azure-storage-file-share-py

$npx mdskill add microsoft/skills/azure-storage-file-share-py

Manage SMB file shares and files in Azure Storage.

  • Enables cloud-native and lift-and-shift file share operations.
  • Depends on Azure Storage File Share SDK and DefaultAzureCredential.
  • Uses context managers and prefers Entra ID authentication.
  • Returns file share, directory, and file operation results.

SKILL.md

.github/skills/azure-storage-file-share-pyView on GitHub ↗
---
name: azure-storage-file-share-py
description: |-
  Azure Storage File Share SDK for Python. Use for SMB file shares, directories, and file operations in the cloud.
  Triggers: "azure-storage-file-share", "ShareServiceClient", "ShareClient", "file share", "SMB".
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
---

# Azure Storage File Share SDK for Python

Manage SMB file shares for cloud-native and lift-and-shift scenarios.

## Installation

```bash
pip install azure-storage-file-share
```

## Environment Variables

```bash
AZURE_STORAGE_ACCOUNT_URL=https://<account>.file.core.windows.net  # Required for Entra ID auth
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
>    - Local dev: `DefaultAzureCredential` works as-is.
>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials.
> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:
>    - Sync: `with <Client>(...) as client:`
>    - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

```python
from azure.storage.fileshare import ShareServiceClient
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

with ShareServiceClient(
    account_url=os.environ["AZURE_STORAGE_ACCOUNT_URL"],
    credential=credential
) as service:
    # Use service here (see following sections for operations)
    ...
```

## Share Operations

### Create Share

```python
share = service.create_share("my-share")
```

### List Shares

```python
for share in service.list_shares():
    print(f"{share.name}: {share.quota} GB")
```

### Get Share Client

```python
share_client = service.get_share_client("my-share")
```

### Delete Share

```python
service.delete_share("my-share")
```

## Directory Operations

### Create Directory

```python
share_client = service.get_share_client("my-share")
share_client.create_directory("my-directory")

# Nested directory
share_client.create_directory("my-directory/sub-directory")
```

### List Directories and Files

```python
directory_client = share_client.get_directory_client("my-directory")

for item in directory_client.list_directories_and_files():
    if item["is_directory"]:
        print(f"[DIR] {item['name']}")
    else:
        print(f"[FILE] {item['name']} ({item['size']} bytes)")
```

### Delete Directory

```python
share_client.delete_directory("my-directory")
```

## File Operations

### Upload File

```python
file_client = share_client.get_file_client("my-directory/file.txt")

# From string
file_client.upload_file("Hello, World!")

# From file
with open("local-file.txt", "rb") as f:
    file_client.upload_file(f)

# From bytes
file_client.upload_file(b"Binary content")
```

### Download File

```python
file_client = share_client.get_file_client("my-directory/file.txt")

# To bytes
data = file_client.download_file().readall()

# To file
with open("downloaded.txt", "wb") as f:
    data = file_client.download_file()
    data.readinto(f)

# Stream chunks
download = file_client.download_file()
for chunk in download.chunks():
    process(chunk)
```

### Get File Properties

```python
properties = file_client.get_file_properties()
print(f"Size: {properties.size}")
print(f"Content type: {properties.content_settings.content_type}")
print(f"Last modified: {properties.last_modified}")
```

### Delete File

```python
file_client.delete_file()
```

### Copy File

```python
source_url = "https://account.file.core.windows.net/share/source.txt"
dest_client = share_client.get_file_client("destination.txt")
dest_client.start_copy_from_url(source_url)
```

## Range Operations

### Upload Range

```python
# Upload to specific range
file_client.upload_range(data=b"content", offset=0, length=7)
```

### Download Range

```python
# Download specific range
download = file_client.download_file(offset=0, length=100)
data = download.readall()
```

## Snapshot Operations

### Create Snapshot

```python
snapshot = share_client.create_snapshot()
print(f"Snapshot: {snapshot['snapshot']}")
```

### Access Snapshot

```python
snapshot_client = service.get_share_client(
    "my-share",
    snapshot=snapshot["snapshot"]
)
```

## Async Client

```python
from azure.storage.fileshare.aio import ShareServiceClient
from azure.identity.aio import DefaultAzureCredential

async def upload_file():
    async with DefaultAzureCredential() as credential:
        async with ShareServiceClient(account_url, credential=credential) as service:
            share = service.get_share_client("my-share")
            file_client = share.get_file_client("test.txt")
            
            await file_client.upload_file("Hello!")
```

## Client Types

| Client | Purpose |
|--------|---------|
| `ShareServiceClient` | Account-level operations |
| `ShareClient` | Share operations |
| `ShareDirectoryClient` | Directory operations |
| `ShareFileClient` | File operations |

## Best Practices

1. **Pick sync OR async and stay consistent.** Do not mix `azure.storage.fileshare` sync clients with `azure.storage.fileshare.aio` async clients in the same call path. Choose one mode per module.
2. **Always use context managers for clients and async credentials.** Wrap every client in `with ShareServiceClient(...) as client:` (sync) or `async with ShareServiceClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
3. **Use `DefaultAzureCredential`** for portable auth across local dev and Azure (avoid connection strings / API keys when possible).
4. **Use Microsoft Entra ID** for production with RBAC
5. **Stream large files** using chunks() to avoid memory issues
6. **Create snapshots** before major changes
7. **Set quotas** to prevent unexpected storage costs
8. **Use ranges** for partial file updates

## Reference Files

| File | Contents |
|------|----------|
| [references/capabilities.md](references/capabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| [references/non-hero-scenarios.md](references/non-hero-scenarios.md) | Dedicated non-hero examples for secondary/advanced scenarios. |

More from microsoft/skills

SkillDescription
agent-framework-azure-ai-pyBuild Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
airunway-aks-setupSet up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: \"setup AI Runway\", \"onboard AKS cluster\", \"install AI Runway\", \"airunway setup\", \"deploy model to AKS\", \"GPU inference on AKS\", \"KAITO setup on AKS\", \"run LLM on AKS\", \"vLLM on AKS\", \"set up model serving on AKS\", \"AI Runway controller\".
appinsights-instrumentationGuidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
applicationinsights-web-tsInstrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
azure-aiUse for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
azure-ai-agents-persistent-dotnet|
azure-ai-agents-persistent-java|
azure-ai-anomalydetector-javaBuild anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
azure-ai-contentsafety-javaBuild content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text/image analysis, blocklist management, or harm detection for hate, violence, sexual content, and self-harm.
azure-ai-contentsafety-py|