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Practical replication strategies always involve a trade-off between the desire to keep data as current as possible and the desire to keep network traffic down to a practical level and provide adequate performance. These trade-offs usually involve not just technical considerations, but business practices and policies as well. For example, consider an order-processing application using the sample database, and assume that order processing is distributed across five different call centers that are geographically distributed around the world. Each call center has its own computer system and database. Incoming orders are checked against the PRODUCTS table to be certain that enough inventory is on hand to fill the order. The PRODUCTS table keeps track of product-on-hand quantities for all of the company s warehouses, worldwide. Suppose the company s policy is that the order-processing clerk must be able to absolutely guarantee a customer that products can be shipped within 24 hours of the time an order is accepted. In this case, the PRODUCTS table must contain absolutely up-to-the-minute data, reflecting the inventory impact of orders taken just seconds earlier. There are only two possible designs for the database in this case. There could be a single, central copy of the PRODUCTS table, shared by all users at all five order-processing sites. Alternatively, there could be a fully mirrored copy of the PRODUCTS table at each of the five sites. The fully mirrored solution is almost certainly impractical because the frequent updates to the PRODUCTS table as each order is taken will cause excessive network traffic to keep the five copies of the table in perfect synchronization.
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But suppose the company believes it can still maintain adequate customer satisfaction with a policy that is slightly less strict for example, it promises to notify any customer within 24 hours if the order cannot be filled immediately and give the customer an opportunity to cancel the order. In this case, a replicated PRODUCTS table becomes an excellent solution. Once a day, updates to the PRODUCTS table can be downloaded to the replicated copy at each of the five sites. During the day, orders are verified against the local copy of the PRODUCTS table, but only the local PRODUCTS table is updated. This prevents the company from taking an order for which there was not adequate stock on hand at the beginning of the day, but it does not prevent orders taken at two or three different sites from exceeding the available stock. The next night, when data communications costs are lower than they are during the day, the orders from each site are transmitted to a central site, which processes them against a central copy of the PRODUCTS table. Orders that cannot be filled from inventory are flagged, and a report of them is generated. When processing is complete, the updated PRODUCTS table, along with the problem orders report, is transmitted back to each of the five sites to prepare for the next day s processing. Which is the correct architecture for supporting the operation of this global business As the example shows, it is not so much a database architecture question as a business policy question. The interdependence of computer systems architectures and business operations is one of the reasons why decisions about replication and data distribution inevitably make certain types of business operations easier and others harder.
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In many cases, it s possible to structure an application that involves replicated data so that conflicts between replica updates are avoided or greatly minimized. The DBMS conflict resolution rules are then applied as a last resort, when a conflict arises despite the design of the application. The next few sections describe some typical replicated table scenarios and the application structure that is often used in each scenario to minimize replication conflicts.
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