Start by clarifying requirements and constraints, then outline a high-level architecture that separates concerns: route generation, pricing computation, real-time data ingestion, experimentation, and fairness enforcement. Dive into the core pricing algorithm, explaining how factors combine and how carpooling and fairness are handled, and finally discuss scalability and trade-offs.
Pro tip: Emphasize idempotency and auditability in pricing calculations, as pricing changes can have legal and customer trust implications. Also, mention how you would A/B test pricing changes safely with guardrail metrics.
Ask questions to understand functional and non-functional requirements: expected scale, latency, fairness constraints, experimentation needs, and integration points. Define key metrics like price accuracy, system availability, and experiment velocity.
Propose a microservices-based architecture with separate services for route generation, pricing, real-time supply/demand tracking, experimentation, and fairness enforcement. Use event-driven updates for real-time data and a rules engine for dynamic pricing factors.
Design a modular pricing function that computes base price from distance and duration, applies multipliers for supply/demand and wait time, adds driver incentives, and adjusts for carpooling by splitting shared segments. Enforce fairness via caps and equity adjustments.
Describe how to support A/B tests for pricing strategies using feature flags and experiment assignment. Explain how fairness constraints (surge caps, neighborhood equity) are enforced as guardrails and monitored.
Discuss scaling the system for high throughput and low latency, using caching, sharding, and async processing. Address trade-offs between accuracy, latency, and fairness, and how to handle failures gracefully.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.