← Snowflake Interview Insights
I got the core solution down pretty quick.
Use a sliding window with two pointers and a hash map to track character frequencies. Expand the right pointer to include new characters, and when the number of distinct characters exceeds k, shrink the window from the left until it's valid again. Track the maximum window length throughout.
Pro tip: Explicitly state that the algorithm runs in O(n) time because each character is added and removed at most once, and mention that the space complexity is O(k) due to the hash map storing at most k+1 distinct characters. This shows you understand the trade-offs and can analyze complexity precisely.
Restate the problem to ensure understanding: find the longest substring with at most k distinct characters. Ask about edge cases like empty string, k=0, or k >= number of distinct characters.
Explain that you'll maintain a window [left, right) and a frequency map. Expand right to include new characters, and when distinct count > k, move left forward until distinct count <= k.
Trace the algorithm on a small example, such as 'eceba' with k=2, showing how the window expands and contracts, and how the max length is updated.
State that time complexity is O(n) because each character is processed at most twice (once by right, once by left). Space complexity is O(k) for the frequency map, as it stores at most k distinct characters.
Mention handling of k=0 (return 0), empty string, and possibly using an array instead of a hash map if the character set is small (e.g., ASCII).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The substring part is easy, just track the start index of the best window.
First, clarify the original problem (likely longest substring without repeating characters) and confirm the expected return format. Then, modify the algorithm to track start and end indices of the optimal window, and finally adapt character comparison to Unicode grapheme clusters using a library or built-in segmentation.
Pro tip: Mention that grapheme clusters can be multiple code points, so using code units (like Java's char) is incorrect; use a grapheme-aware library and consider normalization (NFC) for consistent comparison.
Confirm the original problem (e.g., longest substring without repeating characters) and that the return should be the substring, not just length. Ask about Unicode handling expectations (e.g., grapheme clusters, normalization).
Adjust the sliding window to record the start and end indices of the longest valid substring. Update the result when a longer window is found.
Replace character-based operations with grapheme cluster segmentation. Use a library (e.g., ICU, Intl.Segmenter) to iterate over grapheme clusters and treat each as a unit for uniqueness.
Normalize input to a consistent form (e.g., NFC) to avoid false duplicates. Consider edge cases like empty string, single grapheme, and combining characters.
Discuss time/space complexity: O(n) with sliding window, but grapheme segmentation may add overhead. Mention trade-offs between correctness and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.