The base problem felt manageable, track seen movies with a set and skip duplicates until each row hits 6.
Clarify the problem constraints and edge cases first, then propose an efficient algorithm using a hash set per row to track seen movies while allowing duplicates only after the row reaches 6 unique movies. Discuss trade-offs between time/space complexity and potential optimizations for large-scale data.
Pro tip: Demonstrate awareness of Netflix's scale by mentioning that the solution should handle millions of rows and movies efficiently, and consider whether the deduplication should be done in-place or if a new data structure is needed.
Ask questions to confirm: What defines a duplicate? (e.g., same movie ID) Should duplicates be removed only until the row has 6 unique movies, or can duplicates appear after? Is the order of movies important? What is the expected size of input?
For each row, use a hash set to track unique movies. Iterate through the row, adding movies to the set if not seen; if the set size is less than 6, skip duplicates; once set size reaches 6, allow all subsequent movies (including duplicates).
Time complexity: O(N) where N is total number of movies across all rows. Space complexity: O(U) where U is number of unique movies per row (max 6 for tracking, but could be more if we store the result). Discuss if we can do in-place.
Consider rows with fewer than 6 unique movies (no duplicates allowed), rows with exactly 6 unique movies (duplicates allowed after), and rows with more than 6 unique movies (only first 6 unique kept, rest duplicates allowed? Actually, if row has >6 unique, we keep all unique? The problem says 'Once a row already has 6 movies, duplicates in that row are allowed.' So if row has 7 unique movies, we keep all 7? But then duplicates are allowed? Clarify: The condition is about having 6 movies, not unique. So if row has 6 movies (could be duplicates?), but duplicates are only allowed after 6 movies. So initially, we remove duplicates until we have 6 movies? Wait, re-read: 'remove duplicate movies from the rows. Once a row already has 6 movies, duplicates in that row are allowed.' This implies we process each row: we want to remove duplicates, but if after removing duplicates the row has fewer than 6 movies, we might need to keep some duplicates to reach 6? Or we only remove duplicates until the row has 6 movies? The phrasing is ambiguous. Let's interpret: We want to deduplicate each row, but we allow duplicates only if the row already has 6 movies (i.e., if the row has at least 6 movies, we don't remove duplicates? Or we remove duplicates only if the row has less than 6 movies? Actually, typical interpretation: We want to remove duplicate movies from each row, but if a row has 6 or more movies, we allow duplicates (i.e., we don't remove them). So the rule: For each row, if the number of movies in the row is less than 6, remove duplicates; if it's 6 or more, leave as is? But that seems too simple. Another interpretation: We process the row sequentially, and we only start allowing duplicates once we have added 6 unique movies. So we keep adding movies, but if a movie is a duplicate and we haven't yet reached 6 unique movies, we skip it; once we have 6 unique movies, we allow duplicates. That is more algorithmic. I'll go with that. So edge cases: rows with <6 unique movies: all duplicates removed, resulting in fewer than 6 movies. Rows with exactly 6 unique: duplicates after the 6th unique are kept. Rows with >6 unique: all unique are kept? But then duplicates? Actually, if we have >6 unique, we keep all unique, and then duplicates are allowed? But the condition says 'once a row already has 6 movies', so if we have 7 unique, we have 7 movies, so duplicates are allowed from the start? That would mean we don't remove any duplicates if the row has >=6 movies initially. That's another interpretation. To avoid ambiguity, in the interview, clarify with the interviewer. For the framework, assume the sequential interpretation: we build a new row, adding movies if they are not duplicates or if we already have 6 movies in the new row. So we stop deduplicating after 6 movies. So edge cases: rows with fewer than 6 unique movies will have all duplicates removed; rows with exactly 6 unique will have duplicates after the 6th unique kept; rows with more than 6 unique will have all unique kept and then duplicates allowed? Actually, if we have more than 6 unique, we will keep adding unique movies beyond 6 because they are not duplicates. So the new row will have all unique movies, and then duplicates are allowed. But the condition 'once a row already has 6 movies' means once the new row has 6 movies, we allow duplicates. So if we have 7 unique, we add the 7th unique because it's not a duplicate, so we still add it. So the new row can have more than 6 unique. That's fine. So the algorithm: iterate through original row, maintain a set of seen movies and a count of movies added to new row. For each movie, if it's not in seen, add to seen and to new row. If it is in seen, only add to new row if the new row already has >=6 movies. This ensures duplicates are only added after we have 6 movies. This is a clear interpretation.
Consider if we can avoid extra space by modifying the row in-place (e.g., using two pointers). Discuss if the order matters and if we can use a frequency map instead of a set. Mention that for Netflix scale, we might process rows in parallel or use distributed computing.
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Pretty straightforward refactor once the base case works, just parameterize the threshold.
First, clarify what 'per row dynamic' means: is the threshold derived from row properties (e.g., row length, data distribution) or provided as a per-row parameter? Then, propose a design that computes the threshold for each row, likely using a function or lookup, and adapt the deduplication algorithm to use it. Discuss trade-offs and edge cases.
Pro tip: Netflix values adaptability and ambiguity: show you can handle underspecified requirements by asking clarifying questions and proposing a flexible, testable solution. Also, mention how you'd validate the dynamic threshold's effectiveness with metrics like false positive/negative rates.
Ask whether the threshold should be computed from row data (e.g., row length, entropy) or supplied externally, and what 'row' means in context (e.g., database row, log line).
Propose a method to determine the threshold per row, such as a function of row characteristics or a configuration map. Consider if it should be static per row or adaptive.
Modify the existing dedup logic to accept a threshold parameter per row instead of a global constant. Ensure the algorithm remains efficient and correct.
Discuss how to handle rows with no threshold, invalid values, or performance impact. Consider caching or precomputation if thresholds are expensive.
Outline a testing strategy: unit tests for threshold computation, integration tests for dedup, and metrics to evaluate if dynamic thresholds improve dedup quality.
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Clarify the exact semantics: deduplicate only the first N rows, leaving the rest untouched. Then propose an algorithm that processes the first N rows with a hash set while streaming the remaining rows directly, and analyze time/space complexity and edge cases.
Pro tip: Mention that this is a streaming problem with a bounded deduplication window, and discuss how to handle N larger than the dataset or N=0. Also, note that if the data is sorted, a simpler approach may work, but the general solution should not assume sortedness.
Confirm what 'first N rows' means (e.g., based on input order, sorted order, or arrival time) and whether duplicates within the first N should be removed entirely or only subsequent occurrences. Ask about memory constraints and data size.
Use a hash set to track seen values while processing the first N rows, emitting only new values. For rows beyond N, emit them unconditionally without checking for duplicates.
Time: O(N) for the first part plus O(M) for the rest, where M is total rows. Space: O(min(N, distinct values in first N)). Handle N=0, N > total rows, and duplicate values that appear both within and after the first N.
If N is small, a hash set is fine. If N is large and memory is tight, consider external sorting or a Bloom filter with false positives. Also, if the data is sorted, a simpler adjacent-dedup approach works for the first N.
Walk through a concrete example, e.g., input [1,2,1,3,2,4] with N=3: first three rows [1,2,1] dedup to [1,2], then remaining [3,2,4] are appended as-is, resulting in [1,2,3,2,4].
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So now you need a flag or a set of exempt row identifiers on top of everything else.
First, clarify the exact criteria for special rows and how they should be handled in the output. Then, propose a modified deduplication algorithm that checks for special rows before applying deduplication logic, ensuring they are always retained. Finally, discuss how to integrate this into the existing system with minimal disruption.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that special rows might need to be configurable or driven by metadata, and discuss how to handle edge cases like overlapping special and duplicate rows.
Ask questions to understand what defines a 'special row' and whether the exclusion is based on content, metadata, or external configuration. Confirm that special rows should be kept even if they are duplicates of other rows.
Outline a two-pass approach: first identify all special rows and mark them as protected, then perform deduplication on the remaining rows. Alternatively, use a single pass with a conditional check to skip deduplication for special rows.
Consider scenarios where a special row is identical to a non-special row, or where multiple special rows are duplicates of each other. Decide whether special rows should be deduplicated among themselves or not, and clarify with the interviewer.
Discuss data structures (e.g., hash set for seen rows, separate set for special rows) and time/space complexity. Mention potential optimizations like early filtering or using a bloom filter for large datasets.
Propose test cases: no special rows, all special rows, mixed, and edge cases like special rows at boundaries. Emphasize the importance of verifying that special rows are never removed.
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