Explain external sorting
External sorting is a technique used in computer science to deal with sorting large amounts of data that cannot fit entirely in memory. It involves breaking the data into smaller, manageable chunks, sorting them individually, and then merging the sorted chunks together to obtain the final sorted result.
The reason external sorting is needed is that traditional sorting algorithms, such as Quicksort or Mergesort, require all the data to be loaded into memory at once. However, when dealing with large datasets that exceed the available memory capacity, these algorithms are not feasible.
External sorting typically follows these steps:
1. Phase 1: Chunking - The large dataset is divided into smaller chunks that can fit into memory. These chunks are usually stored on disk.
2. Phase 2: Sorting - Each chunk is individually sorted using an in-memory sorting algorithm. This can be a simple algorithm like Insertion Sort or a more complex one like Quicksort.
3. Phase 3: Merging - The sorted chunks are merged together to create larger chunks. This is done by repeatedly comparing the first elements of each chunk and selecting the smallest one to add to the final sorted result. This process continues until all the elements have been merged.
4. Repeat Phases 2 and 3 - If the merged chunks still exceed the available memory, the process is repeated by sorting and merging the merged chunks again. This continues until the entire dataset is sorted.
5. Final Merge - Once all the chunks have been sorted and merged, a final merge is performed to create the completely sorted dataset.
External sorting can be slower than in-memory sorting due to the additional disk I/O operations involved in reading and writing chunks. However, it is necessary when dealing with large datasets that cannot fit in memory, allowing for efficient sorting of such data.