Firebird Null Guide
NULL behaviour and pitfalls in Firebird SQL
Paul Vinkenoog
Version 1.2, 30 June 2020
Table of Contents
1. What is NULL? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê4
1.1. NULL as a default state. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê4
2. NULL support in Firebird SQL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê6
2.1. Disallowing NULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê6
2.2. Testing for NULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê6
2.3. Assigning NULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê7
2.4. Testing DISTINCTness (Firebird 2+) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê7
2.5. The NULL literal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê8
2.5.1. Firebird 1.5 and below. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê8
2.5.2. Firebird 2.0 and up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê8
3. NULL in operations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê9
3.1. Mathematical and string operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê9
3.2. Boolean operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê10
3.3. More logic (or not) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê11
4. Internal functions and directives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê13
4.1. Internal functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê13
4.2. FIRST, SKIP and ROWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê13
5. Predicates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê14
5.1. The IN predicate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê14
5.1.1. With an empty list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê14
5.1.2. With a NULL test expression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê15
5.1.3. With NULLs in the list. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê15
5.1.4. IN() results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê16
5.1.5. IN() in CHECK constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê17
5.2. The ANY, SOME and ALL quantifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê17
5.2.1. Result values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê18
5.3. EXISTS and SINGULAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê20
5.3.1. EXISTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê20
5.3.2. SINGULAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê20
6. Searches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê22
7. Sorts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê24
8. Aggregate functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê25
8.1. The GROUP BY clause . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê26
8.1.1. Counting frequencies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê27
8.2. The HAVING clause . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê27
9. Conditional statements and loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê29
9.1. IF statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê29
9.2. CASE expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê30
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9.3. WHILE loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê31
9.4. FOR loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê31
10. Keys and unique indices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê33
10.1. Primary keys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê33
10.2. Unique keys and indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê33
10.2.1. Firebird 1.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê33
10.2.2. Firebird 1.5 and higher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê33
10.3. Foreign keys. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê33
11. CHECK constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê35
12. SELECT DISTINCT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê37
13. User-Defined Functions (UDFs). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê38
13.1. NULL <=> non-NULL conversions you didn’t ask for . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê38
13.2. Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê38
13.3. Improvements in Firebird 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê39
13.3.1. “Upgrading” ib_udf functions in an existing database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê39
13.4. Being prepared for undesired conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê40
13.5. More on UDFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê40
14. Converting to and from NULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê41
14.1. Substituting NULL with a value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê41
14.1.1. The COALESCE function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê41
14.1.2. Firebird 1.0: the *NVL functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê41
14.2. Converting values to NULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê42
14.2.1. Firebird 1.5 and up: the NULLIF function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê42
14.2.2. Firebird 1.0: the *nullif UDFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê43
15. Altering populated tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê44
15.1. Adding a non-nullable field to a populated table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê44
15.1.1. Adding a NOT NULL field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê44
15.1.2. Adding a CHECKed column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê46
15.1.3. Using domains to add a non-nullable field. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê47
15.2. Making existing columns non-nullable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê47
15.2.1. Making an existing column NOT NULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê48
15.2.2. Adding a CHECK constraint to an existing column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê48
15.3. Making non-nullable columns nullable again. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê48
15.3.1. Removing a NOT NULL constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê49
15.3.2. Removing a CHECK constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê50
16. Changing the nullability of a domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê52
16.1. Adding and removing domain-level CHECK constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê52
16.2. Altering a domain’s NOT NULL setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê52
17. Testing for NULL and equality in practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê54
17.1. Testing for NULL — if it matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê54
17.2. Equality tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê55
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17.2.1. Firebird 2.0 and up. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê55
17.2.2. Earlier Firebird versions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê55
17.2.3. Summary of (in)equality tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê56
17.3. Finding out if a field has changed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê57
18. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê58
Appendix A: NULL-related bugs in Firebird . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
Bugs that crash the server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
EXECUTE STATEMENT with NULL argument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
EXTRACT from NULL date . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
FIRST and SKIP with NULL argument. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
LIKE with NULL escape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
Other bugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
NULLs in NOT NULL columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
Illegal NULLs returned as 0, '', etc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê60
Primary key with NULL entries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê61
SUBSTRING results described as non-nullable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê61
Gbak -n restoring NOT NULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê61
IN, = ANY and = SOME with indexed subselect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê61
ALL with indexed subselect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê61
SELECT DISTINCT with wrong NULLS FIRST|LAST ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê62
UDFs returning values when they should return NULL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê62
UDFs returning NULL when they should return a value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê62
SINGULAR inconsistent with NULL results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê62
Appendix B: Document history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê63
Appendix C: License notice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê65
Alphabetical index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê66
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Chapter 1. What is NULL?
Time and again, support questions pop up on the Firebird mailing lists about “strange things”
happening with NULLs. The concept seems difficult to grasp — perhaps partly because of the name,
which suggests a kind of “nothing” that won’t do any harm if you add it to a number or stick it to
the back of a string. In reality, performing such operations will render the entire expression NULL.
This guide explores the behaviour of NULL in Firebird SQL, points out common pitfalls and shows
you how to deal safely with expressions that contain NULL or may resolve to NULL.
If you only need a quick reference to refresh your memory, go to the summary at the end of the
guide.
So — what is it?
In SQL, NULL is not a value. It is a state indicating that an item’s value is unknown or nonexistent. It
is not zero or blank or an “empty string” and it does not behave like any of these values. Few things
in SQL lead to more confusion than NULL, and yet its workings shouldn’t be hard to understand as
long as you stick to the following simple definition: NULL means unknown.
Let me repeat that:
NULL means UNKNOWN
Keep this line in mind as you read through the rest of the guide, and most of the seemingly illogical
results you can get with NULL will practically explain themselves.
A few sentences and examples in this guide were taken from the Firebird Quick
Start Guide, first published by IBPhoenix, now part of the Firebird Project.
1.1. NULL as a default state
Because NULL means “value unknown”, it is the logical default state for any field or variable that has
been created but not provided with a value:
• If you declare a variable in a stored procedure or trigger, its value is undefined and its state is
NULL from the moment of creation until some value is assigned to it. The same is true for output
parameters in stored procedures.
• If you insert a record into a table and you only provide values for part of the fields, the
remaining fields will be initialised to NULL except where a default value is in effect or a value is
assigned by a “before insert” trigger.
• If you add a column to a table that already has records, the fields added to the existing records
will be NULL, except if you declare the column as NOT NULL and specify a default value for it. Note
Chapter 1. What is NULL?
4
that both conditions must be satisfied for the fields to become anything other than NULL.
Chapter 1. What is NULL?
5
Chapter 2. NULL support in Firebird SQL
Only a few language elements are purposely designed to give an unambiguous result with NULL
(unambiguous in the sense that some specific action is taken and/or a non-NULL result is returned).
They are discussed in the following paragraphs.
2.1. Disallowing NULL
In a column or domain definition, you can specify that only non-NULL values may be entered by
adding NOT NULL to the definition:
create table MyTable ( i int not null )
create domain DTown as varchar( 32 ) not null
alter table Sales add TransTime timestamp not null
Adding a NOT NULL column to an existing table that already contains records requires special care.
This operation will be discussed in detail in the section Altering populated tables.
2.2. Testing for NULL
If you want to know whether a variable, field or other expression is NULL, use the following syntax:
<expression> IS [NOT] NULL
Examples:
if ( MyField is null ) then YourString = 'Dunno'
select * from Pupils where PhoneNumber is not null
select * from Pupils where not ( PhoneNumber is null )
/* does the same as the previous example */
Chapter 2. NULL support in Firebird SQL
6
update Numbers set Total = A + B + C where A + B + C is not null
delete from Phonebook where PhoneNum is null
Do not use “… = NULL” to test for nullness. This syntax is illegal in Firebird versions up to 1.5.n, and
gives the wrong result in Firebird 2 and up: it returns NULL no matter what you compare. This is by
design, incidentally, and in that sense it’s not really wrong — it just doesn’t give you what you want.
The same goes for “… <> NULL”, so don’t use that either; use IS NOT NULL instead.
IS NULL and IS NOT NULL always return true or false; they never return NULL.
2.3. Assigning NULL
Setting a field or variable to NULL is done with the ‘=’ operator, just like assigning values. You can
also include NULL in an insert list or use it as input parameter to a stored procedure (both are special
types of assignments).
if ( YourString = 'Dunno' ) then MyField = null
update Potatoes set Amount = null where Amount < 0
insert into MyTable values ( 3, '8-May-2004', NULL, 'What?' )
select * from ProcessThis(88, 'Apes', Null)
Remember:
• You cannot — and should not — use the comparison operator ‘=’ to test if something is NULL…
• …but you can — and often must — use the assignment operator ‘=’ to set something to NULL.
2.4. Testing DISTINCTness (Firebird 2+)
In Firebird 2 and higher only, you can test for the null-encompassing equality of two expressions
with “IS [NOT] DISTINCT FROM”:
if ( A is distinct from B ) then...
Chapter 2. NULL support in Firebird SQL
7
if ( Buyer1 is not distinct from Buyer2 ) then...
Fields, variables and other expressions are considered:
• DISTINCT if they have different values or if one of them is NULL and the other isn’t;
• NOT DISTINCT if they have the same value or if both of them are NULL.
[NOT] DISTINCT always returns true or false, never NULL or something else.
With earlier Firebird versions, you have to write special code to obtain the same information. This
will be discussed later.
2.5. The NULL literal
The ability to use NULL literals depends on your Firebird version.
2.5.1. Firebird 1.5 and below
In Firebird 1.5 and below you can only use the literal word “NULL” in a few situations, namely the
ones described in the previous paragraphs plus a few others such as “cast( NULL as <datatype> )”
and “select NULL from MyTable”.
In all other circumstances, Firebird will complain that NULL is an unknown token. If you really must
use NULL in such a context, you have to resort to tricks like “cast( NULL as int )”, or using a field or
variable that you know is NULL, etc.
2.5.2. Firebird 2.0 and up
Firebird 2 allows the use of NULL literals in every context where a normal value can also be entered.
You can e.g. include NULL in an IN() list, write expressions like “if ( MyField = NULL ) then…”, and
so on. However, as a general rule you should not make use of these new possibilities! In almost
every thinkable situation, such use of NULL literals is a sign of poor SQL design and will lead to NULL
results where you meant to get true or false. In that sense the earlier, more restrictive policy was
safer, although you could always bypass it with casts etc. — but at least you had to take deliberate
steps to bypass it.
Chapter 2. NULL support in Firebird SQL
8
Chapter 3. NULL in operations
As many of us have found out to our chagrin, NULL is contagious: use it in a numerical, string or
date/time operation, and the result will invariably be NULL. With boolean operators, the outcome
depends on the type of operation and the value of the other operand.
Please remember that in Firebird versions prior to 2.0 it is mostly illegal to use the constant NULL
directly in operations or comparisons. Wherever you see NULL in the expressions below, read it as “a
field, variable or other expression that resolves to NULL”. In Firebird 2 and above this expression
may also be a NULL literal.
3.1. Mathematical and string operations
The operations in this list always return NULL:
• 1 + 2 + 3 + NULL
• 5 * NULL - 7
• 'Home ' || 'sweet ' || NULL
• MyField = NULL
• MyField <> NULL
• NULL = NULL
If you have difficulty understanding why, remember that NULL means “unknown”. You can also look
at the following table where per-case explanations are provided. In the table we don’t write NULL in
the expressions (as said, this is often illegal); instead, we use two entities A and B that are both NULL.
A and B may be fields, variables, or even composite subexpressions — as long as they’re NULL, they’ll
all behave the same in the enclosing expressions.
Table 1. Operations on null entities A and B
If A and B are NULL, then: Is: Because:
1 + 2 + 3 + A NULL
If A is unknown, then 6 + A is also unknown.
5 * A - 7 NULL
If A is unknown, then 5 * A is also unknown. Subtract
7 and you end up with another unknown.
'Home ' || 'sweet ' || A NULL
If A is unknown, 'Home sweet ' || A is unknown.
MyField = A NULL
If A is unknown, you can’t tell if MyField has the same
value…
MyField <> A NULL
...but you also can’t tell if MyField has a different value!
A = B NULL
With A and B unknown, it’s impossible to know if they
are equal.
Chapter 3. NULL in operations
9
Here is the complete list of math and string operators that return NULL if at least one operand is
NULL:
• +, -, *, and /
• !=, ~=, and ^= (synonyms of <>)
• <, ⇐, >, and >=
• !<, ~<, and ^< (low-precedence synonyms of >=)
• !>, ~>, and ^> (low-precedence synonyms of ⇐)
• ||
• [NOT] BETWEEN
• [NOT] STARTING WITH
• [NOT] LIKE
• [NOT] CONTAINING
The explanations all follow the same pattern: if A is unknown, you can’t tell if it’s greater than B; if
string S1 is unknown, you can’t tell if it contains S2; etcetera.
Using LIKE with a NULL escape character would crash the server in Firebird versions up to and
including 1.5. This bug was fixed in v.1.5.1. From that version onward, such a statement will yield
an empty result set.
3.2. Boolean operations
All the operators examined so far return NULL if any operand is NULL. With boolean operators, things
are a bit more complex:
• not NULL = NULL
• NULL or false = NULL
• NULL or true = true
• NULL or NULL = NULL
• NULL and false = false
• NULL and true = NULL
• NULL and NULL = NULL
In version 2.5 and earlier, Firebird SQL doesn’t have a boolean data type; nor are true and false
existing constants. In the leftmost column of the explanatory table below, “true” and “false”
represent expressions (fields, variables, composites…) that evaluate to true/false.
Table 2. Boolean operations on null entity A
Chapter 3. NULL in operations
10
If A is NULL, then: Is: Because:
not A NULL
If A is unknown, its inverse is also unknown.
A or false NULL
“A or false” always has the same value as A — which is
unknown.
A or true true
“A or true” is always true — A's value doesn’t matter.
A or A NULL
“A or A” always equals A — which is NULL.
A and false false
“A and false” is always false — A's value doesn’t matter.
A and true NULL
“A and true” always has the same value as A — which is
unknown.
A and A NULL
“A and A” always equals A — which is NULL.
All these results are in accordance with boolean logic. The fact that you don’t need to know X's
value to compute “X or true” and “X and false” is also the basis of a feature found in various
programming languages: short-circuit boolean evaluation.
The above results can be generalised as follows for expressions with one type of binary boolean
operator (and | or) and any number of operands:
Disjunctions (“A or B or C or D or …”)
1. If at least one operand is true, the result is true.
2. Else, if at least one operand is NULL, the result is NULL.
3. Else (i.e. if all operands are false) the result is false.
Conjunctions (“A and B and C and D and …”)
1. If at least one operand is false, the result is false.
2. Else, if at least one operand is NULL, the result is NULL.
3. Else (i.e. if all operands are true) the result is true.
Or, shorter:
• TRUE beats NULL in a disjunction (OR-operation);
• FALSE beats NULL in a conjunction (AND-operation);
• In all other cases, NULL wins.
If you have trouble remembering which constant rules which operation, look at the second letter:
tRue prevails with oR — fAlse with And.
3.3. More logic (or not)
The short-circuit results obtained above may lead you to the following ideas:
Chapter 3. NULL in operations
11
• 0 times x equals 0 for every x. Hence, even if x's value is unknown, 0 * x is 0. (Note: this only
holds if x’s datatype only contains numbers, not `NaN or infinities.)
• The empty string is ordered lexicographically before every other string. Therefore, S >= '' is
true whatever the value of S.
• Every value equals itself, whether it’s unknown or not. So, although A = B justifiably returns
NULL if A and B are different NULL entities, A = A should always return true, even if A is NULL. The
same goes for A ⇐ A and A >= A.
By analogous logic, A <> A should always be false, as well as A < A and A > A.
• Every string contains itself, starts with itself and is like itself. So, “S CONTAINING S”, “S STARTING
WITH S” and “S LIKE S” should always return true.
How is this reflected in Firebird SQL? Well, I’m sorry I have to inform you that despite this
compelling logic — and the analogy with the boolean results discussed above — the following
expressions all resolve to NULL:
• 0 * NULL
• NULL >= '' and '' ⇐ NULL
• A = A, A ⇐ A and A >= A
• A <> A, A < A and A > A
• S CONTAINING S, S STARTING WITH S and S LIKE S
So much for consistency.
Chapter 3. NULL in operations
12
Chapter 4. Internal functions and directives
4.1. Internal functions
The following built-in functions return NULL if at least one argument is NULL:
• CAST()
• EXTRACT()
• GEN_ID()
• SUBSTRING()
• UPPER()
• LOWER()
• BIT_LENGTH()
• CHAR[ACTER]_LENGTH()
• OCTET_LENGTH()
• TRIM()
Notes
• In 1.0.0, EXTRACT from a NULL date would crash the server. Fixed in 1.0.2.
• If the first argument to GEN_ID is a valid generator name and the second
argument is NULL, the named generator keeps its current value.
• In versions up to and including 2.0, SUBSTRING results are sometimes returned
as “false emptystrings”. These strings are in fact NULL, but are described by the
server as non-nullable. Therefore, most clients show them as empty strings. See
the bugs list for a detailed description.
4.2. FIRST, SKIP and ROWS
The following two directives crash a Firebird 1.5.n or lower server if given a NULL argument. In
Firebird 2, they treat NULL as the value 0:
• FIRST
• SKIP
This new Firebird 2 directive returns an empty set if any argument is NULL:
• ROWS
In new code, use ROWS, not FIRST and SKIP.
Chapter 4. Internal functions and directives
13
Chapter 5. Predicates
Predicates are statements about objects that return a boolean result: true, false or unknown (= NULL).
In computer code you typically find predicates in places where a yes/no type of decision has to be
taken. For Firebird SQL, that means in WHERE, HAVING, CHECK, CASE WHEN, IF and WHILE clauses.
Comparisons such as “x > y” also return boolean results, but they are generally not called
predicates, although this is mainly a matter of form. An expression like Greater( x, y ) that does
exactly the same would immediately qualify as a predicate. (Mathematicians like predicates to have
a name — such as “Greater” or just plain “G” — and a pair of parentheses to hold the arguments.)
Firebird supports the following SQL predicates: IN, ANY, SOME, ALL, EXISTS and SINGULAR.
It is also perfectly defensible to call “IS [NOT] NULL” and “IS [NOT] DISTINCT FROM”
predicates, despite the absence of parentheses. But, predicates or not, they have
already been introduced and won’t be discussed in this section.
5.1. The IN predicate
The IN predicate compares the expression on its left-hand side to a number of expressions passed in
the argument list and returns true if a match is found. NOT IN always returns the opposite of IN.
Some examples of its use are:
select RoomNo, Floor from Classrooms where Floor in (3, 4, 5)
delete from Customers where upper(Name) in ('UNKNOWN', 'NN', '')
if ( A not in (MyVar, MyVar + 1, YourVar, HisVar) ) then ...
The list can also be generated by a one-column subquery:
select ID, Name, Class from Students
Ê where ID in (select distinct LentTo from LibraryBooks)
5.1.1. With an empty list
If the list is empty (this is only possible with a subquery), IN always returns false and NOT IN always
returns true, even if the test expression is NULL. This makes sense: even if a value is unknown, it is
certain not to occur in an empty list.
Chapter 5. Predicates
14
5.1.2. With a NULL test expression
If the list is not empty and the test expression — called “A” in the examples below — is NULL, the
following predicates will always return NULL, regardless of the expressions in the list:
• A IN ( Expr1, Expr2, …, ExprN )
• A NOT IN ( Expr1, Expr2, …, ExprN )
The first result can be understood by writing out the entire expression as a disjunction (OR-chain) of
equality tests:
A=Expr1 or A=Expr2 or ... or A=ExprN
which, if A is NULL, boils down to
NULL or NULL or ... or NULL
which is NULL.
The nullness of the second predicate follows from the fact that “not (NULL)” equals NULL.
5.1.3. With NULLs in the list
If A has a proper value, but the list contains one or more NULL expressions, things become a little
more complicated:
• If at least one of the expressions in the list has the same value as A:
◦ “A IN( Expr1, Expr2, …, ExprN )” returns true
◦ “A NOT IN( Expr1, Expr2, …, ExprN )” returns false
This is due to the fact that “true or NULL” returns true (see above). Or, more general: a
disjunction where at least one of the elements is true, returns true even if some other elements
are NULL. (Any falses, if present, are not in the way. In a disjunction, true rules.)
• If none of the expressions in the list have the same value as A:
◦ “A IN( Expr1, Expr2, …, ExprN )” returns NULL
◦ “A NOT IN( Expr1, Expr2, …, ExprN )” returns NULL
This is because “false or NULL” returns NULL. In generalised form: a disjunction that has only
false and NULL elements, returns NULL.
Needless to say, if neither A nor any list expression is NULL, the result is always as expected and can
only be true or false.
Chapter 5. Predicates
15
5.1.4. IN() results
The table below shows all the possible results for IN and NOT IN. To use it properly, start with the
first question in the left column. If the answer is No, move on to the next line. As soon as an answer
is Yes, read the results from the second and third columns and you’re done.
Table 3. Results for “A [NOT] IN (<list>)”
Conditions Results
IN() NOT IN()
Is the list empty?
false true
Else, is A NULL?
NULL NULL
Else, is at least one list element equal to A?
true false
Else, is at least one list element NULL?
NULL NULL
Else (i.e. all list elements are non-NULL and unequal to A)
false true
In many contexts (e.g. within IF and WHERE clauses), a NULL result behalves like false in that the
condition is not satisfied when the test expression is NULL. On the one hand this is convenient for
cases where you might expect false but NULL is returned: you simply won’t notice the difference. On
the other hand, this may also lead you to expect true when the expression is inverted (using NOT)
and this is where you’ll run into trouble. In that sense, the most “dangerous” case in the above table
is when you use an expression of the type “A NOT IN (<list>)”, with A indeed not present in the list
(so you’d expect a clear true result), but the list happens to contain one or more NULLs.
Be especially careful if you use NOT IN with a subselect instead of an explicit list,
e.g.
A not in ( select Number from MyTable )
If A is not present in the Number column, the result is true if no Number is NULL, but
NULL if the column does contain a NULL entry. Please be aware that even in a
situation where A is constant and its value is never contained in the Number column,
the result of the expression (and therefore your program flow) may still vary over
time according to the absence or presence of NULLs in the column. Hours of
debugging fun! Of course you can avoid this particular problem simply by adding
“where Number is not NULL” to the subselect.
Bug alert
All Firebird versions before 2.0 contain a bug that causes [NOT] IN to return the
wrong result if an index is active on the subselect and one of the following
conditions is true:
• A is NULL and the subselect doesn’t return any NULLs, or
• A is not NULL and the subselect result set doesn’t contain A but does contain
NULL(s).
Chapter 5. Predicates
16
Please realise that an index may be active even if it has not been created explicitly,
namely if a key is defined on A.
Example: Table TA has a column A with values { 3, 8 }. Table TB has a column B
containing { 2, 8, 1, NULL }. The expressions:
A [not] in ( select B from TB )
should both return NULL for A = 3, because of the NULL in B. But if B is indexed, IN
returns false and NOT IN returns true. As a result, the query
select A from TA where A not in ( select B from TB )
returns a dataset with one record — containing the field with value 3 — while it
should have returned an empty set. Other errors may also occur, e.g. if you use
“NOT IN” in an IF, CASE or WHILE statement.
As an alternative to NOT IN, you can use “<> ALL”. The ALL predicate will be
introduced shortly.
5.1.5. IN() in CHECK constraints
The IN() predicate is often used in CHECK constraints. In that context, NULL expressions have a
surprisingly different effect in Firebird versions 2.0 and up. This will be discussed in the section
CHECK constraints.
5.2. The ANY, SOME and ALL quantifiers
Firebird has two quantifiers that allow you to compare a value to the results of a subselect:
• ALL returns true if the comparison is true for every element in the subselect.
• ANY and SOME (full synonyms) return true if the comparison is true for at least one element in the
subselect.
With ANY, SOME and ALL you provide the comparison operator yourself. This makes it more flexible
than IN, which only supports the (implicit) ‘=’ operator. On the other hand, ANY, SOME and ALL only
accept a subselect as an argument; you can’t provide an explicit list, as with IN.
Valid operators are =, !=, <, >, =<, ⇒ and all their synonyms. You can’t use LIKE, CONTAINING, IS
DISTINCT FROM, or any other operators.
Some usage examples:
•
select name, income from blacksmiths
Chapter 5. Predicates
17
Ê where income > any( select income from goldsmiths )
(returns blacksmiths who earn more than at least one goldsmith)
•
select name, town from blacksmiths
Ê where town != all( select distinct town from goldsmiths )
(returns blacksmiths who live in a goldsmithless town)
•
if ( GSIncome !> some( select income from blacksmiths ) )
Ê then PoorGoldsmith = 1;
Ê else PoorGoldsmith = 0;
(sets PoorGoldsmith to 1 if at least one blacksmith’s income is not less than the value of
GSIncome)
5.2.1. Result values
If the subselect returns an empty set, ALL returns true and ANY|SOME return false, even if the left-
hand side expression is NULL. This follows from the definitions and the rules of formal logic. (Math-
heads will already have noticed that ALL is equivalent to the universal (“A”) quantifier and ANY|SOME
to the existential (“E”) quantifier.)
For non-empty sets, you can write out “A <op> {ANY|SOME} (<subselect>)” as
A <op> E1 or A <op> E2 or ... or A <op> En
with <op> the operator used and E1, E2 etc. the items returned by the subquery.
Likewise, “A <op> ALL (<subselect>)” is the same as
A <op> E1 and A <op> E2 and ... and A <op> En
This should look familiar. The first writeout is equal to that of the IN predicate, except that the
operator may now be something other than ‘=’. The second is different but has the same general
form. We can now work out how nullness of A and/or nullness of subselect results affect the
outcome of ANY|SOME and ALL. This is done in the same way as earlier with IN, so instead of including
all the steps here we will just present the result tables. Again, read the questions in the left column
from top to bottom. As soon as you answer a question with “Yes”, read the result from the second
column and you’re done.
Chapter 5. Predicates
18
Table 4. Results for “A <op> ANY|SOME (<subselect>)”
Conditions Result
ANY|SOME
Does the subselect return an empty set?
false
Else, is A NULL?
NULL
Else, does at least one comparison return true?
true
Else, does at least one comparison return NULL?
NULL
Else (i.e. all comparisons return false)
false
If you think these results look a lot like what we saw with IN(), you’re right: with the ‘=’ operator,
ANY is the same as IN. In the same way, “<> ALL” is equivalent to NOT IN.
Bug alert (revisited)
In versions before 2.0, “= ANY” suffers from the same bug as IN. Under the “right”
circumstances, this can lead to wrong results with expressions of the type “NOT A =
ANY( … )”.
On the bright side, “<> ALL” is not affected and will always return the right result.
Table 5. Results for “A <op> ALL (<subselect>)”
Conditions Result
ALL
Does the subselect return an empty set?
true
Else, is A NULL?
NULL
Else, does at least one comparison return false?
false
Else, does at least one comparison return NULL?
NULL
Else (i.e. all comparisons return true)
true
ALL bug
Although “<> ALL” always works as it should, ALL should nevertheless be
considered broken in all pre-2.0 versions of Firebird: with every operator other
than “<>”, wrong results may be returned if an index is active on the
subselect — with or without NULLs around.
Strictly speaking, the second question in both tables (“is A NULL?”) is redundant and
can be dropped. If A is NULL, all the comparisons return NULL, so that situation will
be caught a little later. And while we’re at it, we could drop the first question too:
the “empty set” situation is just a special case of the final “else”. The whole thing
then once again boils down to “true beats NULL beats false” in disjunctions (ANY
|SOME) and “false beats NULL beats true” in conjunctions (ALL).
Chapter 5. Predicates
19
The reason we included those questions is convenience: you can see if a set is
empty at a glance, and it’s also easier to check if the left-hand side expression is
NULL than to evaluate each and every comparison result. But do feel free to skip
them, or to skip just the second. Do not, however, skip the first question and start
with the second: this will lead to a wrong conclusion if the set is empty!
5.3. EXISTS and SINGULAR
The EXISTS and SINGULAR predicates return information about a subquery, usually a correlated
subquery. You can use them in WHERE, HAVING, CHECK, CASE, IF and WHILE clauses (the latter two are
only available in PSQL, Firebird’s stored procedure and trigger language).
5.3.1. EXISTS
EXISTS tells you whether a subquery returns at least one row of data. Suppose you want a list of
farmers who are also landowners. You could get one like this:
SELECT Farmer FROM Farms WHERE EXISTS
Ê (SELECT * FROM Landowners
Ê WHERE Landowners.Name = Farms.Farmer)
This query returns the names of all farmers who also figure in the Landowners table. The EXISTS
predicate returns true if the result set of the subselect contains at least one row. If it is empty, EXISTS
returns false. EXISTS never returns NULL, because a result set always either has rows, or hasn’t. Of
course the subselect’s search condition may evolve to NULL for certain rows, but that doesn’t cause
any uncertainty: such a row won’t be included in the subresult set.
In reality, the subselect doesn’t return a result set at all. The engine simply steps
through the Landowners records one by one and applies the search condition. If it
evaluates to true, EXISTS returns true immediately and the remaining records
aren’t checked. If it evaluates to false or NULL, the search continues. If all the
records have been searched and there hasn’t been a single true result, EXISTS
returns false.
NOT EXISTS always returns the opposite of EXISTS: false or true, never NULL. NOT EXISTS returns false
immediately if it gets a true result on the subquery’s search condition. Before returning true it must
step through the entire set.
5.3.2. SINGULAR
SINGULAR is an InterBase/Firebird extension to the SQL standard. It is often described as returning
true if exactly one row in the subquery meets the search condition. By analogy with EXISTS this
would make you expect that SINGULAR too will only ever return true or false. After all, a result set
Chapter 5. Predicates
20
has either exactly 1 row or a different number of rows. Unfortunately, all versions of Firebird up to
and including 2.0 have a bug that causes NULL results in a number of cases. The behaviour is pretty
inconsistent, but at the same time fully reproducible. For instance, on a column A containing (1,
NULL, 1), a SINGULAR test with subselect “A=1” returns NULL, but the same test on a column with (1, 1,
NULL) returns false. Notice that only the insertion order is different here!
To make matters worse, all versions prior to 2.0 sometimes return NULL for NOT SINGULAR where
false or true is returned for SINGULAR. In 2.0, this at least doesn’t happen anymore: it’s either false
vs. true or twice NULL.
The code has been fixed for Firebird 2.1; from that version onward SINGULAR will return:
• false if the search condition is never true (this includes the empty-set case);
• true if the search condition is true for exactly 1 row;
• false if the search condition is true for more than 1 row.
Whether the other rows yield false, NULL or a combination thereof, is irrelevant.
NOT SINGULAR will always return the opposite of SINGULAR (as is already the case in 2.0).
In the meantime, if there’s any chance that the search condition may evolve to NULL for one or more
rows, you should always add an IS NOT NULL condition to your [NOT] SINGULAR clauses, e.g. like this:
... SINGULAR( SELECT * from MyTable
Ê WHERE MyField > 38
Ê AND MyField IS NOT NULL )
Chapter 5. Predicates
21
Chapter 6. Searches
If the search condition of a SELECT, UPDATE or DELETE statement resolves to NULL for a certain row, the
effect is the same as if it had been false. Put another way: if the search expression is NULL, the
condition is not met, and consequently the row is not included in the output set (or is not
updated/deleted).
The search condition or search expression is the WHERE clause minus the WHERE
keyword itself.
Some examples (with the search condition in boldface):
SELECT Farmer, Cows FROM Farms WHERE Cows > 0 ORDER BY Cows
The above statement will return the rows for farmers that are known to possess at least one cow.
Farmers with an unknown (NULL) number of cows will not be included, because the expression
“NULL > 0” returns NULL.
SELECT Farmer, Cows FROM Farms WHERE NOT (Cows > 0) ORDER BY Cows
Now, it’s tempting to think that this will return “all the other records” from the Farms table, right?
But it won’t — not if the Cows column contains any NULLs. Remember that not(NULL) is itself NULL. So
for any row where Cows is NULL, “Cows > 0” will be NULL, and “NOT (Cows > 0)” will be NULL as well.
SELECT Farmer, Cows, Sheep FROM Farms WHERE Cows + Sheep > 0
On the surface, this looks like a query returning all the farms that have at least one cow and/or
sheep (assuming that neither Cows nor Sheep can be a negative number). However, if farmer Fred
has 30 cows and an unknown number of sheep, the sum Cows + Sheep becomes NULL, and the entire
search expression boils down to “NULL > 0”, which is… you got it. So despite his 30 cows, our friend
Fred won’t make it into the result set.
As a last example, we shall rewrite the previous statement so that it will return any farm which has
at least one animal of a known kind, even if the other number is NULL. To do that, we exploit the fact
that “NULL or true” returns true — one of the rare occasions where a NULL operand doesn’t render
the entire expression NULL:
SELECT Farmer, Cows, Sheep FROM Farms WHERE Cows > 0 OR Sheep > 0
This time, Fred’s thirty cows will make the first comparison true, while the sheep bit is still NULL. So
we have “true or NULL”, which is true, and the row will be included in the output set.
Chapter 6. Searches
22
Chapter 7. Sorts
In Firebird 2, NULLs are considered “smaller” than anything else when it comes to sorting.
Consequently, they come first in ascending sorts and last in descending sorts. You can override this
default placement by adding a NULLS FIRST or NULLS LAST directive to the ORDER BY clause.
In earlier versions, NULLs were always placed at the end of a sorted set, no matter whether the order
was ascending or descending. For Firebird 1.0, that was the end of the story: NULLs would always
come last in any sorted set, period. Firebird 1.5 introduced the NULLS FIRST/LAST syntax, so you
could force them to the top or bottom.
To sum it all up:
Table 6. NULLs placement in ordered columns
Ordering NULLs placement
Firebird 1 Firebird 1.5 Firebird 2
order by Field [asc]
bottom bottom top
order by Field desc
bottom bottom bottom
order by Field [asc | desc] nulls
first
— top top
order by Field [asc | desc] nulls
last
— bottom bottom
Specifying NULLS FIRST on an ascending or NULLS LAST on a descending sort in Firebird 2 is of course
rather pointless, but perfectly legal. The same is true for NULLS LAST on any sort in Firebird 1.5.
• If you override the default NULLs placement, no index will be used for sorting.
In Firebird 1.5, that is the case with NULLS FIRST. In 2.0 and higher, with NULLS
LAST on ascending and NULLS FIRST on descending sorts.
• If you open a pre-2.0 database with Firebird 2, it will show the old NULL
ordering behaviour (that is: at the bottom, unless overridden by NULLS FIRST). A
backup-restore cycle will fix this, provided that at least the restore is executed
with Firebird 2’s gbak!
• Firebird 2.0 has a bug that causes the NULLS FIRST|LAST directive to fail under
certain circumstances with SELECT DISTINCT. See the bugs list for more details.
Don’t be tempted into thinking that, because NULL is now the “smallest thing” in
sorts, an expression like “NULL < 3” will return true in Firebird 2 and up. It won’t.
Using NULL in this kind of expression will always give a NULL outcome.
Chapter 7. Sorts
24
Chapter 8. Aggregate functions
The aggregate functions — COUNT, SUM, AVG, MAX, MIN and LIST — don’t handle NULL in the same way as
ordinary functions and operators. Instead of returning NULL as soon as a NULL operand is
encountered, they only take non-NULL fields into consideration while computing the outcome. That
is, if you have this table:
MyTable
ID Name Amount
1 John 37
2 Jack
NULL
3 Jim 5
4 Joe 12
5 Josh
NULL
...the statement select sum(Amount) from MyTable returns 54, which is 37 + 5 + 12. Had all five fields
been summed, the result would have been NULL. For AVG, the non-NULL fields are summed and the
sum divided by the number of non-NULL fields.
There is one exception to this rule: COUNT(*) returns the count of all rows, even rows whose fields
are all NULL. But COUNT(FieldName) behaves like the other aggregate functions in that it only counts
rows where the specified field is not NULL.
Another thing worth knowing is that COUNT(*) and COUNT(FieldName) never return NULL: if there are
no rows in the set, both functions return 0. COUNT(FieldName) also returns 0 if all FieldName fields in
the set are NULL. The other aggregate functions return NULL in such cases. Be warned that SUM even
returns NULL if used on an empty set, which is contrary to common logic (if there are no rows, the
average, maximum and minimum are undefined, but the sum is known to be zero).
Now let’s put all that knowledge in a table for your easy reference:
Table 7. Aggregate function results with different column states
Function Results
Empty set All-NULL set or
column
Other sets or columns
COUNT(*)
0 Total number of
rows
Total number of rows
COUNT(Field)
0 0 Number of rows where Field is not
NULL
MAX, MIN
NULL NULL
Max or min value found in the column
SUM NULL NULL
Sum of non-NULL values in the column
Chapter 8. Aggregate functions
25
AVG NULL NULL
Average of non-NULL values in the
column. This equals SUM(Field) /
COUNT(Field).
[1]
LIST
[2]
NULL NULL
Comma-separated string
concatenation of non-NULL values in
the column
8.1. The GROUP BY clause
A GROUP BY clause doesn’t change the aggregate function logic described above, except that it is now
applied to each group individually rather than to the result set as a whole. Suppose you have a table
Employee, with fields Dept and Salary which both allow NULLs, and you run this query:
SELECT Dept, SUM(Salary) FROM Employee GROUP BY Dept
The result may look like this (the row where Dept is <null> may be at the top or bottom, depending
on your Firebird version):
DEPT SUM
====== =====================
<null> 219465.19
000 266643.00
100 155262.50
110 130442.81
115 13480000.00
120 <null>
121 110000.00
123 390500.00
First notice that the people whose department is unknown (NULL) are grouped together, although
you can’t say that they have the same value in the Dept field. But the alternative would have been to
give each of those records a “group” of their own. Not only would this possibly add a huge number
of lines to the output, but it would also defeat the purpose of grouping: those lines wouldn’t be
aggregates, but simple “SELECT Dept, Salary” rows. So it makes sense to group the NULL depts by
their state and the rest by their value.
Anyway, the Dept field is not what interests us most. What does the aggregate SUM column tell us?
That all salaries are non-NULL, except in department 120? No. All we can say is that in every
department except 120, there is at least one employee with a known salary in the database. Each
department may contain NULL salaries; in dept. 120 all the salaries are NULL.
You can find out more by throwing in one or more COUNT() columns. For instance, if you want to
know the number of NULL salaries in each group, add a column “COUNT(*) – COUNT(Salary)”.
Chapter 8. Aggregate functions
26
8.1.1. Counting frequencies
A GROUP BY clause can be used to report the frequencies with which values occur in a table. In that
case you use the same field name several times in the query statement. Let’s say you have a table TT
with a column A whose contents are { 3, 8, NULL, 6, 8, -1, NULL, 3, 1 }. To get a frequencies report, you
could use:
SELECT A, COUNT(A) FROM TT GROUP BY A
which would give you this result:
A COUNT
============ ============
Ê -1 1
Ê 1 1
Ê 3 2
Ê 6 1
Ê 8 2
Ê <null> 0
Oops — something went wrong with the NULL count, but what? Remember that COUNT(FieldName)
skips all NULL fields, so with COUNT(A) the count of the <null> group can only ever be 0. Reformulate
your query like this:
SELECT A, COUNT(*) FROM TT GROUP BY A
and the correct value will be returned (in casu 2).
8.2. The HAVING clause
HAVING clauses can place extra restrictions on the output rows of an aggregate query — just like
WHERE clauses do in record-by-record queries. A HAVING clause can impose conditions on any output
column or combination of columns, aggregate or not.
As far as NULL is concerned, the following two facts are worth knowing (and hardly surprising, I
would guess):
• Rows for which the HAVING condition evaluates to NULL won’t be included in the result set. (“Only
true is good enough.”)
• “HAVING <col> IS [NOT] NULL” is a legal and often useful condition, whether <col> is aggregate or
not. (But if <col> is non-aggregate, you may save the engine some work by changing HAVING to
WHERE and placing the condition before the “GROUP BY” clause. This goes for any condition on non-
aggregate columns.)
Chapter 8. Aggregate functions
27
For instance, adding the following clause to the example query from the “GROUP BY” paragraph:
...HAVING Dept IS NOT NULL
will prevent the first row from being output, whereas this one:
...HAVING SUM(Salary) IS NOT NULL
suppresses the sixth row (the one with Dept = 120).
[1] If Field is of an integer type, AVG is always rounded towards 0. For instance, 6 non-null INT records with a sum of -11 yield an
average of -1, not -2.
[2] LIST was added in Firebird 2.1
Chapter 8. Aggregate functions
28
Chapter 9. Conditional statements and loops
9.1. IF statements
If the test expression of an IF statement resolves to NULL, the THEN clause is skipped and the ELSE
clause — if present — executed. In other words, NULL and false have the same effect in this context.
So in situations where you would logically expect false but NULL is returned, no harm will be done.
However, we’ve already seen examples of NULL being returned where you would expect true, and
that does affect the flow of the code!
Below are some examples of the seemingly paradoxical (but perfectly correct) results you can get if
NULLs creep into your IF statements.
If you use Firebird 2 or higher, you can avoid all the pitfalls discussed here, simply
by using [NOT] DISTINCT instead of the ‘=’ and “<>” operators!
• Equals (‘=’)
if (a = b) then
Ê MyVariable = 'Equal';
else
Ê MyVariable = 'Not equal';
If a and b are both NULL, MyVariable will yet be “Not equal” after executing this code. The reason
is that the expression “a = b” yields NULL if at least one of them is NULL. With a NULL test
expression, the THEN block is skipped and the ELSE block executed.
• Not equals (‘<>’)
if (a <> b) then
Ê MyVariable = 'Not equal';
else
Ê MyVariable = 'Equal';
Here, MyVariable will be “Equal” if a is NULL and b isn’t, or vice versa. The explanation is
analogous to that of the previous example.
So how should you set up equality tests that do give the logical result under all circumstances, even
with NULL operands? In Firebird 2 you can use DISTINCT, as already shown (see Testing
DISTINCTness). With earlier versions, you’ll have to write some more code. This is discussed in the
section Equality tests, later on in this guide. For now, just remember that you have to be very
careful with IF conditions that may resolve to NULL.
Another aspect you shouldn’t forget is the following: a NULL test expression may behave like false in
Chapter 9. Conditional statements and loops
29
an IF condition, but it doesn’t have the value false. It’s still NULL, and that means that its inverse will
also be NULL — not “true”. As a consequence, inverting the test expression and swapping the THEN
and ELSE blocks may change the behaviour of the IF statement. In binary logic, where only true and
false can occur, such a thing could never happen.
To illustrate this, let’s refactor the last example:
• Not not equals (“not (.. <> ..)”)
if (not (a <> b)) then
Ê MyVariable = 'Equal';
else
Ê MyVariable = 'Not equal';
In the original version, if one operand was NULL and the other wasn’t (so they were intuitively
unequal), the result was “Equal”. Here, it’s “Not equal”. The explanation: one operand is NULL,
therefore “a <> b” is NULL, therefore “not(a <> b)” is NULL, therefore ELSE is executed. While this
result is correct where the original had it wrong, there’s no reason to rejoice: in the refactored
version, the result is also “Not equal” if both operands are NULL — something that the original
version “got right”.
Of course, as long as no operand in the test expression can ever be NULL, you can happily formulate
your IF statements like above. Also, refactoring by inverting the test expression and swapping the
THEN and ELSE blocks will always preserve the functionality, regardless of the complexity of the
expressions — as long as they aren’t NULL. What’s especially treacherous is when the operands are
almost always non-NULL, so in the vast majority of cases the results will be correct. In such a
situation those rare NULL cases may go unnoticed for a long time, silently corrupting your data.
9.2. CASE expression
Firebird introduced the CASE construct in version 1.5, with two syntactic variants. The first one is
called the simple syntax:
case <expression>
Ê when <exp1> then <result1>
Ê when <exp2> then <result2>
Ê ...
Ê [else <defaultresult>]
end
This one works more or less like a Pascal case or a C switch construct: <expression> is compared to
<exp1>, <exp2> etc., until a match is found, in which case the corresponding result is returned. If
there is no match and there is an ELSE clause, <defaultresult> is returned. If there is no match and
no ELSE clause, NULL is returned.
Chapter 9. Conditional statements and loops
30
It is important to know that the comparisons are done with the ‘=’ operator, so a null <expression>
will not match a null <expN>. If <expression> is NULL, the only way to get a non-NULL result is via the
ELSE clause.
It is OK to specify NULL (or any other valid NULL expression) as a result.
The second, or searched syntax is:
case
Ê when <condition1> then <result1>
Ê when <condition2> then <result2>
Ê ...
Ê [else <defaultresult>]
end
Here, the <conditionN>s are tests that give a ternary boolean result: true, false, or NULL. Once again,
only true is good enough, so a condition like “A = 3” — or even “A = null” — is not satisfied when A
is NULL. Remember though that “IS [NOT] NULL” never returns NULL: if A is NULL, the condition “A is
null” returns true and the corresponding <resultN> will be returned. In Firebird 2+ you can also use
“IS [NOT] DISTINCT FROM” in your conditions — this operator too will never return NULL.
9.3. WHILE loops
When evaluating the condition of a WHILE loop, NULL has the same effect as in an IF statement: if the
condition resolves to NULL, the loop is not (re)entered — just as if it were false. Again, watch out
with inversion using NOT: a condition like
while ( Counter > 12 ) do
will skip the loop block if Counter is NULL, which is probably what you want, but:
while ( not Counter > 12 ) do
will also skip if Counter is NULL. Maybe this is also exactly what you want — just be aware that these
seemingly complementary tests both exclude NULL counters.
9.4. FOR loops
To avoid any possible confusion, let us emphasise here that FOR loops in Firebird PSQL have a
totally different function than WHILE loops, or for loops in general programming languages. Firebird
FOR loops have the form:
Chapter 9. Conditional statements and loops
31
for <select-statement> into <var-list> do <code-block>
and they will keep executing the code block until all the rows from the result set have been
retrieved, unless an exception occurs or a BREAK, LEAVE or EXIT statement is encountered. Fetching a
NULL, or even row after row filled with NULLs, does not terminate the loop!
Chapter 9. Conditional statements and loops
32
Chapter 10. Keys and unique indices
10.1. Primary keys
NULLs are never allowed in primary keys. A column can only be (part of) a PK if it has been defined
as NOT NULL, either in the column definition or in a domain definition. Note that a “CHECK (XXX IS
NOT NULL)” constraint won’t do: you need a NOT NULL specifier right after the data type.
Firebird 1.5 has a bug that allows primary keys to be defined on a NOT NULL column
with NULL entries. How these NULLs can exist in such a column will be explained
later.
10.2. Unique keys and indices
10.2.1. Firebird 1.0
In Firebird 1.0, unique keys are subject to the same restrictions as primary keys: the column(s)
involved must be defined as NOT NULL. For unique indices, this is not necessary. However, when a
unique index is created the table may not contain any NULLs or duplicate values, or the creation will
fail. Once the index is in place, insertion of NULLs or duplicate values is no longer possible.
10.2.2. Firebird 1.5 and higher
In Firebird 1.5 and up, unique keys and unique indices allow NULLs, and what’s more: they even
allow multiple NULLs. With a single-column key or index, you can insert as many NULLs as you want
in that column, but you can insert each non-NULL value only once.
If the key or index is defined on multiple columns in Firebird 1.5 and higher:
• You can insert multiple rows where all the key columns are NULL;
• But as soon as one or more key columns are non-NULL, each combination of non-NULL values
must be unique in the table. Of course with the understanding that (1, NULL) is not the same as
(NULL, 1).
10.3. Foreign keys
Foreign keys as such impose no restrictions with respect to NULLs. Foreign key columns must always
reference a column (or set of columns) that is a primary key or a unique key. A unique index on the
referenced column(s) is not enough.
Chapter 10. Keys and unique indices
33
In versions up to and including 2.0, if you try to create a foreign key referencing a
target that is neither a primary nor a unique key, Firebird complains that no
unique index can been found on the target — even if such an index does exist. In
2.1, the message correctly states that no unique or primary key could be found.
Even if NULLs are absolutely forbidden in the target key (for instance if the target is a PK), the
foreign key column may still contain NULLs, unless this is prevented by additional constraints.
Chapter 10. Keys and unique indices
34
Chapter 11. CHECK constraints
It has been said several times in this guide that if test expressions return NULL, they have the same
effect as false: the condition is not satisfied. Starting at Firebird 2, this is no longer true for the
CHECK constraint. To comply with SQL standards, a CHECK is now passed if the condition resolves to
NULL. Only an unambiguous false outcome will cause the input to be rejected.
In practice, this means that checks like
check ( value > 10000 )
check ( upper( value ) in ( 'A', 'B', 'X' ) )
check ( value between 30 and 36 )
check ( ColA <> ColB )
check ( Town not like 'Amst%' )
...will reject NULL input in Firebird 1.5, but let it pass in Firebird 2. Existing database creation scripts
will have to be carefully examined before being used under Firebird 2. If a domain or column has
no NOT NULL constraint, and a CHECK constraint may resolve to NULL (which usually — but not
exclusively — happens because the input is NULL), the script has to be adapted. You can extend your
check constraints like this:
check ( value > 10000 and value is not null )
check ( Town not like 'Amst%' and Town is not null )
However, it’s easier and clearer to add NOT NULL to the domain or column definition:
create domain DCENSUS int not null check ( value > 10000 )
create table MyPlaces
(
Ê Town varchar(24) not null check ( Town not like 'Amst%' ),
Ê ...
Chapter 11. CHECK constraints
35
)
If your scripts and/or databases should function consistently under both old and new Firebird
versions, make sure that no CHECK constraint can ever resolve to NULL. Add “or … is null” if you
want to allow NULL input in older versions. Add NOT NULL constraints or “and … is not null”
restrictions to disallow it explicitly in newer Firebird versions.
Chapter 11. CHECK constraints
36
Chapter 12. SELECT DISTINCT
A SELECT DISTINCT statement considers all NULLs to be equal (NOT DISTINCT FROM each other), so if the
select is on a single column it will return at most one NULL.
As mentioned earlier, Firebird 2.0 has a bug that causes the NULLS FIRST|LAST directive to fail under
certain circumstances with SELECT DISTINCT. For more details, see the bugs list.
Chapter 12. SELECT DISTINCT
37
Chapter 13. User-Defined Functions (UDFs)
UDFs (User-Defined Functions) are functions that are not internal to the engine, but defined in
separate modules. Firebird ships with two UDF libraries: ib_udf (a widely used “InterBase library”)
and fbudf. You can add more libraries, e.g. by buying or downloading them, or by writing them
yourself. UDFs can’t be used out of the box; they have to be “declared” to the database first. This
also applies to the UDFs that come with Firebird itself.
13.1. NULL <=> non-NULL conversions you didn’t ask for
Teaching you how to declare, use, and write UDFs is outside the scope of this guide. However, we
must warn you that UDFs can occasionally perform unexpected NULL conversions. This will
sometimes result in NULL input being converted to a regular value, and other times in the
nullification of valid input like '' (an empty string).
The main cause of this problem is that with “old style” UDF calling (inherited from InterBase), it is
not possible to pass NULL as input to the function. When a UDF like LTRIM (left trim) is called with a
NULL argument, the argument is passed to the function as an empty string. (Note: in Firebird 2 and
up, it can also be passed as a null pointer. We’ll get to that later.) From inside the function there is
no way of telling if this argument represents a real empty string or a NULL. So what does the function
implementor do? He has to make a choice: either take the argument at face value, or assume it was
originally a NULL and treat it accordingly.
If the function result type is a pointer, returning NULL is possible even if receiving NULL isn’t. Thus,
the following unexpected things can happen:
• You call a UDF with a NULL argument. It is passed as a value, e.g. 0 or ''. Within the function, this
argument is not changed back to NULL; a non-NULL result is returned.
• You call a UDF with a valid argument like 0 or ''. It is passed as-is (obviously). But the function
code supposes that this value really represents a NULL, treats it as a black hole, and returns NULL
to the caller.
Both conversions are usually unwanted, but the second probably more so than the first (better
validate something NULL than wreck something valid). To get back to our LTRIM example: in Firebird
1.0, this function returns NULL if you feed it an empty string. This is wrong. In 1.5 it never returns
NULL: even NULL strings (passed by the engine as '') are “trimmed” to empty strings. This is also
wrong, but it’s considered the lesser of two evils. Firebird 2 has finally got it right: a NULL string
gives a NULL result, an empty string is trimmed to an empty string — at least if you declare the
function in the right way.
13.2. Descriptors
As early as in Firebird 1.0, a new method of passing UDF arguments and results was introduced:
Chapter 13. User-Defined Functions (UDFs)
38
“by descriptor”. Descriptors allow NULL signalling no matter the type of data. The fbudf library
makes ample use of this technique. Unfortunately, using descriptors is rather cumbersome; it’s
more work and less play for the UDF implementor. But they do solve all the traditional NULL
problems, and for the caller they’re just as easy to use as old-style UDFs.
13.3. Improvements in Firebird 2
Firebird 2 comes with a somewhat improved calling mechanism for old-style UDFs. The engine will
now pass NULL input as a null pointer to the function, if the function has been declared to the
database with a NULL keyword after the argument(s) in question, e.g. like this:
declare external function ltrim
Ê cstring(255) null
Ê returns cstring(255) free_it
Ê entry_point 'IB_UDF_ltrim' module_name 'ib_udf';
This requirement ensures that existing databases and their applications can continue to function
like before. Leave out the NULL keyword and the function will behave like it did under Firebird 1.5
and earlier.
Please note that you can’t just add NULL keywords to your declarations and then expect every
function to handle NULL input correctly. Each function has to be (re)written in such a way that NULLs
are dealt with correctly. Always look at the declarations provided by the function implementor. For
the functions in the ib_udf library, consult ib_udf2.sql in the Firebird UDF directory. Notice the 2 in
the file name; the old-style declarations are in ib_udf.sql.
These are the ib_udf functions that have been updated to recognise NULL input and handle it
properly:
• ascii_char
• lower
• lpad and rpad
• ltrim and rtrim
• substr and substrlen
Most ib_udf functions remain as they were; in any case, passing NULL to an old-style UDF is never
possible if the argument isn’t of a referenced type.
On a side note: don’t use lower, *trim and substr* in new code; use the internal functions LOWER, TRIM
and SUBSTRING instead.
13.3.1. “Upgrading” ib_udf functions in an existing database
If you are using an existing database with one or more of the functions listed above under Firebird
2, and you want to benefit from the improved NULL handling, run the script ib_udf_upgrade.sql
Chapter 13. User-Defined Functions (UDFs)
39
against your database. It is located in the Firebird misc\upgrade\ib_udf directory.
13.4. Being prepared for undesired conversions
The unsolicited NULL <=> non-NULL conversions described earlier usually only happen with legacy
UDFs, but there are a lot of them around (most notably in ib_udf). Also, nothing will stop a careless
implementor from doing the same in a descriptor-style function. So the bottom line is: if you use a
UDF and you don’t know how it behaves with respect to NULL:
1. Look at its declaration to see how values are passed and returned. If it says “by descriptor”, it
should be safe (though it never hurts to make sure). Ditto if arguments are followed by a NULL
keyword. In all other cases, walk through the rest of the steps.
2. If you have the source and you understand the language it’s written in, inspect the function
code.
3. Test the function both with NULL input and with input like 0 (for numerical arguments) and/or ''
(for string arguments).
4. If the function performs an undesired NULL <=> non-NULL conversion, you’ll have to anticipate it
in your code before calling the UDF (see also Testing for NULL — if it matters, elsewhere in this
guide).
The declarations for the shipped UDF libraries can be found in the Firebird subdirectory examples
(v.1.0) or UDF (v.1.5 and up). Look at the files with extension .sql
13.5. More on UDFs
To learn more about UDFs, consult the InterBase 6.0 Developer’s Guide (free at
https://www.ibphoenix.com/downloads/60DevGuide.zip), Using Firebird and the Firebird Reference
Guide (both on CD), or the Firebird Book. CD and book can be purchased via
https://www.ibphoenix.com.
Chapter 13. User-Defined Functions (UDFs)
40
Chapter 14. Converting to and from NULL
14.1. Substituting NULL with a value
14.1.1. The COALESCE function
The COALESCE function in Firebird 1.5 and higher can convert NULL to most anything else. This
enables you to perform an on-the-fly conversion and use the result in your further processing,
without the need for “if (MyExpression is null) then” or similar constructions. The function
signature is:
COALESCE( Expr1, Expr2, Expr3, … )
COALESCE returns the value of the first non-NULL expression in the argument list. If all the
expressions are NULL, it returns NULL.
This is how you would use COALESCE to construct a person’s full name from the first, middle and last
names, assuming that some middle name fields may be NULL:
select FirstName
Ê || coalesce( ' ' || MiddleName, '' )
Ê || ' ' || LastName
from Persons
Or, to create an as-informal-as-possible name from a table that also includes nicknames, and
assuming that both nickname and first name may be NULL:
select coalesce ( Nickname, FirstName, 'Mr./Mrs.' )
Ê || ' ' || LastName
from OtherPersons
COALESCE will only help you out in situations where NULL can be treated in the same way as some
allowed value for the datatype. If NULL needs special handling, different from any other value, your
only option is to use an IF or CASE construct after all.
14.1.2. Firebird 1.0: the *NVL functions
Chapter 14. Converting to and from NULL
41
Firebird 1.0 doesn’t have COALESCE. However, you can use four UDFs that provide a good part of its
functionality. These UDFs reside in the fbudf lib and they are:
• iNVL, for integer arguments
• i64NVL, for bigint arguments
• dNVL, for double precision arguments
• sNVL, for strings
The *NVL functions take exactly two arguments. Like COALESCE, they return the first argument if it’s
not NULL; otherwise, they return the second. Please note that the Firebird 1.0 fbudf lib — and
therefore, the *NVL function set — is only available for Windows.
14.2. Converting values to NULL
Sometimes you want certain values to show up as NULL in the output (or intermediate output). This
doesn’t happen often, but it may for instance be useful if you want to exclude certain values from
summing or averaging. The NULLIF functions can do this for you, though only for one value at the
time.
14.2.1. Firebird 1.5 and up: the NULLIF function
The NULLIF internal function takes two arguments. If their values are equal, the function returns
NULL. Otherwise, it returns the value of the first argument.
A typical use is e.g.
select avg( nullif( Weight, -1 ) ) from FatPeople
which will give you the average weight of the FatPeople population, without counting those with
weight -1. (Remember that aggregate functions like AVG exclude all NULL fields from the
computation.)
Elaborating on this example, suppose that until now you have used the value -1 to indicate “weight
unknown” because you weren’t comfortable with NULLs. After reading this guide, you may feel
brave enough to give the command:
update FatPeople set Weight = nullif( Weight, -1 )
Now unknown weights will really be unknown.
Chapter 14. Converting to and from NULL
42
14.2.2. Firebird 1.0: the *nullif UDFs
Firebird 1.0.x doesn’t have the NULLIF internal function. Instead, it has four user-defined functions
in the fbudf lib that serve the same purpose:
• inullif, for integer arguments
• i64nullif, for bigint arguments
• dnullif, for double precision arguments
• snullif, for strings
Please note that the Firebird 1.0 fbudf lib — and therefore, the *nullif function set — is only
available for Windows.
The Firebird 1 Release Notes state that, because of an engine limitation, these UDFs
return a zero-equivalent if the arguments are equal. This is incorrect: if the
arguments have the same value, the functions all return a true NULL.
However — they also return NULL if the first argument is a real value and the
second argument is NULL. This is a wrong result. The Firebird 1.5 internal NULLIF
function correctly returns the first argument in such a case.
Chapter 14. Converting to and from NULL
43
Chapter 15. Altering populated tables
If your table already contains data, and you want to add a non-nullable column or change the
nullability of an existing column, there are some consequences that you should know about. We’ll
discuss the various possibilities in the sections below.
15.1. Adding a non-nullable field to a populated table
Suppose you have this table:
Table 8. Adventures table
Name Bought Price
Maniac Mansion 12-Jun-1995 $ 49,--
Zak McKracken 9-Oct-1995 $ 54,95
You have already entered some adventure games in this table when you decide to add a non-
nullable ID field. There are two ways to go about this, both with their own specific problems.
15.1.1. Adding a NOT NULL field
This is by far the preferred method in general, but it causes some special problems if used on a
populated table, as you will see in a moment. First, add the field with the following statement:
alter table Adventures add id int not null
Or, if you want to name the constraint explicitly (this makes it easier if you ever want to drop it
later):
alter table Adventures add id int constraint IdNotNull not null
Despite the NOT NULL constraint, the new ID fields that have been added to the existing rows will all
be NULL. In this special case, Firebird allows invalid data to be present in a column. It will even write
the NULLs to a backup without complaining, but it will refuse to restore them, precisely because of
the constraint violation.
Firebird 1.5 (but not 1.0 or 2.0) even allows you to make such a column the
primary key!
Chapter 15. Altering populated tables
44
False reporting of NULLs as zeroes
To make matters worse, Firebird lies to you when you retrieve data from the table. With isql and
many other clients, “SELECT * FROM ADVENTURES” will return this dataset:
Table 9. Result set after adding a NOT NULL column
Name Bought Price ID
Maniac Mansion 12-Jun-1995 $ 49,-- 0
Zak McKracken 9-Oct-1995 $ 54,95 0
Of course this will make most people think “OK, cool: Firebird used a default value of 0 for the new
fields — nothing to worry about”. But you can verify that the ID fields are really NULL with these
queries:
• SELECT * FROM ADVENTURES WHERE ID = 0 (returns empty set)
• SELECT * FROM ADVENTURES WHERE ID IS NULL (returns set shown above, with false 0’s)
• SELECT * FROM ADVENTURES WHERE ID IS NOT NULL (returns empty set)
Another type of query hinting that something fishy is going on is the following:
• SELECT NAME, ID, ID+3 FROM ADVENTURES
Such a query will return 0 in the “ID+3” column. With a true 0 ID it should have been 3. The correct
result would be NULL, of course!
If the added NOT NULL column is of type (VAR)CHAR instead of INT, you will see phoney emptystrings
(''). With a DATE column, phoney “zero dates” of 17 November 1858 (epoch of the Modified Julian
Day). In all cases, the true state of the data is NULL.
What the…
...is going on here?
When a client application like isql queries the server, the conversation passes through several
stages. During one of them — the “describe” phase — the engine reports type and nullability for
each column that will appear in the result set. It does this in a data structure which is later also
used to retrieve the actual row data. For columns flagged as NOT NULL by the server, there is no way
to return NULLs to the client — unless the client flips back the flag before entering the data retrieval
stage. Most client applications don’t do this. After all, if the server assures you that a column can’t
contain NULLs, why would you think you know better, override the server’s decision and check for
NULLs anyway? And yet that’s exactly what you should do if you want to avoid the risk of reporting
false values to your users.
Ensuring the validity of your data
Here’s what you should do to make sure that your data are valid when adding a NOT NULL column to
a populated table:
Chapter 15. Altering populated tables
45
• To prevent the nulls-in-not-null-columns problem from occurring at all, provide a default value
when you add the new column:
alter table Adventures add id int default -1 not null
Default values are normally not applied when adding fields to existing rows, but with NOT NULL
fields they are.
• Else, explicitly set the new fields to the value(s) they should have, right after adding the column.
Verify that they are all valid with a “SELECT … WHERE … IS NULL” query, which should return an
empty set.
• If the damage has already been done and you find yourself with an unrestorable backup, use
gbak's -n switch to ignore validity constraints when restoring. Then fix the data and reinstate the
constraints manually. Again, verify with a “WHERE … IS NULL” query.
Firebird versions up to and including 1.5.0 have an additional bug that causes
gbak to restore NOT NULL constraints even if you specify -n. With those versions,
if you have backed up a database with NULL data in NOT NULL fields, you’re
completely stuck. Solution: install 1.5.1 or higher, restore with gbak -n and fix
your data.
15.1.2. Adding a CHECKed column
Using a CHECK constraint is another way to disallow NULL entries in a column:
alter table Adventures add id int check (id is not null)
If you do it this way, a subsequent SELECT will return:
Table 10. Result set after adding a CHECKed field
Name Bought Price ID
Maniac Mansion 12-Jun-1995 $ 49,-- <null>
Zak McKracken 9-Oct-1995 $ 54,95 <null>
Well, at least now you can see that the fields are NULL! Firebird does not enforce CHECK constraints on
existing rows when you add new fields. The same is true if you add checks to existing fields with
ADD CONSTRAINT or ADD CHECK.
This time, Firebird not only tolerates the presence and the backing up of the NULL entries, but it will
also restore them. Firebird’s gbak tool does restore CHECK constraints, but doesn’t apply them to the
existing data in the backup.
Even with the -n switch, gbak restores CHECK constraints. But since they are not
Chapter 15. Altering populated tables
46
used to validate backed-up data, this will never lead to a failed restore.
This restorability of your NULL data despite the presence of the CHECK constraint is consistent with
the fact that Firebird allows them to be present in the first place, and to be backed up as well. But
from a pragmatical point of view, there’s a downside: you can now go through cycle after cycle of
backup and restore, and your “illegal” data will survive without you even receiving a warning. So
again: make sure that your existing rows obey the new rule immediately after adding the
constrained column. The “default” trick won’t work here; you’ll just have to remember to set the
right value(s) yourself. If you forget it now, chances are that your outlawed NULLs will survive for a
long time, as there won’t be any wake-up calls later on.
The isql command SHOW TABLE lists “CHECK … IS NOT NULL” columns as nullable,
because the column type is not intrinsically NOT NULL. But it also shows the CHECKs,
so you know how things stand.
Likewise, the engine describes these columns as nullable when a query is
executed. This accounts for the fact that NULLs are truthfully reported in this case,
as you’ve seen in the table above.
15.1.3. Using domains to add a non-nullable field
Instead of specifying data types and constraints directly, you can also use domains, e.g. like this:
create domain inn as int not null;
alter table Adventures add id inn;
Or like this:
create domain icnn as int check (value is not null);
alter table Adventures add id icnn;
For the presence of NULLs in the added columns, returning of false 0's, effects of default values etc., it
makes no difference at all whether you take the domain route or the direct approach. The only
difference is that domain-based constraints can’t be removed at the column level. So if you ever
want to drop the constraint later, you must either switch the column to another domain or built-in
type again, or remove the constraint from the entire domain. The latter operation is described in
the section Changing the nullability of a domain.
15.2. Making existing columns non-nullable
Chapter 15. Altering populated tables
47
15.2.1. Making an existing column NOT NULL
You cannot add NOT NULL to an existing column, but there’s a simple workaround. Suppose the
current type is int, then this:
create domain intnn as int not null;
alter table MyTable alter MyColumn type intnn;
will change the column type to “int not null”.
If the table already had records, any NULLs in the column will remain NULL, and again most Firebird
clients will report them as 0 to the user. The situation is almost exactly the same as when you add a
NOT NULL column (see Adding a NOT NULL field). The only difference is that if you give the domain
(and therefore the column) a default value, this time you can’t be sure that it will be applied to the
existing NULL entries. Tests show that sometimes the default is applied to all NULLs, sometimes to
none, and in a few cases to some of the existing entries but not to others! Bottom line: if you change
a column’s type and the new type includes a default, double-check the existing entries — especially
if they “seem to be” 0 or zero-equivalents.
Some Firebird tools allow you to make an existing column NOT NULL with the click
of a button. They do this by poking a value directly into a system table. This
technique is neither recommended nor supported by Firebird, and although until
now it works in practice, this may not be the case in future versions. It’s better to
stay safe and use the SQL given above.
15.2.2. Adding a CHECK constraint to an existing column
To add a CHECK constraint to a column, use one of the following syntaxes:
alter table Stk add check (Amt is not null)
alter table Stk add constraint AmtNotNull check (Amt is not null)
The second form is preferred because it gives you an easy handle to drop the check, but the
constraints themselves function exactly the same. As you may have expected, existing NULLs in the
column will remain, can be backed up and restored, etc. etc. — see Adding a CHECKed column.
15.3. Making non-nullable columns nullable again
Chapter 15. Altering populated tables
48
15.3.1. Removing a NOT NULL constraint
If you gave the NOT NULL constraint a name when you created it, you can simply drop it:
alter table Adventures drop constraint IdNotNull
If you forgot the name, you can retrieve it with isql’s `SHOW TABLE command (i.c. SHOW TABLE
ADVENTURES); other clients may have their own provisions to let you find or browse constraint
names.
If you didn’t name the constraint explicitly, Firebird has created a name for it, but SHOW TABLE won’t
display it. You have to use this piece of SQL to dig it up:
select rc.rdb$constraint_name
from rdb$relation_constraints rc
Ê join rdb$check_constraints cc
Ê on rc.rdb$constraint_name = cc.rdb$constraint_name
where rc.rdb$constraint_type = 'NOT NULL'
Ê and rc.rdb$relation_name = '<TableName>'
Ê and cc.rdb$trigger_name = '<FieldName>'
Don’t break your head over some of the table and field names in this statement; they are illogical
but correct. Make sure to uppercase the names of your table and field if they were defined case-
insensitively. Otherwise, match the case exactly but don’t enclose the names in double-quotes like
you would do in a regular query. Also don’t include the angle brackets (<>). Once you have the
constraint name, you can drop it just like in the previous example.
If the above statement returns an empty set and you are sure that you’ve correctly
filled in the table and field names (including case!), and the constraint did not
come from a domain either (this is discussed in the next sections), it may be that a
third-party tool has made the column NOT NULL by setting a flag in a system table.
In that case it’s probably best to remove it again with the same tool. If that is not
an option, check the field’s NULL flag with:
select rdb$null_flag from rdb$relation_fields
where rdb$relation_name = '<TableName>'
Ê and rdb$field_name = '<FieldName>'
If the flag is NULL or 0, the field is nullable (at least as far as this flag is concerned).
If it’s 1, clear it with:
update rdb$relation_fields set rdb$null_flag = null /* or 0 */
where rdb$relation_name = '<TableName>'
Chapter 15. Altering populated tables
49
Ê and rdb$field_name = '<FieldName>'
followed by a commit.
As soon as you’ve dropped the NOT NULL constraint — by whichever method — SHOW
TABLE will report the column as nullable. Any existing NULLs that were previously
illegal and therefore hidden by most clients (see False reporting of NULLs as zeroes)
will become visible again.
However, before you can insert NULLs into the column, you must commit your
work, close all connections to the database, and reconnect.
Removing a domain-based NOT NULL constraint
If the NOT NULL constraint came with a domain, it is not registered directly with the column. This
means you can’t DROP it from the column either. Instead, change the column’s type to a nullable
domain or built-in data type:
alter table MyTable alter MyColumn type int
Even though this time the constraint was not tied directly to the column, you must again close all
connections and reconnect before NULL input is accepted.
15.3.2. Removing a CHECK constraint
If you used a CHECK constraint to make the column non-nullable, you can simply drop it again:
alter table Stk drop constraint AmtNotNull
If you haven’t named the constraint yourself but added the CHECK directly to the column or table,
you must first find out its name before you can drop it. This can be done with the isql “SHOW TABLE”
command (in this case: SHOW TABLE STK). Unlike NOT NULL constraints, CHECKs will also be shown if
they were created without a user-defined name.
Dropping a column-based CHECK constraint takes effect immediately. You don’t have
to disconnect and reconnect to be able to insert values that would have violated
the check.
Removing a domain-based CHECK constraint
Chapter 15. Altering populated tables
50
You can’t disable a domain-based CHECK constraint on the column level (you can add an extra CHECK,
but the one from the domain will stay in effect as well). So unless you want to remove the CHECK
from the entire domain, you’ll have to change the column’s data type to a domain or built-in type
that allows NULLs, e.g.:
alter table Stk alter Amt type bigint
Chapter 15. Altering populated tables
51
Chapter 16. Changing the nullability of a
domain
When you create a domain, you can optionally specify NOT NULL and/or a CHECK constraint:
create domain posint as int not null check (value > 0)
Domain constraints cannot be overridden or switched off at the column level, but they can be
added to. For instance, you can create a nullable domain but specify NOT NULL for certain columns
based on that domain. Or you can define an additional CHECK on the column level. But sometimes
you may want to change the constraints for the entire domain after it has been used for a while.
The following paragraphs show you how.
16.1. Adding and removing domain-level CHECK
constraints
If the domain doesn’t have a CHECK constraint yet, you can add one like this:
alter domain MyDomain add constraint check (value is not null)
You may leave out the constraint keyword if you wish. The added CHECK takes effect immediately for
all the columns that are based on the domain. However, the check is only applied to new updates
and inserts; existing NULL data will remain in place and will continue to be shown as <null> in result
sets.
A domain can have at most one CHECK constraint. There is no ALTER CHECK statement; if you want to
change the CHECK, you must drop it and create a new one.
This is how you drop a CHECK constraint from a domain:
alter domain MyDomain drop constraint
You must close all connections and reconnect before you can insert values that would have violated
the dropped CHECK in MyDomain-based columns.
16.2. Altering a domain’s NOT NULL setting
Once a domain is created, Firebird doesn’t allow you to add or remove a NOT NULL constraint (DROP
CONSTRAINT will only drop a CHECK). If you want to change a domain-wide NOT NULL setting, the official
procedure is:
1. Create a new domain with the desired characteristics.
2. Switch all the concerned columns over to the new domain.
Chapter 16. Changing the nullability of a domain
52
3. Drop the old domain.
This is fine when it only concerns a few columns, but what if there are dozens or even hundreds? It
is possible to change the setting by going directly to the system table. Be aware however that
Firebird does not recommend or support this type of operation, nor is it guaranteed to keep
working in future versions. That being said, it’s a relatively simple operation and if properly
executed it shouldn’t cause you any problems.
So here’s the SQL, but remember: at your own risk!
update rdb$fields
Ê set rdb$null_flag = <value> where rdb$field_name = '<DomainName>'
To make a domain NOT NULL, <value> should be 1. To remove a NOT NULL constraint, use 0 or NULL.
Write the domain name in all-caps if it was created case-insensitively; otherwise, match the case
exactly. Don’t use double-quotes and don’t include the ‘<>’. Also note that, even when DDL
autocommit is on (which is the default in isql and many other clients), this statement won’t be
autocommitted because technically it’s not DDL. So don’t forget to commit!
If you have set the flag to 1, a subsequent SHOW DOMAIN will immediately report the domain as being
NOT NULL. Likewise, SHOW TABLE will list all columns based on the domain as NOT NULL. But if those
columns already contained NULLs, a SELECT still truthfully reports them as such (the result set
column is still “described” as nullable) — for now. You won’t be able to get any new NULLs in the
column though. Close all connections and reconnect, and any illegal NULLs will appear as zeroes (at
least in most clients; if this surprises you, read False reporting of NULLs as zeroes, earlier in this
guide).
If you have changed the flag from 1 to 0 or NULL — making the domain nullable — SHOW DOMAIN and
SHOW TABLE will immediately report the domain and “its” columns as nullable. But you still can’t
insert NULLs in the columns, and any present NULLs are still shown as zeroes (in most clients). Close
all connections and reconnect to straighten everything out.
Lastly, please be warned again that this type of fiddling with the system tables is not recommended
or supported by Firebird, and not guaranteed to keep working in future versions. If the number of
columns based on the domain is limited, it’s better to switch them over to another domain or built-
in type and then drop the old domain.
Chapter 16. Changing the nullability of a domain
53
Chapter 17. Testing for NULL and equality in
practice
This section contains some practical tips and examples that may be of use to you in your everyday
dealings with NULLs. It concentrates on testing for NULL itself and testing the (in)equality of two
things when NULLs may be involved.
17.1. Testing for NULL — if it matters
Quite often, you don’t need to take special measures for fields or variables that may be NULL. For
instance, if you do this:
select * from Customers where Town = 'Ralston'
you probably don’t want to see the customers whose town is unspecified. Likewise:
if (Age >= 18) then CanVote = 'Yes'
doesn’t include people of unknown age, which is also defensible. But:
if (Age >= 18) then CanVote = 'Yes';
else CanVote = 'No';
seems less justified: if you don’t know a person’s age, you shouldn’t explicitly deny her the right to
vote. Worse, this:
if (Age < 18) then CanVote = 'No';
else CanVote = 'Yes';
won’t have the same effect as the previous. If some of the NULL ages are in reality under 18, you’re
now letting minors vote!
The right approach here is to test for NULL explicitly:
if (Age is null) then CanVote = 'Unsure';
else if (Age >= 18 ) then CanVote = 'Yes';
else CanVote = 'No';
Since this code handles more than two possibilities, using the CASE syntax (supported by Firebird 1.5
and up) is more elegant:
Chapter 17. Testing for NULL and equality in practice
54
CanVote = case
Ê when Age is null then 'Unsure'
Ê when Age >= 18 then 'Yes'
Ê else 'No'
Ê end;
Or, prettier:
CanVote = case
Ê when Age >= 18 then 'Yes'
Ê when Age < 18 then 'No'
Ê else 'Unsure'
Ê end;
17.2. Equality tests
Sometimes you want to find out if two fields or variables are the same and you want to consider
them equal if they are both NULL. The way to do this depends on your Firebird version.
17.2.1. Firebird 2.0 and up
In Firebird 2 and higher, you test for null-encompassing equality with DISTINCT. This has already
been discussed, but here’s a quick recap. Two expressions are considered:
• DISTINCT if they have different values or if one of them is NULL and the other isn’t;
• NOT DISTINCT if they have the same value or if both of them are NULL.
[NOT] DISTINCT always returns true or false, never NULL or something else. Examples:
if (A is distinct from B) then...
if (Buyer1 is not distinct from Buyer2) then...
Skip the next section if you’re not interested in the pre-Firebird-2 stuff.
17.2.2. Earlier Firebird versions
Pre-2.0 versions of Firebird don’t support this use of DISTINCT. Consequently, the tests are a little
more complicated and there are some pitfalls to avoid.
Chapter 17. Testing for NULL and equality in practice
55
The correct equality test for pre-2.0 Firebird versions is:
if (A = B or A is null and B is null) then...
or, if you want to make the precedence of the operations explicit:
if ((A = B) or (A is null and B is null)) then...
A word of warning though: if exactly one of A and B is NULL, the test expression becomes NULL, not
false! This is OK in an if statement, and we can even add an else clause which will be executed if A
and B are not equal (including when one is NULL and the other isn’t):
if (A = B or A is null and B is null)
Ê then ...stuff to be done if A equals B...
Ê else ...stuff to be done if A and B are different...
But don’t get the bright idea of inverting the expression and using it as an inequality test:
/* Don't do this! */
if (not(A = B or A is null and B is null))
Ê then ...stuff to be done if A differs from B...
The above code will work correctly if A and B are both NULL or both non-NULL. But it will fail to
execute the then clause if exactly one of them is NULL.
If you only want something to be done if A and B are different, either use one of the correct
expressions shown above and put a dummy statement in the then clause (starting at 1.5, an empty
begin … end block is also allowed), or use this longer test expression:
/* This is a correct inequality test for pre-2 Firebird: */
if (A <> B
Ê or A is null and B is not null
Ê or A is not null and B is null) then...
Remember, all this is only necessary in pre-2.0 Firebird versions. From version 2 onward, the
inequality test is simply “if (A is distinct from B)”.
17.2.3. Summary of (in)equality tests
Table 11. Testing (in)equality of A and B in different Firebird versions
Test type Firebird version
<= 1.5.x
Ê>= 2.0
Equality
A = B or A is null and B is null A is not distinct from B
Chapter 17. Testing for NULL and equality in practice
56
Inequality
A <> B
or A is null and B is not null
or A is not null and B is null
A is distinct from B
Please keep in mind that with Firebird 1.5.x and earlier:
• the equality test returns NULL if exactly one operand is NULL;
• the inequality test returns NULL if both operands are NULL.
In an IF or WHERE context, these NULL results act as false — which is fine for our purposes. But
remember that an inversion with NOT() will also return NULL — not “true”. Also, if you use the 1.5-
and-earlier tests within CHECK constraints in Firebird 2 or higher, be sure to read the section CHECK
constraints, if you haven’t done so already.
Most JOINs are made on equality of fields in different tables, and use the ‘=’
operator. This will leave out all NULL-NULL pairs. If you want NULL to match NULL, pick
the equality test for your Firebird version from the table above.
17.3. Finding out if a field has changed
In triggers you often want to test if a certain field has changed (including: gone from NULL to non-
NULL or vice versa) or stayed the same (including: kept its NULL state). This is nothing but a special
case of testing the (in)equality of two fields, so here again our approach depends on the Firebird
version.
In Firebird 2 and higher we use this code:
if (New.Job is not distinct from Old.Job)
Ê then ...Job field has stayed the same...
Ê else ...Job field has changed...
And in older versions:
if (New.Job = Old.Job or New.Job is null and Old.Job is null)
Ê then ...Job field has stayed the same...
Ê else ...Job field has changed...
Chapter 17. Testing for NULL and equality in practice
57
Chapter 18. Summary
NULL in a nutshell:
• NULL means unknown.
• Every field or variable that has been created but not initialised is in a NULL state.
• To exclude NULLs from a domain or column, add “NOT NULL” after the type name.
• To find out if A is NULL, use “A IS [NOT] NULL”.
• Assigning NULL is done like assigning values: with “A = NULL” or an insert list.
• To find out if A and B are the same, with the understanding that all NULLs are the same and
different from anything else, use “A IS [NOT] DISTINCT FROM B” in Firebird 2 and up. In earlier
versions the tests are:
equality
A = B or A is null and B is null
inequality
A <> B
or A is null and B is not null
or A is not null and B is null
• In Firebird 2 and up you can use NULL literals in just about every situation where a regular value
is also allowed. In practice this mainly gives you a lot more rope to hang yourself.
• Most of the time, NULL operands make the entire operation return NULL. Noteworthy exceptions
are:
◦ “NULL or true” evaluates to true;
◦ “NULL and false” evaluates to false.
• The IN, ANY|SOME and ALL predicates may (but do not always) return NULL if either the left-hand
side expression or a list/subresult element is NULL.
• The [NOT] EXISTS predicate never returns NULL. The [NOT] SINGULAR predicate never returns NULL
in Firebird 2.1 and up. It is broken in all previous versions.
• In aggregate functions only non-NULL fields are involved in the computation. Exception:
COUNT(*).
• In ordered sets, NULLs are placed…
◦ 1.0: At the bottom;
◦ 1.5: At the bottom, unless NULLS FIRST specified;
◦ 2.0: At the “small end” (top if ascending, bottom if descending), unless overridden by NULLS
FIRST/LAST.
Chapter 18. Summary
58
• If a WHERE or HAVING clause evaluates to NULL, the row is not included in the result set.
• If the test expression of an IF statement is NULL, the THEN block is skipped and the ELSE block
executed.
• A CASE statement returns NULL:
◦ If the selected result is NULL.
◦ If no matches are found (simple CASE) or no conditions are true (searched CASE) and there is
no ELSE clause.
• In a simple CASE statement, “CASE <null_expr>” does not match “WHEN <null_expr>”.
• If the test expression of a WHILE statement evaluates to NULL, the loop is not (re)entered.
• A FOR statement is not exited when NULLs are received. It continues to loop until either all the
rows have been processed or it is interrupted by an exception or a loop-breaking PSQL
statement.
• In Primary Keys, NULLs are never allowed.
• In Unique Keys and Unique Indices, NULLs are
◦ not allowed in Firebird 1.0;
◦ allowed (even multiple) in Firebird 1.5 and higher.
• In Foreign Key columns, multiple NULLs are allowed.
• If a CHECK constraint evaluates to NULL, the input is
◦ rejected under Firebird 1.5 and earlier;
◦ accepted under Firebird 2.0 and higher.
• SELECT DISTINCT considers all NULLs equal: in a single-column select, at most one is returned.
• UDFs sometimes convert NULL <=> non-NULL in a seemingly random manner.
• The COALESCE and *NVL functions can convert NULL to a value.
• The NULLIF family of functions can convert values to NULL.
• If you add a NOT NULL column without a default value to a populated table, all the entries in that
column will be NULL upon creation. Most clients however — including Firebird’s isql tool — will
falsely report them as zeroes (0 for numerical fields, '' for string fields, etc.)
• If you change a column’s datatype to a NOT NULL domain, any existing NULLs in the column will
remain NULL. Again most clients — including isql — will show them as zeroes.
Remember, this is how NULL works in Firebird SQL. There may be (at times subtle) differences with
other RDBMSes.
Chapter 18. Summary
59
Appendix A: NULL-related bugs in Firebird
Attention: both historic and current bugs are listed in the sections below. Always look if and when a
bug has been fixed before assuming that it exists in your version of Firebird.
Bugs that crash the server
EXECUTE STATEMENT with NULL argument
EXECUTE STATEMENT with a NULL argument crashed Firebird 1.5 and 1.5.1 servers. Fixed in 1.5.2.
EXTRACT from NULL date
In 1.0.0, EXTRACT from a NULL date would crash the server. Fixed in 1.0.2.
FIRST and SKIP with NULL argument
FIRST and SKIP crash a Firebird 1.5.n or lower server if given a NULL argument. Fixed in 2.0.
LIKE with NULL escape
Using LIKE with a NULL escape character would crash the server. Fixed in 1.5.1.
Other bugs
NULLs in NOT NULL columns
NULLs can exist in NOT NULL columns in the following situations:
• If you add a NOT NULL column to a populated table, the fields in the newly added column will all
be NULL.
• If you make an existing column NOT NULL, any NULLs already present in the column will remain
in that state.
Firebird allows these NULLs to stay, also backs them up, but refuses to restore them with gbak. See
Adding a NOT NULL field and Making an existing column NOT NULL.
Illegal NULLs returned as 0, '', etc.
If a NOT NULL column contains NULLs (see previous bug), the server will still describe it as non-
nullable to the client. Since most clients don’t question this assurance from the server, they will
Appendix A: NULL-related bugs in Firebird
60
present these NULLs as 0 (or equivalent) to the user. See False reporting of NULLs as zeroes.
Primary key with NULL entries
The following bug appeared in Firebird 1.5: if you had a table with some rows and you added a NOT
NULL column (which automatically creates NULL entries in the existing rows — see above), you could
make that column the primary key even though it had NULL entries. In 1.0 this didn’t work because
of the stricter rules for UNIQUE indices. Fixed in 2.0.
SUBSTRING results described as non-nullable
The engine describes SUBSTRING result columns as non-nullable in the following two cases:
• If the first argument is a string literal, as in “SUBSTRING( 'Ootchie-coo' FROM 5 FOR 2 )”.
• If the first argument is a NOT NULL column.
This is incorrect because even with a known string, substrings may be NULL, namely if the one of the
other arguments is NULL. In versions 1.* this bug didn’t bite: the FROM and FOR args had to be literal
values, so they could never be NULL. But as from Firebird 2, any expression that resolves to the
required data type is allowed. And although the engine correctly returns NULL whenever any
argument is NULL, it describes the result column as non-nullable, so most clients show the result as
an empty string.
This bug seems to be fixed in 2.1.
Gbak -n restoring NOT NULL
Gbak -n[o_validity] restored NOT NULL constraints in early Firebird versions. Fixed in 1.5.1.
IN, = ANY and = SOME with indexed subselect
Let A be the expression on the left-hand side and S the result set of the subselect. In versions prior to
2.0, “IN”, “= ANY” and “= SOME” return false instead of NULL if an index is active on the subselect
column and:
• either A is NULL and S doesn’t contain any NULLs;
• or A is not NULL, A is not found in S, and S contains at least one NULL.
See the warnings in the IN and ANY sections. Workaround: use “<> ALL” instead. Fixed in 2.0.
ALL with indexed subselect
With every operator except ‘<>’, ALL may return wrong results if an index is active on the subselect
column. This can happen with our without NULLs involved. See the ALL bug warning. Fixed in 2.0.
Appendix A: NULL-related bugs in Firebird
61
SELECT DISTINCT with wrong NULLS FIRST|LAST ordering
Firebird 2.0 has the following bug: if a SELECT DISTINCT is combined with an [ASC] NULLS LAST or
DESC NULLS FIRST ordering, and the ordering field(s) form(s) the beginning (but not the whole) of the
select list, every field in the ORDER BY clause that is followed by a field with a different (or no)
ordering gets the NULLs placed at the default relative location, ignoring the NULLS XXX directive. Fixed
in 2.0.1 and 2.1.
UDFs returning values when they should return NULL
This should definitely be considered a bug. If an angle is unknown, don’t tell me that its cosine is 1!
Although the history of these functions is known and we can understand why they behave like they
do (see User-Defined Functions (UDFs)), it’s still wrong. Incorrect results are returned and this
should not happen. Most of the math functions in ib_udf, as well as some others, have this bug.
UDFs returning NULL when they should return a value
This is the complement of the previous bug. LPAD for instance returns NULL if you want to pad an
empty string with 10 dots. This function and others are fixed in 2.0, with the annotation that you
must explicitly declare them with the NULL keyword or they’ll show the old — buggy — behaviour.
LTRIM and RTRIM trim empty strings to NULL in Firebird 1.0.n. This is fixed in 1.5 at the expense of
returning '' when trimming a NULL string, and only fully fixed in 2.0 (if declared with the NULL
keyword).
SINGULAR inconsistent with NULL results
NOT SINGULAR sometimes returns NULL where SINGULAR returns true or false. Fixed in 2.0.
SINGULAR may wrongly return NULL, in an inconsistent but reproducible manner. Fixed in 2.1.
See the section on SINGULAR.
Appendix A: NULL-related bugs in Firebird
62
Appendix B: Document history
The exact file history is recorded in the firebird-documentation git repository; see
https://github.com/FirebirdSQL/firebird-documentation
Revision History
0.1 8 Apr
2005
PV First edition.
0.2 15 Apr
2005
PV Mentioned that Fb 2.0 legalises “A = NULL” comparisons.
Changed text in “Testing if something is NULL”.
Slightly altered “Dealing with NULLs” section.
1.0 24 Jan
2007
PV This is a major update, with so much new material added that the document
has grown to around 4 times its former size. In addition, much of the existing
text has been reorganised and thoroughly reworked. It’s not feasible to give a
summary of all the changes here. Consider this a new guide with 15–25% old
material. The most important additions are:
* NULL literals * IS [NOT] DISTINCT FROM * Internal functions and directives *
Predicates: IN, ANY, SOME, ALL, EXISTS, SINGULAR * Searches (WHERE) * Sorts (ORDER
BY) * GROUP BY and HAVING * CASE, WHILE and FOR * Keys and indices * CHECK
constraints * SELECT DISTINCT * Converting values to NULL with NULLIF *
Altering populated tables * Bugs list * Alphabetical index
1.0.
1
26 Jan
2007
PV Making non-nullable columns nullable again: Provisory fix of error regarding
removal of NOT NULL constraints.
Appendix B: Document history
63
Revision History
1.1 13 Oct
2011
PV What is NULL?: Added subsection NULL as a default state.
NULL support in Firebird SQL :: Assigning NULL: Added 4th example.
Predicates :: EXISTS and SINGULAR: “evolves” → “evaluates” (2x) in Note.
Altering populated tables :: Adding a non-nullable field to a populated table ::
Adding a NOT NULL field: Added para + additional programlisting after first
programlisting.
Altering populated tables :: Adding a non-nullable field to a populated table ::
Adding a CHECKed column: Added second Note.
Altering populated tables :: Adding a non-nullable field to a populated table ::
Adding a non-nullable field using domains: Renamed section to Using domains
to add a non-nullable field.
Altering populated tables :: Adding a non-nullable field to a populated table ::
Using domains to add a non-nullable field: Two programlistings instead of one.
Text heavily edited and extended.
Altering populated tables :: Making existing columns non-nullable :: Making an
existing column NOT NULL: Added Warning.
Altering populated tables :: Making non-nullable columns nullable again:
Largely rewritten, greatly extended and divided into subsections.
Changing the nullability of a domain :: New top-level section. For free!
Summary: Added “Every field or variable that has been created but not
initialised is in a NULL state” as second listitem.
1.2 30 Jun
2020
M
R
Conversion to AsciiDoc, minor copy-editing
Removed section FSQL as linked page no longer exists and no alternative
location found.
Appendix B: Document history
64
Appendix C: License notice
The contents of this Documentation are subject to the Public Documentation License Version 1.0
(the “License”); you may only use this Documentation if you comply with the terms of this License.
Copies of the License are available at https://www.firebirdsql.org/pdfmanual/pdl.pdf (PDF) and
https://www.firebirdsql.org/manual/pdl.html (HTML).
The Original Documentation is titled Firebird Null Guide.
The Initial Writer of the Original Documentation is: Paul Vinkenoog.
Copyright © 2005–2020. All Rights Reserved. Initial Writer contact: paulvink at users dot
sourceforge dot net.
Appendix C: License notice
65
Alphabetical index
A
Adding CHECKed columns, 46
Adding non-nullable columns, 44
Adding NOT NULL columns, 44
using domains, 47
Aggregate functions, 25
GROUP BY, 26
HAVING, 27
ALL, 17
bug with indexed subselect, 61
results, 19
Altering tables, 44
AND operator, 10
ANY, 17
bug with indexed subselect, 61
results, 18
Assigning NULL, 7
AVG, 25
B
Backup, 44
BETWEEN, 10
Boolean operations, 10
Bugs list, 60
other bugs, 60
server-crashers, 60
C
CASE, 30
CHECK
remove, 50
via domain
remove, 50
CHECK constraints, 17, 35
COALESCE, 41
Comparison operations, 9
Conjunctions, 11
CONTAINING, 10
Conversions, 41
from a value to NULL, 42
NULLIF internal function, 42
NULLIF UDFs, 43
from NULL to a value, 41
COALESCE, 41
NVL functions, 41
unwanted, 38
prepare for, 40
COUNT, 25
D
Descriptors, 38
Directives, 13
Disjunctions, 11
DISTINCT
SELECT DISTINCT, 24, 37, 62
testing distinctness, 7, 55
E
Equality tests, 55
in Firebird 1.*, 55
in Firebird 2+, 55
summary, 56
EXISTS, 20
F
False, 10
beating NULL, 11
FIRST, 13
FOR loops, 31
Foreign keys, 33
Functions
aggregate, 25
GROUP BY, 26
internal, 13
G
gbak, 44
-n switch, 46
GROUP BY, 26
H
HAVING, 27
I
IF statements, 29
IN
bug with indexed subselect, 61
IN predicate, 14
in CHECK constraints, 17
results, 16
Alphabetical index
66
Indices, 33
unique, 33
Internal functions, 13
IS [NOT] DISTINCT FROM, 7, 55
IS [NOT] NULL, 6
J
JOIN, 57
K
Keys, 33, 33
foreign, 33
primary, 33
unique, 33
L
LIKE, 10
LIST, 25
M
Mathematical operations, 9
MAX, 25
MIN, 25
N
NOT NULL, 6
direct, 44
remove, 49
via domain, 47
add, 48
remove, 50
NOT operator, 10
NULL, 4
and GROUP BY, 26
as a default, 4
assign, 7
bugs, 60
conversions from, 41
conversions to, 42
conversions to/from, 41
disallow, 6
falsely reported as 0, 45
in a nutshell, 58
in aggregate functions, 25
in boolean operations, 10
in CHECK constraints, 35
in conjunctions, 11
in disjunctions, 11
in foreign keys, 33
in IF statements, 29
in indices, 33
in internal functions, 13
in JOINs, 57
in keys, 33
in NOT NULL columns, 44
in operations, 9
in primary keys, 33
in searches, 22
in sorts, 24
in UDFs, 38
in unique keys and indices, 33
literal, 8
NULL keyword in UDFs, 39
test for, 6
in practice, 54
What is it?, 4
with IN(), 14
NULL keyword in UDFs, 39
NULLIF internal function, 42
NULLIF UDFs, 43
NULLS FIRST, 24
NULLS LAST, 24
NVL functions, 41
O
OR operator, 10
ORDER BY, 24
Ordering, 24
P
Predicates, 14
ANY, SOME and ALL, 17
results, 18
EXISTS, 20
IN, 14
results, 16
SINGULAR, 20
Primary keys, 33
R
Restoring
problem with NULLs, 44
solution, 46
ROWS, 13
Alphabetical index
67
S
Searches, 22
SELECT DISTINCT, 24, 37, 62
SINGULAR, 20
SKIP, 13
SOME, 17
bug with indexed subselect, 61
results, 18
Sorting, 24
STARTING WITH, 10
String operations, 9
SUM, 25
Summary, 58
T
Tables
add CHECK to columns, 48
add CHECKed columns, 46
add non-nullable columns, 44
add NOT NULL columns, 44
using domains, 47
altering, 44
make columns non-nullable, 47
make columns NOT NULL, 48
make columns nullable, 48
Testing for NULL, 6
in practice, 54
True, 10
beating NULL, 11
U
UDFs, 38
by descriptor, 38
links, 40
NULLIF functions, 43
NVL functions, 41
unwanted conversions, 38
prepare for, 40
with NULL keyword, 39
Unique indices, 33
Unique keys, 33
W
WHERE, 22
WHILE loops, 31
Alphabetical index
68
