path: root/doc/src/sgml/typeconv.sgml

<!-- $PostgreSQL: pgsql/doc/src/sgml/typeconv.sgml,v 1.48 2006/09/18 19:54:01 tgl Exp $ -->

<chapter Id="typeconv">
<title>Type Conversion</title>

<indexterm zone="typeconv">
 <primary>data type</primary>
 <secondary>conversion</secondary>
</indexterm>

<para>
<acronym>SQL</acronym> statements can, intentionally or not, require
mixing of different data types in the same expression. 
<productname>PostgreSQL</productname> has extensive facilities for
evaluating mixed-type expressions.
</para>

<para>
In many cases a user will not need
to understand the details of the type conversion mechanism.
However, the implicit conversions done by <productname>PostgreSQL</productname>
can affect the results of a query.  When necessary, these results
can be tailored by using <emphasis>explicit</emphasis> type conversion.
</para>

<para>
This chapter introduces the <productname>PostgreSQL</productname>
type conversion mechanisms and conventions.
Refer to the relevant sections in <xref linkend="datatype"> and <xref linkend="functions">
for more information on specific data types and allowed functions and
operators.
</para>

<sect1 id="typeconv-overview">
<title>Overview</title>

<para>
<acronym>SQL</acronym> is a strongly typed language. That is, every data item
has an associated data type which determines its behavior and allowed usage.
<productname>PostgreSQL</productname> has an extensible type system that is
much more general and flexible than other <acronym>SQL</acronym> implementations.
Hence, most type conversion behavior in <productname>PostgreSQL</productname>
is governed by general rules rather than by <foreignphrase>ad hoc</>
heuristics.  This allows
mixed-type expressions to be meaningful even with user-defined types.
</para>

<para>
The <productname>PostgreSQL</productname> scanner/parser divides lexical
elements into only five fundamental categories: integers, non-integer numbers,
strings, identifiers, and key words.  Constants of most non-numeric types are
first classified as strings. The <acronym>SQL</acronym> language definition
allows specifying type names with strings, and this mechanism can be used in
<productname>PostgreSQL</productname> to start the parser down the correct
path. For example, the query

<screen>
SELECT text 'Origin' AS "label", point '(0,0)' AS "value";

 label  | value
--------+-------
 Origin | (0,0)
(1 row)
</screen>

has two literal constants, of type <type>text</type> and <type>point</type>.
If a type is not specified for a string literal, then the placeholder type
<type>unknown</type> is assigned initially, to be resolved in later
stages as described below.
</para>

<para>
There are four fundamental <acronym>SQL</acronym> constructs requiring
distinct type conversion rules in the <productname>PostgreSQL</productname>
parser:

<variablelist>
<varlistentry>
<term>
Function calls
</term>
<listitem>
<para>
Much of the <productname>PostgreSQL</productname> type system is built around a
rich set of functions. Functions can have one or more arguments.
Since <productname>PostgreSQL</productname> permits function
overloading, the function name alone does not uniquely identify the function
to be called; the parser must select the right function based on the data
types of the supplied arguments.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
Operators
</term>
<listitem>
<para>
<productname>PostgreSQL</productname> allows expressions with
prefix and postfix unary (one-argument) operators,
as well as binary (two-argument) operators.  Like functions, operators can
be overloaded, and so the same problem of selecting the right operator
exists.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
Value Storage
</term>
<listitem>
<para>
<acronym>SQL</acronym> <command>INSERT</command> and <command>UPDATE</command> statements place the results of
expressions into a table. The expressions in the statement must be matched up
with, and perhaps converted to, the types of the target columns.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>UNION</literal>, <literal>CASE</literal>, and related constructs
</term>
<listitem>
<para>
Since all query results from a unionized <command>SELECT</command> statement
must appear in a single set of columns, the types of the results of each
<command>SELECT</> clause must be matched up and converted to a uniform set.
Similarly, the result expressions of a <literal>CASE</> construct must be
converted to a common type so that the <literal>CASE</> expression as a whole
has a known output type.  The same holds for <literal>ARRAY</> constructs,
and for the <function>GREATEST</> and <function>LEAST</> functions.
</para>
</listitem>
</varlistentry>
</variablelist>
</para>

<para>
The system catalogs store information about which conversions, called
<firstterm>casts</firstterm>, between data types are valid, and how to
perform those conversions.  Additional casts can be added by the user
with the <command>CREATE CAST</command> command.  (This is usually
done in conjunction with defining new data types.  The set of casts
between the built-in types has been carefully crafted and is best not
altered.)
</para>

<indexterm>
 <primary>data type</primary>
 <secondary>category</secondary>
</indexterm>

<para>
An additional heuristic is provided in the parser to allow better guesses
at proper behavior for <acronym>SQL</acronym> standard types. There are
several basic <firstterm>type categories</firstterm> defined: <type>boolean</type>,
<type>numeric</type>, <type>string</type>, <type>bitstring</type>, <type>datetime</type>, <type>timespan</type>, <type>geometric</type>, <type>network</type>,
and user-defined. Each category, with the exception of user-defined, has
one or more <firstterm>preferred types</firstterm> which are preferentially
selected when there is ambiguity.
In the user-defined category, each type is its own preferred type.
Ambiguous expressions (those with multiple candidate parsing solutions)
can therefore often be resolved when there are multiple possible built-in types, but
they will raise an error when there are multiple choices for user-defined
types.
</para>

<para>
All type conversion rules are designed with several principles in mind:

<itemizedlist>
<listitem>
<para>
Implicit conversions should never have surprising or unpredictable outcomes.
</para>
</listitem>

<listitem>
<para>
User-defined types, of which the parser has no <foreignphrase>a priori</> knowledge, should be
<quote>higher</quote> in the type hierarchy. In mixed-type expressions, native types shall always
be converted to a user-defined type (of course, only if conversion is necessary).
</para>
</listitem>

<listitem>
<para>
User-defined types are not related. Currently, <productname>PostgreSQL</productname>
does not have information available to it on relationships between types, other than
hardcoded heuristics for built-in types and implicit relationships based on
available functions and casts.
</para>
</listitem>

<listitem>
<para>
There should be no extra overhead from the parser or executor
if a query does not need implicit type conversion.
That is, if a query is well formulated and the types already match up, then the query should proceed
without spending extra time in the parser and without introducing unnecessary implicit conversion
calls into the query.
</para>

<para>
Additionally, if a query usually requires an implicit conversion for a function, and
if then the user defines a new function with the correct argument types, the parser
should use this new function and will no longer do the implicit conversion using the old function.
</para>
</listitem>
</itemizedlist>
</para>

</sect1>

<sect1 id="typeconv-oper">
<title>Operators</title>

<indexterm zone="typeconv-oper">
 <primary>operator</primary>
 <secondary>type resolution in an invocation</secondary>
</indexterm>

  <para>
   The specific operator to be used in an operator invocation is determined
   by following
   the procedure below.  Note that this procedure is indirectly affected
   by the precedence of the involved operators.  See <xref
   linkend="sql-precedence"> for more information.
  </para>

<procedure>
<title>Operator Type Resolution</title>

<step performance="required">
<para>
Select the operators to be considered from the
<classname>pg_operator</classname> system catalog.  If an unqualified
operator name was used (the usual case), the operators
considered are those of the right name and argument count that are
visible in the current search path (see <xref linkend="ddl-schemas-path">).
If a qualified operator name was given, only operators in the specified
schema are considered.
</para>

<substeps>
<step performance="optional">
<para>
If the search path finds multiple operators of identical argument types,
only the one appearing earliest in the path is considered.  But operators of
different argument types are considered on an equal footing regardless of
search path position.
</para>
</step>
</substeps>
</step>

<step performance="required">
<para>
Check for an operator accepting exactly the input argument types.
If one exists (there can be only one exact match in the set of
operators considered), use it.
</para>

<substeps>
<step performance="optional">
<para>
If one argument of a binary operator invocation is of the <type>unknown</type> type,
then assume it is the same type as the other argument for this check.
Other cases involving <type>unknown</type> will never find a match at
this step.
</para>
</step>
</substeps>
</step>

<step performance="required">
<para>
Look for the best match.
</para>
<substeps>
<step performance="required">
<para>
Discard candidate operators for which the input types do not match
and cannot be converted (using an implicit conversion) to match.
<type>unknown</type> literals are
assumed to be convertible to anything for this purpose.  If only one
candidate remains, use it; else continue to the next step.
</para>
</step>
<step performance="required">
<para>
Run through all candidates and keep those with the most exact matches
on input types.  (Domains are considered the same as their base type
for this purpose.)  Keep all candidates if none have any exact matches.
If only one candidate remains, use it; else continue to the next step.
</para>
</step>
<step performance="required">
<para>
Run through all candidates and keep those that accept preferred types (of the
input data type's type category) at the most positions where type conversion
will be required.
Keep all candidates if none accept preferred types.
If only one candidate remains, use it; else continue to the next step.
</para>
</step>
<step performance="required">
<para>
If any input arguments are <type>unknown</type>, check the type
categories accepted at those argument positions by the remaining
candidates.  At each position, select the <type>string</type> category
if any
candidate accepts that category.  (This bias towards string is appropriate
since an unknown-type literal does look like a string.) Otherwise, if
all the remaining candidates accept the same type category, select that
category; otherwise fail because the correct choice cannot be deduced
without more clues.  Now discard
candidates that do not accept the selected type category.  Furthermore,
if any candidate accepts a preferred type at a given argument position,
discard candidates that accept non-preferred types for that argument.
</para>
</step>
<step performance="required">
<para>
If only one candidate remains, use it.  If no candidate or more than one
candidate remains,
then fail.
</para>
</step>
</substeps>
</step>
</procedure>

<para>
Some examples follow.
</para>

<example>
<title>Exponentiation Operator Type Resolution</title>

<para>
There is only one exponentiation
operator defined in the catalog, and it takes arguments of type 
<type>double precision</type>.
The scanner assigns an initial type of <type>integer</type> to both arguments
of this query expression:
<screen>
SELECT 2 ^ 3 AS "exp";

 exp
-----
   8
(1 row)
</screen>

So the parser does a type conversion on both operands and the query
is equivalent to

<screen>
SELECT CAST(2 AS double precision) ^ CAST(3 AS double precision) AS "exp";
</screen>
</para>
</example>

<example>
<title>String Concatenation Operator Type Resolution</title>

<para>
A string-like syntax is used for working with string types as well as for
working with complex extension types.
Strings with unspecified type are matched with likely operator candidates.
</para>

<para>
An example with one unspecified argument:
<screen>
SELECT text 'abc' || 'def' AS "text and unknown";

 text and unknown
------------------
 abcdef
(1 row)
</screen>
</para>

<para>
In this case the parser looks to see if there is an operator taking <type>text</type>
for both arguments. Since there is, it assumes that the second argument should
be interpreted as of type <type>text</type>.
</para>

<para>
Here is a concatenation on unspecified types:
<screen>
SELECT 'abc' || 'def' AS "unspecified";

 unspecified
-------------
 abcdef
(1 row)
</screen>
</para>

<para>
In this case there is no initial hint for which type to use, since no types
are specified in the query. So, the parser looks for all candidate operators
and finds that there are candidates accepting both string-category and
bit-string-category inputs.  Since string category is preferred when available,
that category is selected, and then the 
preferred type for strings, <type>text</type>, is used as the specific
type to resolve the unknown literals to.
</para>
</example>

<example>
<title>Absolute-Value and Negation Operator Type Resolution</title>

<para>
The <productname>PostgreSQL</productname> operator catalog has several
entries for the prefix operator <literal>@</>, all of which implement
absolute-value operations for various numeric data types.  One of these
entries is for type <type>float8</type>, which is the preferred type in
the numeric category.  Therefore, <productname>PostgreSQL</productname>
will use that entry when faced with a non-numeric input:
<screen>
SELECT @ '-4.5' AS "abs";
 abs
-----
 4.5
(1 row)
</screen>
Here the system has performed an implicit conversion from <type>text</type> to <type>float8</type>
before applying the chosen operator.  We can verify that <type>float8</type> and
not some other type was used:
<screen>
SELECT @ '-4.5e500' AS "abs";

ERROR:  "-4.5e500" is out of range for type double precision
</screen>
</para>

<para>
On the other hand, the prefix operator <literal>~</> (bitwise negation)
is defined only for integer data types, not for <type>float8</type>.  So, if we
try a similar case with <literal>~</>, we get:
<screen>
SELECT ~ '20' AS "negation";

ERROR:  operator is not unique: ~ "unknown"
HINT:  Could not choose a best candidate operator. You may need to add explicit
type casts.
</screen>
This happens because the system can't decide which of the several
possible <literal>~</> operators should be preferred.  We can help
it out with an explicit cast:
<screen>
SELECT ~ CAST('20' AS int8) AS "negation";

 negation
----------
      -21
(1 row)
</screen>
</para>
</example>

</sect1>

<sect1 id="typeconv-func">
<title>Functions</title>

<indexterm zone="typeconv-func">
 <primary>function</primary>
 <secondary>type resolution in an invocation</secondary>
</indexterm>

  <para>
   The specific function to be used in a function invocation is determined
   according to the following steps.
  </para>

<procedure>
<title>Function Type Resolution</title>

<step performance="required">
<para>
Select the functions to be considered from the
<classname>pg_proc</classname> system catalog.  If an unqualified
function name was used, the functions
considered are those of the right name and argument count that are
visible in the current search path (see <xref linkend="ddl-schemas-path">).
If a qualified function name was given, only functions in the specified
schema are considered.
</para>

<substeps>
<step performance="optional">
<para>
If the search path finds multiple functions of identical argument types,
only the one appearing earliest in the path is considered.  But functions of
different argument types are considered on an equal footing regardless of
search path position.
</para>
</step>
</substeps>
</step>

<step performance="required">
<para>
Check for a function accepting exactly the input argument types.
If one exists (there can be only one exact match in the set of
functions considered), use it.
(Cases involving <type>unknown</type> will never find a match at
this step.)
</para>
</step>

<step performance="required">
<para>
If no exact match is found, see whether the function call appears
to be a trivial type conversion request.  This happens if the function call
has just one argument and the function name is the same as the (internal)
name of some data type.  Furthermore, the function argument must be either
an unknown-type literal or a type that is binary-compatible with the named
data type.  When these conditions are met, the function argument is converted
to the named data type without any actual function call.
</para>
</step>
<step performance="required">
<para>
Look for the best match.
</para>
<substeps>
<step performance="required">
<para>
Discard candidate functions for which the input types do not match
and cannot be converted (using an implicit conversion) to match.
<type>unknown</type> literals are
assumed to be convertible to anything for this purpose.  If only one
candidate remains, use it; else continue to the next step.
</para>
</step>
<step performance="required">
<para>
Run through all candidates and keep those with the most exact matches
on input types.  (Domains are considered the same as their base type
for this purpose.)  Keep all candidates if none have any exact matches.
If only one candidate remains, use it; else continue to the next step.
</para>
</step>
<step performance="required">
<para>
Run through all candidates and keep those that accept preferred types (of the
input data type's type category) at the most positions where type conversion
will be required.
Keep all candidates if none accept preferred types.
If only one candidate remains, use it; else continue to the next step.
</para>
</step>
<step performance="required">
<para>
If any input arguments are <type>unknown</type>, check the type categories
accepted 
at those argument positions by the remaining candidates.  At each position,
select the <type>string</type> category if any candidate accepts that category.
(This bias towards string
is appropriate since an unknown-type literal does look like a string.)
Otherwise, if all the remaining candidates accept the same type category,
select that category; otherwise fail because
the correct choice cannot be deduced without more clues.
Now discard candidates that do not accept the selected type category.
Furthermore, if any candidate accepts a preferred type at a given argument
position, discard candidates that accept non-preferred types for that
argument.
</para>
</step>
<step performance="required">
<para>
If only one candidate remains, use it.  If no candidate or more than one
candidate remains,
then fail.
</para>
</step>
</substeps>
</step>
</procedure>

<para>
Note that the <quote>best match</> rules are identical for operator and
function type resolution.
Some examples follow.
</para>

<example>
<title>Rounding Function Argument Type Resolution</title>

<para>
There is only one <function>round</function> function with two
arguments.  (The first is <type>numeric</type>, the second is
<type>integer</type>.)  So the following query automatically converts
the first argument of type <type>integer</type> to
<type>numeric</type>:

<screen>
SELECT round(4, 4);

 round
--------
 4.0000
(1 row)
</screen>

That query is actually transformed by the parser to
<screen>
SELECT round(CAST (4 AS numeric), 4);
</screen>
</para>

<para>
Since numeric constants with decimal points are initially assigned the
type <type>numeric</type>, the following query will require no type
conversion and may therefore be slightly more efficient:
<screen>
SELECT round(4.0, 4);
</screen>
</para>
</example>

<example>
<title>Substring Function Type Resolution</title>

<para>
There are several <function>substr</function> functions, one of which
takes types <type>text</type> and <type>integer</type>.  If called
with a string constant of unspecified type, the system chooses the
candidate function that accepts an argument of the preferred category
<literal>string</literal> (namely of type <type>text</type>).

<screen>
SELECT substr('1234', 3);

 substr
--------
     34
(1 row)
</screen>
</para>

<para>
If the string is declared to be of type <type>varchar</type>, as might be the case
if it comes from a table, then the parser will try to convert it to become <type>text</type>:
<screen>
SELECT substr(varchar '1234', 3);

 substr
--------
     34
(1 row)
</screen>

This is transformed by the parser to effectively become
<screen>
SELECT substr(CAST (varchar '1234' AS text), 3);
</screen>
</para>
<para>
<note>
<para>
The parser learns from the <structname>pg_cast</> catalog that
<type>text</type> and <type>varchar</type>
are binary-compatible, meaning that one can be passed to a function that
accepts the other without doing any physical conversion.  Therefore, no
explicit type conversion call is really inserted in this case.
</para>
</note>
</para>

<para>
And, if the function is called with an argument of type <type>integer</type>, the parser will
try to convert that to <type>text</type>:
<screen>
SELECT substr(1234, 3);

 substr
--------
     34
(1 row)
</screen>

This actually executes as
<screen>
SELECT substr(CAST (1234 AS text), 3);
</screen>
This automatic transformation can succeed because there is an
implicitly invocable cast from <type>integer</type> to
<type>text</type>.
</para>
</example>

</sect1>

<sect1 id="typeconv-query">
<title>Value Storage</title>

  <para>
   Values to be inserted into a table are converted to the destination
   column's data type according to the
   following steps.
  </para>

<procedure>
<title>Value Storage Type Conversion</title>

<step performance="required">
<para>
Check for an exact match with the target.
</para>
</step>

<step performance="required">
<para>
Otherwise, try to convert the expression to the target type.  This will succeed
if there is a registered cast between the two types.
If the expression is an unknown-type literal, the contents of
the literal string will be fed to the input conversion routine for the target
type.
</para>
</step>

<step performance="required">
<para>
Check to see if there is a sizing cast for the target type.  A sizing
cast is a cast from that type to itself.  If one is found in the
<structname>pg_cast</> catalog, apply it to the expression before storing
into the destination column.  The implementation function for such a cast
always takes an extra parameter of type <type>integer</type>, which receives
the destination column's declared length (actually, its
<structfield>atttypmod</> value; the interpretation of
<structfield>atttypmod</> varies for different data types).  The cast function
is responsible for applying any length-dependent semantics such as size
checking or truncation.
</para>
</step>

</procedure>

<example>
<title><type>character</type> Storage Type Conversion</title>

<para>
For a target column declared as <type>character(20)</type> the following statement
ensures that the stored value is sized correctly:

<screen>
CREATE TABLE vv (v character(20));
INSERT INTO vv SELECT 'abc' || 'def';
SELECT v, length(v) FROM vv;

          v           | length
----------------------+--------
 abcdef               |     20
(1 row)
</screen>
</para>

<para>
What has really happened here is that the two unknown literals are resolved
to <type>text</type> by default, allowing the <literal>||</literal> operator
to be resolved as <type>text</type> concatenation.  Then the <type>text</type>
result of the operator is converted to <type>bpchar</type> (<quote>blank-padded
char</>, the internal name of the <type>character</type> data type) to match the target
column type.  (Since the types <type>text</type> and
<type>bpchar</type> are binary-compatible, this conversion does
not insert any real function call.)  Finally, the sizing function
<literal>bpchar(bpchar, integer)</literal> is found in the system catalog
and applied to the operator's result and the stored column length.  This
type-specific function performs the required length check and addition of
padding spaces.
</para>
</example>
</sect1>

<sect1 id="typeconv-union-case">
<title><literal>UNION</literal>, <literal>CASE</literal>, and Related Constructs</title>

<indexterm zone="typeconv-union-case">
 <primary>UNION</primary>
 <secondary>determination of result type</secondary>
</indexterm>

<indexterm zone="typeconv-union-case">
 <primary>CASE</primary>
 <secondary>determination of result type</secondary>
</indexterm>

<indexterm zone="typeconv-union-case">
 <primary>ARRAY</primary>
 <secondary>determination of result type</secondary>
</indexterm>

<indexterm zone="typeconv-union-case">
 <primary>VALUES</primary>
 <secondary>determination of result type</secondary>
</indexterm>

<indexterm zone="typeconv-union-case">
 <primary>GREATEST</primary>
 <secondary>determination of result type</secondary>
</indexterm>

<indexterm zone="typeconv-union-case">
 <primary>LEAST</primary>
 <secondary>determination of result type</secondary>
</indexterm>

<para>
SQL <literal>UNION</> constructs must match up possibly dissimilar
types to become a single result set.  The resolution algorithm is
applied separately to each output column of a union query.  The
<literal>INTERSECT</> and <literal>EXCEPT</> constructs resolve
dissimilar types in the same way as <literal>UNION</>.  The
<literal>CASE</>, <literal>ARRAY</>, <literal>VALUES</>,
<function>GREATEST</> and <function>LEAST</> constructs use the identical
algorithm to match up their component expressions and select a result
data type.
</para>

<procedure>
<title>Type Resolution for <literal>UNION</literal>, <literal>CASE</literal>,
and Related Constructs</title>

<step performance="required">
<para>
If all inputs are of type <type>unknown</type>, resolve as type
<type>text</type> (the preferred type of the string category).
Otherwise, ignore the <type>unknown</type> inputs while choosing the result type.
</para>
</step>

<step performance="required">
<para>
If the non-unknown inputs are not all of the same type category, fail.
</para>
</step>

<step performance="required">
<para>
Choose the first non-unknown input type which is a preferred type in
that category or allows all the non-unknown inputs to be implicitly
converted to it.
</para>
</step>

<step performance="required">
<para>
Convert all inputs to the selected type.
</para>
</step>
</procedure>

<para>
Some examples follow.
</para>

<example>
<title>Type Resolution with Underspecified Types in a Union</title>

<para>
<screen>
SELECT text 'a' AS "text" UNION SELECT 'b';

 text
------
 a
 b
(2 rows)
</screen>
Here, the unknown-type literal <literal>'b'</literal> will be resolved as type <type>text</type>.
</para>
</example>

<example>
<title>Type Resolution in a Simple Union</title>

<para>
<screen>
SELECT 1.2 AS "numeric" UNION SELECT 1;

 numeric
---------
       1
     1.2
(2 rows)
</screen>
The literal <literal>1.2</> is of type <type>numeric</>,
and the <type>integer</type> value <literal>1</> can be cast implicitly to
<type>numeric</>, so that type is used.
</para>
</example>

<example>
<title>Type Resolution in a Transposed Union</title>

<para>
<screen>
SELECT 1 AS "real" UNION SELECT CAST('2.2' AS REAL);

 real
------
    1
  2.2
(2 rows)
</screen>
Here, since type <type>real</> cannot be implicitly cast to <type>integer</>,
but <type>integer</> can be implicitly cast to <type>real</>, the union
result type is resolved as <type>real</>.
</para>
</example>

</sect1>
</chapter>