I jumped straight into coding and only realized halfway through that I hadn't asked whether overlapping events were allowed.
Start by clarifying requirements and constraints, then outline a simple data model and state management strategy. Walk through the core CRUD operations and how you would test them, emphasizing trade-offs and edge cases.
Pro tip: Focus on demonstrating clean separation of concerns and testability; mention how you would handle time zones and recurring events as potential extensions, showing foresight without overcomplicating.
Ask questions to understand scope: single user or multi-user? Recurring events? Time zones? Persistence? This ensures you build the right thing.
Define an Event entity with fields like id, title, start, end, description. Consider using immutable data structures for easier state management.
Choose a pattern (e.g., Redux, MobX, or simple observer) to manage events. Emphasize unidirectional data flow and pure functions for updates.
Describe how to create, edit, and delete events. Discuss optimistic updates, validation, and conflict resolution if applicable.
Outline unit tests for reducers/actions and components. Use mocking for time and dependencies. Cover edge cases like overlapping events and invalid inputs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about storing a recurrence rule on the event and generating instances lazily.
Start by clarifying the requirements and constraints of recurring events, then propose a data model that separates the recurrence pattern from individual event instances. Discuss trade-offs between storing materialized instances versus generating them on the fly, and how to handle exceptions and modifications.
Pro tip: Demonstrate awareness of real-world complexities like time zones, DST, and infinite recurrences by suggesting a hybrid approach: store the recurrence rule and materialize instances only for a bounded window, with a mechanism to handle exceptions.
Ask questions to understand the scope: What types of recurrence patterns are needed (daily, weekly, monthly, custom)? How far into the future should events be generated? What are the query patterns (e.g., fetch events for a date range)?
Propose a schema with a RecurrenceRule table (storing RRULE or similar) and an Event table for individual instances. Consider adding an Exception table for modified or cancelled occurrences.
Decide between pre-materializing instances (e.g., for the next year) or generating on-demand. Discuss trade-offs: pre-materialization simplifies queries but requires updates for rule changes; on-demand is flexible but may be complex for range queries.
Explain how to handle edits to a single occurrence (e.g., store an exception with a reference to the original event) and how to handle changes to the entire series (update the rule and regenerate instances).
Discuss time zones, DST, infinite recurrences, and performance considerations (e.g., indexing, caching). Mention how to handle queries efficiently, such as using a materialized view or a separate index for event instances.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what does 'availability' mean (free/busy, working hours), what granularity (15-min slots), and what scale (number of users, events). Then propose a data model (e.g., intervals) and an algorithm to find common free slots, discussing trade-offs between precomputation and on-the-fly merging. Finally, outline how to scale the solution using indexing, caching, and distributed processing.
Pro tip: Mention that you would store busy times as intervals and use a sweep-line algorithm to find free slots, and that you would consider time zones and daylight saving time from the start—this shows attention to real-world complexity.
Ask about the definition of availability (free/busy, working hours), the granularity of slots (e.g., 15 minutes), the number of users and events, and whether the search is for a single day or a range.
Represent busy times as intervals (start, end) per user. Consider storing them in a database with appropriate indexes, or in memory for fast access.
For a set of users, merge their busy intervals and find gaps within the desired time window. Use a sweep-line or interval tree approach for efficiency.
Discuss how to handle large numbers of users: precompute availability, use caching, shard by user or time, and consider distributed processing (e.g., MapReduce) for batch queries.
Account for time zones, daylight saving time, recurring events, all-day events, and partial availability (e.g., user is free for 30 minutes but slot is 60 minutes).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the scope started feeling real.
Start by clarifying the current single-user calendar design and the new requirements for multi-user sharing (e.g., permissions, real-time updates, conflict resolution). Then propose a high-level architecture that introduces a sharing model, access control, and data synchronization, while discussing trade-offs between consistency, latency, and complexity.
Pro tip: Emphasize the importance of defining clear access control roles (owner, editor, viewer) and handling concurrent edits gracefully, as these are common pain points in shared calendars. Also, mention how you would measure success (e.g., user engagement, sync latency) to show product thinking.
Ask questions to understand the scope: How many users per calendar? What sharing permissions are needed? Is real-time collaboration required? What are the consistency and availability requirements?
Propose a schema that supports multiple users per calendar, such as a many-to-many relationship with roles (owner, editor, viewer). Discuss how to enforce permissions at the API and database levels.
Choose a strategy for conflict resolution (e.g., optimistic locking, CRDTs, or operational transforms) and describe how updates propagate to all users (e.g., WebSockets, polling, or push notifications).
Discuss how the design scales with many shared calendars and users: sharding, caching, read replicas, and efficient notification systems. Consider trade-offs between consistency and latency.
Recap key decisions and their implications (e.g., complexity vs. real-time collaboration). Suggest metrics to monitor and potential iterations based on user feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge that adding persistence introduces state and external dependencies, which fundamentally changes test design. Explain how you would shift from pure unit tests to a layered strategy: keep unit tests for business logic, add integration tests for the persistence layer, and use test doubles for the database in higher-level tests. Emphasize the importance of test isolation, data cleanup, and deterministic outcomes.
Pro tip: Mention that you'd use the repository pattern to abstract persistence, allowing you to swap in in-memory implementations for fast unit tests and real databases for integration tests. Also, highlight that you'd version your test data and use transactions to roll back changes, ensuring tests don't interfere with each other.
Recognize that persistence adds state, I/O, and external dependencies, which can make tests slower, flaky, and order-dependent. This requires re-evaluating your test strategy.
Separate unit tests (no persistence) from integration tests (with persistence). Keep unit tests fast and focused on logic, while integration tests verify the interaction with the database.
Abstract persistence behind interfaces (e.g., repositories) so you can inject in-memory fakes for unit tests and real implementations for integration tests. This keeps tests fast and maintainable.
Use transactions, unique test data, or database snapshots to isolate tests. Clean up data after each test to avoid side effects and ensure repeatability.
Integrate persistence tests into CI/CD, monitor for flakiness, and use tools like Testcontainers for consistent environments. Regularly review test performance and reliability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.