Hashing is a fundamental concept in data structure that plays a crucial role in efficient data retrieval. In simple terms, hashing is a process of converting data (such as a string or an integer) into a fixed-size value called a hash code. This hash code is used to index and retrieve data in constant time.

## Why Use Hashing?

Hashing provides several advantages when it comes to storing and retrieving data:

**Fast Access:**Hashing allows for constant-time access to data, regardless of the size of the dataset. This makes it an ideal choice for applications where quick retrieval is essential.**Data Integrity:**Hash codes are unique for each input, which helps ensure the integrity of the data. Even a minor change in the input will result in a completely different hash code.**Distributed Storage:**Hashing enables even distribution of data across multiple storage locations, which can improve overall performance by minimizing the load on individual storage units.

## Types of Hashing

There are different types of hashing techniques that can be employed based on specific requirements and use cases. Let’s explore some of the commonly used hashing types:

### 1. Division Method

The division method is one of the simplest hashing techniques. It involves taking the remainder when dividing the key by the size of the hash table. The resulting remainder becomes the hash code.

### 2. Multiplication Method

In the multiplication method, we multiply the key with a fractional part (0 < A < 1) and extract its fractional part. Then, we multiply it with the size of the hash table to obtain the hash code.

### 3. Folding Method

The folding method involves dividing the key into equal-sized parts and adding them together to obtain the hash code. This technique is particularly useful when dealing with large keys.

### 4. Mid-Square Method

In the mid-square method, we square the key, extract a portion from the middle of the squared value, and use it as the hash code. This technique is widely used when dealing with integer keys.

### 5. Universal Hashing

Universal hashing is a technique where a family of hash functions is randomly selected from a predefined set of functions. The actual hash function is then chosen at runtime based on certain parameters to minimize collisions.

It’s important to note that no hashing technique can guarantee zero collisions, but by choosing an appropriate technique based on your dataset characteristics, you can minimize collision probabilities and achieve efficient data retrieval.

In conclusion, hashing is a powerful concept in data structure that enables fast and efficient data retrieval through unique hash codes. By understanding different types of hashing techniques and their applications, you can choose the right approach for your specific use case and enhance overall performance.