My first instinct was a sorted array with binary search, which actually got a decent response.
Start by clarifying requirements: static vs dynamic, memory constraints, and expected query patterns. Then propose a trie-based solution (e.g., compressed trie or ternary search tree) that supports lexicographic range queries by traversing the trie to find the lower bound and then performing an in-order traversal until the upper bound. Discuss trade-offs with balanced BSTs and sorted arrays, and mention optimizations like storing subtree counts for efficient range size queries.
Pro tip: Demonstrate awareness of real-world constraints: for static datasets, a sorted array with binary search is often fastest and simplest; for dynamic, a balanced BST or trie with parent pointers is better. Also, mention that Google's internal systems often use tries for prefix-based operations, so highlighting trie advantages can resonate.
Ask about static vs dynamic data, expected query frequency, memory limits, and whether strings share prefixes. This shows you consider practical constraints before diving into design.
Suggest a trie (or compressed trie) as the primary structure, explaining how it naturally supports lexicographic order. Mention alternatives like balanced BSTs (e.g., red-black tree) or sorted arrays with binary search, and compare their trade-offs.
Explain how to find the lower bound L by traversing the trie, then perform an in-order traversal to collect strings until exceeding R. For efficiency, describe how to skip subtrees that are entirely outside the range using prefix comparisons.
Discuss time complexity: O(|L| + k) where k is the number of results, plus traversal overhead. Mention optimizations like storing subtree counts for O(1) range size queries, or using a suffix array for static data.
Compare with alternative approaches (e.g., B-trees for disk-based storage, or hash maps for exact match). Discuss how to handle updates (insert/delete) and whether concurrency or persistence is needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.