Suppose you’re responsible for a multi-quarter initiative to reduce median and tail latencies for complex analytic queries by 30%. How would you break this into milestones, choose which parts of the query lifecycle (parsing, optimization, execution, I/O, memory management) to tackle first, and communicate trade-offs and progress to product and engineering stakeholders?

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VirtualInterview.AI · Accepted Answer

I’d treat this as a phased performance program with explicit SLOs. First quarter is measurement and wins: tighten observability (end-to-end tracing, per-operator timings, p95/p99 by query shape), then grab low-risk gains in execution and I/O, since that’s where 70–80% of latency usually sits. For example, in my last role we got a 20% p95 improvement just by fixing skew in hash joins and adding smarter scan pruning.

Next, I’d focus on optimization: better stats, plan caching, and rewriting a few heavy query patterns with product’s buy-in. Parsing is usually last unless we see CPU hotspots there. I’d define milestones as: (Q1) baseline + 10% improvement, (Q2) reach 20–25%, (Q3) hit or exceed 30% and stabilize.

For communication, I’d keep a simple dashboard showing median, p95, p99 by key workloads and a burndown against the 30% goal, and I’d translate trade-offs into language product cares about: “this change speeds dashboard X by 40% but delays feature Y by one sprint.”

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