Externalsort

Externalsort is a sorting algorithm designed to handle datasets that are too large to fit into main memory (RAM). When faced with such massive data, the entire dataset cannot be loaded and sorted directly. Instead, externalsort breaks the problem down into smaller, manageable chunks.

The core idea of externalsort involves two main phases: a run creation phase and a merge phase. In the run creation phase, the algorithm reads portions of the input data that can fit into memory, sorts these chunks using an internal sorting algorithm like quicksort or mergesort, and then writes these sorted chunks back to secondary storage, such as a hard drive, as temporary files. These sorted temporary files are called "runs".

Once all the input data has been processed into sorted runs, the merge phase begins. This phase takes multiple sorted runs and merges them into a single, fully sorted output file. Typically, a k-way merge strategy is employed, where k sorted runs are merged simultaneously. The algorithm reads the first element from each of the k runs, identifies the smallest among them, and writes it to the output file. It then fetches the next element from the run that contained the smallest element and repeats the process until all runs are exhausted and the output file is completely sorted. The number of runs merged at once, k, is usually determined by the amount of available memory to hold the merge buffers.