Missing Data

Overview

Teaching: 15 min
Exercises: 15 min

Questions

• How do databases represent missing information?

• What special handling does missing information require?

Objectives

• Explain how databases represent missing information.

• Explain the three-valued logic databases use when manipulating missing information.

• Write queries that handle missing information correctly.

Real-world data is never complete — there are always holes. Databases represent these holes using a special value called null. null is not zero, False, or the empty string; it is a one-of-a-kind value that means “nothing here”. Dealing with null requires a few special tricks and some careful thinking.

By default, SQLite does not display NULL values in its output. The .nullvalue command causes SQLite to display the value you specify for NULLs. We will use the value -null- to make the NULLs easier to see:

.nullvalue -null-

To start, let’s have a look at the Visit table. There are eight records, but #752 doesn’t have a date — or rather, its date is null:

SELECT * FROM Visit;

Null doesn’t behave like other values. If we select the records that come before 1930:

SELECT * FROM Visit WHERE visit_date < '1930-01-01';

we get two results, and if we select the ones that come during or after 1930:

SELECT * FROM Visit WHERE visit_date >= '1930-01-01';

we get five, but record #752 isn’t in either set of results. The reason is that null<'1930-01-01' is neither true nor false: null means, “We don’t know,” and if we don’t know the value on the left side of a comparison, we don’t know whether the comparison is true or false. Since databases represent “don’t know” as null, the value of null<'1930-01-01' is actually null. null>='1930-01-01' is also null because we can’t answer to that question either. And since the only records kept by a WHERE are those for which the test is true, record #752 isn’t included in either set of results.

Comparisons aren’t the only operations that behave this way with nulls. 1+null is null, 5*null is null, log(null) is null, and so on. In particular, comparing things to null with = and != produces null:

SELECT * FROM Visit WHERE visit_date = NULL;

produces no output, and neither does:

SELECT * FROM Visit WHERE visit_date != NULL;

To check whether a value is null or not, we must use a special test IS NULL:

SELECT * FROM Visit WHERE visit_date IS NULL;

or its inverse IS NOT NULL:

SELECT * FROM Visit WHERE visit_date IS NOT NULL;

Null values can cause headaches wherever they appear. For example, suppose we want to find all the salinity measurements that weren’t taken by Lake. It’s natural to write the query like this:

SELECT * FROM Measurement WHERE type = 'sal' AND person_id != 'lake';

but this query filters omits the records where we don’t know who took the measurement. Once again, the reason is that when person is null, the != comparison produces null, so the record isn’t kept in our results. If we want to keep these records we need to add an explicit check:

SELECT * FROM Measurement WHERE type = 'sal' AND (person_id != 'lake' OR person_id IS NULL);

We still have to decide whether this is the right thing to do or not. If we want to be absolutely sure that we aren’t including any measurements by Lake in our results, we need to exclude all the records for which we don’t know who did the work.

In contrast to arithmetic or Boolean operators, aggregation functions that combine multiple values, such as min, max or avg, ignore null values. In the majority of cases, this is a desirable output: for example, unknown values are thus not affecting our data when we are averaging it. Aggregation functions will be addressed in more detail in the next section.

Sorting by Known Date

Write a query that sorts the records in Visit by date, omitting entries for which the date is not known (i.e., is null).

Solution

SELECT * FROM Visit WHERE visit_date IS NOT NULL ORDER BY visit_date ASC;

visit_id	site_name	visit_date
619	DR-1	1927-02-08
622	DR-1	1927-02-10
734	DR-3	1930-01-07
735	DR-3	1930-01-12
751	DR-3	1930-02-26
837	MSK-4	1932-01-14
844	DR-1	1932-03-22

NULL in a Set

What do you expect the following query to produce?

SELECT * FROM Visit WHERE visit_date IN ('1927-02-08', NULL);

What does it actually produce?

Solution

You might expect the above query to return rows WHERE visit_date is either ‘1927-02-08’ or NULL. Instead it only returns rows WHERE visit_date is ‘1927-02-08’, the same as you would get from this simpler query:

SELECT * FROM Visit WHERE visit_date IN ('1927-02-08');

The reason is that the IN operator works with a set of values, but NULL is by definition not a value and is therefore simply ignored.

If we wanted to actually include NULL, we would have to rewrite the query to use the IS NULL condition:

SELECT * FROM Visit WHERE visit_date = '1927-02-08' OR visit_date IS NULL;

Pros and Cons of Sentinels

Some database designers prefer to use a sentinel value to mark missing data rather than null. For example, they will use the date “0000-00-00” to mark a missing date, or -1.0 to mark a missing salinity or radiation reading (since actual readings cannot be negative). What does this simplify? What burdens or risks does it introduce?

Key Points

• Databases use a special value called NULL to represent missing information.

• Almost all operations on NULL produce NULL.

• Queries can test for NULLs using IS NULL and IS NOT NULL.