This is the foundation everything else builds on so you really cannot rush it.
Start by clarifying requirements and defining the API contract, including return values and failure conditions. Then design a simple data model (e.g., a map of account IDs to balances) and implement each operation with proper validation and atomicity. Discuss trade-offs and potential extensions like concurrency and persistence.
Pro tip: Emphasize the importance of atomicity and consistency in banking operations—mention that transfers must be atomic to avoid partial updates, and discuss how you'd handle concurrent access (e.g., locks or transactions). This shows you think beyond basic functionality.
Ask questions to understand constraints: Are account IDs strings or integers? Should deposit/transfer return new balance or boolean? What are failure cases (e.g., insufficient funds, invalid account)?
Specify method signatures and return types (e.g., createAccount(id) -> bool, deposit(id, amount) -> new balance or None, transfer(from, to, amount) -> bool). Choose a simple in-memory data structure like a hash map for accounts and balances.
Write code for each operation with input validation (e.g., positive amounts, existing accounts) and handle failure by returning None or False. For transfer, ensure atomicity by checking both accounts and updating balances only if all conditions are met.
Walk through test cases: creating duplicate account, depositing negative amount, transferring more than balance, transferring to non-existent account. Verify return values match expectations.
Mention how the design would change for concurrent access (e.g., using locks or transactions) and persistence (e.g., database). Highlight trade-offs between simplicity and robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the data model and requirements first, then propose an efficient aggregation pipeline that computes total outgoing volume per account, filters out zero-volume accounts, and sorts by volume descending with account ID ascending as tiebreaker. Discuss trade-offs between sorting all accounts versus using a heap for top N, and consider scalability for large datasets.
Pro tip: Mention that you would exclude accounts with zero outgoing volume to avoid returning irrelevant results, and discuss how to handle ties at the cutoff (e.g., if multiple accounts have the same volume as the Nth, decide whether to include all or strictly N).
Ask about the schema (e.g., transfers table with from_account, to_account, amount), whether 'outgoing' means from_account, and if volume is sum of amounts. Confirm N is a parameter and ties are broken alphabetically by account ID.
Group transfers by from_account, sum amounts to get total outgoing volume per account. Filter out accounts with zero or null volume if appropriate.
Sort the aggregated results by total volume descending, then by account ID ascending. Use a stable sort or explicit comparator to ensure correct tie-breaking.
If N is small relative to number of accounts, use a min-heap of size N to find top N in O(M log N) time, where M is number of accounts. Otherwise, full sort is O(M log M). Discuss trade-offs.
Consider empty results, N larger than number of accounts, ties at the boundary, and distributed processing (e.g., MapReduce) for massive datasets. Mention indexing on from_account for efficient grouping.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The global payment counter (payment1, payment2, ...) is a small thing that's easy to forget to make truly global rather than per-account.
Clarify requirements (delay precision, persistence, scale, cancellation semantics) and then design a system with a scheduler, a task store, and a cancellation mechanism. Discuss trade-offs between in-memory and distributed approaches, and outline the core algorithm for scheduling and cancelling tasks.
Pro tip: Emphasize idempotency and failure recovery: scheduled payments must not double-execute or get lost if the scheduler crashes. Mention using a persistent store with atomic operations and a unique payment ID to ensure exactly-once execution.
Ask about delay precision, scale (number of scheduled payments), persistence needs, and whether cancellation should work after execution starts. Confirm that payment ID is unique and provided by the client.
Propose components: a scheduler (e.g., priority queue, timer wheel, or external service like Redis ZSET), a persistent task store (database), and a payment executor. Explain how they interact.
Describe how to schedule a payment: insert into a min-heap or sorted set keyed by execution time, with a background worker polling for due tasks. Discuss efficient data structures for large scale.
Explain how to cancel by payment ID: mark the task as cancelled in the store and remove it from the scheduler if possible. Handle race conditions where cancellation arrives as the task is executing.
Discuss failure recovery (persistence, idempotency, retries), exactly-once semantics, and trade-offs between in-memory (fast, not durable) vs. distributed (scalable, complex) solutions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where most people run out of time.
Start by clarifying requirements and scale, then design a data model that supports both current state and historical queries. Propose an append-only ledger or event-sourced system with periodic snapshots for efficient point-in-time balance queries, and describe the merge operation as a transactional event that creates a new merged account while preserving history. Discuss trade-offs between consistency, latency, and storage, and outline how to handle concurrent merges and queries.
Pro tip: Emphasize idempotency and auditability: merges should be idempotent and produce an immutable audit trail, which is critical for financial systems and simplifies rollback and debugging.
Ask about expected throughput, latency requirements, consistency needs, and whether historical queries must be exact or approximate. Confirm what data must be merged (balances, outgoing totals, scheduled payments) and how to handle edge cases like merging accounts with pending transactions.
Propose an append-only ledger of transactions with timestamps, plus periodic snapshots (e.g., daily) to speed up point-in-time queries. Alternatively, use event sourcing with a materialized view for current balances. Discuss how to index by account ID and timestamp.
Describe a transactional merge: create a new account, transfer balances and scheduled payments, and record a merge event linking the source accounts. Ensure idempotency by using a unique merge ID and handling retries. Consider locking or optimistic concurrency to prevent concurrent merges.
Explain how to compute balance at time T: find the latest snapshot before T, then apply all transactions between snapshot and T. For merged accounts, trace back through merge events to include transactions from source accounts. Discuss caching and read replicas for performance.
Compare event sourcing vs. mutable state with audit log; snapshots vs. full replay; strong vs. eventual consistency. Discuss partitioning by account ID, handling hot accounts, and archiving old data. Mention monitoring and alerting for merge failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.