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Google·Software Engineer·Online Assessment (OA)·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Google SWE coding round, got a trie implementation problem. Pretty standard data structures territory but the constraints were tight enough that a sloppy implementation would've timed out.

Questions Asked (1)

Q1

Implement a Trie (prefix tree) that supports insert, exact-word search, and prefix lookup operations, with up to 200k queries and 2 million total characters.

Algorithms & Data Structures
Author's notes

The core structure wasn't the hard part, it's making sure your node traversal doesn't do anything dumb at scale.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a Trie with an array of 26 child pointers per node and a boolean end-of-word flag. Explain the O(L) time complexity for each operation and discuss memory optimizations given the large input size. Finally, walk through the implementation and test with edge cases.

Pro tip: Mention that using a fixed-size array for children is faster but memory-heavy; for 2M characters, consider a hash map or compressed trie to reduce memory. Also, discuss how to handle large inputs with iterative approaches to avoid stack overflow.

1. Clarify requirements and constraints

Ask about character set (lowercase English?), input size limits, and whether memory or speed is more critical. Confirm that operations are insert, search, and startsWith.

2. Design the Trie data structure

Define a TrieNode with children (array of size 26 or hash map) and a boolean isEnd. Explain how each operation traverses the tree character by character.

3. Analyze complexity and trade-offs

State that time complexity is O(L) per operation, where L is word length. Discuss space complexity O(N*L) and compare array vs hash map for children.

4. Implement the operations

Write clean code for insert, search, and startsWith, handling null/empty strings and ensuring proper node creation. Use iterative loops to avoid recursion depth issues.

5. Test and optimize

Walk through examples, test edge cases (empty string, single character, long words), and mention potential optimizations like compressed trie or memory pooling.

Key Points to Mention

  • Time complexity O(L) for insert, search, and prefix lookup
  • Space complexity O(N*L) and trade-offs between array and hash map for children
  • Handling of edge cases: empty string, non-existent prefixes, and duplicate insertions
  • Memory optimization techniques for large input (e.g., compressed trie, ternary search tree)
  • Iterative implementation to avoid stack overflow with deep tries
  • Comparison with alternative data structures like hash set for exact search

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