The recurring meetings part is where I started to drift.
Start by clarifying functional and non-functional requirements, such as scale, recurrence rules, and conflict handling. Then design the data model and APIs for single and recurring meetings, explaining how recurrence is stored and expanded. Finally, discuss scalability, availability, and trade-offs in your design.
Pro tip: Demonstrate maturity by explicitly addressing how you would handle updates and cancellations for recurring meetings—e.g., whether changes apply to a single instance, all future instances, or the entire series—and discuss the data model implications.
Ask about scale (number of users, meetings per day), recurrence patterns (daily, weekly, custom), conflict resolution, time zones, and integration with existing systems like calendars.
Define entities: User, Meeting, RecurrenceRule, and MeetingInstance. Explain how recurring meetings are stored (e.g., RRULE) and how exceptions/overrides are handled.
Specify RESTful endpoints for creating, updating, and cancelling single and recurring meetings, including parameters for recurrence scope (single instance, this and future, all).
Discuss partitioning, caching, asynchronous processing for notifications, and handling high read/write loads. Mention conflict detection and resolution strategies.
Talk about trade-offs between consistency and availability, and potential extensions like integration with video conferencing, reminders, and analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scale, then propose a layered approach: use database transactions with proper isolation levels or optimistic concurrency control to prevent double-booking, and design a conflict detection algorithm that checks for overlapping time intervals. Explain how you handle concurrent requests using locking, versioning, or distributed locks, and discuss trade-offs between consistency and availability.
Pro tip: Mention that you would use a unique constraint or a conditional write (e.g., INSERT ... WHERE NOT EXISTS) to enforce atomicity at the database level, and consider using a distributed lock service like Redis or ZooKeeper for cross-service coordination. Also, highlight the importance of idempotency keys to handle retries safely.
Ask about the expected number of concurrent users, the acceptable latency, and whether the system is distributed. This helps determine the appropriate consistency model and locking strategy.
Propose a schema that includes time ranges for bookings and unique constraints on resources (e.g., room ID, attendee ID) for a given time slot. Mention using database-level constraints to prevent duplicates.
Describe an algorithm that checks for overlapping intervals: for a new booking, query existing bookings for the same resource and check if any overlap. Explain how to do this efficiently with indexing and range queries.
Discuss concurrency control mechanisms: optimistic locking (version numbers), pessimistic locking (SELECT FOR UPDATE), or distributed locks. Explain how to avoid race conditions and ensure atomicity.
Compare approaches: optimistic vs pessimistic locking, centralized vs distributed locks, and their impact on performance, scalability, and consistency. Mention how to handle failures and retries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Normalize everything to UTC first, then it's basically an interval merge problem across N sorted lists.
Start by clarifying requirements (e.g., meeting duration, working hours, room features) and then outline a solution that normalizes all busy intervals to UTC, merges them, and scans for gaps that satisfy constraints. Discuss how to incorporate room availability by treating rooms as additional participants with their own busy intervals.
Pro tip: Mention that you would handle edge cases like DST transitions and overlapping intervals by converting to UTC and using half-open intervals [start, end) to avoid off-by-one errors. Also, suggest precomputing and caching busy times for frequently queried participants to improve performance.
Ask about meeting duration, participant working hours, room requirements (capacity, equipment), and whether the query is one-time or recurring.
Convert all busy intervals to a common time zone (UTC), then merge overlapping intervals for each participant and room to create a unified busy schedule.
Scan the merged busy intervals to identify gaps of at least the required duration, considering working hours and room availability.
Discuss data structures (e.g., interval trees, priority queues) and techniques (e.g., caching, parallel processing) to handle large N efficiently.
Address DST changes, partial overlaps, and constraints like buffer times between meetings or room setup/teardown.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went SQL for the core booking and availability data because of the transactional requirements, and floated a NoSQL store for audit logs and notification queues.
Start by clarifying the specific domain (e.g., rides, trips, payments) and the access patterns, then propose a hybrid data model that uses SQL for transactional integrity and NoSQL for high-volume, low-latency needs. Walk through the core entities, their key fields, and how they relate, explicitly justifying each storage choice based on scale, consistency, and query patterns.
Pro tip: Uber’s scale demands a polyglot persistence approach—don’t default to a single database. Show you understand that different microservices (e.g., payments vs. driver location) have different data modeling requirements, and mention how you’d handle schema evolution and denormalization for performance.
Ask which part of Uber’s system you’re modeling (e.g., trips, drivers, payments) and identify the primary read/write patterns, volume, and latency requirements. This ensures your model addresses the actual problem.
List the main entities (e.g., Rider, Driver, Trip, Payment) and their relationships (one-to-many, many-to-many). This forms the foundation for both SQL and NoSQL designs.
For each entity, specify essential fields (e.g., trip_id, rider_id, driver_id, status, timestamps) and the indexes needed to support frequent queries. Highlight fields that require uniqueness or foreign-key-like constraints.
Justify the storage choice for each entity based on consistency, scale, and query flexibility. For example, use SQL for payments (ACID) and NoSQL for driver location updates (high write throughput, eventual consistency).
Discuss trade-offs like normalization vs. denormalization, sharding strategies, and how you’d handle cross-entity queries in a distributed system. Mention caching or materialized views if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sharding by tenant ID felt obvious and I said so, then talked about secondary indexes on user ID and time range for the free/busy queries.
Start by clarifying the multi-tenant requirements: data volume, access patterns, isolation needs, and scale targets. Then propose a sharding strategy (e.g., tenant-based or composite key) and indexing approach (e.g., tenant-prefixed indexes) that balances performance, isolation, and operational simplicity. Discuss trade-offs and how you would handle hotspots, rebalancing, and cross-tenant queries.
Pro tip: At Uber scale, sharding by tenant alone often creates hotspots; consider sharding by a composite key like (tenant_id, user_id) or using consistent hashing with virtual nodes to distribute load evenly. Also, mention that indexes should be tenant-aware to avoid cross-tenant performance interference.
Ask about tenant size distribution, read/write patterns, isolation requirements, and expected growth. This ensures your design addresses the actual scale and constraints.
Evaluate tenant_id vs. composite keys (e.g., tenant_id + entity_id) based on access patterns. Discuss how the choice affects data distribution, query routing, and hotspot mitigation.
Propose tenant-prefixed indexes to keep tenant data localized and avoid cross-tenant scans. Consider covering indexes and secondary indexes for common query patterns.
Explain how to handle rebalancing, resharding, and failure recovery. Mention tools like Vitess or custom sharding layers, and how to monitor for hotspots.
Compare isolation vs. efficiency, complexity vs. scalability, and consistency vs. availability. Show awareness of Uber's specific challenges like geo-distribution and real-time needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Soft delete with a status field, async notification fan-out via a message queue, and a separate audit table that never gets modified after insert.
Start by clarifying the scope: is this a single meeting cancellation or a bulk operation? Then walk through the end-to-end flow: API endpoint, state transition, notification fan-out, resource cleanup, and audit logging. Finally, address the O(log n) requirement by proposing a data structure like a balanced BST or skip list for efficient lookup and deletion, and discuss trade-offs.
Pro tip: Emphasize idempotency and failure handling: cancellations must be safe to retry, and partial failures (e.g., notification service down) should not leave the system inconsistent. Mention using a saga or outbox pattern for reliability.
Ask if cancellation is for a single meeting or a series, and whether it's user-initiated or system-triggered. Confirm expected scale (e.g., meetings per day) and consistency requirements.
Define an endpoint (e.g., DELETE /meetings/{id}) that validates permissions and transitions the meeting to a 'cancelled' state. Ensure idempotency by checking if already cancelled.
Use an event-driven approach: publish a 'meeting.cancelled' event to a message queue, which triggers notifications (email, push) and resource cleanup (release rooms, calendars) asynchronously.
Write an immutable audit log entry with who, when, why, and what was cancelled. Store in a durable, append-only store (e.g., Kafka or database) for compliance and debugging.
Use a balanced BST or skip list keyed by meeting ID or start time to support O(log n) lookup and deletion. For range cancellations, use an interval tree. Discuss trade-offs vs hash tables (O(1) average but no ordering).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked briefly on the capacity math and just started estimating out loud, which is the right move but felt messy.
Start by clarifying the functional requirements and scale assumptions, then derive nonfunctional requirements from them. Walk through each dimension (QPS, latency, caching, consistency, capacity) with concrete numbers and trade-offs, justifying your choices based on the use case. Conclude by summarizing how these requirements shape the system design.
Pro tip: Always state your assumptions explicitly and show your math—interviewers care more about your reasoning process than exact numbers. Tie each nonfunctional requirement back to user experience and business impact to demonstrate product sense.
Ask questions to understand what the system does, its expected user base, and growth projections. This sets the foundation for estimating QPS and capacity.
Calculate peak and average QPS from daily active users and actions per user. Derive storage, bandwidth, and memory needs using back-of-the-envelope calculations.
Set latency SLOs (e.g., p99 < 200ms) based on user expectations and industry benchmarks. Discuss how latency budgets are allocated across components.
Identify cacheable data, choose cache layers (client, CDN, application, database), and define eviction policies and TTLs. Explain how caching reduces latency and load.
Discuss CAP theorem and choose between strong and eventual consistency for different data types. Explain how consistency choices affect latency, availability, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Idempotency keys on the request, stored in a dedup table with a short TTL.
Start by defining idempotency and its importance in distributed systems, then walk through each operation (create, update, cancel) with concrete strategies like idempotency keys, deduplication windows, and retry policies. Emphasize trade-offs between consistency, latency, and complexity, and tie your answer to real-world scenarios like payment processing or ride requests.
Pro tip: Mention that idempotency is not just about preventing duplicate operations but also about ensuring that retries after partial failures don't corrupt state—use examples like 'exactly-once' semantics via idempotent consumers and idempotent producers.
Explain what idempotency means in the context of APIs and distributed systems, and why it's critical for operations like create, update, and cancel. Clarify that idempotency ensures multiple identical requests have the same effect as a single request.
Discuss using client-generated idempotency keys (e.g., UUIDs) stored server-side with a unique constraint. Explain how to handle duplicate requests by returning the original response or a conflict error, and mention deduplication windows to expire keys.
For updates, use versioning or conditional writes (e.g., ETags) to prevent lost updates. For cancels, treat them as idempotent by design—if already cancelled, return success. Discuss how to handle partial failures and retries with exponential backoff and jitter.
Outline retry strategies: exponential backoff with jitter, max retries, and circuit breakers. Explain deduplication techniques like idempotency keys, request IDs, and message deduplication in queues (e.g., Kafka exactly-once semantics).
Discuss trade-offs: storage overhead for idempotency keys vs. consistency, latency vs. retry aggressiveness, and complexity vs. reliability. Mention edge cases like network partitions, timeouts, and concurrent duplicate requests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.