← Bloomberg Interview Insights
I started with the obvious hashmap plus min-heap combo and the interviewer was fine with it but then pushed on what happens when the upstream is firing thousands of events per second.
Start by clarifying requirements: expected ingestion rate, definition of 'current' (sliding window vs. all-time), and consistency guarantees. Then propose a scalable architecture with a partitioned ingestion layer, efficient in-memory counting using concurrent data structures, and a top-K extraction mechanism that balances accuracy and performance.
Pro tip: Emphasize trade-offs between exact and approximate counting (e.g., Count-Min Sketch) and discuss how to handle hot keys and backpressure. Mention that for financial data, low latency and high throughput are critical, so lock-free or sharded approaches are preferred.
Ask about ingestion rate (e.g., millions of ticks per second), latency requirements, definition of 'current' (time window), and whether exact counts are needed. This shapes the entire design.
Propose a distributed message queue (e.g., Kafka) to buffer incoming ticks, with multiple consumers for parallel processing. Discuss partitioning by ticker symbol to ensure scalability and ordering per symbol.
For exact counts, use a concurrent hash map (e.g., ConcurrentHashMap) with atomic counters. For approximate counts at scale, consider Count-Min Sketch or Space-Saving algorithm. Explain trade-offs.
Maintain a min-heap of size K for top-K, updated on each count change. For distributed settings, merge local top-Ks from shards. Discuss periodic recomputation vs. incremental updates.
Use sharding to reduce contention, lock-free data structures, and atomic operations. Discuss consistency models (eventual vs. strong) and how to handle updates to the top-K list atomically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.