Roblox·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Roblox software engineer interview with a pretty meaty coding problem involving prefix matching and custom sorting. No behavioral fluff, just straight into the algorithm.

Questions Asked (1)

You're given a list of query strings with associated timestamps, plus a list of prefix strings. For each prefix, find all distinct queries that start with it, then return them sorted by descending frequency, breaking ties by the earliest timestamp that query appeared.

Algorithms & Data StructuresSystem Design

Author's notes

The sorting logic is straightforward enough but the scale is the whole point.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints first (e.g., number of queries, prefixes, and whether timestamps are unique). Then design a solution that preprocesses queries into a trie or sorted list to efficiently retrieve queries for each prefix, and for each prefix, aggregate frequencies and track earliest timestamps using a hash map. Finally, sort the results per prefix by descending frequency and ascending earliest timestamp.

Pro tip: Mention the trade-off between preprocessing all queries into a trie (O(total query length) time and space) versus sorting queries and using binary search for each prefix (O(Q log Q + P log Q) time). Also, discuss how to handle large-scale data if the dataset doesn't fit in memory, showing system design awareness.

1. Clarify Requirements and Constraints

Ask about input size, expected query/prefix counts, timestamp uniqueness, and whether queries can have multiple timestamps. Confirm output format and tie-breaking rules.

2. Choose Data Structures

Decide between a trie (for efficient prefix matching) or sorting queries and binary search. Use a hash map to store per-query frequency and earliest timestamp.

3. Preprocess Queries

Build the chosen data structure: insert all queries into a trie, or sort the list of queries lexicographically. Also compute frequency and earliest timestamp for each distinct query.

4. Process Each Prefix

For each prefix, retrieve all matching queries using the data structure. For each matching query, collect its frequency and earliest timestamp.

5. Sort and Return Results

For each prefix, sort the distinct queries by descending frequency, then by ascending earliest timestamp. Return the sorted list.

Key Points to Mention

Trie data structure for efficient prefix matching, with each node storing a list of query indices or a map of queries.
Hash map to aggregate frequency and track earliest timestamp per distinct query.
Sorting comparator: primary key descending frequency, secondary key ascending earliest timestamp.
Time and space complexity analysis: O(total query length + total prefix length + sum of matches log matches) for trie approach.
Handling large datasets: discuss external sorting or distributed processing if data doesn't fit in memory.
Edge cases: empty prefixes, no matching queries, duplicate queries with different timestamps.

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