azure-monitor-opentelemetry-ts
microsoft/skills
Azure Monitor 및 OpenTelemetry를 사용하여 Node.js 애플리케이션에 분산 추적, 메트릭 및 로그 기능을 자동으로 적용합니다.
...모든 것을 확장하십시오TypeScript용 Azure Monitor OpenTelemetry SDK
분산 추적, 메트릭 및 로그 기능을 통해 Node.js 애플리케이션을 자동으로 계측합니다.
설치
# 배포판 (권장 - 자동 계측)
npm install @azure/monitor-opentelemetry
# 저수준 익스포터 (사용자 지정 OpenTelemetry 설정)
npm install @azure/monitor-opentelemetry-exporter
# 사용자 지정 로그 수집
npm install @azure/monitor-ingestion
환경 변수
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...
AZURE_TOKEN_CREDENTIALS=prod # 프로덕션 환경에서 DefaultAzureCredential을 사용하는 경우에만 필요
빠른 시작 (자동 계측)
중요: 다른 모듈을 임포트하기 전에 useAzureMonitor()를 호출하십시오.
import { useAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor({
azureMonitorExporterOptions: {
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
}
});
// 이제 애플리케이션을 가져옵니다
import express from "express";
const app = express();
ESM 지원 (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"
}
}
전체 구성
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
},
// 샘플링
samplingRatio: 1.0, // 0-1, 트레이스 비율
// 기능
enableLiveMetrics: true,
enableStandardMetrics: true,
enablePerformanceCounters: true,
// 계측 라이브러리
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 }
},
// 사용자 정의 리소스
resource: resourceFromAttributes({ "service.name": "my-service" })
};
useAzureMonitor(options);
사용자 정의 트레이스
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");
// 여기에서 작업을 수행하세요
} catch (error) {
span.recordException(error as Error);
span.setStatus({ code: 2, message: (error as Error).message });
} finally {
span.end();
}
사용자 정의 메트릭
import { metrics } from "@opentelemetry/api";
const meter = metrics.getMeter("my-meter");
// 카운터
const counter = meter.createCounter("requests_total");
counter.add(1, { route: "/api/users", method: "GET" });
// 히스토그램
const histogram = meter.createHistogram("request_duration_ms");
histogram.record(150, { route: "/api/users" });
// 관측 가능 게이지
const gauge = meter.createObservableGauge("active_connections");
gauge.addCallback((result) => {
result.observe(getActiveConnections(), { pool: "main" });
});
수동 익스포터 설정
추적 익스포터
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();
메트릭 익스포터
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);
로그 익스포터
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);
사용자 지정 로그 수집
import { DefaultAzureCredential, ManagedIdentityCredential } from "@azure/identity";
import { LogsIngestionClient, isAggregateLogsUploadError } from "@azure/monitor-ingestion";
const endpoint = "https://.ingest.monitor.azure.com";
const ruleId = "";
const streamName = "Custom-MyTable_CL";
// 로컬 개발 환경: DefaultAzureCredential. 프로덕션 환경: AZURE_TOKEN_CREDENTIALS=prod 또는 AZURE_TOKEN_CREDENTIALS=로 설정
const credential = new DefaultAzureCredential({requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"]});
// 또는 프로덕션 환경에서 특정 자격 증명을 직접 사용:
// https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest#credential-classes 참조
// 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("업로드 실패한 로그:", uploadError.failedLogs);
}
}
}
사용자 정의 스팬 프로세서
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 { return Promise.resolve(); }
shutdown(): Promise { return Promise.resolve(); }
onStart(span: Span, context: Context): void {}
onEnd(span: ReadableSpan): void {
// 사용자 정의 속성 추가
span.attributes["CustomDimension"] = "value";
// 내부 스팬 필터링
if (span.kind === SpanKind.INTERNAL) {
span.spanContext().traceFlags = TraceFlags.NONE;
}
}
}
useAzureMonitor({
spanProcessors: [new FilteringSpanProcessor()]
});
샘플링
import { ApplicationInsightsSampler } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
// 트레이스의 75%를 샘플링
const sampler = new ApplicationInsightsSampler(0.75);
const provider = new NodeTracerProvider({ sampler });
종료
import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor();
// 애플리케이션 종료 시
process.on("SIGTERM", async () => {
await shutdownAzureMonitor();
process.exit(0);
});
키 유형
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";
모범 사례
- 다른 모듈을 가져오기 전에먼저 useAzureMonitor()를 호출하세요
- ESM 프로젝트의 경우 ESM 로더를 사용하세요 —
--import @azure/monitor-opentelemetry/loader - 오프라인 저장소 활성화 - 연결이 끊긴 상황에서도 안정적인 텔레메트리 수집을 위해
- 샘플링 비율 설정 - 트래픽이 많은 애플리케이션의 경우
- 사용자 지정 차원 추가 - 데이터 보강을 위해 스팬 프로세서 사용
- 정상적인 종료 -
shutdownAzureMonitor()를호출하여 텔레메트리 데이터를 플러시하세요
---
name: azure-monitor-opentelemetry-ts
description: Auto-instrument Node.js applications with distributed tracing, metrics, and logs using Azure Monitor and OpenTelemetry.
license: MIT
---
# Azure Monitor OpenTelemetry SDK for TypeScript
Auto-instrument Node.js applications with distributed tracing, metrics, and logs.
## Installation
```bash
# 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
```bash
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.
```typescript
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+)
```bash
node --import @azure/monitor-opentelemetry/loader ./dist/index.js
```
**package.json:**
```json
{
"scripts": {
"start": "node --import @azure/monitor-opentelemetry/loader ./dist/index.js"
}
}
```
## Full Configuration
```typescript
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
```typescript
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
```typescript
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
```typescript
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
```typescript
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
```typescript
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
```typescript
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
```typescript
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
```typescript
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
```typescript
import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor();
// On application shutdown
process.on("SIGTERM", async () => {
await shutdownAzureMonitor();
process.exit(0);
});
```
## Key Types
```typescript
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
모든 파일
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