azure-monitor-opentelemetry-ts

TypeScript 版 Azure Monitor OpenTelemetry SDK

通过分布式追踪、指标和日志，自动检测 Node.js 应用程序。

版本和数据边界

本文档介绍现有的 @azure/monitor-opentelemetry 发行版。Microsoft 当前的指导也为新采用场景提供了 @microsoft/opentelemetry；请勿同时初始化两者。所有 OpenTelemetry 包都应使用匹配的版本，并采用该版本的日志提供程序构造函数／处理器 API。配置获准的遥测数据目的地，移除个人身份信息（PII）和机密，并保护离线存储；自动检测可能会导出敏感的请求／日志数据。请参阅 Microsoft 启用指南。

安装

# Distro (recommended - auto-instrumentation)
npm install @azure/monitor-opentelemetry

# Low-level exporters (custom OpenTelemetry setup)
npm install @azure/monitor-opentelemetry-exporter

# Custom logs ingestion
npm install @azure/monitor-ingestion

环境变量

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...

快速入门（自动检测）

重要： 请在导入其他模块之前调用 useAzureMonitor()。

import { useAzureMonitor } from "@azure/monitor-opentelemetry";

useAzureMonitor({
  azureMonitorExporterOptions: {
    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
  }
});

// Static ESM imports are hoisted; load the app after initialization.
const { default: express } = await import("express");
const app = express();

ESM 支持（Node.js 18.19+）

node --import @azure/monitor-opentelemetry/loader ./dist/index.js

package.json：

{
  "scripts": {
    "start": "node --import @azure/monitor-opentelemetry/loader ./dist/index.js"
  }
}

完整配置

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { resourceFromAttributes } from "@opentelemetry/resources";

const options: AzureMonitorOpenTelemetryOptions = {
  azureMonitorExporterOptions: {
    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING,
    storageDirectory: "/path/to/offline/storage",
    disableOfflineStorage: false
  },
  
  // Sampling
  samplingRatio: 1.0,  // 0-1, percentage of traces
  
  // Features
  enableLiveMetrics: true,
  enableStandardMetrics: true,
  enablePerformanceCounters: true,
  
  // Instrumentation libraries
  instrumentationOptions: {
    azureSdk: { enabled: true },
    http: { enabled: true },
    mongoDb: { enabled: true },
    mySql: { enabled: true },
    postgreSql: { enabled: true },
    redis: { enabled: true },
    bunyan: { enabled: false },
    winston: { enabled: false }
  },
  
  // Custom resource
  resource: resourceFromAttributes({ "service.name": "my-service" })
};

useAzureMonitor(options);

自定义追踪

import { trace } from "@opentelemetry/api";

const tracer = trace.getTracer("my-tracer");

const span = tracer.startSpan("doWork");
try {
  span.setAttribute("component", "worker");
  span.setAttribute("operation.id", "42");
  span.addEvent("processing started");
  
  // Your work here
  
} catch (error) {
  span.recordException(error as Error);
  span.setStatus({ code: 2, message: (error as Error).message });
} finally {
  span.end();
}

自定义指标

import { metrics } from "@opentelemetry/api";

const meter = metrics.getMeter("my-meter");

// Counter
const counter = meter.createCounter("requests_total");
counter.add(1, { route: "/api/users", method: "GET" });

// Histogram
const histogram = meter.createHistogram("request_duration_ms");
histogram.record(150, { route: "/api/users" });

// Observable Gauge
const gauge = meter.createObservableGauge("active_connections");
gauge.addCallback((result) => {
  result.observe(getActiveConnections(), { pool: "main" });
});

手动设置导出器

追踪导出器

import { AzureMonitorTraceExporter } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider, BatchSpanProcessor } from "@opentelemetry/sdk-trace-node";

const exporter = new AzureMonitorTraceExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const provider = new NodeTracerProvider({
  spanProcessors: [new BatchSpanProcessor(exporter)]
});

provider.register();

指标导出器

import { AzureMonitorMetricExporter } from "@azure/monitor-opentelemetry-exporter";
import { PeriodicExportingMetricReader, MeterProvider } from "@opentelemetry/sdk-metrics";
import { metrics } from "@opentelemetry/api";

const exporter = new AzureMonitorMetricExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const meterProvider = new MeterProvider({
  readers: [new PeriodicExportingMetricReader({ exporter })]
});

metrics.setGlobalMeterProvider(meterProvider);

日志导出器

import { AzureMonitorLogExporter } from "@azure/monitor-opentelemetry-exporter";
import { BatchLogRecordProcessor, LoggerProvider } from "@opentelemetry/sdk-logs";
import { logs } from "@opentelemetry/api-logs";

const exporter = new AzureMonitorLogExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const loggerProvider = new LoggerProvider();
loggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(exporter));

logs.setGlobalLoggerProvider(loggerProvider);

自定义日志引入

import { DefaultAzureCredential } from "@azure/identity";
import { LogsIngestionClient, isAggregateLogsUploadError } from "@azure/monitor-ingestion";

const endpoint = "https://<dce>.ingest.monitor.azure.com";
const ruleId = "<data-collection-rule-id>";
const streamName = "Custom-MyTable_CL";

const client = new LogsIngestionClient(endpoint, new DefaultAzureCredential());

const logs = [
  {
    Time: new Date().toISOString(),
    Computer: "Server1",
    Message: "Application started",
    Level: "Information"
  }
];

try {
  await client.upload(ruleId, streamName, logs);
} catch (error) {
  if (isAggregateLogsUploadError(error)) {
    for (const uploadError of error.errors) {
      console.error("Failed logs:", uploadError.failedLogs);
    }
  }
}

自定义 Span 处理

在 onStart 中通过 span.setAttribute() 丰富可写入的 Span。请勿在 onEnd 中修改 ReadableSpan.attributes 或 spanContext().traceFlags：已结束的 Span 为只读状态，这也不是可靠的导出过滤方式。请针对锁定的 SDK 版本使用受支持的采样器或经过审核的过滤导出器／处理器管线，并验证导出的数据。

采样

import { ApplicationInsightsSampler } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";

// Sample 75% of traces
const sampler = new ApplicationInsightsSampler(0.75);

const provider = new NodeTracerProvider({ sampler });

关闭

import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";

useAzureMonitor();

// On application shutdown
process.on("SIGTERM", async () => {
  await shutdownAzureMonitor();
  process.exit(0);
});

关键类型

import {
  useAzureMonitor,
  shutdownAzureMonitor,
  AzureMonitorOpenTelemetryOptions,
  InstrumentationOptions
} from "@azure/monitor-opentelemetry";

import {
  AzureMonitorTraceExporter,
  AzureMonitorMetricExporter,
  AzureMonitorLogExporter,
  ApplicationInsightsSampler,
  AzureMonitorExporterOptions
} from "@azure/monitor-opentelemetry-exporter";

import {
  LogsIngestionClient,
  isAggregateLogsUploadError
} from "@azure/monitor-ingestion";

最佳实践

1. 首先调用 useAzureMonitor() - 在导入其他模块之前
2. ESM 项目使用 ESM 加载器 - --import @azure/monitor-opentelemetry/loader
3. 启用离线存储 - 确保在断连场景下可靠地收集遥测数据
4. 设置采样率 - 适用于高流量应用程序
5. 添加自定义维度 - 使用 Span 处理器进行丰富
6. 优雅关闭 - 调用 shutdownAzureMonitor() 刷新遥测数据

适用场景

当需要执行概述中所述的工作流或操作时，可使用此技能。

限制

• 仅当任务明确符合上述范围时，才使用此技能。
• 请勿将输出视为环境特定验证、测试或专家审核的替代品。
• 如果缺少必要的输入、权限、安全边界或成功标准，请停止并请求澄清。