Azure Functions Documentation

Blob Storage Output and Input Bindings for Azure Functions

This document explains how to use Blob Storage bindings in Azure Functions to connect your functions to Azure Blob Storage. These bindings allow your functions to easily read from or write to blobs without requiring explicit SDK code.

Output Bindings

An output binding allows your function to write data to a blob. When you configure an output binding, you specify the blob's path and content. The function runtime handles the creation and upload of the blob.

Configuration

You configure output bindings in your function.json file (for JavaScript, Python, and PowerShell) or via attributes in your code (for C#).

Example function.json for an output binding:

{
  "scriptFile": "__init__.py",
  "bindings": [
    {
      "name": "myBlobOutput",
      "type": "blob",
      "direction": "out",
      "path": "samples-output/output-{name}.txt",
      "connection": "AzureWebJobsStorage"
    }
  ]
}

In this example:

• name: The name of the output binding (e.g., myBlobOutput). This name is used to access the output in your function code.
• type: Set to blob.
• direction: Set to out.
• path: Specifies the container and blob name. Placeholders like {name} can be used to dynamically create blob names based on function input.
• connection: The name of the app setting that contains your Blob Storage connection string. If omitted, it defaults to AzureWebJobsStorage.

Usage in Code

Python Example:

import logging
import azure.functions as func

def main(req: func.HttpRequest, myBlobOutput: func.Out[str]) -> None:
    logging.info('Python HTTP trigger function processed a request.')

    name = req.params.get('name')
    if not name:
        try:
            req_body = req.get_json()
        except ValueError:
            pass
        else:
            name = req_body.get('name')

    if name:
        output_message = f"Hello, {name}. This blob was generated by an Azure Function."
        myBlobOutput.set(output_message)
        logging.info(f"Blob written to output with content: {output_message}")
    else:
        logging.warning("No name provided in request.")

Input Bindings

An input binding allows your function to read data from a blob. The runtime automatically fetches the blob content and makes it available to your function.

Configuration

Example function.json for an input binding:

{
  "scriptFile": "__init__.py",
  "bindings": [
    {
      "name": "myBlobInput",
      "type": "blob",
      "direction": "in",
      "path": "samples-input/input.txt",
      "connection": "AzureWebJobsStorage"
    }
  ]
}

In this example:

• name: The name of the input binding (e.g., myBlobInput).
• type: Set to blob.
• direction: Set to in.
• path: The container and blob name to read from.
• connection: The app setting for the connection string.

Usage in Code

Python Example:

import logging
import azure.functions as func

def main(myBlobInput: str, myBlobOutput: func.Out[str]) -> None:
    logging.info("Python blob trigger function processed blob")
    logging.info(f"Blob content: {myBlobInput}")

    output_message = f"Content from input blob: {myBlobInput}"
    myBlobOutput.set(output_message)
    logging.info(f"Blob written to output with content: {output_message}")

{
  "scriptFile": "__init__.py",
  "bindings": [
    {
      "name": "myBlobTrigger",
      "type": "blobTrigger",
      "direction": "in",
      "path": "samples-trigger/{name}.txt",
      "connection": "AzureWebJobsStorage"
    },
    {
      "name": "myBlobOutput",
      "type": "blob",
      "direction": "out",
      "path": "samples-processed/processed-{name}.txt",
      "connection": "AzureWebJobsStorage"
    }
  ]
}

Common Scenarios

• File Processing: Trigger a function when a file is uploaded to Blob Storage, process the file, and write results to another blob.
• Data Ingestion: Read configuration data or input payloads from blobs to customize function execution.
• Large Data Handling: Use bindings to read and write large files without loading them entirely into memory.