Types of Tries

Trie Types

Introduction to Trie

A Trie or different Types of Tries used for retrieval tree or digital tree, is a tree-like data structure used for efficiently storing and searching a dynamic set of keys, usually strings. Trie is also known as Digital Tree or Prefix Tree.

There are mainly three types of trie : Standard trie, Compressed trie and Suffix trie.

What is Trie ? Describe its types.

A trie is a tree-shaped data structure that stores information in its nodes, and these nodes can possess either one, more than one, or no children.

It is also known as Digital Tree or Radical Tree or Prefix Tree.


Types of Tries in Data Structure

There are mainly three types of tries in data structure :

  • Standard Trie
  • Compressed Trie
  • Suffix Trie

1. Standard Trie

In a standard trie, each node represents a single character in a key or string. The root node is associated with the empty string, and each edge corresponds to a character in the string.

Characteristics of Standard Trie

  • Nodes may have multiple children, each associated with a different character.
  • The key is usually stored at the leaf node, and the path from the root to the leaf represents the key.
  • Efficient for exact match searches and insertion of keys.
  • Can be memory-intensive, especially for datasets with a large number of keys.

2. Compressed Trie

Compressed tries use various techniques to reduce memory usage. One common approach is path compression, where chains of single-child nodes are condensed into a single node.

Characteristics of Compressed Trie

  • Compact representation achieved by eliminating redundant single-child nodes.
  • Reduces memory requirements compared to standard tries.
  • Search and insertion operations may be slightly more complex due to the need for decompression during traversal.
  • Suitable for scenarios where memory efficiency is crucial.

3. Suffix Trie

Suffix tries are specialized structures designed for handling dynamic sets of strings and pattern matching. They store all the suffixes of a given string.

Characteristics of Suffix Trie

  • Efficient for substring searches and pattern matching.
  • Each leaf node represents a suffix of the original string.
  • Useful in applications such as text indexing, bioinformatics (DNA sequence analysis), and data compression algorithms.
  • Construction of a suffix trie is typically more involved and may require additional algorithms, such as Ukkonen’s algorithm.

Components of Trie

  1. Nodes and Edges:

    • A trie is composed of nodes, each representing a single character in a key or string.
    • Edges connect the nodes and correspond to characters, forming paths from the root to the leaf nodes.
  2. Root Node:

    • The topmost node, known as the root, has no associated character but serves as the starting point for traversing the trie.
  3. Leaf Nodes:

    • Leaf nodes represent the end of a key or string and often store additional information associated with that key.

Applications of Tries

Following are the applications of Tries in Data Structure :

To Wrap it up: 

In conclusion, the trie data structure is a powerful tool for organizing and retrieving data efficiently, especially in scenarios where quick searches and pattern matching are critical. In summary, a standard trie provides a straightforward representation of keys, but it can be memory-intensive. Compressed tries optimize memory usage by eliminating redundant nodes. Suffix tries, on the other hand, are specialized for substring searches and pattern matching, storing all suffixes of a string.

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