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When the compiler switches /clr or /clr:pure are used, three exception models must be considered. Obviously, managed exceptions can be thrown when managed code is executed. Native functions called from managed code may throw either C++ exceptions or Win32 structured exception handling (SEH) exceptions. C++/CLI supports handling all of these exceptions in managed code. This will be discussed later in this chapter. The compiler switches /EHs and /EHa can be used to turn on exception handling capabilities. The /EHs switch turns on only C++ exception handling. When you use /EHa, C++ exceptions and Win32 SEH exceptions are supported. The /EHa switch is required for managed compilation with /clr and /clr:pure. Using /EHs causes a compiler error.

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NVL: The NVL function replaces the value in a table column with the value after the comma if the column is null. Thus, the NVL function takes care of column values if the column values are null and converts them to non-null values: SQL> SELECT last_name, title, salary * NVL (commission_pct,0)/100 COMM FROM employees; COALESCE: This function is similar to NVL, but it returns the first non-null value in the list: SQL> COALESCE(region1, region2, region3, region4) DECODE: This function is used to incorporate basic if-then functionality into SQL code. The following example assigns a party name to all the voters in the table based on the value in the affiliation column. If there is no value under the affiliation column, the voter is listed as an independent: SQL> SELECT DECODE(affiliation, 'D', 'Democrat', 'R', 'Republican', 'Independent') FROM voters; CASE: This function provides the same functionality as the DECODE function, but in a much more intuitive and elegant way. Here s a simple example of using the CASE statement, which helps you incorporate if-then logic into your code: SQL> SELECT ename, (CASE deptno WHEN 10 THEN 'Accounting' WHEN 20 THEN 'Research' WHEN 30 THEN 'Sales' WHEN 40 THEN 'Operations' ELSE 'Unknown' END) department FROM employees;

Oracle s SQL analytical functions are powerful tools for business intelligence applications. Oracle claims a potential improvement of 200 to 500 percent in query performance with the use of the SQL analytical functions. The purpose behind using analytical functions is to perform complex summary computations without using a lot of code. Here are the main SQL analytical functions of the Oracle database: Ranking functions: These enable you to rank items in a data set according to some criteria. Oracle has several types of ranking functions, including RANK, DENSE_RANK, CUME_DIST, PERCENT_RANK, and NTILE. Listing A-1 shows a simple example of how a ranking function can help you rank some sales data. Listing A-1. An Example of a Ranking Function SQL> SELECT sales_type, TO_CHAR(SUM(amount_sold), '9,999,999,999') SALES, RANK() OVER (ORDER BY SUM(amount_sold) ) AS original_rank, RANK() OVER (ORDER BY SUM(amount_sold) DESC NULLS LAST) AS derived_rank FROM sales, products, customers, time_frame, sales_types WHERE sales.prod_id=products.prod_id AND sales.cust_id=customers.cust_id AND

sales.time_id=time_frame.time_id AND sales.sales_type_id=sales_types.sales_type_id AND timeframe.calendar_month_desc IN ('2005-07', '2005-08') AND country_id='INDIA' GROUP BY sales_type; SALES_TYPE SALES ORIGINAL_RANK DERIVED_RANK -------------------- -------------- ------------ --------------Direct Sales 5,744,263 5 1 Internet 3,625,993 4 2 Catalog 1,858,386 3 3 Partners 1,500,213 2 4 Tele Sales 604,656 1 5 SQL> Moving-window aggregates: These functions provide cumulative sums and moving averages. Period-over-period comparisons: These functions let you compare two periods (for example, How does the first quarter of 2005 compare with the first quarter of 2006 in terms of percentage growth ). Ratio-to-report comparisons: These make it possible to compare ratios (for example, What is August s enrollment as a percentage of the entire year s enrollment ). Statistical functions: These functions calculate correlations and regression functions so you can see cause and effect relationships among data. Inverse percentiles: These help you find the data corresponding to a percentile value (for example, Get me the names of the salespeople who correspond to the median sales value. ). Hypothetical ranks and distributions: These help you figure out how a new value for a column fits into existing data in terms of its rank and distribution. Histograms: These functions return the number of the histogram data appropriate for each row in a table. First/last aggregates: These functions are appropriate when you are using the GROUP BY clause to sort data into groups. Aggregate functions let you specify the sort order for the groups.

The file locations defined with these variables may vary from installation to installation, so you ll need to make the appropriate changes for your site..