← Hot Agent Startup Interview Insights
I started with the data model which felt safe, got through Flight, Passenger, and Booking tables pretty cleanly.
Start by clarifying requirements (scale, read/write ratio, consistency needs) and then walk through the data model, indexing strategy, and concurrency control. Emphasize how you prevent overselling using transactions, locking, or optimistic concurrency, and discuss trade-offs between consistency and availability.
Pro tip: Mention that overselling is a business risk, not just a technical bug, and propose a two-phase approach: reserve inventory temporarily, then confirm payment, with idempotency keys to handle retries.
Ask about expected traffic, read/write patterns, consistency requirements, and whether the system is global or regional. This shapes your design decisions.
Define entities: Flight (flight_id, origin, destination, departure_time, total_seats, available_seats), Passenger (passenger_id, name, contact), Booking (booking_id, flight_id, passenger_id, seat_number, status, created_at). Consider normalization vs denormalization for performance.
Create indexes on frequently queried fields: flights by (origin, destination, departure_date), bookings by flight_id and passenger_id, and a unique index on (flight_id, seat_number) to prevent double-booking.
Use database transactions with row-level locking (SELECT ... FOR UPDATE) or optimistic concurrency (version column). Alternatively, use a distributed lock or atomic decrement on available_seats with a check constraint.
Compare pessimistic vs optimistic locking, SQL vs NoSQL, and how to scale reads with replicas. Mention handling failures, retries, and idempotency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was more conversational, felt like a relief after the index deep-dive.
Start by outlining the high-level user flow from search to confirmation, then dive into the backend queries and API calls at each step. Emphasize data consistency, idempotency, and scalability, especially for a startup environment.
Pro tip: Highlight trade-offs between consistency and availability, and mention how you'd handle failures like payment timeouts or double bookings. This shows you think about real-world reliability, not just the happy path.
User enters origin, destination, dates, and passenger count. Backend queries flight inventory with filters, often using a search service like Elasticsearch for speed.
User selects a flight from results. Backend fetches detailed flight info, seat availability, and pricing, possibly with caching to reduce database load.
User provides passenger info and payment. Backend validates input, checks seat availability again, and initiates payment processing via a third-party gateway.
On payment success, backend creates a booking record, updates seat inventory, and sends confirmation. Use transactions or sagas to ensure atomicity across services.
Backend triggers email/SMS confirmation, updates loyalty points, and logs analytics. Queries may include inserting into bookings, updating inventory, and writing to event streams.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: scale (millions of votes per second), latency targets (sub-second submission and near-real-time results), and consistency needs (eventual vs. strong). Then propose a horizontally scalable, partitioned architecture using an append-only log (e.g., Kafka) for ingestion, stream processing for aggregation, and a fast in-memory store for results, while discussing trade-offs like exactly-once semantics and cost.
Pro tip: Emphasize idempotency and deduplication at the edge to handle retries and prevent double-counting, and consider using a probabilistic data structure like HyperLogLog for approximate unique counts if exactness isn't critical.
Ask about expected peak QPS, latency SLAs, consistency requirements (e.g., can results be eventually consistent?), and whether votes must be exactly counted or approximate is acceptable.
Propose a scalable, low-latency ingestion tier using a distributed message queue (e.g., Kafka, Pulsar) with partitioning by vote ID or user ID to spread load and ensure ordering per key.
Use stream processing (e.g., Flink, Kafka Streams) to aggregate votes in real-time, with windowing and stateful operators, and write results to a fast store like Redis or an in-memory database.
Serve results via a low-latency API that reads from the aggregated store, possibly with caching and a pub/sub mechanism (e.g., WebSockets) to push updates to clients.
Discuss fault tolerance (replication, checkpointing), scaling strategies (auto-scaling, sharding), and trade-offs between latency, consistency, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered horizontal pod autoscaling on CPU and custom metrics, plus pre-scaling ahead of the known event start time since you'd have a schedule.
Start by clarifying the voting system's architecture and expected traffic patterns, then outline a Kubernetes deployment strategy using managed services for databases and message queues. Focus on autoscaling mechanisms like Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler, and discuss trade-offs between responsiveness and cost.
Pro tip: Emphasize the importance of load testing and gradual rollouts to validate autoscaling behavior before election day, and mention using Pod Disruption Budgets to maintain availability during scaling events.
Ask about expected traffic volume, peak patterns, latency requirements, and data consistency needs to tailor the deployment strategy.
Propose a multi-tier deployment with stateless web/API pods, a managed database (e.g., Cloud SQL), and a message queue (e.g., Pub/Sub) for asynchronous vote processing.
Use Horizontal Pod Autoscaler (HPA) based on CPU/memory or custom metrics (e.g., queue length), and enable Cluster Autoscaler to add nodes when pods are pending.
Configure readiness/liveness probes, Pod Disruption Budgets, and monitoring (e.g., Prometheus, Grafana) to detect and respond to scaling issues.
Pre-warm nodes, use over-provisioning with pause pods, and consider serverless options (e.g., Knative) for burst capacity; discuss trade-offs between cost and responsiveness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.