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Transactions and Multiuser Processing
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When two or more users concurrently access a database, transaction processing takes on a new dimension. Now the DBMS must not only recover properly from system failures or errors, but it must also ensure that the users actions do not interfere with one another. Ideally, each user should be able to access the database as if he or she had exclusive access to it, without worrying about the actions of other users. The SQL transaction model allows a SQL-based DBMS to insulate users from one another in this way. The best way to understand how SQL handles concurrent transactions is to look at the problems that result if transactions are not handled properly. Although they can show up in many different ways, four fundamental problems can occur. The next four sections give a simple example of each problem.
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The Lost Update Problem
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Figure 12-6 shows a simple application where two users accept telephone orders from customers. The order-entry program checks the PRODUCTS file for adequate inventory before accepting the customer s order. In the figure, Joe starts entering an order for 100 ACI-41004 widgets from his customer. At the same time, Mary starts entering her customer s order for 125 ACI-41004 widgets. Each order-entry program does a query on the PRODUCTS file, and each finds that 139 widgets are in stock more than enough to cover the customer s request. Joe asks his customer to confirm the order, and his copy of the order-entry program updates the PRODUCTS file to show (139 100) = 39 widgets remaining for sale and inserts a new order for 100 widgets into the ORDERS table. A few seconds later, Mary asks her customer to confirm the order. Her copy of the order-entry program updates the PRODUCTS file to show (139 125) = 14 widgets remaining in stock and inserts a new order for 125 widgets into the ORDERS table. The handling of the two orders has obviously left the database in an inconsistent state. The first of the two updates to the PRODUCTS file has been lost! Both customers orders have been accepted, but not enough widgets are in inventory to satisfy both orders. Further, the database shows that there are still 14 widgets remaining for sale. This example illustrates the lost update problem that can occur whenever two programs read the same data from the database, use the data as the basis for a calculation, and then try to update the data.
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Transaction Processing
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Figure 12-6.
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The lost update problem
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The Uncommitted Data Problem
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Figure 12-7 shows the same order-processing application as Figure 12-6. Joe again begins taking an order for 100 ACI-41004 widgets from his customer. This time, Joe s copy of the order-processing program queries the PRODUCTS table, finds 139 widgets available, and updates the PRODUCTS table to show 39 widgets remaining after the customer s order. Then Joe begins to discuss with the customer the relative merits of the ACI-41004 and ACI-41005 widgets. In the meantime, Mary s customer tries to order 125 ACI-41004 widgets. Mary s copy of the order-processing program queries the PRODUCTS table, finds only 39 widgets available, and refuses the order. It also generates a notice telling the purchasing manager to buy more ACI-41004 widgets, which are in great demand. Now Joe s customer decides not to order the size 4 widgets after all, and Joe s order-entry program does a ROLLBACK to abort its transaction.
SQL: The Complete Reference
Figure 12-7.
The uncommitted data problem
Because Mary s order-processing program was allowed to see the uncommitted update of Joe s program, the order from Mary s customer was refused, and the purchasing manager will order more widgets, even though 139 of them are still in stock. The situation would have been even worse if Mary s customer had decided to settle for the 39 available widgets. In this case, Mary s program would have updated the PRODUCTS table to show zero units available. But when the ROLLBACK of Joe s transaction occurred, the DBMS would have set the available inventory back to 139 widgets, even though 39 of them are committed to Mary s customer. The problem in this example is that Mary s program has been allowed to see the uncommitted updates from Joe s program and has acted on them, producing the erroneous results. The SQL2 standard refers to this as problem P1, also known as the dirty read problem. In the parlance of the standard, the data that Mary s program has seen is dirty because it has not been committed by Joe s program.
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