structure used in the representation of a set of strings over a finite alphabet set, which allows efficient storage of words with common prefixes. Tries can be efficacious on string-searching algorithms such as predictive text, approximate string matching, and spell checking in comparison to a binary search trees. A trie can be seen as a tree-shaped deterministic finite automaton. Operations Tries support various operations: insertion, deletion, and lookup of a string key. Tries are composed of that contain links that are either references to other child suffix child nodes, or . Except for root, each node is pointed to by just one other node, called the parent. Each node contains links, where is the cardinality of the applicable alphabet, although tries have a substantial number of links. In most cases, the size of array is bitlength of the character encoding - 256 in the case of (unsigned) ASCII. The links within in emphasizes the following characteristics: Characters and string keys are implicitly stored in the trie data structure representation, and include a character sentinel value indicating string-termination. Each node contains one possible link to a prefix of strong keys of the set. A basic structure type of nodes in the trie is as follows; may contain an optional , which is associated with each key stored in the last character of string, or terminal node. Searching Searching a in a trie is guided by the characters in the search string key, as each node in the trie contains a corresponding link to each possible character in the given string. Thus, following the string within the trie yields the associated for the given string key. A link within search execution indicates the inexistence of the key. Following pseudocode implements the search procedure for a given string key () in a rooted trie (). In the above pseudocode, and correspond to the pointer of trie's root node and the string key respectively. The search operation, in a standard trie, takes , is the size of the string parameter , and corresponds to the alphabet size. Binary search trees, on the other hand, take on the worst case, since the search depends on the height of the tree () of the BST (in case of balanced trees), where and being number of keys and the length of the keys. Tries occupy less space in comparison with BST if it encompasses a large number