Iterating over the output of a query is a common requirement in database programming. While SQL is designed for set-based operations, various techniques allow processing individual rows returned by a `SELECT` statement. These methods often involve server-side procedural extensions like stored procedures, functions, or cursors. For example, within a stored procedure, a cursor can fetch rows one by one, enabling row-specific logic to be applied. Alternatively, some database systems provide iterative constructs within their SQL dialects. One example uses a `WHILE` loop in conjunction with a fetch operation to process each row sequentially.
Processing data row by row allows for operations that are not easily achieved with set-based operations. This granular control is essential for tasks like complex data transformations, generating reports with dynamic formatting, or integrating with external systems. Historically, such iterative processing was less efficient than set-based operations. However, database optimizations and advancements in hardware have reduced this performance gap, making row-by-row processing a viable option in many scenarios. It remains critical to carefully evaluate the performance implications and consider set-based alternatives whenever feasible.
