Top-K Analysis System - Variations and Follow-ups
Common Variations
1. Trending Detection
- Identify rapidly rising items
- Compare current vs historical
- Growth rate calculation
- Anomaly detection
2. Personalized Top-K
- Top-K per user segment
- Collaborative filtering
- Recommendation integration
- User preference weighting
3. Multi-dimensional Top-K
- Top-K across multiple dimensions
- Cross-dimension correlation
- Hierarchical aggregation
- Drill-down analysis
4. Real-time Leaderboards
- Gaming leaderboards
- Sales rankings
- Performance metrics
- Live updates
Follow-up Questions
Q: How do you handle high cardinality? A: Count-Min Sketch for space efficiency, Heavy Hitters for top-K, sampling for long tail, dimension reduction.
Q: How do you detect trending items? A: Compare current window vs previous, calculate growth rate, threshold-based detection, exponential smoothing.
Q: How do you ensure accuracy? A: Multiple algorithms (CMS + Heavy Hitters), periodic exact counting, reconciliation, error bounds.
Q: How do you handle late-arriving events? A: Watermarks with grace period, allowed lateness, late data side output, batch reconciliation.
Q: How do you optimize memory? A: Probabilistic structures, sliding window cleanup, state compression, dimension reduction.
Q: How do you handle multiple time windows? A: Separate state per window, hierarchical aggregation, incremental computation, shared base counts.
Q: How do you scale to billions of items? A: Approximate counting, sampling, dimension reduction, distributed processing, sharding.
Q: How do you handle bursty traffic? A: Kafka buffering, backpressure, auto-scaling, rate limiting, graceful degradation.
Edge Cases
Sudden Spikes
- Viral content
- Breaking news
- Flash sales
- Handling: Auto-scaling, sampling, rate limiting
Long Tail Distribution
- Many items with low counts
- Few items with high counts
- Handling: Focus on top-K, ignore long tail, sampling
Tie Breaking
- Multiple items with same count
- Ranking ambiguity
- Handling: Secondary sort (timestamp, lexicographic), stable sorting
Window Boundaries
- Items crossing window boundaries
- Count attribution
- Handling: Clear window semantics, event time processing
These variations demonstrate comprehensive understanding of top-K analysis systems.