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With a star-schema structure, a relational database conceptually provides a good foundation for managing data for business analysis The capability to freely relate information within the database based solely on data values is a good match for the ad hoc, unstructured queries that typify business intelligence applications But there are some serious mismatches between typical business intelligence queries and the capabilities of the core SQL language For example: I Data ordering Many business intelligence queries deal explicitly or implicitly with data ordering they pose questions like What is the top 10 percent What are the top 10 or Which are the worst performing As a set-oriented language, SQL manipulates unordered sets of rows The only support for sorting and ordering data within standard SQL is the ORDER BY clause in the SELECT statement, which is applied only at the end of all other set-oriented processing I Time series.

There is typically a restriction that you cannot transfer control out of a loop or a conditional testing statement, and always a prohibition against transferring control into the middle of such a statement. As in structured programming languages, the use of GOTO statements is discouraged, because it makes stored procedure code harder to understand and debug.

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Many business intelligence queries compare values based on time contrasting this year s results to last year s, or this month s results to the same month last year, or computing year-over-year growth rates, for example It is very hard, and sometimes impossible, to get side-by-side comparisons of data from different time periods within a single row of standard SQL query results, depending on the structure of the underlying database I Comparison to aggregate values Many business intelligence queries compare values for individual entities (for example, office sales results) to an overall total, or to subtotals (such as regional results) These comparisons are difficult to express in standard SQL A report format showing line-item detail, subtotals, and totals is impossible to generate directly from SQL, since all rows of query results must have the same column structure.

One common need for repetition of statements within a stored procedure is when the procedure executes a query and needs to process the query results, row by row All of the major dialects provide a structure for this type of processing Conceptually, the structures parallel the DECLARE CURSOR, OPEN CURSOR, FETCH, and CLOSE CURSOR statements in embedded SQL or in the corresponding SQL API calls However, instead of fetching the query results into the application program, in this case, they are being fetched into the stored procedure, which is executing within the DBMS itself Instead of retrieving the query results into application program variables (host variables), the stored procedure retrieves them into local stored procedure variables To illustrate this capability, assume that you want to populate two tables with data from the ORDERS table One table, named BIGORDERS, should contain customer name and order size for any orders over $10,000.

To deal with these issues, DBMS products on data warehousing have tended to extend the core SQL language For example, the DBMS from Red Brick, one of the data warehousing pioneers and now a part of Informix s product line (which has, in turn,.

The other, SMALLORDERS, should contain the salesperson s name and order size for any orders under $1000 The best and most efficient way to do this would be to use two separate SQL INSERT statements with subqueries, but for purposes of illustration, consider this method instead: 1 Execute a query to retrieve the order amount, customer name, and salesperson name for each order 2 For each row of query results, check the order amount to see whether it falls into the proper range for including in the BIGORDERS or SMALLORDERS tables 3 Depending on the amount, INSERT the appropriate row into the BIGORDERS or SMALLORDERS table 4 Repeat Steps 2 and 3 until all rows of query results are exhausted 5 Commit the updates to the database Figure 20-13 shows an Oracle stored procedure that carries out this method.

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The cursor that defines the query is defined early in the procedure and assigned the name O_CURSOR The variable CURS_ROW is defined as an Oracle row type It is a structured Oracle row variable with individual components (like a C-language structure) By declaring it as having the same row type as the cursor, the individual components of CURS_ROW have the same data types and names as the cursor s query results columns The query described by the cursor is actually carried out by the cursor-based FOR loop It basically tells the DBMS to carry out the query described by the cursor (equivalent to the OPEN statement in embedded SQL) before starting the loop processing.

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