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azure-monitor-opentelemetry-exporter-py

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使用 Python 将 OpenTelemetry 跟踪、指标和日志导出到 Azure Application Insights。

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更新时间 2026-09-19

Azure Monitor OpenTelemetry Python 导出器

用于将 OpenTelemetry 跟踪、指标和日志发送到 Application Insights 的低级导出器。

安装

pip install azure-monitor-opentelemetry-exporter

环境变量

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/  # 所有身份验证方法必需
AZURE_TOKEN_CREDENTIALS=prod # 仅在生产环境中使用 DefaultAzureCredential 时必需

🔑 身份验证与生命周期: 这些导出器在设计上接受连接字符串,但对于 AAD 身份验证的 ingestion(在支持的场景中),建议通过 credential= 参数使用 DefaultAzureCredential — 请参阅 Azure AD 身份验证部分。您与导出器一起创建的任何 Azure SDK 客户端都应包装在 with/async with 块中(来自 azure.identity.aio 的异步凭据也是如此)。

使用场景

场景使用
快速设置,自动仪器化`azure-monitor-opentelemetry` (发行版)
自定义 OpenTelemetry 管道`azure-monitor-opentelemetry-exporter` (本库)
对遥测数据进行细粒度控制`azure-monitor-opentelemetry-exporter` (本库)

跟踪导出器

from azure.identity import DefaultAzureCredential
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# 从环境变量读取 APPLICATIONINSIGHTS_CONNECTION_STRING 以标识资源;
# DefaultAzureCredential 通过 Microsoft Entra ID 对 ingestion 进行身份验证。
exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
)

# 配置跟踪器提供程序
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(exporter)
)

# 使用跟踪器
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("my-span"):
    print("Hello, World!")

指标导出器

from azure.identity import DefaultAzureCredential
from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

# 从环境变量读取 APPLICATIONINSIGHTS_CONNECTION_STRING;通过 DefaultAzureCredential 进行 AAD 身份验证的 ingestion。
exporter = AzureMonitorMetricExporter(
    credential=DefaultAzureCredential(),
)

# 配置度量提供程序
reader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)
metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))

# 使用度量器
meter = metrics.get_meter(__name__)
counter = meter.create_counter("requests_total")
counter.add(1, {"route": "/api/users"})

日志导出器

import logging
from azure.identity import DefaultAzureCredential
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

# 从环境变量读取 APPLICATIONINSIGHTS_CONNECTION_STRING;通过 DefaultAzureCredential 进行 AAD 身份验证的 ingestion。
exporter = AzureMonitorLogExporter(
    credential=DefaultAzureCredential(),
)

# 配置日志提供程序
logger_provider = LoggerProvider()
logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
set_logger_provider(logger_provider)

# 添加到 Python 日志处理程序
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.getLogger().addHandler(handler)

# 使用日志记录
logger = logging.getLogger(__name__)
logger.info("This will be sent to Application Insights")

从环境变量

导出器自动读取 APPLICATIONINSIGHTS_CONNECTION_STRING

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# 来自环境的连接字符串;通过 DefaultAzureCredential 进行 AAD 身份验证的 ingestion。
exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
)

Azure AD 身份验证

from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# 本地开发:DefaultAzureCredential。生产环境:设置 AZURE_TOKEN_CREDENTIALS=prod 或 AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# 或者在生产环境中直接使用特定的凭据:
# 请参阅 https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

exporter = AzureMonitorTraceExporter(
    credential=credential
)
</specific_credential>

采样

使用 ApplicationInsightsSampler 进行一致的采样:

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio
from azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler

# 采样 10% 的跟踪
sampler = ApplicationInsightsSampler(sampling_ratio=0.1)

trace.set_tracer_provider(TracerProvider(sampler=sampler))

离线存储

配置用于重试的离线存储:

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
    storage_directory="/path/to/storage",  # 自定义存储路径
    disable_offline_storage=False  # 启用重试(默认)
)

禁用离线存储

exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
    disable_offline_storage=True  # 失败时不重试
)

主权云

from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.us/",
    credential=credential
)

导出器类型

导出器遥测类型Application Insights 表
`AzureMonitorTraceExporter`跟踪/跨度requests, dependencies, exceptions
`AzureMonitorMetricExporter`指标customMetrics, performanceCounters
`AzureMonitorLogExporter`日志traces, customEvents

配置选项

参数描述默认值
`connection_string`Application Insights 连接字符串来自环境变量
`credential`用于 AAD 身份验证的 Azure 凭据None
`disable_offline_storage`禁用重试存储False
`storage_directory`自定义存储路径临时目录

最佳实践

  1. 选择同步或异步并保持一致。 不要在同一调用路径中混合使用 azure.xxx 同步客户端和 azure.xxx.aio 异步客户端。每个模块选择一种模式。
  2. 在进程退出时刷新并关闭提供程序。 在进程退出时调用 shutdown/flush API(例如 tracer_provider.shutdown()meter_provider.shutdown()logger_provider.shutdown()),以便在进程终止前刷新遥测数据。
  3. 生产环境中使用 BatchSpanProcessor(而非 SimpleSpanProcessor)
  4. 使用 ApplicationInsightsSampler 以实现跨服务的一致性采样
  5. 启用离线存储 以提高生产环境的可靠性
  6. 使用 Microsoft Entra 身份验证 替代仪器化密钥
  7. 设置适合您工作负载的导出间隔
  8. 除非需要自定义管道,否则使用发行版 (azure-monitor-opentelemetry)
在 GitHub 上查看
---
name: azure-monitor-opentelemetry-exporter-py
description: Export OpenTelemetry traces, metrics, and logs to Azure Application Insights using Python.
license: MIT
---

# Azure Monitor OpenTelemetry Exporter for Python

Low-level exporter for sending OpenTelemetry traces, metrics, and logs to Application Insights.

## Installation

```bash
pip install azure-monitor-opentelemetry-exporter
```

## Environment Variables

```bash
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```

> **🔑 Auth & lifecycle:** These exporters take a connection string by design, but for *AAD-authenticated ingestion* (where supported) prefer `DefaultAzureCredential` via the `credential=` parameter — see the [Azure AD Authentication](#azure-ad-authentication) section. Any Azure SDK clients you create alongside the exporter should be wrapped in `with`/`async with` blocks (and async credentials from `azure.identity.aio` likewise).

## When to Use

| Scenario | Use |
|----------|-----|
| Quick setup, auto-instrumentation | `azure-monitor-opentelemetry` (distro) |
| Custom OpenTelemetry pipeline | `azure-monitor-opentelemetry-exporter` (this) |
| Fine-grained control over telemetry | `azure-monitor-opentelemetry-exporter` (this) |

## Trace Exporter

```python
from azure.identity import DefaultAzureCredential
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env to identify the resource;
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID.
exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
)

# Configure tracer provider
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(exporter)
)

# Use tracer
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("my-span"):
    print("Hello, World!")
```

## Metric Exporter

```python
from azure.identity import DefaultAzureCredential
from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorMetricExporter(
    credential=DefaultAzureCredential(),
)

# Configure meter provider
reader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)
metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))

# Use meter
meter = metrics.get_meter(__name__)
counter = meter.create_counter("requests_total")
counter.add(1, {"route": "/api/users"})
```

## Log Exporter

```python
import logging
from azure.identity import DefaultAzureCredential
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorLogExporter(
    credential=DefaultAzureCredential(),
)

# Configure logger provider
logger_provider = LoggerProvider()
logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
set_logger_provider(logger_provider)

# Add handler to Python logging
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.getLogger().addHandler(handler)

# Use logging
logger = logging.getLogger(__name__)
logger.info("This will be sent to Application Insights")
```

## From Environment Variable

Exporters read `APPLICATIONINSIGHTS_CONNECTION_STRING` automatically:

```python
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Connection string from environment; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
)
```

## Azure AD Authentication

```python
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# 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()

exporter = AzureMonitorTraceExporter(
    credential=credential
)
```

## Sampling

Use `ApplicationInsightsSampler` for consistent sampling:

```python
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio
from azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler

# Sample 10% of traces
sampler = ApplicationInsightsSampler(sampling_ratio=0.1)

trace.set_tracer_provider(TracerProvider(sampler=sampler))
```

## Offline Storage

Configure offline storage for retry:

```python
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
    storage_directory="/path/to/storage",  # Custom storage path
    disable_offline_storage=False  # Enable retry (default)
)
```

## Disable Offline Storage

```python
exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
    disable_offline_storage=True  # No retry on failure
)
```

## Sovereign Clouds

```python
from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.us/",
    credential=credential
)
```

## Exporter Types

| Exporter | Telemetry Type | Application Insights Table |
|----------|---------------|---------------------------|
| `AzureMonitorTraceExporter` | Traces/Spans | requests, dependencies, exceptions |
| `AzureMonitorMetricExporter` | Metrics | customMetrics, performanceCounters |
| `AzureMonitorLogExporter` | Logs | traces, customEvents |

## Configuration Options

| Parameter | Description | Default |
|-----------|-------------|---------|
| `connection_string` | Application Insights connection string | From env var |
| `credential` | Azure credential for AAD auth | None |
| `disable_offline_storage` | Disable retry storage | False |
| `storage_directory` | Custom storage path | Temp directory |

## Best Practices

1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
2. **Flush and shut down providers at process exit.** Call the shutdown/flush APIs (e.g. `tracer_provider.shutdown()`, `meter_provider.shutdown()`, `logger_provider.shutdown()`) at process exit to flush telemetry before the process terminates.
3. **Use BatchSpanProcessor** for production (not SimpleSpanProcessor)
4. **Use ApplicationInsightsSampler** for consistent sampling across services
5. **Enable offline storage** for reliability in production
6. **Use Microsoft Entra authentication** instead of instrumentation keys
7. **Set export intervals** appropriate for your workload
8. **Use the distro** (`azure-monitor-opentelemetry`) unless you need custom pipelines

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