The first level felt straightforward until I started second-guessing the return types.
Start by clarifying requirements and constraints (e.g., single-threaded vs concurrent, persistence, transaction semantics). Then design a simple in-memory data model with accounts and balances, and implement core operations with robust error handling. Finally, discuss trade-offs, edge cases, and potential extensions like concurrency control or transaction logs.
Pro tip: Demonstrate awareness of real-world banking concerns like atomicity and idempotency, and mention how you would handle concurrent transfers to avoid race conditions—even if the initial design is single-threaded.
Ask questions to understand expected scale, concurrency needs, error handling expectations, and whether persistence or transaction history is required. This ensures you design the right system for the context.
Define the core entities (e.g., Account with ID and balance) and the operations: createAccount, deposit, transfer. Specify input validation and error conditions (e.g., negative amounts, insufficient funds, non-existent accounts).
Write pseudocode or explain the implementation: use a map for accounts, check preconditions, update balances atomically, and return appropriate errors or exceptions. For transfers, ensure both debit and credit happen or neither.
Discuss handling of concurrent operations (e.g., locks, optimistic concurrency, or serializing transfers), idempotency, and rollback on failure. Mention potential deadlocks and how to avoid them.
Talk about limitations of in-memory storage (e.g., data loss on crash), and how you might extend to persistence, distributed systems, or auditing. Highlight any assumptions made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tie-breaking by lexicographic order is easy to forget under pressure.
Start by clarifying requirements: what defines 'outgoing transfer volume' (sum of amounts, count, etc.), whether the leaderboard is real-time or batch, and the scale (number of accounts, transfers per second). Then propose a data model and algorithm that efficiently maintains a sorted ranking with tie-breaking, discussing trade-offs between different data structures and system designs.
Pro tip: Emphasize that tie-breaking by account ID can be elegantly handled by using a composite key (volume, account ID) in a balanced BST or skip list, and mention that this also ensures deterministic ordering. Also, discuss how to handle updates (new transfers) without recomputing the entire leaderboard, showing awareness of performance at scale.
Ask about the definition of 'outgoing transfer volume' (sum of amounts, count, or both), whether the leaderboard is global or per-region, real-time or periodic, and the expected scale (number of accounts, transfers per second).
Propose maintaining a running total of outgoing volume per account and a data structure that keeps accounts sorted by (volume, account ID) descending. Consider using a balanced BST, skip list, or a heap with lazy updates, and explain how to update on each transfer.
Discuss how to handle high throughput: sharding by account ID, using in-memory stores like Redis sorted sets, or batch processing. Mention trade-offs between consistency and latency.
Explain tie-breaking by account ID (e.g., smaller ID ranks higher) and how to handle accounts with zero volume, negative volumes (if refunds), and updates that change rank.
Outline how the leaderboard would be exposed (e.g., getTopK, getRank) and how it integrates with the existing banking system, including data flow from transfer events to leaderboard updates.
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The globally incrementing ID across all accounts is a small detail that matters a lot.
Start by clarifying functional and non-functional requirements, then design the data model and API for creating and canceling scheduled payments. Focus on how to reliably execute payments at the scheduled time using a scheduler and worker system, ensuring idempotency and exactly-once processing. Discuss trade-offs and failure handling.
Pro tip: Emphasize idempotency and exactly-once execution: scheduled payments must not double-charge if a worker retries. Use a unique payment ID and a state machine to track payment status.
Ask about scale, latency, payment types, and cancellation semantics. Confirm that payment ID is globally unique and incrementing, and that cancellation is only allowed before execution.
Define a Payment entity with fields: id, amount, source, destination, scheduled_time, status (e.g., SCHEDULED, EXECUTED, CANCELED). Design REST endpoints: POST /payments to create, DELETE /payments/{id} to cancel.
Use a scheduler (e.g., cron, delayed queue) to enqueue due payments. Workers pick up payments, check status, execute transaction, and update status. Ensure idempotency via unique payment ID and database transactions.
Discuss retries with exponential backoff, dead-letter queues, and monitoring. For scale, shard by payment ID or time, and use distributed locks to avoid duplicate execution.
Compare polling vs. event-driven scheduling, and discuss consistency vs. availability. Mention using a database with strong consistency for payment state.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This level is where things got genuinely hard.
Start by clarifying requirements and constraints, then propose a data model that supports both current and historical balances, such as an event-sourced ledger or bitemporal tables. Walk through the merge algorithm and historical query mechanism, discussing trade-offs in consistency, performance, and scalability.
Pro tip: Emphasize idempotency and auditability: design the merge as an idempotent operation with a clear audit trail, and use immutable event logs for history to avoid complex temporal joins.
Ask about scale, consistency needs, merge frequency, and query patterns. Confirm whether historical queries must be exact or approximate, and if merges are reversible.
Propose an event-sourced ledger with immutable transactions, or a bitemporal table with valid and transaction time. Discuss how to compute balances from events and handle scheduled payments.
Outline an idempotent merge process: validate accounts, combine balances and outgoing totals, transfer scheduled payments, and record a merge event. Address atomicity and failure recovery.
Explain how to query balance at a timestamp using event replay or temporal indexes. Discuss performance optimizations like snapshots or materialized views.
Compare event sourcing vs. temporal tables, and discuss partitioning, caching, and consistency models. Address how the design scales with account size and query load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.