Automatic data collection and resource detectors for Azure Monitor OpenTelemetry

This article explains how Azure Monitor OpenTelemetry collects telemetry automatically and how resource detectors enrich telemetry with consistent metadata. You learn what signals are collected by default and how resource detectors populate attributes like service identity and environment details so your Application Insights data is easier to filter, correlate, and troubleshoot across .NET, Java, Node.js, and Python applications.

To learn more about OpenTelemetry concepts, see the OpenTelemetry overview.

Note

For Azure Function Apps, see Use OpenTelemetry with Azure Functions.

Included instrumentation libraries

The Azure Monitor Distros automatically collect data by bundling OpenTelemetry instrumentation libraries.

Requests

ASP.NET Core ¹²

Dependencies

Logging

ILogger

For more information about ILogger, see Logging in C# and .NET and code examples.

The Azure Monitor Exporter doesn't include any instrumentation libraries.

To collect dependencies from the Azure Software Development Kits (SDKs), use the following code sample to manually subscribe to the source.

// Create an OpenTelemetry tracer provider builder.
// It is important to keep the TracerProvider instance active throughout the process lifetime.
using var tracerProvider = Sdk.CreateTracerProviderBuilder()
	// The following line subscribes to dependencies emitted from Azure SDKs
    .AddSource("Azure.*")
    .AddAzureMonitorTraceExporter()
    .AddHttpClientInstrumentation(o => o.FilterHttpRequestMessage = (_) =>
	{
    	// Azure SDKs create their own client span before calling the service using HttpClient
		// In this case, we would see two spans corresponding to the same operation
		// 1) created by Azure SDK 2) created by HttpClient
		// To prevent this duplication we are filtering the span from HttpClient
		// as span from Azure SDK contains all relevant information needed.
		var parentActivity = Activity.Current?.Parent;
		if (parentActivity != null && parentActivity.Source.Name.Equals("Azure.Core.Http"))
		{
		    return false;
		}
		return true;
	})
    .Build();

Requests

Java Message Service (JMS) consumers
Kafka consumers
Netty
Quartz
RabbitMQ
Servlets
Spring scheduling

Note

Servlet and Netty autoinstrumentation covers most Java HTTP services, including Java EE, Jakarta EE, Spring Boot, Quarkus, and Micronaut.

Dependencies (plus downstream distributed trace propagation)

Apache HttpClient
Apache HttpAsyncClient
AsyncHttpClient
Google HttpClient
gRPC
java.net.HttpURLConnection
Java 11 HttpClient
JAX-RS client
Jetty HttpClient
JMS
Kafka
Netty client
OkHttp
RabbitMQ

Dependencies (without downstream distributed trace propagation)

Supports Cassandra
Supports Java Database Connectivity (JDBC)
Supports MongoDB (async and sync)
Supports Redis (Lettuce and Jedis)

Metrics

Micrometer Metrics, including Spring Boot Actuator metrics
Java Management Extensions (JMX) Metrics

Logs

Logback (including MDC properties) ¹
Log4j (including MDC/Thread Context properties) ¹
JBoss Logging (including MDC properties) ¹
java.util.logging ¹

Default collection

Telemetry emitted by the following Azure SDKs is automatically collected by default:

Azure App Configuration 1.1.10+
Azure AI Search 11.3.0+
Azure Communication Chat 1.0.0+
Azure Communication Common 1.0.0+
Azure Communication Identity 1.0.0+
Azure Communication Phone Numbers 1.0.0+
Azure Communication SMS (Short Message Service) 1.0.0+
Azure Cosmos DB 4.22.0+
Azure Digital Twins - Core 1.1.0+
Azure Event Grid 4.0.0+
Azure Event Hubs 5.6.0+
Azure Event Hubs - Azure Blob Storage Checkpoint Store 1.5.1+
Azure AI Document Intelligence 3.0.6+
Azure Identity 1.2.4+
Azure Key Vault - Certificates 4.1.6+
Azure Key Vault - Keys 4.2.6+
Azure Key Vault - Secrets 4.2.6+
Azure Service Bus 7.1.0+
Azure Storage - Blobs 12.11.0+
Azure Storage - Blobs Batch 12.9.0+
Azure Storage - Blobs Cryptography 12.11.0+
Azure Storage - Common 12.11.0+
Azure Storage - Files Data Lake 12.5.0+
Azure Storage - Files Shares 12.9.0+
Azure Storage - Queues 12.9.0+
Azure Text Analytics 5.0.4+

Tip

TypeScript samples for Azure Monitor OpenTelemetry (authoritative parity source): https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/monitor/monitor-opentelemetry/samples-dev

The Azure Monitor Application Insights Distro includes the following OpenTelemetry Instrumentation libraries. For more information, see Azure SDK for JavaScript.

Requests

HTTP/HTTPS²

Dependencies

Logs

Important

Bunyan and Winston aren't enabled by default. You can enable instrumentation libraries by setting enabled: true in the instrumentation options.

Expand to view a code sample that shows how to configure instrumentations by using AzureMonitorOpenTelemetryOptions.

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import bunyan from "bunyan";

// Call useAzureMonitor before importing other libraries
const options: AzureMonitorOpenTelemetryOptions = {
  azureMonitorExporterOptions: {
    connectionString:
      process.env.APPLICATIONINSIGHTS_CONNECTION_STRING ||
      "<your-connection-string>",
  },
  // Bunyan is disabled by default — explicitly enable it
  instrumentationOptions: {
    bunyan: { enabled: true },
  },
};

useAzureMonitor(options);

// Create a bunyan logger as usual logs are automatically captured
const logger = bunyan.createLogger({ name: "my-app" });

logger.info("Application started");
logger.warn({ requestId: "abc-123" }, "Slow response detected");
logger.error(new Error("Something failed"), "Unhandled error");

Footnotes

¹: Supports automatic reporting of unhandled/uncaught exceptions
²: Supports OpenTelemetry Metrics

Note

The Azure Monitor OpenTelemetry Distros include custom mapping and logic to automatically emit Application Insights standard metrics. For billing purposes, all OpenTelemetry metrics, whether automatically collected from instrumentation libraries or manually collected from custom coding, are currently considered Application Insights custom metrics. Learn more.

Tip

To reduce or increase the number of logs sent to Azure Monitor, configure logging to set the appropriate log level or apply filters. For example, you can choose to send only WARNING and ERROR logs to Azure Monitor.

Resource detectors

Resource detectors discover environment metadata at startup and populate OpenTelemetry resource attributes such as service.name, cloud.provider, and cloud.resource_id. This metadata powers experiences in Application Insights like Application Map and compute linking, and it improves correlation across traces, metrics, and logs.

Tip

Resource attributes describe the process and its environment. Span attributes describe a single operation. Use resource attributes for app-level properties like service.name.

Supported environments

Environment	How detection works	Notes
Azure App Service	The language SDK or Azure Monitor Distro reads well-known App Service environment variables and host metadata	Works with .NET, Java, Node.js, and Python when you use the guidance in this article.
Azure Functions	See the Azure Functions OpenTelemetry how‑to	All Azure Functions guidance lives there.
Azure Virtual Machines	The language SDK or distro queries the Azure Instance Metadata Service	Ensure the VM has access to the Instance Metadata Service endpoint.
Azure Kubernetes Service (AKS)	Use the OpenTelemetry Collector `k8sattributes` processor to add Kubernetes metadata	Recommended for all languages running in AKS.
Azure Container Apps	Detectors map environment variables and resource identifiers when available	You can also set `OTEL_RESOURCE_ATTRIBUTES` to fill gaps.

OTLP ingestion considerations

cloud.resource_id improves compute linking to Azure resources. If this attribute is missing, some experiences might not show the Azure resource that produced the data.
Application Insights uses service.name to derive Cloud Role Name. Choose a stable name per service to avoid fragmented nodes in Application Map.
cloud.resource_id improves compute linking to Azure resources. If this attribute is missing, some experiences might not show the Azure resource that produced the data.

Troubleshooting, feedback, and support

Tip

The following sections are available across all OpenTelemetry Distro articles.

Troubleshooting

For troubleshooting information, see Troubleshoot OpenTelemetry issues in .NET and Troubleshoot missing application telemetry in Azure Monitor Application Insights.

OpenTelemetry Feedback

To provide feedback:

Fill out the OpenTelemetry community's customer feedback survey.
Tell Microsoft about yourself by joining the OpenTelemetry Early Adopter Community.
Engage with other Azure Monitor users in the Microsoft Tech Community.
Make a feature request at the Azure Feedback Forum.

Support

Select a tab for the language of your choice to discover support options.

For Azure support issues, open an Azure support ticket.
For OpenTelemetry issues, contact the OpenTelemetry .NET community directly.
For a list of open issues related to Azure Monitor Exporter, see the GitHub Issues Page.

Next steps

To review the source code, see the Azure Monitor ASP.NET Core GitHub repository.
To review a sample application, see Azure Monitor OpenTelemetry for ASP.NET Core.
To install the NuGet package, check for updates, or view release notes, see the Azure Monitor AspNetCore NuGet Package page.
To learn more about OpenTelemetry and its community, see the OpenTelemetry .NET GitHub repository.
To enable usage experiences, enable web or browser user monitoring.

To review the source code, see the Azure Monitor OpenTelemetry for JavaScript repository.
To review a sample application, see Azure Monitor OpenTelemetry for Node.js.
To install the npm package and check for updates, see the @azure/monitor-opentelemetry npm Package page.
To learn more about OpenTelemetry and its community, see the OpenTelemetry JavaScript GitHub repository.
To enable usage experiences, enable web or browser user monitoring.

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Last updated on 2026-04-11

Automatic data collection and resource detectors for Azure Monitor OpenTelemetry

Included instrumentation libraries

Resource detectors

Supported environments

OTLP ingestion considerations

Troubleshooting, feedback, and support

Troubleshooting

OpenTelemetry Feedback

Support

Next steps

Feedback

Additional resources