Hashing Techniques in Data Structures and Algorithms

Introduction

Hashing in data structures and algorithms is a method used to map data of arbitrary size to fixed-size values using a hash function. This technique is employed to efficiently store, retrieve, and search for data within large datasets. The hash function computes a hash code for each input key, which is then used as an index in a data structure, typically a hash table. Hashing aims to achieve fast access times by ensuring that keys are evenly distributed across the hash table and by employing collision resolution strategies to handle cases where multiple keys map to the same index.

Understanding Hashing

Hashing is about turning any size of data into a fixed-size value using a hash function. This value is called a hash code or hash digest and serves as a unique identifier for the original data. Hash functions are made to give a different output for each different input, ensuring unique hash codes for different inputs.

Hashing in Data Structures

Hashing is commonly used in data structures like hash tables, hash maps, and hash sets for storing and accessing data efficiently. In hash tables, an array and a hash function are used to store key-value pairs. The hash function calculates an index based on the key, enabling quick access to values linked to the keys.

Different Hashing Techniques

• Division Method: The division method is a hashing technique that maps keys to indices in a hash table by calculating the remainder of the key divided by the size of the table. It's a simple approach but can lead to clustering issues if not managed properly.
• Multiplication Method: In this method, we multiply the key by a constant and then extract a portion of the resulting product as the hash code.
• Universal Hashing: Universal hashing involves randomly selecting a hash function from a family of hash functions, which helps in minimizing collisions and ensuring better performance.
• Double Hashing: Double hashing addresses collision issues by applying a second hash function to calculate an alternative index when a collision occurs.
• Chaining: Chaining is a collision resolution technique where each slot of the hash table stores a linked list of elements with the same hash code.

Code Example

#include <iostream>
using namespace std;

#define N 10

class HashTable {
    int arr[N];
    int size;

public:
    HashTable() {
        size = 0;
        for (int i = 0; i < N; i++) {
            arr[i] = -1;
        }
    }

    void insert(int value) {
        if (size >= N) {
            cout << "Error: Hash table is full" << endl;
            return;
        }
        int index = value % N;
        while (arr[index] != -1) {
            index = (index + 1) % N;
        }
        arr[index] = value;
        size++;
    }

    void display() {
        for (int i = 0; i < N; i++) {
            if (arr[i] != -1) {
                cout << "a[" << i << "]=" << arr[i] << endl;
            }
        }
    }
};

int main() {
    HashTable table;
    cout << "After creating hash table" << endl;
    table.insert(5);
    table.insert(25);
    table.insert(26);
    table.insert(35);

    table.display();
    return 0;
}

Output

Conclusion

Hashing techniques are important in computer science. They help us store and find data quickly and easily. By learning about different hashing methods, like how we turn data into special codes, programmers can make strong systems that handle lots of data well. Whether it's making hash tables, dealing with when two pieces of data have the same code, or making codes work even faster, knowing about hashing techniques is crucial for programmers who want to make awesome software that works well, even with a lot of information.