The concurrency piece is where this gets interesting and also where I fumbled a bit.
Start by clarifying requirements (scale, consistency needs, seat map size) and then propose a layered solution: a database with strong consistency for seat inventory, a temporary hold mechanism with TTL, and an API that exposes seat states with real-time updates. Emphasize concurrency control (e.g., optimistic locking or distributed locks) and discuss trade-offs between consistency and latency.
Pro tip: Mention that you would use a two-phase approach: first, atomically reserve seats in the database with a conditional update (e.g., UPDATE ... WHERE status = 'available'), and second, use a separate hold table with expiration to avoid blocking inventory during checkout. This shows you understand both correctness and user experience.
Ask about expected traffic (e.g., peak concurrent users), consistency requirements (strong vs eventual), and seat map size. This determines whether a single database or distributed system is needed.
Propose a seats table with status (available, held, booked) and a holds table with expiration. Use database transactions with row-level locking or optimistic concurrency control (version column) to prevent double booking.
Outline REST endpoints: GET /seats (returns seat states), POST /holds (creates a hold with TTL), POST /bookings (confirms booking). Use idempotency keys for booking requests and return appropriate HTTP status codes (409 Conflict for double booking attempts).
Describe how the frontend polls or uses WebSockets/SSE to receive seat state changes. Implement optimistic UI updates with rollback on failure, and show hold countdown timers.
Compare optimistic vs pessimistic locking, discuss handling of expired holds, and explain how to scale (e.g., sharding by venue, caching seat maps). Mention monitoring and alerting for concurrency issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Reconciliation problems look straightforward until you start thinking about file delivery guarantees.
Start by clarifying requirements: file formats, frequency, volume, and what constitutes a match. Then propose a pipeline with ingestion, normalization, matching, and reconciliation, using idempotent processing and a durable store for auditability. Finally, discuss trade-offs around consistency, latency, and scalability, and how to handle edge cases like duplicates and mismatches.
Pro tip: Emphasize idempotency and exactly-once semantics: use a unique transaction ID and a deduplication store to handle file re-delivery, which is common in financial systems. Also, mention the importance of an immutable audit log for reconciliation and debugging.
Ask about file frequency, size, format (CSV, JSON, etc.), and expected volume. Determine what defines a match (e.g., transaction ID, amount, date) and what actions to take on discrepancies.
Propose a scalable ingestion layer (e.g., S3 + Lambda, Kafka) that handles periodic files. Normalize data into a common schema, and ensure idempotent processing using file checksums or transaction IDs.
Compare incoming records against internal records using a matching key. Classify entries as missing, duplicate, or mismatched. Use a rules engine or configurable logic to handle different match criteria.
Store flagged entries in a database or queue for operators. Provide APIs or dashboards for review, and integrate with downstream systems via events or webhooks. Ensure retry and dead-letter queues for failures.
Address partitioning, batch vs. stream processing, and consistency models. Trade-offs: latency vs. throughput, strong vs. eventual consistency, and cost vs. complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one felt closest to work I've actually done so I was more comfortable.
Start by clarifying requirements and constraints, then propose a high-level architecture with clear components and data flow. Dive into reliability patterns like idempotency, retries, and partial failure handling, and discuss trade-offs for each decision. Conclude by summarizing how the design meets the goals and scales.
Pro tip: Emphasize idempotency and exactly-once processing semantics early, as they are critical in financial systems and demonstrate deep understanding of distributed systems challenges.
Ask about expected volume, latency, data sources, validation rules, enrichment sources, notification channels, and compliance needs. This ensures the design aligns with business and technical constraints.
Outline components: ingestion (API/file upload), validation service, enrichment service, storage (database/object store), notification service, and orchestration (queue/stream). Describe data flow and interactions.
Discuss idempotency (unique transaction IDs, deduplication), retries with exponential backoff, dead-letter queues, and partial failure strategies (e.g., per-transaction status, compensating actions).
Explain how to ensure consistency across services (e.g., transactional outbox, saga pattern) and choose storage (SQL vs NoSQL) based on query patterns and ACID requirements.
Cover logging, metrics, tracing, alerting, and discuss trade-offs (e.g., latency vs consistency, complexity vs reliability) to show balanced decision-making.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.