Meta·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Meta SWE interview focused on a streaming top-K frequency problem, covering both exact and approximate solutions. Pretty interesting problem once you get into it, though the complexity analysis portion felt like it had more depth than I expected for a single question.

Questions Asked (1)

You're receiving a continuous stream of items that may not fit in memory. How would you design an algorithm to find the top K most frequent items? Cover both an exact approach when distinct items fit in memory and an approximate approach when they don't, and walk through the complexity of each.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This one took me a second to get my footing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying constraints (K, memory, stream rate, exact vs approximate). Present a two-case solution: exact counting with a hash map when distinct items fit in memory, and an approximate heavy-hitters algorithm (e.g., Misra-Gries or Count-Min Sketch) when they don't. For each, analyze time and space complexity, and discuss trade-offs.

Pro tip: Mention that for the approximate case, you can use a two-pass approach: first identify candidate heavy hitters, then in a second pass count them exactly if a second pass is allowed. This shows you consider practical constraints beyond the basic algorithm.

1. Clarify requirements and constraints

Ask about K, memory limits, whether exact counts are needed, if multiple passes are allowed, and the stream's characteristics (e.g., skew). This ensures the solution fits the problem context.

2. Exact approach when distinct items fit in memory

Use a hash map to count frequencies of all items, then maintain a min-heap of size K to track top K. Analyze time O(N log K) and space O(D + K) where D is distinct items.

3. Approximate approach when distinct items don't fit

Use a heavy-hitters algorithm like Misra-Gries or Count-Min Sketch to identify candidate frequent items with bounded memory. Explain the trade-off between accuracy and space.

4. Analyze complexity and trade-offs

Compare time and space for both approaches. For Misra-Gries, time O(N) and space O(K); for Count-Min Sketch, time O(N) and space O(1/ε log 1/δ). Discuss error guarantees and when to choose each.

5. Summarize and recommend

Conclude with a recommendation based on constraints, and mention possible optimizations like parallelization or using a second pass for exact counts on candidates.

Key Points to Mention

Hash map + min-heap for exact top K when distinct items fit in memory
Misra-Gries algorithm for frequent items with O(K) space and error guarantee
Count-Min Sketch for approximate frequency estimation with sublinear space
Time and space complexity analysis for each approach
Trade-offs between exactness, memory, and accuracy
Handling of stream processing and potential need for multiple passes

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