azure-monitor-opentelemetry-ts
microsoft/skills
Instrumente automaticamente aplicativos Node.js com rastreamento distribuído, métricas e logs usando o Azure Monitor e o OpenTelemetry.
...Expandir tudoSDK do Azure Monitor OpenTelemetry para TypeScript
Instrumente automaticamente aplicativos Node.js com rastreamento distribuído, métricas e logs.
Instalação
# Distribuição (recomendado – instrumentação automática)
npm install @azure/monitor-opentelemetry
# Exportadores de baixo nível (configuração personalizada do OpenTelemetry)
npm install @azure/monitor-opentelemetry-exporter
# Ingestão personalizada de logs
npm install @azure/monitor-ingestion
Variáveis de ambiente
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...
AZURE_TOKEN_CREDENTIALS=prod # Necessário apenas se DefaultAzureCredential for usado em produção
Início rápido (instrumentação automática)
IMPORTANTE: Chame useAzureMonitor() ANTES de importar outros módulos.
import { useAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor({
azureMonitorExporterOptions: {
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
}
});
// Agora importe seu aplicativo
import express from "express";
const app = express();
Suporte a ESM (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"
}
}
Configuração completa
import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { resourceFromAttributes } from "@opentelemetry/resources";
const options: AzureMonitorOpenTelemetryOptions = {
azureMonitorExporterOptions: {
connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING,
storageDirectory: "/caminho/para/armazenamento/offline",
disableOfflineStorage: false
},
// Amostragem
samplingRatio: 1.0, // 0-1, porcentagem de traços
// Recursos
enableLiveMetrics: true,
enableStandardMetrics: true,
enablePerformanceCounters: true,
// Bibliotecas de instrumentação
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 }
},
// Recurso personalizado
resource: resourceFromAttributes({ "service.name": "my-service" })
};
useAzureMonitor(options);
Rastreamentos personalizados
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");
// Seu trabalho aqui
} catch (error) {
span.recordException(error as Error);
span.setStatus({ code: 2, message: (error as Error).message });
} finally {
span.end();
}
Métricas personalizadas
import { metrics } from "@opentelemetry/api";
const meter = metrics.getMeter("my-meter");
// Contador
const counter = meter.createCounter("requests_total");
counter.add(1, { route: "/api/users", method: "GET" });
// Histograma
const histogram = meter.createHistogram("request_duration_ms");
histogram.record(150, { route: "/api/users" });
// Indicador observável
const gauge = meter.createObservableGauge("active_connections");
gauge.addCallback((result) => {
result.observe(getActiveConnections(), { pool: "main" });
});
Configuração manual do exportador
Exportador de rastreamento
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();
Exportador de métricas
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);
Exportador de logs
import { AzureMonitorLogExporter } de "@azure/monitor-opentelemetry-exporter";
import { BatchLogRecordProcessor, LoggerProvider } de "@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);
Ingestão de logs personalizados
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";
// Desenvolvimento local: DefaultAzureCredential. Produção: defina AZURE_TOKEN_CREDENTIALS=prod ou AZURE_TOKEN_CREDENTIALS=
const credential = new DefaultAzureCredential({requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"]});
// Ou use uma credencial específica diretamente em produção:
// Consulte 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: "Aplicativo iniciado",
Level: "Informação"
}
];
try {
await client.upload(ruleId, streamName, logs);
} catch (error) {
if (isAggregateLogsUploadError(error)) {
for (const uploadError of error.errors) {
console.error("Logs com falha:", uploadError.failedLogs);
}
}
}
Processador de Span personalizado
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 {
// Adicionar atributos personalizados
span.attributes["CustomDimension"] = "value";
// Filtrar spans internos
if (span.kind === SpanKind.INTERNAL) {
span.spanContext().traceFlags = TraceFlags.NONE;
}
}
}
useAzureMonitor({
spanProcessors: [new FilteringSpanProcessor()]
});
Amostragem
import { ApplicationInsightsSampler } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
// Amostrar 75% dos rastreamentos
const sampler = new ApplicationInsightsSampler(0.75);
const provider = new NodeTracerProvider({ sampler });
Desligamento
import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";
useAzureMonitor();
// Ao encerrar o aplicativo
process.on("SIGTERM", async () => {
await shutdownAzureMonitor();
process.exit(0);
});
Tipos de chave
import {
useAzureMonitor,
shutdownAzureMonitor,
AzureMonitorOpenTelemetryOptions,
InstrumentationOptions
} from "@azure/monitor-opentelemetry";
import {
AzureMonitorTraceExporter,
AzureMonitorMetricExporter,
AzureMonitorLogExporter,
ApplicationInsightsSampler,
AzureMonitorExporterOptions
} de "@azure/monitor-opentelemetry-exporter";
import {
LogsIngestionClient,
isAggregateLogsUploadError
} de "@azure/monitor-ingestion";
Práticas recomendadas
- Chame useAzureMonitor() primeiro — antes de importar outros módulos
- Use o carregador ESM para projetos ESM —
--import @azure/monitor-opentelemetry/loader - Habilite o armazenamento offline — Para telemetria confiável em cenários sem conexão
- Defina a taxa de amostragem — para aplicativos de alto tráfego
- Adicione dimensões personalizadas – Use processadores de intervalo para enriquecimento
- Desligamento controlado – Chame
shutdownAzureMonitor()para liberar os dados de telemetria
---
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
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