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The Phantom Insert Problem
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Figure 12-9 shows an order-processing application once more. This time, the sales manager runs a report program that scans the ORDERS table, printing a list of the orders from customers of Bill Adams and computing their total. In the meantime, a customer calls Bill to place an additional order for $5000. The order is inserted into the database, and the transaction is committed. A short time later, the sales manager's program again scans the ORDERS table, running the very same query. This time, there is an additional order, and the total is $5000 higher than for the first query.
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Figure 12-9: The phantom insert problem
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Like the previous example, the problem here is inconsistent data. The database remains an accurate model of the real-world situation, and its integrity is intact, but the same query executed twice during the same transaction yielded two different results. In the previous example, the query was a single-row query, and the inconsistency in the data was caused by a committed UPDATE statement. A committed DELETE statement could cause the same kind of problem. In the example of Figure 12-9, the problem is caused by a committed INSERT statement. The additional row did not participate in the first query, but it shows up as a "phantom row, out of nowhere" in the second query. Like the inconsistent data problem, the consequences of the phantom insert problem can be inconsistent and incorrect calculations. The SQL2 standard refers to this as "P3," and also uses the name "phantom" to describe it.
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Concurrent Transactions
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As the three multi-user update examples show, when users share access to a database and one or more users is updating data, there is a potential for database corruption. SQL uses its transaction mechanism to eliminate this source of database corruption. In addition to the "all-or-nothing" commitment for the statements in a transaction, a SQLbased DBMS makes this commitment about transactions: During a transaction, the user will see a completely consistent view of the database. The user will never see the uncommitted changes of other users, and even committed changes made by others will not affect data seen by the user in midtransaction. Transactions are thus the key to both recovery and concurrency control in a SQL database. The previous commitment can be restated explicitly in terms of concurrent transaction execution: If two transactions, A and B, are executing concurrently, the DBMS ensures that the results will be the same as they would be if either (a) Transaction A were executed first, followed by Transaction B, or (b) Transaction B were executed first, followed by Transaction A. This concept is known as the serializability of transactions. Effectively, it means that each database user can access the database as if no other users were concurrently accessing the database. The fact that SQL insulates you from the actions of other concurrent users doesn't mean, however, that you can forget all about the other users. In fact, the situation is quite the opposite. Because other users want to concurrently update the database, you should
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keep your transactions as short and simple as possible, to maximize the amount of parallel processing that can occur. Suppose, for example, that you run a program that performs a sequence of three large queries. Since the program doesn't update the database, it might seem that it doesn't need to worry about transactions. It certainly seems unnecessary to use COMMIT statements. But in fact the program should use a COMMIT statement after each query. Why Recall that SQL automatically begins a transaction with the first SQL statement in a program. Without a COMMIT statement, the transaction continues until the program ends. Further, SQL guarantees that the data retrieved during a transaction will be selfconsistent, unaffected by other users' transactions. This means that once your program retrieves a row from the database, no other user can modify the row until your transaction ends, because you might try to retrieve the row again later in your transaction, and the DBMS must guarantee that you will see the same data. Thus, as your program performs its three queries, it will prevent other users from updating larger and larger portions of the database. The moral of this example is simple: you must always worry about transactions when writing programs for a production SQL database. Transactions should always be as short as possible. "COMMIT early and COMMIT often" is good advice when one is using programmatic SQL. In practice, implementing a strict multi-user transaction model can impose a substantial overhead on the operation of a database with dozens, hundreds, or thousands of concurrent users. In addition, the specifics of the application may not require the absolute isolation among the user programs that the SQL transaction model implies. For example, maybe the application designer knows that an order inquiry program has been designed so that it will never attempt to read and then reread a row of the database during a single transaction. In this case, the Inconsistent Data problem can't occur, because of the program structure. Alternatively, maybe the application designer knows that all of a program's access to particular tables of a database is read-only. If the programmer can convey information like this to the DBMS, some of the overhead of SQL transactions can be eliminated. The SQL1 standard did not address this database performance issue, and most of the major DBMS brands implemented proprietary schemes for enhancing the performance of SQL transactions. The SQL2 standard specified a new SET TRANSACTION statement whose function is to specify the level of SQL transaction model support that an application needs. You don't need to use the SET TRANSACTION statement for casual use of SQL or for relatively simple or low-volume SQL transaction processing. To fully understand its operation, it's useful to understand the locking techniques used by commercial DBMS products to implement multi-user SQL transactions. The remainder of this chapter discusses locking and the performance optimizing capabilities of SQL2 and the various DBMS brands that depend on it.
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Locking *
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Virtually all major DBMS products use sophisticated locking techniques to handle concurrent SQL transactions for many simultaneous users. However, the basic concepts behind locking and transactions are very simple. Figure 12-10 shows a simple locking scheme and how it handles contention between two concurrent transactions.
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