azure-monitor-opentelemetry-ts✓ تایید شده

محتوای SKILL.md

---
name: azure-monitor-opentelemetry-ts
description: 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.
package: "@azure/monitor-opentelemetry"
---

# 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=...
```

## 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 } 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);
    }
  }
}
```

## 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

مجوز

مجوز اعلام‌شده: MIT

MIT License

Copyright (c) 2026 microsoft

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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