IntermediateTECHNICAL
Describe a time when you had to debug a hard-to-reproduce production issue in a backend service (for example, intermittent timeouts, memory leaks, or data inconsistencies). What tools and techniques did you use to identify the root cause?
Backend Developer
General

Sample Answer

At my last company, our Node/TypeScript API started throwing intermittent 504s on a checkout endpoint, maybe 0.5% of requests, but it spiked to 3–4% during flash sales. Logs just showed timeouts to a downstream inventory service. It wasn’t reproducible in staging. I added high-cardinality tracing with OpenTelemetry and tagged requests with tenant, region, and feature flags. We also enabled p99 latency and connection-pool metrics in Datadog. The traces showed that timeouts correlated with a specific region and only when a new “bulk pricing” flag was on. From there, I captured heap snapshots and connection-pool stats in that region and found a subtle connection leak in a rarely hit code path: an error branch that skipped pool.release(). Under heavy load, the pool exhausted and new calls stalled until the ELB timed out. Fixing that and adding a safeguard max wait time dropped 5xx on that endpoint from ~3% to under 0.1%, even during 10x traffic spikes.

Keywords

Used distributed tracing and high-cardinality tags to narrow the blast radiusCorrelated timeouts with specific region and feature flag behaviorFound a connection leak in a rare error path via targeted metrics and snapshotsValidated fix under high load and demonstrated clear error-rate reduction
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