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

Source code | Package (PyPI) | API reference documentation | Product documentation | Samples

Getting started

Prerequisites

Install the package

Install the Azure Storage Blobs client library for Python with pip:

pip install azure-storage-blob --pre

Create a storage account

If you wish to create a new storage account, you can use the Azure Portal, Azure PowerShell, or Azure CLI:

# 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 endpoint 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 endpoint URL

You can find the storage account’s blob service endpoint URL using the Azure Portal, Azure PowerShell, or Azure CLI:

# Get the blob service endpoint for the storage account
az storage account show -n my-storage-account-name -g my-resource-group --query "primaryEndpoints.blob"

Types of credentials

The credential parameter may be provided in a number of different forms, depending on the type of authorization you wish to use:

  1. 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.

  2. To use a storage account shared access key, provide the key as a string.

  3. To use an Azure Active Directory (AAD) token credential, provide an instance of the desired credential type obtained from the azure-identity library.

  4. 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

service = BlobServiceClient.from_connection_string(conn_str="my_connection_string")

Key concepts

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.

Clients

Four different clients are provided to to interact with the various components of the Blob Service:

  1. **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 get_container_client or get_blob_client methods.

  2. **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 get_blob_client method.

  3. **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.

  4. **LeaseClient** -

    this client represents lease interactions with a ContainerClient or BlobClient. It provides operations to acquire, renew, release, change, and break a lease on a specified resource.

Blob Types

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

Examples

The following sections provide several code snippets covering some of the most common Storage Blob tasks, including:

Uploading a blob

Upload a blob to 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("./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="my_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()
    my_blob.writelines(blob_data.content_as_bytes())

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.content_as_bytes()
    my_blob.write(data)

Enumerating blobs

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)

Troubleshooting

Storage Blob clients raise exceptions defined in Azure Core. All Blob service operations will throw a StorageErrorException on failure with helpful error codes.

Next steps

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:

Additional documentation

For more extensive documentation on Azure Blob storage, see the Azure Blob storage documentation on docs.microsoft.com.

Contributing

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.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.