Ride Sharing Service — System Design Interview Practice

Interview prompt

Design trip requests, driver availability, geospatial matching, and trip lifecycle so users can request and match a ride reliably at scale.

• Define the source of truth for trip state and payment records and make retries idempotent.
• Use bounded, partitioned state to meet 10M trips per day and 1M location updates per second and match p95 <=2s.
• Separate the critical request path from location streams, pricing, notifications, and analytics.
• Explain consistency, failure recovery, authorization, observability, and a degraded mode.

Requirements and scale assumptions

• Support the core workflow to request and match a ride.
• Expose status, results, and freshness appropriate to trip requests, driver availability, geospatial matching, and trip lifecycle.
• Support authorization, validation, updates, deletion, and recovery semantics.
• Meet match p95 <=2s under normal load.
• Scale to 10M trips per day and 1M location updates per second without a single hot key or unbounded synchronous work.
• Do not lose committed state; make retries and duplicate events safe.
• Degrade safely when downstream workers, caches, or external dependencies fail.
• 10M trips per day and 1M location updates per second
• Partition by the primary tenant, user, item, or geographic key and isolate hot partitions.
• Keep serving state bounded; retain raw events or durable records for replay and auditing.

• Peak scale: 10M trips per day — Capacity assumption that drives partitioning and backpressure.
• Latency target: match p95 <=2s — User-facing budget for the primary request or read path.
• Durable boundary: Committed before async — The source of truth is trip state and payment records.
• Async boundary: At-least-once workers — Keep location streams, pricing, notifications, and analytics off the synchronous path.

Key entities

• InteractioninteractionId, actorId, objectId, type, version, occurredAt

  Canonical ride sharing system interaction with an idempotency key and ordering version.

• ConnectionSessionsessionId, userId, deviceId, roomKey, lastHeartbeat, status

  Ephemeral but observable ride sharing system connection registration used for routing and presence.

• FanoutCursorstreamKey, shard, offset, consumerGroup, updatedAt

  Durable progress marker for ride sharing system fan-out and replay.

• DeliveryReceiptinteractionId, recipientId, channel, attempt, status, deliveredAt

  Deduplicated ride sharing system delivery state for reconnects, retries, or acknowledgements.

Data flow

1. 1. Accept and commit the interactionThe ride sharing system gateway authenticates the actor, validates room or object membership, applies rate limits, and conditionally commits the interaction.
2. 2. Publish an ordered eventAn outbox emits the committed ride sharing system transition with an event ID, partition key, sequence, and replay retention.
3. 3. Fan out by partitionConsumers route ride sharing system events to connected recipients, durable inboxes, or notification channels without making the origin write wait for every recipient.
4. 4. Resume and reconcile connectionsClients reconnect with a cursor; the ride sharing system service replays missed events, deduplicates delivery, and exposes stale or degraded state.
5. 5. Measure latency and recoverOperations tracks ride sharing system publish-to-deliver latency, hot partitions, reconnect storms, dropped events, and consumer lag for replay or repair.

Deep dives and trade-offs

• Ordering, idempotency, and hot keysChoose a ride sharing system partition key that preserves required order while distributing high-volume rooms, users, or objects. Use event IDs, inboxes, consumer offsets, and conditional state transitions for at-least-once delivery. Split or isolate hot partitions without changing the client-visible sequence contract.
• Reconnect and replay semanticsIssue resumable ride sharing system cursors with an expiry and a clear snapshot-plus-delta fallback. Bound replay windows and rebuild from durable state when a cursor is too old. Expose version and freshness so a client can distinguish current, catching up, and degraded state.
• Backpressure and presenceKeep connection heartbeats and ephemeral presence separate from durable ride sharing system interactions. Coalesce safe updates, shed low-value work, and protect critical events during reconnect storms. Measure end-to-end delivery, not only broker publish latency.
• Direct fan-out versus pull-based readsUse push for latency-sensitive ride sharing system deltas and pull or replay for reconnect, history, and recovery. A push-only design loses state when clients disconnect and a pull-only design wastes latency and bandwidth.
• Per-recipient queues versus shared streamsUse shared partitioned streams with per-recipient cursors where fan-out is large, and isolate exceptional high-fanout objects. A queue per recipient becomes expensive and hard to inspect at large scale.
• Strong ordering versus availabilityGuarantee ordering only within the scope the product needs, such as a room, object, or conversation. Global ordering introduces a bottleneck and still does not solve duplicate delivery or reconnect recovery.