Microsoft Azure Blob Storage Client Library for Python
Azure Storage Blobs client library for Python
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
- Python 2.7, or 3.5 or later is required to use this package.
- You must have an Azure subscription and an Azure storage account to use this package.
Install the package
Install the Azure Storage Blobs client library for Python with pip:
pip install azure-storage-blob
Create a storage account
# Create a new resource group to hold the storage account - # if using an existing resource group, skip this step az group create --name my-resource-group --location westus2 # Create the storage account az storage account create -n my-storage-account-name -g my-resource-group
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=credential)
Looking up the account URL
# Get the blob service account url for the storage account az storage account show -n my-storage-account-name -g my-resource-group --query "primaryEndpoints.blob"
Types of credentials
credential parameter may be provided in a number of different forms, depending on the type of
authorization you wish to use:
To use an Azure Active Directory (AAD) token credential, provide an instance of the desired credential type obtained from the azure-identity library. For example, DefaultAzureCredential can be used to authenticate the client.
This requires some initial setup:
- Install azure-identity
- Register a new AAD application and give permissions to access Azure Storage
- Grant access to Azure Blob data with RBAC in the Azure Portal
- Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET
Use the returned token credential to authenticate the client:
from azure.identity import DefaultAzureCredential from azure.storage.blob import BlobServiceClient token_credential = DefaultAzureCredential() blob_service_client = BlobServiceClient( account_url="https://<my_account_name>.blob.core.windows.net", credential=token_credential )
To use a shared access signature (SAS) token, provide the token as a string. If your account URL includes the SAS token, omit the credential parameter. You can generate a SAS token from the Azure Portal under "Shared access signature" or use one of the
generate_sas()functions to create a sas token for the storage account, container, or blob:
from datetime import datetime, timedelta from azure.storage.blob import BlobServiceClient, generate_account_sas, ResourceTypes, AccountSasPermissions sas_token = generate_account_sas( account_name="<storage-account-name>", account_key="<account-access-key>", resource_types=ResourceTypes(service=True), permission=AccountSasPermissions(read=True), expiry=datetime.utcnow() + timedelta(hours=1) ) blob_service_client = BlobServiceClient(account_url="https://<my_account_name>.blob.core.windows.net", credential=sas_token)
To use a storage account shared key (aka account key or access key), provide the key as a string. This can be found in the Azure Portal under the "Access Keys" section or by running the following Azure CLI command:
az storage account keys list -g MyResourceGroup -n MyStorageAccount
Use the key as the credential parameter to authenticate the client:
from azure.storage.blob import BlobServiceClient service = BlobServiceClient(account_url="https://<my_account_name>.blob.core.windows.net", credential="<account_access_key>")
To use anonymous public read access, simply omit the credential parameter.
Creating the client from a connection string
Depending on your use case and authorization method, you may prefer to initialize a client instance with a storage
connection string instead of providing the account URL and credential separately. To do this, pass the storage
connection string to the client's
from_connection_string class method:
from azure.storage.blob import BlobServiceClient connection_string = "DefaultEndpointsProtocol=https;AccountName=xxxx;AccountKey=xxxx;EndpointSuffix=core.windows.net" service = BlobServiceClient.from_connection_string(conn_str=connection_string)
The connection string to your storage account can be found in the Azure Portal under the "Access Keys" section or by running the following CLI command:
az storage account show-connection-string -g MyResourceGroup -n MyStorageAccount
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
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)
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.
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.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
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