Multi-source BFS, pretty standard once you recognize the pattern.
Model the grid as a graph and use multi-source BFS starting from all contaminated cells simultaneously. Track the time (minutes) as BFS levels, and after traversal, check if any fresh cell remains unvisited; if so, return -1, otherwise return the maximum time reached.
Pro tip: Clarify edge cases upfront (e.g., no fresh cells, no contaminated cells) and mention that BFS is optimal because contamination spreads uniformly at one cell per minute. Also, discuss space-time complexity and potential optimizations like using a queue with level tracking.
Restate the problem: grid with empty (0), fresh (1), contaminated (2). Contamination spreads to adjacent fresh cells each minute. Determine if all fresh cells can be contaminated and the minimum time. Discuss edge cases: no fresh cells (return 0), no contaminated cells (return -1 if fresh exist), unreachable fresh cells.
Explain that multi-source BFS is ideal because it simulates simultaneous spread from all contaminated cells level by level, ensuring minimum time. Contrast with DFS which doesn't guarantee shortest time.
Initialize a queue with all contaminated cells and count fresh cells. Process level by level: for each cell, check its 4 neighbors; if fresh, mark contaminated, decrement fresh count, and enqueue. Increment time after each level.
After BFS, if fresh count > 0, return -1. Otherwise, return the time (number of levels processed minus 1, or track separately).
Time complexity O(m*n) since each cell is processed once. Space O(m*n) for queue. Walk through a small example to verify correctness.
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 outline a design using a stack of transaction layers where each layer tracks changes made since its begin. Implement operations by applying changes to the top layer and handling commit/rollback by merging or discarding layers, ensuring nested transactions are correctly managed.
Pro tip: Emphasize the importance of maintaining isolation between transactions and discuss how to handle edge cases like rollback after commit or nested rollbacks. Also, mention that you would write unit tests to verify correctness of nested transactions.
Ask questions to understand expected behavior: Are transactions isolated? What happens on rollback of a nested transaction? Should commit be atomic? Clarify data types and concurrency expectations.
Propose using a stack of transaction layers, each containing a map of key-value changes (or a journal of operations). The base layer represents the committed state.
For get, search from top layer down to base. For set/delete, record the change in the top layer. For begin, push a new empty layer. For commit, merge the top layer into the layer below (or base if only one). For rollback, pop the top layer.
Ensure that commit merges changes into the parent transaction, not directly to base, unless it's the outermost transaction. Rollback discards only the current layer, preserving parent layers.
Talk about time/space complexity, potential optimizations (e.g., using a single log with transaction IDs), and how to extend to concurrency or persistence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.