## Round 1 - System Design ## Problem Design the backend storage and retrieval system for a chat application. Requirements: - Users can send messages in 1:1 and group conversations - Retrieve messages for a conversation paginated by time (newest-first) - Support message search by keyword within a conversation - Message delivery guarantees: at-least-once; UI deduplicates by `message_id` - Scale: 100M DAU, 10B messages/day ## Key Design Decisions - **Data model**: `(conversation_id, message_id, sender_id, content, ts)` — partition by `conversation_id` - **Storage**: Cassandra or HBase (write-heavy, time-series, partition by conv) - **Pagination**: cursor-based using `(ts, message_id)` as the cursor - **Search**: async index to Elasticsearch; acceptable lag of seconds - **Fanout**: for group chats, write to each member's inbox vs. single conv store + pull ## Follow-ups 1. How do you handle message ordering across multiple senders in the same conversation? 2. What are the trade-offs between push fanout vs. pull-on-read for group messages? 3. How do you implement end-to-end encryption and what does that mean for search?

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Chat Messages: Design a Scalable Chat Message Storage and Retrieval System

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Round 1 - System Design Problem Design the backend storage and retrieval system for a chat application. Requirements: Users can send messages in 1:1 and group conversations Retrieve messages for a conversation paginated by time (newest-first) Support message search by keyword within a conversation Message delivery guarantees: at-least-once; UI deduplicates by message_id Scale: 100M DAU, 10B messages/day Key Design Decisions Data model: (conversation_id, message_id, sender_id, content, ts) — part…

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This is a reported interview question from a reddit interview during the phone round.

It covers the following topics: System Design, Coding, Phone, Onsite .

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Chat Messages: Design a Scalable Chat Message Storage and Retrieval System

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