Google·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Google SWE interview, coding round. A follow-up to an earlier trie problem but with the trie explicitly off the table, which was a fun twist I did not fully see coming.

Questions Asked (1)

Given the same prefix/string matching problem from a prior round, solve it without using a trie. How would you approach it using sorting and binary search instead?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I leaned on lower_bound and upper_bound after sorting the dictionary lexicographically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then explain that sorting the strings and using binary search to find the range of strings with the given prefix is an efficient alternative to a trie. Walk through the algorithm step-by-step, analyze time and space complexity, and compare trade-offs with the trie approach.

Pro tip: Mention that binary search can be done using lower_bound and upper_bound with a custom comparator that compares only the prefix length, and highlight that this approach is particularly memory-efficient for large datasets.

1. Clarify the problem

Restate the problem to ensure understanding: given a set of strings and a prefix, find all strings that start with that prefix. Ask about constraints like dataset size, memory limits, and whether the set is static or dynamic.

2. Sort the strings

Sort the list of strings lexicographically. This groups all strings with a common prefix together, enabling efficient range queries.

3. Binary search for prefix range

Use binary search to find the first string that is >= the prefix (lower bound) and the first string that is > the prefix + a large character (upper bound). The range between these indices contains all strings with the given prefix.

4. Analyze complexity and trade-offs

Explain that sorting takes O(N log N) time, each query takes O(M log N) where M is prefix length, and space is O(1) extra beyond the sorted array. Compare with trie: trie offers O(M) query but uses more memory and is better for dynamic insertions.

5. Discuss optimizations and edge cases

Mention possible optimizations like using a custom comparator to avoid creating prefix+large character, handling empty prefix, and dealing with duplicate strings. Also note that if many queries are expected, a trie might be more suitable.

Key Points to Mention

Sorting groups strings with common prefixes together, enabling binary search for range queries.
Use lower_bound to find the first string >= prefix and upper_bound to find the first string > prefix + '~' (or a character larger than any in the alphabet).
Time complexity: O(N log N) preprocessing, O(M log N) per query, where M is prefix length.
Space complexity: O(1) extra space beyond the sorted array, making it memory-efficient.
Trade-offs: Trie provides O(M) query time but uses more memory; sorting+binary search is better for static datasets and memory-constrained environments.
Edge cases: empty prefix returns all strings; prefix not found returns empty range; duplicates are handled naturally.

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