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Distributed transactions are much more difficult to provide than the first two stages of distributed data access. It s impossible to provide distributed transactions without the active cooperation of the individual DBMS systems involved in the transaction. For this reason, the DBMS brands that support distributed transactions almost always support them only for a homogeneous network of databases, all managed by the same DBMS brand (that is, an all-Oracle or all-Sybase network). A special transaction protocol, called the two-phase commit protocol, is used to implement distributed transactions and ensure that they provide the all-or-nothing requirement of a SQL transaction. The details of this protocol are described later in the section The Two-Phase Commit Protocol.

The final stage of distributed data access in the IBM model is a distributed request, shown in Figure 23-12. At this stage, a single SQL statement may reference tables from two or more databases located on different computer systems. The DBMS is responsible for automatically carrying out the statement across the network. A sequence of distributed request statements can be grouped together as a transaction. As in the previous distributed transaction stage, the DBMS must guarantee the integrity of the distributed transaction on all systems that are involved. The distributed request stage doesn t make any new demands on the DBMS transaction-processing logic, because the DBMS already had to support transactions across system boundaries at the previous distributed transaction stage. However, distributed requests pose major new challenges for the DBMS optimization logic. The optimizer must now consider network speed when it evaluates alternate methods for carrying out a SQL statement. If the local DBMS must repeatedly access part of a remote table (for example, when making a join), it may be faster to copy part of the table across the network in one large bulk transfer rather than repeatedly retrieving individual rows across the network.

[CONSTRAINT constraint_name] CHECK (condition) Example: CONSTRAINT CK_CUSTOMER_DEPOSIT_AMOUNT CHECK (CUSTOMER_DEPOSIT_AMOUNT >= 0 OR CUSTOMER_DEPOSIT_AMOUNT IS NULL)

23:

Figure 23-12.

The relative sizes of the tables on the local and remote system are also relevant optimization factors, as well as the selectivity of any search conditions in the SELECT clause For some queries, the search conditions may select only one or a few rows on the local system and hundreds of rows on the remote system, so they should be applied locally first For other queries involving the same tables, the relative selectivity may be reversed, and the remote search condition should be applied For still other queries, the join condition itself may limit the rows that participate in both the local and remote systems, and it may be most efficient to apply it first.

In each case, the cost of the query is not just the cost of the database access but also the cost of shipping the results of intermediate query execution steps back and forth across the network The optimizer must also decide which copy of the DBMS should handle statement execution If most of the tables are on a remote system, it may be a good idea for the remote DBMS on that system to execute the statement However, that may be a bad choice if the remote system is heavily loaded Thus, the optimizer s task is both more complex and much more important in a distributed request Ultimately, the goal of the distributed request stage is to make the entire distributed database look like one large database to the user.

This check constraint prevents a negative amount from being stored in the CUSTOMER_DEPOSIT_AMOUNT column. Notice the OR in the expression that also allows null values in the column. If this were not included, any attempt to store a null value would fail because a null value is not greater than or equal to zero. UNIQUE constraint

Ideally, the user would have full access to any table in the distributed database and could use SQL transactions without knowing anything about the physical location of the data Unfortunately, this ideal scenario would quickly prove impractical in real networks In a network of any size, the number of tables in the distributed database would quickly become very large, and users would find it impossible to find data of interest The user-ids of every database in the organization would have to be coordinated to make sure that a given user-id uniquely identified a user in all databases Database administration would also be very difficult In practice, therefore, distributed requests must be implemented selectively Database administrators must decide which remote tables are to be made visible to local users and which will remain hidden.