What is sample sort?

Sample sort is a parallel sorting algorithm that is often used in distributed systems where data is spread across multiple machines. The algorithm works by dividing the data into smaller sets and sorting each set independently. These sets are then merged together to give a final sorted output.

The first step of sample sort is to select a set of samples from the dataset. These samples are used to create a map of the data, which partitions the full dataset into smaller subsets. Each partition is then sorted independently using any sorting algorithm. Finally, the sorted sets are merged together to create the final sorted output.

This algorithm has several advantages over other parallel sorting algorithms. For example, it requires fewer communication rounds between machines, which makes it more scalable. Additionally, the use of samples reduces the overhead of sorting large datasets and improves the efficiency of the algorithm.

However, sample sort has some limitations. The algorithm assumes that the data is randomly distributed, which may not always be the case. Furthermore, the selection of samples can impact the accuracy of the final output, so it’s important to choose a representative sample set.