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Robinhood·Frontend Engineer·Onsite - System Design / Architecture·Senior

Senior
May 2026

Summary

Robinhood frontend interview that went deeper into system design than I expected. The whole session was basically one big design problem with follow-ups stacked on top of each other.

Questions Asked (1)

Q1

Design and implement a calendar system like Google Calendar. It should support creating events with start and end times, querying events for a specific day or date range, detecting overlapping event conflicts, and ideally recurring events, invitations, and reminders. What data structures would you use and what are the complexity tradeoffs?

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

I went with a sorted map of intervals first because it felt more approachable to explain, then they pushed me on why not an interval tree.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a layered architecture: a data model for events, an efficient in-memory index for querying, and a conflict detection mechanism. Discuss trade-offs between simple array-based approaches and more advanced structures like interval trees or segment trees, and address recurring events and reminders with appropriate strategies.

Pro tip: Emphasize the frontend perspective: how you'd optimize rendering and data fetching for a calendar UI, such as virtualizing the event list, using Web Workers for conflict detection, and leveraging IndexedDB for offline support. This shows you understand the unique challenges of building a complex UI at scale.

1. Clarify Requirements and Scale

Ask about expected number of events, users, query patterns, and whether real-time collaboration is needed. This determines the choice of data structures and whether to use client-side or server-side processing.

2. Design the Data Model

Define an Event object with id, title, start, end, recurrence rule, attendees, and reminders. Consider how to represent recurring events (e.g., RRULE) and how to handle exceptions.

3. Choose Data Structures for Querying and Conflict Detection

For a single day or range query, consider sorted arrays by start time with binary search, interval trees, or segment trees. For conflict detection, use interval overlap logic; for recurring events, expand occurrences on-the-fly or precompute.

4. Analyze Complexity Trade-offs

Compare time and space complexity of different approaches: e.g., sorted array O(log n + k) for range query but O(n) insertion; interval tree O(log n + k) query and O(log n) insertion; segment tree for dynamic updates. Discuss when to use each.

5. Address Recurring Events, Invitations, and Reminders

Explain how to handle recurrence (e.g., using RRULE expansion), invitations (e.g., separate attendee list with status), and reminders (e.g., scheduling notifications with a priority queue or setTimeout).

Key Points to Mention

  • Interval tree or segment tree for efficient range queries and conflict detection
  • Binary search on sorted array for simple range queries with O(log n + k) complexity
  • Recurring events: use RRULE (RFC 5545) and expand occurrences lazily or cache them
  • Conflict detection: check overlap condition (start1 < end2 && start2 < end1)
  • Reminders: use a min-heap or priority queue to schedule notifications
  • Frontend optimizations: virtual scrolling, Web Workers for heavy computations, IndexedDB for offline storage

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