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azure-storage-blob-py

Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle. Triggers on blob storage, BlobServiceClient, ContainerClient, BlobClient, upload blob, download blob.

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2026年2月13日 08:47
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SKILL.md
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name
azure-storage-blob-py
description
Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle. Triggers on blob storage, BlobServiceClient, ContainerClient, BlobClient, upload blob, download blob.
package
azure-storage-blob
# Azure Blob Storage SDK for Python Client library for Azure Blob Storage — object storage for unstructured data. ## Installation ```bash pip install azure-storage-blob azure-identity ``` ## Environment Variables ```bash AZURE_STORAGE_ACCOUNT_NAME=<your-storage-account> # Or use full URL AZURE_STORAGE_ACCOUNT_URL=https://<account>.blob.core.windows.net ``` ## Authentication ```python 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 ```python container_client = blob_service_client.get_container_client("mycontainer") container_client.create_container() ``` ### Upload Blob ```python # 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 ```python 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 ```python 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 ```python blob_client.delete_blob() # Delete with snapshots blob_client.delete_blob(delete_snapshots="include") ``` ## Performance Tuning ```python # 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 ```python 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 ```python # 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 ```python 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 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
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