How do different duplicate removal methods compare in performance?

How do different duplicate removal methods compare in performance?

There are several methods for duplicate removal, including hashing, sorting, and using data structures like sets. Each has its own performance characteristics depending on the dataset size and structure.

Can you explain how hashing works for duplicate removal?

Sure! Hashing involves creating a unique hash value for each item in the dataset. When a new item is added, its hash is compared to existing hashes to check for duplicates, which is generally very fast.

What about the sorting method? Is it efficient?

Sorting can be efficient, especially for smaller datasets. Once sorted, duplicates are adjacent, making it easy to identify them. However, it has a higher time complexity compared to hashing.

Which method would you recommend for large datasets?

For large datasets, I recommend using hashing due to its average O(1) time complexity for lookups, making it faster than sorting.