The cashback scheduling part is what tripped me up first.
Start by clarifying requirements and scale, then design the core data model and APIs for accounts, transactions, and transfers. Address the ranking and scheduled cashback as separate concerns, explaining how you would implement them efficiently and reliably.
Pro tip: Emphasize idempotency and exactly-once processing for transfers and cashback, as financial systems demand strong consistency and fault tolerance. Also, discuss how you would handle race conditions and ensure data integrity under high concurrency.
Ask about expected user base, transaction volume, consistency needs, and latency requirements. Clarify whether the ranking is global or per-user, and if cashback is per transaction or aggregated.
Define entities: User, Account, Transaction, Transfer, and Cashback. Specify APIs for account creation, deposit, transfer, pay, and top-activity ranking. Choose a relational database for ACID guarantees.
Detail how deposits and transfers work with transactions and locking to prevent race conditions. For transfers, use a two-phase commit or saga pattern if distributed. Ensure idempotency with unique request IDs.
For ranking, maintain a sorted set (e.g., Redis ZSET) updated on each outgoing transaction, or compute periodically from a materialized view. For cashback, use a delayed job queue (e.g., RabbitMQ with TTL, or a scheduler) to credit 2% after 24 hours, ensuring idempotency.
Discuss sharding, replication, and caching for read-heavy ranking. For cashback, ensure exactly-once processing with retries and dead-letter queues. Monitor and alert on failures.
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, such as whether the merge is permanent and how payment IDs are structured. Then propose a data model that supports merging, focusing on maintaining a mapping from old payment IDs to the merged account and aggregating balances and totals. Finally, discuss trade-offs between different approaches, such as physical merge vs. logical merge, and how to handle pending cashbacks and ranking updates.
Pro tip: Emphasize idempotency and atomicity: ensure the merge operation can be safely retried and that payment lookups remain consistent even if the merge fails midway. Also, consider using an alias table or a union-find structure to efficiently redirect old payment IDs.
Ask questions to understand the scope: Is the merge permanent? Can accounts be merged multiple times? How are payment IDs generated and stored? What are the consistency and latency requirements for payment lookups?
Propose a schema that supports merging, such as an accounts table with a merged_into field or a separate account_aliases table. Consider how to store combined balance and outgoing totals, and how to associate pending cashbacks with the merged account.
Design a mechanism to map old payment IDs to the merged account, such as a payment_id_mapping table or embedding the account ID in the payment ID. Ensure lookups are efficient and can handle multiple merges.
Outline the steps to perform the merge atomically: update balances, transfer pending cashbacks, update outgoing totals for ranking, and create mappings for old payment IDs. Discuss how to handle failures and ensure idempotency.
Compare approaches: physical merge (updating all records) vs. logical merge (using aliases). Discuss trade-offs in terms of read/write performance, storage, and complexity. Address how the solution scales with many merges and high lookup volume.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and merge semantics: the ranking is based on total outgoing transaction amounts per account, and a merge combines two accounts into one. Then propose a solution that recomputes or incrementally updates the merged account's total and adjusts the ranking data structure (e.g., a balanced BST or heap) to reflect the new total, ensuring correctness and efficiency.
Pro tip: Mention that you would handle the merge atomically and consider concurrency: use a transaction or lock to prevent ranking queries from seeing an inconsistent state during the merge. Also, discuss how to handle frequent merges by batching or using a lazy update strategy if needed.
Ask about the scale (number of accounts, transactions), frequency of merges, and whether the ranking must be real-time or can be eventually consistent. Confirm that the ranking is global and based on total outgoing amount.
Propose a data structure that supports efficient updates and queries, such as a balanced binary search tree (e.g., order-statistic tree) or a skip list, where each node stores the account ID and total outgoing amount, sorted by amount.
When merging account A into account B, compute the new total outgoing amount for B as sum(A.total, B.total). Remove A from the ranking structure and update B's total in the structure (which may involve rebalancing).
Perform the merge and ranking updates within a single transaction or under a lock to prevent concurrent reads from seeing an inconsistent state. Consider using a versioned or snapshot approach for read-heavy workloads.
If merges are frequent, discuss incremental updates versus periodic recomputation. For very large scale, consider sharding the ranking by amount ranges or using a distributed system with eventual consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a relief after the merge question.
Clarify the data model and cashback rules, then design an efficient algorithm that retrieves the account, computes due cashbacks based on time or transaction history, and returns the updated balance or null. Discuss trade-offs between precomputing cashbacks and calculating on-the-fly, and handle edge cases like missing accounts and concurrent updates.
Pro tip: Demonstrate awareness of real-world constraints: cashback accrual might be asynchronous, so consider idempotency and consistency (e.g., using timestamps or versioning) to avoid double-counting when GET_BALANCE is called multiple times.
Ask about the account data structure, how cashbacks are stored (e.g., pending vs applied), and the rules for when a cashback becomes due (time-based, transaction-based, etc.).
Outline steps: fetch account by ID, if not found return null; otherwise, identify all due cashbacks, apply them to the balance, and return the new balance. Consider whether to update the stored balance or compute on the fly.
Address scenarios like no due cashbacks, multiple cashbacks, concurrent calls, and idempotency. Discuss locking or atomic operations if needed.
Evaluate time and space complexity of your approach. Compare precomputing cashbacks (faster reads, complex writes) vs computing on read (simpler writes, potentially slower reads).
Walk through test cases: existing account with/without due cashbacks, non-existent account, multiple cashbacks, and boundary conditions (e.g., cashback exactly at due time).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.