Tracing for Root Cause Analysis

Modern applications built with microservices and distributed architectures make root cause analysis (RCA) increasingly complex. When failures occur across dozens of interconnected services, traditional debugging methods fall short. This guide explains how combining distributed tracing with synthetic monitoring creates a powerful approach for faster incident resolution through vastly improved time to understand the root cause of failure.

One senior operations engineer I spoke to after a webinar mentioned his frustration with root cause analysis in his team:

Consider these common scenarios:

• A checkout process fails intermittently, but logs show no errors.
• API latency spikes during peak hours with no clear origin.
• A frontend component breaks silently after a backend deployment.

All of these types of failures could have happened at a SaaS company ten years ago, and while they wouldn’t be fun to troubleshoot at any time, now instead of a code monolith to analyze, we’re likely working with dozens of microservices all interacting in complex ways. Traditional troubleshooting methods struggle because:

1. Logs are fragmented across services and systems.
2. Metrics alone don’t show request flows.
3. Alerts lack context and only show the error message received by users, not the scope or suggested causes.

While we can use tools like synthetic monitoring to know that something isn’t working correctly, it’s become increasingly difficult to remediate failures, much less find the root cause.

Traces provide a complete map of requests as they flow through your system:

• Visualize service dependencies
• Identify latency bottlenecks
• See exact failure points in complex workflows

Example trace showing a failed payment process:

[Frontend] → [API Gateway] → [Payments Service] → [Fraud Check] → [Database]
                       ↑
                500ms timeout

Proactively test critical user journeys with:

• Scripted browser checks (e.g., login → add to cart → checkout)
• API test sequences with assertions
• Geographic performance testing

When combined, these approaches:

1. Detect issues early before users report them
2. Preserve context across frontend and backend
3. Accelerate RCA with complete request histories

• Use synthetic checks for key user flows.
• Add OpenTelemetry for tracing.
• Correlate trace IDs with check results.

When failures occur:

1. Check synthetic monitors for failures
2. Pull traces using the test’s trace ID
3. Follow the path to identify:
   • Which service failed
   • How long it took to fail
   • What dependencies were involved

Scenario: Customers report payment failures during peak hours

Investigation Steps:

1. Synthetic check for checkout flow begins failing

2. Trace shows:

   [Web] → [API] → [Payments] → [Fraud Service]
                          ↑
                 Timeout after 2s
   

3. Metrics reveal fraud service CPU saturation

4. Resolution: Scale fraud service pods + add circuit breaker

Result: MTTR reduced from 47 minutes to 8 minutes

1. Faster Detection
   • Catch issues before users do
   • Get complete failure context immediately
2. Clearer Diagnostics
   • See the full path of broken requests
   • Stop guessing between frontend vs backend issues
3. Prevent Recurrence
   • Identify weak points in architecture
   • Build resilience based on real failure patterns

1. Start small: Instrument one critical user journey
2. Correlate data: Connect synthetic checks to traces
3. Expand coverage: Add more checks as you grow

With tracing and synthetic monitoring working together, teams can shift from reactive debugging to proactive reliability engineering.

Ready to implement? See our guide on Setting Up OpenTelemetry with Checkly.