Introduction to Blob Storage Bindings
Azure Functions provides powerful input and output bindings for Azure Blob Storage. These bindings simplify the process of reading from and writing to blob containers without requiring you to write explicit SDK code for storage operations. This allows you to focus on your business logic.
Blob Storage bindings can be configured in your function's function.json file or by using attributes in C# or other languages.
Common Use Cases
- Triggering a function when a new blob is created or updated.
- Reading blob content as input to your function.
- Writing function output to a blob.
- Uploading processed data to a blob.
Input Bindings
Input bindings allow you to automatically load blob content into your function's parameters.
Blob Trigger
The blob trigger starts your function when a blob is created or updated in a specified container.
Example: function.json (Blob Trigger)
{
"scriptFile": "__init__.py",
"bindings": [
{
"name": "myblob",
"type": "blobTrigger",
"direction": "in",
"path": "samples-workitems/{name}",
"connection": "AzureWebJobsStorage"
}
]
}
Example: Python (Blob Trigger)
import logging
import azure.functions as func
def main(myblob: func.InputStream):
logging.info(f"Python blob trigger function processed blob")
logging.info(f"Name: {myblob.name}")
logging.info(f"Size: {myblob.length} Bytes")
logging.info(f"Content-Type: {myblob.content_type}")
blob_content = myblob.read()
logging.info(f"Blob Content (first 100 bytes): {blob_content[:100]}...")
Blob Input Binding
Use a blob input binding to read the content of a specific blob into your function.
Example: function.json (Blob Input)
{
"scriptFile": "__init__.py",
"bindings": [
{
"name": "myblob",
"type": "blobTrigger",
"direction": "in",
"path": "samples-workitems/{name}",
"connection": "AzureWebJobsStorage"
},
{
"name": "inputblob",
"type": "blob",
"direction": "in",
"path": "input-data/{name}",
"connection": "AzureWebJobsStorage"
}
]
}
Example: Python (Blob Input)
import logging
import azure.functions as func
def main(myblob: func.InputStream, inputblob: bytes):
logging.info(f"Python blob trigger function processed blob")
logging.info(f"Input blob content: {inputblob.decode('utf-8')}")
Output Bindings
Output bindings allow you to easily write data to Azure Blob Storage from your function.
Blob Output Binding
Write data from your function to a blob. The path can include dynamic elements like the function's input blob name.
Example: function.json (Blob Output)
{
"scriptFile": "__init__.py",
"bindings": [
{
"name": "myblob",
"type": "blobTrigger",
"direction": "in",
"path": "samples-workitems/{name}",
"connection": "AzureWebJobsStorage"
},
{
"name": "outputblob",
"type": "blob",
"direction": "out",
"path": "output-processed/{name}.processed",
"connection": "AzureWebJobsStorage"
}
]
}
Example: Python (Blob Output)
import logging
import azure.functions as func
def main(myblob: func.InputStream, outputblob: func.Out[str]):
logging.info(f"Python blob trigger function processed blob")
processed_data = f"Processed content for {myblob.name}"
outputblob.set(processed_data)
logging.info(f"Wrote processed data to output blob.")
AzureWebJobsStorage in local.settings.json or application settings in Azure) are correctly configured with your Azure Storage account connection string.
path property in bindings can use tokens like {name} and {rand-guid} to create dynamic blob names. This is very powerful for routing and organization.
Supported Data Types
Blob bindings can bind to various data types, including:
stringfor text content.byte[]for binary content.Streamfor streaming access.- Language-specific types (e.g.,
azure.functions.InputStreamin Python,CloudBlockBlobin C#).
Advanced Scenarios
You can also bind to blob metadata, use different blob types (e.g., page blobs, append blobs), and configure bindings for specific storage tiers. For more complex operations, consider using the Azure Storage SDK directly within your function.