I got the frequency map part pretty quickly but fumbled the bucket sort explanation for a bit.
Use a hash map to count co-occurrences: for each session, iterate over the other product IDs and increment a counter for each (target, other) pair. Then sort the resulting products by count descending and product ID ascending, and return the top k. Discuss trade-offs between time and space, and how to handle large-scale data.
Pro tip: Clarify assumptions upfront: whether the target product can appear multiple times in a session, whether sessions can be empty, and if k exceeds the number of unique co-occurring products. Also, mention that in a real eBay-scale system, you'd likely use a distributed approach like MapReduce to handle the data volume.
Ask about input size, whether the target product is guaranteed to be in each session, and how to handle ties. Confirm output format and if k can be larger than the number of unique products.
Use a hash map to count co-occurrences: for each session, iterate over other product IDs and increment their count. Ensure the target product itself is excluded from the counts.
Convert the hash map entries to a list and sort by count descending, then by product ID ascending. Return the first k elements. Discuss using a heap for better efficiency if k is small relative to the number of unique products.
State time complexity: O(N + M log M) where N is total number of product occurrences across sessions and M is number of unique co-occurring products. Space complexity: O(M). Mention that a heap can reduce sorting to O(M log k).
For large-scale data, propose distributed counting (e.g., MapReduce) or streaming approaches. Mention memory considerations and potential use of approximate algorithms if exact counts are not required.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current solution's pain points and production requirements, then propose a refactoring plan that applies Java Streams for declarative data processing, immutable maps for thread-safe configuration or caching, and layered separation of concerns (e.g., controller/service/repository). Emphasize trade-offs like readability vs. performance and testability, and tie your choices to eBay's scale and reliability needs.
Pro tip: Mention that you'd introduce the refactoring incrementally behind tests, using feature flags if needed, to avoid breaking existing behavior—this shows you understand production risk management, not just code aesthetics.
Ask about expected data volume, latency, concurrency, and existing dependencies to ground your refactoring decisions in real production needs.
Point out issues like mutable shared state, mixed I/O and business logic, or imperative loops that obscure intent, and map them to clean architecture layers.
Show how Streams can replace imperative loops for filtering/transforming collections, and how immutable maps (e.g., Map.of, Collectors.toUnmodifiableMap) eliminate side effects and enable safe sharing.
Propose extracting distinct responsibilities into classes or interfaces (e.g., a service for business logic, a repository for data access, a mapper for DTO conversion) and using dependency injection.
Describe how you'd write unit tests for each layer, benchmark performance if needed, and weigh readability, memory overhead, and team familiarity against the benefits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.