The surface area of this question is deceptive.
Start by clarifying functional and non-functional requirements, then design the data model and API, focusing on the complexities of recurring events and time zones. Discuss trade-offs between different approaches for recurrence expansion, notification delivery, and consistency, and finally address scalability and reliability concerns.
Pro tip: Emphasize the importance of idempotency and exactly-once semantics for event operations and notifications, as these are critical in a distributed system like a calendar service. Also, consider how to handle conflicts and concurrency, such as when multiple users edit the same event simultaneously.
Ask clarifying questions to understand the scope: expected scale (users, events), consistency requirements, supported recurrence patterns, notification channels, and integration needs (e.g., with email).
Outline the main components: API gateway, calendar service, event store, notification service, and external integrations. Sketch the data flow for key operations like event creation and RSVP.
Design schemas for users, calendars, events, recurrences, invitations, and reminders. Choose appropriate databases (e.g., relational for core data, NoSQL for scalability) and discuss indexing for efficient queries.
Explain how to handle recurring events (e.g., using RRULE, expansion on read vs. write), time zone conversions (store UTC, convert on display), and reminders (scheduling with a delay queue).
Discuss partitioning strategies, caching, handling failures, and trade-offs between consistency and availability. Address how to ensure notifications are delivered reliably and how to handle concurrent edits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I spent the most time and probably lost the most points.
Start by clarifying requirements: what types of recurrence (daily, weekly, monthly by day or date), what exceptions (skip, modify, add), and query patterns (range queries, single event lookup). Then propose a data model that separates the recurrence rule from exception overrides, and discuss trade-offs between storing expanded instances vs. computing on the fly. Finally, outline how to handle edge cases like time zones, DST, and infinite recurrence.
Pro tip: Mention that you'd store the recurrence rule in a standard format like iCalendar RRULE and use a library to expand occurrences, but cache or precompute for performance. Also, highlight that exceptions should be stored as overrides keyed by the original occurrence's start time, which simplifies lookups and modifications.
Ask about the types of recurrence patterns, exception types (skip, modify, add), query patterns (range vs. single), and scale (number of events, frequency of changes). This ensures the model fits the use case.
Use a standard like iCalendar RRULE (RFC 5545) to define the pattern, or design a custom schema if needed. Discuss how to store it (e.g., as a string or structured fields).
Store exceptions separately, keyed by the original occurrence's start time (or a unique occurrence ID). For skips, mark as cancelled; for modifications, store the changed fields. This avoids duplicating the entire series.
Compare computing occurrences on the fly (using the rule and exceptions) vs. precomputing and storing instances. Discuss trade-offs: on-the-fly is flexible but may be slow for large ranges; precomputed is fast for reads but requires updates when the rule changes.
Cover time zones, daylight saving time, infinite recurrence (use bounded queries), and how to handle updates to the series (e.g., 'this and future' vs. 'all'). Mention indexing for efficient range queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about optimistic locking and vector clocks.
Start by clarifying the requirements and constraints, then compare optimistic vs pessimistic concurrency control, and finally propose a concrete solution with trade-offs. Emphasize how you'd handle conflicts in a distributed system, including detection, resolution, and user experience.
Pro tip: Mention that conflict resolution is not just technical but also a product decision—sometimes it's better to merge changes or prompt the user rather than silently overwrite. Also, highlight the importance of idempotency and versioning to avoid duplicate updates.
Ask about the expected concurrency level, consistency requirements, and whether the system is distributed. Clarify if users need immediate feedback or if eventual consistency is acceptable.
Discuss optimistic concurrency control (e.g., version numbers, ETags) vs pessimistic locking. Explain when each is appropriate, considering latency, scalability, and user experience.
Describe how to detect conflicts (e.g., version mismatch) and resolve them (e.g., last-write-wins, merge, user prompt). Mention techniques like CRDTs for automatic merging if applicable.
Cover issues like clock skew, network partitions, and consistency models (e.g., strong vs eventual). Explain how to ensure correctness across multiple devices or data centers.
Summarize the trade-offs of your approach (e.g., complexity, performance, user experience) and mention edge cases like offline edits or partial failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a change-log table and per-device sync tokens.
Start by clarifying requirements: multi-device push sync, offline-first, conflict resolution, and scale. Then propose a hybrid architecture using a central sync service with change logs, push notifications, and client-side local databases, explaining how CalDAV principles (resources, ETags, sync-tokens) map to your design. Finally, discuss trade-offs around consistency, latency, and conflict handling.
Pro tip: Emphasize that offline-first means the client is the source of truth for local changes, and the server must reconcile concurrent edits—use vector clocks or version vectors to detect conflicts, and always provide a deterministic merge strategy (e.g., last-write-wins with server timestamp or CRDTs for specific fields).
Ask about scale (number of devices, users), data types (calendar events, contacts), consistency needs, and whether push must be real-time. Confirm offline-first expectations: clients can read/write without network, and sync when connectivity returns.
Propose a RESTful API with resources (e.g., events) identified by URIs, using ETags for optimistic concurrency and a sync-token (like CalDAV's sync-collection) to fetch changes since last sync. Include a change log on the server to track modifications.
Use WebSockets or long-polling for real-time push notifications to online devices. For offline devices, queue notifications and deliver upon reconnection. Consider using a pub/sub system (e.g., Redis Pub/Sub, Kafka) to fan out changes to connected devices.
Clients store data locally (e.g., SQLite) and queue changes. On sync, send local changes with version vectors; server detects conflicts and applies merge policy. Return conflicts to client for resolution if needed, or auto-merge using CRDTs for certain fields.
Compare push vs. pull, consistency models (strong vs. eventual), and conflict resolution strategies. Address scalability: sharding by user, caching, and rate limiting. Mention monitoring and failure recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (scale, latency, delivery guarantees, channels) and then propose a distributed, event-driven architecture that decouples ingestion from delivery. Focus on partitioning, idempotency, and backpressure to handle billions of events, and discuss trade-offs between consistency and availability.
Pro tip: Emphasize the importance of idempotency and deduplication at scale, and mention how you would handle failures gracefully with retries and dead-letter queues to avoid notification storms.
Ask about expected event volume, latency SLAs, delivery guarantees (at-least-once, exactly-once), supported channels (email, push, SMS), and user preferences. This ensures the design meets actual needs.
Propose an event-driven pipeline: ingestion via API/gateway, message queue (e.g., Kafka) for buffering, stream processing for filtering/aggregation, and a delivery service that dispatches to providers. Use a separate scheduler for reminders.
Explain how to partition data by user ID or event type to distribute load, use sharding for the database, and scale consumers horizontally. Discuss how to handle hot partitions and ensure even distribution.
Describe mechanisms for idempotency (deduplication keys), retries with exponential backoff, dead-letter queues for failed deliveries, and monitoring/alerting. Ensure no single point of failure.
Discuss trade-offs between latency and throughput, consistency vs. availability, and cost. Mention optimizations like batching, rate limiting, and using CDNs for static content.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.