Design a time-series database that ingests millions of metrics per second and answers range queries fast.
Metrics, traces, and IoT data are append-heavy, time-ordered, and seldom updated. A general database handles this poorly. The wins come from columnar layout, compression tuned for timestamps, downsampling, and retention. Here is the time-series design interviewers want.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Metrics, traces, and IoT data are append-heavy, time-ordered, and seldom updated. A general database handles this poorly. The wins come from columnar layout, compression tuned for timestamps, downsampling, and retention. Here is the time-series design interviewers want.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
