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.

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.

docnext count = 20

With `NULL`s 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.

`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 1. Results for “`A [NOT] IN (<list>)`”
Conditions	Results
Conditions	`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.

Caution

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.

Warning

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).

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.

`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 [nullguide-check-constraints].

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
  where income > any( select income from goldsmiths )

(returns blacksmiths who earn more than at least one goldsmith)

select name, town from blacksmiths
  where town != all( select distinct town from goldsmiths )

(returns blacksmiths who live in a goldsmithless town)

if ( GSIncome !> some( select income from blacksmiths ) )
  then PoorGoldsmith = 1;
  else PoorGoldsmith = 0;

(sets PoorGoldsmith to 1 if at least one blacksmith’s income is not less than the value of GSIncome)

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.

Table 1. Results for “`A <op> ANY|SOME (<subselect>)`”
Conditions	Result
Conditions	`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.

Warning

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 2. Results for “`A <op> ALL (<subselect>)`”
Conditions	Result
Conditions	`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`

Warning

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.

Note

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).

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!

`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).

`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
  (SELECT * FROM Landowners
   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.

Note

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.

`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 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
              WHERE MyField > 38
              AND MyField IS NOT NULL )

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).

Note	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.

Caution

If your search condition contains one or more IN predicates, there is the additional complication that some of the list elements (or subselect results) may be NULL.The implications of this are discussed in [nullguide-pred-in].

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 1. `NULL`s placement in ordered columns
Ordering	`NULL`s placement
Ordering	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.

Note

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.

Warning

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.

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 1. Aggregate function results with different column states
Function	Results
Function	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
`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

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

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> 219465.19000 266643.00100 155262.50110 130442.81115 13480000.00120 <null>121 110000.00123 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 __group__ing: 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({asterisk}) – COUNT(Salary)```".

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
============ ============
          -1            1
           1            1
           3            2
           6            1
           8            2
      <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).

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.)

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).

Conditional statements and loops

`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.

Tip	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
  MyVariable = 'Equal';
else
  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
  MyVariable = 'Not equal';
else
  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 [nullguide-testing-equality], 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 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
  MyVariable = 'Equal';
else
  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.

`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>
  when <exp1> then <result1>
  when <exp2> then <result2>
  ...
  [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.

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
  when <condition1> then <result1>
  when <condition2> then <result2>
  ...
  [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.

`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.

`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:

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!

With An Empty List

With an empty list

With a NULL test expression

docnext count = 20

With NULLs in the list

IN() results

IN() in CHECK constraints

The ANY, SOME and ALL quantifiers

Result values

EXISTS and SINGULAR

EXISTS

SINGULAR

Searches

Sorts

Aggregate functions

The GROUP BY clause

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Counting frequencies

The HAVING clause

Conditional statements and loops

IF statements

CASE expression

WHILE loops

FOR loops

With a `NULL` test expression

With `NULL`s in the list

`IN()` results

`IN()` in `CHECK` constraints

The `ANY`, `SOME` and `ALL` quantifiers

`EXISTS` and `SINGULAR`

`EXISTS`

`SINGULAR`

The `GROUP BY` clause

The `HAVING` clause

`IF` statements

`CASE` expression

`WHILE` loops

`FOR` loops