How can Application Insights be used to trace a request across multiple Azure services in a distributed application?

In a distributed application, a single business transaction rarely remains inside one application. For example, a customer request may enter an ASP.NET Core API, trigger an Azure Function, publish a message to Azure Service Bus, invoke another service, access Azure SQL, and finally call an external API. If each component produces independent logs, identifying the actual source of a failure or performance problem becomes difficult. Application Insights, as part of Azure Monitor, provides telemetry and distributed-tracing capabilities that help developers correlate activity across application components.

The first principle is to think in terms of a trace rather than an individual log entry. A trace represents the journey of a request or business operation, while spans/telemetry represent individual operations within that journey—such as an HTTP request, database dependency, outbound API call, or messaging operation. This allows developers to investigate questions such as: Where did the request spend most of its time? Which dependency failed? Did the failure originate in our application or an external service?

A typical architecture could look like:

Client → API Management → ASP.NET Core API → Azure Function → Service Bus → Worker → Azure SQL

Suppose the customer receives a timeout. Looking only at the API log might show a 30-second request duration, but it does not necessarily explain why. Distributed telemetry can help reveal that the API called a downstream service, which waited for a Service Bus operation, while the worker subsequently experienced slow database queries. The investigation therefore moves from symptom → correlated operations → dependency → root cause.

Developers should pay particular attention to:

• Request and dependency telemetry
• Distributed trace correlation
• Exceptions and failure rates
• Dependency duration
• Database and HTTP dependency failures
• Application logs and structured logging
• Availability tests
• Custom business telemetry where appropriate
• Sampling and telemetry volume
• Alerting and dashboards
• Sensitive information contained in telemetry

A common mistake is to instrument every application component without establishing a consistent observability strategy. More telemetry does not automatically mean better diagnostics. Excessive logging can increase cost, create noise, and make incident investigation harder.

For working professionals, the deeper question is how to design observability as part of the architecture. Critical business operations may require meaningful custom telemetry—for example, recording an order-processing stage or payment-processing outcome—without logging sensitive customer or authentication information.

Real-world scenario

Consider an online ordering platform:

Order API → Inventory Service → Payment Service → Order Processor → SQL Database

A customer reports:

“The order was submitted, but the application showed an error.”

The development team can use telemetry to investigate:

1. Did the initial API request succeed?
2. Which downstream dependencies were called?
3. Was inventory successfully reserved?
4. Did the payment API respond?
5. Did the message reach the order processor?
6. Did the database operation fail?
7. Was the user-facing failure caused by the original request or a downstream timeout?

This is where observability becomes an engineering capability rather than simply a logging mechanism.

Key point: Application Insights should be treated as part of a broader observability strategy covering metrics, logs, traces, alerts, and operational diagnostics.