This was the main question and it ate up most of the session.
Start by clarifying functional and non-functional requirements, then design a scalable, event-driven architecture that decouples order intake from processing. Focus on peak-hour spikes by incorporating asynchronous queues, auto-scaling, and caching, while discussing trade-offs between consistency and availability.
Pro tip: Emphasize the importance of backpressure and graceful degradation during spikes—showing you prioritize system resilience over perfect consistency demonstrates senior-level thinking.
Ask questions to understand expected scale (e.g., orders per second during peak), latency requirements, consistency needs, and budget constraints. Define core entities like users, restaurants, orders, and payments.
Sketch a microservices-based architecture with separate services for order intake, restaurant management, dispatch, and notifications. Use API gateways, load balancers, and CDNs for static content.
Introduce asynchronous processing with message queues (e.g., Kafka, RabbitMQ) to buffer orders. Implement auto-scaling for stateless services, and use caching (Redis) for hot data like restaurant menus and user sessions.
Choose databases based on access patterns: NoSQL for high-throughput order writes, relational for transactional integrity. Discuss sharding, replication, and eventual consistency trade-offs.
Design for failure with circuit breakers, retries, and idempotency. Set up monitoring (Prometheus, Grafana) and alerting for key metrics like queue depth, latency, and error rates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about dynamic pricing adjustments and pre-reserving courier slots based on historical patterns.
Start by clarifying the business context and requirements for surge pricing and capacity reservation, then propose a scalable, event-driven architecture that dynamically adjusts prices and reserves capacity based on real-time demand signals. Emphasize trade-offs between consistency, availability, and latency, and discuss how to handle edge cases like fairness and abuse prevention.
Pro tip: Demonstrate awareness of eBay's unique auction-based model and the need to balance surge pricing with seller/buyer fairness—mention how you'd prevent price gouging or ensure equitable access during high-demand events like limited-edition drops.
Ask about the specific use cases (e.g., auctions, buy-it-now), expected traffic patterns, latency requirements, and business rules for surge pricing and reservations. Identify non-functional requirements like scalability, consistency, and fairness.
Propose a data model for items, pricing rules, and reservations. Outline a pricing engine that computes surge multipliers based on real-time demand (e.g., bid velocity, page views) and historical data, using a rules engine or ML model.
Describe an event-driven architecture with message queues (e.g., Kafka) to ingest demand signals, a stream processing layer (e.g., Flink) to compute dynamic prices, and a distributed cache (e.g., Redis) to serve prices and reservations with low latency.
Explain how to handle reservations atomically using distributed locks or optimistic concurrency, ensuring no overselling. Discuss consistency models (e.g., eventual vs. strong) and fallback strategies during failures.
Cover fairness (e.g., rate limiting per user), abuse prevention (e.g., bots), and graceful degradation. Outline monitoring and alerting for pricing anomalies, reservation conflicts, and system health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements, then systematically address hotspots at multiple layers: data partitioning, caching, load balancing, and asynchronous processing. Emphasize trade-offs between consistency, latency, and cost, and propose a combination of techniques like sharding by geography, read replicas, and queue-based order processing.
Pro tip: Mention that hotspots are often transient and can be mitigated with dynamic scaling and intelligent request routing, but be careful not to over-engineer; sometimes a simple cache with a short TTL solves 90% of the problem.
Ask about expected peak load, latency SLAs, consistency requirements, and budget. This shows you understand that solutions must be tailored to business needs.
Analyze where hotspots occur: database writes, read queries, API rate limits, or downstream services. Determine if the issue is read-heavy or write-heavy.
Propose solutions at different layers: caching (CDN, Redis), database sharding/replication, load balancing with consistent hashing, and asynchronous order processing via queues.
Compare options like strong vs. eventual consistency, cost of scaling vs. throttling, and complexity of sharding vs. caching. Show you can make informed decisions.
Emphasize the need for real-time monitoring, auto-scaling, and dynamic rebalancing to handle hotspots as they emerge.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the payment idempotency piece specifically.
Structure your answer around a layered reliability strategy: first prevent failures where possible, then detect and recover from partial failures using retries with backoff and jitter, and finally ensure correctness through idempotency and reconciliation. Use concrete examples from orders and payments to illustrate each layer, emphasizing how you avoid duplicate charges and inconsistent order states.
Pro tip: Show you understand that retries without idempotency are dangerous—especially for payments—and that the real challenge is designing for exactly-once effects in an at-least-once delivery world. Mention that you'd use idempotency keys generated by the client and stored server-side, and that you'd reconcile with external providers to catch edge cases.
Identify where partial failures can occur: network timeouts, service crashes, database failures, and third-party payment provider errors. Clarify which operations are idempotent by nature and which need explicit idempotency handling.
For orders and payments, require a client-generated idempotency key on all mutating requests. Store the key with the operation result in a durable store, and return the same result for duplicate requests. Use database unique constraints to prevent duplicate order creation.
Use exponential backoff with jitter for retries, and set a maximum retry limit. Distinguish between retryable errors (e.g., timeouts, 5xx) and non-retryable errors (e.g., 4xx). For payments, consider using a circuit breaker to avoid overwhelming a failing provider.
For multi-step processes like order fulfillment and payment capture, use a saga pattern with compensating transactions to roll back on failure. Alternatively, use a two-phase commit if strong consistency is required, but be aware of its limitations.
Implement logging and tracing to detect partial failures. Periodically reconcile internal state with external providers (e.g., payment gateway) to catch discrepancies. Set up alerts for anomalies like high retry rates or stuck orders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part I was least prepared for.
Start by outlining a layered observability strategy covering metrics, logs, traces, and events, with emphasis on real-time monitoring during surge periods. Then define SLOs that are dynamic, with separate targets for normal and surge conditions, and explain how you'd measure and alert on them. Finally, tie it back to business impact and user experience.
Pro tip: During surge periods, traditional SLOs may be too strict; consider using error budgets that scale with traffic or define 'surge SLOs' with relaxed thresholds, but always communicate the trade-offs to stakeholders.
Map critical user flows (e.g., search, checkout) and the metrics that matter most during surges, such as latency, error rate, and throughput.
Implement metrics (RED/USE), distributed tracing, structured logging, and real-time dashboards with anomaly detection to handle surge traffic.
Set baseline SLOs for normal periods and separate, more lenient SLOs for surge periods, based on historical data and capacity limits.
Calculate error budgets for both normal and surge SLOs, and configure alerts that trigger when burn rates exceed thresholds, with escalation paths.
Review SLO performance after each surge, adjust targets, and ensure stakeholders understand the trade-offs between reliability and cost during peak events.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.