This is the core question and it ate up most of the session.
Start by clarifying functional and non-functional requirements, such as scale, latency, and delivery guarantees. Then propose a high-level architecture with real-time WebSocket connections and a fallback mechanism using a message queue and notification service. Dive into key components like data storage, presence tracking, and delivery guarantees, discussing trade-offs at each step.
Pro tip: Emphasize idempotency and message ordering to prevent duplicates and ensure conversations make sense, especially when falling back to SMS/email. Also, discuss how to handle offline users gracefully without blocking the sender.
Ask about scale (DAU, messages per second), latency expectations, delivery guarantees (at-least-once, exactly-once), and fallback preferences (SMS vs email).
Sketch the main components: API gateway, chat service, presence service, message queue, notification service, and databases. Explain how they interact.
Detail the WebSocket-based approach for online users, including connection management, heartbeats, and message routing.
Describe how to detect offline users and trigger SMS/email via a notification service, ensuring idempotency and retries.
Discuss database choices (e.g., Cassandra for messages, Redis for presence), consistency models, and trade-offs between latency, cost, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They asked me to raise this as a clarifying question before diving in.
Start by clarifying the product requirements and expected scale with the interviewer, then propose a flexible data model that supports both one-to-one and group threads from the start, explaining the trade-offs. Emphasize that designing for groups early avoids costly migrations later, but keep the initial implementation simple by leveraging a unified conversation model.
Pro tip: Demonstrate awareness of future features like read receipts, typing indicators, and message reactions, which are easier to support with a group-ready model. Also, mention that you'd validate assumptions with product managers to avoid over-engineering.
Ask the interviewer about the product roadmap, expected user scale, and whether group messaging is a planned feature. This shows you don't make assumptions and align with business goals.
Discuss the pros and cons of designing for one-to-one only versus group-ready from the start, including development speed, complexity, and future migration costs.
Suggest a data model where a conversation can have multiple participants, with a type field to distinguish between direct and group threads. This abstracts the difference and simplifies code.
Explain how the model handles high write throughput, message ordering, and efficient querying for both direct and group conversations, considering sharding and indexing strategies.
If starting with one-to-one, describe how to evolve the schema later with minimal disruption, such as adding a participants table and backfilling data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said at-least-once with dedup by message ID, which is the right call, but I fumbled explaining why exactly-once is basically impossible in a distributed system without massive tradeoffs.
Start by clarifying the business context and requirements, such as whether message loss is acceptable and what the cost of duplicates is. Then compare at-least-once with client deduplication and exactly-once, highlighting trade-offs in complexity, latency, and reliability. Finally, recommend a pragmatic approach based on the specific use case, often favoring at-least-once with idempotent consumers for simplicity and scalability.
Pro tip: Emphasize that exactly-once delivery is often a distributed systems myth; true exactly-once requires end-to-end coordination and is rarely worth the complexity. Instead, focus on idempotency and deduplication to achieve effectively-once semantics.
Ask about the business impact of message loss and duplicates. Determine if the system can tolerate occasional loss or if every message must be processed.
Explain at-least-once (messages may be duplicated but not lost) and exactly-once (no loss, no duplicates) in the context of the system.
Compare complexity, performance, and reliability. At-least-once with deduplication is simpler and more scalable; exactly-once requires transactional guarantees and can introduce latency.
Discuss how to implement deduplication (e.g., idempotent consumers, unique message IDs) and the challenges of exactly-once (e.g., distributed transactions, two-phase commit).
Propose a solution based on the use case. For many systems, at-least-once with idempotent processing is sufficient and more practical.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the delivery semantics and constraints (e.g., at-least-once vs exactly-once, message ordering, client capabilities). Then describe a protocol where the client acknowledges messages only after processing, and on reconnect, it resumes from the last acknowledged offset or uses idempotent processing with deduplication. Finally, discuss trade-offs and how to handle edge cases like partial acknowledgments.
Pro tip: Mention that exactly-once delivery is impossible without idempotency or transactional coordination, so the practical solution is at-least-once delivery with client-side deduplication. This shows you understand the theoretical limits and real-world engineering trade-offs.
Ask about delivery guarantees, message ordering, client state persistence, and whether the system can tolerate duplicates. This sets the stage for a precise answer.
Explain that the client sends an ack only after successfully processing a message, and the server tracks the last acknowledged offset per client. This ensures no loss if the client disconnects before acking.
On reconnect, the client presents its last acknowledged offset (or the server uses a session token) to resume delivery from that point. This guarantees no messages are skipped.
Since the client may receive duplicates (e.g., if ack is lost), use a unique message ID and a client-side deduplication cache to discard already-processed messages, achieving effectively-once semantics.
Cover trade-offs like storage overhead for deduplication, latency from acking, and handling partial acks or out-of-order messages. Mention alternatives like transactional outbox or idempotent consumers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a compare-and-swap on the cursor value, take the max of the two positions rather than a blind overwrite.
Start by clarifying the requirements: is the read cursor per-user or per-device, and what consistency guarantees are needed? Then propose a design that uses a monotonic, conflict-free update strategy (e.g., last-write-wins with versioning or CRDTs) and explain how to handle concurrent writes from multiple devices. Finally, discuss trade-offs and how to ensure eventual consistency without losing updates.
Pro tip: Emphasize that read cursors should be monotonic (never go backwards) and that you can use a simple 'max' merge strategy to resolve conflicts, which avoids complex coordination. Also mention that you might store the cursor per user, not per device, to simplify consistency.
Ask whether the read cursor is per-user or per-device, what consistency level is needed (strong vs eventual), and if the system must handle offline devices. This shows you understand the problem before jumping to solutions.
Propose storing the cursor as a single value per user with a version or timestamp, and use a merge function like 'max' to resolve concurrent updates. Alternatively, consider a CRDT (e.g., grow-only set of read message IDs) if more complex semantics are needed.
Explain how updates are sent to the backend (e.g., via API with optimistic concurrency using ETags or version numbers) and how the server applies the merge. Mention using a database with atomic operations or a distributed store with last-write-wins.
Describe how other devices learn about the updated cursor (e.g., via push notifications or polling) and how you handle stale reads. Highlight that eventual consistency is acceptable for read cursors and that monotonicity prevents regressions.
Address scenarios like offline updates, clock skew, and network partitions. Compare approaches (e.g., LWW vs CRDT) in terms of complexity, latency, and correctness, and justify your choice for Whatnot's use case.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Shard by conversation ID so one shard owns the sequence counter for a given conversation.
Start by clarifying the requirements: strict per-conversation ordering means messages within a conversation must be totally ordered, but global ordering across conversations is not required. Then propose a solution that centralizes sequence assignment per conversation, such as routing all messages for a conversation to a single partition or using a distributed lock/consensus protocol, and discuss trade-offs like latency, scalability, and fault tolerance.
Pro tip: Mention that you can avoid distributed locks by using a per-conversation message queue (e.g., Kafka partition keyed by conversation ID) where a single consumer assigns sequence numbers, and highlight that this maintains ordering while scaling horizontally across conversations.
Confirm that ordering is only required per conversation, not globally, and identify the scale (number of conversations, messages per second) and consistency needs (e.g., strict vs. eventual).
Explain that with sharded storage and multiple app servers, concurrent writes to the same conversation can lead to race conditions in sequence number assignment, causing out-of-order messages.
Suggest routing all messages for a given conversation to a single logical entity (e.g., a partition leader, a dedicated sequencer service, or a distributed lock) that assigns monotonically increasing sequence numbers.
Compare approaches like per-conversation Kafka partitions, Redis INCR with Lua scripts, ZooKeeper/etcd locks, or database sequences, and analyze their impact on latency, throughput, and availability.
Explain how to handle sequencer failures (e.g., leader election, replication) and how to scale by sharding conversations across multiple sequencers, ensuring no single point of contention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Kept the same fan-out model but replace the single recipient with a participant list.
Start by clarifying the existing core delivery path and its abstractions, then propose an extension that introduces a group abstraction layer without modifying the core. Focus on how to reuse the existing delivery mechanism by treating a group as a virtual recipient or by adding a fan-out step that is transparent to the core.
Pro tip: Emphasize backward compatibility and incremental rollout: show how you can add group support behind a feature flag and migrate gradually, minimizing risk to the existing system.
Ask questions to understand the core delivery path: what are the key components, interfaces, and assumptions? Identify what 'without rewriting' means in terms of constraints.
Determine where group semantics can be layered on top of the existing path, such as at the message routing, fan-out, or storage layers, without altering the core logic.
Introduce a group entity that maps to multiple recipients, and design a fan-out mechanism that delivers to each member using the existing delivery path.
Discuss how the extension handles increased load, consistency, and failure scenarios. Consider options like asynchronous fan-out, batching, or sharding.
Outline a migration strategy with feature flags, A/B testing, and metrics to ensure the core path remains unaffected and performance is maintained.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Aggregate notifications with a short delay window, say 30 seconds, and send one summary rather than one per message.
Start by clarifying the requirements and constraints, then propose a solution that combines idempotency (using unique message IDs and deduplication) with debouncing (batching notifications within a time window). Explain how you would handle offline users by queuing notifications and delivering a summary when they come online, while ensuring exactly-once delivery semantics.
Pro tip: Emphasize the importance of idempotency keys and a centralized notification service to avoid duplicate sends, and mention that debouncing should be configurable per user to balance timeliness and spam reduction.
Ask about the expected scale, latency requirements, and whether the user can receive a summary or must get individual messages. Confirm that the goal is to reduce notification spam while ensuring no critical messages are lost.
Use unique message IDs and an idempotency key (e.g., user ID + message ID) to deduplicate. Store sent notifications in a database with a unique constraint to prevent duplicates, and use a message queue with at-least-once delivery and consumer deduplication.
Introduce a debounce window (e.g., 5 minutes) where notifications for a user are aggregated. Use a scheduler or delayed queue to hold notifications and send a single summary after the window expires or when the user comes online.
Track user online/offline status and queue notifications accordingly. When the user comes online, deliver a consolidated summary. Ensure the queue is durable and can survive restarts.
Address trade-offs between latency and spam reduction, and how to handle urgent messages that bypass debouncing. Consider failure scenarios like duplicate sends due to retries and how to monitor and alert on them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.