Merge sort’s performance remains consistent regardless of the initial ordering of the data because it does not attempt to optimise based on how sorted the list already is. Unlike algorithms such as bubble sort or insertion sort, which can complete faster on nearly sorted data, merge sort always follows the same divide-and-conquer structure. It recursively splits the list into halves until individual elements remain and then performs a series of merge operations to combine the sorted sublists. This means the number of operations and comparisons depends solely on the size of the input, not its order. The algorithm always performs log base 2 of n recursive levels, and each level involves comparing and merging all n elements. Therefore, the total time complexity stays at O(n log n) in all cases—best, average, and worst. This consistency makes merge sort a reliable choice when performance guarantees are needed, regardless of the nature of the data.

Despite its reliable performance, merge sort has practical limitations, especially concerning memory usage. During the merging phase, the algorithm requires temporary arrays to store the merged results. This means it uses additional space proportional to the size of the input—O(n) space complexity. In systems with limited memory, or when working with extremely large datasets, this overhead can become problematic. Furthermore, merge sort’s recursive structure can also lead to a large number of function calls, which may result in stack overflow errors in languages or environments with limited call stack depth. It also tends to have more overhead in terms of operations compared to simpler sorting algorithms for small datasets, where something like insertion sort may actually perform faster in practice. As a result, while merge sort is efficient in theory, in practice it’s often used in hybrid approaches—only applied to large parts of the dataset, or in situations where stability and consistent performance are more important than memory usage.

Merge sort is naturally stable because it preserves the original relative ordering of elements with equal keys during the merging process. When two elements being compared are equal, the algorithm typically selects the element from the left sublist first. Since the left sublist contains elements that appeared earlier in the original list, this behaviour ensures that ties are broken in favour of original order. For example, if sorting a list of students by grade, and multiple students have the same grade, merge sort will keep those students in the same order they appeared before the sort. This is essential in cases where the data has multiple fields, and stability needs to be preserved for secondary sorting criteria. Maintaining stability is particularly useful in chained sorting operations, where the list has already been sorted by one field, and merge sort is used to sort by another without disrupting the previous ordering of equal elements.

Yes, merge sort can be implemented iteratively, although the recursive approach is more commonly taught due to its clarity and direct relation to the divide-and-conquer paradigm. An iterative merge sort typically works by treating the list as a collection of small sorted segments and gradually merging these segments in passes of increasing size. In the first pass, adjacent elements are grouped and merged in pairs to form two-element sorted lists. In the second pass, these two-element lists are merged into four-element sorted lists, and so on, until the entire list is merged. This approach simulates the recursive merging process using loops instead of recursive calls. It avoids the risk of stack overflow from deep recursion and can be more memory-efficient in environments where function calls are expensive. However, the implementation is more complex, as it requires careful management of indices and merging boundaries. Despite being iterative, this version still has the same O(n log n) time complexity.

Merge sort is particularly well-suited for sorting large datasets, especially when the data does not fit entirely into main memory. This makes it an ideal algorithm for external sorting, where the dataset is stored on external media such as hard drives or SSDs. Since merge sort works in large part by sequentially accessing data, it performs efficiently on storage systems where random access is expensive or slow. The divide-and-conquer strategy is adapted for external sorting by splitting the dataset into chunks that fit into memory, sorting each chunk in memory (using any efficient algorithm), and then writing these sorted chunks back to disk. After all chunks are sorted, a k-way merge process combines them in a manner similar to the standard merge step. Because merge sort handles merging efficiently and predictably, it avoids excessive re-reading of data and leverages buffering effectively. As a result, it is commonly used in large-scale data processing systems like databases and search engines.