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Although the concept of NULL is not part of the relational model, virtually every relational database product supports the use of the NULL value. NULL is a special data element that can be stored in a database column. A NULL has no meaning, apparently distinguishing it from a blank, empty string, or zero value. This extension to SQL has been widely criticized on a number of grounds, most notably for the fact that it appears to confuse data with metadata and for the twisted logical system it implies. While this book is not intended to argue over such matters, the use of NULLs in the data warehouse causes significant difficulties when it comes to data analysis. These difficulties are evident in two situations: the use of NULL as a dimension value and the use of NULL as a foreign key column. Practical solutions can avoid some of these problems, although they exhibit some of the same issues to which theoreticians object.
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In most relational database management systems, the use of NULLs is allowed by default. Unless the database administrator explicitly specifies that a column must not contain NULL, it is possible to insert one. NULLs need not be explicitly inserted; when a row is added to a table, if a value is not specified for a particular column, it will default to NULL. Data warehouse practitioners often learn of the pitfalls of these NULL values the hard way. In developing reports, NULLs cause myriad problems that can make the calmest of analysts start pulling their hair out. The problems they encounter all stem from the special status of the NULL: it is not a value and has no meaning. This sounds innocent enough, until you look at what it means in terms of actually using the data. Understanding the headaches caused by NULLs does not require delving into the academic or philosophical underpinnings of the relational model. One need look no further than the impact of NULL on the process of writing queries. Because a NULL does not represent anything, it cannot be considered equal to anything else not even another NULL. At the same time, a NULL cannot be considered not equal to anything else.
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Any traditional comparison will fail if a NULL is involved. Database vendors have, therefore, added special features to SQL that permit testing for NULLs. Suppose, for example, that a customer table contains a column indicating whether a customer has tax exempt status. In keeping with 3 s advice to spell out flag values, assume that this column does not contain Boolean true/false values but instead contains the values Tax Exempt and Not Tax Exempt. Let us further assume that, for whatever reason, this value is not recorded for a particular customer, Hal Smith. In his case, the column contains a NULL. If you want to generate a report for all customers who do not have a tax exempt status, you probably want Hal Smith to be counted. Unschooled in the dangers of the NULL, you might try to use the not equal comparison operator:
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WHERE tax_exampt_status <> "Tax Exempt"
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WHERE tax_exampt_status <> "Tax Exempt" OR tax_exempt_status IS NULL
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Combining this with additional comparisons now creates an exercise in the balancing of parentheses. Similar issues are faced when searching strings to ensure they do not contain a particular value, using greater than or less than comparison operators, and so forth. The problems extend into aggregation as well; if you count customers with and without tax exempt status, the total will match the number of customers. When NULLs are stored in the database, analysts need to jump through a lot of hoops to create accurate reports. Needless to say, the potential for error is strong, and business users can certainly not be expected to construct reports properly, even when using a business intelligence tool. The presence of NULLs therefore increases the report creation burden shouldered by the data warehouse team. Even when trained analysts are responsible for producing all reports, it can be confusing just explaining what a report means when NULLs are allowed. NULLs also make for unusual conversations between developers and users. Imagine having to ask the question Do you want customers with a tax_exempt status of Not Tax Exempt, or would you prefer to include customers who do not have a tax exempt status This question will be rewarded with a blank stare. The largest problem created by NULLs cannot be avoided by entrusting all database interaction to developers. While a well-trained analyst can deal with NULLs, there is nothing to stop businesspeople from looking at two reports and misinterpreting the results. If one report shows January sales for tax exempt customers and another shows January sales for customers who are not tax exempt, a reasonable assumption would be that the two figures together represent all of January sales. Unfortunately, this is not the case.
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