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Discord·Software Engineer·Onsite - System Design / Architecture·Senior

Senior
Jul 2026

Summary

Discord system design round for a software engineer role. The whole thing was one big deep-dive into building a Slack-style chat system, and they clearly wanted more than a surface-level answer. Covered a lot of ground fast.

Questions Asked (1)

Q1

Design a workspace-based chat system with channels, direct messages, real-time delivery, message history, edits, deletes, typing indicators, read receipts, and presence.

System DesignTechnical Trade-offsData Modeling
Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then sketch a high-level architecture that separates real-time delivery from persistent storage. Focus on data modeling for channels, messages, and user state, and discuss trade-offs for each component.

Pro tip: Emphasize the importance of idempotency and ordering in message delivery, and how you would handle failures gracefully. Show awareness of Discord's specific challenges like massive fan-out and low-latency requirements.

1. Clarify Requirements and Scale

Ask about expected number of users, channels, messages per day, and latency requirements. Clarify features like message retention, search, and multi-device support.

2. High-Level Architecture

Outline components: API gateway, chat service, presence service, message storage, and real-time delivery via WebSockets. Discuss how services interact.

3. Data Modeling and Storage

Design schemas for channels, messages, and user-channel relationships. Choose databases (e.g., Cassandra for messages, Redis for presence) and explain partitioning and indexing.

4. Real-Time Delivery and State Management

Detail WebSocket connections, pub/sub for message fan-out, and how to handle typing indicators, read receipts, and presence updates efficiently.

5. Trade-offs and Scalability

Discuss trade-offs: consistency vs. availability, push vs. pull for presence, and how to scale horizontally. Mention caching, sharding, and failure handling.

Key Points to Mention

  • Use WebSockets for real-time bidirectional communication, with a pub/sub system like Redis or Kafka for message distribution.
  • Model messages with a unique ID (e.g., Snowflake) for ordering and idempotency; store in a distributed database like Cassandra with time-based partitioning.
  • Handle edits and deletes by appending events or updating records, and ensure clients reconcile state via versioning or timestamps.
  • Presence and typing indicators are ephemeral; use in-memory stores like Redis with TTL and publish updates via pub/sub.
  • Read receipts require tracking last-read message per user per channel; consider trade-offs between storage and accuracy.
  • Ensure scalability by sharding channels, using consistent hashing, and designing for fault tolerance with replication.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.