Start by clarifying requirements and constraints, then design a data model that supports efficient timestamp-ordered operations. Use an ordered data structure like a balanced BST or skip list to maintain transactions, and ensure each operation is processed in timestamp order. Finally, discuss trade-offs and potential optimizations for scale.
Pro tip: Demonstrate awareness of real-world banking constraints like idempotency and exactly-once processing, and mention how you would handle out-of-order timestamps or late-arriving transactions.
Ask about expected scale, consistency requirements, and whether timestamps are provided by clients or generated server-side. Confirm if operations must be strictly ordered or if eventual consistency is acceptable.
Define Account and Transaction entities. Use a map for accounts and an ordered structure (e.g., balanced BST, skip list, or sorted list) to store transactions by timestamp for each account or globally.
For each operation, validate inputs, check account existence, and apply the transaction in timestamp order. Use locking or optimistic concurrency to handle concurrent access.
Explain how to process out-of-order timestamps (e.g., buffer and sort) and ensure thread safety. Discuss using a priority queue or log-structured storage for high throughput.
Talk about partitioning by account ID, using distributed logs (e.g., Kafka), and trade-offs between consistency and availability. Mention potential bottlenecks and mitigation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sorting by outgoing spend sounds simple but you need to track cumulative withdrawals and transfers separately per account as you go.
Clarify the query semantics (time window, outgoing definition, ranking ties) and then design a solution that balances query performance with write overhead. Propose a data model and algorithm, such as maintaining a running total per account or using a batch aggregation, and discuss trade-offs.
Pro tip: Mention that you would first check if the system already has a ledger or transaction log that can be leveraged, and consider whether the query needs to be real-time or can be served from a periodically updated materialized view.
Ask about the definition of 'outgoing' (e.g., debits, transfers, withdrawals), the time window (all-time, monthly), and whether ties should be broken by account ID or another criterion.
Decide whether to compute totals on the fly from transactions or maintain a pre-aggregated balance per account. Consider adding a 'total_outgoing' field to the account record or a separate summary table.
If pre-aggregating, update the total on each outgoing transaction. For querying, sort accounts by total descending. If computing on the fly, aggregate transactions per account and then sort.
Discuss indexing (e.g., on total_outgoing), caching, or using a heap for top-K if only top N are needed. Consider sharding or distributed aggregation if data is large.
Address concurrency (e.g., locking or atomic updates), negative amounts, and how to handle updates or reversals. Ensure the query reflects a consistent snapshot.
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 and API for immediate and scheduled transfers with cancellation. Discuss trade-offs between in-memory scheduling and persistent job queues, and outline how to ensure correctness and idempotency.
Pro tip: Emphasize idempotency and failure recovery—interviewers at Meta care deeply about systems that handle retries and partial failures gracefully, so mention how you'd prevent duplicate transfers if a scheduled job runs twice.
Ask about expected scale, latency requirements, persistence needs, and whether transfers must be atomic across accounts. Confirm if cancellation is only for pending scheduled transfers and how far in advance scheduling is allowed.
Define core entities: Transfer (immediate), ScheduledTransfer (with status: PENDING, EXECUTED, CANCELLED), and a unique transfer ID. Specify API endpoints: POST /transfer, POST /schedule_transfer, DELETE /schedule_transfer/{id}.
Decide between in-memory timers (e.g., priority queue) and persistent job queue (e.g., database-backed scheduler). Discuss trade-offs: in-memory is fast but not durable; persistent is reliable but adds latency and complexity.
Ensure transfers are idempotent using unique keys and transactional updates. For cancellation, mark the scheduled transfer as CANCELLED and ensure the executor checks status before processing. Handle race conditions between cancellation and execution.
Explain how to scale the scheduler (e.g., sharding by user ID) and recover from failures (e.g., re-queue missed jobs, use dead-letter queues). Mention monitoring and alerting for failed transfers.
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 supports account merging with full transaction history and pending transfers. Discuss the merge process step by step, covering data migration, consistency, and handling edge cases like duplicate transactions or conflicting scheduled transfers.
Pro tip: Emphasize idempotency and atomicity: the merge should be safe to retry and either fully complete or roll back, which is critical in distributed systems like Meta's.
Ask about scale, consistency requirements, and whether the merge is one-time or reversible. Confirm what 'full transaction history' and 'pending scheduled transfers' entail.
Propose a schema that links transactions and scheduled transfers to accounts, ensuring historical data remains intact after merge. Consider using a merge log or tombstone for the source account.
Describe steps: validate accounts, lock them, reassign transactions and scheduled transfers, update balances, and mark source account as merged. Ensure atomicity via transactions or sagas.
Address duplicate transactions, conflicting scheduled transfers, currency mismatches, and partial failures. Discuss idempotency and retry logic.
Compare approaches: immediate vs. lazy migration, synchronous vs. asynchronous processing, and impact on read/write performance. Justify choices based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.