Google·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Google SWE interview centered entirely on a trie-based autocomplete design problem. It went deeper than I expected, covering data structure choices, complexity analysis, and a full dry-run trace through several operations.

Questions Asked (1)

Design and implement an autocomplete service using a prefix tree. Support insert(word, weight), update(word, newWeight), delete(word), and topK(prefix, k) returning the k highest-weight words under that prefix, with lexicographic tiebreaking. Walk through the data structures you'd store at each node to make topK efficient, analyze time and space complexity per operation, and discuss trade-offs between caching sorted results at nodes versus computing on demand.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This was the whole interview basically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then design a trie where each node stores a map of children and a data structure (e.g., a min-heap or sorted list) for top-K words under that prefix. Walk through each operation, analyzing time and space complexity, and discuss the trade-offs between caching sorted results at nodes (faster queries, more memory, slower updates) versus computing on demand (slower queries, less memory, faster updates).

Pro tip: Emphasize that the choice between caching and on-demand computation depends on the read/write ratio and memory constraints; for Google-scale systems, a hybrid approach with periodic caching or approximate top-K might be necessary.

1. Clarify requirements and assumptions

Ask about expected scale, read/write ratio, memory limits, and whether weights can change frequently. Confirm that topK should return words sorted by weight descending, then lexicographically ascending.

2. Design the trie node structure

Each node contains a map of children (e.g., hash map or array), a flag indicating if it's a word end, the word's weight if applicable, and a data structure to maintain top-K words under this prefix (e.g., a min-heap of size K or a sorted list).

3. Implement operations and analyze complexity

For insert/update/delete, traverse the trie, update the word's weight, and propagate changes to the top-K structures along the path. For topK, traverse to the prefix node and return the precomputed top-K list. Analyze time and space for each operation.

4. Discuss trade-offs

Compare caching sorted results at nodes (O(1) topK, O(L * K log K) update) versus computing on demand (O(L + S) topK, O(L) update). Consider memory overhead, update frequency, and query patterns.

5. Optimize and extend

Mention potential optimizations like lazy propagation, using a balanced BST for top-K, or approximate top-K for very large K. Discuss how to handle concurrent updates and persistence.

Key Points to Mention

Trie node structure: children map, isEnd flag, weight, and top-K cache (e.g., min-heap or sorted list).
Time complexity: insert/update/delete O(L * K log K) with caching, topK O(L + K) if cached; without caching, topK O(L + S) where S is subtree size.
Space complexity: O(N * L) for trie plus O(N * K) for caches, where N is number of words and L is average length.
Trade-offs: caching improves read performance but increases memory and write latency; on-demand saves memory but makes reads expensive.
Lexicographic tiebreaking: ensure comparator sorts by weight descending, then word ascending.
Handling updates: when a word's weight changes, update the top-K structures along its path; consider lazy updates for efficiency.

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