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The periodic snapshot need not be limited to storing facts that measure status. While this kind of fact is the raison d etre of the periodic snapshot, additional facts may also prove useful. Facts that summarize the snapshot period can prevent the need to refer to the transaction star when working with the snapshot. In some cases, it is possible that a nonadditive fact, which cannot be reduced to fully additive components, may also be called for. The monthly snapshot in Figure 11-4 illustrates these enhancements. When reporting on end-of-period status, we often want to know what the status was at the beginning of the period. By storing period-begin and period-end balances, we avoid the need to perform correlated subqueries or reporting tricks to provide this data in a single row. The fact table in Figure 11-4 provides both period-begin and period-end balances. Both facts are semi-additive. They are also redundant; each period-begin balance will be the same as the period-end balance recorded for the account in the snapshot of the prior period. It can also be useful to provide summary-level information on the activities that occurred during the period. These may include counts of different kinds of transactions, as well as their total magnitudes. In Figure 11-4, additional facts have been added to indicate the number of credit and debit transactions during the snapshot period, as well as the total dollar amount

must basically do the same processing to interpret the BIGORDERS view definition. As these examples show, the SQL1 standard and mainstream DBMS products today are relatively restrictive in their permitted use of expressions involving individual data items, sets of data items, rows, and tables. The SQL2 standard includes a number of advanced query capabilities that are focused on removing these restrictions, and making the SQL language more general. The spirit of these SQL2 capabilities tends to be "a user should be able to write a query expression that 'makes sense' and have the query expression be a legal SQL query." Because these SQL2 capabilities constitute a major expansion of the language over the SQL1 standard, most of them are required at a Full Compliance level of the standard only.

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MONTH ACCOUNT_MONTHLY_ SNAPSHOT_FACTS month_key account_key branch_key_account acct_holder_key_primary period_begin_balance period_end_balance number_transactions credit_amount debit_amount average_daily_balance period_balance_sum Semi-additive facts (Not additive across months) Additive facts Nonadditive fact Fully additive alternative to nonadditive fact is preferable, can be used to compute averages across other dimensions

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Scalar-Valued Expressions (SQL2)

Figure 11-4

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of each kind of transaction Notice that these four facts are fully additive; they can be meaningfully aggregated across any and all dimensions, including the snapshot period There may also be interest in recording one or more nonadditive facts with the snapshot For example, the bank may be interested in the average daily balance of an account over the snapshot period The average daily balance is nonadditive; it makes no sense to add together averages across accounts, time periods, or any other dimension If the average daily balance was to be included in each snapshot record, it would only be useful when studying individual accounts The average daily balance can be decomposed into fully additive components: number of days and a sum of account balances This latter measurement can be stored in the fact table.

The simplest extended query capabilities in SQL2 are those that provide more data manipulation and calculation power involving individual data values (called scalars in the SQL2 standard). Within the SQL language, individual data values tend to have three sources: The value of an individual column within an individual row of a table A literal value, such as 125.7 or "ABC" A user-supplied data value, entered into a program In this SQL query: SELECT NAME, EMPL_NUM, HIRE_DATE, (QUOTA * .9) FROM SALESREPS WHERE (REP_OFFICE = 13) OR TITLE = 'VP SALES' the column names NAME, EMPL_NUM, HIRE_DATE, and QUOTA generate individual data values for each row of query results, as do the column names REP_OFFICE and TITLE in the WHERE clause. The numbers .9 and 13 and the character string "VP SALES" similarly generate individual data values. If this SQL statement appeared within an embedded SQL program (described in 17), the program variable office_num might contain an individual ata value, and the query might appear as: SELECT NAME, EMPL_NUM, HIRE_DATE, (QUOTA * .9) FROM SALESREPS WHERE (REP_OFFICE = :office_num) OR TITLE = 'VP SALES' As this query and many previous examples have shown, individual data values can be combined in simple expressions, like the calculated value QUOTA * .9. To these basic SQL1 expressions, SQL2 adds the CAST operator for explicit data type conversion, the CASE operator for decision-making, the NULLIF operation for conditionally creating a NULL value, and the COALESCE operator for conditionally creating non-NULL values.

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