Netflix·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

Netflix coding interview, sliding window problem on arrays/strings. Pretty standard algorithmic round but the O(n) constraint means you can't brute force your way through it.

Questions Asked (1)

Given an array or string, find the length of the longest contiguous subarray where all elements are unique. They wanted an O(n) solution.

Algorithms & Data Structures

Author's notes

I knew sliding window was the move but fumbled the part about tracking last-seen positions with a hash map versus just a set.

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AI HintsAI Generated

Suggested Approach

Use a sliding window with two pointers and a hash map to track the last seen index of each character. Expand the right pointer, and when a duplicate is found, move the left pointer to the maximum of its current position and the duplicate's last index plus one. Keep track of the maximum window length throughout.

Pro tip: Clarify upfront whether the input is a string (e.g., ASCII) or an array of integers, as this affects the choice of data structure (array vs. hash map) and edge cases. Also, mention that the algorithm runs in O(n) time and O(min(n, m)) space, where m is the size of the character set.

1. Clarify input and constraints

Ask whether the input is a string or an array, and what the element range is (e.g., ASCII characters, integers). This determines if you can use a fixed-size array instead of a hash map for O(1) lookups.

2. Explain the sliding window approach

Describe maintaining a window [left, right] that contains only unique elements. Use a hash map (or array) to store the last seen index of each element.

3. Walk through the algorithm

Iterate right from 0 to n-1. If the current element is in the map and its last index >= left, update left = last index + 1. Then update the map with the current index and compute the window length.

4. Analyze complexity and edge cases

State that time complexity is O(n) because each element is visited at most twice, and space is O(min(n, m)) where m is the alphabet size. Discuss edge cases like empty input, all unique, all duplicates.

5. Test with examples

Walk through a small example (e.g., 'abcabcbb') to demonstrate correctness, and optionally mention alternative approaches like brute force for comparison.

Key Points to Mention

Sliding window technique with two pointers (left and right).
Hash map (or array) to store the last seen index of each element.
Time complexity O(n) and space complexity O(min(n, m)).
Handling duplicates by moving the left pointer to max(left, last_seen[element] + 1).
Edge cases: empty input, all unique elements, all same elements.
Comparison with brute force O(n^2) approach to highlight efficiency.

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