Meta·Software Engineer·Technical Phone Screen·Senior

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Jul 2026

Summary

Meta data engineer interview with a coding problem that looked straightforward on the surface but had enough edge cases to keep me busy. One question, heap-based, category grouping with tie-breaking rules.

Questions Asked (1)

Given a list of items each with a content_id, category, and rating, write a function that returns the top 3 items per category by rating. Ties should be broken by higher rating first, then lexicographically smaller content_id. The result for each category should be sorted by descending rating, and the solution should aim for O(n log k) per category.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I got the basic grouping by category pretty fast but the tie-breaking tripped me up for a bit.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and constraints, then propose a solution using a hash map to group items by category and a min-heap of size 3 per category to efficiently track the top items. Define a custom comparator that orders by rating descending and content_id ascending, and analyze the time and space complexity.

Pro tip: Mention that you can use a single pass to group and maintain heaps, and that the heap size is fixed at 3, so the log factor is constant, making the solution effectively O(n). Also, discuss how to handle ties and edge cases like fewer than 3 items per category.

1. Clarify Requirements and Constraints

Ask about input size, data types, whether categories can have fewer than 3 items, and if the result should include categories with no items. Confirm the tie-breaking rules and expected output format.

2. Design the Algorithm

Propose using a hash map to group items by category. For each category, maintain a min-heap of size 3 based on the custom comparator (rating descending, content_id ascending). Iterate through items, pushing to the heap and popping the smallest when size exceeds 3.

3. Implement the Solution

Write code that iterates through the list, updates the hash map with heaps, and then extracts the top 3 from each heap, sorting them in descending order by rating (and ascending content_id for ties). Ensure the comparator is correctly implemented.

4. Analyze Complexity and Trade-offs

Explain that the time complexity is O(n log k) per category, where k=3, so effectively O(n). Space complexity is O(n) for the hash map and heaps. Discuss alternative approaches like sorting all items per category (O(n log n)) and why the heap is more efficient.

5. Test with Edge Cases

Walk through examples: categories with exactly 3 items, more than 3, fewer than 3, ties in rating, and ties in both rating and content_id. Verify the output order and correctness.

Key Points to Mention

Use a hash map to group items by category for O(1) average access.
Maintain a min-heap of size 3 per category to efficiently keep the top items.
Define a custom comparator: higher rating first, then lexicographically smaller content_id.
Time complexity: O(n log k) per category, with k=3, so O(n) overall; space O(n).
Handle edge cases: fewer than 3 items, ties, and categories with no items.
Alternative: sorting each category's items takes O(n log n), which is less efficient for large n.

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