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Meta·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026

Summary

Meta software engineering interview with a design-heavy coding question around a payment ranking system. The question had multiple layers and required thinking through data structures out loud, which I was not fully prepared for.

Questions Asked (1)

Q1

Implement a function topNPayers(n) that returns the n accounts with the highest total outgoing payment amounts, formatted as 'accountId:totalOutgoing'. The system should support live updates as new debits come in. Walk through your choice of data structure, how you handle ties, and the time and space complexity for both updates and queries.

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

The core logic clicked pretty fast but I fumbled explaining the tie-breaking rules.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: define 'total outgoing payment amounts', the expected scale (number of accounts, update frequency), and whether ties should be broken by accountId or insertion order. Then propose a data structure that supports efficient updates and top-n queries, such as a hash map for account totals combined with a balanced BST or heap for ordering, and discuss trade-offs. Finally, analyze time and space complexity for both updates and queries, and mention how to handle ties consistently.

Pro tip: Emphasize that the choice of data structure depends on the read/write ratio: if queries are frequent, a sorted structure like a balanced BST or skip list may be better; if updates dominate, a heap with lazy deletion could be more efficient. Also, mention that for live updates, you might need to maintain the top-n incrementally rather than recomputing from scratch.

1. Clarify requirements and assumptions

Ask about the scale (number of accounts, update rate), definition of 'outgoing payment', and tie-breaking rules. Confirm whether the function should return a snapshot or support live queries.

2. Choose data structures

Propose a hash map to store accountId -> total outgoing amount for O(1) updates, and a secondary structure (e.g., balanced BST, max-heap, or skip list) to maintain ordering for top-n queries. Discuss trade-offs between update and query efficiency.

3. Handle ties and ordering

Define a deterministic tie-breaking rule, such as sorting by accountId when totals are equal. Ensure the chosen data structure supports this ordering.

4. Analyze complexity

For each operation (update, query top-n), state the time and space complexity. For example, with a hash map + balanced BST: update O(log n), query O(n) to traverse top n, space O(n).

5. Discuss scalability and optimizations

Mention potential improvements like caching top-n results, using a heap with lazy deletion for updates, or sharding for distributed systems. Address how to handle live updates efficiently.

Key Points to Mention

  • Hash map for O(1) account total updates
  • Balanced BST or heap for maintaining order and top-n queries
  • Tie-breaking strategy (e.g., by accountId)
  • Time complexity: update O(log n) with BST, query O(n) to retrieve top n
  • Space complexity: O(n) for storing all accounts
  • Trade-offs between update-heavy vs query-heavy workloads
  • Handling live updates: incremental maintenance vs recomputation

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