Distributed Tracing Is Not Optional: Debugging Microservices Without It Is Finding a Needle in a Haystack

Background and Project Context

Recently, I implemented a new feature that spans 10 microservices. This feature is not a simple request-response flow. It is a pipeline-style workflow where one service triggers another, propagating through multiple downstream services.

From a functional perspective, the implementation was successful. From an operational perspective, debugging quickly became the main challenge.

The Problem: Debugging Without Distributed Tracing

When issues occurred, debugging relied entirely on logs:

• Manually opening logs for each service
• Searching by timestamps
• Guessing which log entries belonged to the same request
• Repeating this process across multiple services

This approach has fundamental problems in distributed systems:

• Logs are service-scoped, not request-scoped
• Correlating logs across services is manual and error-prone
• Understanding the full execution path requires mental reconstruction

As the number of microservices increases, this approach becomes unsustainable.

Without distributed tracing, debugging a multi-service workflow is effectively blind debugging. It feels like finding a needle in a haystack with no visibility into where to look.

Solution: Introducing Distributed Tracing

To address this, I implemented distributed tracing using Jaeger.

Architecture overview:

• All microservices emit trace data
• Trace context is propagated across service boundaries
• Business-relevant tags are attached to spans
• Jaeger is deployed on Amazon ECS
• Jaeger uses its built-in storage, backed by Amazon EFS
• No external backend (OpenSearch or Elasticsearch) is used

This setup is intentionally minimal and cost-efficient.

Results and Observability Improvements

After enabling distributed tracing, debugging changed fundamentally:

• Entire workflows became visible end-to-end
• Dependencies between microservices were immediately clear
• Latency bottlenecks were easy to identify
• Failures could be pinpointed to a specific service and operation
• Debugging time was reduced dramatically

Instead of jumping between logs, I could:

• Search by a single tag in Jaeger
• See the complete pipeline across all 10 services
• Understand exactly how a request flowed through the system

This transformed debugging from guesswork into deterministic analysis.

Cost vs. Benefit Analysis

Cost:

• One ECS service
• One EFS mount
• No managed search backend
• Minimal operational overhead

Benefit:

• Full visibility into distributed workflows
• Faster root cause analysis
• Reduced operational risk
• Significantly lower debugging time

The return on investment is extremely high. The cost is negligible compared to the productivity and reliability gains.

Key Takeaway

Distributed tracing is not a nice-to-have feature. For any distributed system with multiple microservices:

• Logs alone are insufficient
• Visibility into request flow is mandatory
• Tracing provides system-level understanding that logs cannot

Without tracing, distributed systems are opaque. With tracing, they become observable.

In microservice architectures, distributed tracing is a foundational capability, not an optional enhancement.