Google·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Google SWE interview with a classic string algorithms problem. Nothing too wild but the requirement to go beyond naive O(n^2) made it more interesting than it looked at first glance.

Questions Asked (1)

Given a string of lowercase letters, find the longest substring that appears at least twice (overlapping occurrences are allowed). Return an empty string if no such substring exists. Your solution should be meaningfully faster than a naive O(n^2) approach.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The 'banana' -> 'ana' example is easy to see but getting to an efficient solution is where it gets real.

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AI HintsAI Generated

Suggested Approach

Use binary search on the length of the repeated substring combined with a rolling hash (Rabin-Karp) to check for duplicates in O(n) time per length, achieving O(n log n) overall. Alternatively, build a suffix array and compute the longest common prefix between adjacent suffixes to find the maximum LCP in O(n log n) or O(n) with advanced techniques. Clearly explain the trade-offs between these approaches and why they beat the naive O(n^2) method.

Pro tip: Mention that you would use double hashing or a suffix automaton to avoid collisions and achieve deterministic O(n) time, showing awareness of edge cases and production-quality code. Also, discuss how you would handle very large inputs and memory constraints, as Google values scalability.

1. Clarify the problem and constraints

Confirm that overlapping occurrences are allowed, the string contains only lowercase letters, and the goal is to return the longest repeated substring. Ask about input size to determine if O(n log n) is acceptable or if O(n) is needed.

2. Propose a binary search + rolling hash approach

Explain that you can binary search on the length L, and for each L, use a rolling hash to check if any substring of length L appears at least twice in O(n) time. This yields O(n log n) overall, which is faster than naive O(n^2).

3. Discuss alternative efficient algorithms

Mention suffix array with LCP array (O(n log n) or O(n) with SA-IS) or suffix automaton (O(n)) as alternatives. Compare their trade-offs in terms of implementation complexity, memory, and constant factors.

4. Handle collisions and edge cases

For rolling hash, use double hashing or a large prime modulus to minimize collisions. For suffix array, ensure correct handling of empty string and no repeated substring. Discuss how to retrieve the actual substring, not just its length.

5. Analyze complexity and conclude

Summarize the time and space complexity of your chosen approach, emphasizing why it is meaningfully faster than O(n^2). If time permits, mention that O(n) is possible with suffix automaton but may have higher constant factors.

Key Points to Mention

Binary search on substring length combined with rolling hash (Rabin-Karp) for O(n log n) time.
Suffix array and LCP array to find the longest repeated substring in O(n log n) or O(n).
Suffix automaton for deterministic O(n) time, though more complex to implement.
Use of double hashing or a large prime to avoid hash collisions.
Handling overlapping occurrences and returning the actual substring, not just its length.
Trade-offs between time complexity, space complexity, and implementation difficulty.

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