This is a big one and I underestimated how much the interviewer wanted me to decompose it upfront.
Start by clarifying requirements and scale (e.g., number of concurrent users, message throughput, latency targets, consistency needs). Then design a high-level architecture covering message flow, storage, and real-time delivery, and dive into key components like partitioning, fan-out, and unread count management. Discuss trade-offs and justify your choices.
Pro tip: Focus on the unique challenges of group chat: fan-out to many recipients and maintaining unread counts efficiently. Propose a hybrid approach (e.g., push for small groups, pull for large) and explain how you'd handle message ordering and idempotency.
Ask about expected number of users, groups, messages per second, latency requirements, and consistency guarantees. Define functional and non-functional requirements.
Sketch the main components: clients, gateways, chat service, message queue, storage, and presence service. Explain how messages flow from sender to recipients.
Design schemas for messages, conversations, and user-conversation mappings. Choose databases (e.g., Cassandra for messages, Redis for unread counts) and explain partitioning and indexing strategies.
Detail how to deliver messages in real-time using WebSockets or long polling. Discuss fan-out strategies (push vs. pull) and how to handle large groups efficiently.
Explain how to maintain unread counts per user per conversation, using counters and read receipts. Address scalability, fault tolerance, and trade-offs.
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I asked about group size limits and got partial credit for that.
Start by acknowledging that clarifying questions are essential to scope the problem and avoid over-engineering. Then, walk through the key dimensions of a group chat system (scale, features, consistency, etc.), explaining how each answer would pivot your design decisions. Emphasize that you would prioritize questions based on impact and iterate as you learn more.
Pro tip: Frame your clarifying questions around the product's core value proposition—e.g., 'Is this for real-time coordination or asynchronous communication?'—because at Airbnb, design decisions should always tie back to user needs and business goals.
Ask about the primary use case: Is it for 1:1 messaging, small groups, or large communities? What features are must-have (e.g., message history, read receipts, media sharing)?
Determine expected user base, concurrent users, message volume, and latency requirements. This drives decisions on architecture (e.g., monolithic vs. microservices) and storage.
Ask about message ordering, delivery guarantees (at-least-once vs. exactly-once), and offline support. These affect choices like using WebSockets vs. polling, and database consistency models.
Inquire about encryption, data retention policies, and moderation needs. This influences design around authentication, authorization, and storage.
Summarize how each answer would change your design: e.g., if scale is small, a simple client-server with a relational DB suffices; if large, consider sharding, pub/sub, and CDN for media.
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I went with wall-clock timestamps initially and the interviewer pushed back immediately.
Start by outlining the core entities (User, Conversation, Message, Participant) and their relationships, then dive into the message ordering challenge. Explain how you'd use a monotonic sequence per conversation and discuss the trade-offs between server-assigned timestamps, client-generated IDs, and distributed sequence generators. Finally, describe how the client and server collaborate to ensure messages are displayed in the correct order, even with network delays or concurrent sends.
Pro tip: Acknowledge that perfect global ordering is impossible in distributed systems, but per-conversation ordering can be guaranteed with a single writer or a consensus protocol. Mention that Airbnb likely uses a sharded architecture, so you'd shard by conversation ID to keep ordering local and scalable.
List the main entities: User, Conversation, Message, and Participant. Describe key fields (e.g., Message has conversation_id, sender_id, content, sequence_number, timestamp) and relationships (one conversation has many messages, many users).
Explain that messages within a conversation must be totally ordered, but ordering across conversations is not required. Discuss the need for a consistent order that all participants agree on, even with concurrent sends.
Propose a per-conversation monotonic sequence number assigned by a single authority (e.g., a dedicated service or the database). Compare alternatives like Lamport timestamps, vector clocks, or client-side sequence numbers with server reconciliation.
Describe how to handle simultaneous sends: use optimistic concurrency or a queue per conversation. Discuss failure scenarios (e.g., network partitions) and how to ensure the sequence remains consistent (e.g., via consensus or idempotent writes).
Explain how clients use the sequence number to order messages, handle out-of-order delivery, and reconcile local optimistic messages with server-assigned sequence numbers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
WebSockets were obvious but the routing part tripped me up.
Start by clarifying requirements (scale, latency, message types, delivery guarantees) and then propose a high-level architecture using a pub/sub backbone with persistent connections (e.g., WebSockets) and a distributed connection layer. Dive into trade-offs around state management, fan-out, and reliability, and discuss how to handle failures and scale horizontally.
Pro tip: Emphasize the importance of connection state and backpressure; showing awareness of these operational realities distinguishes senior engineers. Also, relate your design to Airbnb's use cases (e.g., chat, notifications) to demonstrate product empathy.
Ask about scale (millions of connections), latency targets, message types (1:1, group, broadcast), delivery guarantees (at-least-once, exactly-once), and client types (mobile, web).
Propose a layered design: connection gateways (WebSocket servers) for persistent connections, a pub/sub system (e.g., Kafka, Redis Pub/Sub) for message routing, and a service layer for business logic.
Detail how connection gateways maintain state, how messages are routed to the right gateway (e.g., via consistent hashing or a registry), and how to handle fan-out for group messages.
Discuss trade-offs: push vs. pull, stateful vs. stateless gateways, message ordering, delivery guarantees, and handling reconnections and missed messages.
Explain horizontal scaling of gateways, load balancing, failure recovery (e.g., reconnect with exponential backoff), and monitoring (e.g., connection counts, latency).
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Storing a last-read sequence number per user per conversation is elegant and I actually got this one right without much prompting.
Start by clarifying requirements: scale (messages per second, users, conversations), read latency, and accuracy needs. Then propose a data model that stores unread counts per user per conversation, updated atomically on message send and read events, and discuss trade-offs between push (increment/decrement) and pull (compute on read) approaches. Finally, address consistency, idempotency, and scalability concerns.
Pro tip: Mention that you would use a per-user counter with atomic increments/decrements and a last-read timestamp to handle out-of-order events, and consider a fallback to recompute from the message store if counters drift.
Ask about expected read/write throughput, number of participants per conversation, and whether eventual consistency is acceptable. This determines whether a simple counter or a more complex distributed solution is needed.
Propose a table or key-value store mapping (user_id, conversation_id) to an unread_count and last_read_message_id/timestamp. Consider using a wide-column store like Cassandra or a relational DB with proper indexing.
On new message, atomically increment unread_count for all participants except sender. On read, atomically decrement or reset the count based on the read position, using idempotent operations to avoid double-counting.
Use transactions or atomic operations (e.g., Redis INCR/DECR, DynamoDB atomic counters) to prevent race conditions. Implement idempotency keys for read receipts and handle out-of-order events with versioning or timestamps.
Discuss sharding by user_id or conversation_id, caching hot counters, and fallback mechanisms to recompute counts from the message store if drift occurs. Compare push vs. pull models and their impact on latency and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward once you've committed to partitioning by conversation ID.
Start by clarifying the requirements and constraints, such as message volume, read/write patterns, and latency expectations. Then propose a cursor-based pagination scheme with a stable sort key (e.g., timestamp + message ID) and discuss storage/indexing strategies to support efficient queries. Finally, address edge cases like new messages arriving during pagination and consistency guarantees.
Pro tip: Mention that you would avoid offset-based pagination because it leads to duplicates or missed messages when new messages arrive, and instead use a cursor that encodes the last seen message's sort key. Also, consider using a composite index on (conversation_id, created_at, message_id) to make queries efficient.
Ask about expected message volume per conversation, read/write ratio, latency requirements, and whether messages are immutable. This determines the appropriate pagination strategy and storage design.
Propose cursor-based pagination using a stable sort key (e.g., timestamp + message ID) to avoid duplicates and missed messages. Explain why offset-based pagination is problematic for real-time conversations.
Describe how messages are stored (e.g., in a wide-column store like Cassandra or a relational DB) and the need for a composite index on (conversation_id, created_at, message_id) to support efficient range queries.
Discuss how to handle new messages arriving during pagination (e.g., using a snapshot or accepting eventual consistency), and how to ensure the cursor remains valid even if messages are deleted.
Mention caching strategies (e.g., caching recent messages), using a dedicated service for message history, and potentially sharding by conversation ID to distribute load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, then propose a durable, transactional approach to message handling. Explain how to use a write-ahead log or persistent queue with atomic operations to ensure the message is safely stored before acknowledgment, and discuss how to handle fan-out asynchronously with idempotency and retries.
Pro tip: Emphasize that guaranteeing no message loss requires a trade-off between latency and durability; acknowledging after persistence adds latency but ensures reliability. Also, mention that idempotency is crucial to handle duplicate deliveries during retries.
Ask about the expected throughput, latency tolerance, and consistency requirements to tailor the solution appropriately.
Propose persisting the message to a write-ahead log or a replicated message queue before sending an acknowledgment to the sender.
Ensure that the acknowledgment is sent only after the message is durably stored, using transactions or two-phase commit if necessary.
Use a separate consumer process to read from the durable log and perform fan-out, with retries and dead-letter queues for failures.
Design the fan-out to be idempotent, using unique message IDs and deduplication to avoid duplicate processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Fan-out on write becomes completely infeasible at that scale, which I said immediately.
Start by acknowledging that scaling from hundreds to 100,000+ members fundamentally changes the design constraints, shifting from a simple fan-out model to a highly partitioned, asynchronous system. Focus on the core challenges: message delivery at scale, storage, and real-time updates, and propose a sharded, event-driven architecture with trade-offs.
Pro tip: Emphasize the importance of defining clear SLAs for message delivery latency and consistency, as these will drive architectural decisions and prevent over-engineering. Also, mention the need for graceful degradation and backpressure to handle peak loads.
Ask about expected read/write patterns, latency requirements, consistency needs, and whether all members need real-time updates. This sets the stage for design decisions.
Analyze how the existing design (for a few hundred members) would fail at 100k+, such as database write contention, fan-out on write, and memory limits.
Outline a sharded, partitioned system with asynchronous message queues, a distributed cache, and possibly a pub/sub model to handle high fan-out.
Discuss storage strategies: time-series databases for messages, cold storage for older messages, and efficient indexing for quick retrieval.
Highlight trade-offs like consistency vs. availability, cost implications, and how to handle failures (e.g., message loss, delayed delivery).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: soft deletes with a tombstone or an edit event appended to the log, then propagate via the same fan-out path.
Start by clarifying the requirements: what does 'consistent' mean (eventual vs. strong), and how do edit/delete/recall differ in semantics? Then propose a design that treats each message as an immutable event with a versioned state, using a central authority to order operations and a real-time sync mechanism to propagate updates. Finally, discuss trade-offs between consistency, latency, and complexity.
Pro tip: Emphasize that 'recall' is not just a delete—it's a state change that must be visible to all participants, and often has a time limit. Mention that you'd use a monotonic version number per message to handle out-of-order updates, which shows you understand distributed systems pitfalls.
Ask about consistency level (strong vs. eventual), who can edit/delete/recall, time limits, and whether history is preserved. Define what 'consistent view' means for all participants.
Model messages as immutable events with a unique ID and version. Define APIs for edit, delete, and recall that create new events or update state, and specify how clients fetch the latest state.
Use a central sequencer (e.g., per-conversation queue) to order operations, or a distributed consensus protocol if needed. Assign monotonic version numbers to each message to resolve conflicts and ensure all participants converge.
Push updates via WebSocket or long-polling to all participants. Include the message ID, new version, and operation type so clients can apply changes idempotently and handle out-of-order delivery.
Address offline clients, network partitions, and late-joining participants. Discuss how to reconcile conflicting edits (e.g., last-write-wins vs. manual merge) and the cost of strong consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.