What Is Hashing in Data Structure Using C?

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Larry Thompson

Hashing is a fundamental concept in data structures that plays a crucial role in organizing and retrieving data efficiently. In this article, we will explore what hashing is and how it is implemented using the C programming language.

What is Hashing?

Hashing is a technique used to map data to a fixed-size array or table, known as a hash table. It involves applying a hash function to the data being stored, which then generates a unique hash value or index. This index is used to store the data in the hash table.

The primary goal of hashing is to provide constant-time complexity for basic operations such as insertion, deletion, and retrieval of data elements. By using an appropriate hash function and handling collisions effectively, hashing can significantly improve the performance of these operations.

Hash Functions

A hash function takes an input (data) and produces a fixed-size string of characters, which represents the hashed value or index. The ideal scenario is when each input produces a unique hashed value. However, this is not always possible due to the limited size of the hash table.

Example:

    
        unsigned int hashFunction(char* key) {
            unsigned int sum = 0;
            for (int i = 0; key[i] != '\0'; i++) {
                sum += key[i];
            }
            return sum % TABLE_SIZE;
        }
    

In this example, we have implemented a simple hash function that calculates the sum of ASCII values of characters in the input string ‘key’. The modulus operation ensures that the resulting index falls within the range of our predefined TABLE_SIZE.

Collision Handling

A collision occurs when two different keys produce the same hashed value or index. It is a common occurrence in hashing, as the hash function cannot guarantee unique indices for all possible inputs.

Open Addressing:

In open addressing, when a collision occurs, we search for the next available slot in the hash table to store the data. This process continues until an empty slot is found. There are various techniques for open addressing, including linear probing, quadratic probing, and double hashing.

Chaining:

In chaining, instead of storing data directly in the hash table slots, we use linked lists to handle collisions. Each slot in the hash table maintains a pointer to a linked list that stores all the elements with the same hashed value. When a collision occurs, we simply add the new element to the linked list at that slot.

Advantages of Hashing

  • Fast retrieval: Hashing provides constant-time complexity for retrieving data elements from the hash table.
  • Efficient storage utilization: Hashing ensures optimal usage of memory by storing data elements based on their hashed values.
  • Supports large data sets: Hash tables can efficiently handle large amounts of data by providing fast access to individual elements.

Disadvantages of Hashing

  • Potential collisions: Collisions can degrade performance if not handled properly.
  • Extra memory overhead: Chaining requires additional memory for maintaining linked lists.
  • Inefficient search for non-existent keys: In some cases, searching for a non-existent key can be less efficient in hashing compared to other data structures like binary search trees.

Conclusion

Hashing is a powerful technique used in data structures to improve the efficiency of storing and retrieving data. By applying a hash function and handling collisions effectively, we can achieve constant-time complexity for basic operations on a hash table. Understanding the concepts of hashing is crucial for any programmer working with large datasets or looking to optimize their code.

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