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The SELECT statement for simple queries like this one includes only the two required clauses. The SELECT clause names the requested columns; the FROM clause names the table that contains them. Conceptually, SQL processes the query by going through the table named in the FROM clause, one row at a time, as shown in Figure 6-3. For each row, SQL takes the values of the columns requested in the select list and produces a single row of query results. The query results thus contain one row of data for each row in the table.

6. Optionally, select Query | Perform Index Analysis to rerun the same query and to make recommendations on index implementation. This option works only for certain types of queries that can be analyzed by SQL Query Analyzer.

Access Language Structured Query Language (SQL)

It is often useful to be able to limit the resources used by a single query or transaction in SQL Server. For example, executing a query that asks for the sum of all values in a 3GB table would be quite costly to perform. Other users would suffer from slow response times, and the database server itself might be significantly slowed. In many cases, such a transaction might be executed by mistake. If the transaction must be carried out, it is a good idea to schedule it to occur at a later time. SQL Server 7 includes a server configuration parameter that can allow an administrator to limit the resources that can be used by a single operation. This option, the query governor cost limit, sets the longest time (in seconds) that a query may run. To set these options for all databases on a server, you can use the sp_configure stored procedure, as follows:

Interactive (OLAP)

Figure 6-3: Simple query processing (no WHERE clause)

Before this setting will take effect, you need to stop and restart SQL Server. Finally, the query governor cost limit can be set on a per-transaction basis by using the following statement as part of a transaction:

Still other tools assemble cubes from the results of queries against a relational database. This enables them to offer limited slicing and dicing in the context of a business intelligence tool. These are sometimes referred to as ROLAP products. The distinctions between multidimensional and relational technology are beginning to fade. Many tools that we used to think of as relational database management systems (RDBMS) now incorporate multidimensional storage capabilities, often as a way to provide increased performance for relational data. These database products are now better described by the more general term database management system (DBMS). Additional innovations include SQL extensions for interaction with dimensional structures, the automated generation of cubes from tables, and the rewrite of queries from the SQL access language to MDX or vice versa. Vendors in these hybrid environments are also boosting the scalability of the cube, by allowing the database administrator to control the amount of precalculated intersections that are stored.

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Each of the three data warehouse architectures from 2, Data Warehouse Architectures, can be adapted to incorporate the use of cubes Used as a primary data store, the cube replaces a star schema to store dimensional data; as a derived data store, it supplements a star When cubes are used as the primary storage for dimensional data, the solution may be limited by the scalability of the cube A smaller number of dimensional attributes may be practical, and the grain of the cube may be limited This may be a concern in a stand-alone data mart, or in a dimensional data warehouse architecture, where the dimensional data store is loaded directly from operational data Any aggregation performed during this step represents analytic detail lost The benefits may include the more expedient delivery of a solution and a high-performance analytic environment.

A value of 0 will set no limit on the maximum query execution time. Any values greater than 0 will specify the number of seconds that a query may run. Note that the same query might take differing amounts of time to complete, based on server hardware configurations.

This trade-off has lesser significance in a Corporate Information Factory, where a separate repository of atomic data is always maintained Typically, the cube will replace relational storage as the primary data store in specific subject areas where the data sets are smaller These subject areas are often supported by packaged solutions that provide a very tight analytic experience within this limited domain A common example is budgeting, where vendors offer prebuilt analytics based on dimensional storage Instead of displacing relational storage, the cube can also be used to supplement it This practice allows the cube to participate in any of the architectures described in 2 as an additional layer By supplementing relational storage with one or more cubes, developers can ensure comprehensive storage of detailed dimensional data in stars, while taking advantage of the performance and expressive capability of the cube TIP Stars and cubes work well together.