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Today, major applications from the large packaged enterprise software vendors are all based on SQL and relational databases. Examples include large Enterprise Resource Planning (ERP), Supply Chain Management (SCM), Human Resources Management (HRM), Customer Relationship Management (CRM), financial management, and other packages from vendors such as SAP, BAAN, PeopleSoft, Vantive, Clarify, Siebel Systems, i2 Technologies, Manugistics, and others. These large-scale applications typically run on large UNIX-based server systems and place a heavy workload on the supporting DBMS. To isolate the applications and DBMS processing, and apply more total processing power to the application, they often use a three-tier architecture shown in Figure 23-17. Even with the use of stored procedures to minimize network traffic, the network and database access demands of the most data-intensive of these enterprise applications can outstrip the available network bandwidth and DBMS transaction rates. For example, consider a supply chain planning application that helps a manufacturing company determine the parts that it must order from suppliers. To generate a complete plan, the application must examine every open order and apply the product bill-of-materials to it. A complex product might involve hundreds of parts, some of which are themselves subassemblies consisting of dozens or hundreds of parts. If written using straightforward programming techniques, the planning application must perform a database inquiry to determine the parts makeup of every product, and then every subassembly, for every order, and it will accumulate the total needed information in the planning database for each of these parts. Using this technique, the application will take hours to process the hundreds of thousands of orders that may be currently on the books. In fact, the application will probably run so long that it cannot
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Three-tier architecture of a major enterprisewide application
possibly complete its work during the typical overnight low-volume batch processing window of time during which the company normally runs such applications. To deliver acceptable performance, all data-intensive enterprise applications employ caching techniques, pulling the data forward out of the database server, closer to the application. In most cases, the application uses relatively primitive caching techniques. For example, it might read the bill-of-materials once and load it into main-memory data tables within the application program. By eliminating the heavily repeated product-structure queries, the program can dramatically improve its performance. Recently, enterprise application vendors have begun to use more complex caching techniques. They may replicate the most heavily accessed data (the hot data) in a duplicate database table, on the same system as the application itself. Main-memory databases offer an even higher-performance alternative and are already being used where there is a relatively small amount of hot data (tens to hundreds of megabytes). With the advent of 64-bit operating system architectures and continuing declines in memory prices, it is becoming practical to cache larger amounts of data (several gigabytes or tens of gigabytes). Advanced caching and replication will become more important in response to emerging business requirements. Leading-edge manufacturing companies want to move toward real-time planning, where incoming customer orders and changes immediately impact production plans. They want to offer more customized products, in more configurations, to more closely match customer desires. These and similar trends will continue to raise the volume and complexity of database access.
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High-Volume Internet Data Management
High-volume Internet applications are also driving the trend to database caching and replication in networked database architectures. For example, financial services firms are competing for online brokerage clients by offering more and more advanced real-time stock reporting and analysis capabilities. The data management to support this application involves real-time data feeds (to insure that pricing and volume information in the database is current) and peak-load database inquiries of tens of thousands of transactions per second. Similar volume demands are found in applications for managing and monitoring high-volume Internet sites. The trend to personalize web sites (determining on the fly which banner ads to display, which products to feature, and so on) and measure the effectiveness of such personalization is another trend driving peak-load data access and data capture rates. The Web has already shown to be an effective architecture for dealing with these types of peak-load Internet volume demands through web site caching. Copies of heavily accessed web pages are pulled forward in the network and replicated. As a result, the total network capacity for serving web pages is increased, and the amount of network traffic associated with those page hits is reduced. Similar architectures are beginning to emerge for high-volume Internet database management, as shown in Figure 23-18. In this case, an Internet information services application caches hot data, such as the most recent news and financial information, in a very high-performance main-memory database from a vendor such as TimesTen Performance Software. It also stores summary user profile information in a main-memory database, which is used to personalize users experiences as they interact with the web site. As Figure 23-18 shows, the methods for handling high-performance data management are beginning to follow those already established for high-performance web page management. The issues for databases are more complex because of database integrity issues, but the emerging techniques are similar replication, high-volume read access, memory-resident databases, and highly fault-tolerant architectures. These demands will only grow as Internet traffic and personalization continues to increase, leading to more advanced network database architectures.
This chapter described the distributed data management capabilities offered by various DBMS products and the trade-offs involved in providing access to remote data: I A distributed database is implemented by a network of computer systems, each running its own copy of the DBMS software and operating autonomously for local data access. The copies of the DBMS cooperate to provide remote data access when required.
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