The skeleton they gave was pretty clean so I didn't waste time on structure.
Start by clarifying requirements: single-level or nested transactions, concurrency needs, and operations. Then propose a design using a transaction stack where each transaction maintains a local write buffer and reads fall through to the committed store or lower transactions. Implement the class with methods for begin, get, set, commit, and rollback, ensuring atomicity and isolation.
Pro tip: Mention that you would use a stack of maps to support nested transactions, and that commit merges the top map into the parent (or main store if no parent), while rollback simply discards the top map. This shows you understand transactional semantics and efficient data structures.
Ask about transaction nesting, concurrency, and expected operations. Confirm if reads should see uncommitted changes within the same transaction.
Propose using a main key-value store (e.g., hash map) for committed data and a stack of transaction layers, each with its own write buffer (hash map).
For begin, push a new empty map onto the stack. For get, check the top transaction's buffer first, then lower transactions, then the main store. For set, write to the top transaction's buffer.
Commit: merge the top transaction's buffer into the parent transaction's buffer (or main store if no parent) and pop the stack. Rollback: simply pop the top transaction's buffer without merging.
Discuss time complexity (O(1) for get/set/begin, O(n) for commit/rollback where n is number of keys in transaction) and handle edge cases like rollback with no active transaction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said last-write-wins first, which they clearly weren't thrilled about.
Start by clarifying the context: is this a single-node database, a distributed system, or an application-level conflict? Then outline the standard mechanisms (locking, optimistic concurrency, timestamps) and discuss trade-offs like latency, throughput, and consistency. Finally, tie your answer to Cursor's likely use case (e.g., collaborative editing or database transactions) by emphasizing practical implications.
Pro tip: Mention that the best solution depends on the workload: for low-contention scenarios, optimistic concurrency with retries is often simpler and faster, while high-contention requires pessimistic locking or conflict-free replicated data types (CRDTs). Show you understand that 'it depends' is a valid answer when backed by reasoning.
Ask whether the conflict occurs in a single database, a distributed system, or an application-level shared resource. This determines the available mechanisms and constraints.
List common approaches: pessimistic locking (e.g., SELECT FOR UPDATE), optimistic concurrency control (version checks), timestamp ordering, and multi-version concurrency control (MVCC).
Compare strategies on latency, throughput, complexity, and consistency guarantees. For example, locking prevents conflicts but reduces concurrency; optimistic control scales better under low contention but requires retries.
Select the most appropriate strategy for the given scenario, explaining why it fits. Consider factors like contention level, latency requirements, and system architecture.
Mention practical details: retry logic, deadlock avoidance, idempotency, and how to handle failures. Also consider distributed scenarios like two-phase commit or CRDTs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one actually tripped me up more than I expected.
Explain that this is a classic write skew anomaly under snapshot isolation, where each transaction reads a different key and writes the other, so no write-write conflict is detected. Propose using serializable isolation (e.g., SSI) or explicit conflict detection via read/write sets to abort one transaction.
Pro tip: Mention that many production systems use snapshot isolation by default, so you must either upgrade to serializable or implement application-level conflict detection to prevent write skew.
Recognize that under snapshot isolation, each transaction reads a different key and writes the other, so no write-write conflict occurs, leading to write skew.
Point out that snapshot isolation only detects write-write conflicts on the same key, but here tx1 writes B and tx2 writes A, so both can commit.
Suggest using serializable isolation (e.g., Serializable Snapshot Isolation) which tracks read/write dependencies and aborts one transaction if a cycle is detected.
Mention application-level locking (e.g., lock both keys upfront), or using a database that supports predicate locking or materialized conflict detection.
Weigh the overhead of serializable isolation versus the complexity of manual conflict detection, and note that aborting one transaction ensures the swap becomes a no-op.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.