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This type of mirrored table approach can be highly effective if the ratio of database queries to database updates is very high (for example, 95 percent read access/5 percent update access). If the percentage of updates is higher, the potential for conflicts and the replication overhead can diminish the effectiveness and scalability of the overall configuration. Efficiency also decreases with each increase in the number of replicated systems, since the replication overhead rises. One common way to get more efficiency out of a mirrored table configuration like the one in Figure 23-8 is to divide updates to the table based on some rule. For example, if the mirrored table is a customer table, the primary key may be the customer name. The front-end load-balancing software can then be written so that updates for customer names starting with A through M are routed to the one system and updates for customer names starting with N through Z are routed to the other system. This eliminates the possibility of update conflicts. Because the table remains fully replicated under this scenario, read access requests can still be distributed randomly between the two systems to balance the workload. This type of approach can be quite effective in achieving scalable database performance with replicated tables. It can be fairly easily extended from a two-way scheme to an N-way scheme, where updates are split among three or more database servers.
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Over the last several years, research into fully distributed database access has slowly but surely found its way into commercial products. Today, many of the mainstream enterprise database products offer at least some level of transparent distributed database access. As noted earlier in the Remote Data Transparency section, the performance implications of distributed database access and updates can be very substantial. Two very similar-looking queries can create massively different amounts of network traffic and overhead. A single query, carried out in a brute force method or an optimized method, can create the same differences, depending on the quality of the optimization done by the DBMS. Because of these challenges, all of the vendors have taken a step-by-step approach to delivering distributed database access. When IBM first announced its blueprint for distributed data management in its SQL products, it defined a four-stage approach. IBM s four stages, shown in Table 23-1, provide an excellent framework for understanding distributed data management capabilities and their implications. The IBM scheme provides a simple model for defining the distributed data access problem: a user of one computer system needs to access data stored on one or more other computer systems. The sophistication of the distributed access increases at each stage. Thus, the capabilities provided by a given DBMS can be described in terms of which stage it has reached. In addition, within each stage, a distinction can be made between read-only access (with the SELECT statement) and update access (with the INSERT, DELETE, and UPDATE statements). A DBMS product often provides read-only capability for a given stage before full update capability is provided.
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23:
SQL Networking and Distributed Databases
Stage
Remote request Remote transaction
Description
Each SQL statement accesses a single remote database; each statement is a transaction. Each SQL statement accesses a single remote database; multistatement transactions are supported for a single database. Each SQL statement accesses a single remote database; multistatement transactions are supported across multiple databases. Each SQL statement may access multiple databases; multistatement transactions are supported across multiple databases.
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Distributed transaction
Distributed request
Table 23-1.
IBM s Four-Stage Approach for Distributed Database Access
Remote Requests
The first stage of distributed data access, as defined by IBM, is a remote request, shown in Figure 23-9. In this stage, the PC user may issue a SQL statement that queries or updates data in a single remote database. Each individual SQL statement operates as its own transaction, similar to the autocommit mode provided by many interactive SQL programs. The user can issue a sequence of SQL statements for various databases, but the DBMS doesn t support multistatement transactions. Remote requests are very useful when a PC user needs to query corporate data. Usually, the required data is located within a single database, such as a database of order-processing or manufacturing data. Using a remote request, the PC program can retrieve the remote data for processing by a PC spreadsheet, graphics program, or desktop publishing package. The remote request capability is not powerful enough for most transactionprocessing applications. For example, consider a PC-based order entry application that accesses a corporate database. To process a new order, the PC program must check inventory levels, add the order to the database, decrease the inventory totals, and adjust customer and sales totals, involving perhaps half a dozen different SQL statements. As explained in 11, database integrity can be corrupted if these statements do not execute as a single transaction. However, the remote request stage does not support multistatement transactions, so it cannot support this application.
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