Google·Software Engineer·Technical Phone Screen·Senior

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Jun 2026

Summary

Google SWE interview with a tricky stream-matching problem that's a step beyond the classic LC 1032. The twist of consuming matched words from the stream made it meaningfully harder than what I'd prepared for.

Questions Asked (1)

You're receiving a character stream one character at a time, and you have a list of target words. After each new character, check if any target word matches the current suffix of the stream. If it does, return that word and also 'consume' it from the stream so the next characters extend only the prefix before the match.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew LC 1032 going in, so I felt okay for about 30 seconds.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient solution using a trie (prefix tree) to track suffixes of the stream. Explain how to handle consumption by resetting the stream state after a match, and analyze time and space complexity.

Pro tip: Mention that a trie can be built once for the target words and then reused for each character, achieving O(1) amortized time per character. Also, discuss how to handle overlapping matches and the order of checking (e.g., longest match first).

1. Clarify requirements and edge cases

Ask about the size of the target word list, character set, whether matches should be case-sensitive, and what to do if multiple words match (e.g., return the longest or shortest).

2. Design data structure

Propose using a trie (prefix tree) built from the reversed target words to efficiently check suffixes of the stream. Alternatively, consider Aho-Corasick for multiple pattern matching.

3. Algorithm for streaming and consumption

Maintain a pointer in the trie as characters arrive. When a match is found, return the word and reset the stream state (e.g., clear the buffer or reset the trie pointer) to consume the matched suffix.

4. Complexity analysis

Analyze time: O(L) per character where L is the maximum word length, but with trie it's O(1) amortized per character. Space: O(total characters in target words) for the trie.

5. Discuss trade-offs and alternatives

Compare trie vs. hash set of suffixes vs. Aho-Corasick. Discuss memory vs. speed, and how consumption affects the stream state.

Key Points to Mention

Trie (prefix tree) for efficient suffix matching
Handling consumption by resetting the stream state
Time and space complexity analysis
Edge cases: overlapping matches, multiple matches, empty stream
Alternative approaches: Aho-Corasick, rolling hash, suffix automaton
Scalability for large word lists and long streams

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