Whatnot·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Whatnot software engineering interview that was basically one meaty coding problem about tries and user journey reconstruction. The interviewer pushed for an optimized single-pass solution after I got the naive version working, which I did not fully see coming.

Questions Asked (1)

Given a list of log records in the form [user_id, timestamp, action], reconstruct each user's chronological action sequence and insert all sequences into a shared trie where each node's count reflects how many users passed through that prefix. Return the trie with counts.

Algorithms & Data StructuresData Modeling

Author's notes

Got the basic approach pretty quick: group actions by user, sort by timestamp, walk each user's sequence and insert into a trie while incrementing counts.

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AI HintsAI Generated

Suggested Approach

First, group log records by user_id and sort each group by timestamp to reconstruct chronological action sequences. Then, insert each sequence into a shared trie, incrementing the count at each node along the path. Finally, return the root of the trie with counts representing the number of users who passed through each prefix.

Pro tip: Clarify edge cases upfront, such as duplicate timestamps or missing actions, and discuss how to handle them (e.g., stable sort or tie-breaking rules). Also, mention the trade-off between memory and speed when choosing to store counts as integers versus other structures.

1. Clarify requirements and edge cases

Ask about input size, timestamp format, duplicate timestamps, and whether actions are strings or enums. Confirm that each user's sequence should be ordered by timestamp and that the trie is shared across all users.

2. Group and sort logs per user

Use a hash map to group records by user_id, then sort each user's records by timestamp. If timestamps are equal, define a stable tie-breaker (e.g., original order or action name).

3. Build the trie with counts

Initialize a trie node with a count and children map. For each user's sorted action sequence, traverse the trie, creating nodes as needed, and increment the count at each node along the path.

4. Return the trie root

After processing all users, return the root node of the trie. The count at each node represents the number of users whose action sequence includes that prefix.

Key Points to Mention

Time complexity: O(N log N) due to sorting, where N is total number of log records; trie insertion is O(total actions).
Space complexity: O(total unique prefixes) for the trie, plus O(N) for grouping and sorting.
Handling duplicate timestamps: use a stable sort or a secondary key to ensure deterministic ordering.
Trie node structure: store count and a map of children (e.g., HashMap or array if actions are limited).
Incrementing counts: each node's count is incremented once per user that passes through it, not per occurrence.
Potential optimizations: if actions are from a small set, use an array for children; consider streaming if data is too large to fit in memory.

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