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Bytedance·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Bytedance SWE interview with a verbal coding question that was pretty much a classic Top K problem dressed up in a social media context.

Questions Asked (1)

Q1

Given a large set of content creators and their like counts, how would you efficiently find the top K creators by likes?

Algorithms & Data Structures
Author's notes

The KOL framing threw me for a second but it's just Top K under the hood.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., data size, memory limits, whether likes are static or dynamic). Then propose a min-heap of size K to find the top K in O(N log K) time, which is efficient for large N. Discuss trade-offs with other approaches like sorting or quickselect, and mention scalability considerations for distributed systems.

Pro tip: Mention that if K is small relative to N, a min-heap is optimal; but if K is large, quickselect (average O(N)) might be better. Also, highlight that in a real system like ByteDance, you'd likely use a distributed approach with MapReduce or streaming algorithms.

1. Clarify Requirements

Ask about the size of the dataset, memory constraints, whether the data is static or streaming, and if exact top K is needed or approximate is acceptable.

2. Choose Algorithm

Select an algorithm based on constraints: min-heap for O(N log K) time and O(K) space, quickselect for O(N) average time, or sorting for simplicity if N is small.

3. Analyze Complexity

Compare time and space complexity of options, and discuss trade-offs. For example, min-heap is better when K << N, while quickselect has better average time but worse worst-case.

4. Handle Scalability

If data is too large for one machine, propose a distributed approach: partition data, compute local top K, then merge. Or use a streaming algorithm like count-min sketch for approximate top K.

5. Consider Edge Cases

Address duplicates, ties, K > N, and dynamic updates (if likes change frequently, consider a different data structure like a balanced BST or a heap with lazy updates).

Key Points to Mention

  • Min-heap of size K: iterate through creators, maintain heap of top K, time O(N log K), space O(K).
  • Quickselect: average O(N) time, but worst-case O(N^2); good when K is large or memory is tight.
  • Sorting: O(N log N) time, simple but inefficient for large N.
  • Distributed processing: MapReduce or Spark to compute top K per partition then merge.
  • Streaming/approximate algorithms: count-min sketch or space-saving algorithm for real-time or memory-limited scenarios.
  • Trade-offs: exact vs approximate, time vs space, static vs dynamic data.

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