Start by clarifying requirements and constraints, then propose a data structure that efficiently supports the three operations. For each operation, discuss the algorithm, time/space complexity, and trade-offs. Finally, address edge cases like expiration during operations and concurrency.
Pro tip: Mention that you would use a min-heap or balanced BST keyed by expiry time for efficient earliest-expiry-first consumption, and discuss how to handle lazy expiration versus eager cleanup. Also, consider atomicity and concurrency control for multi-user scenarios.
Ask about expected scale (number of users, lots, operations per second), consistency requirements, and whether operations need to be thread-safe or distributed. Clarify if credits can be negative or if partial consumption is allowed.
Propose a per-user ledger with lots stored in a min-heap or balanced BST ordered by expiry time. Consider also maintaining a total balance for quick queries, and discuss how to handle expired lots (lazy vs eager).
For add: insert lot and update total. For subtract: check total balance, then iterate lots in expiry order, consuming until amount satisfied; if insufficient, rollback. For query: return total and list of non-expired lots with remaining credits.
Discuss time complexity: add O(log n), subtract O(k log n) where k is number of lots consumed, query O(n) or O(1) with caching. Compare heap vs BST vs sorted list, and lazy vs eager expiration.
Handle expiration during operations (e.g., lot expires mid-subtraction), atomicity (use transactions or locks), and multi-user concurrency (per-user locks or optimistic concurrency). Discuss failure scenarios and rollback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly defining the ledger operations and their expected complexities, then analyze each operation's time and space complexity. Discuss how to achieve O(log n) per update using balanced trees or skip lists, and cover persistence strategies like snapshots and write-ahead logs. Finally, address optimization for high volume through batching, caching, and sharding.
Pro tip: Emphasize the trade-offs between different data structures and persistence methods, and relate them to real-world systems like databases or blockchain ledgers. Show awareness of concurrency and failure recovery.
List the core ledger operations (e.g., add entry, query balance, verify integrity) and state their expected time and space complexities. Explain why O(log n) per update is desirable.
Describe data structures like balanced BSTs (e.g., AVL, Red-Black) or skip lists that provide O(log n) insertion and lookup. Mention how to maintain order and balance.
Discuss techniques like batching updates, using in-memory caching, sharding by account or time, and asynchronous processing to handle high throughput.
Explain strategies for persisting ledger state: write-ahead logging, periodic snapshots, and incremental backups. Describe how to restore state efficiently after a crash.
Summarize trade-offs between consistency, latency, and throughput. Mention how to scale horizontally and ensure fault tolerance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.