Azure Documentation

Integrate Azure Storage with Azure Functions

This tutorial guides you through the process of seamlessly integrating Azure Storage services with Azure Functions. This integration allows your serverless functions to interact with data stored in Azure Blob Storage, Table Storage, and Queue Storage, enabling powerful data-driven applications.

Prerequisites

• An Azure Subscription.
• Azure Functions Core Tools installed.
• Azure CLI installed.
• Basic understanding of C# or JavaScript/TypeScript.

Step 1: Create an Azure Storage Account

First, you need an Azure Storage account to store your data. You can create one using the Azure portal, Azure CLI, or Azure PowerShell.

Using Azure CLI:

az storage account create \
    --name mystoragesaunique \
    --resource-group MyResourceGroup \
    --location eastus \
    --sku Standard_LRS
                    

Replace mystoragesaunique with a unique name for your storage account and MyResourceGroup with your desired resource group name.

Step 2: Create an Azure Function App

Next, create a Function App, which is the execution environment for your functions.

Using Azure Functions Core Tools (for local development):

func init MyFunctionApp --worker-runtime csharp # or node for JavaScript/TypeScript
cd MyFunctionApp
func new --name BlobTriggerFunction --template "Azure Blob Storage trigger"
                    

This creates a new function project and adds a Blob trigger function. You'll be prompted for connection string settings and container names.

Step 3: Configure Storage Bindings

Azure Functions uses bindings to connect to Azure services. For storage integration, you'll typically use input, output, or trigger bindings.

Blob Trigger Example (C#):

The function.json file defines the bindings. For a Blob trigger, it might look like this:

{
  "scriptFile": "BlobTriggerFunction.cs",
  "bindings": [
    {
      "name": "myBlob",
      "type": "blobTrigger",
      "direction": "in",
      "path": "samples-workitems/{name}",
      "connection": "AzureWebJobsStorage"
    }
  ]
}
                    

The connection property refers to an app setting named AzureWebJobsStorage, which typically holds the connection string to your default storage account. The path specifies the container and blob name pattern to monitor.

Blob Output Binding Example (C#):

To write data to Blob Storage:

{
  "scriptFile": "BlobOutputFunction.cs",
  "bindings": [
    {
      "name": "inputQueueItem",
      "type": "queueTrigger",
      "direction": "in",
      "queueName": "myqueue-items",
      "connection": "AzureWebJobsStorage"
    },
    {
      "name": "outputBlob",
      "type": "blob",
      "direction": "out",
      "path": "output-blobs/{name}.txt",
      "connection": "AzureWebJobsStorage"
    }
  ]
}
                    

The C# code would use the outputBlob parameter to write content:

using Microsoft.Azure.WebJobs;
using Microsoft.Extensions.Logging;

public static class BlobOutputFunction
{
    [FunctionName("BlobOutputFunction")]
    public static void Run(
        [QueueTrigger("myqueue-items", Connection = "AzureWebJobsStorage")] string myQueueItem,
        [Blob("output-blobs/{name}.txt", FileAccess.Write, Connection = "AzureWebJobsStorage")] out string outputBlob,
        ILogger log)
    {
        log.LogInformation($"C# Queue trigger function processed: {myQueueItem}");
        outputBlob = $"Processed item: {myQueueItem}";
    }
}
                    

Step 4: Deploy Your Function

Once you have your function configured, deploy it to Azure.

az functionapp deploy --resource-group MyResourceGroup --name MyFunctionApp --src-path .
                    

Step 5: Test the Integration

Trigger your function by uploading a file to the blob container specified in your trigger path or by sending a message to the queue. Monitor the execution logs in the Azure portal or Azure Functions Core Tools to verify that your function correctly reads from or writes to Azure Storage.

Working with Other Storage Services

• Table Storage: Use input/output bindings with Microsoft.Azure.Cosmos.Table to read from and write to Azure Table Storage.
• Queue Storage: Use trigger and output bindings to process messages from and send messages to Azure Queue Storage.

Next Steps

Explore advanced scenarios like implementing robust error handling, managing large files, and optimizing performance for your Azure Functions and Storage integrations.