# What Is Linear Hashing in Data Structure?

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Scott Campbell

In the world of data structures, Linear Hashing is a powerful technique that is widely used for efficient storage and retrieval of data. It is particularly useful when dealing with large datasets that need to be stored and accessed quickly.

## Understanding Linear Hashing

Linear Hashing is a hashing technique that allows for dynamic expansion and contraction of hash tables. It was first introduced by Witold Litwin in 1980 as an extension to the traditional hashing method.

Unlike traditional hashing, where collisions occur when multiple keys map to the same hash value, Linear Hashing uses a dynamic number of buckets to minimize collisions. This makes it suitable for applications where the number of keys can change frequently.

## The Basics of Linear Hashing

Linear Hashing works by dividing the hash table into multiple buckets or segments. Each bucket can hold one or more key-value pairs. Initially, the hash table starts with a single bucket, known as Bucket 0.

When a new key-value pair needs to be added to the hash table, it is hashed using a hash function. The resulting hash value determines which bucket the pair should be placed in. If the bucket is full, an overflow area is created to accommodate additional key-value pairs.

The interesting aspect of Linear Hashing is its ability to dynamically split buckets when they become full. When a bucket reaches its maximum capacity, it undergoes a split operation. During this process, half of the elements from the overflowing bucket are moved to a new bucket called Bucket n+1.

This splitting process continues as more and more elements are inserted into the hash table. Over time, multiple buckets are created, allowing for efficient storage and retrieval of data. This dynamic nature ensures that performance remains consistent even with increasing amounts of data.

• Dynamic Expansion: Linear Hashing allows for the dynamic expansion of hash tables, ensuring efficient storage and retrieval even with large amounts of data.
• Reduced Collisions: By splitting overflowing buckets and distributing the elements across multiple buckets, Linear Hashing minimizes collisions, leading to improved performance.
• Adaptability: The dynamic nature of Linear Hashing makes it suitable for applications where the number of keys can change frequently. It can handle both insertions and deletions efficiently.

## Conclusion

Linear Hashing is a powerful technique in data structures that offers efficient storage and retrieval of data through dynamic expansion and contraction of hash tables. By minimizing collisions and adapting to changing data sizes, it ensures optimal performance in various applications.

Whether you are working on a project that requires handling large datasets or just want to expand your knowledge of data structures, understanding Linear Hashing is essential. Its ability to handle dynamic changes in data makes it a valuable tool in the world of computer science.