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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.
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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. 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. The cooperating DBMS copies must translate user-ids from one system to another, allowing each database to be administered autonomously while
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providing security for remote data access. Distributed requests that would consume too many DBMS or network resources must be detected and prohibited before they impact overall DBMS performance. Because of their complexity, distributed requests are not fully supported by any commercial SQL-based DBMS today, and it will be some time before even a majority of their features are available. One major step toward distributed processing across database brands has been the standardization of a distributed transaction protocol. The XA protocol, originally developed to coordinate among multiple transaction monitors, is being actively applied to distributed database transactions as well. A Java version of the same capability, called Java Transaction Protocol (JTP), provides a distributed transaction interface for Java-based applications and application servers. Today, most commercial DBMS products designed to be used in a network environment support XA and JTA interfaces.
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The Two-Phase Commit Protocol *
A distributed DBMS must preserve the all-or-nothing quality of a SQL transaction if it is to provide distributed transactions. The user of the distributed DBMS expects that a committed transaction will be committed on all of the systems where data resides, and that a rolled back transaction will be rolled back on all of the systems as well. Further, failures in a network connection or in one of the systems should cause the DBMS to abort a transaction and roll it back, rather than leaving the transaction in a partially committed state. All commercial DBMS systems that support distributed transactions use a technique called two-phase commit to provide that support. You don t have to understand the twophase commit scheme to use distributed transactions. In fact, the whole point of the scheme is to support distributed transactions without your knowing it. However, understanding the mechanics of a two-phase commit can help you plan efficient database access. To understand why a special two-phase commit protocol is needed, consider the database in Figure 23-13. The user, located on System A, has updated a table on System B and a table on System C and now wants to commit the transaction. Suppose the DBMS software on System A tried to commit the transaction by simply sending a COMMIT message to System B and System C, and then waiting for their affirmative replies. This strategy works as long as Systems B and C can both successfully commit their part of the transaction. But what happens if a problem such as a disk failure or a deadlock condition prevents System C from committing as requested System B will commit its part of the transaction and send back an acknowledgment, System C will roll back its part of the transaction because of the error and send back an error message, and the user ends up with a partially committed, partially rolled back transaction. Note that System A can t change its mind at this point and ask System B to roll back the transaction. The transaction on System B has been committed, and other users may already have modified the data on System B based on the changes made by the transaction.
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