Modern Root Cause Analysis

Root cause analysis (RCA) in modern distributed systems has evolved beyond manual log digging and static alerts sending emails whenever CPU usage went above 90%. Traditional methods struggle with dynamic, cloud-native environments—where failures cascade across microservices, Kubernetes clusters, and serverless functions. Modern observability transforms RCA by unifying metrics, logs, traces, and events into single data sources, enabling teams to pinpoint issues faster.

Not every team will have the depth of Checkly’s synthetic monitoring to watch every common user path and test any critical interaction with a site and its API.

The Checkly dashboard packs in check frequency, retries, response times, and geographic distribution in one dashboard

Still most teams can rely on some basic monitoring that constantly checks if our services are available, whether a simple pinger or a backend tool like Prometheus black box exporter. That, along with improved tools for users to get in touch with their product teams, means on-call team can rely on knowing about outages within minutes of their start. Why then, is it so hard to find the cause of this outage? Starting with this high level dashboard, sadly many developer’s next stop is here:

Amazon CloudWatch logs, a great tool but pretty tough if you’re desperate to know what’s broken where without any clues except the time of day

Going from high level dashboards to logs is still common for operations engineers today, but it’s not a great experience for quickly finding root causes. First, without more detail we can’t effectively query and filter logs to see only relevant log lines. Second, modern apps are highly decomposed into microservices. As a result their logs can be stored in multiple places, with multiple conventions, making it exceedingly difficult to connect requests into a clear narrative.

To belabor an analogy: imagine you’ve lost your wallet on the beach. You have a tool that tells you what part of the world the beach was in, like a high level dashboard. But logs are like these closeup photos of the sand from various beaches. It’s very hard to find what you’re looking for without a great deal of scrolling, and these closeup photos are exceedingly hard to connect with each other.

As cloud architectures expand into greater complexity, broadening our search for a root cause, we need modern techniques enabled by tools like OpenTelemetry, Jaeger, and Checkly to tackle the problems of modern RCA.

1. Automated Correlation – With distributed tracing and context propagation, we shouldn’t be trying to line up time codes to correlate observability data from multiple services
2. Context-Aware Alerts – Alerts prioritize issues impacting business metrics (like checkout errors vs. low-severity logs). Sophisticated routing and status updates on tools like Rootly mean only the right team members get woken up during incidents.
3. Proactive Detection – Once solely used for end-to-end testing, synthetic user testing is now available to development and operations teams. Any activity frequently performed by users can be performed in automatic test sessions with deep assertions to make sure the responses look correct.

A payment service fails intermittently. Traditional RCA might check logs in isolation, wasting hours. With modern observability tools:

• Metrics show elevated error rates.
• Traces reveal timeouts between checkout and fraud-detection microservices.
• Logs highlight a third-party API throttling connections.
• Dashboards correlate this with a recent deployment, suggesting a rollback.

• OpenTelemetry (unified data collection for the back end)
• Prometheus/Grafana (metrics visualization)
• Jaeger (distributed tracing)
• Checkly (Synthetic monitoring of the frontend, correlated with OpenTelemetry traces)

Modern RCA reduces MTTR by replacing guesswork with data-driven decisions.

Modern root cause analysis is no longer about digging through logs. Today’s systems are too complex for old methods. But with the right tools—unified tracing, smart alerts, and synthetic checks—teams can find and fix issues fast.

Instead of guessing, you get:

• Connected data (logs, traces, metrics in one place)
• Clear paths (see how failures spread across services)
• Faster fixes (know what broke and why)

Tools like OpenTelemetry, Checkly, and Jaeger turn chaos into clarity. The result? Less downtime, happier users, and fewer late-night fire drills.

Want to improve your RCA? Start by connecting your OpenTelemetry tracing to Checkly—before the next outage hits.