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azure-data-tables-py

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提供使用 Azure Tables SDK for Python 進行 NoSQL 鍵值儲存、實體 CRUD 操作、批次操作以及針對 Azure Storage Tables 或 Cosmos DB Table API 執行查詢的程式碼範例與最佳實務。

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更新時間 2026-09-13

Azure Tables Python SDK

適用於結構化資料的 NoSQL 鍵值儲存庫(Azure Storage Tables 或 Cosmos DB Table API)。

安裝

pip install azure-data-tables azure-identity

環境變數

# Azure Storage Tables
AZURE_STORAGE_ACCOUNT_URL=https://.table.core.windows.net  # Azure Storage Tables 所需

# Cosmos DB Table API
COSMOS_TABLE_ENDPOINT=https://.table.cosmos.azure.com  # Cosmos DB Table API 必填
AZURE_TOKEN_CREDENTIALS=prod # 僅在生產環境中使用 DefaultAzureCredential 時才需設定

驗證與生命週期

🔑 以下每個程式碼範例均適用兩項規則:

  1. 優先使用DefaultAzureCredential它可在本地端(Azure CLI / VS Code / Developer CLI)及 Azure 環境(託管身分識別、工作負載身分識別)中運作,且無需修改程式碼。請避免使用連線字串、帳戶/API 金鑰——這些會繞過 Entra 稽核與輪替機制。
    • 本地開發:DefaultAzureCredential可直接使用。
    • 生產環境:設定AZURE_TOKEN_CREDENTIALS=prod(或AZURE_TOKEN_CREDENTIALS= ),以將憑證鏈限制為符合生產環境安全標準的憑證。
  2. 將每個客戶端封裝在上下文管理器中,以確保 HTTP 傳輸、套接字和憑證快取能以可預測的方式釋放:
    • 同步模式:使用 `(...) as client:`
    • 非同步:使用 `(...)` 作為 `client` 的 `async ` ,以及使用 `DefaultAzureCredential()` 作為 `credential` 的 `async`:(來自azure.identity.aio

程式碼片段可能會簡化此設定,但生產環境的程式碼應始終遵循這兩項規則。

import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.data.tables import TableServiceClient, TableClient

# 本地開發環境:使用 DefaultAzureCredential。生產環境:設定 AZURE_TOKEN_CREDENTIALS=prod 或 AZURE_TOKEN_CREDENTIALS=
credential = DefaultAzureCredential(require_envvar=True)
# 或者在生產環境中直接使用特定憑證:
# 參見 https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

endpoint = "https://.table.core.windows.net"

# 服務客戶端(管理資料表)
with TableServiceClient(endpoint=endpoint, credential=credential) as service_client:
    # 在此處使用 service_client(操作詳見後續章節)
    ...

# 表格客戶端(處理實體)
with TableClient(endpoint=endpoint, table_name="mytable", credential=credential) as table_client:
    # 在此處使用 table_client(操作請參閱後續章節)
    ...

客戶端類型

客戶端 用途
TableServiceClient 建立/刪除資料表、列出資料表
TableClient 實體 CRUD 操作、查詢

表格操作

# 建立資料表
service_client.create_table("mytable")

# 若不存在則建立
service_client.create_table_if_not_exists("mytable")

# 刪除資料表
service_client.delete_table("mytable")

# 列出資料表
for table in service_client.list_tables():
    print(table.name)

# 取得資料表客戶端
table_client = service_client.get_table_client("mytable")

實體操作

重要:每個實體都需要PartitionKeyRowKey(兩者共同構成唯一識別碼)。

建立實體

entity = {
    "PartitionKey": "sales",
    "RowKey": "order-001",
    "product": "Widget",
    "quantity": 5,
    "price": 9.99,
    "shipped": False
}

# 建立(若已存在則失敗)
table_client.create_entity(entity=entity)

# Upsert(建立或替換)
table_client.upsert_entity(entity=entity)

取得實體

# 透過鍵值擷取(最快)
entity = table_client.get_entity(
    partition_key="sales",
    row_key="order-001"
)
print(f"產品:{entity['product']}")

更新實體

# 替換整個實體
entity["quantity"] = 10
table_client.update_entity(entity=entity, mode="replace")

# 合併(僅更新特定欄位)
update = {
    "PartitionKey": "sales",
    "RowKey": "order-001",
    "shipped": True
}
table_client.update_entity(entity=update, mode="merge")

刪除實體

table_client.delete_entity(
    partition_key="sales",
    row_key="order-001"
)

查詢實體

在分區內查詢

# 依分區查詢(高效)
entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales'"
)
for entity in entities:
    print(entity)

使用篩選條件查詢

# 根據屬性篩選
entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales' and quantity gt 3"
)

# 帶參數 (更安全)
entities = table_client.query_entities(
    query_filter="PartitionKey eq @pk and price lt @max_price",
    parameters={"pk": "sales", "max_price": 50.0}
)

選取特定屬性

entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales'",
    select=["RowKey", "product", "price"]
)

列出所有實體

# 列出所有實體(跨分區 - 請謹慎使用)
for entity in table_client.list_entities():
    print(entity)

批次操作

from azure.data.tables import TableTransactionError

# 批次操作(僅限同一分區!)
operations = [
    ("create", {"PartitionKey": "batch", "RowKey": "1", "data": "first"}),
    ("create", {"PartitionKey": "batch", "RowKey": "2", "data": "second"}),
    ("upsert", {"PartitionKey": "batch", "RowKey": "3", "data": "third"}),
]

try:
    table_client.submit_transaction(operations)
except TableTransactionError as e:
    print(f"交易失敗:{e}")

非同步客戶端

from azure.data.tables.aio import TableServiceClient, TableClient
from azure.identity.aio import DefaultAzureCredential

async def table_operations():
    async with DefaultAzureCredential() as credential:
        async with TableClient(
            endpoint="https://.table.core.windows.net",
            table_name="mytable",
            credential=credential
        ) as client:
            # 建立
            await client.create_entity(entity={
                "PartitionKey": "async",
                "RowKey": "1",
                "data": "test"
            })
            
            # 查詢
            async for entity in client.query_entities("PartitionKey eq 'async'"):
                print(entity)

import asyncio
asyncio.run(table_operations())

資料類型

Python 資料型別 表儲存類型
str 字串
int Int64
float Double
bool 布林值
datetime DateTime
位元組 二進位
UUID GUID

最佳實務

  1. 請選擇「同步」或「非同步」模式,並保持一致。請勿在同一個呼叫路徑中混合使用azure.data.tables的同步客戶端與azure.data.tables.aio的非同步客戶端。每個模組應僅採用一種模式。
  2. 請務必為客戶端和非同步憑證使用上下文管理器。將每個客戶端以 TableClient(...) as client:(sync)(同步模式)或TableClient(...) as client:(async)(非同步模式)進行封裝。 對於來自azure.identity.aio 的非同步DefaultAzureCredential,也請搭配 credential:參數使用非同步模式,以便妥善清理憑證和傳輸資料。
  3. 請使用DefaultAzureCredential實現本地開發環境與 Azure 之間的可移植驗證(盡可能避免使用連線字串/API 金鑰)。
  4. 根據查詢模式設計分區鍵,並確保均勻分佈
  5. 盡可能在分區內進行查詢(跨分區查詢成本較高)
  6. 針對同一分區內的多個實體,請使用批次操作
  7. 使用` upsert_entity` 進行幺正寫入
  8. 使用參數化查詢以防止注入攻擊
  9. 保持實體大小適中— 每個實體最大 1MB
  10. 在高吞吐量情境下使用非同步客戶端
在 GitHub 上查看
---
name: azure-data-tables-py
description: Provides code samples and best practices for using the Azure Tables SDK for Python to perform NoSQL key-value storage, entity CRUD, batch operations, and queries against Azure Storage Tables or Cosmos DB Table API.
license: MIT
---

# Azure Tables SDK for Python

NoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).

## Installation

```bash
pip install azure-data-tables azure-identity
```

## Environment Variables

```bash
# Azure Storage Tables
AZURE_STORAGE_ACCOUNT_URL=https://<account>.table.core.windows.net  # Required for Azure Storage Tables

# Cosmos DB Table API
COSMOS_TABLE_ENDPOINT=https://<account>.table.cosmos.azure.com  # Required for Cosmos DB Table API
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
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.data.tables import TableServiceClient, TableClient

# 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()

endpoint = "https://<account>.table.core.windows.net"

# Service client (manage tables)
with TableServiceClient(endpoint=endpoint, credential=credential) as service_client:
    # Use service_client here (see following sections for operations)
    ...

# Table client (work with entities)
with TableClient(endpoint=endpoint, table_name="mytable", credential=credential) as table_client:
    # Use table_client here (see following sections for operations)
    ...
```

## Client Types

| Client | Purpose |
|--------|---------|
| `TableServiceClient` | Create/delete tables, list tables |
| `TableClient` | Entity CRUD, queries |

## Table Operations

```python
# Create table
service_client.create_table("mytable")

# Create if not exists
service_client.create_table_if_not_exists("mytable")

# Delete table
service_client.delete_table("mytable")

# List tables
for table in service_client.list_tables():
    print(table.name)

# Get table client
table_client = service_client.get_table_client("mytable")
```

## Entity Operations

**Important**: Every entity requires `PartitionKey` and `RowKey` (together form unique ID).

### Create Entity

```python
entity = {
    "PartitionKey": "sales",
    "RowKey": "order-001",
    "product": "Widget",
    "quantity": 5,
    "price": 9.99,
    "shipped": False
}

# Create (fails if exists)
table_client.create_entity(entity=entity)

# Upsert (create or replace)
table_client.upsert_entity(entity=entity)
```

### Get Entity

```python
# Get by key (fastest)
entity = table_client.get_entity(
    partition_key="sales",
    row_key="order-001"
)
print(f"Product: {entity['product']}")
```

### Update Entity

```python
# Replace entire entity
entity["quantity"] = 10
table_client.update_entity(entity=entity, mode="replace")

# Merge (update specific fields only)
update = {
    "PartitionKey": "sales",
    "RowKey": "order-001",
    "shipped": True
}
table_client.update_entity(entity=update, mode="merge")
```

### Delete Entity

```python
table_client.delete_entity(
    partition_key="sales",
    row_key="order-001"
)
```

## Query Entities

### Query Within Partition

```python
# Query by partition (efficient)
entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales'"
)
for entity in entities:
    print(entity)
```

### Query with Filters

```python
# Filter by properties
entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales' and quantity gt 3"
)

# With parameters (safer)
entities = table_client.query_entities(
    query_filter="PartitionKey eq @pk and price lt @max_price",
    parameters={"pk": "sales", "max_price": 50.0}
)
```

### Select Specific Properties

```python
entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales'",
    select=["RowKey", "product", "price"]
)
```

### List All Entities

```python
# List all (cross-partition - use sparingly)
for entity in table_client.list_entities():
    print(entity)
```

## Batch Operations

```python
from azure.data.tables import TableTransactionError

# Batch operations (same partition only!)
operations = [
    ("create", {"PartitionKey": "batch", "RowKey": "1", "data": "first"}),
    ("create", {"PartitionKey": "batch", "RowKey": "2", "data": "second"}),
    ("upsert", {"PartitionKey": "batch", "RowKey": "3", "data": "third"}),
]

try:
    table_client.submit_transaction(operations)
except TableTransactionError as e:
    print(f"Transaction failed: {e}")
```

## Async Client

```python
from azure.data.tables.aio import TableServiceClient, TableClient
from azure.identity.aio import DefaultAzureCredential

async def table_operations():
    async with DefaultAzureCredential() as credential:
        async with TableClient(
            endpoint="https://<account>.table.core.windows.net",
            table_name="mytable",
            credential=credential
        ) as client:
            # Create
            await client.create_entity(entity={
                "PartitionKey": "async",
                "RowKey": "1",
                "data": "test"
            })
            
            # Query
            async for entity in client.query_entities("PartitionKey eq 'async'"):
                print(entity)

import asyncio
asyncio.run(table_operations())
```

## Data Types

| Python Type | Table Storage Type |
|-------------|-------------------|
| `str` | String |
| `int` | Int64 |
| `float` | Double |
| `bool` | Boolean |
| `datetime` | DateTime |
| `bytes` | Binary |
| `UUID` | Guid |

## Best Practices

1. **Pick sync OR async and stay consistent.** Do not mix `azure.data.tables` sync clients with `azure.data.tables.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 TableClient(...) as client:` (sync) or `async with TableClient(...) 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. **Design partition keys** for query patterns and even distribution
5. **Query within partitions** whenever possible (cross-partition is expensive)
6. **Use batch operations** for multiple entities in same partition
7. **Use `upsert_entity`** for idempotent writes
8. **Use parameterized queries** to prevent injection
9. **Keep entities small** — max 1MB per entity
10. **Use async client** for high-throughput scenarios

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