Optimizing transaction data performance in database management systems

Abstract

One indicator of the quality of an information system is the speed of data processing. A database's most common data processing operations are adding, displaying, changing, and deleting data. The amount of data stored in the database significantly impacts the performance of data processing and, therefore, the performance of information systems. The update command changes some or all of the data in a table. The update command works by retrieving the data in the table to be changed, entering the new data in a form, and then sending it back to the database. The update command is often combined with a condition specifying which data rows must be changed. This research is an experimental study that compares the use of the update command with a stored procedure to the use of the update command without a stored procedure. The results showed that the average processing time for the update command with the stored procedure was 348.896 milliseconds for the minimum data category, 266.462 milliseconds for the medium data category, and 279.543 milliseconds for the maximum data category. The average processing time for the update command without a stored procedure was 297.132 milliseconds for the minimum data category, 747.670 milliseconds for the medium data category, and 1256.273 milliseconds for the maximum data category. These results suggest that the update command with a stored procedure is more efficient than the one without a stored procedure. This is because the stored procedure can pre-compile the SQL statement, which reduces the time it takes to execute the statement.