Spent way too long on clarifying questions at the start.
Start by clarifying requirements and constraints, then propose a baseline in-memory solution using backtracking with constraint propagation. Next, discuss scaling challenges and outline a distributed architecture that partitions the puzzle or dictionary, leveraging parallel search and caching. Finally, compare trade-offs between the two approaches and suggest a hybrid solution.
Pro tip: Emphasize that the core challenge is search space reduction, and show how techniques like constraint satisfaction and heuristics (e.g., letter frequency) can drastically improve performance before scaling out.
Ask about puzzle size, dictionary size, time limits, and whether the system needs to solve arbitrary puzzles or a specific format. This demonstrates adaptability and ensures you address the right problem.
Propose a backtracking algorithm with constraint propagation (e.g., using AC-3) and heuristics like most-constrained-variable. Discuss data structures for efficient dictionary lookup (trie, DAWG).
Analyze limitations of in-memory approach: memory for large dictionaries, single-machine compute limits, and inability to handle massive parallelism. This sets the stage for distributed processing.
Outline a distributed system that partitions the search space (e.g., by puzzle regions or dictionary subsets) across workers, using a master-worker pattern with task queue and result aggregation. Mention fault tolerance and load balancing.
Discuss trade-offs: in-memory is simpler and faster for small puzzles; distributed is necessary for large-scale or real-time solving. Suggest a hybrid where in-memory optimizations are used within each worker.
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