This one I felt okay about until the backpressure piece came up.
Start by clarifying requirements and scale (throughput, retention, ordering, consumer semantics), then sketch a high-level architecture with partitions, replication, and consumer groups. Dive into data models for offsets and storage, and discuss trade-offs around consistency, durability, and failure recovery.
Pro tip: Emphasize how you would handle partition rebalancing and consumer group coordination without downtime, as this is a common pain point in production systems. Also, mention how you would monitor lag and ensure exactly-once semantics if needed.
Ask about expected throughput, message size, retention period, ordering guarantees, and consumer semantics (at-least-once, exactly-once). Establish non-functional requirements like latency, durability, and availability.
Propose a partitioned log-based system with brokers, producers, and consumers. Explain how partitions enable parallelism and ordering, and how replication ensures durability.
Detail how messages are stored (e.g., append-only log segments), how offsets are tracked per consumer group, and how retention is enforced (time or size-based). Discuss indexing for efficient replay.
Describe how consumers coordinate within a group (e.g., via a coordinator), how offsets are committed, and how rebalancing works. Mention strategies for avoiding duplicate processing.
Explain replication (leader-follower), handling broker failures, and ensuring data durability. Discuss trade-offs between consistency, availability, and latency (e.g., acks=all vs acks=1).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The rider-selects-driver twist genuinely surprised me.
Start by clarifying requirements and scale, then design a state machine for the trip lifecycle with explicit states (e.g., REQUESTED, DRIVERS_NOTIFIED, RIDER_SELECTED, DRIVER_CONFIRMING, ASSIGNED, CANCELLED, TIMED_OUT). Propose a distributed architecture using a matching service, notification service, and a strongly consistent datastore with optimistic concurrency control to handle race conditions and duplicate requests.
Pro tip: Emphasize idempotency and exactly-once semantics for critical operations (e.g., driver confirmation) using idempotency keys and conditional writes, and discuss how to handle partial failures with compensating actions or retries.
Ask about expected QPS, number of drivers/rider, geographic distribution, and consistency requirements. Define functional requirements: rider sees multiple drivers, picks one, driver confirms, trip assigned; handle cancellations, timeouts, race conditions, duplicates.
Define states and transitions for a trip request (e.g., REQUESTED, DRIVERS_NOTIFIED, RIDER_SELECTED, DRIVER_CONFIRMING, ASSIGNED, CANCELLED, TIMED_OUT). Specify a data model with trip ID, rider ID, selected driver ID, status, timestamps, and version for optimistic locking.
Design services: Matching Service to find drivers and notify them, Rider Service to present options and accept selection, Driver Service to handle confirmation. Use a message queue for asynchronous notifications and a consistent datastore for trip state.
Use optimistic concurrency control (version checks) or distributed locks to prevent race conditions. Implement timeouts with TTLs or scheduled jobs to expire requests. Handle cancellations by allowing state transitions only from valid states and notifying all parties.
Assign idempotency keys to rider selection and driver confirmation requests. Use conditional writes (e.g., 'update if status = X') to ensure duplicate requests don't cause multiple assignments. Log and deduplicate events.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.