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If a source record can undergo multiple significant type 2 changes between loads, or if it will be necessary to load past history into the fact table, this procedure will be ineffective. Knowing which version of a product is current at the end of the day is not enough to determine which should be assigned to a transaction. It will be necessary to replace the current_flag in the dimension design with an effective date and time and expiration date and time. These values can be used to pinpoint the precise version of the product record that was in effect at the time of each transaction. If the dimension design allows for type 2 changes and does not include a current_ version or time stamps, the lookup will be much more complicated. It will be necessary to include all the type 2 attributes in the lookup. In this example, it would necessitate knowing the brand_code and brand_name associated with each transaction. These attribute values would be included in the product lookup. TIP Before transactions can be inserted into a fact table, they must be assigned the appropriate foreign key values. For each dimension, the natural key value associated with the transaction is used as an index into the dimension table, and the corresponding surrogate key is retrieved. If type 2 changes are allowed, a natural key value may appear in the dimension more than once. The lookup must be supplemented to identify the correct version. Dimension design can aid this process: Adding a current_version column to the dimension makes it easy to identify the most recent version for a natural key value. Adding effective and expiration dates makes it easy to identify the correct version for a natural key value at any point in time.

A value of less than 10% indicates additional storage space is needed. If high, disk performance may not be sufficient. High values indicate many and/or large file transfers to and from the server. Indicates current activity; used to compare loads on different machines. If sustained greater than 40%, performance may be decreasing. Large file transfers may be occurring to/from this server.

If neither of these mechanisms is available, it will be necessary to fetch all type 2 attributes with source transactions and include all of them in the lookup process.

*These guidelines should be taken as generalizations for sustained levels. It is common for pages/second, for example, to go above the recommended value for short periods of time.

request The database engine on the server processes the request and scans the database, which also resides on the server When the result is calculated, the database engine sends it back across the network as a single reply to the initial request, and the front-end application displays it on the PC screen The client/server architecture reduces the network traffic and splits the database workload User-intensive functions, such as handling input and displaying data, are concentrated on the user's PC Data-intensive functions, such as file I/O and query processing, are concentrated in the database server Most importantly, the SQL language provides a well-defined interface between the front-end and back-end systems, communicating database access requests in an efficient manner By the mid-1990s, these advantages made the client/server architecture the most popular scheme for implementing new applications.

Developers are almost always under pressure to develop a load process that is fast and efficient. For loads that operate in a batch, the available time window is likely to be shared with processes that construct derived and aggregate tables, build cubes, rebuild indexes, execute scheduled reports, and cache their results. For a real-time load, the continuous flow of new transactions results in a similar requirement for processing efficiency. Various kinds of bottlenecks are possible when loading a star schema, but some are fundamental. These result from the need to perform slow change processing. In the worstcase scenario, it is necessary to inspect each source record for new and changed dimensions. Doing so may require as many as three different comparisons to data in the dimension table, as Figure 17-2 illustrated. For example, each time the product dimension is updated, it may be necessary to review all products in the source system for new records (step 1.4), type 1 changes (step 1.5), and type 2 changes (step 1.7). Some of these comparisons, in turn, may be resource-intensive. Finding type 2 changes to a customer record, for example, may require comparing dozens of type 2 attributes. The fundamental bottlenecks of slow change processing can be addressed in two ways: reduce the amount of data to be processed, and simplify the necessary processing. Many

Table 3-5.