This challenge builds on RideShare 1 - City Launch. Complete it first for the best experience.
ZipRide has proven product-market fit and is expanding to 20 cities. The engineering challenges multiply:
- Surge pricing - dynamically adjust prices based on supply/demand ratio per geographic zone, recalculated every 60 seconds.•Data growth - ride history, driver trajectories, and payment records are growing fast. The single database is becoming a bottleneck.•Analytics pipeline - the business team needs real-time dashboards showing rides/hour, revenue, and driver utilization per city.•Peak handling - Friday/Saturday nights see 5× average load.
Design a system that scales horizontally across cities while keeping the matching fast and pricing accurate.
Expand to 20 cities, add surge pricing, and handle 1 M rides/day. Build this architecture under realistic production constraints, then validate tradeoffs in the design lab simulation.
Clarify requirements: Expanding to 20 cities. Peak demand at commute hours. Driver dispatch is becoming slow -- too many drivers to scan.
Estimate scale: 1M rides/day across 20 cities. ~60k concurrent drivers. Peak at 5x average on Fri/Sat nights.
Pick components:
Key tradeoffs to discuss: