Azure Functions Documentation

Blob Input Bindings

Blob input bindings allow your Azure Function to read data directly from Azure Blob Storage. This simplifies common scenarios like processing files uploaded to a blob container.

When a blob input binding is configured, the function runtime automatically retrieves the specified blob and makes its content available as a parameter in your function code. This eliminates the need for manual SDK calls to download blob content.

How it Works

The binding maps a blob in your storage account to a parameter in your function. You specify the blob path using a combination of container name and blob name. The function runtime handles the rest:

• It connects to your Azure Storage account using connection string settings.
• It resolves the blob based on the provided path.
• It downloads the blob content.
• It passes the content to your function parameter in the appropriate type (e.g., string, byte array, stream).

You can use a blob trigger to invoke a function when a new blob is created, or you can use an input binding to read a specific blob within a function triggered by another event.

Configuration

Blob input bindings are configured in your function's function.json file (for JavaScript, Python, and PowerShell) or via attributes in your code (for C# and Java).

function.json Example:

{
  "bindings": [
    {
      "name": "myBlob",
      "type": "blob",
      "direction": "in",
      "path": "samples-workitems/{name}.txt",
      "connection": "AzureWebJobsStorage"
    }
  ]
}

Key properties:

• name: The name of the parameter in your function code.
• type: Must be "blob" for blob input.
• direction: Must be "in" for input bindings.
• path: Specifies the container and blob name. You can use binding expressions, such as {name}, which can be populated from a trigger or other bindings.
• connection: The name of an app setting that contains the Azure Storage connection string. Defaults to "AzureWebJobsStorage".

Attribute Example (C#):

[Blob("samples-workitems/{name}.txt", Connection = "AzureWebJobsStorage")] Stream myBlob

Examples

C#

Reading a blob as a string:

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

public static class ReadBlob
{
    [FunctionName("ReadBlobContent")]
    public static void Run(
        [BlobTrigger("samples-workitems/{name}.txt", Connection = "AzureWebJobsStorage")] string myBlob,
        string name,
        ILogger log)
    {
        log.LogInformation($"C# Blob trigger function Processed blob\\n Name:{name} \\n Size: {myBlob.Length} Bytes");
        log.LogInformation($"Blob Content: {myBlob}");
    }
}

Reading a blob as a stream:

using Microsoft.Azure.WebJobs;
using Microsoft.Azure.WebJobs.Extensions.Http;
using Microsoft.AspNetCore.Http;
using Microsoft.Extensions.Logging;
using System.IO;

public static class ReadBlobStream
{
    [FunctionName("ReadBlobStreamContent")]
    public static void Run(
        [BlobTrigger("samples-workitems/{name}.txt", Connection = "AzureWebJobsStorage")] Stream myBlobStream,
        string name,
        ILogger log)
    {
        log.LogInformation($"C# Blob trigger function Processed blob\\n Name:{name}");

        using (var reader = new StreamReader(myBlobStream))
        {
            string content = reader.ReadToEnd();
            log.LogInformation($"Blob Content: {content}");
        }
    }
}

JavaScript (Node.js)

Reading a blob as a string:

module.exports = async function (context, myBlob) {
    context.log('JavaScript blob trigger function processed blob');
    context.log('Name: ' + context.bindingData.name);
    context.log('Blob Size: ' + myBlob.length + ' Bytes');
    context.log('Blob Content: ' + myBlob);
};

Python

Reading a blob as a string:

import logging

import azure.functions as func

def main(myBlob: func.InputStream) -> None:
    logging.info(f"Python blob trigger function processed blob\n"
                 f"Name: {myBlob.name}\n"
                 f"Blob Size: {myBlob.length} Bytes")

    content = myBlob.read().decode('utf-8')
    logging.info(f"Blob Content: {content}")

Advanced Topics

• Binding Expressions: Use binding expressions like {name}, {extension}, or custom parameters from other triggers to dynamically specify the blob path.
• Mime Types: For HTTP triggers, you can specify the MIME type of the blob content.
• Multiple Blobs: You can bind to collections of blobs, though this is less common for simple input scenarios.
• Error Handling: Implement robust error handling for cases where the blob might not exist or the connection fails.