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

SeniorPrefer not to say
Jun 2026Remote

Summary

Discord system design round, deep dive into distributed replication. They wanted a real implementation-level discussion, not just hand-waving at Raft. Harder than I expected and I definitely left some things on the table around split-brain prevention.

Questions Asked (1)

Q1

Design a leader-follower replication system similar to what etcd or Redis uses. Cover leader election, log replication, consistency semantics, failure detection, follower catch-up, split-brain prevention, and read paths. Show enough of the data plane to demonstrate writes propagating from leader to follower and surviving a leader failover.

System DesignTechnical Trade-offs
Author's notes

I went with Redis-style async replication because I could actually defend it, and pivoted to Raft semantics when they pushed on consistency guarantees.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then walk through the core components: leader election (e.g., Raft or Redis Sentinel), log replication with quorum acknowledgment, and consistency guarantees. Use a concrete write example to show data flow from leader to follower, and explain how failover preserves durability and prevents split-brain. Finally, discuss read paths and trade-offs between consistency and latency.

Pro tip: Emphasize the importance of term numbers and commit indexes in Raft-like protocols to prevent split-brain and ensure linearizability; mention how Discord’s scale might influence choices like using a consensus layer for metadata vs. a simpler replication for high-throughput channels.

1. Clarify Requirements and Scope

Ask about expected scale, consistency needs (strong vs. eventual), latency tolerance, and failure scenarios. This shapes whether you choose Raft, Paxos, or a Redis-style replication.

2. Design Leader Election and Failure Detection

Describe a term-based election with randomized timeouts, quorum voting, and heartbeats for failure detection. Explain how a new leader is chosen and how old leaders step down.

3. Detail Log Replication and Consistency

Walk through the write path: leader appends to log, replicates to followers, commits after quorum acknowledgment, and applies to state machine. Discuss consistency models (linearizable writes, sequential reads) and how followers catch up via log matching.

4. Address Split-Brain and Failover

Explain how quorum and term numbers prevent split-brain. Show a failover scenario: leader crashes, new leader elected, uncommitted entries handled, and clients redirected.

5. Cover Read Paths and Trade-offs

Discuss read options: linearizable reads via leader or quorum, stale reads from followers, and lease-based reads. Highlight trade-offs between consistency, latency, and availability.

Key Points to Mention

  • Quorum-based commit and election to ensure safety and liveness.
  • Term numbers and log matching property to prevent split-brain and ensure consistency.
  • Follower catch-up mechanisms: log truncation, snapshot transfer, and incremental sync.
  • Read consistency options: linearizable reads, lease reads, and follower reads with staleness bounds.
  • Failure detection via heartbeats and timeouts, with randomized election timeouts to avoid split votes.
  • Trade-offs: latency vs. consistency, availability during partitions, and impact of network delays.

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