A **Trie data structure**, also known as a **prefix tree**, is a specialized tree-based data structure that is commonly used for efficient storage and retrieval of strings. It is particularly useful when dealing with tasks such as autocomplete, spell checking, and searching for words with similar prefixes. In this article, we will explore the Trie data structure in depth and provide examples to illustrate its usage and benefits.

## Structure of a Trie

A Trie is made up of nodes, where each node represents a character. The root node represents an empty string or null character.

Each node can have multiple child nodes corresponding to different characters. The edges connecting the nodes represent the characters.

In a Trie, each path from the root to a leaf node represents a complete word. The leaf nodes denote the end of a word and may contain additional information such as frequency count or pointers to further data.

### Example:

Let’s consider building a Trie for the following words: “apple”, “application”, “apply”, “banana”, and “bat”.

We start with an empty root node:

Node: (root)
Edges: None

We insert the word “apple” into the Trie:

Node: (root)
Edges: a -> p -> p -> l -> e

Next, we insert “application”:

Node: (root)
Edges:
a -> p -> p -> l -> e (end of word)
p -> l -> i -> c -> a -> t -> i -> o -> n (end of word)

The word “apply” is inserted next:

Node: (root)
Edges:
a -> p -> p -> l -> e (end of word)
p -> l -> i -> c -> a -> t -> i -> o -> n (end of word)
a -> p -> p -> l -> y (end of word)

Then, we insert “banana”:

Node: (root)

Edges:

a -> p -> p -> l -> e (end of word)

p -> l -> i -> c

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