Heap Operations for Streaming Statistics and Medians Questions
Master using two heaps (max-heap for lower half, min-heap for upper half) to maintain running median or percentiles from streaming data efficiently. Understand insertion, deletion, heap balancing, and O(log n) retrieval. Know when to choose max-heap vs min-heap and how to maintain invariants. Practice implementing and debugging heap operations in Python.
EasyBehavioral
81 practiced
Behavioral: Describe a time when you had to debug a subtle data-structure invariant (not necessarily heaps) in production. Use STAR format: Situation, Task, Action, Result. Highlight how you communicated with stakeholders and prevented regressions.
HardTechnical
50 practiced
Complexity proof: Prove that insertion into the two-heap running median structure and median retrieval meet the time complexity bounds O(log n) and O(1) respectively. Include assumptions about heap operations.
HardTechnical
82 practiced
Performance tuning: you find that Python's heapq is the bottleneck under heavy load. Discuss optimizations you could apply without changing high-level algorithm (two heaps). Consider micro-optimizations, data layout, use of C extensions, and alternative data structures.
HardTechnical
48 practiced
Concurrency: If multiple producer threads call add(num) concurrently on a single StreamingMedian instance in Python, what race conditions could occur? Propose synchronized approaches and evaluate performance trade-offs (locks, lock-free structures, per-thread buffering).
MediumTechnical
79 practiced
Edge case handling: What should your median implementation return or do when receiving NaN, None, or non-comparable types in the stream? Propose a defensive strategy and how you'd document behavior for users of your library.
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