I started with the connection layer and spent probably too long on WebSocket vs polling before getting nudged toward the actual storage design.
Start by clarifying requirements and scale (e.g., 1B users, message delivery guarantees, group size limits), then design the core architecture focusing on message flow, storage, and real-time delivery. Walk through the design from client to backend, highlighting trade-offs in data modeling, consistency, and scalability.
Pro tip: Emphasize the separation of message persistence from real-time delivery, and discuss how to handle offline users and message ordering—these are critical for chat systems and show you understand real-world constraints.
Ask about functional requirements (1:1 and group chat, message types, online/offline status) and non-functional requirements (latency, availability, consistency). Estimate scale: number of users, messages per day, group size limits.
Outline the main components: clients, API gateway, chat servers (WebSocket), message queue, storage (message DB, user DB), and notification service. Explain how messages flow from sender to receiver.
Design schemas for users, conversations, messages, and group memberships. Discuss choices like SQL vs NoSQL, sharding by conversation ID, and indexing for efficient retrieval.
Detail how to maintain WebSocket connections, route messages to online users, and handle offline users (push notifications, message queuing). Discuss presence service and heartbeat mechanisms.
Address scaling: partitioning, replication, load balancing, and handling failures. Discuss trade-offs like consistency vs availability, message ordering guarantees, and group message fan-out strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came as a follow-up and I was a bit flat on it.
Start by clarifying requirements and scale, then design a state machine for message lifecycle with server as source of truth and client-side reconciliation. Discuss trade-offs between push/pull, idempotency, ordering, and offline handling, and how to scale with sharding and caching.
Pro tip: Emphasize that 'read' receipts are inherently racy and privacy-sensitive; propose per-conversation monotonic sequence numbers and explicit user consent to avoid incorrect states and regulatory issues.
Ask about scale (DAU, messages/sec), consistency needs (per-conversation ordering vs global), client types (mobile/web), and privacy constraints for read receipts.
Model states (sent, delivered, read) with transitions triggered by server events; ensure idempotency and monotonic progression using per-message versioning.
Persist message state in a durable store (e.g., sharded DB) with per-conversation sequence numbers; use a message queue for async fan-out and idempotent updates.
Use push (WebSocket/APNs/FCM) for real-time updates with fallback to pull on reconnect; clients reconcile via sequence numbers and handle offline queuing and deduplication.
Discuss trade-offs: push vs pull latency/cost, strong vs eventual consistency, read receipt privacy, and scaling via sharding by conversation ID and caching hot conversations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what level of ordering is needed (per-conversation vs global), and what consistency guarantees are acceptable. Then propose a solution using a sequencer service or logical clocks to assign order, and discuss trade-offs like latency, scalability, and fault tolerance.
Pro tip: Mention that perfect global ordering is often unnecessary; per-conversation ordering with idempotency and conflict resolution is usually sufficient and more scalable. This shows you understand real-world constraints at Meta's scale.
Ask about the expected scale, latency requirements, and whether ordering is needed per conversation or globally. Confirm if clients can tolerate slight delays or reordering.
Propose using a centralized sequencer (e.g., a service that assigns monotonically increasing sequence numbers per conversation) or a distributed approach like logical clocks (Lamport timestamps) with tie-breaking.
Discuss how to detect and resolve conflicts when messages arrive nearly simultaneously, using client-generated unique IDs and server-side deduplication. Ensure messages are idempotent to avoid duplicates.
Explain how to partition conversations across sequencers (e.g., by conversation ID) to scale horizontally, and how to handle sequencer failures with replication and failover.
Compare centralized sequencing (strong ordering, higher latency) vs. distributed logical clocks (lower latency, potential conflicts). Mention that eventual consistency with client-side ordering may suffice for some use cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: what devices, sync latency expectations, message types, and consistency needs. Then propose a design using a central message store with per-device cursors and a sync protocol that handles incremental updates, conflicts, and offline scenarios.
Pro tip: Emphasize idempotency and monotonic versioning (e.g., per-conversation sequence numbers) to make sync robust and avoid duplicates; also discuss how to handle device-specific state like read receipts without overcomplicating the core message sync.
Ask about number of devices, expected sync latency, message types (text, media), offline support, and consistency guarantees (e.g., eventual vs strong). This shows you avoid assumptions and tailor the design.
Propose a central message store (e.g., per-conversation log with sequence numbers) and per-device sync state (last synced sequence). Discuss how to handle message ordering and deduplication.
Outline a pull-based or push-based sync mechanism: devices request messages after their last cursor, server returns incremental updates. Include handling of offline devices, retries, and idempotent writes.
Explain how to resolve conflicts (e.g., server-authoritative ordering, client-generated IDs for dedup) and how to handle read state, deletions, and edits across devices.
Mention partitioning by user/conversation, caching, and trade-offs between latency and consistency. Also touch on security (auth per device) and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.