← Bytedance Interview Insights
Start by clarifying the problem constraints: data stream characteristics, K value, memory limits, and accuracy requirements. Then propose a two-phase approach: first use a streaming algorithm like Count-Min Sketch to estimate frequencies with bounded memory, then maintain a min-heap of size K to track the top K elements. Discuss trade-offs between exact and approximate solutions, and how to handle updates and queries efficiently.
Pro tip: Emphasize the space-accuracy trade-off and mention that for heavy hitters, algorithms like Misra-Gries or Space-Saving can provide guarantees, showing depth beyond basic heap-based solutions.
Ask about data stream volume, velocity, memory limits, K size, and whether exact or approximate results are acceptable. Also consider if the stream is infinite and if elements can be evicted.
Select an appropriate algorithm like Count-Min Sketch for frequency estimation or Misra-Gries for frequent items, balancing memory and accuracy. Explain why it fits the constraints.
Use a min-heap of size K to maintain the top K elements, updating it as frequency estimates change. Discuss how to handle updates efficiently and when to recompute.
If the stream is distributed, propose a mergeable sketch or parallel processing with local top-K and global aggregation. Mention consistency and latency trade-offs.
Compare exact vs approximate, memory vs accuracy, and handling of concept drift. Suggest monitoring and adaptive parameters if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.