Design a Simple Spell Checker (Trie/Set)

To design a spell checker, I first need to clarify if we only check existence or require prefix suggestions. Assuming a standard dictionary lookup, a Hash Set is the most straightforward approach. It offers O(1) average time complexity for checking if a word exists, making it incredibly fast for simple validation. However, Hash Sets struggle with partial matches or finding words sharing common prefixes efficiently. For a more robust solution, especially one that might support features like auto-complete, a Trie is superior. A Trie stores characters in a tree structure where each node represents a letter. This allows us to insert and search words in O(M) time, where M is the length of the word, independent of the total dictionary size. Crucially, the Trie naturally groups words by prefixes, enabling us to quickly find all words starting with 'com' without scanning the entire dataset. While a Trie consumes more memory due to node overhead compared to a flat set, its structural advantages align well with systems handling massive datasets, such as those at IBM. If memory is extremely constrained and only exact matching is needed, the Hash Set remains the pragmatic choice. Ultimately, I would recommend the Trie for its versatility in handling both exact spelling checks and predictive text features simultaneously.