Roblox·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Roblox technical phone screen with a trie-based autocomplete design problem. Pretty classic LC-adjacent stuff but the follow-up on trade-offs had some teeth to it.

Questions Asked (1)

Given parallel arrays of query strings and their timestamps, plus a list of prefix strings, design a data structure that returns matching queries for each prefix ordered by frequency descending and then by earliest timestamp. Walk through preprocessing time, query time, and memory trade-offs.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went straight to a trie and started coding before I'd thought through what to store at each node.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., number of queries, prefixes, and expected query frequency) and then propose a trie-based solution where each node stores a sorted list of queries for the prefix ending at that node. Explain how to preprocess the data by inserting each query into the trie and maintaining the sorted order based on frequency and timestamp, then discuss the time and space trade-offs of this approach.

Pro tip: Mention that you can optimize memory by storing only the top K queries per prefix if the system only needs to return a limited number of results, and discuss how to handle updates if new queries arrive dynamically.

1. Clarify Requirements and Constraints

Ask about the scale of data (number of queries, prefixes), whether the data is static or dynamic, and if there's a limit on the number of results per prefix. This ensures your solution aligns with the expected use case.

2. Propose a Trie-Based Data Structure

Describe building a trie where each node represents a prefix and stores a list of queries that have that prefix, sorted by frequency descending and then by earliest timestamp. Explain how to insert queries and maintain the sorted order.

3. Analyze Preprocessing and Query Time

Preprocessing: inserting each query into the trie takes O(L) per query, where L is the query length, plus sorting the lists at each node. Query time: traversing the trie to the prefix node takes O(P) where P is prefix length, then retrieving the sorted list is O(1) if precomputed.

4. Discuss Memory Trade-offs and Optimizations

The trie uses O(total characters in all queries) space, but storing sorted lists at each node can increase memory. Optimize by storing only top K results per node or using a heap to merge results on the fly if K is small.

5. Consider Alternatives and Extensions

Mention alternative approaches like using a hash map for prefixes or a combination of trie and heap, and discuss how to handle dynamic updates (e.g., new queries) by updating the trie and re-sorting affected nodes.

Key Points to Mention

Trie data structure for prefix matching
Sorting criteria: frequency descending, then earliest timestamp
Preprocessing time complexity: O(N * L + N * log N) where N is number of queries and L is average length
Query time complexity: O(P) for prefix traversal, O(1) for retrieving precomputed list
Memory optimization: storing top K results per node or using heaps
Handling dynamic updates and trade-offs between preprocessing and query time

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