azure-monitor-opentelemetry-exporter-java

Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights. Triggers: "AzureMonitorExporter java", "opentelemetry azure java", "application insights java otel", "azure monitor tracing java". Note: This package is DEPRECATED. Migrate to azure-monitor-opentelemetry-autoconfigure.

npx skills add https://github.com/microsoft/skills --skill azure-monitor-opentelemetry-exporter-java

Azure Monitor OpenTelemetry Exporter for Java

⚠️ DEPRECATION NOTICE: This package is deprecated. Migrate to azure-monitor-opentelemetry-autoconfigure.

See Migration Guide for detailed instructions.

Export OpenTelemetry telemetry data to Azure Monitor / Application Insights.

Installation (Deprecated)

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
    <version>1.0.0-beta.x</version>
</dependency>

Recommended: Use Autoconfigure Instead

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
    <version>LATEST</version>
</dependency>

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/

Basic Setup with Autoconfigure (Recommended)

Using Environment Variable

import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdk;
import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdkBuilder;
import io.opentelemetry.api.OpenTelemetry;
import com.azure.monitor.opentelemetry.exporter.AzureMonitorExporter;

// Connection string from APPLICATIONINSIGHTS_CONNECTION_STRING env var
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder);
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

With Explicit Connection String

AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder, "{connection-string}");
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

Creating Spans

import io.opentelemetry.api.trace.Tracer;
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.context.Scope;

// Get tracer
Tracer tracer = openTelemetry.getTracer("com.example.myapp");

// Create span
Span span = tracer.spanBuilder("myOperation").startSpan();

try (Scope scope = span.makeCurrent()) {
    // Your application logic
    doWork();
} catch (Throwable t) {
    span.recordException(t);
    throw t;
} finally {
    span.end();
}

Adding Span Attributes

import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;

Span span = tracer.spanBuilder("processOrder")
    .setAttribute("order.id", "12345")
    .setAttribute("customer.tier", "premium")
    .startSpan();

try (Scope scope = span.makeCurrent()) {
    // Add attributes during execution
    span.setAttribute("items.count", 3);
    span.setAttribute("total.amount", 99.99);
    
    processOrder();
} finally {
    span.end();
}

Custom Span Processor

import io.opentelemetry.sdk.trace.SpanProcessor;
import io.opentelemetry.sdk.trace.ReadWriteSpan;
import io.opentelemetry.sdk.trace.ReadableSpan;
import io.opentelemetry.context.Context;

private static final AttributeKey<String> CUSTOM_ATTR = AttributeKey.stringKey("custom.attribute");

SpanProcessor customProcessor = new SpanProcessor() {
    @Override
    public void onStart(Context context, ReadWriteSpan span) {
        // Add custom attribute to every span
        span.setAttribute(CUSTOM_ATTR, "customValue");
    }

    @Override
    public boolean isStartRequired() {
        return true;
    }

    @Override
    public void onEnd(ReadableSpan span) {
        // Post-processing if needed
    }

    @Override
    public boolean isEndRequired() {
        return false;
    }
};

// Register processor
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder);

sdkBuilder.addTracerProviderCustomizer(
    (sdkTracerProviderBuilder, configProperties) -> 
        sdkTracerProviderBuilder.addSpanProcessor(customProcessor)
);

OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

Nested Spans

public void parentOperation() {
    Span parentSpan = tracer.spanBuilder("parentOperation").startSpan();
    try (Scope scope = parentSpan.makeCurrent()) {
        childOperation();
    } finally {
        parentSpan.end();
    }
}

public void childOperation() {
    // Automatically links to parent via Context
    Span childSpan = tracer.spanBuilder("childOperation").startSpan();
    try (Scope scope = childSpan.makeCurrent()) {
        // Child work
    } finally {
        childSpan.end();
    }
}

Recording Exceptions

Span span = tracer.spanBuilder("riskyOperation").startSpan();
try (Scope scope = span.makeCurrent()) {
    performRiskyWork();
} catch (Exception e) {
    span.recordException(e);
    span.setStatus(StatusCode.ERROR, e.getMessage());
    throw e;
} finally {
    span.end();
}

Metrics (via OpenTelemetry)

import io.opentelemetry.api.metrics.Meter;
import io.opentelemetry.api.metrics.LongCounter;
import io.opentelemetry.api.metrics.LongHistogram;

Meter meter = openTelemetry.getMeter("com.example.myapp");

// Counter
LongCounter requestCounter = meter.counterBuilder("http.requests")
    .setDescription("Total HTTP requests")
    .setUnit("requests")
    .build();

requestCounter.add(1, Attributes.of(
    AttributeKey.stringKey("http.method"), "GET",
    AttributeKey.longKey("http.status_code"), 200L
));

// Histogram
LongHistogram latencyHistogram = meter.histogramBuilder("http.latency")
    .setDescription("Request latency")
    .setUnit("ms")
    .ofLongs()
    .build();

latencyHistogram.record(150, Attributes.of(
    AttributeKey.stringKey("http.route"), "/api/users"
));

Key Concepts

ConceptDescription
Connection StringApplication Insights connection string with instrumentation key
TracerCreates spans for distributed tracing
SpanRepresents a unit of work with timing and attributes
SpanProcessorIntercepts span lifecycle for customization
ExporterSends telemetry to Azure Monitor

Migration to Autoconfigure

The azure-monitor-opentelemetry-autoconfigure package provides:

  • Automatic instrumentation of common libraries
  • Simplified configuration
  • Better integration with OpenTelemetry SDK

Migration Steps

  1. Replace dependency:

    <!-- Remove -->
    <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
    </dependency>
    
    <!-- Add -->
    <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
    </dependency>
    
  2. Update initialization code per Migration Guide

Best Practices

  1. Use autoconfigure — Migrate to azure-monitor-opentelemetry-autoconfigure
  2. Set meaningful span names — Use descriptive operation names
  3. Add relevant attributes — Include contextual data for debugging
  4. Handle exceptions — Always record exceptions on spans
  5. Use semantic conventions — Follow OpenTelemetry semantic conventions
  6. End spans in finally — Ensure spans are always ended
  7. Use try-with-resources — Scope management with try-with-resources pattern

Reference Links

ResourceURL
Maven Packagehttps://central.sonatype.com/artifact/com.azure/azure-monitor-opentelemetry-exporter
GitHubhttps://github.com/Azure/azure-sdk-for-java/tree/main/sdk/monitor/azure-monitor-opentelemetry-exporter
Migration Guidehttps://github.com/Azure/azure-sdk-for-java/blob/main/sdk/monitor/azure-monitor-opentelemetry-exporter/MIGRATION.md
Autoconfigure Packagehttps://central.sonatype.com/artifact/com.azure/azure-monitor-opentelemetry-autoconfigure
OpenTelemetry Javahttps://opentelemetry.io/docs/languages/java/
Application Insightshttps://learn.microsoft.com/azure/azure-monitor/app/app-insights-overview

Lebih banyak skill dari microsoft

oss-growth
microsoft
Persona peretas pertumbuhan OSS
agent-framework-azure-ai-py
microsoft
Bangun agen Azure AI Foundry menggunakan Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Gunakan saat membuat agen persisten dengan AzureAIAgentsProvider, menggunakan alat yang dihosting (code interpreter, file search, web search), mengintegrasikan server MCP, mengelola utas percakapan, atau mengimplementasikan respons streaming. Mencakup alat fungsi, keluaran terstruktur, dan agen multi-alat.
development
airunway-aks-setup
microsoft
Siapkan AI Runway di AKS — dari klaster kosong hingga model berjalan. Mencakup verifikasi klaster, instalasi controller, penilaian GPU, penyiapan penyedia, dan deployment pertama. KAPAN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Panduan untuk instrumentasi aplikasi web dengan Azure Application Insights. Menyediakan pola telemetri, pengaturan SDK, dan referensi konfigurasi. KAPAN: cara menginstrumentasi aplikasi, SDK App Insights, pola telemetri, apa itu App Insights, panduan Application Insights, contoh instrumentasi, praktik terbaik APM.
devops
applicationinsights-web-ts
microsoft
Instrumentasi aplikasi browser/web dengan Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Digunakan untuk Real User Monitoring (RUM) — tampilan halaman, klik, dependensi AJAX/fetch, pengecualian, peristiwa kustom, dan jejak agen GenAI sisi browser yang dikorelasikan dengan jejak OpenTelemetry backend. Mencakup pengaturan SDK Loader Script dan npm, ekstensi kerangka kerja (React, React Native, Angular), Click Analytics, inisialisasi telemetri, dan konvensi semantik OTel GenAI untuk span agen/alat/model yang dipancarkan dari browser.
devops
azure-ai-anomalydetector-java
microsoft
Bangun aplikasi deteksi anomali dengan Azure AI Anomaly Detector SDK untuk Java. Gunakan saat mengimplementasikan deteksi anomali univariat/multivariat, analisis deret waktu, atau pemantauan bertenaga AI.
development
azure-ai-language-conversations-py
microsoft
Implementasikan Pemahaman Bahasa Percakapan (CLU) menggunakan SDK Python azure-ai-language-conversations. Gunakan saat bekerja dengan ConversationAnalysisClient untuk menganalisis maksud dan entitas percakapan, membangun fitur NLP, atau mengintegrasikan pemahaman bahasa ke dalam aplikasi.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 untuk Python. Gunakan untuk ruang kerja ML, pekerjaan, model, kumpulan data, komputasi, dan pipeline. Pemicu: "azure-ai-ml", "MLClient", "ruang kerja", "registri model", "pekerjaan pelatihan", "kumpulan data".
development