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The locking techniques described in the preceding sections are the most widely used techniques for supporting concurrent multiuser transaction processing in relational DBMS products. Locking is sometimes called a pessimistic approach to concurrency, because by locking parts of the database, the DBMS is implicitly assuming that concurrent transactions will probably interfere with one another. In recent years, a different approach to concurrency, called versioning, has been implemented in some DBMS products and has been increasing in popularity. Versioning is sometimes called an optimistic approach to concurrency because in this approach, the DBMS implicitly assumes that concurrent transactions will not interfere with one another. With a locking (pessimistic) architecture, the DBMS internally maintains one and only one copy of the data for each row in the database. As multiple users access the database, the locking scheme arbitrates the access to this row of data among the users (more precisely, among their concurrent transactions). In contrast, with a versioning (optimistic) architecture, the DBMS will create two or more copies of the data for a row in the database when a user attempts to update the row. One copy of the row will contain the old data for the row, before the update; the other copy of the row will contain the new data for the row, after the update. The DBMS internally keeps track of which transactions should see which version of the row, depending on their isolation levels.
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Figure 12-17 shows a simple versioning architecture in action. Transaction A starts the action, reading a row of the PRODUCTS table, and finding 139 units of ACI-41004 size 4 widgets available. Transaction B comes along next and updates the same row, reducing the quantity available to 39 units. In response, the DBMS internally creates a new copy of the row. From this point on, if Transaction B rereads the contents of the row, the contents will come from this new copy, since it reflects Transaction B s updated quantity on hand (39 units). Next, Transaction C comes along and tries to read the same row. Because Transaction B s update has not yet been committed, the DBMS gives Transaction C the data from the old copy of the row, showing 139 units available. The same thing happens a few seconds later for Transaction D; it will also see 139 units available. Now Transaction B performs a COMMIT operation, making its update of the row permanent. A short time later, Transaction E attempts to read the row. With Transaction B s update now committed, the DBMS will give Transaction E the data from the new copy, showing 100 units. Finally, Transactions C, D, and E end their database activity with a COMMIT operation. The activity shown in Figure 12-17 meets the serializability requirement for proper DBMS operation. The sequential transaction series A-C-D-B-E would produce the same results shown in the Figure. (In fact, the series A-D-C-B-E would also produce these results.) Furthermore, the versioning implementation delivers the correct operation without causing any of the transactions to wait. This is not true of the typical locking implementation, as shown in Figure 12-18.
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In Figure 12-18, Transaction A again starts the action, finding 139 units of ACI-41004 widgets available. Internally, the DBMS places a shared lock on the row. Transaction B next tries to update the row, reducing quantity on hand to 39 units. If Transaction A is operating at a strict isolation level (such as REPEATABLE READ), Transaction B will be held at this point, because it cannot acquire the required exclusive lock. If Transaction A is operating at a less strict isolation level, the DBMS can allow Transaction B to proceed, giving it an exclusive lock on the row and actually updating the data. The internal row in the database (recall that there is only a single copy of the row in this locking architecture) now shows 39 units available. When Transaction C comes along, it must wait for Transaction B to release
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