Model the problem as a graph where each word is a node and edges connect words that differ by one letter. Use BFS to find the shortest path from start to end, returning the number of substitutions or -1 if unreachable.
Pro tip: Precompute wildcard patterns (e.g., 'h*t') to efficiently find neighbors, reducing time complexity from O(N^2 * L) to O(N * L^2). Also, consider bidirectional BFS for large dictionaries to significantly speed up the search.
Confirm that start and end words are of equal length and that all words are in the dictionary. Check edge cases: start equals end (return 0), start or end not in dictionary (return -1).
Create a mapping from wildcard patterns to words to efficiently find neighbors. Alternatively, if dictionary is small, generate neighbors by changing each character.
Use a queue to explore words level by level, starting from start word. Track visited words to avoid cycles. For each word, generate all possible one-letter variations and enqueue those in the dictionary.
If end word is reached, return the current level (number of substitutions). If queue exhausts without reaching end, return -1.
For large dictionaries, consider bidirectional BFS to reduce search space. Discuss time and space complexity trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the neighbor generation problem and why caching helps, then describe the LRU cache design with eviction policy, and finally analyze time/space complexity and trade-offs like cache size vs. hit rate and memory overhead. Emphasize how this applies to ML tasks like graph-based recommendations at LinkedIn.
Pro tip: Mention that LRU is a good default but consider workload patterns—if some neighbors are accessed far more frequently, a LFU or hybrid policy might be better; also discuss cache warming and invalidation strategies for dynamic graphs.
Explain that neighbor generation can be expensive (e.g., graph traversal, similarity computation) and is often repeated for the same nodes, so caching results reduces latency and compute.
Describe a function that takes a node ID and returns its neighbors, wrapped with an LRU cache that stores a fixed number of entries, using a hash map and doubly linked list for O(1) get/put.
Detail how LRU evicts the least recently used entry when the cache is full, and discuss why this is suitable for temporal locality in access patterns.
Compare time complexity: cache hit O(1) vs. miss O(neighbor generation cost); space complexity O(cache size). Discuss trade-offs: larger cache improves hit rate but increases memory; smaller cache reduces memory but may cause more misses.
Mention cache size tuning, thread safety, distributed caching for scale, and alternative policies (LFU, FIFO) or hybrid approaches based on access distribution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one shifted into system design territory fast and I wasn't ready for it.
Start by clarifying the read/write ratio and consistency requirements, then propose a copy-on-write or immutable snapshot approach for the dictionary to allow lock-free reads. Explain how you would invalidate caches using versioning or event-driven invalidation, and discuss trade-offs between consistency and latency.
Pro tip: Mention that you would use a read-write lock or a concurrent data structure like a persistent trie, and that cache invalidation can be handled via a version number or a pub/sub mechanism to avoid stale reads. Also, highlight the importance of monitoring cache hit rates and consistency metrics.
Ask about the expected read/write ratio, consistency requirements (strong vs eventual), and latency constraints to tailor the solution.
Propose using immutable snapshots with atomic reference swapping (copy-on-write) or a concurrent data structure to ensure thread-safe reads without locks.
Describe a versioning scheme where each dictionary update increments a version, and caches check the version before serving; or use an event bus to notify caches to invalidate.
Discuss trade-offs: copy-on-write may increase memory and write latency, while locking can reduce read throughput; choose based on read/write ratio.
Suggest monitoring cache hit rates, consistency violations, and latency to validate the approach and adjust as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.