Which data structure is most efficient for calculating the sum of transaction amounts for unique account numbers?

Using a hash table is the most efficient choice for calculating the sum of transaction amounts for unique account numbers due to its ability to provide average-case constant time complexity, O(1), for insertions and lookups.

When processing transactions, each account number can be used as a key in the hash table, where the associated value would be the cumulative sum of the transaction amounts for that account. By utilizing this data structure, as transactions are processed, the program can quickly check if the account number already exists in the hash table. If it does, you can simply update the sum; if not, you can create a new entry in the hash table for that account number with the initial transaction amount.

This efficiency is especially beneficial when dealing with a large dataset, as it significantly reduces the time it would take to calculate sums compared to other data structures. For instance, linked lists would require sequential searches through the list to access each account number, making it inefficient for this specific task. Similarly, a two-dimensional array would also involve more complex indexing and potentially wasted space, while a comma-delimited string would require parsing and is less structured for rapid access and updating.

In scenarios where rapid access and modification for unique keys is required, hash tables excel and stand