Discord·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Discord technical screen for a software engineer role, basically one meaty coding problem about stream processing and session detection. No behavioral stuff, just the problem and a lot of follow-up edge cases.

Questions Asked (1)

You're building a backend component that reads a continuous stream of newline-delimited JSON events, each representing a user sending a message to a channel. Define a user session as a sequence of events from the same user where no two consecutive events are more than 30 minutes apart. Implement something that parses this stream and emits a completed session record (with user ID, start/end timestamps, message count, and top channel) whenever a session can be finalized. The stream is potentially infinite, so you can't buffer everything.

System DesignAlgorithms & Data StructuresData Modeling

Author's notes

The 'infinite stream' constraint is what makes this non-trivial.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a streaming solution using a hash map to track active sessions per user. Explain how to detect session boundaries based on the 30-minute gap and emit completed sessions, possibly using a timer or watermark mechanism. Finally, discuss scalability and trade-offs.

Pro tip: Mention that you would use a min-heap or timing wheel to efficiently expire sessions, and that you'd handle out-of-order events with a watermark or allowed lateness. This shows you think about real-world streaming complexities.

1. Clarify requirements and assumptions

Ask about event ordering, timestamp source, session finalization semantics (e.g., when to emit), and whether late events are possible. Confirm that sessions are per-user and that the 30-minute gap is between consecutive events.

2. Design data structures

Use a hash map keyed by user ID to store the current session state (start time, last event time, message count, channel counts). Also maintain a priority queue or timer to track when each session should be finalized (last event time + 30 minutes).

3. Process stream and emit sessions

For each incoming event, update the user's session: if no active session or gap > 30 minutes, finalize the old session (emit it) and start a new one. Update the session's last event time, message count, and channel counts. Use the timer to emit sessions when they expire.

4. Handle out-of-order and late events

If events can be out-of-order, use a watermark or allowed lateness to delay finalization. Discuss how to handle late events that belong to an already finalized session (e.g., drop, update, or emit correction).

5. Discuss scalability and optimizations

Address memory usage (only active sessions in memory), potential bottlenecks (single point of failure), and scaling strategies (partition by user ID). Mention alternatives like using a stream processing framework (Flink, Kafka Streams).

Key Points to Mention

Session state management per user with a hash map
Efficient expiration using a min-heap or timing wheel
Handling out-of-order events with watermarks or allowed lateness
Emitting session records with required fields: user ID, start/end timestamps, message count, top channel
Scalability considerations: partitioning by user ID, using a distributed stream processor
Trade-offs between latency and completeness (e.g., waiting for late events)

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