What is distributed tracing and telemetry correlation?

Modern cloud and microservices architectures have enabled simple, independently deployable services that reduce costs while increasing availability and throughput. However, it has made overall systems more difficult to reason about and debug. Distributed tracing solves this problem by providing a performance profiler that works like call stacks for cloud and microservices architectures.

Azure Monitor provides two experiences for consuming distributed trace data: the transaction diagnostics view for a single transaction/request and the application map view to show how systems interact.

Application Insights can monitor each component separately and detect which component is responsible for failures or performance degradation by using distributed telemetry correlation. This article explains the data model, context-propagation techniques, protocols, and implementation of correlation tactics on different languages and platforms used by Application Insights.

Enable distributed tracing

To enable distributed tracing for an application, add the right agent, SDK, or library to each service based on its programming language.

Enable via Application Insights through autoinstrumentation or SDKs

The Application Insights agents and SDKs for .NET, .NET Core, Java, Node.js, and JavaScript all support distributed tracing natively. Instructions for installing and configuring each Application Insights SDK are available for:

With the proper Application Insights SDK installed and configured, tracing information is automatically collected for popular frameworks, libraries, and technologies by SDK dependency auto-collectors. The full list of supported technologies is available in the Dependency auto-collection documentation.

Any technology also can be tracked manually with a call to TrackDependency on the TelemetryClient.

Enable via OpenTelemetry

Application Insights now supports distributed tracing through OpenTelemetry. OpenTelemetry provides a vendor-neutral instrumentation to send traces, metrics, and logs to Application Insights. Initially, the OpenTelemetry community took on distributed tracing. Metrics and logs are still in progress.

A complete observability story includes all three pillars. Check the status of our Azure Monitor OpenTelemetry-based offerings to see the latest status on what's included, which offerings are generally available, and support options.

The following pages consist of language-by-language guidance to enable and configure Microsoft's OpenTelemetry-based offerings. Importantly, we share the available functionality and limitations of each offering so you can determine whether OpenTelemetry is right for your project.

Enable via OpenCensus

In addition to the Application Insights SDKs, Application Insights also supports distributed tracing through OpenCensus. OpenCensus is an open-source, vendor-agnostic, single distribution of libraries to provide metrics collection and distributed tracing for services. It also enables the open-source community to enable distributed tracing with popular technologies like Redis, Memcached, or MongoDB. Microsoft collaborates on OpenCensus with several other monitoring and cloud partners.

For more information on OpenCensus for Python, see Set up Azure Monitor for your Python application.

The OpenCensus website maintains API reference documentation for Python, Go, and various guides for using OpenCensus.

Data model for telemetry correlation

Application Insights defines a data model for distributed telemetry correlation. To associate telemetry with a logical operation, every telemetry item has a context field called operation_Id. Every telemetry item in the distributed trace shares this identifier. So even if you lose telemetry from a single layer, you can still associate telemetry reported by other components.

A distributed logical operation typically consists of a set of smaller operations that are requests processed by one of the components. Request telemetry defines these operations. Every request telemetry item has its own id that identifies it uniquely and globally. And all telemetry items (such as traces and exceptions) that are associated with the request should set the operation_parentId to the value of the request id.

Dependency telemetry represents every outgoing operation, such as an HTTP call to another component. It also defines its own id that's globally unique. Request telemetry, initiated by this dependency call, uses this id as its operation_parentId.

You can build a view of the distributed logical operation by using operation_Id, operation_parentId, and request.id with dependency.id. These fields also define the causality order of telemetry calls.

In a microservices environment, traces from components can go to different storage items. Every component can have its own connection string in Application Insights. To get telemetry for the logical operation, Application Insights queries data from every storage item.

When the number of storage items is large, you need a hint about where to look next. The Application Insights data model defines two fields to solve this problem: request.source and dependency.target. The first field identifies the component that initiated the dependency request. The second field identifies which component returned the response of the dependency call.

For information on querying from multiple disparate instances by using the app query expression, see app() expression in Azure Monitor query.


Let's look at an example. An application called Stock Prices shows the current market price of a stock by using an external API called Stock. The Stock Prices application has a page called Stock page that the client web browser opens by using GET /Home/Stock. The application queries the Stock API by using the HTTP call GET /api/stock/value.

You can analyze the resulting telemetry by running a query:

(requests | union dependencies | union pageViews)
| where operation_Id == "STYz"
| project timestamp, itemType, name, id, operation_ParentId, operation_Id

In the results, all telemetry items share the root operation_Id. When an Ajax call is made from the page, a new unique ID (qJSXU) is assigned to the dependency telemetry, and the ID of the pageView is used as operation_ParentId. The server request then uses the Ajax ID as operation_ParentId.

itemType name ID operation_ParentId operation_Id
pageView Stock page STYz STYz
dependency GET /Home/Stock qJSXU STYz STYz
request GET Home/Stock KqKwlrSt9PA= qJSXU STYz
dependency GET /api/stock/value bBrf2L7mm2g= KqKwlrSt9PA= STYz

When the call GET /api/stock/value is made to an external service, you need to know the identity of that server so you can set the dependency.target field appropriately. When the external service doesn't support monitoring, target is set to the host name of the service. An example is stock-prices-api.com. But if the service identifies itself by returning a predefined HTTP header, target contains the service identity that allows Application Insights to build a distributed trace by querying telemetry from that service.

Correlation headers using W3C TraceContext

Application Insights is transitioning to W3C Trace-Context, which defines:

  • traceparent: Carries the globally unique operation ID and unique identifier of the call.
  • tracestate: Carries system-specific tracing context.

The latest version of the Application Insights SDK supports the Trace-Context protocol, but you might need to opt in to it. (Backward compatibility with the previous correlation protocol supported by the Application Insights SDK is maintained.)

The correlation HTTP protocol, also called Request-Id, is being deprecated. This protocol defines two headers:

  • Request-Id: Carries the globally unique ID of the call.
  • Correlation-Context: Carries the name-value pairs collection of the distributed trace properties.

Application Insights also defines the extension for the correlation HTTP protocol. It uses Request-Context name-value pairs to propagate the collection of properties used by the immediate caller or callee. The Application Insights SDK uses this header to set the dependency.target and request.source fields.

The W3C Trace-Context and Application Insights data models map in the following way:

Application Insights W3C TraceContext
Id of Request and Dependency parent-id
Operation_Id trace-id
Operation_ParentId parent-id of this span's parent span. This field must be empty if it's a root span.

For more information, see Application Insights telemetry data model.

Enable W3C distributed tracing support for .NET apps

W3C TraceContext-based distributed tracing is enabled by default in all recent .NET Framework/.NET Core SDKs, along with backward compatibility with legacy Request-Id protocol.

Enable W3C distributed tracing support for Java apps

Java 3.0 agent

Java 3.0 agent supports W3C out of the box, and no more configuration is needed.

Java SDK

  • Incoming configuration

    For Java EE apps, add the following code to the <TelemetryModules> tag in ApplicationInsights.xml:

    <Add type="com.microsoft.applicationinsights.web.extensibility.modules.WebRequestTrackingTelemetryModule>
       <Param name = "W3CEnabled" value ="true"/>
       <Param name ="enableW3CBackCompat" value = "true" />

    For Spring Boot apps, add these properties:

    • azure.application-insights.web.enable-W3C=true
    • azure.application-insights.web.enable-W3C-backcompat-mode=true
  • Outgoing configuration

    Add the following code to AI-Agent.xml:

      <BuiltIn enabled="true">
        <HTTP enabled="true" W3C="true" enableW3CBackCompat="true"/>


    Backward compatibility mode is enabled by default, and the enableW3CBackCompat parameter is optional. Use it only when you want to turn backward compatibility off.

    Ideally, you'll' turn off this mode when all your services are updated to newer versions of SDKs that support the W3C protocol. We highly recommend that you move to these newer SDKs as soon as possible.

It's important to make sure the incoming and outgoing configurations are exactly the same.

Enable W3C distributed tracing support for web apps

This feature is enabled by default for Javascript and the headers are automatically included when the hosting page domain is the same as the domain the requests are sent to (for example, the hosting page is example.com and the Ajax requests are sent to example.com). To change the distributed tracing mode, use the distributedTracingMode configuration field. AI_AND_W3C is provided by default for backward compatibility with any legacy services instrumented by Application Insights.

If the XMLHttpRequest or Fetch Ajax requests are sent to a different domain host, including sub-domains, the correlation headers are not included by default. To enable this feature, set the enableCorsCorrelation configuration field to true. If you set enableCorsCorrelation to true, all XMLHttpRequest and Fetch Ajax requests include the correlation headers. As a result, if the application on the server that is being called doesn't support the traceparent header, the request may fail, depending on whether the browser / version can validate the request based on which headers the server will accept.


To see all configurations required to enable correlation, see the JavaScript correlation documentation.

Telemetry correlation in OpenCensus Python

OpenCensus Python supports W3C Trace-Context without requiring extra configuration.

For a reference, you can find the OpenCensus data model on this GitHub page.

Incoming request correlation

OpenCensus Python correlates W3C Trace-Context headers from incoming requests to the spans that are generated from the requests themselves. OpenCensus correlates automatically with integrations for these popular web application frameworks: Flask, Django, and Pyramid. You just need to populate the W3C Trace-Context headers with the correct format and send them with the request.

Explore this sample Flask application. Install Flask, OpenCensus, and the extensions for Flask and Azure.

pip install flask opencensus opencensus-ext-flask opencensus-ext-azure

You need to add your Application Insights connection string to the environment variable.


Sample Flask Application

from flask import Flask
from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.ext.flask.flask_middleware import FlaskMiddleware
from opencensus.trace.samplers import ProbabilitySampler

app = Flask(__name__)
middleware = FlaskMiddleware(
        connection_string='<appinsights-connection-string>', # or set environment variable APPLICATION_INSIGHTS_CONNECTION_STRING

def hello():
    return 'Hello World!'

if __name__ == '__main__':
    app.run(host='localhost', port=8080, threaded=True)

This code runs a sample Flask application on your local machine, listening to port 8080. To correlate trace context, you send a request to the endpoint. In this example, you can use a curl command:

curl --header "traceparent: 00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-01" localhost:8080

By looking at the Trace-Context header format, you can derive the following information:

version: 00

trace-id: 4bf92f3577b34da6a3ce929d0e0e4736

parent-id/span-id: 00f067aa0ba902b7

trace-flags: 01

If you look at the request entry that was sent to Azure Monitor, you can see fields populated with the trace header information. You can find the data under Logs (Analytics) in the Azure Monitor Application Insights resource.

Screenshot that shows Request telemetry in Logs (Analytics).

The id field is in the format <trace-id>.<span-id>, where trace-id is taken from the trace header that was passed in the request and span-id is a generated 8-byte array for this span.

The operation_ParentId field is in the format <trace-id>.<parent-id>, where both trace-id and parent-id are taken from the trace header that was passed in the request.

Log correlation

OpenCensus Python enables you to correlate logs by adding a trace ID, a span ID, and a sampling flag to log records. You add these attributes by installing OpenCensus logging integration. The following attributes are added to Python LogRecord objects: traceId, spanId, and traceSampled (applicable only for loggers that are created after the integration).

Install the OpenCensus logging integration:

python -m pip install opencensus-ext-logging

Sample application

import logging

from opencensus.trace import config_integration
from opencensus.trace.samplers import AlwaysOnSampler
from opencensus.trace.tracer import Tracer

logging.basicConfig(format='%(asctime)s traceId=%(traceId)s spanId=%(spanId)s %(message)s')
tracer = Tracer(sampler=AlwaysOnSampler())

logger = logging.getLogger(__name__)
logger.warning('Before the span')
with tracer.span(name='hello'):
    logger.warning('In the span')
logger.warning('After the span')

When this code runs, the following prints in the console:

2019-10-17 11:25:59,382 traceId=c54cb1d4bbbec5864bf0917c64aeacdc spanId=0000000000000000 Before the span
2019-10-17 11:25:59,384 traceId=c54cb1d4bbbec5864bf0917c64aeacdc spanId=70da28f5a4831014 In the span
2019-10-17 11:25:59,385 traceId=c54cb1d4bbbec5864bf0917c64aeacdc spanId=0000000000000000 After the span

Notice that there's a spanId present for the log message that's within the span. The spanId is the same as that which belongs to the span named hello.

You can export the log data by using AzureLogHandler. For more information, see Set up Azure Monitor for your Python application.

We can also pass trace information from one component to another for proper correlation. For example, consider a scenario where there are two components, module1 and module2. Module1 calls functions in Module2. To get logs from both module1 and module2 in a single trace, we can use the following approach:

# module1.py
import logging

from opencensus.trace import config_integration
from opencensus.trace.samplers import AlwaysOnSampler
from opencensus.trace.tracer import Tracer
from module_2 import function_1

    format="%(asctime)s traceId=%(traceId)s spanId=%(spanId)s %(message)s"
tracer = Tracer(sampler=AlwaysOnSampler())

logger = logging.getLogger(__name__)
logger.warning("Before the span")

with tracer.span(name="hello"):
    logger.warning("In the span")
    function_1(logger, tracer)
logger.warning("After the span")
# module_2.py
import logging

from opencensus.trace import config_integration
from opencensus.trace.samplers import AlwaysOnSampler
from opencensus.trace.tracer import Tracer

    format="%(asctime)s traceId=%(traceId)s spanId=%(spanId)s %(message)s"
logger = logging.getLogger(__name__)
tracer = Tracer(sampler=AlwaysOnSampler())

def function_1(logger=logger, parent_tracer=None):
    if parent_tracer is not None:
        tracer = Tracer(
        tracer = Tracer(sampler=AlwaysOnSampler())

    with tracer.span("function_1"):
        logger.info("In function_1")

Telemetry correlation in .NET

Correlation is handled by default when onboarding an app. No special actions are required.

.NET runtime supports distributed with the help of Activity and DiagnosticSource

The Application Insights .NET SDK uses DiagnosticSource and Activity to collect and correlate telemetry.

Telemetry correlation in Java

Java agent supports automatic correlation of telemetry. It automatically populates operation_id for all telemetry (like traces, exceptions, and custom events) issued within the scope of a request. It also propagates the correlation headers that were described earlier for service-to-service calls via HTTP, if the Java SDK agent is configured.


Application Insights Java agent autocollects requests and dependencies for JMS, Kafka, Netty/Webflux, and more. For Java SDK, only calls made via Apache HttpClient are supported for the correlation feature. Automatic context propagation across messaging technologies like Kafka, RabbitMQ, and Azure Service Bus isn't supported in the SDK.

To collect custom telemetry, you need to instrument the application with Java 2.6 SDK.

Role names

You might want to customize the way component names are displayed in Application Map. To do so, you can manually set cloud_RoleName by taking one of the following actions:

  • For Application Insights Java, set the cloud role name as follows:

      "role": {
        "name": "my cloud role name"

    You can also set the cloud role name by using the environment variable APPLICATIONINSIGHTS_ROLE_NAME.

  • With Application Insights Java SDK 2.5.0 and later, you can specify cloud_RoleName by adding <RoleName> to your ApplicationInsights.xml file:

    Screenshot that shows Application Insights overview and connection string.

    <?xml version="1.0" encoding="utf-8"?>
    <ApplicationInsights xmlns="http://schemas.microsoft.com/ApplicationInsights/2013/Settings" schemaVersion="2014-05-30">
       <RoleName>** Your role name **</RoleName>
  • If you use Spring Boot with the Application Insights Spring Boot Starter, set your custom name for the application in the application.properties file:


You can also set the cloud role name via environment variable or system property. See Configuring cloud role name for details.

Next steps