Trie memory optimization using hash map

26 Sep, 2017
We introduced and discussed an implementation in below post.
Trie | (Insert and Search) – GeeksforGeeks
The implementation used in above post uses an array of alphabet size with every node. It can be made memory efficient. One way to implementing Trie is linked set of nodes, where each node contains an array of child pointers, one for each symbol in the alphabet. This is not efficient in terms of time as we can’t quickly find a particular child.The efficient way is an implementation where we use hash map to store children of a node. Now we allocate memory only for alphabets in use, and don’t waste space storing null pointers.
// A memory optimized CPP implementation of trie// using unordered_map#include <iostream>#include <unordered_map>using namespace std;  struct Trie {      // isEndOfWord is true if the node    // represents end of a word    bool isEndOfWord;      /* nodes store a map to child node */    unordered_map<char, Trie*> map;};  /*function to make a new trie*/Trie* getNewTrieNode(){    Trie* node = new Trie;    node->isEndOfWord = false;    return node;}  /*function to insert in trie*/void insert(Trie*& root, const string& str){    if (root == nullptr)        root = getNewTrieNode();      Trie* temp = root;    for (int i = 0; i < str.length(); i++) {        char x = str[i];          /* make a new node if there is no path */        if (temp->map.find(x) == temp->map.end())            temp->map[x] = getNewTrieNode();          temp = temp->map[x];    }      temp->isEndOfWord = true;}  /*function to search in trie*/bool search(Trie* root, const string& str){    /*return false if Trie is empty*/    if (root == nullptr)        return false;      Trie* temp = root;    for (int i = 0; i < str.length(); i++) {          /* go to next node*/        temp = temp->map[str[i]];          if (temp == nullptr)            return false;    }      return temp->isEndOfWord;}  /*Driver function*/int main(){    Trie* root = nullptr;      insert(root, "geeks");    cout << search(root, "geeks") << " ";      insert(root, "for");    cout << search(root, "for") << " ";      cout << search(root, "geekk") << " ";      insert(root, "gee");    cout << search(root, "gee") << " ";      insert(root, "science");    cout << search(root, "science") << endl;      return 0;}
Output:
1 1 0 1 1

Space used here with every node here is proportional to number of children which is much better than proportional to alphabet size, especially if alphabet is large.
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Count of strings whose prefix match with the given string to a given length k
Overview of Data Structures | Set 3 (Graph, Trie, Segment Tree and Suffix Tree)
Segment Tree | Set 2 (Range Minimum Query)
2-3 Trees | (Search, Insert and Deletion)
Extendible Hashing (Dynamic approach to DBMS)
Advantages of Trie Data Structure
Segment tree | Efficient implementation
Trie | (Delete)
Find shortest unique prefix for every word in a given list | Set 1 (Using Trie)
Difference between Backtracking and Branch-N-Bound technique