System Design Interview
Ride sharing platform with matching, trip management, and notifications.
Start with a single service for all ride sharing logic.
What was missing: No separation of concerns, no redundancy, no async, no observability.
Why that's risky: Single point of failure, no protection from spikes, no monitoring.
What gets added: Nothing yet (MVP).
Trade-offs: Simple, but not production ready.
Introduce edge, API gateway, and rate limiting for security and performance.
What was missing: No edge security, no rate limiting, no separation of gateway and matching.
Why that's risky: Vulnerable to DDoS, no traffic shaping, gateway logic not isolated.
What gets added: Edge, API gateway, and rate limiter.
Trade-offs: Slightly more complex.
Add load balancer and multiple matching engines for scale and redundancy.
What was missing: No redundancy, no horizontal scaling, single matching engine bottleneck.
Why that's risky: Single point of failure, cannot handle spikes.
What gets added: Load balancer, multiple matching engines.
Trade-offs: More complex deployment.
Split trip and location responsibilities, add caching for geo queries.
What was missing: No trip/location separation, no caching, all geo queries hit DB.
Why that's risky: DB can be overloaded, no geo query optimization.
What gets added: Trip/location split, geo cache.
Trade-offs: More moving parts, cache invalidation complexity.
Add event streaming, worker pool, and notification service for async updates.
What was missing: No async events, no worker pool, no notification service, all updates synchronous.
Why that's risky: Spikes can overload trip/matching, slow updates block users.
What gets added: Async event stream, worker pool, notification service, idempotency.
Trade-offs: More moving parts, eventual consistency for notifications.
Add analytics DB, monitoring/logging, and circuit breaker for observability and resilience.
What was missing: No analytics DB, no monitoring, no circuit breaker, no observability.
Why that's risky: Failures can cascade, no alerting, no analytics for tuning.
What gets added: Analytics DB, monitoring/logging, circuit breaker.
Trade-offs: More moving parts, more operational complexity.
Add multi-region, sharding, and cross-region failover for global scale.
What was missing: No multi-region, no sharding, no cross-region failover.
Why that's risky: No global scale, regional outages impact all users.
What gets added: Multi-region, sharding, cross-region failover.
Trade-offs: More moving parts, cross-region complexity.
Add global analytics, feedback loop, and advanced monitoring for continuous improvement.
What was missing: No global analytics, no feedback loop, no advanced monitoring.
Why that's risky: No continuous improvement, no global visibility, no feedback for tuning.
What gets added: Global analytics, feedback loop, advanced monitoring.
Trade-offs: More moving parts, more operational complexity.
Use soft-state location caches with TTLs and tolerate small staleness while updating positions frequently.
Apply rate limiting at the gateway and buffer non-critical events through queues with autoscaling workers.
Shard by city or region to keep hot ride data local and reduce cross-region latency.
Matching is latency-sensitive and benefits from isolated scaling and caching.
Caching nearby drivers reduces repeated database lookups for real-time location reads.
Event streams decouple real-time ride actions from downstream analytics and notifications.
You're in the middle of an interview session. Leaving now will end your current attempt.
Explore concept overviews, real-system examples, key tradeoffs, and interview talking points for each roadmap section.
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