IntermediatePROBLEM_SOLVING
Your service experiences significant latency and occasional timeouts under increased load. Describe the steps you would take to diagnose the root cause, metrics and tools you would use, and potential short-term and long-term fixes.
Backend Developer
General

Sample Answer

First I’d reproduce load in a staging environment and gather p50/p95/p99 latencies, error rates, CPU, memory, GC, DB query times, and queue/backpressure metrics. I’d use Prometheus + Grafana for metrics, Jaeger for traces, and pprof/Flamegraphs for CPU hotspots. In one incident we saw p99 jump to 3s and 10% timeouts; traces pointed to a blocking external API call and a hot DB index. Short-term I added circuit breakers, increased replicas from 4 to 8 behind the load balancer, introduced a 500ms request timeout, and cached responses in Redis — latency dropped from 1200ms median to ~200ms and timeouts fell to <0.1% within 48 hours. Long-term I refactored the slow path to async processing, added read replicas and composite indexes, and introduced rate-limiting to protect the DB. I coordinated this across a 5-person platform and data team over three sprints.

Keywords

Collect p50/p95/p99, error rates, CPU/GC, DB query times, queue depthUse tracing (Jaeger) and profilers (pprof) to find hotspotsShort-term: scale replicas, caching, circuit breakers; Long-term: async work, indexing, sharding
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