A hash table is a data structure that allows efficient insertion, deletion, and retrieval of elements. One common implementation of a hash table is the linear probing hash table. In this article, we will dive deeper into what a linear probing hash table is and how it works.

## What Is a Linear Probing Hash Table?

A linear probing hash table is a data structure that uses an array to store key-value pairs. It uses a hashing function to map keys to array indices, allowing for constant-time (O(1)) access to values based on their keys.

### How Does Linear Probing Work?

In a linear probing hash table, collision resolution is handled by searching for the next available slot in the array when there is a collision. When two keys map to the same index, the second key-value pair is placed in the next available slot in the array.

**For example:**

- We have an array of size 10
- The hashing function maps key “apple” to index 2
- The hashing function maps key “banana” to index 2 (collision)
- The value “banana” is stored at index 3 instead

This process continues until an empty slot or the end of the array is reached. When searching for a value based on its key, if the desired key is not found at its hashed index, we can simply traverse the array sequentially until we find either the desired value or an empty slot.

### Advantages of Linear Probing Hash Tables

**Efficient Memory Usage:**Linear probing hash tables can be more memory-efficient compared to other collision resolution techniques like chaining, as they store key-value pairs directly in the array.**Cache-Friendly:**Linear probing hash tables tend to have good cache performance due to their contiguous memory layout.**Simple Implementation:**Implementing a linear probing hash table is relatively straightforward compared to other collision resolution techniques.

### Disadvantages of Linear Probing Hash Tables

**Primary Clustering:**Linear probing hash tables are prone to primary clustering, where consecutive collisions result in longer probe sequences, leading to decreased performance.**Poor Worst-Case Performance:**In the worst-case scenario, linear probing hash tables may degrade to O(n) access time if the probe sequence becomes lengthy due to primary clustering.

__Note:__

A load factor is often used in linear probing hash tables to determine when it is necessary to resize the array. The load factor is the ratio of the number of elements stored in the hash table to the size of the array. When this ratio exceeds a certain threshold, the array is resized and rehashed to maintain a desirable average number of elements per slot.

## In Conclusion

A linear probing hash table provides an efficient way to store and retrieve key-value pairs using an array-based approach. While it has its advantages in terms of memory usage and cache-friendliness, it can suffer from primary clustering and poor worst-case performance. Understanding these trade-offs can help you make informed decisions when choosing a hash table implementation for your specific use case.