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

SeniorPrefer not to say

Apr 2026Remote

Summary

System design round at Temporal for a software engineer role. The whole interview was one big question about building a delayed event firing system at massive scale, and it went pretty deep pretty fast.

Questions Asked (1)

Design a system that schedules and fires a very large volume of events at specific future times, guaranteeing each event fires exactly once at its target time. Cover storage, dispatcher design, scaling, retry semantics, fault tolerance, clock skew, and how you'd push it to billions of events.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This is the kind of question where you feel good for the first ten minutes and then realize you've barely scratched the surface.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a distributed architecture using a time-ordered store (e.g., sharded DB or Kafka) and a dispatcher that pulls due events. Address exactly-once semantics via idempotency and deduplication, and discuss scaling, fault tolerance, and clock skew handling.

Pro tip: Emphasize that exactly-once firing is impossible without idempotent consumers; design for at-least-once delivery with deduplication to achieve effectively-once semantics. Also, mention using a hierarchical timing wheel or priority queue for efficient due event retrieval.

1. Clarify Requirements and Scale

Ask about event volume, latency tolerance, and exactly-once expectations. Confirm if events can be delayed or dropped, and understand the read/write patterns.

2. Design Storage Layer

Choose a scalable, time-ordered store like a sharded database (e.g., Cassandra) or a log (e.g., Kafka) partitioned by time buckets. Ensure efficient range queries for due events.

3. Design Dispatcher and Scaling

Use a distributed set of dispatcher nodes that poll for due events, with leader election or sharding to avoid duplicate work. Scale horizontally by adding partitions and dispatchers.

4. Handle Exactly-Once and Retries

Implement idempotent event processing with unique event IDs and deduplication. Use at-least-once delivery with retries and dead-letter queues for failures.

5. Address Fault Tolerance and Clock Skew

Replicate storage and use consensus for coordination. Use NTP or atomic clocks, and design for clock skew by adding tolerance windows or using logical clocks.

Key Points to Mention

Sharding by time buckets to distribute load and enable efficient range scans.
Using a timing wheel or priority queue for in-memory scheduling of near-term events.
Idempotency keys and deduplication to achieve exactly-once semantics.
Retry policies with exponential backoff and dead-letter queues.
Fault tolerance via replication, leader election, and checkpointing.
Clock skew mitigation: NTP, tolerance windows, and logical timestamps.

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