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azure-monitor-opentelemetry-ts

Add monitoring to Node.js applications with Azure Monitor and OpenTelemetry. Collect traces that follow application work, performance measurements, and logs in Application Insights, with guidance for automatic collection, custom measurements, sampling, and shutdown.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.

연동 도구Azure

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Adding automatic monitoring to a Node.js application
  • Recording custom operations and performance measurements
  • Sending custom logs to Azure Monitor
  • Configuring trace sampling and shutdown data delivery

포함된 파일(1)

  • SKILL.md8.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Azure Monitor OpenTelemetry SDK for TypeScript

Auto-instrument Node.js applications with distributed tracing, metrics, and logs.

Installation

# 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

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Quick Start (Auto-Instrumentation)

IMPORTANT: Call useAzureMonitor() BEFORE importing other modules.

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

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

// Now import your application
import express from "express";
const app = express();

ESM Support (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"
  }
}

Full Configuration

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);

Custom Traces

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();
}

Custom Metrics

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" });
});

Manual Exporter Setup

Trace Exporter

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();

Metric Exporter

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);

Log Exporter

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);

Custom Logs Ingestion

import { DefaultAzureCredential, ManagedIdentityCredential } 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";

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
const credential = new DefaultAzureCredential({requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"]});
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest#credential-classes
// const credential = new ManagedIdentityCredential();

const client = new LogsIngestionClient(endpoint, credential);

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);
    }
  }
}

Custom Span Processor

import { SpanProcessor, ReadableSpan } from "@opentelemetry/sdk-trace-base";
import { Span, Context, SpanKind, TraceFlags } from "@opentelemetry/api";
import { useAzureMonitor } from "@azure/monitor-opentelemetry";

class FilteringSpanProcessor implements SpanProcessor {
  forceFlush(): Promise<void> { return Promise.resolve(); }
  shutdown(): Promise<void> { return Promise.resolve(); }
  onStart(span: Span, context: Context): void {}
  
  onEnd(span: ReadableSpan): void {
    // Add custom attributes
    span.attributes["CustomDimension"] = "value";
    
    // Filter out internal spans
    if (span.kind === SpanKind.INTERNAL) {
      span.spanContext().traceFlags = TraceFlags.NONE;
    }
  }
}

useAzureMonitor({
  spanProcessors: [new FilteringSpanProcessor()]
});

Sampling

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 });

Shutdown

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

useAzureMonitor();

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

Key Types

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";

Best Practices

  1. Call useAzureMonitor() first - Before importing other modules
  2. Use ESM loader for ESM projects - --import @azure/monitor-opentelemetry/loader
  3. Enable offline storage - For reliable telemetry in disconnected scenarios
  4. Set sampling ratio - For high-traffic applications
  5. Add custom dimensions - Use span processors for enrichment
  6. Graceful shutdown - Call shutdownAzureMonitor() to flush telemetry

리뷰

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