The question is basically 'design Google Calendar but justify every decision.' I started with the data model because that felt most concrete, but the interviewer kept pulling me toward scalability before I'd even finished the schema.
Start by clarifying requirements and scale, then design a high-level architecture that separates concerns: event storage, scheduling, notification, and access control. Dive into data modeling for recurring events and time zones, and discuss trade-offs in consistency, partitioning, and caching.
Pro tip: Emphasize how you handle recurring events efficiently by storing recurrence rules and expanding instances on-demand, rather than precomputing all occurrences. Also, discuss how you'd shard data by user or calendar to achieve horizontal scalability.
Ask about expected read/write patterns, consistency needs, and features like real-time updates. Confirm scale: hundreds of millions of users, billions of events.
Outline core services: API gateway, calendar service, event service, notification service, and storage layers. Discuss partitioning and replication strategies.
Design schemas for users, calendars, events, attendees, and recurrence rules. Address time zone handling and indexing for efficient queries.
Explain how to store recurrence rules (e.g., RRULE) and generate instances on-the-fly. Describe reminder scheduling using a distributed job queue.
Define permission models (ACLs) for shared calendars and events. Discuss how to enforce access control at scale with caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and choosing REST or gRPC based on client needs, then define resource-oriented endpoints for core operations. Explicitly address idempotency, pagination, and rate limiting with concrete mechanisms, and discuss trade-offs.
Pro tip: Demonstrate maturity by acknowledging that idempotency and pagination are often afterthoughts that cause production issues; propose idempotency keys for writes and cursor-based pagination for lists from the start.
Ask about clients (web, mobile, third-party), expected scale, and consistency needs. Choose REST for simplicity and broad compatibility, or gRPC for performance and streaming; justify your choice.
Outline RESTful endpoints for events (POST /events, PATCH /events/{id}), listing (GET /events?start=...&end=...), attendees (POST /events/{id}/attendees), RSVP (PUT /events/{id}/attendees/{userId}/rsvp), search (GET /events/search?q=...), and shares (POST /calendars/{id}/shares).
Explain how to make write operations idempotent: use client-generated idempotency keys for POST requests, and design PUT/PATCH to be naturally idempotent. Discuss storing keys with TTL and returning the same response for duplicates.
For pagination, use cursor-based pagination (e.g., ?pageToken=...) for large, changing datasets like event lists. For rate limiting, describe token bucket or sliding window algorithms, and where to enforce (API gateway vs. service).
Highlight trade-offs: REST vs. gRPC, cursor vs. offset pagination, strict vs. eventual consistency for RSVPs, and rate limiting granularity (per user, per IP). Mention error handling and versioning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Drew out Users, Calendars, Events, Attendees, RecurrenceRules, and an Exceptions table.
Start by clarifying the system's core entities and relationships, then walk through the schema table by table, explaining primary keys, foreign keys, and normalization choices. Explicitly address soft deletes and audit fields, discussing trade-offs like query complexity vs. data retention and how they impact indexing and performance.
Pro tip: Mention that soft deletes require careful handling in unique constraints and queries (e.g., partial indexes on `deleted_at IS NULL`), and that audit fields should be immutable and set via triggers or application logic to ensure consistency.
Ask clarifying questions about the system's scope, expected scale, and data retention needs. Identify the main entities (e.g., users, posts, connections) and their relationships.
For each entity, define a table with a primary key (e.g., auto-incrementing bigint or UUID). Add foreign keys to enforce referential integrity, and consider junction tables for many-to-many relationships.
Add a `deleted_at` timestamp column (nullable) to tables where soft deletes are needed. Explain how to filter active records and handle unique constraints with partial indexes.
Include `created_at`, `updated_at`, and optionally `created_by`, `updated_by` columns. Discuss how to populate them (e.g., database triggers, ORM hooks) and their role in auditing and debugging.
Talk about performance implications (indexing, query patterns), storage overhead, and alternatives like hard deletes with archival tables. Mention how soft deletes affect cascading deletes and data consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The conflict detection query tripped me up.
Start by clarifying the data model and assumptions (e.g., tables for events, attendees, recurrence rules, exceptions) and the exact semantics of 'visible' and 'time range'. Then walk through each query, explaining the logic and any edge cases, and finally discuss how to handle recurrence materialization and conflict detection efficiently.
Pro tip: Demonstrate awareness of time zones and DST when comparing timestamps, and mention that recurrence rules should be expanded using a library or a recursive CTE rather than ad-hoc string parsing.
Ask clarifying questions about visibility (e.g., public, private, shared), time range boundaries (inclusive/exclusive), and the schema for events, attendees, recurrence rules, and exceptions.
Construct a SQL query that joins events with visibility/attendee tables and filters by the time range, considering time zone conversions if needed.
Write a query that checks for overlapping events for the user within the proposed time slot, using interval overlap logic (start < new_end AND end > new_start).
Explain how to expand a recurrence rule (e.g., using a recursive CTE or application logic) to generate occurrences, then apply exceptions and select the one matching the given time.
Mention indexing on time columns, handling large recurrence sets, and edge cases like all-day events, time zones, and DST transitions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partitioning by user ID or calendar ID was the obvious answer and I said both, which probably wasn't crisp enough.
Start by clarifying the scale and access patterns (e.g., number of events, users, read/write ratio) to ground your design. Then propose a sharding strategy based on access patterns (e.g., by user or event ID) and discuss caching layers (e.g., Redis for hot data) and search indexing (e.g., Elasticsearch). Finally, weigh the trade-offs between precomputing recurring event instances in the background versus computing on the fly, considering factors like query latency, storage cost, and complexity.
Pro tip: Demonstrate awareness of LinkedIn's specific scale and existing infrastructure (e.g., Espresso, Venice, Galene) by suggesting how your design could integrate with or leverage these systems. This shows you understand the company's tech stack and can make pragmatic choices.
Ask questions to understand the expected data volume, read/write patterns, latency requirements, and query types (e.g., fetching events by user, searching by keyword). This ensures your design is appropriately tailored.
Choose a sharding key that aligns with the most common access pattern (e.g., user ID for user-centric queries) and discuss strategies like consistent hashing to distribute load evenly. Mention how to handle hot shards and rebalancing.
Identify hot data (e.g., upcoming events for active users) and propose a multi-level cache (e.g., local cache, Redis) with appropriate TTLs and invalidation strategies. Discuss cache coherence and fallback to persistent storage.
For search functionality, propose an inverted index (e.g., Elasticsearch) and discuss how to keep it in sync with the primary data store (e.g., via change data capture). Consider indexing fields like title, description, and location.
Compare precomputing instances in the background (e.g., via a batch job) versus computing on the fly. Discuss trade-offs: precomputation reduces read latency but increases storage and write complexity; on-the-fly saves storage but may increase query time and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with optimistic locking using a version field on events.
Structure your answer by first addressing concurrency control (optimistic locking, versioning), then idempotency (idempotency keys, deduplication), and finally distributed transactions vs. outbox pattern. Discuss eventual consistency for search and notifications by explaining how to use change data capture (CDC) and message queues to propagate updates asynchronously.
Pro tip: Emphasize the trade-offs: distributed transactions (2PC) provide strong consistency but hurt availability and scalability, while the outbox pattern with eventual consistency is more resilient and fits LinkedIn's scale. Mention that idempotency is crucial for retries and can be achieved with unique request IDs and upsert semantics.
Explain how to handle concurrent edits using optimistic locking (version numbers) or pessimistic locking. Discuss conflict resolution strategies (last-write-wins, merge) and how to detect conflicts.
Describe how to ensure retries don't cause duplicate side effects. Use idempotency keys, deduplication tables, or idempotent operations (e.g., upserts). Mention that clients should generate unique request IDs.
Compare 2PC (strong consistency, but blocking and not scalable) with the outbox pattern (eventual consistency, scalable). Explain when to use each: 2PC for critical financial transactions within a single service boundary, outbox for cross-service workflows where availability is key.
Describe how to propagate changes asynchronously using CDC or domain events. For search, use a message queue to update the index; for notifications, use a similar pipeline. Discuss handling failures, retries, and ensuring at-least-once delivery with idempotent consumers.
Mention the need for monitoring (e.g., lag, failures) and reconciliation jobs to detect and fix inconsistencies. Highlight that eventual consistency requires observability and compensating actions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Reminder delivery I covered with a scheduled job scanning upcoming events and pushing to a notification queue.
Start by clarifying requirements and scale, then design the system in layers: scheduling and delivery, ICS import/export, and external calendar sync. For each layer, discuss data models, APIs, and trade-offs, and finally address abuse prevention and quotas with rate limiting, validation, and monitoring.
Pro tip: Emphasize idempotency and fault tolerance in delivery and sync, and mention how you'd handle time zones and recurring events—common pitfalls in calendar systems. Also, tie abuse prevention to LinkedIn's scale and trust & safety needs.
Ask about expected volume, latency, delivery guarantees, and supported calendar providers. Establish non-functional requirements like reliability, security, and compliance.
Propose a scalable architecture using a message queue and workers, with idempotent delivery, retries, and dead-letter queues. Discuss storage for reminders and user preferences.
Define a service to parse and generate ICS files, handling time zones, recurring events, and validation. Ensure secure file upload and size limits.
Use provider APIs (Google, Outlook) with OAuth, incremental sync via sync tokens, and conflict resolution. Design for eventual consistency and error handling.
Apply rate limiting per user/IP, quota management, input validation, and anomaly detection. Integrate with monitoring and alerting for abuse patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said relational for the core event and user metadata since you need joins and transactional guarantees.
Start by clarifying the system's requirements and data access patterns, then map each database type to the components where its strengths align. Propose a primary store that handles the core workload, and justify polyglot additions for specialized needs like search, caching, or analytics.
Pro tip: Emphasize that polyglot persistence adds operational complexity, so only introduce additional stores when there's a clear, measurable benefit. Show you understand the trade-offs between consistency, scalability, and team expertise.
Ask about data volume, velocity, variety, consistency needs, and access patterns (read/write ratio, query complexity). This sets the context for database choices.
For each major component (e.g., user profiles, activity feed, messaging, analytics), identify which database type fits best based on its characteristics.
Select the database that handles the most critical, high-volume workload as the primary store, justifying why it's the best fit.
Suggest additional databases for specialized needs (e.g., search, caching, time-series metrics) and explain how they integrate with the primary store.
Address consistency, data synchronization, operational overhead, and how to avoid over-engineering. Highlight any potential challenges.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.