I knew LRU cold but LFU tripped me up at first because the tie-breaking rule (least recently used among the least frequent) means you need two layers of ordering.
Start by clarifying the LFU cache requirements: O(1) get and put, and tie-breaking for eviction when multiple keys have the same lowest frequency. Then describe a design using a frequency map and a doubly linked list per frequency, explaining how nodes move between lists on access and how eviction picks the least recently used among the lowest frequency. Finally, walk through the implementation details and edge cases.
Pro tip: Mention that LFU is often used in caching systems like Redis and that the tie-breaking policy (LRU among same frequency) is crucial for correctness and performance. Also, note that you can optimize by maintaining a min frequency pointer to avoid scanning for the lowest frequency.
Confirm that get and put must be O(1), and discuss how to handle ties when multiple keys have the same lowest frequency. Ask if the cache size is fixed and if updates to existing keys count as access.
Propose a hash map for key-to-node lookup, a frequency map mapping frequency to a doubly linked list of nodes, and a min frequency variable. Each node stores key, value, frequency, and pointers for the linked list.
For get: if key exists, increment its frequency, move it to the appropriate frequency list, and update min frequency if needed. For put: if key exists, update value and increment frequency; if new, insert with frequency 1 and evict if at capacity.
When evicting, remove the least recently used node from the list at min frequency. If that list becomes empty, increment min frequency. This ensures O(1) eviction and correct tie-breaking.
Confirm O(1) time for both operations. Mention edge cases: cache size 1, updating existing key, and handling frequency overflow (though unlikely). Optionally, discuss alternative implementations like using a min-heap (but that would be O(log n)).
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Start by framing the problem as approximate frequency estimation over a distributed stream, then propose per-node probabilistic data structures like Count-Min Sketch or Space-Saving, and finally describe how to merge them globally using a hierarchical aggregation with error bounds. Emphasize trade-offs between accuracy, memory, and communication cost, and tie it back to ML use cases like feature frequency or caching.
Pro tip: Mention that Count-Min Sketch merges are linear and commutative, which is crucial for distributed aggregation, and discuss how to handle heavy hitters with a separate algorithm like Space-Saving or Misra-Gries to avoid missing important items.
Ask about data volume, latency, accuracy needs, and whether exact counts are impossible due to memory or bandwidth. Define what 'frequency' means (e.g., item occurrences, feature counts) and the goal (e.g., top-K, caching, anomaly detection).
Select structures like Count-Min Sketch for frequency estimation, Space-Saving for heavy hitters, or HyperLogLog for cardinality. Justify based on memory, update speed, and mergeability.
Propose a hierarchical merge: each node sends its sketch to a aggregator, which merges them by summing counters (for CMS) or merging heaps (for Space-Saving). Discuss communication efficiency and potential use of a tree topology.
Explain how to set parameters (e.g., width/depth of CMS) to achieve desired error guarantees, and discuss trade-offs between accuracy, memory, and network overhead. Mention that merging increases error but remains bounded.
Connect to ML use cases like feature hashing, frequency-based embeddings, or caching. Discuss practical issues: handling concept drift, sliding windows, and integration with systems like Apache Flink or Spark Streaming.
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Talked about watermarks and a sliding window approach.
Start by clarifying the requirements: what is the event time semantics, how much lateness is acceptable, and what is the desired decay function? Then describe a windowing strategy with allowed lateness and triggers, and explain how to implement time-based decay using exponential or sliding window aggregation. Finally, discuss trade-offs between accuracy, latency, and resource usage, and mention how to handle out-of-order events with watermarks and retractions.
Pro tip: Emphasize that you would use event-time processing with watermarks and allowed lateness, and that you would implement decay via a time-weighted aggregation (e.g., exponential decay) that can be updated incrementally. Also, mention that you would monitor late-event rates and adjust allowed lateness dynamically to balance accuracy and cost.
Ask about event-time vs processing-time, acceptable lateness, and the desired decay function (e.g., exponential, linear). Confirm whether exact results are needed or approximate is acceptable.
Propose using event-time windows with watermarks to track progress, and allowed lateness to handle out-of-order events. Explain how triggers (e.g., early, on-time, late) emit results.
Describe how to apply decay: for exponential decay, maintain a weighted sum where each event's weight decays over time. For sliding windows, use a decay factor per window and combine. Mention incremental updates to avoid recomputation.
Explain that late events are processed if within allowed lateness, and results are updated (retractions or upserts). If beyond allowed lateness, they are dropped or sent to a side output for later reconciliation.
Compare accuracy vs latency vs resource usage. Mention techniques like dynamic allowed lateness, approximate algorithms (e.g., sketches), and state management (e.g., RocksDB) for scalability.
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Standard trade-off question but it felt more pointed here because I'd already committed to specific design choices.
Start by clearly defining the problem and the constraints, then explain the trade-offs between accuracy, memory, and latency in your design. Justify your choices by linking them to business requirements and performance goals, and discuss how you validated or monitored the trade-offs.
Pro tip: Quantify the trade-offs with concrete numbers (e.g., 'we accepted 1% error to reduce memory by 50%') and tie them to Amazon's leadership principles like Customer Obsession and Dive Deep.
Briefly describe the distributed frequency tracking problem, its scale, and the key constraints (e.g., real-time updates, memory limits, accuracy requirements).
Discuss how accuracy, memory, and latency are interrelated: improving one often degrades the others. Give examples of design choices that affect each.
Explain why you prioritized certain aspects over others, linking to business needs (e.g., low latency for real-time recommendations) and technical constraints.
Describe how you measured the impact of your trade-offs (e.g., A/B tests, metrics) and how you monitor and adjust in production.
Conclude with the overall impact of your choices and what you learned, showing a balance between theoretical and practical considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.