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PRODUCTS Table 12:00 MFR_ID PRODUCT_ID QTY_ON_HAND ACI
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The inconsistent data problem
For example, if the program is accumulating totals or calculating statistics, it cannot be sure that the statistics reflect a stable, consistent view of the data. The problem in this case is that Mary s program has been allowed to see committed updates from Joe s program that affect rows that Mary s program has already examined. The SQL standard refers to this problem as P2, also known as the nonrepeatable read problem. The name comes from the fact that Mary s program can t repeat the same read access to the database and obtain the same results.
The Phantom Insert Problem
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 (still operating within its initial transaction) 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.
12:
Tr a n s a c t i o n P r o c e s s i n g
Update Program
ORDERS Table 12:00 ORDER_NUM 112961 113012 AMOUNT $31,500.00 $3,745.00
Report Program 12:00 SELECT * FROM ORDERS 12:01 Answer: 112961, $31,500 12:02
12:04 INSERT INTO ORDERS VALUES (118102,......5,000.00) 12:05 COMMIT 12:04 ORDER_NUM 112961 118102 113012
Answer: 113012, $3,745 AMOUNT $31,500.00 $5,000.00 $3,745.00 12:10 SELECT * FROM ORDERS 12:11 Answer: 112961, $31,500 12:12 Answer: 118102, $5,000 12:13 Answer: 113012, $3,745
PART III
FIGURE 12-9
The phantom insert problem
As in 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. As with the inconsistent data problem, the consequences of the phantom insert problem can be inconsistent and incorrect calculations. The SQL standard refers to this as P3, and also uses the name phantom to describe it.
Part III:
Updating Data
Concurrent Transactions
As the three multiuser 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 SQL-based 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 mid transaction. Transactions are thus the key to both recovery and concurrency control in a SQL database. The preceding 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 rst, followed by Transaction B, or (b) Transaction B were executed rst, 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. In practice, dozens or hundreds of transactions may be concurrently executing within a large production database. The serializability concept can be directly extended to cover this situation. Serializability guarantees that, if some number, N, concurrent transactions are executing, the DBMS must ensure that its results are the same as if they had been executed in some sequence one at a time. The concept does not specify which sequence of transactions must be used, only that the results must match the results of some sequence. The fact that a DBMS 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 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, if the current session is in implicit transaction mode, the program should use a COMMIT statement after each query. Why Recall that SQL in implicit transaction mode 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 self-consistent, 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 you are using
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