9.1第十章

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类型转换(Type Conversion)

SQL statements can, intentionally or not, require the mixing of different data types in the same expression.

PostgreSQL has extensive facilities for evaluating mixed-type expressions.

In many cases a user does not need to understand the details of the type conversion mechanism. However, implicit

conversions done by PostgreSQL can affect the results of a query. When necessary, these results can be tailored by

using explicit type conversion.

This chapter introduces the PostgreSQL type conversion mechanisms and conventions. Refer to the relevant sections

in Chapter 8 and Chapter 9 for more information on specific data types and allowed functions and operators.

概述(Overview)

SQL is a strongly typed language. That is, every data item has an associated data type which determines its

behavior and allowed usage. PostgreSQL has an extensible type system that is more general and flexible than other

SQL implementations. Hence, most type conversion behavior in PostgreSQL is governed by general rules rather than by

ad hoc heuristics. This allows the use of mixed-type expressions even with user-defined types.

The PostgreSQL scanner/parser divides lexical elements into five fundamental categories: integers, non-integer

numbers, strings, identifiers, and key words. Constants of most non-numeric types are first classified as strings.

The SQL language definition allows specifying type names with strings, and this mechanism can be used in PostgreSQL

to start the parser down the correct path. For example, the query:

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

 label  | value
--------+-------
 Origin | (0,0)
(1 row)

has two literal constants, of type text and point. If a type is not specified for a string literal, then the

placeholder type unknown is assigned initially, to be resolved in later stages as described below.

There are four fundamental SQL constructs requiring distinct type conversion rules in the PostgreSQL parser:

Function calls Much of the PostgreSQL type system is built around a rich set of functions. Functions can have one or more

arguments. Since PostgreSQL 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.

Operators PostgreSQL allows expressions with prefix and postfix unary (one-argument) operators, as well as binary (two-

argument) operators. Like functions, operators can be overloaded, so the same problem of selecting the right

operator exists.

Value Storage SQL INSERT and UPDATE 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.

UNION, CASE, and related constructs Since all query results from a unionized SELECT statement must appear in a single set of columns, the types of the

results of each SELECT clause must be matched up and converted to a uniform set. Similarly, the result expressions

of a CASE construct must be converted to a common type so that the CASE expression as a whole has a known output

type. The same holds for ARRAY constructs, and for the GREATEST and LEAST functions.

The system catalogs store information about which conversions, or casts, exist between which data types, and how to

perform those conversions. Additional casts can be added by the user with the CREATE CAST command. (This is usually

done in conjunction with defining new data types. The set of casts between built-in types has been carefully

crafted and is best not altered.)

An additional heuristic provided by the parser allows improved determination of the proper casting behavior among

groups of types that have implicit casts. Data types are divided into several basic type categories, including

boolean, numeric, string, bitstring, datetime, timespan, geometric, network, and user-defined. (For a list see

Table 45-49; but note it is also possible to create custom type categories.) Within each category there can be one

or more preferred types, which are preferred when there is a choice of possible types. With careful selection of

preferred types and available implicit casts, it is possible to ensure that ambiguous expressions (those with

multiple candidate parsing solutions) can be resolved in a useful way.

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

Implicit conversions should never have surprising or unpredictable outcomes.

There should be no extra overhead in the parser or executor if a query does not need implicit type conversion. That

is, if a query is well-formed and the types already match, then the query should execute without spending extra

time in the parser and without introducing unnecessary implicit conversion calls in the query.

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 no longer do implicit

conversion to use the old function.

操作(Operators)

The specific operator that is referenced by an operator expression is determined using the following procedure. Note that this procedure is indirectly affected by the precedence of the involved operators, since that will determine which sub-expressions are taken to be the inputs of which operators. See Section 4.1.6 for more information.

Operator Type Resolution

Select the operators to be considered from the pg_operator system catalog. If a non-schema-qualified operator name was used (the usual case), the operators considered are those with the matching name and argument count that are visible in the current search path (see Section 5.7.3). If a qualified operator name was given, only operators in the specified schema are considered.

If the search path finds multiple operators with identical argument types, only the one appearing earliest in the path is considered. Operators with different argument types are considered on an equal footing regardless of search path position.

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.

If one argument of a binary operator invocation is of the unknown type, then assume it is the same type as the other argument for this check. Invocations involving two unknown inputs, or a unary operator with an unknown input, will never find a match at this step.

Look for the best match.

Discard candidate operators for which the input types do not match and cannot be converted (using an implicit conversion) to match. unknown literals are assumed to be convertible to anything for this purpose. If only one candidate remains, use it; else continue to the next step.

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 exact matches. If only one candidate remains, use it; else continue to the next step.

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.

If any input arguments are unknown, check the type categories accepted at those argument positions by the remaining candidates. At each position, select the string category if any candidate accepts that category. (This bias towards string is appropriate since an unknown-type literal looks 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 in that category, discard candidates that accept non-preferred types for that argument.

If only one candidate remains, use it. If no candidate or more than one candidate remains, then fail.

Some examples follow.

Example 10-1. Factorial Operator Type Resolution

There is only one factorial operator (postfix !) defined in the standard catalog, and it takes an argument of type bigint. The scanner assigns an initial type of integer to the argument in this query expression:

SELECT 40 ! AS "40 factorial";

                   40 factorial
--------------------------------------------------
 815915283247897734345611269596115894272000000000
(1 row)

So the parser does a type conversion on the operand and the query is equivalent to:

SELECT CAST(40 AS bigint) ! AS "40 factorial";

Example 10-2. String Concatenation Operator Type Resolution

A string-like syntax is used for working with string types and for working with complex extension types. Strings with unspecified type are matched with likely operator candidates.

An example with one unspecified argument:

SELECT text 'abc' || 'def' AS "text and unknown";

 text and unknown
------------------
 abcdef
(1 row)

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

Here is a concatenation on unspecified types:

SELECT 'abc' || 'def' AS "unspecified";

 unspecified
-------------
 abcdef
(1 row)

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, text, is used as the specific type to resolve the unknown literals as.

Example 10-3. Absolute-Value and Negation Operator Type Resolution

The PostgreSQL operator catalog has several entries for the prefix operator @, all of which implement absolute-value operations for various numeric data types. One of these entries is for type float8, which is the preferred type in the numeric category. Therefore, PostgreSQL will use that entry when faced with an unknown input:

SELECT @ '-4.5' AS "abs";
 abs
-----
 4.5
(1 row)

Here the system has implicitly resolved the unknown-type literal as type float8 before applying the chosen operator. We can verify that float8 and not some other type was used:

SELECT @ '-4.5e500' AS "abs";

ERROR: "-4.5e500" is out of range for type double precision On the other hand, the prefix operator ~ (bitwise negation) is defined only for integer data types, not for float8. So, if we try a similar case with ~, we get:

SELECT ~ '20' AS "negation";

ERROR: operator is not unique: ~ "unknown" HINT: Could not choose a best candidate operator. You might need to add explicit type casts. This happens because the system cannot decide which of the several possible ~ operators should be preferred. We can help it out with an explicit cast:

SELECT ~ CAST('20' AS int8) AS "negation";

 negation
----------
      -21
(1 row)

函数(Functions)

The specific function that is referenced by a function call is determined using the following procedure.

Function Type Resolution

Select the functions to be considered from the pg_proc system catalog. If a non-schema-qualified function name was used, the functions considered are those with the matching name and argument count that are visible in the current search path (see Section 5.7.3). If a qualified function name was given, only functions in the specified schema are considered.

If the search path finds multiple functions of identical argument types, only the one appearing earliest in the path is considered. Functions of different argument types are considered on an equal footing regardless of search path position.

If a function is declared with a VARIADIC array parameter, and the call does not use the VARIADIC keyword, then the function is treated as if the array parameter were replaced by one or more occurrences of its element type, as needed to match the call. After such expansion the function might have effective argument types identical to some non-variadic function. In that case the function appearing earlier in the search path is used, or if the two functions are in the same schema, the non-variadic one is preferred.

Functions that have default values for parameters are considered to match any call that omits zero or more of the defaultable parameter positions. If more than one such function matches a call, the one appearing earliest in the search path is used. If there are two or more such functions in the same schema with identical parameter types in the non-defaulted positions (which is possible if they have different sets of defaultable parameters), the system will not be able to determine which to prefer, and so an "ambiguous function call" error will result if no better match to the call can be found.

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 unknown will never find a match at this step.)

If no exact match is found, see if the function call appears to be a special 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-coercible to the named data type, or a type that could be converted to the named data type by applying that type's I/O functions (that is, the conversion is either to or from one of the standard string types). When these conditions are met, the function call is treated as a form of CAST specification. [1]

Look for the best match.

Discard candidate functions for which the input types do not match and cannot be converted (using an implicit conversion) to match. unknown literals are assumed to be convertible to anything for this purpose. If only one candidate remains, use it; else continue to the next step.

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 exact matches. If only one candidate remains, use it; else continue to the next step.

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.

If any input arguments are unknown, check the type categories accepted at those argument positions by the remaining candidates. At each position, select the string category if any candidate accepts that category. (This bias towards string is appropriate since an unknown-type literal looks 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 in that category, discard candidates that accept non-preferred types for that argument.

If only one candidate remains, use it. If no candidate or more than one candidate remains, then fail.

Note that the "best match" rules are identical for operator and function type resolution. Some examples follow.

Example 10-4. Rounding Function Argument Type Resolution

There is only one round function that takes two arguments; it takes a first argument of type numeric and a second argument of type integer. So the following query automatically converts the first argument of type integer to numeric:

SELECT round(4, 4);

 round
--------
 4.0000
(1 row)

That query is actually transformed by the parser to:

SELECT round(CAST (4 AS numeric), 4);

Since numeric constants with decimal points are initially assigned the type numeric, the following query will require no type conversion and therefore might be slightly more efficient:

SELECT round(4.0, 4);

Example 10-5. Substring Function Type Resolution

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

SELECT substr('1234', 3);

 substr
--------
     34
(1 row)

If the string is declared to be of type varchar, as might be the case if it comes from a table, then the parser will try to convert it to become text:

SELECT substr(varchar '1234', 3);

 substr
--------
     34
(1 row)

This is transformed by the parser to effectively become:

SELECT substr(CAST (varchar '1234' AS text), 3);

Note: The parser learns from the pg_cast catalog that text and varchar are binary-compatible, meaning that one can be passed to a function that accepts the other without doing any physical conversion. Therefore, no type conversion call is really inserted in this case.

And, if the function is called with an argument of type integer, the parser will try to convert that to text:

SELECT substr(1234, 3);

ERROR: function substr(integer, integer) does not exist HINT: No function matches the given name and argument types. You might need to add explicit type casts. This does not work because integer does not have an implicit cast to text. An explicit cast will work, however:

SELECT substr(CAST (1234 AS text), 3);

 substr
--------
     34
(1 row)
Notes

[1] The reason for this step is to support function-style cast specifications in cases where there is not an actual cast function. If there is a cast function, it is conventionally named after its output type, and so there is no need to have a special case. See CREATE CAST for additional commentary.

值存储(Value Storage)

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

Value Storage Type Conversion

Check for an exact match with the target.

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.

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 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 integer, which receives the destination column's declared length (actually, its atttypmod value; the interpretation of atttypmod varies for different data types). The cast function is responsible for applying any length-dependent semantics such as size checking or truncation.

Example 10-6. character Storage Type Conversion

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

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

          v           | length
----------------------+--------
 abcdef               |     20
(1 row)

What has really happened here is that the two unknown literals are resolved to text by default, allowing the || operator to be resolved as text concatenation. Then the text result of the operator is converted to bpchar ("blank-padded char", the internal name of the character data type) to match the target column type. (Since the conversion from text to bpchar is binary-coercible, this conversion does not insert any real function call.) Finally, the sizing function bpchar(bpchar, integer) 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.

UNION, CASE, 和Related Constructs(UNION, CASE, and Related Constructs)

SQL 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 INTERSECT and EXCEPT constructs resolve dissimilar types in the same way as UNION. The CASE, ARRAY, VALUES, GREATEST and LEAST constructs use the identical algorithm to match up their component expressions and select a result data type.

Type Resolution for UNION, CASE, and Related Constructs

If all inputs are of the same type, and it is not unknown, resolve as that type. Otherwise, replace any domain types in the list with their underlying base types.

If all inputs are of type unknown, resolve as type text (the preferred type of the string category). Otherwise, unknown inputs are ignored.

If the non-unknown inputs are not all of the same type category, fail.

Choose the first non-unknown input type which is a preferred type in that category, if there is one.

Otherwise, choose the last non-unknown input type that allows all the preceding non-unknown inputs to be implicitly converted to it. (There always is such a type, since at least the first type in the list must satisfy this condition.)

Convert all inputs to the selected type. Fail if there is not a conversion from a given input to the selected type.

Some examples follow.

Example 10-7. Type Resolution with Underspecified Types in a Union

SELECT text 'a' AS "text" UNION SELECT 'b';

 text
------
 a
 b
(2 rows)

Here, the unknown-type literal 'b' will be resolved to type text.

Example 10-8. Type Resolution in a Simple Union

SELECT 1.2 AS "numeric" UNION SELECT 1;

 numeric
---------
       1
     1.2
(2 rows)

The literal 1.2 is of type numeric, and the integer value 1 can be cast implicitly to numeric, so that type is used.

Example 10-9. Type Resolution in a Transposed Union

SELECT 1 AS "real" UNION SELECT CAST('2.2' AS REAL);

 real
------
    1
  2.2
(2 rows)

Here, since type real cannot be implicitly cast to integer, but integer can be implicitly cast to real, the union result type is resolved as real.

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