azure-monitor-opentelemetry-exporter-py
microsoft/skills
使用 Python 將 OpenTelemetry 跟蹤、指標和日誌匯出到 Azure Application Insights。
...展開全部Azure Monitor OpenTelemetry Python 匯出器
用於將 OpenTelemetry 跟蹤、指標和日誌傳送到 Application Insights 的低階匯出器。
安裝
pip install azure-monitor-opentelemetry-exporter
環境變數
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/ # 所有身份驗證方法必需
AZURE_TOKEN_CREDENTIALS=prod # 僅在生產環境中使用 DefaultAzureCredential 時必需
🔑 身份驗證與生命週期: 這些匯出器在設計上接受連線字串,但對於 AAD 身份驗證的 ingestion(在支援的場景中),建議透過
credential=引數使用DefaultAzureCredential— 請參閱 Azure AD 身份驗證部分。您與匯出器一起建立的任何 Azure SDK 客戶端都應包裝在with/async with塊中(來自azure.identity.aio的非同步憑據也是如此)。
使用場景
| 場景 | 使用 |
|---|---|
| 快速設定,自動儀器化 | `azure-monitor-opentelemetry` (發行版) |
| 自定義 OpenTelemetry 管道 | `azure-monitor-opentelemetry-exporter` (本庫) |
| 對遙測資料進行細粒度控制 | `azure-monitor-opentelemetry-exporter` (本庫) |
跟蹤匯出器
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
# 從環境變數讀取 APPLICATIONINSIGHTS_CONNECTION_STRING 以標識資源;
# DefaultAzureCredential 透過 Microsoft Entra ID 對 ingestion 進行身份驗證。
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential(),
)
# 配置跟蹤器提供程式
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
BatchSpanProcessor(exporter)
)
# 使用跟蹤器
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("my-span"):
print("Hello, World!")
指標匯出器
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
# 從環境變數讀取 APPLICATIONINSIGHTS_CONNECTION_STRING;透過 DefaultAzureCredential 進行 AAD 身份驗證的 ingestion。
exporter = AzureMonitorMetricExporter(
credential=DefaultAzureCredential(),
)
# 配置度量提供程式
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"})
日誌匯出器
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
# 從環境變數讀取 APPLICATIONINSIGHTS_CONNECTION_STRING;透過 DefaultAzureCredential 進行 AAD 身份驗證的 ingestion。
exporter = AzureMonitorLogExporter(
credential=DefaultAzureCredential(),
)
# 配置日誌提供程式
logger_provider = LoggerProvider()
logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
set_logger_provider(logger_provider)
# 新增到 Python 日誌處理程式
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.getLogger().addHandler(handler)
# 使用日誌記錄
logger = logging.getLogger(__name__)
logger.info("This will be sent to Application Insights")
從環境變數
匯出器自動讀取 APPLICATIONINSIGHTS_CONNECTION_STRING:
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# 來自環境的連線字串;透過 DefaultAzureCredential 進行 AAD 身份驗證的 ingestion。
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential(),
)
Azure AD 身份驗證
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# 本地開發:DefaultAzureCredential。生產環境:設定 AZURE_TOKEN_CREDENTIALS=prod 或 AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# 或者在生產環境中直接使用特定的憑據:
# 請參閱 https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
exporter = AzureMonitorTraceExporter(
credential=credential
)
</specific_credential>取樣
使用 ApplicationInsightsSampler 進行一致的取樣:
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio
from azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler
# 取樣 10% 的跟蹤
sampler = ApplicationInsightsSampler(sampling_ratio=0.1)
trace.set_tracer_provider(TracerProvider(sampler=sampler))
離線儲存
配置用於重試的離線儲存:
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential(),
storage_directory="/path/to/storage", # 自定義儲存路徑
disable_offline_storage=False # 啟用重試(預設)
)
禁用離線儲存
exporter = AzureMonitorTraceExporter(
credential=DefaultAzureCredential(),
disable_offline_storage=True # 失敗時不重試
)
主權雲
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
)
匯出器型別
| 匯出器 | 遙測型別 | Application Insights 表 |
|---|---|---|
| `AzureMonitorTraceExporter` | 跟蹤/跨度 | requests, dependencies, exceptions |
| `AzureMonitorMetricExporter` | 指標 | customMetrics, performanceCounters |
| `AzureMonitorLogExporter` | 日誌 | traces, customEvents |
配置選項
| 引數 | 描述 | 預設值 |
|---|---|---|
| `connection_string` | Application Insights 連線字串 | 來自環境變數 |
| `credential` | 用於 AAD 身份驗證的 Azure 憑據 | None |
| `disable_offline_storage` | 禁用重試儲存 | False |
| `storage_directory` | 自定義儲存路徑 | 臨時目錄 |
最佳實踐
- 選擇同步或非同步並保持一致。 不要在同一呼叫路徑中混合使用
azure.xxx同步客戶端和azure.xxx.aio非同步客戶端。每個模組選擇一種模式。 - 在程序退出時重新整理並關閉提供程式。 在程序退出時呼叫 shutdown/flush API(例如
tracer_provider.shutdown()、meter_provider.shutdown()、logger_provider.shutdown()),以便在程序終止前重新整理遙測資料。 - 生產環境中使用 BatchSpanProcessor(而非 SimpleSpanProcessor)
- 使用 ApplicationInsightsSampler 以實現跨服務的一致性取樣
- 啟用離線儲存 以提高生產環境的可靠性
- 使用 Microsoft Entra 身份驗證 替代儀器化金鑰
- 設定適合您工作負載的匯出間隔
- 除非需要自定義管道,否則使用發行版 (
azure-monitor-opentelemetry)
---
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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