Azure Storage Blobs client library for Python - Version 12.3.0
Azure Blob storage is Microsoft's object storage solution for the cloud. Blob storage is optimized for storing massive amounts of unstructured data, such as text or binary data.
Blob storage is ideal for:
- Serving images or documents directly to a browser
- Storing files for distributed access
- Streaming video and audio
- Storing data for backup and restore, disaster recovery, and archiving
- Storing data for analysis by an on-premises or Azure-hosted service
1: Set up your local development environment
If you haven't already, follow all the instructions on Configure your local Python dev environment for Azure.
Be sure to create a service principal for local development, and create and activate a virtual environment for this project.
2. Create a storage account
3. Install the package
pip install azure-storage-blob
4. Create the client
The Azure Storage Blobs client library for Python allows you to interact with three types of resources: the storage account itself, blob storage containers, and blobs. Interaction with these resources starts with an instance of a client. To create a client object, you will need the storage account's blob service account URL and a credential that allows you to access the storage account:
from azure.storage.blob import BlobServiceClient service = BlobServiceClient(account_url="https://<my-storage-account-name>.blob.core.windows.net/", credential=new DefaultAzureCredential())
Notes: If you have created a service principal following the configure your local environment documentation,
Default Azure Credential works without additional parameters. For additional configuration options see Authorizing access to data in Azure Storage.
You can find the storage account's blob service URL using the Azure Portal.
The following sections provide several code snippets covering some of the most common Storage Blob tasks, including:
Note that a container must be created before to upload or download a blob.
Create a container
Create a container from where you can upload or download blobs.
from azure.storage.blob import ContainerClient container_client = ContainerClient.from_connection_string(conn_str="<connection_string>", container_name="my_container") container_client.create_container()
Use the async client to upload a blob
from azure.storage.blob.aio import ContainerClient container_client = ContainerClient.from_connection_string(conn_str="<connection_string>", container_name="my_container") await container_client.create_container()
Uploading a blob
Upload a blob to your container
from azure.storage.blob import BlobClient blob = BlobClient.from_connection_string(conn_str="<connection_string>", container_name="my_container", blob_name="my_blob") with open("./SampleSource.txt", "rb") as data: blob.upload_blob(data)
Use the async client to upload a blob
from azure.storage.blob.aio import BlobClient blob = BlobClient.from_connection_string(conn_str="<connection_string>", container_name="my_container", blob_name="my_blob") with open("./SampleSource.txt", "rb") as data: await blob.upload_blob(data)
Downloading a blob
Download a blob from your container
from azure.storage.blob import BlobClient blob = BlobClient.from_connection_string(conn_str="my_connection_string", container_name="my_container", blob_name="my_blob") with open("./BlockDestination.txt", "wb") as my_blob: blob_data = blob.download_blob() blob_data.readinto(my_blob)
Download a blob asynchronously
from azure.storage.blob.aio import BlobClient blob = BlobClient.from_connection_string(conn_str="my_connection_string", container_name="my_container", blob_name="my_blob") with open("./BlockDestination.txt", "wb") as my_blob: stream = await blob.download_blob() data = await stream.readall() my_blob.write(data)
List the blobs in your container
from azure.storage.blob import ContainerClient container = ContainerClient.from_connection_string(conn_str="my_connection_string", container_name="my_container") blob_list = container.list_blobs() for blob in blob_list: print(blob.name + '\n')
List the blobs asynchronously
from azure.storage.blob.aio import ContainerClient container = ContainerClient.from_connection_string(conn_str="my_connection_string", container_name="my_container") blob_list =  async for blob in container.list_blobs(): blob_list.append(blob) print(blob_list)
Understanding the Examples
The following components make up the Azure Blob Service:
- The storage account itself
- A container within the storage account
- A blob within a container
The Azure Storage Blobs client library for Python allows you to interact with each of these components through the use of a dedicated client object.
Four different clients are provided to to interact with the various components of the Blob Service:
- BlobServiceClient -
this client represents interaction with the Azure storage account itself, and allows you to acquire preconfigured
client instances to access the containers and blobs within. It provides operations to retrieve and configure the
account properties as well as list, create, and delete containers within the account. To perform operations on a
specific container or blob, retrieve a client using the
- ContainerClient -
this client represents interaction with a specific container (which need not exist yet), and allows you to acquire
preconfigured client instances to access the blobs within. It provides operations to create, delete, or configure a
container and includes operations to list, upload, and delete the blobs within it. To perform operations on a
specific blob within the container, retrieve a client using the
- BlobClient - this client represents interaction with a specific blob (which need not exist yet). It provides operations to upload, download, delete, and create snapshots of a blob, as well as specific operations per blob type.
- BlobLeaseClient -
this client represents lease interactions with a
BlobClient. It provides operations to acquire, renew, release, change, and break a lease on a specified resource.
Once you've initialized a Client, you can choose from the different types of blobs:
- Block blobs store text and binary data, up to approximately 4.75 TiB. Block blobs are made up of blocks of data that can be managed individually
- Append blobs are made up of blocks like block blobs, but are optimized for append operations. Append blobs are ideal for scenarios such as logging data from virtual machines
- Page blobs store random access files up to 8 TiB in size. Page blobs store virtual hard drive (VHD) files and serve as disks for Azure virtual machines
Optional keyword arguments that can be passed in at the client and per-operation level.
Retry Policy configuration
Use the following keyword arguments when instantiating a client to configure the retry policy:
- retry_total (int): Total number of retries to allow. Takes precedence over other counts.
retry_total=0if you do not want to retry on requests. Defaults to 10.
- retry_connect (int): How many connection-related errors to retry on. Defaults to 3.
- retry_read (int): How many times to retry on read errors. Defaults to 3.
- retry_status (int): How many times to retry on bad status codes. Defaults to 3.
- retry_to_secondary (bool): Whether the request should be retried to secondary, if able.
This should only be enabled of RA-GRS accounts are used and potentially stale data can be handled.
Use the following keyword arguments when instantiating a client to configure encryption:
- require_encryption (bool): If set to True, will enforce that objects are encrypted and decrypt them.
- key_encryption_key (object): The user-provided key-encryption-key. The instance must implement the following methods:
wrap_key(key)--wraps the specified key using an algorithm of the user's choice.
get_key_wrap_algorithm()--returns the algorithm used to wrap the specified symmetric key.
get_kid()--returns a string key id for this key-encryption-key.
- key_resolver_function (callable): The user-provided key resolver. Uses the kid string to return a key-encryption-key implementing the interface defined above.
Other client / per-operation configuration
Other optional configuration keyword arguments that can be specified on the client or per-operation.
Client keyword arguments:
- connection_timeout (int): Optionally sets the connect and read timeout value, in seconds.
- transport (Any): User-provided transport to send the HTTP request.
Per-operation keyword arguments:
- raw_response_hook (callable): The given callback uses the response returned from the service.
- raw_request_hook (callable): The given callback uses the request before being sent to service.
- client_request_id (str): Optional user specified identification of the request.
- user_agent (str): Appends the custom value to the user-agent header to be sent with the request.
- logging_enable (bool): Enables logging at the DEBUG level. Defaults to False. Can also be passed in at the client level to enable it for all requests.
- headers (dict): Pass in custom headers as key, value pairs. E.g.
This library uses the standard logging library for logging. Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO level.
Detailed DEBUG level logging, including request/response bodies and unredacted
headers, can be enabled on a client with the
import sys import logging from azure.storage.blob import BlobServiceClient # Create a logger for the 'azure.storage.blob' SDK logger = logging.getLogger('azure.storage.blob') logger.setLevel(logging.DEBUG) # Configure a console output handler = logging.StreamHandler(stream=sys.stdout) logger.addHandler(handler) # This client will log detailed information about its HTTP sessions, at DEBUG level service_client = BlobServiceClient.from_connection_string("your_connection_string", logging_enable=True)
logging_enable can enable detailed logging for a single operation,
even when it isn't enabled for the client:
More sample code
Get started with our Blob samples.
Several Storage Blobs Python SDK samples are available to you in the SDK's GitHub repository. These samples provide example code for additional scenarios commonly encountered while working with Storage Blobs:
- Set up Access Policy for container
- Set up a container
- Create a block, page, or append blob
- Upload blobs
- Download blobs
- Delete blobs
- From a connection string
- From a shared access key
- From a shared access signature token
- From active directory
- Get account information
- Get and set service properties
- Get service statistics
- Create, list, and delete containers
- Get the Blob or Container client
- Create a container and delete containers
- Set metadata on containers
- Get container properties
- Acquire a lease on container
- Set an access policy on a container
- Upload, list, delete blobs in container
- Get the blob client to interact with a specific blob
- Create a snapshot
- Delete a blob snapshot
- Soft delete a blob
- Undelete a blob
- Acquire a lease on a blob
- Copy a blob from a URL
blob_samples_directory_interface.py - Examples for interfacing with Blob storage as if it were a directory on a filesystem:
- Copy (upload or download) a single file or directory
- List files or directories at a single level or recursively
- Delete a single file or recursively delete a directory
For more extensive documentation on Azure Blob storage, see the Azure Blob storage documentation on docs.microsoft.com.
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