Start by clarifying requirements and constraints (data source format, expected load, latency, consistency). Then walk through a simple, scalable design: ingestion pipeline, storage choice, and a read-optimized lookup endpoint. Emphasize trade-offs and how you'd evolve the design as scale increases.
Pro tip: Show you're thinking about production concerns early: idempotent ingestion, data freshness, and graceful degradation. Stripe values reliability and developer experience, so mention how you'd version the API and handle errors consistently.
Ask about data source (file, API, stream), update frequency, expected QPS, latency SLA, and consistency needs. This shapes storage and caching decisions.
Outline how to load data: batch vs. streaming, validation, deduplication, and idempotency. Mention scheduling (cron, Airflow) or event-driven triggers.
Select a database (e.g., PostgreSQL for relational, Redis for caching) based on access patterns. Define schema: station ID, location, capacity, availability, last_updated.
Define RESTful endpoint (e.g., GET /stations/{id}) with clear response schema, error codes, and pagination for list queries. Discuss caching and rate limiting.
Explain how to scale reads (replicas, CDN), handle failures (retries, circuit breakers), and evolve the API (versioning, backward compatibility).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I added filtering first because routing felt heavier and I wanted something working.
Start by clarifying the current service architecture and what 'stations' represent, then propose a filtering/routing mechanism that balances flexibility, performance, and maintainability. Discuss trade-offs between client-side filtering, server-side query parameters, and dedicated routing logic, and recommend an approach with clear reasoning.
Pro tip: Demonstrate awareness of Stripe's API design principles by emphasizing idempotency, backward compatibility, and clear error handling for invalid filters or routes. Also, mention how you would test and monitor the new functionality to ensure reliability at scale.
Ask questions to understand what 'stations' are, the expected filter criteria, routing rules, and non-functional requirements like latency and throughput. Confirm whether this is a public API change and what compatibility guarantees are needed.
Outline possible approaches: adding query parameters for filtering, implementing a routing layer with rules, or using a separate service. Compare them on complexity, performance, and extensibility.
Discuss pros and cons of each option, focusing on API usability, scalability, and maintenance. Consider how filtering vs. routing affects data consistency and error handling.
Recommend a specific approach with justification, such as adding optional filter parameters to the existing endpoint and a lightweight routing table for station selection. Explain how it aligns with Stripe's API design principles.
Outline steps for implementation, including validation, backward compatibility, and monitoring. Describe how you would test edge cases and ensure performance under load.
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 constraints of the existing system, then propose a layered architecture that separates concerns: graph algorithms for routing, a service layer for availability, and an event-driven pipeline for real-time updates. Emphasize incremental delivery, testing, and observability to ensure each layer integrates smoothly without disrupting existing functionality.
Pro tip: Show that you think about failure modes and data consistency early—e.g., how stale real-time data affects routing decisions—and propose concrete mitigations like versioned graphs or fallback to cached data. This demonstrates production maturity beyond just algorithmic correctness.
Ask about scale (number of stations, update frequency), latency SLAs, consistency needs, and existing architecture. This ensures your design addresses real needs and integrates with what's already built.
Propose separate layers: a graph service for shortest path (e.g., using Dijkstra or A*), an availability service that tracks station status, and a real-time update pipeline (e.g., pub/sub or streaming). Explain how they interact via well-defined APIs.
For shortest path, discuss trade-offs between Dijkstra, A*, and precomputed routes; for availability, consider in-memory caches with TTL or a database with read replicas; for real-time updates, evaluate push vs. pull and event sourcing.
Explain how to handle stale data (e.g., versioned graphs, eventual consistency), fallback strategies (e.g., use last known good data), and idempotent updates to avoid inconsistencies during network partitions.
Describe how to deploy each layer independently with feature flags, monitor performance (e.g., latency, error rates), and test with synthetic load and chaos experiments to ensure resilience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.