I started with users and rides tables and felt okay for the first few minutes, but then they pushed on things like driver availability, pricing history, and how you'd handle trip states over time.
Start by clarifying the core requirements and scale of the ride-sharing app, then design a normalized schema for the main entities (riders, drivers, trips, payments) before addressing high-volume needs like location tracking and matching. Discuss trade-offs between normalization and denormalization, and how to handle real-time updates and scalability.
Pro tip: Emphasize that the schema must support high write throughput for location updates and low-latency reads for matching; consider using a hybrid approach with a relational database for transactional data and a NoSQL or in-memory store for real-time location and session data.
Ask about expected user base, trip volume, read/write patterns, and consistency needs. This determines whether to prioritize normalization, denormalization, or a polyglot persistence approach.
List the main entities: Rider, Driver, Trip, Payment, Location, and Rating. Define their attributes and relationships, ensuring to capture cardinality (e.g., one rider to many trips).
Create tables with primary keys, foreign keys, and appropriate indexes for frequent queries (e.g., finding nearby drivers, trip history). Consider using UUIDs for distributed systems.
For driver location updates and trip matching, propose a separate store (e.g., Redis for geospatial indexing, Cassandra for time-series location data) and discuss data synchronization with the core relational database.
Explain choices like normalization vs. denormalization, SQL vs. NoSQL, and how to handle sharding, replication, and eventual consistency. Mention how the schema supports features like surge pricing and ride history.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.