Trie Nodeby Isai Damier, Android Engineer @ Google

```/***************************************************************************
* Author: Isai Damier
* Title: Trie.java
* Project: geekviewpoint
* Package: datastructure
*
* Description:
*  A trie is a tree data-structure that stores words by compressing common
*  prefixes. To illustrate, following is a word list and its resulting trie.
*
*   WORDS: rat, rats, rattle, rate, rates, rating, can, cane,
*          canny, cant, cans, cat, cats, cattle, cattles.
*
*   TRIE:
*         ___________|____________
*        |                        |
*        r                        c
*        a                ________a___________
*        t~              |                    |
*    ____|_____          n~                   t~
*   |   |  |   |    _____|_____        _______|______
*   s~  t  e~  i   |   |   |   |      |              t
*       l  s~  n   e~  n   t~  s~     s~             l
*       e~     g~      y~                            e~
*                                                    s~
*
*   Each ~ in the figure indicates where a prefix is a word.
*
*   Generally, a trie has all the benefits of a hash table without
*
*   --------------------------------------------------------------
*          | HASH TABLE | TRIE     | explanations
*   --------------------------------------------------------------
*   Memory |    O(n)    |  < O(n)  | trie uses prefix compression.
*          |            |          | Hence it does not store each
*          |            |          | word explicitly
*  ----------------------------------------------------------------
*   Search |   O(1)     |  O(1)    | trie is technically faster.
*          |            | pseudo-  | Given a word, computing a
*          |            | constant | hash takes at least as long
*          |            |          | as traversing a trie. Plus,
*          |            |          | trie has no collision.
*  ----------------------------------------------------------------
*
*  Tries are particularly superior to hash tables when it comes to solving
*  problems such as word puzzles like boggle. In such puzzles the objective
*  is to find how many words in a given list are valid. So if for example
*  at a particular instance in boggle you have a list of one billion words
*  all starting with zh-, whereas the dictionary has no words starting with
*  zh-; then: if the dictionary is a hash table, you must compute the entire
*  hashcode for each word and do one billion look-ups; if on the other hand
*  the dictionary is a trie, you only do the equivalent of partially
*  computing one hashcode! That's a saving of over one billion fold!
*
*  This implementations of trie uses an array to store the children
*  nodes, where the numerical value of each char serves as index.
**************************************************************************/
/********************************************************************
* Author: Isai Damier
* Title: TrieNode
* Project: geekviewpoint
* Package: algorithms.trie
*
* Description:
*  This is a very simple node class for the Trie data structure. An array
*  is used to hold the references/pointers to the children/next nodes.
*  The numerical value of each char serves as index. The array could be
*
*   TrieNode[] next = new TrieNode[256];//256 or 26 or etc.
*
*  Different implementation of the TrieNode presents different conveniences
*  and restrictions. For instance, using an array as opposed to a hashmap
*  for storing the pointers to the next nodes means not having access to
*  a cheap isEmpty() function. Hence we must declare and track our own
*  nextIsEmpty variable.
*
*  Map<Character,TrieNode> next = new HashMap<>();//map alternative
*******************************************************************/
package algorithms.trie;

public class TrieNode {

public char value;
boolean word = false;
TrieNode[] next = new TrieNode[256];
private int nextLength = 0;

public TrieNode(char c) {
value = c;
}

void setChild(char c, TrieNode node){
next[c]=node;
nextLength++;
}
void clearNext() {
next = new TrieNode[256];
nextLength = 0;
}

boolean nextIsEmpty(){
return nextLength == 0;
}

int nextSize(){
return nextLength;
}
}```