azure portal, open the Function App resource and select Delete.
  2. Enter the app name and select Delete to confirm.

Next steps

Read the articles below to learn more about Service Connector concepts and how it helps Azure Functions connect to other cloud services.

Learn about Service Connector concepts

Use Service Connector to connect Azure Functions to other services