What is collision resolution in hash tables?

Collision resolution in hash tables refers to the process of handling situations where two or more keys are mapped to the same index or bucket in the hash table. This can occur due to the limited number of available indices or the nature of the hashing function used.

There are several methods for resolving collisions in hash tables, including:

1. Separate Chaining: In this method, each bucket in the hash table contains a linked list of elements that hash to the same index. When a collision occurs, the new element is simply appended to the linked list at that index.

2. Open Addressing: In this method, when a collision occurs, the algorithm searches for the next available index in the hash table to place the element. This can be done using various techniques such as linear probing (checking the next index in a linear manner), quadratic probing (checking indices in a quadratic manner), or double hashing (using a second hash function to determine the next index).

3. Robin Hood Hashing: This is a variation of open addressing where elements are placed in a way that minimizes the average search time. When a collision occurs, the algorithm checks the distance between the current element and its ideal position. If the distance is smaller than the collided element, it swaps the elements, ensuring that elements with smaller distances are closer to their ideal positions.

4. Cuckoo Hashing: This is another open addressing technique where each key is associated with two hash functions and two separate hash tables. When a collision occurs, the algorithm tries to relocate the collided element to its alternative position in the other hash table. This process continues until a vacant position is found or a maximum number of relocations is reached.

The choice of collision resolution method depends on factors such as the expected number of collisions, the load factor of the hash table, and the desired performance characteristics. Each method has its advantages and disadvantages in terms of memory usage, search time, and insertion/deletion complexity.