azure-monitor-opentelemetry-ts

CategoryGeneral
AuthorAgentic Awesome Skills 社区
LicenseMIT
Rating4.80/5
Uses8.3K

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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