The tricky part isn't the validation itself, it's that earlier transactions affect whether later ones are valid.
Start by clarifying the requirements and edge cases, then outline a validation function that checks each transaction against the rules before applying it. Discuss the data structures for accounts and rejected transactions, and analyze the time and space complexity of your solution.
Pro tip: Emphasize the importance of atomicity and idempotency in transaction processing, and suggest that validation should be centralized to avoid duplication and ensure consistency.
Ask questions to confirm the exact rules: what constitutes a registered account, how to handle negative or zero amounts, and whether rejected transactions should include detailed reasons. Also clarify if transactions should be processed in order and if partial application is allowed.
Choose appropriate data structures: a hash map for account balances (keyed by account ID) for O(1) lookups, and a list to collect rejected transactions with reasons. Consider if account registration status needs separate tracking.
For each transaction, validate in a clear order: check if both accounts are registered, then if the amount is positive, then if the sender has sufficient funds. If any check fails, record the rejection with a specific reason and skip applying the transaction.
For valid transactions, update the sender's and receiver's balances atomically. Ensure that the balance updates are consistent and that no partial updates occur if an error happens mid-way.
Discuss the time complexity (O(n) for n transactions) and space complexity (O(m) for m accounts plus rejected transactions). Mention potential trade-offs: e.g., using a database transaction for atomicity vs. in-memory processing, and how to handle concurrency if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came as a follow-up and I think I answered it correctly but my explanation was a bit circular at first.
Start by clarifying the requirements: are we dealing with a single-threaded or concurrent environment, and what consistency guarantees are needed? Then propose a solution that tracks transaction outcomes and conditionally applies later transactions, discussing trade-offs between optimistic and pessimistic approaches.
Pro tip: Mention that this is essentially a dependency resolution problem and that Stripe likely values solutions that are both correct and scalable, so consider how your approach handles high throughput and failures.
Ask questions to understand the context: Is this a single-threaded or concurrent system? What are the consistency requirements? Are transactions applied in order?
Represent transactions as nodes in a dependency graph, where edges indicate that one transaction's legality depends on another's outcome.
Decide between optimistic (e.g., speculative execution with rollback) and pessimistic (e.g., locking or serialization) approaches based on performance and consistency needs.
Explain how to detect and resolve conflicts, ensure atomicity, and maintain correctness under concurrent access.
Compare the chosen approach with alternatives in terms of latency, throughput, complexity, and fault tolerance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rattled off the obvious ones: negative amounts, unknown account IDs, self-transfers, zero-amount transfers.
Start by clarifying the transaction schema and validation rules, then systematically enumerate edge cases across structural, semantic, and business-rule dimensions. Prioritize cases by risk and explain how you would test them, including expected behavior and error handling.
Pro tip: Emphasize idempotency and error codes—Stripe cares deeply about safe retries and clear, actionable errors for developers. Mentioning how you'd test for duplicate submissions and consistent error responses shows you understand real-world payment systems.
Ask about the expected schema, required fields, data types, and validation rules to establish a baseline for what constitutes a valid transaction.
List malformed inputs such as missing fields, wrong data types, extra fields, null values, empty strings, and oversized payloads.
Consider invalid values like negative amounts, unsupported currencies, expired cards, future dates, and violations of business constraints (e.g., amount limits).
Include duplicate submissions, out-of-order events, race conditions, and idempotency key reuse to ensure system integrity.
For each case, specify the expected error code, message, and system behavior; then prioritize based on likelihood and impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.