Here are the key explanation of Internal and External Sorting:
Internal Sorting:
Internal sorting refers to the sorting of data that is entirely held in memory (RAM) during the sorting process.
• The entire dataset to be sorted fits within the available memory, and the sorting operations are performed using the data in memory without the need for external storage.
Characteristics of Internal Sorting:
Memory Usage:
The entire dataset is stored in RAM. Sorting operations are performed on the in-memory data structures.
Efficiency:
Generally faster than external sorting due to direct access to data in memory.
Complexity:
Algorithms for internal sorting are often simpler and more straightforward.
Suitability:
Suitable for datasets that can fit into the available memory.
Examples:
QuickSort, MergeSort, Insertion Sort, HeapSort, Bubble Sort, Selection Sort.
External Sorting:
External sorting refers to the sorting that is used when the dataset to be sorted is too large to fit entirely into the available memory.
• It involves dividing the data into smaller chunks, sorting these chunks in memory, and then merging the sorted chunks to produce the final sorted result.
• External sorting minimizes the need for random access to external storage, such as disk drives, during the sorting process.
Characteristics of External Sorting:
Memory Usage:
Only a portion of the dataset is loaded into memory at a time.
Sorting operations involve multiple passes over the data.
Efficiency:
Slower than internal sorting due to the need for external storage access.
More efficient for large datasets that don’t fit into memory.
Complexity:
Requires additional complexity for managing data chunks and merging.
Suitability:
Suitable for sorting large datasets that cannot fit into the available memory.
Examples:
External sorting algorithms include Merge sort, Tag sort, Polyphase sort, Four tape sort, External radix sort, Internal merge sort, etc.
Difference between Internal and External Sorting:
Here are the key difference between Internal and External sorting:
| Aspect | Internal Sorting | External Sorting |
|---|---|---|
| Data | Entire dataset fits in main memory (RAM) | Dataset exceeds main memory capacity, stored in secondary storage (disk) |
| Speed | Generally faster due to direct access to memory | Slower due to disk access which is slower compared to main memory |
| Memory Usage | Requires enough memory to hold entire dataset | Uses memory for buffering and processing chunks of data |
| I/O Operations | Minimal I/O operations, typically limited to input and output | Involves frequent I/O operations for reading from and writing to disk |
| Sorting Algorithm | Can use algorithms like quicksort, mergesort, heapsort, etc. | Often employs external sorting algorithms like external mergesort, polyphase mergesort, etc. |
| Efficiency | Highly efficient for small to medium-sized datasets | Efficient for large datasets but may suffer from slower performance compared to internal sorting for smaller datasets |
| Application | Suitable for datasets that easily fit into memory | Necessary for handling large datasets that cannot fit into memory |
| Examples | Sorting small arrays, lists, or database tables in memory | Sorting large databases, files, or datasets stored on disk |
