This is the kind of question where you feel okay for the first ten minutes and then realize you've only covered like 15% of what they want.
Start by clarifying functional and non-functional requirements, then design a high-level architecture with core services for search, booking, and inventory. Dive into data models and APIs for each component, addressing challenges like multi-leg search, seat locking, and consistency during changes/cancellations.
Pro tip: Emphasize idempotency and concurrency control in booking and cancellation flows, as these are critical for preventing double-booking and ensuring a smooth user experience. Also, discuss how you would handle partial failures in multi-leg itineraries.
Ask questions to understand scope: expected scale (users, flights), consistency needs, integration with external systems (GDS), and key features like multi-leg search, seat selection, and cancellation policies.
Outline main components: API gateway, search service, booking service, inventory service, payment service, and notification service. Discuss data stores (SQL for transactions, NoSQL for search) and caching.
Define core entities (Flight, Seat, Booking, Passenger) and relationships. Design RESTful APIs for search, book, cancel, and change, specifying request/response formats and error handling.
Explain multi-leg search algorithm (e.g., graph traversal with time constraints), seat locking mechanism (e.g., distributed locks or optimistic concurrency), and handling itinerary changes/cancellations (e.g., compensating transactions, refunds).
Discuss scaling strategies (sharding, read replicas), fault tolerance (retries, circuit breakers), and consistency trade-offs (CAP theorem). Mention monitoring and logging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Spent too long on the API surface and not enough on the data model.
Start by clarifying the core use cases and constraints (e.g., consistency, latency, scale) for each operation, then propose a hybrid API design: REST for user-facing CRUD and gRPC for internal high-performance services. Walk through a normalized data model that separates static flight schedule data from dynamic inventory and reservations, explaining how each entity supports the four operations.
Pro tip: Emphasize idempotency and concurrency control (e.g., optimistic locking with version numbers) for booking and cancellation APIs, as these are critical in distributed systems and often overlooked by candidates.
Ask about expected traffic, consistency needs (e.g., strong vs. eventual), and whether the APIs are public or internal. This shapes the choice between REST and gRPC and the data model design.
Define endpoints/RPCs for search, booking, modification, and cancellation. For REST, use resource-oriented URLs (e.g., /flights, /bookings); for gRPC, define services with clear request/response messages. Highlight idempotency keys for booking and cancellation.
Describe the schema for flights, legs, fares, seat inventory, reservations, and users. Explain relationships (e.g., a flight has multiple legs, a leg has seat inventory per fare class) and how they support the operations.
Explain how you handle concurrent bookings (e.g., seat locking, optimistic concurrency) and ensure data integrity across services. Mention transaction boundaries and potential use of event sourcing or CQRS if relevant.
Compare REST vs. gRPC for each operation, and discuss how the data model scales (e.g., sharding by flight ID, caching search results). Mention monitoring, versioning, and error handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The idempotency part I actually felt decent about, talked through client-generated keys and dedup at the reservation service level.
Start by clarifying requirements and constraints (e.g., scale, consistency needs, failure modes), then propose a design that uses a central inventory service with atomic reservations and idempotent booking operations. Discuss trade-offs between strong consistency (e.g., distributed transactions) and eventual consistency (e.g., compensating transactions) for multi-segment flights, and explain how idempotency keys prevent duplicate bookings.
Pro tip: Emphasize that idempotency is not just about deduplication but also about ensuring the same response is returned for repeated requests, which requires storing the result of the original operation. Also, mention that overbooking is sometimes intentional (e.g., airlines overbook to account for no-shows), so clarify whether the goal is to prevent it entirely or manage it within limits.
Ask about scale (requests per second, number of flights), consistency requirements (strong vs. eventual), and failure scenarios (e.g., network partitions, service crashes). Clarify whether overbooking is allowed and to what extent.
Propose a central inventory service that tracks seat availability per flight segment. Use atomic operations (e.g., database transactions with row-level locking or optimistic concurrency control) to reserve seats and prevent overselling.
For connecting flights, use a two-phase commit or saga pattern to reserve seats across segments atomically. If any segment fails, compensate by releasing previously reserved seats.
Require clients to send an idempotency key with each booking request. Store the key and the result of the operation in a durable store (e.g., database) with a unique constraint. On retry, return the stored result instead of reprocessing.
Explain trade-offs: strong consistency may reduce availability and increase latency; eventual consistency may lead to temporary overselling. Describe how to handle failures (e.g., timeouts, retries) and ensure idempotency across distributed components.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with strong consistency for the booking write path and eventual for search/read replicas.
Start by clarifying the booking system's core requirements: inventory accuracy, user experience, and global latency. Then propose a hybrid consistency model where strong consistency is used for critical booking operations (e.g., seat reservation) and eventual consistency for non-critical data (e.g., user reviews, recommendations). Explain how you would implement this using techniques like quorum reads/writes, conflict-free replicated data types (CRDTs), and regional caching with invalidation.
Pro tip: Emphasize that consistency is a spectrum and the key is to align it with business impact—over-engineering strong consistency everywhere hurts scalability, while under-engineering it can cause double bookings. Mention that you would measure and monitor consistency-related metrics (e.g., stale reads, conflict rates) to continuously tune the system.
Ask about booking volume, geographic distribution, latency SLAs, and tolerance for temporary inconsistency. Identify which operations are read-heavy vs. write-heavy and their criticality.
Classify operations: booking/reservation requires strong consistency (linearizability or serializability) to prevent double-booking; user profiles, search, and recommendations can be eventually consistent.
Propose a multi-region architecture with a primary region for writes (strong consistency) and read replicas in other regions (eventual consistency). Use quorum-based replication (e.g., Paxos/Raft) for critical data and asynchronous replication for non-critical data.
Discuss latency vs. consistency trade-offs (e.g., CAP theorem), conflict resolution strategies (e.g., last-write-wins, CRDTs), and how to handle network partitions. Mention fallback mechanisms like optimistic locking or reservation timeouts.
Explain how you would instrument the system to track consistency violations, latency, and user impact, and use that data to adjust the consistency model over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the core booking flow and then design each integration as a decoupled service with clear contracts and idempotent operations. Emphasize reliability patterns like event-driven communication, retries, and compensation for refunds. Conclude by discussing how you would test and monitor the end-to-end flow.
Pro tip: Show that you treat payments and refunds as distributed transactions: use idempotency keys, outbox pattern, and saga orchestration to avoid double charges or lost refunds. Mention that you'd start with a simple synchronous flow but design for eventual consistency and failure recovery.
Ask about expected scale, payment providers, ticketing model (reserved vs. general admission), notification channels, and refund policies. This ensures your design addresses real needs.
Outline a synchronous flow: user selects seats, payment is authorized, booking is confirmed, and ticket is issued. Use idempotency keys to prevent duplicate charges.
After payment success, publish an event to generate tickets and send notifications via a message queue. This decouples services and improves resilience.
Implement a saga or orchestration to reverse steps: void tickets, trigger refund via payment provider, and notify the user. Ensure idempotency and retries for refund operations.
Discuss retries, dead-letter queues, circuit breakers, and reconciliation jobs. Mention end-to-end tests with mocked providers and observability for each integration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Flexible-date search caught me a bit flat-footed.
Start by clarifying functional and non-functional requirements, then propose a high-level architecture that separates the search index from the primary datastore. Walk through how each filter (origin, destination, date, cabin) maps to index fields, and explain strategies for flexible-date search, caching, and pagination. Conclude by discussing trade-offs and how you would scale the solution.
Pro tip: Emphasize that flexible-date search is best handled by precomputing date ranges or using a date-range index rather than scanning all dates at query time. Also, mention that caching should be layered (e.g., CDN, application cache, query cache) and invalidated intelligently to avoid stale results.
Ask about expected query volume, latency SLAs, data freshness, and whether flexible-date search means ±N days or a range. Confirm if cabin class is a single value or multiple, and if results need to be sorted by price, duration, etc.
Define fields for origin, destination, departure date, cabin class, and other attributes like price and airline. Use appropriate data types (e.g., keyword for origin/destination, date for departure, integer for cabin class) and consider composite keys or nested documents for multi-leg flights.
Precompute and store date ranges (e.g., a week or month) or use a date-range query with a sliding window. Alternatively, maintain a separate index for date-flexible searches that groups flights by route and cabin, with min/max dates and prices.
Cache frequent queries at the edge (CDN) and in-memory (Redis) with TTLs based on data volatility. For pagination, use cursor-based pagination (e.g., search_after in Elasticsearch) to avoid deep pagination issues and ensure consistent results.
Explain how to shard the index by route or date, use read replicas, and handle hot shards. Discuss trade-offs between index size, query latency, and freshness, and how to monitor and reindex when needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through sharding by route or by booking ID, and separate read replicas for search.
Start by clarifying the system's requirements and constraints, then walk through scaling strategies for reads and writes, including sharding and replication. Finally, outline a resiliency plan covering failover, retries, and saga-based rollback, emphasizing trade-offs and alignment with Axon's domain (e.g., evidence management, real-time data).
Pro tip: Tie your answer to Axon's specific challenges, such as handling large volumes of video evidence and ensuring data integrity for law enforcement. Mention how you'd monitor and test resiliency mechanisms (e.g., chaos engineering) to validate your design.
Ask about expected scale, read/write ratios, latency SLAs, consistency needs, and budget. This ensures your design is grounded in real needs.
Propose horizontal scaling with sharding (e.g., by user ID, geography, or time) and discuss shard key selection, rebalancing, and routing.
For read-heavy: use caching, read replicas, CDNs. For write-heavy: use async writes, batching, LSM trees, and queue-based ingestion.
Cover failover (multi-AZ, active-active), retries with exponential backoff and jitter, circuit breakers, and idempotency.
Explain saga pattern for long-running transactions: choreography vs. orchestration, compensating actions for rollback, and ensuring eventual consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the multi-tenant angle is where I felt most out of my depth.
Start by contrasting single-tenant and multi-tenant architectures, focusing on data isolation, scalability, and operational complexity. Then dive into partner API integration, covering abstraction layers, resilience patterns, and rate limiting strategies. Emphasize trade-offs and how you'd evolve the system as requirements grow.
Pro tip: Show you understand that multi-tenancy isn't just about sharing resources—it's about enforcing isolation at every layer (data, compute, network) while maintaining cost efficiency. Mention that rate limiting must be adaptive and tenant-aware to prevent noisy neighbors.
Ask about expected number of tenants, traffic patterns, and SLAs to ground your design. This shows you avoid over-engineering and tailor solutions to actual needs.
Discuss data isolation models (silo, pool, bridge), resource sharing, and operational overhead. Highlight how multi-tenancy introduces complexity in authentication, authorization, and metering.
Propose an abstraction layer (e.g., adapter pattern) to normalize diverse partner APIs. Cover resilience patterns like circuit breakers, retries with backoff, and idempotency.
Explain strategies: token bucket, leaky bucket, sliding window. Emphasize per-tenant and per-partner limits, distributed enforcement (e.g., Redis), and graceful degradation.
Mention observability (metrics, tracing) to detect bottlenecks and abuse. Discuss how to evolve from single to multi-tenant incrementally, e.g., via feature flags or sharding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.