Walk me through how you would design and implement a solution for a high-traffic API endpoint that is currently experiencing latency issues. What data would you collect, what technical approaches might you consider (e.g., caching, indexing, batching, async processing), and how would you validate that your changes improved performance?

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VirtualInterview.AI · Accepted Answer

I’d start by instrumenting the endpoint end-to-end: add tracing, log request/response times, and break down latency by segments (API, DB, external calls). I’d look at P50/P95/P99 latencies, QPS, and error rates over at least a week of traffic to see patterns by endpoint parameters and clients.

In a recent case, we saw P99 at ~2.5s for a 1k RPS endpoint, mostly due to two unindexed joins and a chatty downstream service. We added proper DB indexes, introduced a read-through Redis cache for the hottest 10% of keys (with a 5-minute TTL), and batched downstream calls with a fan-out/fan-in pattern using a worker pool. That brought P99 under 500ms and cut DB load by ~60%. We validated via load tests in staging, canary rollout to 10% of traffic, and a dashboard comparing before/after latency and saturation metrics.

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