Preparing Archive
azure-storage-blob-py
Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle.
Architectural Overview
"This module is grounded in ai engineering patterns and exposes 1 core capabilities across 1 execution phases."
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() |
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():
credential = DefaultAzureCredential()
async with 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 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
- Use DefaultAzureCredential instead of connection strings
- Use context managers for async clients
- Set
overwrite=Trueexplicitly when re-uploading - Use
max_concurrencyfor large file transfers - Prefer
readinto()overreadall()for memory efficiency - Use
walk_blobs()for hierarchical listing - 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.
Primary Stack
Python
Tooling Surface
Guide only
Workspace Path
.agents/skills/azure-storage-blob-py
Operational Ecosystem
The complete hardware and software toolchain required.
Module Topology
Antigravity Core
Principal Engineering Agent
Recommended for this workflow
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