How can I avoid NULLs in my database, while also representing missing data?

In SQL and Relational Theory (C.J. Date, 2009) chapter 4 advocates avoiding duplicate rows, and also to avoid NULL attributes in the data we store. While I have no troubles avoiding duplicate rows, I am struggling to see how I can model data without making use of NULL. Take the following, for example – which is a bit from work.

We have an artist table, which has, amongst other columns, a gender column. This is a foreign key to the gender table. However, for some artists, we don’t know their gender – for example we’ve been given a list of new music which has no descriptions of the artist. How, without using NULL is one meant to represent this data? The only solution I see is to add a new gender, “unknown”, to the gender table.

While I am thoroughly enjoying this book, I was really disappointed when the chapter concluded with:

Of course, if nulls are prohibited, then missing information will have to be handled by some other means. Unfortunately, those other means are much too complex to be discussed in detail here.

Which is a real shame – because this was the solution I was waiting to read about! There is a reference to read the appendix which has lots of publications to read, but I was hoping for a little bit more of a down to earth summary before I dived into reading these.

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I’m getting a few people commenting that they don’t understand why I wish to avoid ‘NULL’ so I will quote the book again. Take the following query:

SELECT s.sno, p.pno
  FROM s, p
 WHERE s.city <> p.city
    OR p.city <> 'Paris'

Now, take the example that s.city is London, and p.city is Paris. In this case, London <> Paris, so the query is true. Now take the case that p.city is not Paris, and is infact xyz. In this case, (London <> xyz) OR (xyz <> Paris) is also True. So, given any data – this query is true. However, if xyz is ‘NULL’ the scenario changes. In this case both of these expressions are neither True nor False, they are in fact, Unknown. And in this case because the result is unknown you will not get any rows returned.

The move from 2 value logic to 3 value logic can easily introduce bugs like this. Infact, I just introduced one at work which motivated this very post. I wanted all rows where the type != 0 However, this actually ends up matching type == 0 OR type IS NULL – confusing behavior.

Whether or not I model my data with or without NULL in the future is unclear, but I’m very curious what the other solutions are. (I too have always been of the argument that if you don’t know, you should use NULL).

Answer

Good on you, for eliminating Nulls. I have never allowed Nulls in any of my databases.

Of course, if nulls are prohibited, then missing information will have to be handled by some other means. Unfortunately, those other means are much too complex to be discussed in detail here.

Actually it is not so hard at all. There are three alternatives.

1. Here’s a paper on How To Handle Missing Information Without Using NULL by H Darwen, that may help to get your head around the problem.

   1.1. Sixth Normal Form is the answer. But you do not have to normalise your entire database to 6NF. For each column that is optional, you need a child table off the main table, with just the PK, which is also the FK, because it is a 1::0-1 relation. Other than the PK, the only column is the optional column.

   Look at this Data Model; AssetSerial on page 4 is a classic case: not allAssets have SerialNumbers; but when they do, I want them to store them; more important I want to ensure that they are Unique.

   (For the OO people out there, incidentally, that is a three level class diagram in Relational notation, a “Concrete Table Inheritance”, no big deal, we’ve had it fro 30 years.)

   1.2. For each such table, use a View to provide the 5NF form of the table. Sure, use Null (or any value that is appropriate for the column) to identify the absence of the column for any row. But do not update via the view.

   1.3 Do not use straight joins to grab the 6NF column. Do not use outer joins, either (and have the server fill in a Null for the missing rows). Use a subquery to populate the column, and specify the value that you want returned for a missing value (except if you have Oracle, because its Subquery processing is even worse than its set processing). Eg. and just an eg. you can convert a numeric column to string, and use “Missing” for the missing rows.

When you do not want to go that far (6NF), you have two more options.

2. You can use Null substitutes. I use CHAR(0) for character colomns and 0 for numeric. But I do not allow that for FKs. Obviously you need a value that is outside the normal range of data. This does not allow Three Valued Logic.

3. In addition to (2), for each Nullable column, you need a boolean Indicator. For the example of the Sex column, the Indicator would be something like SexIsMissing or SexLess (sorry). This allows very tight Three Valued Logic. Many people in that 5% like it because the db remains at 5NF (and less tables); the columns with missing info are loaded with values that are never used; they are only used if the Indicator is false. If you have an enterprise db, you can wrap that in a Function, and always use the UDF, not the raw column.

Of course, in all cases, you can never get away from writing code that is required to handle the missing info. Whether it is ISNULL(), or a subquery for the 6NF column, or an Indicator to check before using the value, or an UDF.

If Null has a specific meaning … then it is not a Null! By definition, Null is the Unknown Value.