Hash collision is a phenomenon that occurs in data structures when two different keys generate the same hash value. In other words, it is a situation where two or more elements are mapped to the same location in a hash table. This can lead to various complications and inefficiencies in handling data.

**How does hashing work?**

Hashing is a technique used to map data elements to locations within an array, known as the hash table, based on their respective keys. The goal is to minimize the time required to search for and retrieve data.

When an element is inserted into the hash table, its key is transformed into a numerical value using a hash function. This value determines the index or location where the element will be stored.

__Types of hash functions__

There are different types of hash functions used in various applications, such as division hashing, multiplication hashing, and universal hashing. These functions aim to distribute the elements uniformly across the hash table to minimize collisions.

**The problem with collision**

Despite efforts made by different hashing techniques, collision remains a possibility due to various factors like limited array size or poor choice of hash function. When two or more elements are assigned the same index in a hash table, they need to be stored together using additional data structures like linked lists or binary trees.

__Effects on performance__

Hash collisions can have several negative impacts on performance. Firstly, they increase the time complexity of operations like insertion and retrieval since we need to traverse through linked lists or trees associated with colliding elements. This slows down access times and defeats the purpose of using hashing for efficient data retrieval.

**Handling collisions**

To handle collisions effectively, several collision resolution techniques are employed:

### 1. Separate Chaining

In this approach, each index in the hash table contains a linked list or any other suitable data structure that holds colliding elements.

When a collision occurs, the new element is simply appended to the existing list. This method ensures all elements are stored, but it requires additional memory for the linked lists.

### 2. Open Addressing

Open addressing resolves collisions by finding an alternative location within the hash table to store the colliding element.

Various techniques like linear probing, quadratic probing, and double hashing are used to find the next available index for insertion. Although it saves memory compared to separate chaining, it can lead to clustering and increased search times.

### 3. Robin Hood Hashing

Robin Hood hashing is a variation of open addressing that attempts to minimize clustering by swapping elements during insertion when a better location is found. This technique reduces the average search time and provides good performance overall.

**Conclusion**

Hash collision is an inherent challenge in data structures that utilize hashing for efficient storage and retrieval of data elements. While collision resolution techniques can mitigate the effects, it’s important to choose appropriate hash functions and consider factors like load factor and table size to minimize collisions.