The following data structures can be used for storing strings efficiently:

Hashing Tables

Hashing tables use a hash function to map strings to indices in a hash table(array). This enables searching, insertion and deletion in constant time. Hashing tables are suitable for storing strings when fast retrieval based on keys is required, but they may have collisions.

Binary Search Trees

Binary search trees (BSTs) are tree-based data structures that maintain a sorted order of strings. Each node in the BST contains a string and two child nodes (left and right).All strings in the left subtree are smaller than the current node, and all strings in the right subtree are larger. BSTs provide efficient search, insert, and delete operations with a time complexity of O(log n) in the average case and O(n) in the worst case.

• Tries: Tries store characters of strings as nodes in a tree structure, where each path from the root to a leaf node represents a complete string. Tries have a time complexity of O(m), where m is the length of the string being searched. Tries are commonly used in applications involving autocomplete, spell checkers, and IP routing.

Ternary Search Trees

Ternary search trees (TSTs) combine characteristics of tries and binary search trees. Each node in a TST contains a character, and the tree branches out based on whether the character is smaller, equal to, or larger than the current node's character. TSTs have a time complexity of O(log n + k), where n is the number of nodes in the tree and k is the length of the search string.