CommentsProcedural SQL (PSQL) Statements

NULLs Position

The keyword NULLS defines where NULL in the associated column will fall in the sort order: NULLS FIRST places the rows with the NULL column above rows ordered by that column’s value;NULLS LAST places those rows after the ordered rows.

NULLS FIRST is the default.

Ordering `UNION`-ed Sets

Not-parenthesized query expressions contributing to a UNION cannot take an ORDER BY clause.You can order the entire output, using one ORDER BY clause at the end of the overall query, or use parenthesized query expressions, which do allow ORDER BY.

The simplest — and, in some cases, the only — method for specifying the sort order is by the ordinal column position.However, it is also valid to use the column names or aliases, from the first contributing query only.

The ASC/DESC and/or NULLS directives are available for this global set.

If discrete ordering within the contributing set is required, use parenthesized query expressions, derived tables, or common table expressions for those sets.

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Examples of `ORDER BY`

Sorting the result set in ascending order, ordering by the RDB$CHARACTER_SET_ID and RDB$COLLATION_ID columns of the RDB$COLLATIONS table:

SELECT
  RDB$CHARACTER_SET_ID AS CHARSET_ID,
  RDB$COLLATION_ID AS COLL_ID,
  RDB$COLLATION_NAME AS NAME
FROM RDB$COLLATIONS
ORDER BY RDB$CHARACTER_SET_ID, RDB$COLLATION_ID;

The same, but sorting by the column aliases:

SELECT
  RDB$CHARACTER_SET_ID AS CHARSET_ID,
  RDB$COLLATION_ID AS COLL_ID,
  RDB$COLLATION_NAME AS NAME
FROM RDB$COLLATIONS
ORDER BY CHARSET_ID, COLL_ID;

Sorting the output data by the column position numbers:

SELECT
  RDB$CHARACTER_SET_ID AS CHARSET_ID,
  RDB$COLLATION_ID AS COLL_ID,
  RDB$COLLATION_NAME AS NAME
FROM RDB$COLLATIONS
ORDER BY 1, 2;

Sorting a SELECT * query by position numbers — possible, but nasty and not recommended:

SELECT *
FROM RDB$COLLATIONS
ORDER BY 3, 2;

Sorting by the second column in the BOOKS table, or — if BOOKS has only one column — the FILMS.DIRECTOR column:

SELECT
    BOOKS.*,
    FILMS.DIRECTOR
FROM BOOKS, FILMS
ORDER BY 2;

Sorting in descending order by the values of column PROCESS_TIME, with NULLs placed at the beginning of the set:

SELECT *
FROM MSG
ORDER BY PROCESS_TIME DESC NULLS FIRST;

Sorting the set obtained by a UNION of two queries.Results are sorted in descending order for the values in the second column, with NULLs at the end of the set;and in ascending order for the values of the first column with NULLs at the beginning.

SELECT
  DOC_NUMBER, DOC_DATE
FROM PAYORDER
UNION ALL
SELECT
  DOC_NUMBER, DOC_DATE
FROM BUDGORDER
ORDER BY 2 DESC NULLS LAST, 1 ASC NULLS FIRST;

`ROWS`

Retrieves a slice of rows from an ordered set

Syntax

SELECT <columns> FROM ...
  [WHERE ...]
  [ORDER BY ...]
  ROWS <value-expression> [TO <value-expression>]

Table 1. Arguments for the `ROWS` Clause
Argument	Description
value-expression	Any integer expressions

Note	`ROWS` is non-standard syntax `ROWS` is a Firebird-specific clause.Use the SQL-standard [fblangref50-dml-select-offsetfetch] syntax wherever possible.

ROWS limits the amount of rows returned by the SELECT statement to a specified number or range.

The ROWS clause also does the same job as the FIRST and SKIP clauses, but neither are SQL-compliant.Unlike FIRST and SKIP, and OFFSET and FETCH, the ROWS and TO clauses accept any type of integer expression as their arguments, without parentheses.Of course, parentheses may still be needed for nested evaluations inside the expression, and a subquery must always be enclosed in parentheses.

Important

Numbering of rows in the intermediate set — the overall set cached on disk before the “slice” is extracted — starts at 1.
OFFSET/FETCH, FIRST/SKIP, and ROWS can all be used without the ORDER BY clause, although it rarely makes sense to do so — except perhaps when you want to take a quick look at the table data and don’t care that rows will be in a non-deterministic order.For this purpose, a query like “SELECT * FROM TABLE1 ROWS 20” would return the first 20 rows instead of a whole table that might be rather big.

Characteristics of Using `ROWS m` Without a `TO` Clause:

Calling ROWS m retrieves the first m records from the set specified.

If m is greater than the total number of records in the intermediate data set, the entire set is returned
If m = 0, an empty set is returned
If m < 0, the SELECT statement call fails with an error

Characteristics of Using `ROWS m` With a `TO` Clause:

Calling ROWS m TO n retrieves the rows from the set, starting at row m and ending after row n — the set is inclusive.

If m is greater than the total number of rows in the intermediate set and n >= m, an empty set is returned
If m is not greater than n and n is greater than the total number of rows in the intermediate set, the result set will be limited to rows starting from m, up to the end of the set
If m < 1 and n < 1, the SELECT statement call fails with an error
If n = m - 1, an empty set is returned
If n < m - 1, the SELECT statement call fails with an error

Not Possible to Use a `TO` Clause Without a `ROWS` Clause:

While ROWS is an alternative to the FIRST and SKIP syntax, there is one situation where the ROWS syntax does not provide the same behaviour: specifying SKIP n on its own returns the entire intermediate set, without the first n rows.The ROWS … TO syntax needs a little help to achieve this.

With the ROWS syntax, you need a ROWS clause in association with the TO clause and deliberately make the second (n) argument greater than the size of the intermediate data set.This is achieved by creating an expression for n that uses a subquery to retrieve the count of rows in the intermediate set and adds 1 to it, or use a literal with a sufficiently large value.

Replacing of `FIRST`/`SKIP` and `OFFSET`/`FETCH`

The ROWS clause can be used instead of the SQL-standard OFFSET/FETCH or non-standard FIRST/SKIP clauses, except the case where only OFFSET or SKIP is used, that is when the whole result set is returned except for skipping the specified number of rows from the beginning.

To implement this behaviour using ROWS, you must specify the TO clause with a value larger than the size of the returned result set.

Mixing `ROWS` and `FIRST`/`SKIP` or `OFFSET`/`FETCH`

ROWS syntax cannot be mixed with FIRST/SKIP or OFFSET/FETCH in the same SELECT expression.Using the different syntaxes in different subqueries in the same statement is allowed.

`ROWS` Syntax in `UNION` Queries

When ROWS is used in a UNION query, the ROWS directive is applied to the unioned set and must be placed after the last SELECT statement.

If a need arises to limit the subsets returned by one or more SELECT statements inside UNION, there are a couple of options:

Use FIRST/SKIP syntax in these SELECT statements — bearing in mind that an ordering clause (ORDER BY) cannot be applied locally to the discrete queries, but only to the combined output.
Convert the queries to derived tables with their own ROWS clauses.
Use parenthesized query expressions with OFFSET/FETCH

Examples of `ROWS`

The following examples rewrite the examples used in the section about FIRST and SKIP, earlier in this chapter.

Retrieve the first ten names from the output of a sorted query on the PEOPLE table:

SELECT id, name
FROM People
ORDER BY name ASC
ROWS 1 TO 10;

or its equivalent

SELECT id, name
FROM People
ORDER BY name ASC
ROWS 10;

Return all records from the PEOPLE table except for the first 10 names:

SELECT id, name
FROM People
ORDER BY name ASC
ROWS 11 TO (SELECT COUNT(*) FROM People);

And this query will return the last 10 records (pay attention to the parentheses):

SELECT id, name
FROM People
ORDER BY name ASC
ROWS (SELECT COUNT(*) - 9 FROM People)
TO (SELECT COUNT(*) FROM People);

This one will return rows 81-100 from the PEOPLE table:

SELECT id, name
FROM People
ORDER BY name ASC
ROWS 81 TO 100;

Note	`ROWS` can also be used with the `UPDATE` and `DELETE` statements.

`OFFSET`, `FETCH`

Retrieves a slice of rows from an ordered set

Syntax

SELECT <columns> FROM ...
  [WHERE ...]
  [ORDER BY ...]
  [OFFSET <offset-fetch-expression> { ROW | ROWS }]
  [FETCH { FIRST | NEXT }
   [<offset-fetch-expression>] { ROW | ROWS } ONLY]

<offset-fetch-expression> ::=
    <integer-literal>
  | <query-parameter>

Table 1. Arguments for the `OFFSET` and `FETCH` Clause
Argument	Description
integer-literal	Integer literal
query-parameter	Query parameter place-holder.`?` in DSQL and `:paramname` in PSQL

The OFFSET and FETCH clauses are an SQL standard-compliant equivalent for FIRST/SKIP, and an alternative for ROWS.The OFFSET clause specifies the number of rows to skip.The FETCH clause specifies the number of rows to fetch.

When <offset-fetch-expression> is left out of the FETCH clause (e.g. FETCH FIRST ROW ONLY), one row will be fetched.

The choice between ROW or ROWS, or FIRST or NEXT in the clauses is just for aesthetic purposes (e.g. making the query more readable or grammatically correct).There is no difference between OFFSET 10 ROW or OFFSET 10 ROWS, or FETCH NEXT 10 ROWS ONLY or FETCH FIRST 10 ROWS ONLY.

As with SKIP and FIRST, OFFSET and FETCH clauses can be applied independently, in both top-level and nested query expressions.

Note

Firebird doesn’t support the percentage FETCH nor the FETCH … WITH TIES defined in the SQL standard.
The FIRST/SKIP and ROWS clause are non-standard alternatives.
The OFFSET and/or FETCH clauses cannot be combined with ROWS or FIRST/SKIP on the same query expression.
Expressions, column references, etc. are not allowed within either clause.
Contrary to the ROWS clause, OFFSET and FETCH are only available on SELECT statements.

Examples of `OFFSET` and `FETCH`

Return all rows except the first 10, ordered by column COL1

SELECT *
FROM T1
ORDER BY COL1
OFFSET 10 ROWS

Return the first 10 rows, ordered by column COL1

SELECT *
FROM T1
ORDER BY COL1
FETCH FIRST 10 ROWS ONLY

Using OFFSET and FETCH clauses in a derived table and in the outer query

SELECT *
FROM (
  SELECT *
  FROM T1
  ORDER BY COL1 DESC
  OFFSET 1 ROW
  FETCH NEXT 10 ROWS ONLY
) a
ORDER BY a.COL1
FETCH FIRST ROW ONLY

The following examples rewrite the FIRST/SKIP examples and ROWS examples earlier in this chapter.

Retrieve the first ten names from the output of a sorted query on the PEOPLE table:

SELECT id, name
FROM People
ORDER BY name ASC
FETCH NEXT 10 ROWS ONLY;

Return all records from the PEOPLE table except for the first 10 names:

SELECT id, name
FROM People
ORDER BY name ASC
OFFSET 10 ROWS;

And this query will return the last 10 records.Contrary to FIRST/SKIP and ROWS we cannot use expressions (including sub-queries).To retrieve the last 10 rows, reverse the sort to the first (last) 10 rows, and then sort in the right order.

SELECT id, name
FROM (
  SELECT id, name
  FROM People
  ORDER BY name DESC
  FETCH FIRST 10 ROWS ONLY
) a
ORDER BY name ASC;

This one will return rows 81-100 from the PEOPLE table:

SELECT id, name
FROM People
ORDER BY name ASC
OFFSET 80 ROWS
FETCH NEXT 20 ROWS;

`FOR UPDATE [OF]`

Syntax

SELECT ... FROM single_table
  [WHERE ...]
  FOR UPDATE [OF <column-name-list>]

FOR UPDATE does not do what its name suggests.Its only effect currently is to disable the pre-fetch buffer.

Tip	It is likely to change in a future Firebird version: the plan is to validate cursors marked with `FOR UPDATE` if they are truly updatable and reject positioned updates and deletes for cursors evaluated as non-updatable.

The OF sub-clause does not do anything at all, and is only provided for syntax compatibility with other database systems.

`WITH LOCK`

Applies limited pessimistic locking

Syntax

SELECT ... FROM single_table
  [WHERE ...]
  [FOR UPDATE [OF <column_list>]]
  WITH LOCK [SKIP LOCKED]

WITH LOCK provides a limited explicit pessimistic locking capability for cautious use in conditions where the affected row set is:

extremely small (ideally singleton), and
precisely controlled by the application code.

Caution

This is for experts only!

The need for a pessimistic lock in Firebird is very rare indeed and should be well understood before use of this extension is considered.

It is essential to understand the effects of transaction isolation and other transaction attributes before attempting to implement explicit locking in your application.

If the WITH LOCK clause succeeds, it will secure a lock on the selected rows and prevent any other transaction from obtaining write access to any of those rows, or their dependants, until your transaction ends.

WITH LOCK can only be used with a top-level, single-table SELECT statement.It is not available:

in a subquery specification
for joined sets
with the DISTINCT operator, a GROUP BY clause or any other aggregating operation
with a view
with the output of a selectable stored procedure
with an external table
with a UNION query

As the engine considers, in turn, each record falling under an explicit lock statement, it returns either the record version that is the most currently committed, regardless of database state when the statement was submitted, or an exception.

When the optional SKIP LOCKED clause is specified, records locked by a different transaction are skipped.

Note	If a statement has both `SKIP LOCKED` and `OFFSET`/`SKIP`/`ROWS` subclauses, locked rows may be skipped before `OFFSET`/`SKIP`/`ROWS` subclause can account for them, thus skipping more rows than specified in `OFFSET`/`SKIP`/`ROWS`.

Wait behaviour and conflict reporting depend on the transaction parameters specified in the TPB block:

Table 1. How TPB settings affect explicit locking
TPB mode	Behaviour
isc_tpb_consistency	Explicit locks are overridden by implicit or explicit table-level locks and are ignored.
isc_tpb_concurrency + isc_tpb_nowait	If a record is modified by any transaction that was committed since the transaction attempting to get explicit lock started, or an active transaction has performed a modification of this record, an update conflict exception is raised immediately.
isc_tpb_concurrency + isc_tpb_wait	If the record is modified by any transaction that has committed since the transaction attempting to get explicit lock started, an update conflict exception is raised immediately. If an active transaction is holding ownership on this record (via explicit locking or by a normal optimistic write-lock) the transaction attempting the explicit lock waits for the outcome of the blocking transaction and, when it finishes, attempts to get the lock on the record again.This means that, if the blocking transaction committed a modified version of this record, an update conflict exception will be raised.
isc_tpb_read_committed + isc_tpb_nowait	If there is an active transaction holding ownership on this record (via explicit locking or normal update), an update conflict exception is raised immediately.
isc_tpb_read_committed + isc_tpb_wait	If there is an active transaction holding ownership on this record (via explicit locking or by a normal optimistic write-lock), the transaction attempting the explicit lock waits for the outcome of blocking transaction and when it finishes, attempts to get the lock on the record again. Update conflict exceptions can never be raised by an explicit lock statement in this TPB mode.

Usage with a `FOR UPDATE` Clause

If the FOR UPDATE sub-clause precedes the WITH LOCK sub-clause, buffered fetches are suppressed.Thus, the lock will be applied to each row, one by one, at the moment it is fetched.It becomes possible, then, that a lock which appeared to succeed when requested will nevertheless fail subsequently, when an attempt is made to fetch a row which has become locked by another transaction in the meantime.This can be avoided by also using SKIP LOCKED.

Tip	As an alternative, it may be possible in your access components to set the size of the fetch buffer to 1.This would enable you to process the currently-locked row before the next is fetched and locked, or to handle errors without rolling back your transaction.

See also

FOR UPDATE [OF]

How the engine deals with `WITH LOCK`

When an UPDATE statement tries to access a record that is locked by another transaction, it either raises an update conflict exception or waits for the locking transaction to finish, depending on TPB mode.Engine behaviour here is the same as if this record had already been modified by the locking transaction.

No special error codes are returned from conflicts involving pessimistic locks.

The engine guarantees that all records returned by an explicit lock statement are locked and do meet the search conditions specified in WHERE clause, as long as the search conditions do not depend on any other tables, via joins, subqueries, etc.It also guarantees that rows not meeting the search conditions will not be locked by the statement.It can not guarantee that there are no rows which, though meeting the search conditions, are not locked.

Note	This situation can arise if other, parallel transactions commit their changes during the course of the locking statement’s execution.

The engine locks rows at fetch time.This has important consequences if you lock several rows at once.Many access methods for Firebird databases default to fetching output in packets of a few hundred rows (“buffered fetches”).Most data access components cannot bring you the rows contained in the last-fetched packet, when an error occurred.

Caveats using `WITH LOCK`

Rolling back of an implicit or explicit savepoint releases record locks that were taken under that savepoint, but it doesn’t notify waiting transactions.Applications should not depend on this behaviour as it may get changed in the future.
While explicit locks can be used to prevent and/or handle unusual update conflict errors, the volume of deadlock errors will grow unless you design your locking strategy carefully and control it rigorously.
Most applications do not need explicit locks at all.The main purposes of explicit locks are:
1. to prevent expensive handling of update conflict errors in heavily loaded applications, and
2. to maintain integrity of objects mapped to a relational database in a clustered environment.
If your use of explicit locking doesn’t fall in one of these two categories, then it’s probably the wrong way to do the task in Firebird.
Explicit locking is an advanced feature;do not misuse it!While solutions for these kinds of problems may be important for websites handling thousands of concurrent writers, or for ERP/CRM systems operating in large corporations, most application programs do not need to work in such conditions.

Examples using explicit locking

Simple:

SELECT * FROM DOCUMENT WHERE ID=? WITH LOCK;

Multiple rows, one-by-one processing with DSQL cursor:

SELECT * FROM DOCUMENT WHERE PARENT_ID=?
  FOR UPDATE WITH LOCK;

`OPTIMIZE FOR`

Syntax

SELECT ...
  [WITH LOCK [SKIP LOCKED]]
  OPTIMIZE FOR {FIRST | ALL} ROWS

The OPTIMIZE FOR clause can only occur on a top-level SELECT.

This feature allows the optimizer to consider another (hopefully better) plan if only a subset or rows is fetched initially by the user application (with the remaining rows being fetched on demand), thus improving the response time.

It can also be specified at the session level using the SET OPTIMIZE management statement.

The default behaviour can be specified globally using the OptimizeForFirstRows setting in firebird.conf or databases.conf.

`INTO`

Passes SELECT output into variables

Available in

PSQL

Syntax

In PSQL the INTO clause is placed at the end of the SELECT statement.

SELECT [...] <column-list>
FROM ...
[...]
[INTO <variable-list>]

<variable-list> ::= <variable> [, <variable> ...]

<variable> ::= varname | ':' varname

Note	The colon prefix before local variable names in PSQL is optional in the `INTO` clause.

In PSQL code (triggers, stored procedures and executable blocks), the results of a SELECT statement can be loaded row-by-row into local variables.It is often the only way to do anything with the returned values at all, unless an explicit or implicit cursor name is specified.The number, order and types of the variables must match the columns in the output row.

A “plain” SELECT statement can only be used in PSQL if it returns at most one row, i.e. if it is a singleton select.For multi-row selects, PSQL provides the FOR SELECT loop construct, discussed later in the PSQL chapter.PSQL also supports the DECLARE CURSOR statement, which binds a named cursor to a SELECT statement.The cursor can then be used to walk the result set.

Examples

Selecting aggregated values and passing them into previously declared variables min_amt, avg_amt and max_amt:
```
select min(amount), avg(cast(amount as float)), max(amount)
  from orders
  where artno = 372218
  into min_amt, avg_amt, max_amt;
```
Note

The CAST serves to make the average a real number;otherwise, since amount is presumably an integer field, SQL rules would truncate it to the nearest lower integer.
A PSQL trigger that retrieves two values as a BLOB field (using the LIST() function) and assigns it INTO a third field:
```
select list(name, ', ')
  from persons p
  where p.id in (new.father, new.mother)
  into new.parentnames;
```

Common Table Expressions (“`WITH … AS … SELECT`”)

Syntax

<query-expression> ::=
  [<with-clause>] <query-expression-body> [<order-by-clause>]
    [{ <rows-clause>
     | [<result-offset-clause>] [<fetch-first-clause>] }]

<with-clause> ::=
  WITH [RECURSIVE] <with-list-element> [, <with-list-element> ...]

<with-list-element> ::=
  query-name [(<column-name-list>)] AS (<query-expression>)

<column-name-list> ::= column-name [, column-name ...]

Table 1. Arguments for Common Table Expressions
Argument	Description
query-name	Alias for a table expression
column-name	Alias for a column in a table expression

A common table expression or CTE can be described as a virtual table or view, defined in a preamble to a main query, and going out of scope after the main query’s execution.The main query can reference any CTEs defined in the preamble as if they were regular tables or views.CTEs can be recursive, i.e. self-referencing, but they cannot be nested.

CTE Notes

A CTE definition can contain any legal query-expression, as long as it doesn’t have a “WITH…” preamble of its own (no nesting).
CTEs defined for the same main query can reference each other, but care should be taken to avoid loops.
CTEs can be referenced from anywhere in the main query.
Each CTE can be referenced multiple times in the main query, using different aliases if necessary.
When enclosed in parentheses, CTE constructs can be used as subqueries in SELECT statements, but also in UPDATEs, MERGEs etc.

In PSQL, CTEs are also supported in FOR loop headers:

for
  with my_rivers as (select * from rivers where owner = 'me')
    select name, length from my_rivers into :rname, :rlen
do
begin
  ..
end

Example

with dept_year_budget as (
  select fiscal_year,
         dept_no,
         sum(projected_budget) as budget
  from proj_dept_budget
  group by fiscal_year, dept_no
)
select d.dept_no,
       d.department,
       dyb_2008.budget as budget_08,
       dyb_2009.budget as budget_09
from department d
     left join dept_year_budget dyb_2008
       on d.dept_no = dyb_2008.dept_no
       and dyb_2008.fiscal_year = 2008
     left join dept_year_budget dyb_2009
       on d.dept_no = dyb_2009.dept_no
       and dyb_2009.fiscal_year = 2009
where exists (
  select * from proj_dept_budget b
  where d.dept_no = b.dept_no
);

Recursive CTEs

A recursive (self-referencing) CTE is a UNION which must have at least one non-recursive member, called the anchor.The non-recursive member(s) must be placed before the recursive member(s).Recursive members are linked to each other and to their non-recursive neighbour by UNION ALL operators.The unions between non-recursive members may be of any type.

Recursive CTEs require the RECURSIVE keyword to be present right after WITH.Each recursive union member may reference itself only once, and it must do so in a FROM clause.

A great benefit of recursive CTEs is that they use far less memory and CPU cycles than an equivalent recursive stored procedure.

Execution Pattern

The execution pattern of a recursive CTE is as follows:

The engine begins execution from a non-recursive member.
For each row evaluated, it starts executing each recursive member one by one, using the current values from the outer row as parameters.
If the currently executing instance of a recursive member produces no rows, execution loops back one level and gets the next row from the outer result set.

Example of recursive CTEs

WITH RECURSIVE DEPT_YEAR_BUDGET AS (
  SELECT
      FISCAL_YEAR,
      DEPT_NO,
      SUM(PROJECTED_BUDGET) BUDGET
  FROM PROJ_DEPT_BUDGET
  GROUP BY FISCAL_YEAR, DEPT_NO
),
DEPT_TREE AS (
  SELECT
      DEPT_NO,
      HEAD_DEPT,
      DEPARTMENT,
      CAST('' AS VARCHAR(255)) AS INDENT
  FROM DEPARTMENT
  WHERE HEAD_DEPT IS NULL
  UNION ALL
  SELECT
      D.DEPT_NO,
      D.HEAD_DEPT,
      D.DEPARTMENT,
      H.INDENT || ' '
  FROM DEPARTMENT D
    JOIN DEPT_TREE H ON H.HEAD_DEPT = D.DEPT_NO
)
SELECT
    D.DEPT_NO,
    D.INDENT || D.DEPARTMENT DEPARTMENT,
    DYB_2008.BUDGET AS BUDGET_08,
    DYB_2009.BUDGET AS BUDGET_09
FROM DEPT_TREE D
    LEFT JOIN DEPT_YEAR_BUDGET DYB_2008 ON
      (D.DEPT_NO = DYB_2008.DEPT_NO) AND
      (DYB_2008.FISCAL_YEAR = 2008)
    LEFT JOIN DEPT_YEAR_BUDGET DYB_2009 ON
      (D.DEPT_NO = DYB_2009.DEPT_NO) AND
      (DYB_2009.FISCAL_YEAR = 2009);

The next example returns the pedigree of a horse.The main difference is that recursion occurs simultaneously in two branches of the pedigree.

WITH RECURSIVE PEDIGREE (
  CODE_HORSE,
  CODE_FATHER,
  CODE_MOTHER,
  NAME,
  MARK,
  DEPTH)
AS (SELECT
      HORSE.CODE_HORSE,
      HORSE.CODE_FATHER,
      HORSE.CODE_MOTHER,
      HORSE.NAME,
      CAST('' AS VARCHAR(80)),
      0
    FROM
      HORSE
    WHERE
      HORSE.CODE_HORSE = :CODE_HORSE
    UNION ALL
    SELECT
      HORSE.CODE_HORSE,
      HORSE.CODE_FATHER,
      HORSE.CODE_MOTHER,
      HORSE.NAME,
      'F' || PEDIGREE.MARK,
      PEDIGREE.DEPTH + 1
    FROM
      HORSE
      JOIN PEDIGREE
        ON HORSE.CODE_HORSE = PEDIGREE.CODE_FATHER
    WHERE
      PEDIGREE.DEPTH < :MAX_DEPTH
    UNION ALL
    SELECT
      HORSE.CODE_HORSE,
      HORSE.CODE_FATHER,
      HORSE.CODE_MOTHER,
      HORSE.NAME,
      'M' || PEDIGREE.MARK,
      PEDIGREE.DEPTH + 1
    FROM
      HORSE
      JOIN PEDIGREE
        ON HORSE.CODE_HORSE = PEDIGREE.CODE_MOTHER
    WHERE
      PEDIGREE.DEPTH < :MAX_DEPTH
)
SELECT
  CODE_HORSE,
  NAME,
  MARK,
  DEPTH
FROM
  PEDIGREE

Notes on recursive CTEs

Aggregates (DISTINCT, GROUP BY, HAVING) and aggregate functions (SUM, COUNT, MAX etc) are not allowed in recursive union members.
A recursive reference cannot participate in an outer join.
The maximum recursion depth is 1024.

Full `SELECT` Syntax

The previous sections used incomplete or simplified fragments of the SELECT syntax.Following is the full syntax.

Note

Where possible, the syntax below uses syntax names from the SQL standard, which do not necessarily match the syntax names in the Firebird source.In some cases, syntax productions have been collapsed, because the productions in the SQL standard are verbose as they are also used to add additional rules or definitions to a syntax element.

Although this is intended as the full syntax, some productions are not shown (e.g. <value-expression>) and assumed to be clear for the reader, and in some cases we take shortcuts like using query-name or column-alias for identifiers in a syntax production.

If you come across situations where these shortcuts do result in lack of clarity or other issues, let us know on https://github.com/FirebirdSQL/firebird-documentation or on firebird-devel.

The syntax below does not include the PSQL SELECT … INTO syntax, which is essentially <cursor-specification> INTO <variable-list>.

<cursor-specification> ::=
  <query-expression> [<updatability-clause>] [<lock-clause>]

<query-expression> ::=
  [<with-clause>] <query-expression-body> [<order-by-clause>]
    [{ <rows-clause>
     | [<result-offset-clause>] [<fetch-first-clause>] }]

<with-clause> ::=
  WITH [RECURSIVE] <with-list-element> [, <with-list-element> ...]

<with-list-element> ::=
  query-name [(<column-name-list>)] AS (<query-expression>)

<column-name-list> ::= column-name [, column-name ...]

<query-expression-body> ::=
    <query-term>
  | <query-expression-body> UNION [{ DISTINCT | ALL }] <query-term>

<query-term> ::= <query-primary>

<query-primary> ::=
    <query-specification>
  | (<query-expression-body> [<order-by-clause>]
     [<result-offset-clause>] [<fetch-first-clause>])

<query-specification> ::=
  SELECT <limit-clause> [{ ALL | DISTINCT }] <select-list>
    FROM <table-reference> [, <table-reference> ...]
    [WHERE <search-condition>]
    [GROUP BY <value-expression> [, <value-expression> ...]]
    [HAVING <search-condition>]
    [WINDOW <window-definition> [, <window-definition> ...]]
    [PLAN <plan-expression>]

<limit-clause> ::= [FIRST <limit-expression>] [SKIP <limit-expression>]

<limit-expression> ::=
    <integer-literal>
  | <query-parameter>
  | (<value-expression>)

<select-list> ::= * | <select-sublist> [, <select-sublist> ...]

<select-sublist> ::=
    table-alias.*
  | <value-expression> [[AS] column-alias]

<table-reference> ::= <table-primary> | <joined-table>

<table-primary> ::=
    <table-or-query-name> [[AS] correlation-name]
  | [LATERAL] <derived-table> [<correlation-or-recognition>]
  | <parenthesized-joined-table>

<table-or-query-name> ::=
    table-name
  | query-name
  | [package-name.]procedure-name [(<procedure-args>)]

<procedure-args> ::= <value-expression [, <value-expression> ...]

<correlation-or-recognition> ::=
  [AS] correlation-name [(<column-name-list>)]

<derived-table> ::= (<query-expression>)

<parenthesized-joined-table> ::=
    (<parenthesized-joined-table)
  | (<joined-table>)

<joined-table> ::=
    <cross-join>
  | <natural-join>
  | <qualified-join>

<cross-join>
  <table-reference> CROSS JOIN <table-primary>

<natural-join> ::=
  <table-reference> NATURAL [<join-type>] JOIN <table-primary>

<join-type> ::= INNER | { LEFT | RIGHT | FULL } [OUTER]

<qualified-join> ::=
  <table-reference> [<join-type>] JOIN <table-primary>
  { ON <search-condition>
  | USING (<column-name-list>) }

<window-definition> ::=
  new-window-name AS (<window-specification-details>)

<window-specification-details> ::=
  [existing-window-name]
    [<window-partition-clause>]
    [<order-by-clause>]
    [<window-frame-clause>]

<window-partition-clause> ::=
  PARTITION BY <value-expression> [, <value-expression> ...]

<order-by-clause> ::=
  ORDER BY <sort-specification [, <sort-specification> ...]

<sort-specification> ::=
  <value-expression> [<ordering-specification>] [<null-ordering>]

<ordering-specification> ::=
    ASC  | ASCENDING
  | DESC | DESCENDING

<null-ordering> ::=
    NULLS FIRST
  | NULLS LAST

<window-frame-clause> ::= { RANGE | ROWS } <window-frame-extent>

<window-frame-extent> ::=
    <window-frame-start>
  | <window-frame-between>

<window-frame-start> ::=
    UNBOUNDED PRECEDING
  | <value-expression> PRECEDING
  | CURRENT ROW

<window-frame-between> ::=
  BETWEEN { UNBOUNDED PRECEDING | <value-expression> PRECEDING
          | CURRENT ROW | <value-expression> FOLLOWING }
  AND { <value-expression> PRECEDING | CURRENT ROW
      | <value-expression> FOLLOWING | UNBOUNDED FOLLOWING }

<rows-clause> ::= ROWS <value-expression> [TO <value-expression>]

<result-offset-clause> :: =
  OFFSET <offset-fetch-expression> { ROW | ROWS }

<offset-fetch-expression> ::=
    <integer-literal>
  | <query-parameter>

<fetch-first-clause> ::=
  [FETCH { FIRST | NEXT }
   [<offset-fetch-expression>] { ROW | ROWS } ONLY]

<updatability-clause> ::= FOR UPDATE [OF <column-name-list>]

<lock-clause> ::= WITH LOCK [SKIP LOCKED]

The `SELECT` Columns List

The columns list contains one or more comma-separated value expressions.Each expression provides a value for one output column.Alternatively, * (“star” or “all”) can be used to stand for all the columns of all relations in the FROM clause.

Syntax

SELECT
  [...]
  [{ ALL | DISTINCT }] <select-list>
  [...]
  FROM ...

<select_list> ::= * | <select-sublist> [, <select-sublist> ...]

<select-sublist> ::=
    table-alias.*
  | <value-expression> [[AS] column-alias]

<value-expression> ::=
    [table-alias.]col_name
  | [table-alias.]selectable_SP_outparm
  | <literal>
  | <context-variable>
  | <function-call>
  | <single-value-subselect>
  | <CASE-construct>
  | any other expression returning a single
    value of a Firebird data type or NULL

<function-call> ::=
    <normal-function>
  | <aggregate-function>
  | <window-function>

<normal-function> ::=
  !! See Built-in Scalar Functions !!

<aggregate-function> ::=
  !! See Aggregate Functions !!

<window-function> ::=
  !! See Window Functions !!

Table 1. Arguments for the `SELECT` Columns List
Argument	Description
table-alias	Name of relation (view, stored procedure, derived table), or its alias
col_name	Name of a table or view column, or its alias
selectable_SP_outparm	Declared name of an output parameter of a selectable stored procedure
literal	A literal
context-variable	Context variable
function-call	Scalar, aggregate, or window function expression
single-value-subselect	A subquery returning one scalar value (singleton)
CASE-construct	CASE construct setting conditions for a return value

It is always valid to qualify a column name (or “*”) with the name or alias of the table, view or selectable SP to which it belongs, followed by a dot (‘.’).For example, relationname.columnname, relationname.*, alias.columnname, alias.*.Qualifying is required if the column name occurs in more than one relation taking part in a join.Qualifying “*” is required if it is not the only item in the column list.

Important

Aliases hide the original relation name: once a table, view or procedure has been aliased, only the alias can be used as its qualifier throughout the query.The relation name itself becomes unavailable.

The column list may optionally be preceded by one of the keywords DISTINCT or ALL:

DISTINCT filters out any duplicate rows.That is, if two or more rows have the same values in every corresponding column, only one of them is included in the result set
ALL is the default: it returns all rows, including duplicates.ALL is rarely used;it is allowed for compliance with the SQL standard.

A COLLATE clause of a value-expression will not change the appearance of the column as such.However, if the specified collation changes the case or accent sensitivity of the column, it may influence:

The ordering, if an ORDER BY clause is also present, and it involves that column
Grouping, if the column is part of a GROUP BY clause
The rows retrieved (and hence the total number of rows in the result set), if DISTINCT is used

Examples of `SELECT` queries with different types of column lists

A simple SELECT using only column names:

select cust_id, cust_name, phone
  from customers
  where city = 'London'

A query featuring a concatenation expression and a function call in the columns list:

select 'Mr./Mrs. ' || lastname, street, zip, upper(city)
  from contacts
  where date_last_purchase(id) = current_date

A query with two subselects:

select p.fullname,
  (select name from classes c where c.id = p.class) as class,
  (select name from mentors m where m.id = p.mentor) as mentor
from pupils p

The following query accomplishes the same as the previous one using joins instead of subselects:

select p.fullname,
  c.name as class,
  m.name as mentor
  join classes c on c.id = p.class
from pupils p
  join mentors m on m.id = p.mentor

This query uses a CASE construct to determine the correct title, e.g. when sending mail to a person:

select case upper(sex)
    when 'F' then 'Mrs.'
    when 'M' then 'Mr.'
    else ''
  end as title,
  lastname,
  address
from employees

Query using a window function, ranks employees by salary.

SELECT
  id,
  salary,
  name ,
  DENSE_RANK() OVER (ORDER BY salary) AS EMP_RANK
FROM employees
ORDER BY salary;

Querying a selectable stored procedure:

select * from interesting_transactions(2010, 3, 'S')
  order by amount

Selecting from columns of a derived table.A derived table is a parenthesized SELECT statement whose result set is used in an enclosing query as if it were a regular table or view.The derived table is shown in bold here:

select fieldcount,
  count(relation) as num_tables
from (select r.rdb$relation_name as relation,
        count(*) as fieldcount
      from rdb$relations r
        join rdb$relation_fields rf
          on rf.rdb$relation_name = r.rdb$relation_name
      group by relation)
group by fieldcount

Asking the time through a context variable (CURRENT_TIME):

select current_time from rdb$database

For those not familiar with RDB$DATABASE: this is a system table that is present in all Firebird databases and is guaranteed to contain exactly one row.Although it wasn’t created for this purpose, it has become standard practice among Firebird programmers to select from this table if you want to select “from nothing”, i.e. if you need data that are not bound to a table or view, but can be derived from the expressions in the output columns alone.Another example is:

select power(12, 2) as twelve_squared, power(12, 3) as twelve_cubed
  from rdb$database

Finally, an example where you select some meaningful information from RDB$DATABASE itself:

select rdb$character_set_name from rdb$database

As you may have guessed, this will give you the default character set of the database.

The `FROM` clause

The FROM clause specifies the source(s) from which the data are to be retrieved.In its simplest form, this is a single table or view.However, the source can also be a selectable stored procedure, a derived table, or a common table expression.Multiple sources can be combined using various types of joins.

This section focuses on single-source selects.Joins are discussed in a following section.

Syntax

SELECT
  ...
  FROM <table-reference> [, <table-reference> ...]
  [...]

<table-reference> ::= <table-primary> | <joined-table>

<table-primary> ::=
    <table-or-query-name> [[AS] correlation-name]
  | [LATERAL] <derived-table> [<correlation-or-recognition>]
  | <parenthesized-joined-table>

<table-or-query-name> ::=
    table-name
  | query-name
  | [package-name.]procedure-name [(<procedure-args>)]

<procedure-args> ::= <value-expression [, <value-expression> ...]

<derived-table> ::= (<query-expression>)

<correlation-or-recognition> ::=
  [AS] correlation-name [(<column-name-list>)]

<column-name-list> ::= column-name [, column-name ...]

Table 1. Arguments for the `FROM` Clause
Argument	Description
table-name	Name of a table or view
query-name	Name of a CTE
package-name	Name of a package
procedure-name	Name of a selectable stored procedure
procedure-args	Selectable stored procedure arguments
derived-table	Derived table query expression
correlation-name	The alias of a data source (table, view, procedure, CTE, derived table)
column-name	Name or alias for a column in a relation, CTE or derived table

Selecting `FROM` a table or view

When selecting from a single table or view, the FROM clause requires nothing more than the name.An alias may be useful or even necessary if there are subqueries that refer to the main select statement (as they often do — subqueries like this are called correlated subqueries).

Examples

select id, name, sex, age from actors
where state = 'Ohio'

select * from birds
where type = 'flightless'
order by family, genus, species

select firstname,
  middlename,
  lastname,
  date_of_birth,
  (select name from schools s where p.school = s.id) schoolname
from pupils p
where year_started = '2012'
order by schoolname, date_of_birth

Important

Never mix column names with column aliases!

If you specify an alias for a table or a view, you must always use this alias in place of the table name whenever you query the columns of the relation (and wherever else you make a reference to columns, such as ORDER BY, GROUP BY and WHERE clauses).

Correct use:

SELECT PEARS
FROM FRUIT;

SELECT FRUIT.PEARS
FROM FRUIT;

SELECT PEARS
FROM FRUIT F;

SELECT F.PEARS
FROM FRUIT F;

Incorrect use:

SELECT FRUIT.PEARS
FROM FRUIT F;

Selecting `FROM` a stored procedure

A selectable stored procedure is a procedure that:

contains at least one output parameter, and
utilizes the SUSPEND keyword so the caller can fetch the output rows one by one, like selecting from a table or view.

The output parameters of a selectable stored procedure correspond to the columns of a regular table.

Selecting from a stored procedure without input parameters is like selecting from a table or view:

select * from suspicious_transactions
  where assignee = 'John'

Any required input parameters must be specified after the procedure name, enclosed in parentheses:

select name, az, alt from visible_stars('Brugge', current_date, '22:30')
  where alt >= 20
  order by az, alt

Values for optional parameters (that is, parameters for which default values have been defined) may be omitted or provided.However, if you provide them only partly, the parameters you omit must all be at the tail end.

Supposing that the procedure visible_stars from the previous example has two optional parameters: min_magn numeric(3,1) and spectral_class varchar(12), the following queries are all valid:

select name, az, alt
from visible_stars('Brugge', current_date, '22:30');

select name, az, alt
from visible_stars('Brugge', current_date, '22:30', 4.0);

select name, az, alt
from visible_stars('Brugge', current_date, '22:30', 4.0, 'G');

But this one isn’t, because there’s a “hole” in the parameter list:

select name, az, alt
from visible_stars('Brugge', current_date, '22:30', 'G');

An alias for a selectable stored procedure is specified after the parameter list:

select
  number,
  (select name from contestants c where c.number = gw.number)
from get_winners('#34517', 'AMS') gw

If you refer to an output parameter (“column”) by qualifying it with the full procedure name, the procedure alias should be omitted:

select
  number,
  (select name from contestants c where c.number = get_winners.number)
from get_winners('#34517', 'AMS')

See also

Stored Procedures, CREATE PROCEDURE

Selecting `FROM` a derived table

A derived table is a valid SELECT statement enclosed in parentheses, optionally followed by a table alias and/or column aliases.The result set of the statement acts as a virtual table which the enclosing statement can query.

Syntax

(<query-expression>) [<correlation-or-recognition>]

<correlation-or-recognition> ::=
  [AS] correlation-name [(<column-name-list>)]

<column-name-list> ::= column-name [, column-name ...]

Note	The SQL standard requires the <correlation-or-recognition>, and not providing one makes it hard to reference the derived table or its columns.For maximum compatibility and portability, we recommend always specifying an alias (correlation-name).

The result set returned by this “SELECT … FROM (SELECT FROM …)” style of statement is a virtual table that can be queried within the enclosing statement, as if it were a regular table or view.

`LATERAL` Derived Tables

The keyword LATERAL marks a table as a lateral derived table.Lateral derived tables can reference tables (including other derived tables) that occur earlier in the FROM clause.See [fblangref50-dml-select-joins-lateral] for more information.

Example using a derived table

The derived table in the query below returns the list of table names in the database, and the number of columns in each table.A “drill-down” query on the derived table returns the counts of fields and the counts of tables having each field count:

SELECT
  FIELDCOUNT,
  COUNT(RELATION) AS NUM_TABLES
FROM (SELECT
        R.RDB$RELATION_NAME RELATION,
        COUNT(*) AS FIELDCOUNT
      FROM RDB$RELATIONS R
        JOIN RDB$RELATION_FIELDS RF
        ON RF.RDB$RELATION_NAME = R.RDB$RELATION_NAME
        GROUP BY RELATION)
GROUP BY FIELDCOUNT

A trivial example demonstrating how the alias of a derived table and the list of column aliases (both optional) can be used:

SELECT
  DBINFO.DESCR, DBINFO.DEF_CHARSET
FROM (SELECT *
      FROM RDB$DATABASE) DBINFO
        (DESCR, REL_ID, SEC_CLASS, DEF_CHARSET)

Note

More about Derived Tables

Derived tables can

be nested
be unions, and can be used in unions
contain aggregate functions, subqueries and joins
be used in aggregate functions, subqueries and joins
be calls to selectable stored procedures or queries to them
have WHERE, ORDER BY and GROUP BY clauses, FIRST/SKIP or ROWS directives, et al.

Furthermore,

Each column in a derived table must have a name.If it does not have a name, such as when it is a constant or a run-time expression, it should be given an alias, either in the regular way or by including it in the list of column aliases in the derived table’s specification.
- The list of column aliases is optional but, if it exists, it must contain an alias for every column in the derived table
The optimizer can process derived tables very effectively.However, if a derived table is included in an inner join and contains a subquery, the optimizer will be unable to use any other join order.

A more useful example

Suppose we have a table COEFFS which contains the coefficients of a number of quadratic equations we have to solve.It has been defined like this:

create table coeffs (
  a double precision not null,
  b double precision not null,
  c double precision not null,
  constraint chk_a_not_zero check (a <> 0)
)

Depending on the values of a, b and c, each equation may have zero, one or two solutions.It is possible to find these solutions with a single-level query on table COEFFS, but the code will look messy and several values (like the discriminant) will have to be calculated multiple times per row.A derived table can help keep things clean here:

select
  iif (D >= 0, (-b - sqrt(D)) / denom, null) sol_1,
  iif (D >  0, (-b + sqrt(D)) / denom, null) sol_2
  from
    (select b, b*b - 4*a*c, 2*a from coeffs) (b, D, denom)

If we want to show the coefficients next to the solutions (which may not be a bad idea), we can alter the query like this:

select
  a, b, c,
  iif (D >= 0, (-b - sqrt(D)) / denom, null) sol_1,
  iif (D >  0, (-b + sqrt(D)) / denom, null) sol_2
  from
    (select a, b, c, b*b - 4*a*c as D, 2*a as denom
     from coeffs)

Notice that whereas the first query used a column aliases list for the derived table, the second adds aliases internally where needed.Both methods work, as long as every column is guaranteed to have a name.

Important

All columns in the derived table will be evaluated as many times as they are specified in the main query.This is important, as it can lead to unexpected results when using non-deterministic functions.The following shows an example of this.

SELECT
  UUID_TO_CHAR(X) AS C1,
  UUID_TO_CHAR(X) AS C2,
  UUID_TO_CHAR(X) AS C3
FROM (SELECT GEN_UUID() AS X
      FROM RDB$DATABASE) T;

The result if this query produces three different values:

C1  80AAECED-65CD-4C2F-90AB-5D548C3C7279
C2  C1214CD3-423C-406D-B5BD-95BF432ED3E3
C3  EB176C10-F754-4689-8B84-64B666381154

To ensure a single result of the GEN_UUID function, you can use the following method:

SELECT
  UUID_TO_CHAR(X) AS C1,
  UUID_TO_CHAR(X) AS C2,
  UUID_TO_CHAR(X) AS C3
FROM (SELECT GEN_UUID() AS X
      FROM RDB$DATABASE
      UNION ALL
      SELECT NULL FROM RDB$DATABASE WHERE 1 = 0) T;

This query produces a single result for all three columns:

C1  80AAECED-65CD-4C2F-90AB-5D548C3C7279
C2  80AAECED-65CD-4C2F-90AB-5D548C3C7279
C3  80AAECED-65CD-4C2F-90AB-5D548C3C7279

An alternative solution is to wrap the GEN_UUID query in a subquery:

SELECT
  UUID_TO_CHAR(X) AS C1,
  UUID_TO_CHAR(X) AS C2,
  UUID_TO_CHAR(X) AS C3
FROM (SELECT
        (SELECT GEN_UUID() FROM RDB$DATABASE) AS X
      FROM RDB$DATABASE) T;

This is an artifact of the current implementation.This behaviour may change in a future Firebird version.

Selecting `FROM` a Common Table Expression (CTE)

A common table expression — or CTE — is a more complex variant of the derived table, but it is also more powerful.A preamble, starting with the keyword WITH, defines one or more named CTEs, each with an optional column aliases list.The main query, which follows the preamble, can then access these CTEs as if they were regular tables or views.The CTEs go out of scope once the main query has run to completion.

For a full discussion of CTEs, please refer to the section [fblangref50-dml-select-cte].

The following is a rewrite of our derived table example as a CTE:

with vars (b, D, denom) as (
  select b, b*b - 4*a*c, 2*a from coeffs
)
select
  iif (D >= 0, (-b - sqrt(D)) / denom, null) sol_1,
  iif (D >  0, (-b + sqrt(D)) / denom, null) sol_2
from vars

Except for the fact that the calculations that have to be made first are now at the beginning, this isn’t a great improvement over the derived table version.However, we can now also eliminate the double calculation of sqrt(D) for every row:

with vars (b, D, denom) as (
  select b, b*b - 4*a*c, 2*a from coeffs
),
vars2 (b, D, denom, sqrtD) as (
  select b, D, denom, iif (D >= 0, sqrt(D), null) from vars
)
select
  iif (D >= 0, (-b - sqrtD) / denom, null) sol_1,
  iif (D >  0, (-b + sqrtD) / denom, null) sol_2
from vars2

The code is a little more complicated now, but it might execute more efficiently (depending on what takes more time: executing the SQRT function or passing the values of b, D and denom through an extra CTE).Incidentally, we could have done the same with derived tables, but that would involve nesting.

Important

All columns in the CTE will be evaluated as many times as they are specified in the main query.This is important, as it can lead to unexpected results when using non-deterministic functions.The following shows an example of this.

WITH T (X) AS (
  SELECT GEN_UUID()
  FROM RDB$DATABASE)
SELECT
  UUID_TO_CHAR(X) as c1,
  UUID_TO_CHAR(X) as c2,
  UUID_TO_CHAR(X) as c3
FROM T

The result if this query produces three different values:

C1  80AAECED-65CD-4C2F-90AB-5D548C3C7279
C2  C1214CD3-423C-406D-B5BD-95BF432ED3E3
C3  EB176C10-F754-4689-8B84-64B666381154

To ensure a single result of the GEN_UUID function, you can use the following method:

WITH T (X) AS (
  SELECT GEN_UUID()
  FROM RDB$DATABASE
  UNION ALL
  SELECT NULL FROM RDB$DATABASE WHERE 1 = 0)
SELECT
  UUID_TO_CHAR(X) as c1,
  UUID_TO_CHAR(X) as c2,
  UUID_TO_CHAR(X) as c3
FROM T;

This query produces a single result for all three columns:

C1  80AAECED-65CD-4C2F-90AB-5D548C3C7279
C2  80AAECED-65CD-4C2F-90AB-5D548C3C7279
C3  80AAECED-65CD-4C2F-90AB-5D548C3C7279

An alternative solution is to wrap the GEN_UUID query in a subquery:

WITH T (X) AS (
  SELECT (SELECT GEN_UUID() FROM RDB$DATABASE)
  FROM RDB$DATABASE)
SELECT
  UUID_TO_CHAR(X) as c1,
  UUID_TO_CHAR(X) as c2,
  UUID_TO_CHAR(X) as c3
FROM T;

This is an artifact of the current implementation.This behaviour may change in a future Firebird version.

See also

[fblangref50-dml-select-cte].

Joins

Joins combine data from two sources into a single set.This is done on a row-by-row basis and usually involves checking a join condition to determine which rows should be merged and appear in the resulting dataset.There are several types (INNER, OUTER) and classes (qualified, natural, etc.) of joins, each with its own syntax and rules.

Since joins can be chained, the datasets involved in a join may themselves be joined sets.

Syntax

SELECT
   ...
   FROM <table-reference> [, <table-reference> ...]
   [...]

<table-reference> ::= <table-primary> | <joined-table>

<table-primary> ::=
    <table-or-query-name> [[AS] correlation-name]
  | [LATERAL] <derived-table> [<correlation-or-recognition>]
  | <parenthesized-joined-table>

<table-or-query-name> ::=
    table-name
  | query-name
  | [package-name.]procedure-name [(<procedure-args>)]

<procedure-args> ::= <value-expression [, <value-expression> ...]

<correlation-or-recognition> ::=
  [AS] correlation-name [(<column-name-list>)]

<column-name-list> ::= column-name [, column-name ...]

<derived-table> ::= (<query-expression>)

<parenthesized-joined-table> ::=
    (<parenthesized-joined-table)
  | (<joined-table>)

<joined-table> ::=
    <cross-join>
  | <natural-join>
  | <qualified-join>

<cross-join>
  <table-reference> CROSS JOIN <table-primary>

<natural-join> ::=
  <table-reference> NATURAL [<join-type>] JOIN <table-primary>

<join-type> ::= INNER | { LEFT | RIGHT | FULL } [OUTER]

<qualified-join> ::=
  <table-reference> [<join-type>] JOIN <table-primary>
  { ON <search-condition>
  | USING (<column-name-list>) }

Table 1. Arguments for `JOIN` Clauses
Argument	Description
table-name	Name of a table or view
query-name	Name of a CTE
package-name	Name of a package
procedure-name	Name of a selectable stored procedure
procedure-args	Selectable stored procedure input parameter(s)
derived-table	Derived table query expression
correlation-name	The alias of a data source (table, view, procedure, CTE, derived table)
column-name	Name or alias for a column in a relation, CTE or derived table
search-condition	Join condition (criterion)
column-name-list	List of aliases of the columns of a derived table, or the list of columns used for an equi-join

Inner vs. Outer Joins

A join combines data rows from two sets (usually referred to as the left set and the right set).By default, only rows that meet the join condition (i.e. that match at least one row in the other set when the join condition is applied) make it into the result set.This default type of join is called an inner join.Suppose we have the following two tables:

Table A
ID	S
87	Just some text
235	Silence

Table B
CODE	X
-23	56.7735
87	416.0

If we join these tables like this:

select *
  from A
  join B on A.id = B.code;

then the result set will be:

CODE

Just some text

416.0

The first row of A has been joined with the second row of B because together they met the condition “A.id = B.code”.The other rows from the source tables have no match in the opposite set and are therefore not included in the join.Remember, this is an INNER join.We can make that fact explicit by writing:

select *
  from A
  inner join B on A.id = B.code;

However, since INNER is the default, it is usually omitted.

It is perfectly possible that a row in the left set matches several rows from the right set or vice versa.In that case, all those combinations are included, and we can get results like:

ID	S	CODE	X
87	Just some text	87	416.0
87	Just some text	87	-1.0
-23	Don’t know	-23	56.7735
-23	Still don’t know	-23	56.7735
-23	I give up	-23	56.7735

Sometimes we want (or need) all the rows of one or both of the sources to appear in the joined set, even if they don’t match a record in the other source.This is where outer joins come in.A LEFT outer join includes all the records from the left set, but only matching records from the right set.In a RIGHT outer join it’s the other way around.A FULL outer joins include all the records from both sets.In all outer joins, the “holes” (the places where an included source record doesn’t have a match in the other set) are filled up with NULLs.

To make an outer join, you must specify LEFT, RIGHT or FULL, optionally followed by the keyword OUTER.

Below are the results of the various outer joins when applied to our original tables A and B:

select *
  from A
  left outer join B on A.id = B.code;

ID	S	CODE	X
87	Just some text	87	416.0
235	Silence	<null>	<null>

select *
  from A
  right outer join B on A.id = B.code

ID	S	CODE	X
<null>	<null>	-23	56.7735
87	Just some text	87	416.0

select *
  from A
  full outer join B on A.id = B.code

ID	S	CODE	X
<null>	<null>	-23	56.7735
87	Just some text	87	416.0
235	Silence	<null>	<null>

Qualified joins

Qualified joins specify conditions for the combining of rows.This happens either explicitly in an ON clause or implicitly in a USING clause.

Syntax

<qualified-join> ::=
  <table-reference> [<join-type>] JOIN <table-primary>
  { ON <search-condition>
  | USING (<column-name-list>) }

<join-type> ::= INNER | {LEFT | RIGHT | FULL} [OUTER]

Explicit-condition joins

Most qualified joins have an ON clause, with an explicit condition that can be any valid Boolean expression, but usually involves a comparison between the two sources involved.

Often, the condition is an equality test (or a number of ANDed equality tests) using the “=” operator.Joins like these are called equi-joins.(The examples in the section on inner and outer joins were all equi-joins.)

Examples of joins with an explicit condition:

/* Select all Detroit customers who made a purchase
   in 2013, along with the purchase details: */
select * from customers c
  join sales s on s.cust_id = c.id
  where c.city = 'Detroit' and s.year = 2013;

/* Same as above, but include non-buying customers: */
select * from customers c
  left join sales s on s.cust_id = c.id
  where c.city = 'Detroit' and s.year = 2013;

/* For each man, select the women who are taller than he.
   Men for whom no such woman exists are not included. */
select m.fullname as man, f.fullname as woman
  from males m
  join females f on f.height > m.height;

/* Select all pupils with their class and mentor.
   Pupils without a mentor are also included.
   Pupils without a class are not included. */
select p.firstname, p.middlename, p.lastname,
       c.name, m.name
  from pupils p
  join classes c on c.id = p.class
  left join mentors m on m.id = p.mentor;

Named columns joins

Equi-joins often compare columns that have the same name in both tables.If this is the case, we can also use the second type of qualified join: the named columns join.

Note	Named columns joins are not supported in Dialect 1 databases.

Named columns joins have a USING clause which states only the column names.So instead of this:

select * from flotsam f
  join jetsam j
  on f.sea = j.sea
  and f.ship = j.ship;

we can also write:

select * from flotsam
  join jetsam using (sea, ship)

which is considerably shorter.The result set is a little different though — at least when using “SELECT *”:

The explicit-condition join — with the ON clause — will contain each of the columns SEA and SHIP twice: once from table FLOTSAM, and once from table JETSAM.Obviously, they will have the same values.
The named columns join — with the USING clause — will contain these columns only once.

If you want all the columns in the result set of the named columns join, set up your query like this:

select f.*, j.*
  from flotsam f
  join jetsam j using (sea, ship);

This will give you the same result set as the explicit-condition join.

For an OUTER named columns join, there’s an additional twist when using “SELECT *” or an unqualified column name from the USING list:

If a row from one source set doesn’t have a match in the other but must still be included because of the LEFT, RIGHT or FULL directive, the merged column in the joined set gets the non-NULL value.That is fair enough, but now you can’t tell whether this value came from the left set, the right set, or both.This can be especially deceiving when the value came from the right hand set, because “*” always shows combined columns in the left hand part — even in the case of a RIGHT join.

Whether this is a problem or not depends on the situation.If it is, use the “a.*, b.*” approach shown above, with a and b the names or aliases of the two sources.Or better yet, avoid “*” altogether in your serious queries and qualify all column names in joined sets.This has the additional benefit that it forces you to think about which data you want to retrieve and where from.

It is your responsibility to make sure the column names in the USING list are of compatible types between the two sources.If the types are compatible but not equal, the engine converts them to the type with the broadest range of values before comparing the values.This will also be the data type of the merged column that shows up in the result set if “SELECT *” or the unqualified column name is used.Qualified columns on the other hand will always retain their original data type.

Tip

If, when joining by named columns, you are using a join column in the WHERE clause, always use the qualified column name, otherwise an index on this column will not be used.

SELECT 1 FROM t1 a JOIN t2 b USING (x) WHERE x = 0;

-- PLAN JOIN (A NATURAL , B INDEX (RDB$2))

However:

SELECT 1 FROM t1 a JOIN t2 b USING (x) WHERE a.x = 0;
-- PLAN JOIN (A INDEX (RDB$1), B INDEX (RDB$2))

SELECT 1 FROM t1 a JOIN t2 b USING (x) WHERE b.x = 0;
-- PLAN JOIN (A INDEX (RDB$1), B INDEX (RDB$2))

The fact is, the unspecified column in this case is implicitly replaced by `COALESCE(a.x, b.x).This trick is used to disambiguate column names, but it also interferes with the use of the index.

Natural joins

Taking the idea of the named columns join a step further, a natural join performs an automatic equi-join on all the columns that have the same name in the left and right table.The data types of these columns must be compatible.

Note	Natural joins are not supported in Dialect 1 databases.

Syntax

<natural-join> ::=
  <table-reference> NATURAL [<join-type>] JOIN <table-primary>

<join-type> ::= INNER | {LEFT | RIGHT | FULL} [OUTER]

Given these two tables:

create table TA (
  a bigint,
  s varchar(12),
  ins_date date
);

create table TB (
  a bigint,
  descr varchar(12),
  x float,
  ins_date date
);

A natural join on TA and TB would involve the columns a and ins_date, and the following two statements would have the same effect:

select * from TA
  natural join TB;

select * from TA
  join TB using (a, ins_date);

Like all joins, natural joins are inner joins by default, but you can turn them into outer joins by specifying LEFT, RIGHT or FULL before the JOIN keyword.

Caution

If there are no columns with the same name in the two source relations, a CROSS JOIN is performed.We’ll get to this type of join next.

Cross joins

A cross join produces the full set product — or Cartesian product — of the two data sources.This means that it successfully matches every row in the left source to every row in the right source.

Syntax

FROM <table-reference> [, <table-reference> ...]

<cross-join>
  <table-reference> CROSS JOIN <table-primary>

Use of the comma syntax is discouraged, and we recommend using the explicit join syntax.

Cross-joining two sets is equivalent to joining them on a tautology (a condition that is always true).The following two statements have the same effect:

select * from TA
  cross join TB;

select * from TA
  join TB on TRUE;

Cross joins are inner joins, because they only include matching records –- it just so happens that every record matches!An outer cross join, if it existed, wouldn’t add anything to the result, because what outer joins add are non-matching records, and these don’t exist in cross joins.

Cross joins are seldom useful, except if you want to list all the possible combinations of two or more variables.Suppose you are selling a product that comes in different sizes, different colors and different materials.If these variables are each listed in a table of their own, this query would return all the combinations:

select m.name, s.size, c.name
  from materials m
  cross join sizes s
  cross join colors c;

Implicit Joins

In the SQL:89 standard, the tables involved in a join were specified as a comma-delimited list in the FROM clause (in other words, a cross join).The join conditions were then specified in the WHERE clause among other search terms.This type of join is called an implicit join.

An example of an implicit join:

/*
 * A sample of all Detroit customers who
 * made a purchase.
 */
SELECT *
FROM customers c, sales s
WHERE s.cust_id = c.id AND c.city = 'Detroit'

Mixing Explicit and Implicit Joins

Mixing explicit and implicit joins is not recommend, but is allowed.However, some types of mixing are not supported by Firebird.

For example, the following query will raise the error “Column does not belong to referenced table”

SELECT *
FROM TA, TB
JOIN TC ON TA.COL1 = TC.COL1
WHERE TA.COL2 = TB.COL2

That is because the explicit join cannot see the TA table.However, the next query will complete without error, since the restriction is not violated.

SELECT *
FROM TA, TB
JOIN TC ON TB.COL1 = TC.COL1
WHERE TA.COL2 = TB.COL2

A Note on Equality

Important

This note about equality and inequality operators applies everywhere in Firebird’s SQL language, not only in JOIN conditions.

The “=” operator, which is explicitly used in many conditional joins and implicitly in named column joins and natural joins, only matches values to values.According to the SQL standard, NULL is not a value and hence two NULLs are neither equal nor unequal to one another.If you need NULLs to match each other in a join, use the IS NOT DISTINCT FROM operator.This operator returns true if the operands have the same value or if they are both NULL.

select *
  from A join B
  on A.id is not distinct from B.code;

Likewise, when you want to join on inequality, use IS DISTINCT FROM, not “<>”, if you want NULL to be considered different from any value and two NULLs considered equal:

select *
  from A join B
  on A.id is distinct from B.code;

Ambiguous field names in joins

Firebird rejects unqualified field names in a query if these field names exist in more than one dataset involved in a join.This is even true for inner equi-joins where the field name figures in the ON clause like this:

select a, b, c
  from TA
  join TB on TA.a = TB.a;

There is one exception to this rule: with named columns joins and natural joins, the unqualified field name of a column taking part in the matching process may be used legally and refers to the merged column of the same name.For named columns joins, these are the columns listed in the USING clause.For natural joins, they are the columns that have the same name in both relations.But please notice again that, especially in outer joins, plain colname isn’t always the same as left.colname or right.colname.Types may differ, and one of the qualified columns may be NULL while the other isn’t.In that case, the value in the merged, unqualified column may mask the fact that one of the source values is absent.

Joins with `LATERAL` Derived Tables

A derived table defined with the LATERAL keyword is called a lateral derived table.If a derived table is defined as lateral, then it is allowed to refer to other tables in the same FROM clause, but only those declared before it in the FROM clause.

Lateral Derived Table Examples

/* select customers with their last order date and number */
select c.name, ox.order_date as last_order, ox.number
from customer c
  left join LATERAL (
    select first 1 o.order_date, o.number
    from orders o
    where o.id_customer = c.id
    order by o.ORDER_DATE desc
  ) as ox on true
--
select dt.population, dt.city_name, c.country_name
from (select distinct country_name from cities) AS c
  cross join LATERAL (
    select first 1 city_name, population
    from cities
    where cities.country_name = c.country_name
    order by population desc
  ) AS dt;
--
select salespeople.name,
       max_sale.amount,
       customer_of_max_sale.customer_name
from salespeople,
  LATERAL ( select max(amount) as amount
            from all_sales
            where all_sales.salesperson_id = salespeople.id
  ) as max_sale,
  LATERAL ( select customer_name
            from all_sales
            where all_sales.salesperson_id = salespeople.id
            and all_sales.amount = max_sale.amount
  ) as customer_of_max_sale;

The `WHERE` clause

The WHERE clause serves to limit the rows returned to the ones that the caller is interested in.The condition following the keyword WHERE can be as simple as a check like “AMOUNT = 3” or it can be a multilayered, convoluted expression containing subselects, predicates, function calls, mathematical and logical operators, context variables and more.

The condition in the WHERE clause is often called the search condition, the search expression or simply the search.

In DSQL and ESQL, the search condition may contain parameters.This is useful if a query has to be repeated a number of times with different input values.In the SQL string as it is passed to the server, question marks are used as placeholders for the parameters.These question marks are called positional parameters because they can only be told apart by their position in the string.Connectivity libraries often support named parameters of the form :id, :amount, :a etc.These are more user-friendly;the library takes care of translating the named parameters to positional parameters before passing the statement to the server.

The search condition may also contain local (PSQL) or host (ESQL) variable names, preceded by a colon.

Syntax

SELECT ...
  FROM ...
  [...]
  WHERE <search-condition>
  [...]

Table 1. Argument of `WHERE`
Parameter	Description
search-condition	A Boolean expression returning `TRUE`, `FALSE` or `UNKNOWN` (`NULL`)

Only those rows for which the search condition evaluates to TRUE are included in the result set.Be careful with possible NULL outcomes: if you negate a NULL expression with NOT, the result will still be NULL and the row will not pass.This is demonstrated in one of the examples below.

Examples

select genus, species from mammals
  where family = 'Felidae'
  order by genus;

select * from persons
  where birthyear in (1880, 1881)
     or birthyear between 1891 and 1898;

select name, street, borough, phone
  from schools s
  where exists (select * from pupils p where p.school = s.id)
  order by borough, street;

select * from employees
  where salary >= 10000 and position <> 'Manager';

select name from wrestlers
  where region = 'Europe'
    and weight > all (select weight from shot_putters
                      where region = 'Africa');

select id, name from players
  where team_id = (select id from teams where name = 'Buffaloes');

select sum (population) from towns
  where name like '%dam'
  and province containing 'land';

select password from usertable
  where username = current_user;

The following example shows what can happen if the search condition evaluates to NULL.

Suppose you have a table listing children’s names and the number of marbles they possess.At a certain moment, the table contains this data:

CHILD	MARBLES
Anita	23
Bob E.	12
Chris	<null>
Deirdre	1
Eve	17
Fritz	0
Gerry	21
Hadassah	<null>
Isaac	6

CHILD

MARBLES

Anita

Bob E.

Chris

<null>

Deirdre

Eve

Fritz

Gerry

Hadassah

<null>

Isaac

First, please notice the difference between NULL and 0: Fritz is known to have no marbles at all, Chris’s and Hadassah’s marble counts are unknown.

Now, if you issue this SQL statement:

select list(child) from marbletable where marbles > 10;

you will get the names Anita, Bob E., Eve and Gerry.These children all have more than 10 marbles.

If you negate the expression:

select list(child) from marbletable where not marbles > 10

it’s the turn of Deirdre, Fritz and Isaac to fill the list.Chris and Hadassah are not included, because they aren’t known to have ten or fewer marbles.Should you change that last query to:

select list(child) from marbletable where marbles <= 10;

the result will still be the same, because the expression NULL <= 10 yields UNKNOWN.This is not the same as TRUE, so Chris and Hadassah are not listed.If you want them listed with the “poor” children, change the query to:

select list(child) from marbletable
where marbles <= 10 or marbles is null;

Now the search condition becomes true for Chris and Hadassah, because “marbles is null” obviously returns TRUE in their case.In fact, the search condition cannot be NULL for anybody now.

Lastly, two examples of SELECT queries with parameters in the search.It depends on the application how you should define query parameters and even if it is possible at all.Notice that queries like these cannot be executed immediately: they have to be prepared first.Once a parameterized query has been prepared, the user (or calling code) can supply values for the parameters and have it executed many times, entering new values before every call.How the values are entered and the execution started is up to the application.In a GUI environment, the user typically types the parameter values in one or more text boxes and then clicks an “Execute”, “Run” or “Refresh” button.

select name, address, phone frome stores
  where city = ? and class = ?;

select * from pants
  where model = :model and size = :size and color = :col;

The last query cannot be passed directly to the engine;the application must convert it to the other format first, mapping named parameters to positional parameters.

The `GROUP BY` clause

GROUP BY merges output rows that have the same combination of values in its item list into a single row.Aggregate functions in the select list are applied to each group individually instead of to the dataset as a whole.

If the select list only contains aggregate columns or, more generally, columns whose values don’t depend on individual rows in the underlying set, GROUP BY is optional.When omitted, the final result set consists of a single row (provided that at least one aggregated column is present).

If the select list contains both aggregate columns and columns whose values may vary per row, the GROUP BY clause becomes mandatory.

Syntax

SELECT ... FROM ...
  GROUP BY <grouping-item> [, <grouping-item> ...]
  [HAVING <grouped-row-condition>]
  ...

<grouping-item> ::=
    <non-aggr-select-item>
  | <non-aggr-expression>

<non-aggr-select-item> ::=
    column-copy
  | column-alias
  | column-position

Table 1. Arguments for the `GROUP BY` Clause
Argument	Description
<grouping-item>	Expression to group on;in the rest of this chapter, we use <value-expression> in `GROUP BY` syntax
non-aggr-expression	Any non-aggregating expression that is not included in the `SELECT` list, i.e. unselected columns from the source set or expressions that do not depend on the data in the set at all
column-copy	A literal copy, from the `SELECT` list, of an expression that contains no aggregate function
column-alias	The alias, from the `SELECT` list, of an expression (column) that contains no aggregate function
column-position	The position number, in the `SELECT` list, of an expression (column) that contains no aggregate function

A general rule of thumb is that every non-aggregate item in the SELECT list must also be in the GROUP BY list.You can do this in three ways:

By copying the item verbatim from the select list, e.g. “class” or “'D:' || upper(doccode)”.
By specifying the column alias, if it exists.
By specifying the column position as an integer literal between 1 and the number of columns.Integer values resulting from expressions or parameter substitutions are simple constant values and not column position and will be used as such in the grouping.They will have no effect though, as their value is the same for each row.

Note

If you group by a column position, the expression at that position is copied internally from the select list.If it concerns a subquery, that subquery will be executed again in the grouping phase.That is to say, grouping by the column position, rather than duplicating the subquery expression in the grouping clause, saves keystrokes and bytes, but it is not a way of saving processing cycles!

In addition to the required items, the grouping list may also contain:

Columns from the source table that are not in the select list, or non-aggregate expressions based on such columns.Adding such columns may further subdivide the groups.However, since these columns are not in the select list, you can’t tell which aggregated row corresponds to which value in the column.So, in general, if you are interested in this information, you also include the column or expression in the select list — which brings you back to the rule: “every non-aggregate column in the select list must also be in the grouping list”.
Expressions that aren’t dependent on the data in the underlying set, e.g. constants, context variables, single-value non-correlated subselects etc.This is only mentioned for completeness, as adding such items is utterly pointless: they don’t affect the grouping at all.“Harmless but useless” items like these may also figure in the select list without being copied to the grouping list.

Examples

When the select list contains only aggregate columns, GROUP BY is not mandatory:

select count(*), avg(age) from students
  where sex = 'M';

This will return a single row listing the number of male students and their average age.Adding expressions that don’t depend on values in individual rows of table STUDENTS doesn’t change that:

select count(*), avg(age), current_date from students
  where sex = 'M';

The row will now have an extra column showing the current date, but other than that, nothing fundamental has changed.A GROUP BY clause is still not required.

However, in both the above examples it is allowed.This is perfectly valid:

select count(*), avg(age) from students
  where sex = 'M'
  group by class;

This will return a row for each class that has boys in it, listing the number of boys and their average age in that particular class.(If you also leave the current_date field in, this value will be repeated on every row, which is not very exciting.)

The above query has a major drawback though: it gives you information about the different classes, but it doesn’t tell you which row applies to which class.To get that extra bit of information, add the non-aggregate column CLASS to the select list:

select class, count(*), avg(age) from students
  where sex = 'M'
  group by class;

Now we have a useful query.Notice that the addition of column CLASS also makes the GROUP BY clause mandatory.We can’t drop that clause anymore, unless we also remove CLASS from the column list.

The output of our last query may look something like this:

CLASS	COUNT	AVG
2A	12	13.5
2B	9	13.9
3A	11	14.6
3B	12	14.4
…	…	…

The headings “COUNT” and “AVG” are not very informative.In a simple case like this, you might get away with that, but in general you should give aggregate columns a meaningful name by aliasing them:

select class,
       count(*) as num_boys,
       avg(age) as boys_avg_age
  from students
  where sex = 'M'
  group by class;

Adding more non-aggregate (or, row-dependent) columns requires adding them to the GROUP BY clause too.For instance, you might want to see the above information for girls as well;and you may also want to differentiate between boarding and day students:

select class,
       sex,
       boarding_type,
       count(*) as number,
       avg(age) as avg_age
  from students
  group by class, sex, boarding_type;

This may give you the following result:

CLASS	SEX	BOARDING_TYPE	NUMBER	AVG_AGE
2A	F	BOARDING	9	13.3
2A	F	DAY	6	13.5
2A	M	BOARDING	7	13.6
2A	M	DAY	5	13.4
2B	F	BOARDING	11	13.7
2B	F	DAY	5	13.7
2B	M	BOARDING	6	13.8
…	…	…	…	…

CLASS

SEX

BOARDING_TYPE

NUMBER

AVG_AGE

BOARDING

13.3

DAY

13.5

BOARDING

13.6

DAY

13.4

BOARDING

13.7

DAY

13.7

BOARDING

13.8

…

Each row in the result set corresponds to one particular combination of the columns CLASS, SEX and BOARDING_TYPE.The aggregate results — number and average age — are given for each of these groups individually.In a query like this, you don’t see a total for boys as a whole, or day students as a whole.That’s the tradeoff: the more non-aggregate columns you add, the more you can pinpoint specific groups, but the more you also lose sight of the general picture.Of course, you can still obtain the “coarser” aggregates through separate queries.

`HAVING`

Just as a WHERE clause limits the rows in a dataset to those that meet the search condition, so the HAVING sub-clause imposes restrictions on the aggregated rows in a grouped set.HAVING is optional, and can only be used in conjunction with GROUP BY.

The condition(s) in the HAVING clause can refer to:

Any aggregated column in the select list.This is the most widely used case.
Any aggregated expression that is not in the select list, but allowed in the context of the query.This is sometimes useful too.
Any column in the GROUP BY list.While legal, it is more efficient to filter on these non-aggregated data at an earlier stage: in the WHERE clause.
Any expression whose value doesn’t depend on the contents of the dataset (like a constant or a context variable).This is valid but not useful, because it will either suppress the entire set or leave it untouched, based on conditions that have nothing to do with the set itself.

A HAVING clause can not contain:

Non-aggregated column expressions that are not in the GROUP BY list.
Column positions.An integer in the HAVING clause is just an integer, not a column position.
Column aliases –- not even if they appear in the GROUP BY clause!

Examples

Building on our earlier examples, this could be used to skip small groups of students:

select class,
       count(*) as num_boys,
       avg(age) as boys_avg_age
  from students
  where sex = 'M'
  group by class
  having count(*) >= 5;

To select only groups that have a minimum age spread:

select class,
       count(*) as num_boys,
       avg(age) as boys_avg_age
  from students
  where sex = 'M'
  group by class
  having max(age) - min(age) > 1.2;

Notice that if you’re interested in this information, you’ll likely also include min(age) and max(age) — or the expression “max(age) - min(age)”.

To include only 3rd classes:

select class,
       count(*) as num_boys,
       avg(age) as boys_avg_age
  from students
  where sex = 'M'
  group by class
  having class starting with '3';

Better would be to move this condition to the WHERE clause:

select class,
       count(*) as num_boys,
       avg(age) as boys_avg_age
  from students
  where sex = 'M' and class starting with '3'
  group by class;

The `WINDOW` Clause

The WINDOW clause defines one or more named windows that can be referenced by window functions in the current query specification.

Syntax

<query_spec> ::=
  SELECT
    [<limit_clause>]
    [<distinct_clause>]
    <select_list>
    <from_clause>
    [<where_clause>]
    [<group_clause>]
    [<having_clause>]
    [<named_windows_clause>]
    [<plan_clause>]

<named_windows_clause> ::=
  WINDOW <window_definition> [, <window_definition> ...]

<window definition> ::=
  new-window-name AS (<window-specification-details>)

<window-specification-details> ::=
  !! See Window (Analytical) Functions !!

In a query with multiple SELECT and WINDOW clauses (for example, with subqueries), the scope of the `new_window_name_ is confined to its query context.That means a window name from an inner context cannot be used in an outer context, nor vice versa.However, the same window name can be used independently in different contexts, though to avoid confusion it might be better to avoid this.

For more information, see [fblangref50-windowfuncs].

Example Using Named Windows

select
  id,
  department,
  salary,
  count(*) over w1,
  first_value(salary) over w2,
  last_value(salary) over w2
from employee
window w1 as (partition by department),
       w2 as (w1 order by salary)
order by department, salary;

The `PLAN` Clause

The PLAN clause enables the user to submit a data retrieval plan, thus overriding the plan that the optimizer would have generated automatically.

Syntax

PLAN <plan-expression>

<plan-expression> ::=
    (<plan-item> [, <plan-item> ...])
  | <sorted-item>
  | <joined-item>
  | <merged-item>
  | <hash-item>

<sorted-item> ::= SORT (<plan-item>)

<joined-item> ::=
  JOIN (<plan-item>, <plan-item> [, <plan-item> ...])

<merged-item> ::=
  [SORT] MERGE (<sorted-item>, <sorted-item> [, <sorted-item> ...])

<hash-item> ::=
  HASH (<plan-item>, <plan-item> [, <plan-item> ...])

<plan-item> ::= <basic-item> | <plan-expression>

<basic-item> ::=
  <relation> { NATURAL
             | INDEX (<indexlist>)
             | ORDER index [INDEX (<indexlist>)] }

<relation> ::= table | view [table]

<indexlist> ::= index [, index ...]

Table 1. Arguments for the `PLAN` Clause
Argument	Description
table	Table name or its alias
view	View name
index	Index name

Every time a user submits a query to the Firebird engine, the optimizer computes a data retrieval strategy.Most Firebird clients can make this retrieval plan visible to the user.In Firebird’s own isql utility, this is done with the command SET PLAN ON.If you are only interested in looking at query plans, SET PLANONLY ON will show the plan without executing the query.Use SET PLANONLY OFF to execute the query and show the plan.

Note

A more detailed plan can be obtained when you enable an advanced plan.In isql this can be done with SET EXPLAIN ON.The advanced plan displays more detailed information about the access methods used by the optimizer, however it cannot be included in the PLAN clause of a statement.The description of the advanced plan is beyond the scope of this Language Reference.

In most situations, you can trust that Firebird will select the optimal query plan for you.However, if you have complicated queries that seem to be underperforming, it may be worth your while to examine the plan and see if you can improve on it.

Simple Plans

The simplest plans consist of a relation name followed by a retrieval method.For example, for an unsorted single-table select without a WHERE clause:

select * from students
  plan (students natural);

Advanced plan:

Select Expression
  -> Table "STUDENTS" Full Scan

If there’s a WHERE or a HAVING clause, you can specify the index to be used for finding matches:

select * from students
  where class = '3C'
  plan (students index (ix_stud_class));

Advanced plan:

Select Expression
  -> Filter
    -> Table "STUDENTS" Access By ID
      -> Bitmap
        -> Index "IX_STUD_CLASS" Range Scan (full match)

The INDEX directive is also used for join conditions (to be discussed a little later).It can contain a list of indexes, separated by commas.

ORDER specifies the index for sorting the set if an ORDER BY or GROUP BY clause is present:

select * from students
  plan (students order pk_students)
  order by id;

Advanced plan:

Select Expression
  -> Table "STUDENTS" Access By ID
    -> Index "PK_STUDENTS" Full Scan

ORDER and INDEX can be combined:

select * from students
  where class >= '3'
  plan (students order pk_students index (ix_stud_class))
  order by id;

Advanced plan:

Select Expression
  -> Filter
    -> Table "STUDENTS" Access By ID
      -> Index "PK_STUDENTS" Full Scan
        -> Bitmap
          -> Index "IX_STUD_CLASS" Range Scan (lower bound: 1/1)

It is perfectly OK if ORDER and INDEX specify the same index:

select * from students
  where class >= '3'
  plan (students order ix_stud_class index (ix_stud_class))
  order by class;

Advanced plan:

Select Expression
  -> Filter
    -> Table "STUDENTS" Access By ID
      -> Index "IX_STUD_CLASS" Range Scan (lower bound: 1/1)
        -> Bitmap
          -> Index "IX_STUD_CLASS" Range Scan (lower bound: 1/1)

For sorting sets when there’s no usable index available (or if you want to suppress its use), leave out ORDER and prepend the plan expression with SORT:

select * from students
  plan sort (students natural)
  order by name;

Advanced plan:

Select Expression
  -> Sort (record length: 128, key length: 56)
    -> Table "STUDENTS" Full Scan

Or when an index is used for the search:

select * from students
  where class >= '3'
  plan sort (students index (ix_stud_class))
  order by name;

Advanced plan:

elect Expression
  -> Sort (record length: 136, key length: 56)
    -> Filter
      -> Table "STUDENTS" Access By ID
        -> Bitmap
          -> Index "IX_STUD_CLASS" Range Scan (lower bound: 1/1)

Notice that SORT, unlike ORDER, is outside the parentheses.This reflects the fact that the data rows are retrieved unordered and sorted afterward by the engine.

When selecting from a view, specify the view and the table involved.For instance, if you have a view FRESHMEN that selects the first-year students:

select * from freshmen
  plan (freshmen students natural);

Advanced plan:

Select Expression
  -> Table "STUDENTS" as "FRESHMEN" Full Scan

Or, for instance:

select * from freshmen
  where id > 10
  plan sort (freshmen students index (pk_students))
  order by name desc;

Advanced plan:

Select Expression
  -> Sort (record length: 144, key length: 24)
    -> Filter
      -> Table "STUDENTS" as "FRESHMEN" Access By ID
        -> Bitmap
          -> Index "PK_STUDENTS" Range Scan (lower bound: 1/1)

Important

If a table or view has been aliased, it is the alias, not the original name, that must be used in the PLAN clause.

Composite Plans

When a join is made, you can specify the index which is to be used for matching.You must also use the JOIN directive on the two streams in the plan:

select s.id, s.name, s.class, c.mentor
  from students s
  join classes c on c.name = s.class
  plan join (s natural, c index (pk_classes));

Advanced plan:

Select Expression
  -> Nested Loop Join (inner)
    -> Table "STUDENTS" as "S" Full Scan
    -> Filter
      -> Table "CLASSES" as "C" Access By ID
        -> Bitmap
          -> Index "PK_CLASSES" Unique Scan

The same join, sorted on an indexed column:

select s.id, s.name, s.class, c.mentor
  from students s
  join classes c on c.name = s.class
  plan join (s order pk_students, c index (pk_classes))
  order by s.id;

Advanced plan:

Select Expression
  -> Nested Loop Join (inner)
    -> Table "STUDENTS" as "S" Access By ID
      -> Index "PK_STUDENTS" Full Scan
    -> Filter
      -> Table "CLASSES" as "C" Access By ID
        -> Bitmap
          -> Index "PK_CLASSES" Unique Scan

And on a non-indexed column:

select s.id, s.name, s.class, c.mentor
  from students s
  join classes c on c.name = s.class
  plan sort (join (s natural, c index (pk_classes)))
  order by s.name;

Advanced plan:

Select Expression
  -> Sort (record length: 152, key length: 12)
    -> Nested Loop Join (inner)
      -> Table "STUDENTS" as "S" Full Scan
      -> Filter
        -> Table "CLASSES" as "C" Access By ID
          -> Bitmap
            -> Index "PK_CLASSES" Unique Scan

With a search condition added:

select s.id, s.name, s.class, c.mentor
  from students s
  join classes c on c.name = s.class
  where s.class <= '2'
  plan sort (join (s index (fk_student_class), c index (pk_classes)))
  order by s.name;

Advanced plan:

Select Expression
  -> Sort (record length: 152, key length: 12)
    -> Nested Loop Join (inner)
      -> Filter
        -> Table "STUDENTS" as "S" Access By ID
          -> Bitmap
            -> Index "FK_STUDENT_CLASS" Range Scan (lower bound: 1/1)
      -> Filter
        -> Table "CLASSES" as "C" Access By ID
          -> Bitmap
            -> Index "PK_CLASSES" Unique Scan

As a left outer join:

select s.id, s.name, s.class, c.mentor
  from classes c
  left join students s on c.name = s.class
  where s.class <= '2'
  plan sort (join (c natural, s index (fk_student_class)))
  order by s.name;

Advanced plan:

Select Expression
  -> Sort (record length: 192, key length: 56)
    -> Filter
      -> Nested Loop Join (outer)
        -> Table "CLASSES" as "C" Full Scan
        -> Filter
          -> Table "STUDENTS" as "S" Access By ID
            -> Bitmap
              -> Index "FK_STUDENT_CLASS" Range Scan (full match)

If there are no indices available to match the join condition (or if you don’t want to use it), then it is possible connect the streams using HASH or MERGE method.

To connect using the HASH method in the plan, the HASH directive is used instead of the JOIN directive.In this case, the smaller (secondary) stream is materialized completely into an internal buffer.While reading this secondary stream, a hash function is applied and a pair {hash, pointer to buffer} is written to a hash table.Then the primary stream is read and its hash key is tested against the hash table.

select *
  from students s
  join classes c on c.cookie = s.cookie
  plan hash (c natural, s natural)

Advanced plan:

Select Expression
  -> Filter
    -> Hash Join (inner)
      -> Table "STUDENTS" as "S" Full Scan
      -> Record Buffer (record length: 145)
        -> Table "CLASSES" as "C" Full Scan

For a MERGE join, the plan must first sort both streams on their join column(s) and then merge.This is achieved with the SORT directive (which we’ve already seen) and MERGE instead of JOIN:

select * from students s
  join classes c on c.cookie = s.cookie
  plan merge (sort (c natural), sort (s natural));

Adding an ORDER BY clause means the result of the merge must also be sorted:

select * from students s
  join classes c on c.cookie = s.cookie
  plan sort (merge (sort (c natural), sort (s natural)))
  order by c.name, s.id;

Finally, we add a search condition on two indexable columns of table STUDENTS:

select * from students s
  join classes c on c.cookie = s.cookie
  where s.id < 10 and s.class <= '2'
  plan sort (merge (sort (c natural),
                    sort (s index (pk_students, fk_student_class))))
  order by c.name, s.id;

As follows from the formal syntax definition, JOINs and MERGEs in the plan may combine more than two streams.Also, every plan expression may be used as a plan item in an encompassing plan.This means that plans of certain complicated queries may have various nesting levels.

Finally, instead of MERGE you may also write SORT MERGE.As this makes no difference and may create confusion with “real” SORT directives (the ones that do make a difference), it’s best to stick to plain MERGE.

In addition to the plan for the main query, you can specify a plan for each subquery.For example, the following query with multiple plans will work:

select *
from color
where exists (
  select *
  from hors
  where horse.code_color = color.code_color
  plan (horse index (fk_horse_color)))
plan (color natural)

Warning

Occasionally, the optimizer will accept a plan and then not follow it, even though it does not reject it as invalid.One such example was

MERGE (unsorted stream, unsorted stream)

It is advisable to treat such as plan as “deprecated”.

`UNION`

The UNION clause concatenates two or more datasets, thus increasing the number of rows but not the number of columns.Datasets taking part in a UNION must have the same number of columns, and columns at corresponding positions must be of the same type.

By default, a union suppresses duplicate rows.UNION ALL shows all rows, including any duplicates.The optional DISTINCT keyword makes the default behaviour explicit.

Syntax

<query-expression> ::=
  [<with-clause>] <query-expression-body> [<order-by-clause>]
    [{ <rows-clause>
     | [<result-offset-clause>] [<fetch-first-clause>] }]

<query-expression-body> ::=
    <query-term>
  | <query-expression-body> UNION [{ DISTINCT | ALL }] <query-term>

<query-term> ::= <query-primary>

<query-primary> ::=
    <query-specification>
  | (<query-expression-body> [<order-by-clause>]
     [<result-offset-clause>] [<fetch-first-clause>])

<query-specification> ::=
  SELECT <limit-clause> [{ ALL | DISTINCT }] <select-list>
    FROM <table-reference> [, <table-reference> ...]
    [WHERE <search-condition>]
    [GROUP BY <value-expression> [, <value-expression> ...]]
    [HAVING <search-condition>]
    [WINDOW <window-definition> [, <window-definition> ...]]
    [PLAN <plan-expression>]

Note	See also [fblangref50-dml-select-full-syntax] for the full syntax.

Unions take their column names from the first select query.If you want to alias union columns, do so in the column list of the topmost SELECT.Aliases in other participating selects are allowed and may even be useful, but will not propagate to the union level.

If a union has an ORDER BY clause, the only allowed sort items are integer literals indicating 1-based column positions, optionally followed by an ASC/DESC and/or a NULLS {FIRST | LAST} directive.This also implies that you cannot order a union by anything that isn’t a column in the union.(You can, however, wrap it in a derived table, which gives you back all the usual sort options.)

Unions are allowed in subqueries of any kind and can themselves contain subqueries.They can also contain joins, and can take part in a join when wrapped in a derived table.

Examples

This query presents information from different music collections in one dataset using unions:

select id, title, artist, length, 'CD' as medium
  from cds
union
select id, title, artist, length, 'LP'
  from records
union
select id, title, artist, length, 'MC'
  from cassettes
order by 3, 2  -- artist, title;

If id, title, artist and length are the only fields in the tables involved, the query can also be written as:

select c.*, 'CD' as medium
  from cds c
union
select r.*, 'LP'
  from records r
union
select c.*, 'MC'
  from cassettes c
order by 3, 2  -- artist, title;

Qualifying the “stars” is necessary here because they are not the only item in the column list.Notice how the “c” aliases in the first and third select do not conflict with each other: their scopes are not union-wide but apply only to their respective select queries.

The next query retrieves names and phone numbers from translators and proofreaders.Translators who also work as proofreaders will show up only once in the result set, provided their phone number is the same in both tables.The same result can be obtained without DISTINCT.With ALL, these people would appear twice.

select name, phone from translators
  union distinct
select name, telephone from proofreaders;

A UNION within a subquery:

select name, phone, hourly_rate from clowns
where hourly_rate < all
  (select hourly_rate from jugglers
     union
   select hourly_rate from acrobats)
order by hourly_rate;

Using parenthesized query expressions to show the employees with the highest and lowest salaries:

(
  select emp_no, salary, 'lowest' as type
  from employee
  order by salary asc
  fetch first row only
)
union all
(
  select emp_no, salary, 'highest' as type
  from employee
  order by salary desc
  fetch first row only
);

`INSERT`

Inserts rows of data into a table or updatable view

Syntax

INSERT INTO target
  { DEFAULT VALUES
  | [(<column_list>)] [<override_opt>] <value-source> }
  [RETURNING <returning_list> [INTO <variables>]]

<column_list> ::= col_name [, col_name ...]

<override_opt> ::=
  OVERRIDING {USER | SYSTEM} VALUE

<value-source> ::= VALUES (<value-list>) | <query-expression>

<value-list> ::= <ins-value> [, <ins-value> ...]

<ins-value> :: = <value-expression> | DEFAULT

<returning_list> ::= * | <output_column> [, <output_column]

<output_column> ::=
    target.*
  | <return_expression> [COLLATE collation] [[AS] alias]

<return_expression> ::=
    <value-expression>
  | [target.]col_name

<value-expression> ::=
    <literal>
  | <context-variable>
  | any other expression returning a single
    value of a Firebird data type or NULL

<variables> ::= [:]varname [, [:]varname ...]

Table 1. Arguments for the `INSERT` Statement Parameters
Argument	Description
target	The name of the table or view to which a new row, or batch of rows, should be added
col_name	Name of a table or view column
value-expression	An expression whose value is used for inserting into the table or for returning
return_expression	The expression to be returned in the `RETURNING` clause
literal	A literal
context-variable	Context variable
varname	Name of a PSQL local variable

The INSERT statement is used to add rows to a table or to one or more tables underlying a view:

If the column values are supplied in a VALUES clause, exactly one row is inserted
The values may be provided instead by a SELECT expression, in which case zero to many rows may be inserted
With the DEFAULT VALUES clause, no values are provided at all and exactly one row is inserted.

Note	Restrictions Columns returned to the `NEW.column_name` context variables in DML triggers should not have a colon (“`:`”) prefixed to their names No column may appear more than once in the column list.

Important

ALERT : BEFORE INSERT Triggers

Regardless of the method used for inserting rows, be mindful of any columns in the target table or view that are populated by BEFORE INSERT triggers, such as primary keys and case-insensitive search columns.Those columns should be excluded from both the column_list and the VALUES list if, as they should, the triggers test the NEW.column_name for NULL.

`INSERT … VALUES`

The VALUES list must provide a value for every column in the column list, in the same order and of the correct type.The column list need not specify every column in the target but, if the column list is absent, the engine requires a value for every column in the table or view (computed columns excluded).

The expression DEFAULT allows a column to be specified in the column list, but instructs Firebird to use the default value (either NULL or the value specified in the DEFAULT clause of the column definition).For identity columns, specifying DEFAULT will generate the identity value.It is possible to include calculated columns in the column list and specifying DEFAULT as the column value.

Note	Introducer syntax provides a way to identify the character set of a value that is a string constant (literal).Introducer syntax works only with literal strings: it cannot be applied to string variables, parameters, column references or values that are expressions.

Examples

INSERT INTO cars (make, model, year)
VALUES ('Ford', 'T', 1908);

INSERT INTO cars
VALUES ('Ford', 'T', 1908, 'USA', 850);

-- notice the '_' prefix (introducer syntax)
INSERT INTO People
VALUES (_ISO8859_1 'Hans-Jörg Schäfer');

`INSERT … SELECT`

For this method of inserting, the output columns of the SELECT statement (or <query-expression>) must provide a value for every target column in the column list, in the same order and of the correct type.

Literal values, context variables or expressions of compatible type can be substituted for any column in the source row.In this case, a source column list and a corresponding VALUES list are required.

If the column list is absent — as it is when SELECT * is used for the source expression — the column_list must contain the names of every column in the target table or view (computed columns excluded).

Examples

INSERT INTO cars (make, model, year)
  SELECT make, model, year
  FROM new_cars;

INSERT INTO cars
  SELECT * FROM new_cars;

INSERT INTO Members (number, name)
  SELECT number, name FROM NewMembers
    WHERE Accepted = 1
UNION ALL
  SELECT number, name FROM SuspendedMembers
    WHERE Vindicated = 1

INSERT INTO numbers(num)
  WITH RECURSIVE r(n) as (
    SELECT 1 FROM rdb$database
    UNION ALL
    SELECT n+1 FROM r WHERE n < 100
  )
SELECT n FROM r

Of course, the column names in the source table need not be the same as those in the target table.Any type of SELECT statement is permitted, as long as its output columns exactly match the insert columns in number, order and type.Types need not be the same, but they must be assignment-compatible.

Note

Since Firebird 5.0, an INSERT … SELECT with a RETURNING clause produces zero or more rows, and the statement is described as type isc_info_sql_stmt_select.In other words, an INSERT … SELECT … RETURNING will no longer produce a “multiple rows in singleton select” error when the select produces multiple rows.

For the time being, a INSERT … VALUES (…) or INSERT … DEFAULT VALUES with a RETURNING clause is still described as isc_info_sql_stmt_exec_procedure.This behaviour may change in a future Firebird version.

`INSERT … DEFAULT VALUES`

The DEFAULT VALUES clause allows insertion of a record without providing any values at all, either directly or from a SELECT statement.This is only possible if every NOT NULL or CHECKed column in the table either has a valid default declared or gets such a value from a BEFORE INSERT trigger.Furthermore, triggers providing required field values must not depend on the presence of input values.

Specifying DEFAULT VALUES is equivalent to specifying a values list with expression DEFAULT for all columns.

Example

INSERT INTO journal
  DEFAULT VALUES
RETURNING entry_id;

`OVERRIDING`

The OVERRIDING clause controls the behaviour of an identity column for this statement only.

OVERRIDING SYSTEM VALUE: The user-provided value for the identity column is used, and no value is generated using the identity.In other words, for this insert, the identity will behave as if it is GENERATED BY DEFAULT.This option can only be specified for tables with a GENERATED ALWAYS identity column.

This can be useful when merging or importing data from another source.After such an insert, it may be necessary to change the next value of the identity sequence using ALTER TABLE to prevent subsequent inserts from generating colliding identity values.
OVERRIDING USER VALUE: The user-provided value for the identity column is ignored, and the column value is generated using the identity.In other words, for this insert, the identity will behave as if it is GENERATED ALWAYS, while allowing the identity column in the column-list.This option can be specified for both types of identity columns.

It is usually simpler to leave out the identity column to achieve the same effect.

Examples of OVERRIDING

-- for ALWAYS
-- value 11 is used anyway
insert into objects_always (id, name)
  OVERRIDING SYSTEM VALUE values (11, 'Laptop');

-- for both ALWAYS and BY DEFAULT
-- value 12 is not used
insert into objects_default (id, name)
  OVERRIDING USER VALUE values (12, 'Laptop');

The `RETURNING` Clause

An INSERT statement may optionally include a RETURNING clause to return values from the inserted rows.The clause, if present, need not contain all columns referenced in the insert statement and may also contain other columns or expressions.The returned values reflect any changes that may have been made in BEFORE INSERT triggers.

The user executing the statement needs to have SELECT privileges on the columns specified in the RETURNING clause.

The syntax of the returning_list is similar to the column list of a SELECT clause.It is possible to reference all columns using * or table_name.*.

The optional INTO sub-clause is only valid in PSQL.

Note	Caveats for updatable views The values reported by `RETURNING` for updatable views may be incorrect if the view is made updatable through triggers.See [fblangref50-ddl-view-updatable-returning].

Important

Multiple INSERTs

In DSQL, an INSERT … VALUES (…) RETURNING or INSERT … DEFAULT VALUES RETURNING returns only one row, and a INSERT … SELECT … RETURNING can return zero or more rows.

In PSQL, if the RETURNING clause is specified and more than one row is inserted by the INSERT statement, the statement fails and a “multiple rows in singleton select” error is returned.This behaviour may change in future Firebird versions.

Examples

INSERT INTO Scholars (firstname, lastname, address,
  phone, email)
VALUES ('Henry', 'Higgins', '27A Wimpole Street',
  '3231212', NULL)
RETURNING lastname, fullname, id;

INSERT INTO Scholars (firstname, lastname, address,
  phone, email)
VALUES (
  'Henry', 'Higgins', '27A Wimpole Street',
  '3231212', NULL)
RETURNING *;

INSERT INTO Dumbbells (firstname, lastname, iq)
  SELECT fname, lname, iq
FROM Friends
  ORDER BY iq ROWS 1
  RETURNING id, firstname, iq
INTO :id, :fname, :iq;

Note

In DSQL, an INSERT … VALUES (…) RETURNING always returns exactly one row.This behaviour may change in a future Firebird version.
In DSQL, an INSERT … DEFAULT VALUES RETURNING always returns exactly one row.
In DSQL, an INSERT … SELECT … RETURNING returns zero or more rows.
In PSQL, if multiple rows are returned, the statement fails with a “multiple rows in singleton select” error.This behaviour may change in a future Firebird version.
In PSQL, if no row was inserted, nothing is returned, and the target variables keep their existing values.

Inserting into `BLOB` columns

Inserting into BLOB columns is only possible under the following circumstances:

The client application has made special provisions for such inserts, using the Firebird API.In this case, the modus operandi is application-specific and outside the scope of this manual.

The value inserted is a string literal of no more than 65,533 bytes (64KB - 3).

Note	A limit, in characters, is calculated at run-time for strings that are in multi-byte character sets, to avoid overrunning the bytes limit.For example, for a UTF8 string (max. 4 bytes/character), the run-time limit is likely to be about (floor(65533/4)) = 16383 characters.

You are using the “INSERT … SELECT” form and one or more columns in the result set are BLOBs.

`UPDATE`

Updates existing rows in tables and updatable views

Syntax

UPDATE target [[AS] alias]
  SET col_name = <upd_value> [, col_name = <upd_value> ...]
  [WHERE {<search-conditions> | CURRENT OF cursorname}]
  [PLAN <plan_items>]
  [ORDER BY <sort_items>]
  [ROWS m [TO n]]
  [SKIP LOCKED]
  [RETURNING <returning_list> [INTO <variables>]]

<upd_value> ::= <value-expression> | DEFAULT

<returning_list> ::= * | <output_column> [, <output_column]

<output_column> ::=
    target.* | NEW.* | OLD.*
  | <return_expression> [COLLATE collation] [[AS] alias]

<return_expression> ::=
    <value-expression>
  | [target.]col_name
  | NEW.col_name
  | OLD.col_name

<value-expression> ::=
    <literal>
  | <context-variable>
  | any other expression returning a single
    value of a Firebird data type or NULL

<variables> ::= [:]varname [, [:]varname ...]

Table 1. Arguments for the UPDATE Statement Parameters
Argument	Description
target	The name of the table or view where the records are updated
alias	Alias for the table or view
col_name	Name or alias of a column in the table or view
value-expression	Expression for the new value for a column that is to be updated in the table or view by the statement, or a value to be returned
search-conditions	A search condition limiting the set of the rows to be updated
cursorname	The name of the cursor through which the row(s) to be updated are positioned
plan_items	Clauses in the query plan
sort_items	Columns listed in an `ORDER BY` clause
m, n	Integer expressions for limiting the number of rows to be updated
return_expression	A value to be returned in the `RETURNING` clause
literal	A literal
context-variable	Context variable
varname	Name of a PSQL local variable

The UPDATE statement changes values in a table or in one or more of the tables that underlie a view.The columns affected are specified in the SET clause.The rows affected may be limited by the WHERE and ROWS clauses.If neither WHERE nor ROWS is present, all records in the table will be updated.

Using an alias

If you assign an alias to a table or a view, the alias must be used when specifying columns and also in any column references included in other clauses.

Example

Correct usage:

update Fruit set soort = 'pisang' where ...

update Fruit set Fruit.soort = 'pisang' where ...

update Fruit F set soort = 'pisang' where ...

update Fruit F set F.soort = 'pisang' where ...

Not possible:

update Fruit F set Fruit.soort = 'pisang' where ...

The `SET` Clause

In the SET clause, the assignment expressions, containing the columns with the values to be set, are separated by commas.In an assignment expression, column names are on the left and the values or expressions to assign are on the right.A column may be assigned only once in the SET clause.

A column name can be used in expressions on the right.The old value of the column will always be used in these right-side values, even if the column was already assigned a new value earlier in the SET clause.

Using the expression DEFAULT will set the column to its default value (either NULL or the value specified on the DEFAULT clause of the column definition).For an identity column, specifying DEFAULT will generate a new identity value.It is possible to “update” calculated columns in the SET clause if and only if the assigned value is DEFAULT.

Note	It is not possible to assign `DEFAULT` as a parameter value.

Here is an example

Data in the TSET table:

A B
---
1 0
2 0

The statement:

UPDATE tset SET a = 5, b = a;

will change the values to:

A B
---
5 1
5 2

Notice that the old values (1 and 2) are used to update the b column even after the column was assigned a new value (5).

The `WHERE` Clause

The WHERE clause sets the conditions that limit the set of records for a searched update.

In PSQL, if a named cursor is being used for updating a set, using the WHERE CURRENT OF clause, the action is limited to the row where the cursor is currently positioned.This is a positioned update.

Note	To be able to use the `WHERE CURRENT OF` clause in DSQL, the cursor name needs to be set on the statement handle before executing the statement.

Examples

UPDATE People
  SET firstname = 'Boris'
  WHERE lastname = 'Johnson';

UPDATE employee e
  SET salary = salary * 1.05
  WHERE EXISTS(
         SELECT *
           FROM employee_project ep
           WHERE e.emp_no = ep.emp_no);

UPDATE addresses
  SET city = 'Saint Petersburg', citycode = 'PET'
  WHERE city = 'Leningrad'

UPDATE employees
  SET salary = 2.5 * salary
  WHERE title = 'CEO'

For string literals with which the parser needs help to interpret the character set of the data, the introducer syntax may be used.The string literal is preceded by the character set name, prefixed with an underscore character:

-- notice the '_' prefix

UPDATE People
SET name = _ISO8859_1 'Hans-Jörg Schäfer'
WHERE id = 53662;

The `ORDER BY` and `ROWS` Clauses

The ORDER BY and ROWS clauses make sense only when used together.However, they can be used separately.

If ROWS has one argument, m, the rows to be updated will be limited to the first m rows.

Points to note

If m > the number of rows being processed, the entire set of rows is updated
If m = 0, no rows are updated
If m < 0, an error occurs and the update fails

If two arguments are used, m and n, ROWS limits the rows being updated to rows from m to n inclusively.Both arguments are integers and start from 1.

Points to note

If m > the number of rows being processed, no rows are updated
If n > the number of rows, rows from m to the end of the set are updated
If m < 1 or n < 1, an error occurs and the update fails
If n = m - 1, no rows are updated
If n < m -1, an error occurs and the update fails

ROWS Example

UPDATE employees
SET salary = salary + 50
ORDER BY salary ASC
ROWS 20;

The `SKIP LOCKED` Clause

When the SKIP LOCKED clause is specified, records locked by a different transaction are skipped by the statement and are not updated.

When a ROWS clause is specified, the “skip locked” check is performed after skipping the requested number of rows specified, and before counting the number of rows to update.

The `RETURNING` Clause

An UPDATE statement may include RETURNING to return some values from the updated rows.RETURNING may include data from any column of the row, not only the columns that are updated by the statement.It can include literals or expressions not associated with columns, if there is a need for that.

The user executing the statement needs to have SELECT privileges on the columns specified in the RETURNING clause.

When the RETURNING set contains data from the current row, the returned values report changes made in the BEFORE UPDATE triggers, but not those made in AFTER UPDATE triggers.

The context variables OLD.fieldname and NEW.fieldname can be used as column names.If OLD. or NEW. is not specified, or if the table name (target) is specified instead, the column values returned are the NEW. ones.

The syntax of the returning_list is similar to the column list of a SELECT clause.It is possible to reference all columns using *, or table_name.*, NEW.* and/or OLD.*.

In DSQL, a positioned update statement (WHERE CURRENT OF …) with RETURNING always returns a single row, a normal update statement can return zero or more rows.The update is executed to completion before rows are returned.In PSQL, attempts to execute an UPDATE … RETURNING that affects multiple rows will result in the error “multiple rows in singleton select”.This behaviour may change in a future Firebird version.

The `INTO` Sub-clause

In PSQL, the INTO clause can be used to pass the returning values to local variables.It is not available in DSQL.If no records are updated, nothing is returned and variables specified in RETURNING will keep their previous values.

RETURNING Example (DSQL)

UPDATE Scholars
SET firstname = 'Hugh', lastname = 'Pickering'
WHERE firstname = 'Henry' and lastname = 'Higgins'
RETURNING id, old.lastname, new.lastname;

Updating `BLOB` columns

Updating a BLOB column always replaces the entire contents.Even the BLOB ID, the “handle” that is stored directly in the column, is changed.BLOBs can be updated if:

The client application has made special provisions for this operation, using the Firebird API.In this case, the modus operandi is application-specific and outside the scope of this manual.

The new value is a string literal of no more than 65,533 bytes (64KB - 3).

Note	A limit, in characters, is calculated at run-time for strings that are in multi-byte character sets, to avoid overrunning the bytes limit.For example, for a UTF8 string (max. 4 bytes/character), the run-time limit is likely to be about (floor(65533/4)) = 16383 characters.

The source is itself a BLOB column or, more generally, an expression that returns a BLOB.
You use the INSERT CURSOR statement (ESQL only).

`UPDATE OR INSERT`

Updates existing rows in a table or updatable view, or — if it does not exist — inserts it

Syntax

UPDATE OR INSERT INTO
  target [(<column_list>)]
  [<override_opt>]
  VALUES (<value_list>)
  [MATCHING (<column_list>)]
  [ORDER BY <ordering-list>]
  [ROWS <m> [TO <n>]]
  [RETURNING <returning_list> [INTO <variables>]]

<column_list> ::= col_name  [, col_name ...]

<override_opt> ::=
  OVERRIDING {USER | SYSTEM} VALUE

<value_list> ::= <ins_value> [, <ins_value> ...]

<ins_value> ::= <value> | DEFAULT

<returning_list> ::= * | <output_column> [, <output_column]

<output_column> ::=
    target.* | NEW.* | OLD.*
  | <return_expression> [COLLATE collation] [[AS] alias]

<return_expression> ::=
    <value-expression>
  | [target.]col_name
  | NEW.col_name
  | OLD.col_name

<value-expression> ::=
    <literal>
  | <context-variable>
  | any other expression returning a single
    value of a Firebird data type or NULL

<variables> ::= [:]varname [, [:]varname ...]

Table 1. Arguments for the `UPDATE OR INSERT` Statement Parameters
Argument	Description
target	The name of the table or view where the record(s) is to be updated or a new record inserted
col_name	Name of a column in the table or view
value-expression	An expression whose value is to be used for inserting or updating the table, or returning a value
return_expression	An expression returned in the RETURNING clause
varname	Variable name — PSQL only

UPDATE OR INSERT inserts a new record or updates one or more existing records.The action taken depends on the values provided for the columns in the MATCHING clause (or, if the latter is absent, in the primary key).If there are records found matching those values, they are updated.If not, a new record is inserted.A match only counts if all the columns in the MATCHING clause or primary key columns are equal.Matching is done with the IS NOT DISTINCT operator, so one NULL matches another.

Note

Restrictions

If the table has no primary key, the MATCHING clause is mandatory.
In the MATCHING list as well as in the update/insert column list, each column name may occur only once.
The “INTO <variables>” subclause is only available in PSQL.
When values are returned into the context variable NEW, this name must not be preceded by a colon (“:”).

The `ORDER BY` and `ROWS` Clauses

See [fblangref50-dml-update-orderbyclause] for UPDATE.

The `RETURNING` Clause

The optional RETURNING clause, if present, need not contain all the columns mentioned in the statement and may also contain other columns or expressions.The returned values reflect any changes that may have been made in BEFORE triggers, but not those in AFTER triggers.OLD.fieldname and NEW.fieldname may both be used in the list of columns to return;for field names not preceded by either of these, the new value is returned.

The user executing the statement needs to have SELECT privileges on the columns specified in the RETURNING clause.

The syntax of the returning_list is similar to the column list of a SELECT clause.It is possible to reference all columns using *, or table_name.*, NEW.* and/or OLD.*.

In DSQL, a statement with a RETURNING clause can return zero or more rows.The update or insert is executed to completion before rows are returned.In PSQL, if a RETURNING clause is present and more than one matching record is found, an error “multiple rows in singleton select” is raised.This behaviour may change in a future Firebird version.

The optional INTO sub-clause is only valid in PSQL.

Example of `UPDATE OR INSERT`

Modifying data in a table, using UPDATE OR INSERT in a PSQL module.The return value is passed to a local variable, whose colon prefix is optional.

UPDATE OR INSERT INTO Cows (Name, Number, Location)
  VALUES ('Suzy Creamcheese', 3278823, 'Green Pastures')
  MATCHING (Number)
  RETURNING rec_id into :id;

UPDATE OR INSERT INTO Cows (Name, Number, Location)
  VALUES ('Suzy Creamcheese', 3278823, 'Green Pastures')
  MATCHING (Number)
  RETURNING old.*, new.*;

`DELETE`

Deletes rows from a table or updatable view

Syntax

DELETE
  FROM target [[AS] alias]
  [WHERE {<search-conditions> | CURRENT OF cursorname}]
  [PLAN <plan_items>]
  [ORDER BY <sort_items>]
  [ROWS m [TO n]]
  [SKIP LOCKED]
  [RETURNING <returning_list> [INTO <variables>]]

<returning_list> ::= * | <output_column> [, <output_column]

<output_column> ::=
    target.*
  | <return_expression> [COLLATE collation] [[AS] alias]

<return_expression> ::=
    <value-expression>
  | [target.]col_name

<value-expression> ::=
    <literal>
  | <context-variable>
  | any other expression returning a single
    value of a Firebird data type or NULL

<variables> ::=
  [:]varname [, [:]varname ...]

Table 1. Arguments for the `DELETE` Statement Parameters
Argument	Description
target	The name of the table or view from which the records are to be deleted
alias	Alias for the target table or view
search-conditions	Search condition limiting the set of rows being targeted for deletion
cursorname	The name of the cursor in which current record is positioned for deletion
plan_items	Query plan clause
sort_items	`ORDER BY` clause
m, n	Integer expressions for limiting the number of rows being deleted
return_expression	An expression to be returned in the `RETURNING` clause
value-expression	An expression whose value is used for returning
varname	Name of a PSQL variable

DELETE removes rows from a database table or from one or more of the tables that underlie a view.WHERE and ROWS clauses can limit the number of rows deleted.If neither WHERE nor ROWS is present, DELETE removes all the rows in the relation.

Aliases

If an alias is specified for the target table or view, it must be used to qualify all field name references in the DELETE statement.

Examples

Supported usage:

delete from Cities where name starting 'Alex';

delete from Cities where Cities.name starting 'Alex';

delete from Cities C where name starting 'Alex';

delete from Cities C where C.name starting 'Alex';

Not possible:

delete from Cities C where Cities.name starting 'Alex';

`WHERE`

The WHERE clause sets the conditions that limit the set of records for a searched delete.

In PSQL, if a named cursor is being used for deleting a set, using the WHERE CURRENT OF clause, the action is limited to the row where the cursor is currently positioned.This is a positioned delete.

Note	To be able to use the `WHERE CURRENT OF` clause in DSQL, the cursor name needs to be set on the statement handle before executing the statement.

Examples

DELETE FROM People
  WHERE firstname <> 'Boris' AND lastname <> 'Johnson';

DELETE FROM employee e
  WHERE NOT EXISTS(
    SELECT *
    FROM employee_project ep
     WHERE e.emp_no = ep.emp_no);

DELETE FROM Cities
  WHERE CURRENT OF Cur_Cities;  -- ESQL and PSQL only

`PLAN`

A PLAN clause allows the user to optimize the operation manually.

Example

DELETE FROM Submissions
  WHERE date_entered < '1-Jan-2002'
  PLAN (Submissions INDEX ix_subm_date);

`ORDER BY` and `ROWS`

The ORDER BY clause orders the set before the actual deletion takes place.It only makes sense in combination with ROWS, but is also valid without it.

The ROWS clause limits the number of rows being deleted.Integer literals or any integer expressions can be used for the arguments m and n.

If ROWS has one argument, m, the rows to be deleted will be limited to the first m rows.

Points to note

If m > the number of rows being processed, the entire set of rows is deleted
If m = 0, no rows are deleted
If m < 0, an error occurs and the deletion fails

If two arguments are used, m and n, ROWS limits the rows being deleted to rows from m to n inclusively.Both arguments are integers and start from 1.

Points to note

If m > the number of rows being processed, no rows are deleted
If m > 0 and <= the number of rows in the set and n is outside these values, rows from m to the end of the set are deleted
If m < 1 or n < 1, an error occurs and the deletion fails
If n = m - 1, no rows are deleted
If n < m -1, an error occurs and the deletion fails

Examples

Deleting the oldest purchase:

DELETE FROM Purchases
  ORDER BY date ROWS 1;

Deleting the highest custno(s):

DELETE FROM Sales
  ORDER BY custno DESC ROWS 1 to 10;

Deleting all sales, ORDER BY clause pointless:

DELETE FROM Sales
  ORDER BY custno DESC;

Deleting one record starting from the end, i.e. from Z…:

DELETE FROM popgroups
  ORDER BY name DESC ROWS 1;

Deleting the five oldest groups:

DELETE FROM popgroups
  ORDER BY formed ROWS 5;

No sorting (ORDER BY) is specified so 8 found records, starting from the fifth one, will be deleted:

DELETE FROM popgroups
  ROWS 5 TO 12;

`SKIP LOCKED`

When the SKIP LOCKED clause is specified, records locked by a different transaction are skipped by the statement and are not deleted.

When a ROWS clause is specified, the “skip locked” check is performed after skipping the requested number of rows specified, and before counting the number of rows to delete.

`RETURNING`

A DELETE statement may optionally include a RETURNING clause to return values from the deleted rows.The clause, if present, need not contain all the relation’s columns and may also contain other columns or expressions.

The user executing the statement needs to have SELECT privileges on the columns specified in the RETURNING clause.

The syntax of the returning_list is similar to the column list of a SELECT clause.It is possible to reference all columns using *, or table_name.*.

Note

In DSQL, a positioned delete statement (WHERE CURRENT OF …) with RETURNING always returns a singleton, never a multi-row set.If no records is deleted, the returned columns contain NULL.
A normal DELETE statement can return zero or more rows;the deletion is executed to completion before rows are returned.
In PSQL, if a RETURNING clause is present and more than one matching record is found, an error “multiple rows in singleton select” is raised.This behaviour may change in a future Firebird version.
The INTO clause is available only in PSQL
- If no row is deleted, nothing is returned and the target variables keep their values

Examples

DELETE FROM Scholars
  WHERE firstname = 'Henry' and lastname = 'Higgins'
  RETURNING lastname, fullname, id;

DELETE FROM Scholars
  WHERE firstname = 'Henry' and lastname = 'Higgins'
  RETURNING *;

DELETE FROM Dumbbells
  ORDER BY iq DESC
  ROWS 1
  RETURNING lastname, iq into :lname, :iq;

`MERGE`

Merges data from a source set into a target table or updatable view

Syntax

MERGE INTO target [[AS] target_alias]
  USING <table-reference>
  ON <join_condition>
  <merge_when> [<merge_when> ...]
  [PLAN <plan-expr>]
  [ORDER BY <ordering-list>]
  [RETURNING <returning_list> [INTO <variables>]]

<merge_when> ::=
    <merge_when_matched>
  | <merge_when_not_matched_target>
  | <merge_when_not_matched_source>

<merge_when_matched> ::=
  WHEN MATCHED [AND <condition>] THEN
  { UPDATE SET <assignment-list>
  | DELETE }

<merge_when_not_matched_target> ::=
  WHEN NOT MATCHED [BY TARGET] [AND <condition>] THEN
  INSERT [( <column_list> )] [<override_opt>]
  VALUES ( <value_list> )

<merge_when_not_matched_source> ::=
  WHEN NOT MATCHED BY SOURCE [ AND <condition> ] THEN
  { UPDATE SET <assignment-list>
  | DELETE }

<table-reference> ::= <table-primary> | <joined-table>

<table-primary> ::=
    <table-or-query-name> [[AS] correlation-name]
  | [LATERAL] <derived-table> [<correlation-or-recognition>]
  | <parenthesized-joined-table>

<assignment_list ::=
  col_name = <m_value> [, <col_name> = <m_value> ...]]

<override_opt> ::=
  OVERRIDING {USER | SYSTEM} VALUE

<column_list> ::= colname [, colname ...]

<value_list> ::= <m_value> [, <m_value> ...]

<m_value> ::= <value-expression> | DEFAULT

<returning_list> ::= * | <output_column> [, <output_column]

<output_column> ::=
    target.* | NEW.* | OLD.*
  | <return_expression> [COLLATE collation] [[AS] alias]

<return_expression> ::=
    <value-expression>
  | [target.]col_name
  | NEW.col_name
  | OLD.col_name

<value-expression> ::=
    <literal>
  | <context-variable>
  | any other expression returning a single
    value of a Firebird data type or NULL

<variables> ::=
  [:]varname [, [:]varname ...]

Table 1. Arguments for the `MERGE` Statement Parameters
Argument	Description
target	Name of target relation (table or updatable view)
table-reference	Data source.It can be a table, a view, a stored procedure, a derived table or a parenthesized joined table
target_alias	Alias for the target relation (table or updatable view)
join_conditions	The (`ON`) condition(s) for matching the source records with those in the target
condition	Additional test condition in `WHEN MATCHED` or `WHEN NOT MATCHED` clause
col_name	Name of a column in the target relation
value-expression	The value assigned to a column in the target table.This expression may be a literal value, a PSQL variable, a column from the source, or a compatible context variable
return_expression	The expression to be returned in the `RETURNING` clauseCan be a column reference to source or target, or a column reference of the `NEW` or `OLD` context of the target, or a value.
ret_alias	Alias for the value expression in the `RETURNING` clause
varname	Name of a PSQL local variable

The MERGE statement merges records from a source <table-reference> into a target table or updatable view.The source may be a table, view or “anything you can SELECT from” in general.Each source record will be used to update one or more target records, insert a new record in the target table, delete a record from the target table or do nothing.

The action taken depends on the supplied join condition, the WHEN clause(s), and the — optional — condition in the WHEN clause.The join condition and condition in the WHEN will typically contain a comparison of fields in the source and target relations.

Multiple WHEN MATCHED and WHEN NOT MATCHED clauses are allowed.For each row in the source, the WHEN clauses are checked in the order they are specified in the statement.If the condition in the WHEN clause does not evaluate to true, the clause is skipped, and the next clause will be checked.This will be done until the condition for a WHEN clause evaluates to true, or a WHEN clauses without condition matches, or there are no more WHEN clauses.If a matching clause is found, the action associated with the clause is executed.For each row in the source, at most one action is executed.If the WHEN MATCHED clause is present, and several records match a single record in the target table, an error is raised.

Contrary to the other WHEN clauses, the WHEN NOT MATCHED BY SOURCE clauses evaluates records in the target which match no record in source.

Warning

At least one WHEN clause must be present.

WHEN NOT MATCHED is evaluated from the source viewpoint, that is, the table or set specified in USING.It has to work this way because if the source record does not match a target record, INSERT is executed.Of course, if there is a target record which does not match a source record, nothing is done.

Currently, in PSQL, the ROW_COUNT variable returns the value 1, even if more than one record is modified or inserted.For details and progress, refer to firebird#4722.

The `ORDER BY` Clause

The ORDER BY can be used to influence the order in which rows are evaluated.The primary use case is when combined with RETURNING, to influence the order rows are returned.

The `RETURNING` Clause

A MERGE statement can contain a RETURNING clause to return rows added, modified or removed.The merge is executed to completion before rows are returned.The RETURNING clause can contain any columns from the target table (or updatable view), as well as other columns (eg from the source) and expressions.

The user executing the statement needs to have SELECT privileges on the columns specified in the RETURNING clause.

In PSQL, If a RETURNING clause is present and more than one matching record is found, an error “multiple rows in singleton select” is raised.This behaviour may change in a future Firebird version.

The optional INTO sub-clause is only valid in PSQL.

Column names can be qualified by the OLD or NEW prefix to define exactly what value to return: before or after modification. The returned values include the changes made by BEFORE triggers.

The syntax of the returning_list is similar to the column list of a SELECT clause.It is possible to reference all columns using *, or table_name.*, NEW.* and/or OLD.*.

For the UPDATE or INSERT action, unqualified column names, or those qualified by the target table name or alias will behave as if qualified by NEW, while for the DELETE action as if qualified by OLD.

The following example modifies the previous example to affect one line, and adds a RETURNING clause to return the old and new quantity of goods, and the difference between those values.

Using MERGE with a RETURNING clause

MERGE INTO PRODUCT_INVENTORY AS TARGET
USING (
  SELECT
    SL.ID_PRODUCT,
    SUM(SL.QUANTITY)
  FROM SALES_ORDER_LINE SL
  JOIN SALES_ORDER S ON S.ID = SL.ID_SALES_ORDER
  WHERE S.BYDATE = CURRENT_DATE
  AND SL.ID_PRODUCT =: ID_PRODUCT
  GROUP BY 1
) AS SRC (ID_PRODUCT, QUANTITY)
ON TARGET.ID_PRODUCT = SRC.ID_PRODUCT
WHEN MATCHED AND TARGET.QUANTITY - SRC.QUANTITY <= 0 THEN
  DELETE
WHEN MATCHED THEN
  UPDATE SET
    TARGET.QUANTITY = TARGET.QUANTITY - SRC.QUANTITY,
    TARGET.BYDATE = CURRENT_DATE
RETURNING OLD.QUANTITY, NEW.QUANTITY, SRC.QUANTITY
INTO : OLD_QUANTITY, :NEW_QUANTITY, :DIFF_QUANTITY

Examples of `MERGE`

Update books when present, or add new record if absent

MERGE INTO books b
  USING purchases p
  ON p.title = b.title and p.type = 'bk'
  WHEN MATCHED THEN
    UPDATE SET b.desc = b.desc || '; ' || p.desc
  WHEN NOT MATCHED THEN
    INSERT (title, desc, bought) values (p.title, p.desc, p.bought);

Using a derived table

MERGE INTO customers c
  USING (SELECT * from customers_delta WHERE id > 10) cd
  ON (c.id = cd.id)
  WHEN MATCHED THEN
    UPDATE SET name = cd.name
  WHEN NOT MATCHED THEN
    INSERT (id, name) values (cd.id, cd.name);

Together with a recursive CTE

MERGE INTO numbers
  USING (
    WITH RECURSIVE r(n) AS (
      SELECT 1 FROM rdb$database
      UNION ALL
      SELECT n+1 FROM r WHERE n < 200
    )
    SELECT n FROM r
  ) t
  ON numbers.num = t.n
  WHEN NOT MATCHED THEN
    INSERT(num) VALUES(t.n);

Using DELETE clause

MERGE INTO SALARY_HISTORY
USING (
  SELECT EMP_NO
  FROM EMPLOYEE
  WHERE DEPT_NO = 120) EMP
ON SALARY_HISTORY.EMP_NO = EMP.EMP_NO
WHEN MATCHED THEN DELETE

The following example updates the PRODUCT_INVENTORY table daily based on orders processed in the SALES_ORDER_LINE table.If the stock level of the product would drop to zero or lower, then the row for that product is removed from the PRODUCT_INVENTORY table.

MERGE INTO PRODUCT_INVENTORY AS TARGET
USING (
  SELECT
    SL.ID_PRODUCT,
    SUM (SL.QUANTITY)
  FROM SALES_ORDER_LINE SL
  JOIN SALES_ORDER S ON S.ID = SL.ID_SALES_ORDER
  WHERE S.BYDATE = CURRENT_DATE
  GROUP BY 1
) AS SRC (ID_PRODUCT, QUANTITY)
ON TARGET.ID_PRODUCT = SRC.ID_PRODUCT
WHEN MATCHED AND TARGET.QUANTITY - SRC.QUANTITY <= 0 THEN
  DELETE
WHEN MATCHED THEN
  UPDATE SET
    TARGET.QUANTITY = TARGET.QUANTITY - SRC.QUANTITY,
    TARGET.BYDATE = CURRENT_DATE

`EXECUTE PROCEDURE`

Executes a stored procedure

Syntax

EXECUTE PROCEDURE procname
   [{ <inparam-list | ( <inparam-list> ) }]
   [RETURNING_VALUES { <outvar-list> | ( <outvar-list ) }]

<inparam-list> ::=
  <inparam> [, <inparam> ...]

<outvar-list> ::=
  <outvar> [, <outvar> ...]

<outvar> ::= [:]varname

Table 1. Arguments for the `EXECUTE PROCEDURE` Statement Parameters
Argument	Description
procname	Name of the stored procedure
inparam	An expression evaluating to the declared data type of an input parameter
varname	A PSQL variable to receive the return value

Executes an executable stored procedure, taking a list of one or more input parameters, if they are defined for the procedure, and returning a one-row set of output values, if they are defined for the procedure.

“Executable” Stored Procedure

The EXECUTE PROCEDURE statement is most commonly used to invoke “executable” stored procedures to perform some data-modifying task at the server side — those that do not contain any SUSPEND statements in their code.They can be designed to return a result set, consisting of only one row, which is usually passed, via a set of RETURNING_VALUES() variables, to another stored procedure that calls it.Client interfaces usually have an API wrapper that can retrieve the output values into a single-row buffer when calling EXECUTE PROCEDURE in DSQL.

Invoking “selectable” stored procedures is also possible with EXECUTE PROCEDURE, but it returns only the first row of an output set which is almost surely designed to be multi-row.Selectable stored procedures are designed to be invoked by a SELECT statement, producing output that behaves like a virtual table.

Note

In PSQL and DSQL, input parameters may be any expression that resolves to the expected type.
Although parentheses are not required after the name of the stored procedure to enclose the input parameters, their use is recommended for the sake of readability.
Where output parameters have been defined in a procedure, the RETURNING_VALUES clause can be used in PSQL to retrieve them into a list of previously declared variables that conforms in sequence, data type and number with the defined output parameters.
The list of RETURNING_VALUES may be optionally enclosed in parentheses and their use is recommended.
When DSQL applications call EXECUTE PROCEDURE using the Firebird API or some form of wrapper for it, a buffer is prepared to receive the output row and the RETURNING_VALUES clause is not used.

Examples of `EXECUTE PROCEDURE`

In PSQL, with optional colons and without optional parentheses:

EXECUTE PROCEDURE MakeFullName
  :FirstName, :MiddleName, :LastName
  RETURNING_VALUES :FullName;

In Firebird’s command-line utility isql, with literal parameters and optional parentheses:
```
EXECUTE PROCEDURE MakeFullName ('J', 'Edgar', 'Hoover');
```
Note

In DSQL (e.g. in isql), RETURNING_VALUES is not used.Any output values are captured by the application and displayed automatically.

A PSQL example with expression parameters and optional parentheses:

EXECUTE PROCEDURE MakeFullName
  ('Mr./Mrs. ' || FirstName, MiddleName, upper(LastName))
  RETURNING_VALUES (FullName);

`EXECUTE BLOCK`

Creates an “anonymous” block of PSQL code in DSQL for immediate execution

Available in

DSQL

Syntax

EXECUTE BLOCK [(<inparams>)]
  [RETURNS (<outparams>)]
  <psql-module-body>

<inparams> ::=  <param_decl> = ? [, <inparams> ]

<outparams> ::=  <param_decl> [, <outparams>]

<param_decl> ::=
  paramname <domain_or_non_array_type> [NOT NULL] [COLLATE collation]

<domain_or_non_array_type> ::=
  !! See Scalar Data Types Syntax !!

<psql-module-body> ::=
  !! See Syntax of a Module Body !!

Table 1. Arguments for the `EXECUTE BLOCK` Statement Parameters
Argument	Description
param_decl	Name and description of an input or output parameter
paramname	The name of an input or output parameter of the procedural block, up to 63 characters long.The name must be unique among input and output parameters and local variables in the block
collation	Collation

Executes a block of PSQL code as if it were a stored procedure, optionally with input and output parameters and variable declarations.This allows the user to perform “on-the-fly” PSQL within a DSQL context.

Examples

This example injects the numbers 0 through 127 and their corresponding ASCII characters into the table ASCIITABLE:

EXECUTE BLOCK
AS
declare i INT = 0;
BEGIN
  WHILE (i < 128) DO
  BEGIN
    INSERT INTO AsciiTable VALUES (:i, ascii_char(:i));
    i = i + 1;
  END
END

The next example calculates the geometric mean of two numbers and returns it to the user:
```
EXECUTE BLOCK (x DOUBLE PRECISION = ?, y DOUBLE PRECISION = ?)
RETURNS (gmean DOUBLE PRECISION)
AS
BEGIN
  gmean = SQRT(x*y);
  SUSPEND;
END
```
Because this block has input parameters, it has to be prepared first.Then the parameters can be set and the block executed.It depends on the client software how this must be done and even if it is possible at all — see the notes below.

Our last example takes two integer values, smallest and largest.For all the numbers in the range smallest…largest, the block outputs the number itself, its square, its cube and its fourth power.

EXECUTE BLOCK (smallest INT = ?, largest INT = ?)
RETURNS (number INT, square BIGINT, cube BIGINT, fourth BIGINT)
AS
BEGIN
  number = smallest;
  WHILE (number <= largest) DO
  BEGIN
    square = number * number;
    cube   = number * square;
    fourth = number * cube;
    SUSPEND;
    number = number + 1;
  END
END

Again, it depends on the client software if and how you can set the parameter values.

Input and output parameters

Executing a block without input parameters should be possible with every Firebird client that allows the user to enter their own DSQL statements.If there are input parameters, things get trickier: these parameters must get their values after the statement is prepared, but before it is executed.This requires special provisions, which not every client application offers.(Firebird’s own isql, for one, doesn’t.)

The server only accepts question marks (“?”) as placeholders for the input values, not “:a”, “:MyParam” etc., or literal values.Client software may support the “:xxx” form though, and will preprocess it before sending it to the server.

If the block has output parameters, you must use SUSPEND or nothing will be returned.

Output is always returned in the form of a result set, just as with a SELECT statement.You can’t use RETURNING_VALUES or execute the block INTO some variables, even if there is only one result row.

PSQL Links

For more information about writing PSQL, consult Chapter Procedural SQL (PSQL) Statements.

Statement Terminators

Some SQL statement editors — specifically the isql utility that comes with Firebird, and possibly some third-party editors — employ an internal convention that requires all statements to be terminated with a semicolon.This creates a conflict with PSQL syntax when coding in these environments.If you are unacquainted with this problem and its solution, please study the details in the PSQL chapter in the section entitled Switching the Terminator in isql.

Nulls Position

NULLs Position

Ordering UNION-ed Sets

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Examples of ORDER BY

ROWS

Characteristics of Using ROWS m Without a TO Clause:

Characteristics of Using ROWS m With a TO Clause:

Not Possible to Use a TO Clause Without a ROWS Clause:

Replacing of FIRST/SKIP and OFFSET/FETCH

Mixing ROWS and FIRST/SKIP or OFFSET/FETCH

ROWS Syntax in UNION Queries

Examples of ROWS

OFFSET, FETCH

Examples of OFFSET and FETCH

FOR UPDATE [OF]

WITH LOCK

Usage with a FOR UPDATE Clause

How the engine deals with WITH LOCK

Caveats using WITH LOCK

Examples using explicit locking

OPTIMIZE FOR

INTO

Examples

Common Table Expressions (“WITH …​ AS …​ SELECT”)

CTE Notes

Example

Recursive CTEs

Execution Pattern

Example of recursive CTEs

Full SELECT Syntax

The SELECT Columns List

Examples of SELECT queries with different types of column lists

The FROM clause

Selecting FROM a table or view

Examples

Selecting FROM a stored procedure

Selecting FROM a derived table

LATERAL Derived Tables

Example using a derived table

A more useful example

Selecting FROM a Common Table Expression (CTE)

Joins

Inner vs. Outer Joins

Qualified joins

Explicit-condition joins

Named columns joins

Natural joins

Cross joins

Implicit Joins

Mixing Explicit and Implicit Joins

A Note on Equality

Ambiguous field names in joins

Joins with LATERAL Derived Tables

The WHERE clause

Examples

The GROUP BY clause

Examples

HAVING

Examples

The WINDOW Clause

Example Using Named Windows

The PLAN Clause

Simple Plans

Composite Plans

UNION

Examples

INSERT

INSERT …​ VALUES

INSERT …​ SELECT

INSERT …​ DEFAULT VALUES

OVERRIDING

The RETURNING Clause

Inserting into BLOB columns

UPDATE

Using an alias

Example

The SET Clause

The WHERE Clause

The ORDER BY and ROWS Clauses

Ordering `UNION`-ed Sets

Examples of `ORDER BY`

`ROWS`

Characteristics of Using `ROWS m` Without a `TO` Clause:

Characteristics of Using `ROWS m` With a `TO` Clause:

Not Possible to Use a `TO` Clause Without a `ROWS` Clause:

Replacing of `FIRST`/`SKIP` and `OFFSET`/`FETCH`

Mixing `ROWS` and `FIRST`/`SKIP` or `OFFSET`/`FETCH`

`ROWS` Syntax in `UNION` Queries

Examples of `ROWS`

`OFFSET`, `FETCH`

Examples of `OFFSET` and `FETCH`

`FOR UPDATE [OF]`

`WITH LOCK`

Usage with a `FOR UPDATE` Clause

How the engine deals with `WITH LOCK`

Caveats using `WITH LOCK`

`OPTIMIZE FOR`

`INTO`

Common Table Expressions (“`WITH … AS … SELECT`”)

Full `SELECT` Syntax

The `SELECT` Columns List

Examples of `SELECT` queries with different types of column lists

The `FROM` clause

Selecting `FROM` a table or view

Selecting `FROM` a stored procedure

Selecting `FROM` a derived table

`LATERAL` Derived Tables

Selecting `FROM` a Common Table Expression (CTE)

Joins with `LATERAL` Derived Tables

The `WHERE` clause

The `GROUP BY` clause

`HAVING`

The `WINDOW` Clause

The `PLAN` Clause

`UNION`

`INSERT`

`INSERT … VALUES`

`INSERT … SELECT`

`INSERT … DEFAULT VALUES`

`OVERRIDING`

The `RETURNING` Clause

Inserting into `BLOB` columns

`UPDATE`

The `SET` Clause

The `WHERE` Clause

The `ORDER BY` and `ROWS` Clauses

The `SKIP LOCKED` Clause

The `RETURNING` Clause

The `INTO` Sub-clause

Updating `BLOB` columns

`UPDATE OR INSERT`

The `ORDER BY` and `ROWS` Clauses

The `RETURNING` Clause

Example of `UPDATE OR INSERT`

`DELETE`

`WHERE`

`PLAN`

`ORDER BY` and `ROWS`

`SKIP LOCKED`

`RETURNING`

`MERGE`

The `ORDER BY` Clause

The `RETURNING` Clause

Examples of `MERGE`

`EXECUTE PROCEDURE`

Examples of `EXECUTE PROCEDURE`

`EXECUTE BLOCK`