Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Oct 2026Remote

Summary

Phone screen at Google for a software engineering role that threw me completely off with a file-based string problem I had no prep for. Spent most of the call fumbling through hints and never actually wrote any code, which still stings.

Questions Asked (1)

Given a 4GB file, find the minimum length of a substring that does not appear anywhere in the file.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was brute force with hashing and the interviewer basically shut that down immediately.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: the file is a sequence of bytes (or characters), and we need the shortest string not present as a contiguous substring. The key insight is that the answer is at most O(log n) in length, so we can use a sliding window and a hash set to check all substrings of increasing length until we find one missing. For a 4GB file, we must consider memory and I/O constraints, so we should discuss streaming and external algorithms.

Pro tip: Mention that the answer is bounded by the logarithm of the file size (base alphabet size), so we only need to check a few lengths, and that a Bloom filter or external sorting can handle the memory constraints.

1. Clarify the problem and constraints

Confirm the alphabet (e.g., bytes, ASCII), whether the file is static, and the definition of 'substring'. Discuss memory limits and whether we can use external storage.

2. Analyze the theoretical bound

Show that the minimum missing substring length is at most ceil(log_A(n)) + 1, where A is the alphabet size and n is the file length. For a 4GB file with byte alphabet (A=256), this is at most 5.

3. Design an algorithm for small lengths

For each length L from 1 upward, scan the file and insert all substrings of length L into a hash set (or use a rolling hash). If the set size is less than A^L, a missing substring exists. Return the first L where this happens.

4. Address memory and I/O constraints

For large L, the number of substrings may exceed memory. Propose using external sorting, Bloom filters, or a trie on disk. Alternatively, use a probabilistic approach with Bloom filters to detect missing substrings with high probability.

5. Optimize and discuss trade-offs

Compare time vs. space: rolling hash reduces memory but has collisions; external sorting is exact but slower. Mention that for L=1, we can use a 256-bit bitmap, and for L=2, a 65536-bit bitmap, etc.

Key Points to Mention

The answer length is O(log n) due to the pigeonhole principle: there are only A^L possible strings of length L.
Use a sliding window with a hash set or rolling hash to efficiently enumerate substrings of a given length.
For a 4GB file, even L=5 requires checking up to 4 billion substrings, which may not fit in memory; consider external algorithms.
A Bloom filter can probabilistically determine if all possible substrings of length L are present, but false positives require verification.
For small L (1, 2, 3), bitmaps are very memory-efficient: 256 bits for L=1, 64KB for L=2, 16MB for L=3.
The problem is related to the 'shortest absent word' problem in combinatorics on words, and can be solved in O(n) time for fixed alphabet using suffix automata or suffix arrays.

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