Pretty standard starting point but I fumbled the error handling spec a bit.
Start by clarifying requirements and defining the core data model (accounts, balances, transactions). Then outline the API and walk through the happy path for account creation, deposits, and transfers. Finally, systematically address error cases (unknown users, insufficient funds) and discuss trade-offs like atomicity, consistency, and concurrency.
Pro tip: Emphasize idempotency and atomicity for transfers—use a transaction-like approach to ensure either both debit and credit succeed or neither does. Also, mention how you'd handle concurrency (e.g., locking or optimistic concurrency) to prevent race conditions.
Ask questions to understand expected scale, consistency needs, and whether this is single-threaded or concurrent. Confirm that in-memory means no persistence and that we need basic error handling.
Define a simple Account class with userId and balance, and a Bank class managing accounts. Specify methods: createAccount(userId), deposit(userId, amount), transfer(fromUserId, toUserId, amount).
Walk through the logic for each operation: createAccount checks for duplicates; deposit validates user and positive amount; transfer validates both users, checks sufficient funds, and atomically updates balances.
For each operation, define specific exceptions or error returns: UnknownUserException, InsufficientFundsException, InvalidAmountException. Ensure transfers are atomic—if any step fails, no changes are made.
Talk about concurrency control (e.g., synchronized methods, locks, or optimistic locking), idempotency for deposits/transfers, and potential extensions like transaction history or multi-currency support.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went straight to a heap and the interviewer seemed fine with that.
Clarify the data model and constraints first, then propose a min-heap of size K for O(n log K) time and O(K) space, or a balanced BST if dynamic updates are needed. Define tie-breaking explicitly (e.g., account ID ascending) and discuss how to handle ties at the K-th boundary.
Pro tip: Mention that tie-breaking should be deterministic and documented, and consider whether ties should be included (returning more than K) or truncated—this shows attention to product requirements and edge cases.
Ask about the data source (e.g., list of accounts with spend), whether the method is called once or repeatedly, and if K is small relative to N. Confirm the definition of 'outgoing spend' and tie-breaking expectations.
For a one-time query, use a min-heap of size K to track the top K accounts. For dynamic updates, consider a balanced BST or a combination of hash map and heap. Explain the trade-offs.
Specify a deterministic secondary key, such as account ID ascending, to ensure consistent ordering. Discuss whether to include all accounts tied at the K-th spend or truncate arbitrarily.
State time and space complexity (O(N log K) time, O(K) space for heap). Handle edge cases: K=0, K>N, negative spend, and ties at the boundary.
Outline the algorithm: iterate through accounts, maintain heap, and extract top K. If needed, sort the final K elements by spend descending and account ID ascending.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then design a data model for scheduled payments with unique IDs and cancellation. Propose a time-advancement mechanism (e.g., a priority queue or sorted structure) to efficiently execute due payments, and discuss trade-offs between in-memory and persistent storage.
Pro tip: Mention idempotency and exactly-once execution to prevent duplicate payments, and discuss how to handle failures during execution (e.g., retries with exponential backoff).
Ask about scale, persistence needs, time granularity, and whether payments can be modified after scheduling. Confirm that unique IDs are system-generated and cancellation is allowed before execution.
Define a ScheduledPayment entity with fields: id (UUID), amount, currency, recipient, executionTime, status (scheduled/cancelled/executed). Use a database table or in-memory store with an index on executionTime for efficient querying.
Introduce a TimeProvider interface to abstract current time. For execution, use a min-heap (priority queue) of payments ordered by executionTime, or a scheduler that polls the database for due payments. Discuss trade-offs: heap is efficient for in-memory but loses state on restart; database polling is durable but may have latency.
For cancellation, mark the payment as cancelled (soft delete) and remove from the heap if present. For execution, atomically update status to executed and process payment, ensuring idempotency via unique ID and status check.
Address distributed scenarios: use a distributed lock or leader election for a single executor, or shard payments by ID. Consider persistence, crash recovery, and exactly-once semantics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then design a data model that preserves transaction history and handles scheduled payments. Walk through the merge process step-by-step, covering data migration, idempotency, and rollback strategies. Emphasize consistency, auditability, and edge cases.
Pro tip: Propose a two-phase approach: first, mark the source account as 'merging' to prevent new transactions, then perform the merge asynchronously with idempotent operations. This shows foresight about production safety and scalability.
Ask about expected data volume, consistency requirements, downtime tolerance, and regulatory constraints. Confirm whether the merge is reversible and how scheduled payments should be handled.
Propose a schema that links transactions to a user ID and supports reassignment. Consider using a separate mapping table or updating foreign keys, and ensure audit logs capture the merge.
Outline steps: validate both accounts, freeze source account, reassign transactions and scheduled payments, update balances, and mark source as merged. Ensure idempotency and atomicity where possible.
Identify all future-dated payments involving the source user, update them to reference the target user, and notify relevant parties. Consider recurring payments and third-party integrations.
Discuss handling of concurrent operations, failures during merge, and rollback procedures. Include monitoring, logging, and alerting for the merge process.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.