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 = .. . 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 } from "@azure/identity" ; import { LogsIngestionClient , isAggregateLogsUploadError } from "@azure/monitor-ingestion" ; const endpoint = "https://.ingest.monitor.azure.com" ; const ruleId = "" ; 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 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 Call useAzureMonitor() first - Before importing other modules Use ESM loader for ESM projects - --import @azure/monitor-opentelemetry/loader Enable offline storage - For reliable telemetry in disconnected scenarios Set sampling ratio - For high-traffic applications Add custom dimensions - Use span processors for enrichment Graceful shutdown - Call shutdownAzureMonitor() to flush telemetry When to Use This skill is applicable to execute the workflow or actions described in the overview.