Design Ride Sharing System - Video Solutions

Problem Overview: Design a ride sharing system that processes a continuous stream of ride requests and driver availability updates. The system must efficiently match riders with drivers while maintaining fast lookups and ordered processing of requests.

Approach 1: Queue Simulation + Linear Search (O(n) per operation, O(n) space)

The straightforward implementation uses a queue to store incoming ride requests and a list or map of available drivers. Each time a request arrives, you iterate through the driver list to find a suitable match. When a driver becomes available, you scan the queued requests to assign the earliest compatible rider. This approach mirrors the real-world first-come-first-served process but requires scanning potentially many elements for each assignment.

The key operations are queue insertion for incoming requests and linear iteration for matching. While simple to implement, the repeated scans cause O(n) time per match operation, which becomes slow as the number of riders and drivers grows. Space complexity remains O(n) because all active requests and drivers must be stored in memory.

Approach 2: Sorted Set + Hash Table (O(log n) per operation, O(n) space)

A more scalable design combines a sorted structure for ordering with a hash table for constant-time lookups. The hash table maps rider or driver IDs to their current state, while a sorted set maintains ordering based on attributes such as request time, priority, or distance. This allows the system to always retrieve the most suitable match without scanning the entire dataset.

When a new ride request arrives, insert it into the sorted set and record its metadata in the hash table. When a driver becomes available, query the sorted set to fetch the best candidate in O(log n) time. After a match, remove both entities from the sorted structure and update the hash table. This design supports fast updates and efficient matching even under heavy request streams, which is typical in data stream style problems.

The combination works because the sorted set guarantees efficient ordering while the hash table provides direct access to objects during updates or cancellations. Insertions, deletions, and queries all run in O(log n) time, making the system responsive even with thousands of active rides.

Recommended for interviews: Interviewers typically expect the Sorted Set + Hash Table design. A basic queue simulation shows you understand the workflow, but the optimized design demonstrates knowledge of scalable system design and efficient data structures. Mentioning why linear scans fail at scale and replacing them with ordered retrieval is the key insight that signals strong problem-solving ability.