What are the differences between hashing and binary search trees?

What are the differences between hashing and binary search trees?

Hashing and binary search trees are both data structures used for storing and retrieving data efficiently. Hashing uses a hash function to map keys to values, allowing for average-case constant time complexity for lookups. In contrast, binary search trees maintain a sorted structure, enabling logarithmic time complexity for lookups, insertions, and deletions.

Which one is better for searching large datasets?

It depends on the use case. Hashing is generally faster for lookups in large datasets due to its average constant time complexity. However, binary search trees can be more efficient for ordered data and range queries.

Are there any drawbacks to using hashing?

Yes, hashing can lead to collisions, where two keys hash to the same index. This can degrade performance. Additionally, it doesn't maintain order, which can be a limitation for certain applications.

What about the drawbacks of binary search trees?

Binary search trees can become unbalanced, leading to worst-case time complexities of O(n) for operations. Self-balancing trees like AVL or Red-Black trees can mitigate this issue.