data type conversion SQL statements can, intentionally or not, require mixing of different data types in the same expression. PostgreSQL has extensive facilities for evaluating mixed-type expressions. In many cases a user will not need to understand the details of the type conversion mechanism. However, the 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 and for more information on specific data types and allowed functions and operators. 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 much 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 mixed-type expressions to be meaningful even with user-defined types. The PostgreSQL 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 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, and 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, called casts, between data types are valid, 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 the built-in types has been carefully crafted and is best not altered.) data type category An additional heuristic is provided in the parser to allow better guesses at proper behavior for SQL standard types. There are several basic type categories defined: boolean, numeric, string, bitstring, datetime, timespan, geometric, network, and user-defined. Each category, with the exception of user-defined, has one or more preferred types 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. All type conversion rules are designed with several principles in mind: Implicit conversions should never have surprising or unpredictable outcomes. User-defined types, of which the parser has no a priori knowledge, should be higher 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). User-defined types are not related. Currently, PostgreSQL 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. 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. 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. operator type resolution in an invocation 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 for more information. Select the operators to be considered from the pg_operator 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 ). If a qualified operator name was given, only operators in the specified schema are considered. 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. 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. Other cases involving unknown 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 any 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 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. If only one candidate remains, use it. If no candidate or more than one candidate remains, then fail. Some examples follow. There is only one exponentiation operator defined in the catalog, and it takes arguments of type double precision. The scanner assigns an initial type of integer to both arguments of this query expression: SELECT 2 ^ 3 AS "exp"; exp ----- 8 (1 row) So the parser does a type conversion on both operands and the query is equivalent to SELECT CAST(2 AS double precision) ^ CAST(3 AS double precision) AS "exp"; 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. 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 of 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 to. 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 a non-numeric input: SELECT @ '-4.5' AS "abs"; abs ----- 4.5 (1 row) Here the system has performed an implicit conversion from text to 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 may need to add explicit type casts. This happens because the system can't 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) function type resolution in an invocation The specific function to be used in a function invocation is determined according to the following steps. Select the functions to be considered from the pg_proc 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 ). 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. But functions of different argument types are considered on an equal footing regardless of search path position. 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 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. 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 any 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 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. 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. There is only one round function with two arguments. (The first is numeric, the second is 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 may therefore be slightly more efficient: SELECT round(4.0, 4); 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); 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 explicit 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); substr -------- 34 (1 row) This actually executes as SELECT substr(CAST (1234 AS text), 3); This automatic transformation can succeed because there is an implicitly invocable cast from integer to text. Values to be inserted into a table are converted to the destination column's data type according to the following steps. 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. 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 types text and bpchar are binary-compatible, 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 determination of result type CASE determination of result type ARRAY determination of result type VALUES determination of result type GREATEST determination of result type LEAST determination of result type 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. If all inputs are of type unknown, resolve as type text (the preferred type of the string category). Otherwise, ignore the unknown inputs while choosing the result type. 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 or allows all the non-unknown inputs to be implicitly converted to it. Convert all inputs to the selected type. Some examples follow. SELECT text 'a' AS "text" UNION SELECT 'b'; text ------ a b (2 rows) Here, the unknown-type literal 'b' will be resolved as type text. 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. 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.