Trie (also known as prefix tree) is a tree-based data structure that is used to store an associative array where the keys are sequences (usually strings). Some advantages of using a trie data structure include:
- Fast search: Tries support fast search operations, as we can search for a key by traversing down the tree from the root, and the search time is directly proportional to the length of the key. This makes tries an efficient data structure for searching for keys in a large dataset.
- Auto-complete: Tries are widely used in applications that require auto-complete functionality, such as search engines or predictive text input.
- Efficient insertion and deletion: Tries support fast insertion and deletion of keys, as we can simply add or delete nodes from the tree as needed.
- Efficient sorting: Tries can be used to sort a large dataset efficiently, as they support fast search and insertion operations.
- Compact representation: Tries provide a compact representation of a large dataset, as they store only the characters that are present in the keys. This makes them an ideal data structure for storing large dictionaries or lexicons.
Dynamic insertion: Tries allow for dynamic insertion of new strings into the data set.
Compression: Tries can be used to compress a data set of strings by identifying and storing only the common prefixes among the strings.
Autocomplete and spell-checking: Tries are commonly used in autocomplete and spell-checking systems.
Handling large dataset: Tries can handle large datasets as they are not dependent on the length of the strings, rather on the number of unique characters in the dataset.
Multi-language support: Tries can store strings of any language, as they are based on the characters of the strings rather than their encoding.
Issues with Trie :-Â
The main disadvantage of tries is that they need a lot of memory for storing the strings. For each node we have too many node pointers(equal to number of characters of the alphabet), if space is concerned, then Ternary Search Tree can be preferred for dictionary implementations. In Ternary Search Tree, the time complexity of search operation is O(h) where h is the height of the tree. Ternary Search Trees also supports other operations supported by Trie like prefix search, alphabetical order printing, and nearest neighbor search.Â
The final conclusion regarding tries data structure is that they are faster but require huge memory for storing the strings.
Applications :-
- Tries are used to implement data structures and algorithms like dictionaries, lookup tables, matching algorithms, etc.
- They are also used for many practical applications like auto-complete in editors and mobile applications.
- They are used inphone book search applications where efficient searching of a large number of records is required.