What is a self-balancing hash table and how is it different from a hash table?

A self-balancing hash table, also known as a balanced hash table or a balanced search tree, is a data structure that combines the properties of a hash table and a balanced search tree. It aims to provide efficient lookup, insertion, and deletion operations while maintaining a balanced structure.

In a regular hash table, data elements are stored in an array using a hash function to determine their index. However, collisions can occur when multiple elements are mapped to the same index, leading to performance degradation. To handle collisions, techniques like chaining or open addressing are used, but they can still result in inefficient lookup times.

On the other hand, a self-balancing hash table uses a balanced search tree, such as an AVL tree, red-black tree, or a B-tree, to store the data elements. These trees ensure that the height of the tree remains balanced, which guarantees efficient search operations with a time complexity of O(log n).

The main difference between a self-balancing hash table and a regular hash table lies in the handling of collisions. In a self-balancing hash table, collisions are resolved by storing the colliding elements in a balanced search tree instead of using chaining or open addressing. This allows for faster search, insertion, and deletion operations, even in the presence of collisions.

The self-balancing property of the hash table ensures that the tree remains balanced, which in turn guarantees that the worst-case time complexity for search, insertion, and deletion operations is logarithmic. This is in contrast to a regular hash table, where the worst-case time complexity for these operations can be linear in the number of elements stored.

Overall, a self-balancing hash table provides the benefits of both a hash table and a balanced search tree. It offers efficient lookup times similar to a hash table, while also maintaining a balanced structure to handle collisions effectively. This makes it a suitable choice for scenarios where fast search operations and collision resolution are both important requirements.