The two-level dedup structure clicked for me pretty quickly.
Clarify the deduplication rules: a show is removed if it has been seen earlier in the same row (local) or anywhere above in the entire feed (global). Then implement a single pass over the 2D list, maintaining a global set and a per-row set, and only keep a show if it's not in either set, adding it to both sets when kept.
Pro tip: Discuss the trade-off between using a global set for O(1) lookups versus sorting or other approaches, and mention that the order of scanning (top-to-bottom, left-to-right) ensures deterministic output. Also, consider edge cases like empty rows or null values.
Ask if the deduplication should be case-sensitive, if show names are unique identifiers, and if the output should preserve the original order. Confirm that local duplicates are removed even if not seen globally.
Use two sets: a global set for all kept shows and a local set for the current row. Iterate through each row, and for each show, check if it's in either set; if not, keep it and add to both sets.
Time complexity is O(N) where N is total number of shows, as each show is processed once. Space complexity is O(N) for the sets, but can be optimized by clearing the local set per row.
Consider empty input, rows with no shows, duplicate shows within a row, and shows that appear in multiple rows. Ensure the function returns a new 2D list without modifying the original.
Walk through a sample input to verify correctness, such as [[A, B, A], [B, C, D], [A, E]] resulting in [[A, B], [C, D], [E]].
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements (e.g., QPS, latency, memory constraints) and then propose a layered architecture that separates per-user and global deduplication. Discuss the trade-offs of using Redis Bloom Filters for probabilistic dedup, TTL strategies for freshness, and how dedup integrates with the ranking layer to avoid filtering out relevant items.
Pro tip: Emphasize that deduplication should be configurable and observable—different surfaces (e.g., recommendations vs. search) may need different dedup windows and granularity, and you should monitor false positive rates and memory usage.
Ask about expected QPS, number of users, item cardinality, latency SLA, and memory budget. Understand what 'deduplication' means here: avoiding showing the same item twice in a session, across sessions, or globally?
Propose separate mechanisms: per-user dedup (e.g., Redis sets or Bloom filters keyed by user) for personalized freshness, and global dedup (e.g., a shared Bloom filter) for trending or popular items. Discuss trade-offs in memory and accuracy.
Evaluate Redis Bloom Filters for memory efficiency vs. exact sets for accuracy. Define TTLs based on content lifecycle (e.g., 24 hours for recommendations, longer for evergreen content) and consider sliding windows or time-bucketed keys.
Explain how dedup fits into the ranking pipeline: either as a pre-filter (remove duplicates before ranking) or post-filter (after ranking, to preserve relevance). Discuss how to handle fallbacks when dedup removes too many items.
Cover sharding, replication, and failover for Redis; consider using a distributed cache like Memcached or a custom service. Discuss monitoring, false positive rates, and how to handle cache misses without impacting latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.