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To implement the delete function, you should delete the trie nodes of the word if they are not shared with other words.
You should keep for each trie node a counter of how many words share this node.
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The erase operation traverses the word path in the trie and decreases the prefix count for each visited node. At the final node, it also decreases the word-ending counter. This ensures that future prefix and word queries reflect the updated state of the data structure.
Yes, trie-based design problems are common in FAANG and other top tech interviews. They test understanding of prefix trees, efficient string handling, and the ability to design data structures that support multiple operations efficiently.
A Trie (prefix tree) is the most suitable data structure because it efficiently handles prefix-based operations. By storing character links and counters in each node, the structure supports fast insertion, prefix counting, and exact word frequency queries.
The optimal approach is to use a Trie where each node stores counters for how many words pass through it and how many words end there. This allows efficient insert, prefix queries, and deletion operations. Each operation runs in O(L) time, where L is the length of the word.
