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Meta·Software Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Meta Research Scientist interview with a system design coding question around a priority-based ticket management system. Pretty involved for what felt like a single round, lots of back-and-forth on design tradeoffs.

Questions Asked (1)

Q1

Design a ticket management class that supports adding tickets, updating existing tickets in-place, and retrieving the next highest-priority ticket. Priority is determined by severity first, then most recent timestamp as a tiebreaker.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I went straight to a max-heap and they seemed fine with that, but then they asked how I'd handle updates and I kind of stumbled.

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AI HintsAI Generated

Suggested Approach

Clarify requirements and constraints, then propose a data structure (e.g., heap with lazy deletion or balanced BST) that supports O(log n) add/update and O(1) or O(log n) retrieval. Discuss trade-offs between time complexity, memory, and implementation complexity, and handle edge cases like duplicate priorities and updates to existing tickets.

Pro tip: Mention that updates can be handled efficiently with lazy deletion in a heap, avoiding O(n) removal, and that a balanced BST (like a TreeSet) can provide O(log n) updates and retrieval if you need strict ordering. This shows you understand practical optimizations beyond textbook solutions.

1. Clarify Requirements and Constraints

Ask about expected frequency of operations, ticket volume, whether updates can change severity, and if concurrency is a concern. This informs data structure choice and trade-offs.

2. Define the Data Model and Interface

Specify the Ticket class with id, severity, timestamp, and other fields. Define methods: addTicket, updateTicket, getNextTicket. Discuss how to handle updates that change priority.

3. Choose Data Structures and Algorithms

Propose a max-heap with lazy deletion for O(log n) add and O(1) peek, or a balanced BST (e.g., TreeSet) for O(log n) add, update, and retrieval. Explain how to maintain ordering by severity then timestamp.

4. Analyze Trade-offs and Complexity

Compare time and space complexity of heap vs. BST, and discuss pros/cons like memory overhead, implementation complexity, and worst-case performance. Mention handling of duplicate priorities.

5. Handle Edge Cases and Concurrency

Address scenarios like updating a ticket to a higher priority, removing the next ticket, and thread safety if needed. Suggest synchronization or lock-free approaches if concurrency is required.

Key Points to Mention

  • Use a max-heap with lazy deletion to avoid O(n) removal on updates, achieving O(log n) add and O(1) peek.
  • Alternatively, use a balanced BST (e.g., TreeSet) for O(log n) add, update, and retrieval, with strict ordering.
  • Define a comparator that orders by severity descending, then timestamp descending (most recent first).
  • For updates, if the ticket is in the heap, mark it as stale and re-insert the updated version; clean up stale entries when peeking.
  • Discuss trade-offs: heap uses less memory but may have stale entries; BST uses more memory but guarantees no stale entries and supports efficient deletion.
  • Consider concurrency: use locks or concurrent data structures if multiple threads access the ticket manager.

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