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

Senior
Apr 2026

Summary

Did a system design round at Meta for a software engineer role, got asked to design a distributed messaging system along the lines of Kafka. Not a lot of context to share beyond the question itself, but it's the kind of thing that can go sideways fast if you haven't thought through partitioning and consumer group semantics before walking in.

Questions Asked (1)

Q1

Design a distributed queue service similar to a Kafka-style messaging system.

System DesignTechnical Trade-offsData Modeling
Author's notes

This is the kind of question where you can talk for 45 minutes and still feel like you barely scratched the surface.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture with core components like producers, brokers, topics, partitions, and consumers. Dive into critical details such as replication, durability, ordering, and delivery semantics, and discuss trade-offs for each design decision.

Pro tip: Emphasize how your design handles failures and scales horizontally, and proactively compare your choices to Kafka's approach to show deep understanding.

1. Clarify Requirements and Constraints

Ask about expected throughput, latency, durability, ordering guarantees, and consumer models to scope the design appropriately.

2. High-Level Architecture

Outline the main components: producers, brokers, topics, partitions, consumers, and a coordination service (e.g., ZooKeeper/KRaft). Explain how they interact.

3. Data Model and Storage

Describe how messages are stored (e.g., append-only log, segments), partitioned for parallelism, and indexed for efficient consumption.

4. Reliability and Scalability

Discuss replication for fault tolerance, leader election, handling broker failures, and scaling partitions and consumers.

5. Delivery Semantics and Trade-offs

Explain at-least-once, at-most-once, and exactly-once semantics, and trade-offs between consistency, availability, and latency.

Key Points to Mention

  • Partitioning strategy for scalability and ordering guarantees
  • Replication and ISR (in-sync replicas) for durability and fault tolerance
  • Consumer groups and offset management for parallel consumption
  • Message retention policies and log compaction
  • Delivery semantics (at-least-once, at-most-once, exactly-once) and idempotent producers
  • Backpressure handling and flow control

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