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Alur, Deepak, Dan Malks, and John Crupi. Core J2EE Design Patterns. New York: Prentice Hall 2001. Beck, Kent. Extreme Programming Explained. Boston: Addison-Wesley, 2000. Coulouris, George, Jean Dollimore, and Tim Kindberg. Distributed Systems Concepts and Design. London: Addison-Wesley, 2001. John Davies, Rod Johnson, Cedric Buest, Tyler Jewell, Andrew Longshaw, et al. Professional Java Server Programming J2EE 1.3 Edition. Birmingham, UK: Wrox Press Ltd., 2001. Fowler, Martin, and Kendall Scott. UML Distilled. Boston: Addison-Wesley, 2000. Gamma, Erich, Richard Helm, Ralph Johnson, and John Vlissides. Design Patterns. Boston: Addison-Wesley, 1995. Hatcher, Erik, and Steve Loughran. Java Software Development with Ant. Greenwich, CT: Manning Publications, 2002. Husted, Ted. Scaffolding: Developing Web Applications with Struts. Greenwich, CT: Manning Publications, 2002. Kay, Michael. XSLT. Birmingham, UK: Wrox Press Ltd., 2001. Kruchten, Phillippe. The Rational Unified Process: An Introduction. New York: AddisonWesley, 2000. McLaughlin, Brett. Java & XML, 2d ed. Cambridge: O Reilly and Associates, 2001. Mohr, Steven, Jonathan Pinnock, and Brian Loesgen. Professional XML. Birmingham, UK: Wrox Press Ltd., 2000. Mullender, Sape, ed. Distributed Systems. New York: Addison-Wesley, 1998.

7

Using Your Mobile PC in Meetings The hand below the folder indicates that the folder is now being shared.

T-SQL programming often involves the need to materialize data temporarily . Temporary tables are just one solution; other ways for handling an independent physical or logical materialization of a set include table variables and table expressions such as views, inline user-defined functions (UDFs), derived tables, and common table expressions (CTEs) . You might need to physically persist interim states of your data for performance reasons, or just as a staging area . Examples of such scenarios include:

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Materializing aggregated data to some level of granularity (for example, employee and month), and issuing running, sliding, and other statistical reports against that data Materializing a result of a query for paging purposes Materializing result sets of interim queries, and querying the materialized data Materializing the result of a query with the GROUPING SETS, CUBE and ROLLUP options, and issuing queries against that data Walking through the output of a cursor and saving information you read or calculate per row for further manipulation Pivoting data from an Open Schema environment to a more traditional form, and issuing queries against the pivoted data Creating a result set that contains a hierarchy with additional attributes such as materialized paths or levels, and issuing reports against the result Holding data that needs to be scrubbed before it can be inserted

Seely, Scott. SOAP. New York: Prentice Hall, 2001. Vint, Danny. XML Family of Specifications. Greenwich, CT: Manning Publications, 2002. Vought, Eric, and Armin Begtrup. Unit Testing for Java Programmers: Using JUnit and Ant. Greenwich, CT: Manning Publications, 2002. Wesley, Ajamu. Web Services Explained. Greenwich, CT: Manning Publications, 2002.

One of the benefits of materializing data in a temporary table is that it can be more compact than the base data, with preprocessed calculations, and you can index it when it might be inefficient or impractical to index all the base data . In terms of performance, you typically benefit from materializing the data when you need to access it multiple times, but in some cases, even when all you have is a single query against the data, you benefit . You might also need to materialize interim sets logically in virtual temporary tables (table expressions) to develop solutions in a modular approach . I ll show examples in this chapter that address this need as well . Either way, there are many cases in which using temporary tables, table variables, or table expressions can be useful .

There s a lot of confusion around choosing the appropriate type of temporary object for a given task, and there are many myths regarding the differences between temporary tables and table variables . Furthermore, temporary tables and table variables are often misused because of lack of knowledge of efficient set-based programming . In this chapter, I will try to provide you with a clear picture of how the different temporary object types behave, in which circumstances you should use each, and whether you should use them at all . At the end of the chapter, I ll provide a summary table (Table 7-1) that contrasts and compares the different types . This table covers the factors you should take into consideration before making your choice .

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