Azure Monitor Opentelemetry Exporter Py

作者 microsoft354361d83247MIT收录于 2026年10月8日更新于 2026年10月8日

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".

精选仅含说明Software DevelopmentDevOps & Cloud
AI 生成的概览

指导 Python 开发者将 OpenTelemetry 跟踪、指标和日志导出到 Azure Application Insights。

功能
该技能提供使用 azure-monitor-opentelemetry-exporter Python 包将 OpenTelemetry 遥测数据发送到 Application Insights 的参考说明和代码示例。内容涵盖跟踪、指标和日志导出器、使用 DefaultAzureCredential 进行身份验证、采样、离线存储、主权云以及配置选项。它产出的是指导和示例代码片段，而非可执行脚本。
适用场景
适用于在 Python 中构建需要低层控制、将遥测数据导出到 Application Insights 的自定义 OpenTelemetry 管道时。也适用于为此类导出器配置身份验证、采样或离线存储的场景。
运行要求
需要 azure-monitor-opentelemetry-exporter Python 包、azure-identity 包以及 OpenTelemetry SDK 包。需要 Application Insights 连接字符串；若使用 Microsoft Entra 身份验证，还需要 Azure 凭据。需要访问 Azure 摄取端点的网络连接。不附带脚本。

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 methodsAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

  1. Prefer DefaultAzureCredential for ingestion auth when supported. APPLICATIONINSIGHTS_CONNECTION_STRING identifies the target Application Insights resource, and credential=DefaultAzureCredential(...) provides Microsoft Entra authentication.
    • 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. Providers are not context managers. Flush and shut down telemetry providers explicitly at process exit so buffers are exported deterministically.

Snippets may abbreviate this setup, but production code should always follow both rules.

When to Use

ScenarioUse
Quick setup, auto-instrumentationazure-monitor-opentelemetry (distro)
Custom OpenTelemetry pipelineazure-monitor-opentelemetry-exporter (this)
Fine-grained control over telemetryazure-monitor-opentelemetry-exporter (this)

Trace Exporter

python
from azure.identity import DefaultAzureCredentialfrom opentelemetry import tracefrom opentelemetry.sdk.trace import TracerProviderfrom opentelemetry.sdk.trace.export import BatchSpanProcessorfrom 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 providertrace.set_tracer_provider(TracerProvider())trace.get_tracer_provider().add_span_processor(    BatchSpanProcessor(exporter))
# Use tracertracer = trace.get_tracer(__name__)with tracer.start_as_current_span("my-span"):    print("Hello, World!")

Metric Exporter

python
from azure.identity import DefaultAzureCredentialfrom opentelemetry import metricsfrom opentelemetry.sdk.metrics import MeterProviderfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReaderfrom azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.exporter = AzureMonitorMetricExporter(    credential=DefaultAzureCredential(),)
# Configure meter providerreader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))
# Use metermeter = metrics.get_meter(__name__)counter = meter.create_counter("requests_total")counter.add(1, {"route": "/api/users"})

Log Exporter

python
import loggingfrom azure.identity import DefaultAzureCredentialfrom opentelemetry._logs import set_logger_providerfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandlerfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessorfrom azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.exporter = AzureMonitorLogExporter(    credential=DefaultAzureCredential(),)
# Configure logger providerlogger_provider = LoggerProvider()logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))set_logger_provider(logger_provider)
# Add handler to Python logginghandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)logging.getLogger().addHandler(handler)
# Use logginglogger = 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 DefaultAzureCredentialfrom 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, ManagedIdentityCredentialfrom 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 TracerProviderfrom opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatiofrom azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler
# Sample 10% of tracessampler = ApplicationInsightsSampler(sampling_ratio=0.1)
trace.set_tracer_provider(TracerProvider(sampler=sampler))

Offline Storage

Configure offline storage for retry:

python
from azure.identity import DefaultAzureCredentialfrom 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, DefaultAzureCredentialfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Azure Governmentcredential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)exporter = AzureMonitorTraceExporter(    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.us/",    credential=credential)

Exporter Types

ExporterTelemetry TypeApplication Insights Table
AzureMonitorTraceExporterTraces/Spansrequests, dependencies, exceptions
AzureMonitorMetricExporterMetricscustomMetrics, performanceCounters
AzureMonitorLogExporterLogstraces, customEvents

Configuration Options

ParameterDescriptionDefault
connection_stringApplication Insights connection stringFrom env var
credentialAzure credential for AAD authNone
disable_offline_storageDisable retry storageFalse
storage_directoryCustom storage pathTemp 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. Call provider.shutdown() / force_flush() at process exit to flush telemetry — providers are not context managers.
  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

Reference Files

FileContents
references/capabilities.md [blocked]Additional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.md [blocked]Dedicated non-hero examples for secondary/advanced scenarios.

来源与署名

来源：microsoft/skills位于.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-py提交354361d

许可证： MIT

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架

更多来自 microsoft/skills 的技能

Customize

microsoft

精选

以交互式引导流程部署 Azure OpenAI 模型，可自定义版本、SKU、容量和内容筛选。

DevOps & Cloud2026年10月8日

Discover Azure Skills

microsoft

精选

搜索 GitHub 上的 Azure 技能目录，并推荐与 Azure 任务匹配的可安装智能体技能。

AI & Agents2026年10月8日

Azure Resource Visualizer

microsoft

精选

分析 Azure 资源组，并生成包含详细 Mermaid 架构图及资源关系说明的 Markdown 文档。

DevOps & Cloud2026年10月8日

Azure Resource Lookup

microsoft

精选

使用 Azure Resource Graph 查询和 MCP 工具，跨订阅列出和查找 Azure 资源。

DevOps & Cloud2026年10月8日

Azure Messaging

microsoft

精选

排查并解决 Azure 事件中心和 Service Bus SDK 的连接、身份验证和消息处理问题。

DevOps & Cloud2026年10月8日

Azure Kusto

microsoft

精选

在 Azure Data Explorer（Kusto）中执行 KQL 查询并浏览架构，用于日志、遥测和时间序列分析。

Data & Analytics2026年10月8日

更多Software Development技能

Playground

anthropics

精选

构建自包含的交互式 HTML 演练场，包含控件、实时预览和可复制的提示词输出。

Software Development37K今天更新

M5 Onboard

anthropics

精选

通过 USB 检测 M5Stack ESP32 开发板，刷写 UIFlow 2.0 固件并安装 MicroPython 应用包。

Software Development37K今天更新

Cardputer Buddy

anthropics

精选

指导迭代 Cardputer-Adv 的 MicroPython 应用包：添加应用、通过 USB 串口推送文件、查看日志并运行 REPL 命令。

Software Development37K今天更新

Testing Strategy

anthropics

精选

设计测试策略与测试计划，涵盖测试类型、覆盖率目标与示例测试用例。

Software Development27K今天更新

Tech Debt

anthropics

精选

识别、分类并排序技术债务，产出评分后的待办清单与分阶段整改计划。

Software Development27K今天更新

System Design

anthropics

精选

通过需求、高层设计、深入设计、扩展与权衡分析来指导系统与架构设计。

Software Development27K今天更新