What Is Hashing Techniques in Data Structure?


Heather Bennett

Hashing is a fundamental concept in data structures that plays a crucial role in various applications and algorithms. In this article, we will explore the concept of hashing techniques and how they are used to efficiently store and retrieve data.

What is Hashing?

Hashing is the process of converting data (such as a string or an object) into a fixed-size numerical value called a hash code or hash value. This hash value is used to uniquely identify the data and is typically much smaller in size compared to the original data.

Hashing functions are designed to be fast and efficient, ensuring that the computation time remains constant regardless of the size of the input data. This makes hashing techniques ideal for applications where quick access to data is required, such as in databases or search algorithms.

How Does Hashing Work?

The process of hashing involves applying a hash function to the input data. A hash function takes an input (such as a string) and produces a fixed-size output (the hash value). The output is determined solely by the input, meaning that even a small change in the input will result in a significantly different hash value.

The key idea behind hashing is that each unique input should have its own unique hash value. However, it is possible for different inputs to produce the same hash value, known as a collision. While collisions are rare, they can be handled using various collision resolution techniques.

Common Hashing Techniques

In practice, there are several commonly used hashing techniques:

  • Division Method: In this technique, the key is divided by some predetermined number (usually prime) and the remainder becomes the hash value.
  • Multiplication Method: This technique involves multiplying the key by a constant value between 0 and 1, and then extracting the fractional part of the product as the hash value.
  • Folding Method: The folding method involves dividing the key into equal-size parts (usually of fixed length) and then adding or XORing them together to obtain the hash value.
  • Mid-Square Method: In this technique, the square of the key is calculated, and then a portion of the resulting digits are extracted as the hash value.

Advantages of Hashing

Hashing provides several advantages:

  • Fast Access: Hashing allows for constant-time access to data, regardless of its size. This makes it ideal for applications that require quick retrieval of information.
  • Data Integrity: Hash functions ensure data integrity by producing unique hash values for each input. Any change in the input will result in a different hash value, making it useful for data verification purposes.
  • Efficient Memory Usage: Since hash values are typically much smaller than the original data, hashing techniques enable efficient memory usage, especially when dealing with large datasets.


In summary, hashing is a powerful technique used in data structures to efficiently store and retrieve data. It converts complex input into simple numerical values that can be used to identify and access information quickly. With its fast access times and efficient memory usage, hashing plays a critical role in various applications and algorithms.

If you are interested in learning more about hashing techniques or want to explore how they are implemented in different programming languages, consider diving deeper into this fascinating topic!

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