Databricks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Databricks SWE coding round, pretty much a sliding window problem the whole way through. They pushed hard for the linear solution so knowing your way around the naive approach isn't enough here.

Questions Asked (1)

Given two strings, find the starting indices of all substrings in the first string that are anagrams of the second string. What's the most efficient solution you can come up with?

Algorithms & Data Structures

Author's notes

I started with the obvious brute force and they let me finish before asking if I could do better.

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AI HintsAI Generated

Suggested Approach

Use a sliding window of length equal to the pattern string and compare character frequency counts. Maintain a frequency map for the pattern and update the window's frequency map incrementally to achieve O(n) time. Alternatively, use a rolling hash to compare sorted strings or frequency arrays, but the frequency map approach is more efficient.

Pro tip: Clarify edge cases upfront, such as when the pattern is longer than the text or when there are duplicate characters. Also, mention that the solution can be optimized by using an array of size 26 for lowercase English letters to reduce overhead.

1. Clarify the problem

Confirm that anagrams are case-sensitive and consider only lowercase English letters. Ask if the output should be sorted or in any order.

2. Choose the right data structure

Use a frequency array or hash map to count characters in the pattern and the sliding window. For fixed alphabet, an array of size 26 is optimal.

3. Implement sliding window

Initialize the window with the first len(pattern) characters. Slide the window one character at a time, updating the frequency counts by removing the left character and adding the right character.

4. Compare frequencies efficiently

Maintain a count of how many characters have matching frequencies to avoid comparing entire arrays each time. When the match count equals the number of distinct characters in the pattern, record the start index.

5. Analyze complexity and edge cases

State that time complexity is O(n) and space O(1) for fixed alphabet. Discuss edge cases like empty strings, pattern longer than text, and repeated characters.

Key Points to Mention

Sliding window technique with fixed window size equal to pattern length
Frequency counting using array or hash map
Incremental update of window frequencies to avoid recomputation
Use of a match counter to achieve O(1) comparison per window
Time complexity O(n) and space complexity O(1) for lowercase English letters
Handling edge cases such as pattern longer than text or empty strings

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