Geo-partitioned ride-hailing database
Design a geo-partitioned database for a ride-hailing platform where most reads and writes for a trip stay within one city, but cross-city operations (a rider traveling, fleet analytics) exist. Scale is 500k active trips, 40k writes/sec concentrated in dense metros, with a 50ms in-region write SLA. Some metros are far hotter than others. How do you partition data by geography, place leaders, and handle a metro that suddenly doubles in traffic?
What a strong answer looks like
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts (data model, bottlenecks, consistency, failure modes) and name the trade-offs you are making.
Clarify5:30 left
Estimate5:30 planned
Design16:30 planned
Deep dive13:30 planned
Failure9:00 planned
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.