azure-storage-blob-py – DevOps & Infrastructure skill for AI agents

Azure Blob Storage SDK for Python

Client library for Azure Blob Storage — object storage for unstructured data.

Installation

pip install azure-storage-blob azure-identity

Environment Variables

AZURE_STORAGE_ACCOUNT_NAME=<your-storage-account>
# Or use full URL
AZURE_STORAGE_ACCOUNT_URL=https://<account>.blob.core.windows.net

Authentication

from azure.identity import DefaultAzureCredential
from azure.storage.blob import BlobServiceClient

credential = DefaultAzureCredential()
account_url = "https://<account>.blob.core.windows.net"

blob_service_client = BlobServiceClient(account_url, credential=credential)

Client Hierarchy

| Client | Purpose | Get From |

|--------|---------|----------|

| BlobServiceClient | Account-level operations | Direct instantiation |

| ContainerClient | Container operations | blob_service_client.get_container_client() |

| BlobClient | Single blob operations | container_client.get_blob_client() |

Execution boundaries

Confirm the account, container and exact blob before writes or deletion. overwrite=True replaces content; deleting snapshots can be irreversible without an independently verified recovery policy. Use least-privilege RBAC and short-lived read-only SAS where needed; never log SAS URLs or account keys. A BytesIO buffer still retains the entire download in memory; use a file stream for large data.

Core Workflow

Create Container

container_client = blob_service_client.get_container_client("mycontainer")
container_client.create_container()

Upload Blob

# From file path
blob_client = blob_service_client.get_blob_client(
    container="mycontainer",
    blob="sample.txt"
)

with open("./local-file.txt", "rb") as data:
    blob_client.upload_blob(data, overwrite=True)

# From bytes/string
blob_client.upload_blob(b"Hello, World!", overwrite=True)

# From stream
import io
stream = io.BytesIO(b"Stream content")
blob_client.upload_blob(stream, overwrite=True)

Download Blob

blob_client = blob_service_client.get_blob_client(
    container="mycontainer",
    blob="sample.txt"
)

# To file
with open("./downloaded.txt", "wb") as file:
    download_stream = blob_client.download_blob()
    file.write(download_stream.readall())

# To memory
download_stream = blob_client.download_blob()
content = download_stream.readall()  # bytes

# Read into existing buffer
stream = io.BytesIO()
num_bytes = blob_client.download_blob().readinto(stream)

List Blobs

container_client = blob_service_client.get_container_client("mycontainer")

# List all blobs
for blob in container_client.list_blobs():
    print(f"{blob.name} - {blob.size} bytes")

# List with prefix (folder-like)
for blob in container_client.list_blobs(name_starts_with="logs/"):
    print(blob.name)

# Walk blob hierarchy (virtual directories)
for item in container_client.walk_blobs(delimiter="/"):
    if item.get("prefix"):
        print(f"Directory: {item['prefix']}")
    else:
        print(f"Blob: {item.name}")

Delete Blob

blob_client.delete_blob()

# Delete with snapshots
blob_client.delete_blob(delete_snapshots="include")

Performance Tuning

# Configure chunk sizes for large uploads/downloads
blob_client = BlobClient(
    account_url=account_url,
    container_name="mycontainer",
    blob_name="large-file.zip",
    credential=credential,
    max_block_size=4 * 1024 * 1024,  # 4 MiB blocks
    max_single_put_size=64 * 1024 * 1024  # 64 MiB single upload limit
)

# Parallel upload
blob_client.upload_blob(data, max_concurrency=4)

# Parallel download
download_stream = blob_client.download_blob(max_concurrency=4)

SAS Tokens

from datetime import datetime, timedelta, timezone
from azure.storage.blob import generate_blob_sas, BlobSasPermissions

sas_token = generate_blob_sas(
    account_name="<account>",
    container_name="mycontainer",
    blob_name="sample.txt",
    account_key="<account-key>",  # Or use user delegation key
    permission=BlobSasPermissions(read=True),
    expiry=datetime.now(timezone.utc) + timedelta(hours=1)
)

# Use SAS token
blob_url = f"https://<account>.blob.core.windows.net/mycontainer/sample.txt?{sas_token}"

Blob Properties and Metadata

# Get properties
properties = blob_client.get_blob_properties()
print(f"Size: {properties.size}")
print(f"Content-Type: {properties.content_settings.content_type}")
print(f"Last modified: {properties.last_modified}")

# Set metadata
blob_client.set_blob_metadata(metadata={"category": "logs", "year": "2024"})

# Set content type
from azure.storage.blob import ContentSettings
blob_client.set_http_headers(
    content_settings=ContentSettings(content_type="application/json")
)

Async Client

from azure.identity.aio import DefaultAzureCredential
from azure.storage.blob.aio import BlobServiceClient

async def upload_async():
    async with DefaultAzureCredential() as credential, BlobServiceClient(account_url, credential=credential) as client:
        blob_client = client.get_blob_client("mycontainer", "sample.txt")
        
        with open("./file.txt", "rb") as data:
            await blob_client.upload_blob(data, overwrite=True)

# Download async
async def download_async():
    async with DefaultAzureCredential() as credential, BlobServiceClient(account_url, credential=credential) as client:
        blob_client = client.get_blob_client("mycontainer", "sample.txt")
        
        stream = await blob_client.download_blob()
        data = await stream.readall()

Best Practices

1. Use DefaultAzureCredential instead of connection strings

2. Use context managers for async clients

3. Set overwrite=True explicitly when re-uploading

4. Use max_concurrency for large file transfers

5. Prefer readinto() over readall() for memory efficiency

6. Use walk_blobs() for hierarchical listing

7. Set appropriate content types for web-served blobs

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.