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I started with the happy path which was probably the wrong move.
Start by clarifying requirements and scale, then outline the high-level components (API, database, payment integration). Focus on the core flows: adding credit, purchasing, and refunding, ensuring idempotency and consistency. Conclude with trade-offs and potential optimizations.
Pro tip: Emphasize idempotency and transactional integrity in payment operations, as these are critical for financial systems and demonstrate production maturity. Also, discuss how you would handle failures and retries gracefully.
Ask questions to understand expected load, consistency requirements, and integration with external payment providers. This sets the stage for design decisions.
Define RESTful endpoints for adding credit, purchasing, and refunding, including request/response schemas and error handling. Consider idempotency keys for safe retries.
Propose tables for users, accounts, transactions, and items, with appropriate indexes and constraints. Discuss how to maintain consistency and audit trails.
Explain step-by-step how each operation works, including validation, database updates, and interaction with external payment systems. Highlight transaction boundaries and idempotency.
Address potential bottlenecks, consistency vs. availability, and how to scale. Mention monitoring, logging, and security considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly a question I hadn't thought about before.
Start by clarifying the business context and requirements, then compare quantity vs. total value inputs across dimensions like pricing flexibility, validation, and client complexity. Conclude with a recommendation that balances simplicity, correctness, and future extensibility, possibly suggesting a hybrid approach.
Pro tip: Mention that accepting quantity is generally safer because it centralizes pricing logic server-side, reducing the risk of price mismatches and fraud, but be open to total value for use cases like donations or when pricing is dynamic and client-controlled.
Ask about the use case: Is this for a fixed-price catalog, dynamic pricing, or donations? Understand who controls pricing and how often it changes.
Discuss pros: server controls pricing, easier validation, consistent totals. Cons: less flexible for custom amounts, requires server to know current price.
Discuss pros: flexible for arbitrary amounts, simpler for clients that compute totals. Cons: risk of price mismatch, harder to validate, potential for fraud or errors.
Compare on dimensions: security, consistency, client complexity, API evolution, and error handling. Consider idempotency and rounding issues.
Propose a solution, e.g., accept quantity by default, with an optional total value for specific cases, or use a separate endpoint. Explain how it aligns with business needs and engineering best practices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scenario: client retries on timeouts or network failures can cause duplicate purchase requests. Then walk through your idempotency key design: how keys are generated, stored, and validated, and how you handle concurrent requests and edge cases. Emphasize trade-offs like storage cost, TTL, and failure modes.
Pro tip: Mention that idempotency keys should be scoped to a specific operation and user, and that you must handle the case where the first request is still in-flight when the retry arrives—using a lock or 'pending' state to avoid race conditions.
Restate the issue: client retries can cause duplicate charges or orders. Ask about scale, latency requirements, and whether the client can generate keys.
Explain how the key is generated (e.g., client-generated UUID, or hash of request payload + user ID) and its scope (per user, per operation).
Describe the storage layer (e.g., Redis, database) with TTL, and how you check for existing keys before processing. Mention atomic operations to avoid race conditions.
Discuss locking or 'pending' state to ensure only one request processes while others wait or return the same result.
Cover key expiration, storage cost, failure scenarios (e.g., key stored but request fails), and how to clean up.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard once you've seen it before.
Start by defining the atomicity requirement: both the credit balance update and inventory decrement must succeed or fail together. Then explain how to wrap both operations in a single database transaction with appropriate isolation level and error handling, and discuss how to handle concurrency and failures.
Pro tip: Mention that you would use SELECT ... FOR UPDATE to lock the rows before updating, and that you would consider using optimistic concurrency control with version numbers if contention is low, showing awareness of trade-offs.
Confirm that the purchase involves updating a user's credit balance and decrementing inventory, and that both must be atomic. Ask about expected concurrency and consistency needs.
Decide to wrap both updates in a single transaction. Select an isolation level (e.g., Read Committed or Repeatable Read) that prevents lost updates and dirty reads, and explain why.
Use SELECT ... FOR UPDATE to lock the credit and inventory rows, then perform the updates. Alternatively, use optimistic concurrency with version checks if contention is low.
Ensure that any failure (e.g., insufficient credit or inventory) triggers a rollback, leaving both balances unchanged. Use try-catch or transaction management to guarantee atomicity.
Mention that for high concurrency, you might use a distributed transaction or saga pattern, but for a single database, transactions suffice. Also note the importance of idempotency and retry logic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I mapped out a few states: refund requested, refund processing, refunded, failed.
Start by defining the refund flow's scope and actors, then walk through the state machine from initiation to completion, highlighting failure modes and idempotency. Emphasize how you'd design for consistency, observability, and reconciliation in a distributed system.
Pro tip: Show maturity by discussing idempotency keys and compensating transactions—interviewers at Databricks care about correctness under failures, not just happy paths.
Ask clarifying questions about the refund flow: who initiates it (customer, support, system), what payment methods are involved, and whether it's full or partial. Define the boundaries of the system you'll describe.
Enumerate the states (e.g., INITIATED, PENDING, PROCESSING, SUCCEEDED, FAILED, CANCELLED, REFUNDED) and the transitions between them. Explain what triggers each transition and who owns it.
Discuss what can go wrong at each transition: network timeouts, duplicate requests, partial failures, inconsistent state between services, and external provider errors. Mention how to detect and handle them.
Propose mechanisms like idempotency keys, retries with backoff, compensating transactions (sagas), and reconciliation jobs to ensure eventual consistency and prevent double refunds.
Wrap up by highlighting observability (logging, metrics, tracing) and how this design scales. Relate it to Databricks' need for reliable data pipelines and transactional integrity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.