This challenge builds on RideShare 2 - Multi-City & Surge Pricing. Complete it first for the best experience.
ZipRide is now a global platform operating in 50 countries with a mixed fleet of human drivers and autonomous vehicles. The system must handle:
- 10 million rides per day across all regions.•Autonomous vehicle telemetry - each AV streams 50+ sensor readings per second (location, speed, battery, obstacle detection). The platform must ingest, process, and act on this data in real time for safety-critical decisions.•ML-based dispatch - a machine learning model predicts optimal driver/rider matches considering ETA, driver rating, vehicle type, and predicted demand 15 minutes into the future.•Regulatory compliance - different countries have different data residency laws, licensing requirements, and payment processors.•Five-nines availability (99.999%) - the platform handles safety-critical operations; downtime could leave riders stranded or autonomous vehicles without instructions.
This is the hardest system design problem: a globally distributed, real-time, safety-critical platform at extreme scale.
Operate in 50 countries with autonomous vehicles, ML-based dispatch, and five-nines uptime. Build this architecture under realistic production constraints, then validate tradeoffs in the design lab simulation.
Clarify requirements: Need real-time surge pricing (demand > supply -> price increases). Need historical analytics for business intelligence. Operational DB cannot handle analytics queries.
Estimate scale: 10M rides/day globally. Analytics queries scan millions of rows -- would kill the operational DB if run there.
Pick components:
Key tradeoffs to discuss: