← Netflix Interview Insights

Netflix·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Netflix coding screen, one problem the whole session. The question was about deduplication logic for a streaming app home page shelf layout. Took me a while to fully internalize the two-phase dedupe rules before I could even start writing anything.

Questions Asked (1)

Q1

Given a list of shelves (each shelf is a list of title IDs) and a viewport width X, filter each shelf's titles according to a two-phase deduplication rule: for the first X output titles on any shelf, skip titles already seen in the visible region of previous shelves (global dedupe); beyond position X on a shelf, only skip titles that already appeared within that same shelf (local dedupe). Return the filtered shelves preserving original order.

Algorithms & Data StructuresSystem Design
Author's notes

The problem sounds manageable until you sit with the phase boundary.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the two-phase deduplication rule and edge cases (e.g., X=0, empty shelves, duplicate titles within a shelf). Then propose an efficient algorithm using a global set for the first X positions and a local set per shelf for positions beyond X, iterating through shelves and titles while preserving order. Analyze time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Emphasize that the global set only needs to track titles from the first X positions of previous shelves, not all titles, which reduces memory and aligns with the 'visible region' concept. Also, consider streaming or lazy evaluation for large datasets, as Netflix deals with massive scale.

1. Clarify requirements and edge cases

Restate the problem in your own words to ensure understanding, and ask clarifying questions about X (e.g., can it be 0 or negative?), shelf sizes, and whether titles can repeat within a shelf. Discuss edge cases like empty shelves or X larger than shelf length.

2. Design the algorithm

Propose using a global set to track titles seen in the first X positions of all previous shelves, and a local set per shelf for titles seen beyond position X. Iterate through each shelf, maintaining a position counter, and for each title decide whether to include it based on the phase.

3. Analyze complexity and optimizations

State the time complexity O(total titles) and space complexity O(total unique titles in first X positions + max shelf size). Discuss potential optimizations like early termination if X is small, or using a Bloom filter for approximate deduplication if memory is constrained.

4. Consider system design implications

If relevant, discuss how this algorithm scales in a distributed environment, e.g., processing shelves in parallel with shared state, or using a streaming approach to handle large datasets without loading everything into memory.

5. Test with examples

Walk through a concrete example to validate the algorithm, showing how the global and local sets evolve. Mention unit tests for edge cases and performance testing for large inputs.

Key Points to Mention

  • Two-phase deduplication: global for first X positions, local for beyond X
  • Use of hash sets for O(1) lookups and efficient deduplication
  • Preservation of original order and shelf structure
  • Handling edge cases: X=0, X > shelf length, empty shelves, duplicates within a shelf
  • Time and space complexity analysis
  • Scalability considerations for large datasets (e.g., streaming, distributed processing)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.