function operator PostgreSQL provides a large number of functions and operators for the built-in data types. This chapter describes most of them, although additional special-purpose functions appear in relevant sections of the manual. Users can also define their own functions and operators, as described in . The psql commands \df and \do can be used to list all available functions and operators, respectively. The notation used throughout this chapter to describe the argument and result data types of a function or operator is like this: repeat ( text, integer ) text which says that the function repeat takes one text and one integer argument and returns a result of type text. The right arrow is also used to indicate the result of an example, thus: repeat('Pg', 4) PgPgPgPg If you are concerned about portability then note that most of the functions and operators described in this chapter, with the exception of the most trivial arithmetic and comparison operators and some explicitly marked functions, are not specified by the SQL standard. Some of this extended functionality is present in other SQL database management systems, and in many cases this functionality is compatible and consistent between the various implementations. operator logical Boolean operators operators, logical The usual logical operators are available: AND (operator) OR (operator) NOT (operator) conjunction disjunction negation boolean AND boolean boolean boolean OR boolean boolean NOT boolean boolean SQL uses a three-valued logic system with true, false, and null, which represents unknown. Observe the following truth tables: a b a AND b a OR b TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE NULL NULL TRUE FALSE FALSE FALSE FALSE FALSE NULL FALSE NULL NULL NULL NULL NULL a NOT a TRUE FALSE FALSE TRUE NULL NULL The operators AND and OR are commutative, that is, you can switch the left and right operands without affecting the result. (However, it is not guaranteed that the left operand is evaluated before the right operand. See for more information about the order of evaluation of subexpressions.) comparison operators The usual comparison operators are available, as shown in . Operator Description datatype < datatype boolean Less than datatype > datatype boolean Greater than datatype <= datatype boolean Less than or equal to datatype >= datatype boolean Greater than or equal to datatype = datatype boolean Equal datatype <> datatype boolean Not equal datatype != datatype boolean Not equal <> is the standard SQL notation for not equal. != is an alias, which is converted to <> at a very early stage of parsing. Hence, it is not possible to implement != and <> operators that do different things. These comparison operators are available for all built-in data types that have a natural ordering, including numeric, string, and date/time types. In addition, arrays, composite types, and ranges can be compared if their component data types are comparable. It is usually possible to compare values of related data types as well; for example integer > bigint will work. Some cases of this sort are implemented directly by cross-type comparison operators, but if no such operator is available, the parser will coerce the less-general type to the more-general type and apply the latter's comparison operator. As shown above, all comparison operators are binary operators that return values of type boolean. Thus, expressions like 1 < 2 < 3 are not valid (because there is no < operator to compare a Boolean value with 3). Use the BETWEEN predicates shown below to perform range tests. There are also some comparison predicates, as shown in . These behave much like operators, but have special syntax mandated by the SQL standard. Predicate Description Example(s) datatype BETWEEN datatype AND datatype boolean Between (inclusive of the range endpoints). 2 BETWEEN 1 AND 3 t 2 BETWEEN 3 AND 1 f datatype NOT BETWEEN datatype AND datatype boolean Not between (the negation of BETWEEN). 2 NOT BETWEEN 1 AND 3 f datatype BETWEEN SYMMETRIC datatype AND datatype boolean Between, after sorting the two endpoint values. 2 BETWEEN SYMMETRIC 3 AND 1 t datatype NOT BETWEEN SYMMETRIC datatype AND datatype boolean Not between, after sorting the two endpoint values. 2 NOT BETWEEN SYMMETRIC 3 AND 1 f datatype IS DISTINCT FROM datatype boolean Not equal, treating null as a comparable value. 1 IS DISTINCT FROM NULL t (rather than NULL) NULL IS DISTINCT FROM NULL f (rather than NULL) datatype IS NOT DISTINCT FROM datatype boolean Equal, treating null as a comparable value. 1 IS NOT DISTINCT FROM NULL f (rather than NULL) NULL IS NOT DISTINCT FROM NULL t (rather than NULL) datatype IS NULL boolean Test whether value is null. 1.5 IS NULL f datatype IS NOT NULL boolean Test whether value is not null. 'null' IS NOT NULL t datatype ISNULL boolean Test whether value is null (nonstandard syntax). datatype NOTNULL boolean Test whether value is not null (nonstandard syntax). boolean IS TRUE boolean Test whether boolean expression yields true. true IS TRUE t NULL::boolean IS TRUE f (rather than NULL) boolean IS NOT TRUE boolean Test whether boolean expression yields false or unknown. true IS NOT TRUE f NULL::boolean IS NOT TRUE t (rather than NULL) boolean IS FALSE boolean Test whether boolean expression yields false. true IS FALSE f NULL::boolean IS FALSE f (rather than NULL) boolean IS NOT FALSE boolean Test whether boolean expression yields true or unknown. true IS NOT FALSE t NULL::boolean IS NOT FALSE t (rather than NULL) boolean IS UNKNOWN boolean Test whether boolean expression yields unknown. true IS UNKNOWN f NULL::boolean IS UNKNOWN t (rather than NULL) boolean IS NOT UNKNOWN boolean Test whether boolean expression yields true or false. true IS NOT UNKNOWN t NULL::boolean IS NOT UNKNOWN f (rather than NULL) BETWEEN BETWEEN SYMMETRIC The BETWEEN predicate simplifies range tests: a BETWEEN x AND y is equivalent to a >= x AND a <= y Notice that BETWEEN treats the endpoint values as included in the range. BETWEEN SYMMETRIC is like BETWEEN except there is no requirement that the argument to the left of AND be less than or equal to the argument on the right. If it is not, those two arguments are automatically swapped, so that a nonempty range is always implied. The various variants of BETWEEN are implemented in terms of the ordinary comparison operators, and therefore will work for any data type(s) that can be compared. The use of AND in the BETWEEN syntax creates an ambiguity with the use of AND as a logical operator. To resolve this, only a limited set of expression types are allowed as the second argument of a BETWEEN clause. If you need to write a more complex sub-expression in BETWEEN, write parentheses around the sub-expression. IS DISTINCT FROM IS NOT DISTINCT FROM Ordinary comparison operators yield null (signifying unknown), not true or false, when either input is null. For example, 7 = NULL yields null, as does 7 <> NULL. When this behavior is not suitable, use the IS NOT DISTINCT FROM predicates: a IS DISTINCT FROM b a IS NOT DISTINCT FROM b For non-null inputs, IS DISTINCT FROM is the same as the <> operator. However, if both inputs are null it returns false, and if only one input is null it returns true. Similarly, IS NOT DISTINCT FROM is identical to = for non-null inputs, but it returns true when both inputs are null, and false when only one input is null. Thus, these predicates effectively act as though null were a normal data value, rather than unknown. IS NULL IS NOT NULL ISNULL NOTNULL To check whether a value is or is not null, use the predicates: expression IS NULL expression IS NOT NULL or the equivalent, but nonstandard, predicates: expression ISNULL expression NOTNULL null valuecomparing Do not write expression = NULL because NULL is not equal to NULL. (The null value represents an unknown value, and it is not known whether two unknown values are equal.) Some applications might expect that expression = NULL returns true if expression evaluates to the null value. It is highly recommended that these applications be modified to comply with the SQL standard. However, if that cannot be done the configuration variable is available. If it is enabled, PostgreSQL will convert x = NULL clauses to x IS NULL. If the expression is row-valued, then IS NULL is true when the row expression itself is null or when all the row's fields are null, while IS NOT NULL is true when the row expression itself is non-null and all the row's fields are non-null. Because of this behavior, IS NULL and IS NOT NULL do not always return inverse results for row-valued expressions; in particular, a row-valued expression that contains both null and non-null fields will return false for both tests. In some cases, it may be preferable to write row IS DISTINCT FROM NULL or row IS NOT DISTINCT FROM NULL, which will simply check whether the overall row value is null without any additional tests on the row fields. IS TRUE IS NOT TRUE IS FALSE IS NOT FALSE IS UNKNOWN IS NOT UNKNOWN Boolean values can also be tested using the predicates boolean_expression IS TRUE boolean_expression IS NOT TRUE boolean_expression IS FALSE boolean_expression IS NOT FALSE boolean_expression IS UNKNOWN boolean_expression IS NOT UNKNOWN These will always return true or false, never a null value, even when the operand is null. A null input is treated as the logical value unknown. Notice that IS UNKNOWN and IS NOT UNKNOWN are effectively the same as IS NULL and IS NOT NULL, respectively, except that the input expression must be of Boolean type. Some comparison-related functions are also available, as shown in . Function Description Example(s) num_nonnulls num_nonnulls ( VARIADIC "any" ) integer Returns the number of non-null arguments. num_nonnulls(1, NULL, 2) 2 num_nulls num_nulls ( VARIADIC "any" ) integer Returns the number of null arguments. num_nulls(1, NULL, 2) 1 Mathematical operators are provided for many PostgreSQL types. For types without standard mathematical conventions (e.g., date/time types) we describe the actual behavior in subsequent sections. shows the mathematical operators that are available for the standard numeric types. Unless otherwise noted, operators shown as accepting numeric_type are available for all the types smallint, integer, bigint, numeric, real, and double precision. Operators shown as accepting integral_type are available for the types smallint, integer, and bigint. Except where noted, each form of an operator returns the same data type as its argument(s). Calls involving multiple argument data types, such as integer + numeric, are resolved by using the type appearing later in these lists. Operator Description Example(s) numeric_type + numeric_type numeric_type Addition 2 + 3 5 + numeric_type numeric_type Unary plus (no operation) + 3.5 3.5 numeric_type - numeric_type numeric_type Subtraction 2 - 3 -1 - numeric_type numeric_type Negation - (-4) 4 numeric_type * numeric_type numeric_type Multiplication 2 * 3 6 numeric_type / numeric_type numeric_type Division (for integral types, division truncates the result towards zero) 5.0 / 2 2.5000000000000000 5 / 2 2 (-5) / 2 -2 numeric_type % numeric_type numeric_type Modulo (remainder); available for smallint, integer, bigint, and numeric 5 % 4 1 numeric ^ numeric numeric double precision ^ double precision double precision Exponentiation 2 ^ 3 8 Unlike typical mathematical practice, multiple uses of ^ will associate left to right by default: 2 ^ 3 ^ 3 512 2 ^ (3 ^ 3) 134217728 |/ double precision double precision Square root |/ 25.0 5 ||/ double precision double precision Cube root ||/ 64.0 4 @ numeric_type numeric_type Absolute value @ -5.0 5.0 integral_type & integral_type integral_type Bitwise AND 91 & 15 11 integral_type | integral_type integral_type Bitwise OR 32 | 3 35 integral_type # integral_type integral_type Bitwise exclusive OR 17 # 5 20 ~ integral_type integral_type Bitwise NOT ~1 -2 integral_type << integer integral_type Bitwise shift left 1 << 4 16 integral_type >> integer integral_type Bitwise shift right 8 >> 2 2 shows the available mathematical functions. Many of these functions are provided in multiple forms with different argument types. Except where noted, any given form of a function returns the same data type as its argument(s); cross-type cases are resolved in the same way as explained above for operators. The functions working with double precision data are mostly implemented on top of the host system's C library; accuracy and behavior in boundary cases can therefore vary depending on the host system. Function Description Example(s) abs abs ( numeric_type ) numeric_type Absolute value abs(-17.4) 17.4 cbrt cbrt ( double precision ) double precision Cube root cbrt(64.0) 4 ceil ceil ( numeric ) numeric ceil ( double precision ) double precision Nearest integer greater than or equal to argument ceil(42.2) 43 ceil(-42.8) -42 ceiling ceiling ( numeric ) numeric ceiling ( double precision ) double precision Nearest integer greater than or equal to argument (same as ceil) ceiling(95.3) 96 degrees degrees ( double precision ) double precision Converts radians to degrees degrees(0.5) 28.64788975654116 div div ( y numeric, x numeric ) numeric Integer quotient of y/x (truncates towards zero) div(9, 4) 2 exp exp ( numeric ) numeric exp ( double precision ) double precision Exponential (e raised to the given power) exp(1.0) 2.7182818284590452 factorial factorial ( bigint ) numeric Factorial factorial(5) 120 floor floor ( numeric ) numeric floor ( double precision ) double precision Nearest integer less than or equal to argument floor(42.8) 42 floor(-42.8) -43 gcd gcd ( numeric_type, numeric_type ) numeric_type Greatest common divisor (the largest positive number that divides both inputs with no remainder); returns 0 if both inputs are zero; available for integer, bigint, and numeric gcd(1071, 462) 21 lcm lcm ( numeric_type, numeric_type ) numeric_type Least common multiple (the smallest strictly positive number that is an integral multiple of both inputs); returns 0 if either input is zero; available for integer, bigint, and numeric lcm(1071, 462) 23562 ln ln ( numeric ) numeric ln ( double precision ) double precision Natural logarithm ln(2.0) 0.6931471805599453 log log ( numeric ) numeric log ( double precision ) double precision Base 10 logarithm log(100) 2 log10 log10 ( numeric ) numeric log10 ( double precision ) double precision Base 10 logarithm (same as log) log10(1000) 3 log ( b numeric, x numeric ) numeric Logarithm of x to base b log(2.0, 64.0) 6.0000000000000000 min_scale min_scale ( numeric ) integer Minimum scale (number of fractional decimal digits) needed to represent the supplied value precisely min_scale(8.4100) 2 mod mod ( y numeric_type, x numeric_type ) numeric_type Remainder of y/x; available for smallint, integer, bigint, and numeric mod(9, 4) 1 pi pi ( ) double precision Approximate value of π pi() 3.141592653589793 power power ( a numeric, b numeric ) numeric power ( a double precision, b double precision ) double precision a raised to the power of b power(9, 3) 729 radians radians ( double precision ) double precision Converts degrees to radians radians(45.0) 0.7853981633974483 round round ( numeric ) numeric round ( double precision ) double precision Rounds to nearest integer. For numeric, ties are broken by rounding away from zero. For double precision, the tie-breaking behavior is platform dependent, but round to nearest even is the most common rule. round(42.4) 42 round ( v numeric, s integer ) numeric Rounds v to s decimal places. Ties are broken by rounding away from zero. round(42.4382, 2) 42.44 round(1234.56, -1) 1230 scale scale ( numeric ) integer Scale of the argument (the number of decimal digits in the fractional part) scale(8.4100) 4 sign sign ( numeric ) numeric sign ( double precision ) double precision Sign of the argument (-1, 0, or +1) sign(-8.4) -1 sqrt sqrt ( numeric ) numeric sqrt ( double precision ) double precision Square root sqrt(2) 1.4142135623730951 trim_scale trim_scale ( numeric ) numeric Reduces the value's scale (number of fractional decimal digits) by removing trailing zeroes trim_scale(8.4100) 8.41 trunc trunc ( numeric ) numeric trunc ( double precision ) double precision Truncates to integer (towards zero) trunc(42.8) 42 trunc(-42.8) -42 trunc ( v numeric, s integer ) numeric Truncates v to s decimal places trunc(42.4382, 2) 42.43 width_bucket width_bucket ( operand numeric, low numeric, high numeric, count integer ) integer width_bucket ( operand double precision, low double precision, high double precision, count integer ) integer Returns the number of the bucket in which operand falls in a histogram having count equal-width buckets spanning the range low to high. Returns 0 or count+1 for an input outside that range. width_bucket(5.35, 0.024, 10.06, 5) 3 width_bucket ( operand anycompatible, thresholds anycompatiblearray ) integer Returns the number of the bucket in which operand falls given an array listing the lower bounds of the buckets. Returns 0 for an input less than the first lower bound. operand and the array elements can be of any type having standard comparison operators. The thresholds array must be sorted, smallest first, or unexpected results will be obtained. width_bucket(now(), array['yesterday', 'today', 'tomorrow']::timestamptz[]) 2 shows functions for generating random numbers. Function Description Example(s) random random ( ) double precision Returns a random value in the range 0.0 <= x < 1.0 random() 0.897124072839091 setseed setseed ( double precision ) void Sets the seed for subsequent random() calls; argument must be between -1.0 and 1.0, inclusive setseed(0.12345) The random() function uses a deterministic pseudo-random number generator. It is fast but not suitable for cryptographic applications; see the module for a more secure alternative. If setseed() is called, the series of results of subsequent random() calls in the current session can be repeated by re-issuing setseed() with the same argument. Without any prior setseed() call in the same session, the first random() call obtains a seed from a platform-dependent source of random bits. shows the available trigonometric functions. Each of these functions comes in two variants, one that measures angles in radians and one that measures angles in degrees. Function Description Example(s) acos acos ( double precision ) double precision Inverse cosine, result in radians acos(1) 0 acosd acosd ( double precision ) double precision Inverse cosine, result in degrees acosd(0.5) 60 asin asin ( double precision ) double precision Inverse sine, result in radians asin(1) 1.5707963267948966 asind asind ( double precision ) double precision Inverse sine, result in degrees asind(0.5) 30 atan atan ( double precision ) double precision Inverse tangent, result in radians atan(1) 0.7853981633974483 atand atand ( double precision ) double precision Inverse tangent, result in degrees atand(1) 45 atan2 atan2 ( y double precision, x double precision ) double precision Inverse tangent of y/x, result in radians atan2(1, 0) 1.5707963267948966 atan2d atan2d ( y double precision, x double precision ) double precision Inverse tangent of y/x, result in degrees atan2d(1, 0) 90 cos cos ( double precision ) double precision Cosine, argument in radians cos(0) 1 cosd cosd ( double precision ) double precision Cosine, argument in degrees cosd(60) 0.5 cot cot ( double precision ) double precision Cotangent, argument in radians cot(0.5) 1.830487721712452 cotd cotd ( double precision ) double precision Cotangent, argument in degrees cotd(45) 1 sin sin ( double precision ) double precision Sine, argument in radians sin(1) 0.8414709848078965 sind sind ( double precision ) double precision Sine, argument in degrees sind(30) 0.5 tan tan ( double precision ) double precision Tangent, argument in radians tan(1) 1.5574077246549023 tand tand ( double precision ) double precision Tangent, argument in degrees tand(45) 1 Another way to work with angles measured in degrees is to use the unit transformation functions radians() and degrees() shown earlier. However, using the degree-based trigonometric functions is preferred, as that way avoids round-off error for special cases such as sind(30). shows the available hyperbolic functions. Function Description Example(s) sinh sinh ( double precision ) double precision Hyperbolic sine sinh(1) 1.1752011936438014 cosh cosh ( double precision ) double precision Hyperbolic cosine cosh(0) 1 tanh tanh ( double precision ) double precision Hyperbolic tangent tanh(1) 0.7615941559557649 asinh asinh ( double precision ) double precision Inverse hyperbolic sine asinh(1) 0.881373587019543 acosh acosh ( double precision ) double precision Inverse hyperbolic cosine acosh(1) 0 atanh atanh ( double precision ) double precision Inverse hyperbolic tangent atanh(0.5) 0.5493061443340548 This section describes functions and operators for examining and manipulating string values. Strings in this context include values of the types character, character varying, and text. Except where noted, these functions and operators are declared to accept and return type text. They will interchangeably accept character varying arguments. Values of type character will be converted to text before the function or operator is applied, resulting in stripping any trailing spaces in the character value. SQL defines some string functions that use key words, rather than commas, to separate arguments. Details are in . PostgreSQL also provides versions of these functions that use the regular function invocation syntax (see ). The string concatenation operator (||) will accept non-string input, so long as at least one input is of string type, as shown in . For other cases, inserting an explicit coercion to text can be used to have non-string input accepted. Function/Operator Description Example(s) character string concatenation text || text text Concatenates the two strings. 'Post' || 'greSQL' PostgreSQL text || anynonarray text anynonarray || text text Converts the non-string input to text, then concatenates the two strings. (The non-string input cannot be of an array type, because that would create ambiguity with the array || operators. If you want to concatenate an array's text equivalent, cast it to text explicitly.) 'Value: ' || 42 Value: 42 normalized Unicode normalization text IS NOT form NORMALIZED boolean Checks whether the string is in the specified Unicode normalization form. The optional form key word specifies the form: NFC (the default), NFD, NFKC, or NFKD. This expression can only be used when the server encoding is UTF8. Note that checking for normalization using this expression is often faster than normalizing possibly already normalized strings. U&'\0061\0308bc' IS NFD NORMALIZED t bit_length bit_length ( text ) integer Returns number of bits in the string (8 times the octet_length). bit_length('jose') 32 char_length character string length length of a character string character string, length char_length ( text ) integer character_length character_length ( text ) integer Returns number of characters in the string. char_length('josé') 4 lower lower ( text ) text Converts the string to all lower case, according to the rules of the database's locale. lower('TOM') tom normalize Unicode normalization normalize ( text , form ) text Converts the string to the specified Unicode normalization form. The optional form key word specifies the form: NFC (the default), NFD, NFKC, or NFKD. This function can only be used when the server encoding is UTF8. normalize(U&'\0061\0308bc', NFC) U&'\00E4bc' octet_length octet_length ( text ) integer Returns number of bytes in the string. octet_length('josé') 5 (if server encoding is UTF8) octet_length octet_length ( character ) integer Returns number of bytes in the string. Since this version of the function accepts type character directly, it will not strip trailing spaces. octet_length('abc '::character(4)) 4 overlay overlay ( string text PLACING newsubstring text FROM start integer FOR count integer ) text Replaces the substring of string that starts at the start'th character and extends for count characters with newsubstring. If count is omitted, it defaults to the length of newsubstring. overlay('Txxxxas' placing 'hom' from 2 for 4) Thomas position position ( substring text IN string text ) integer Returns first starting index of the specified substring within string, or zero if it's not present. position('om' in 'Thomas') 3 substring substring ( string text FROM start integer FOR count integer ) text Extracts the substring of string starting at the start'th character if that is specified, and stopping after count characters if that is specified. Provide at least one of start and count. substring('Thomas' from 2 for 3) hom substring('Thomas' from 3) omas substring('Thomas' for 2) Th substring ( string text FROM pattern text ) text Extracts the first substring matching POSIX regular expression; see . substring('Thomas' from '...$') mas substring ( string text SIMILAR pattern text ESCAPE escape text ) text substring ( string text FROM pattern text FOR escape text ) text Extracts the first substring matching SQL regular expression; see . The first form has been specified since SQL:2003; the second form was only in SQL:1999 and should be considered obsolete. substring('Thomas' similar '%#"o_a#"_' escape '#') oma trim trim ( LEADING | TRAILING | BOTH characters text FROM string text ) text Removes the longest string containing only characters in characters (a space by default) from the start, end, or both ends (BOTH is the default) of string. trim(both 'xyz' from 'yxTomxx') Tom trim ( LEADING | TRAILING | BOTH FROM string text , characters text ) text This is a non-standard syntax for trim(). trim(both from 'yxTomxx', 'xyz') Tom upper upper ( text ) text Converts the string to all upper case, according to the rules of the database's locale. upper('tom') TOM Additional string manipulation functions and operators are available and are listed in . (Some of these are used internally to implement the SQL-standard string functions listed in .) There are also pattern-matching operators, which are described in , and operators for full-text search, which are described in . Function/Operator Description Example(s) character string prefix test text ^@ text boolean Returns true if the first string starts with the second string (equivalent to the starts_with() function). 'alphabet' ^@ 'alph' t ascii ascii ( text ) integer Returns the numeric code of the first character of the argument. In UTF8 encoding, returns the Unicode code point of the character. In other multibyte encodings, the argument must be an ASCII character. ascii('x') 120 btrim btrim ( string text , characters text ) text Removes the longest string containing only characters in characters (a space by default) from the start and end of string. btrim('xyxtrimyyx', 'xyz') trim chr chr ( integer ) text Returns the character with the given code. In UTF8 encoding the argument is treated as a Unicode code point. In other multibyte encodings the argument must designate an ASCII character. chr(0) is disallowed because text data types cannot store that character. chr(65) A concat concat ( val1 "any" [, val2 "any" [, ...] ] ) text Concatenates the text representations of all the arguments. NULL arguments are ignored. concat('abcde', 2, NULL, 22) abcde222 concat_ws concat_ws ( sep text, val1 "any" [, val2 "any" [, ...] ] ) text Concatenates all but the first argument, with separators. The first argument is used as the separator string, and should not be NULL. Other NULL arguments are ignored. concat_ws(',', 'abcde', 2, NULL, 22) abcde,2,22 format format ( formatstr text [, formatarg "any" [, ...] ] ) text Formats arguments according to a format string; see . This function is similar to the C function sprintf. format('Hello %s, %1$s', 'World') Hello World, World initcap initcap ( text ) text Converts the first letter of each word to upper case and the rest to lower case. Words are sequences of alphanumeric characters separated by non-alphanumeric characters. initcap('hi THOMAS') Hi Thomas left left ( string text, n integer ) text Returns first n characters in the string, or when n is negative, returns all but last |n| characters. left('abcde', 2) ab length length ( text ) integer Returns the number of characters in the string. length('jose') 4 lpad lpad ( string text, length integer , fill text ) text Extends the string to length length by prepending the characters fill (a space by default). If the string is already longer than length then it is truncated (on the right). lpad('hi', 5, 'xy') xyxhi ltrim ltrim ( string text , characters text ) text Removes the longest string containing only characters in characters (a space by default) from the start of string. ltrim('zzzytest', 'xyz') test md5 md5 ( text ) text Computes the MD5 hash of the argument, with the result written in hexadecimal. md5('abc') 900150983cd24fb0&zwsp;d6963f7d28e17f72 parse_ident parse_ident ( qualified_identifier text [, strict_mode boolean DEFAULT true ] ) text[] Splits qualified_identifier into an array of identifiers, removing any quoting of individual identifiers. By default, extra characters after the last identifier are considered an error; but if the second parameter is false, then such extra characters are ignored. (This behavior is useful for parsing names for objects like functions.) Note that this function does not truncate over-length identifiers. If you want truncation you can cast the result to name[]. parse_ident('"SomeSchema".someTable') {SomeSchema,sometable} pg_client_encoding pg_client_encoding ( ) name Returns current client encoding name. pg_client_encoding() UTF8 quote_ident quote_ident ( text ) text Returns the given string suitably quoted to be used as an identifier in an SQL statement string. Quotes are added only if necessary (i.e., if the string contains non-identifier characters or would be case-folded). Embedded quotes are properly doubled. See also . quote_ident('Foo bar') "Foo bar" quote_literal quote_literal ( text ) text Returns the given string suitably quoted to be used as a string literal in an SQL statement string. Embedded single-quotes and backslashes are properly doubled. Note that quote_literal returns null on null input; if the argument might be null, quote_nullable is often more suitable. See also . quote_literal(E'O\'Reilly') 'O''Reilly' quote_literal ( anyelement ) text Converts the given value to text and then quotes it as a literal. Embedded single-quotes and backslashes are properly doubled. quote_literal(42.5) '42.5' quote_nullable quote_nullable ( text ) text Returns the given string suitably quoted to be used as a string literal in an SQL statement string; or, if the argument is null, returns NULL. Embedded single-quotes and backslashes are properly doubled. See also . quote_nullable(NULL) NULL quote_nullable ( anyelement ) text Converts the given value to text and then quotes it as a literal; or, if the argument is null, returns NULL. Embedded single-quotes and backslashes are properly doubled. quote_nullable(42.5) '42.5' regexp_count regexp_count ( string text, pattern text [, start integer [, flags text ] ] ) integer Returns the number of times the POSIX regular expression pattern matches in the string; see . regexp_count('123456789012', '\d\d\d', 2) 3 regexp_instr regexp_instr ( string text, pattern text [, start integer [, N integer [, endoption integer [, flags text [, subexpr integer ] ] ] ] ] ) integer Returns the position within string where the N'th match of the POSIX regular expression pattern occurs, or zero if there is no such match; see . regexp_instr('ABCDEF', 'c(.)(..)', 1, 1, 0, 'i') 3 regexp_instr('ABCDEF', 'c(.)(..)', 1, 1, 0, 'i', 2) 5 regexp_like regexp_like ( string text, pattern text [, flags text ] ) boolean Checks whether a match of the POSIX regular expression pattern occurs within string; see . regexp_like('Hello World', 'world$', 'i') t regexp_match regexp_match ( string text, pattern text [, flags text ] ) text[] Returns substrings within the first match of the POSIX regular expression pattern to the string; see . regexp_match('foobarbequebaz', '(bar)(beque)') {bar,beque} regexp_matches regexp_matches ( string text, pattern text [, flags text ] ) setof text[] Returns substrings within the first match of the POSIX regular expression pattern to the string, or substrings within all such matches if the g flag is used; see . regexp_matches('foobarbequebaz', 'ba.', 'g') {bar} {baz} regexp_replace regexp_replace ( string text, pattern text, replacement text [, start integer ] [, flags text ] ) text Replaces the substring that is the first match to the POSIX regular expression pattern, or all such matches if the g flag is used; see . regexp_replace('Thomas', '.[mN]a.', 'M') ThM regexp_replace ( string text, pattern text, replacement text, start integer, N integer [, flags text ] ) text Replaces the substring that is the N'th match to the POSIX regular expression pattern, or all such matches if N is zero; see . regexp_replace('Thomas', '.', 'X', 3, 2) ThoXas regexp_split_to_array regexp_split_to_array ( string text, pattern text [, flags text ] ) text[] Splits string using a POSIX regular expression as the delimiter, producing an array of results; see . regexp_split_to_array('hello world', '\s+') {hello,world} regexp_split_to_table regexp_split_to_table ( string text, pattern text [, flags text ] ) setof text Splits string using a POSIX regular expression as the delimiter, producing a set of results; see . regexp_split_to_table('hello world', '\s+') hello world regexp_substr regexp_substr ( string text, pattern text [, start integer [, N integer [, flags text [, subexpr integer ] ] ] ] ) text Returns the substring within string that matches the N'th occurrence of the POSIX regular expression pattern, or NULL if there is no such match; see . regexp_substr('ABCDEF', 'c(.)(..)', 1, 1, 'i') CDEF regexp_substr('ABCDEF', 'c(.)(..)', 1, 1, 'i', 2) EF repeat repeat ( string text, number integer ) text Repeats string the specified number of times. repeat('Pg', 4) PgPgPgPg replace replace ( string text, from text, to text ) text Replaces all occurrences in string of substring from with substring to. replace('abcdefabcdef', 'cd', 'XX') abXXefabXXef reverse reverse ( text ) text Reverses the order of the characters in the string. reverse('abcde') edcba right right ( string text, n integer ) text Returns last n characters in the string, or when n is negative, returns all but first |n| characters. right('abcde', 2) de rpad rpad ( string text, length integer , fill text ) text Extends the string to length length by appending the characters fill (a space by default). If the string is already longer than length then it is truncated. rpad('hi', 5, 'xy') hixyx rtrim rtrim ( string text , characters text ) text Removes the longest string containing only characters in characters (a space by default) from the end of string. rtrim('testxxzx', 'xyz') test split_part split_part ( string text, delimiter text, n integer ) text Splits string at occurrences of delimiter and returns the n'th field (counting from one), or when n is negative, returns the |n|'th-from-last field. split_part('abc~@~def~@~ghi', '~@~', 2) def split_part('abc,def,ghi,jkl', ',', -2) ghi starts_with starts_with ( string text, prefix text ) boolean Returns true if string starts with prefix. starts_with('alphabet', 'alph') t string_to_array string_to_array ( string text, delimiter text , null_string text ) text[] Splits the string at occurrences of delimiter and forms the resulting fields into a text array. If delimiter is NULL, each character in the string will become a separate element in the array. If delimiter is an empty string, then the string is treated as a single field. If null_string is supplied and is not NULL, fields matching that string are replaced by NULL. See also array_to_string. string_to_array('xx~~yy~~zz', '~~', 'yy') {xx,NULL,zz} string_to_table string_to_table ( string text, delimiter text , null_string text ) setof text Splits the string at occurrences of delimiter and returns the resulting fields as a set of text rows. If delimiter is NULL, each character in the string will become a separate row of the result. If delimiter is an empty string, then the string is treated as a single field. If null_string is supplied and is not NULL, fields matching that string are replaced by NULL. string_to_table('xx~^~yy~^~zz', '~^~', 'yy') xx NULL zz strpos strpos ( string text, substring text ) integer Returns first starting index of the specified substring within string, or zero if it's not present. (Same as position(substring in string), but note the reversed argument order.) strpos('high', 'ig') 2 substr substr ( string text, start integer , count integer ) text Extracts the substring of string starting at the start'th character, and extending for count characters if that is specified. (Same as substring(string from start for count).) substr('alphabet', 3) phabet substr('alphabet', 3, 2) ph to_ascii to_ascii ( string text ) text to_ascii ( string text, encoding name ) text to_ascii ( string text, encoding integer ) text Converts string to ASCII from another encoding, which may be identified by name or number. If encoding is omitted the database encoding is assumed (which in practice is the only useful case). The conversion consists primarily of dropping accents. Conversion is only supported from LATIN1, LATIN2, LATIN9, and WIN1250 encodings. (See the module for another, more flexible solution.) to_ascii('Karél') Karel to_hex to_hex ( integer ) text to_hex ( bigint ) text Converts the number to its equivalent hexadecimal representation. to_hex(2147483647) 7fffffff translate translate ( string text, from text, to text ) text Replaces each character in string that matches a character in the from set with the corresponding character in the to set. If from is longer than to, occurrences of the extra characters in from are deleted. translate('12345', '143', 'ax') a2x5 unistr unistr ( text ) text Evaluate escaped Unicode characters in the argument. Unicode characters can be specified as \XXXX (4 hexadecimal digits), \+XXXXXX (6 hexadecimal digits), \uXXXX (4 hexadecimal digits), or \UXXXXXXXX (8 hexadecimal digits). To specify a backslash, write two backslashes. All other characters are taken literally. If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that's not possible. This function provides a (non-standard) alternative to string constants with Unicode escapes (see ). unistr('d\0061t\+000061') data unistr('d\u0061t\U00000061') data The concat, concat_ws and format functions are variadic, so it is possible to pass the values to be concatenated or formatted as an array marked with the VARIADIC keyword (see ). The array's elements are treated as if they were separate ordinary arguments to the function. If the variadic array argument is NULL, concat and concat_ws return NULL, but format treats a NULL as a zero-element array. See also the aggregate function string_agg in , and the functions for converting between strings and the bytea type in . format The function format produces output formatted according to a format string, in a style similar to the C function sprintf. format(formatstr text [, formatarg "any" [, ...] ]) formatstr is a format string that specifies how the result should be formatted. Text in the format string is copied directly to the result, except where format specifiers are used. Format specifiers act as placeholders in the string, defining how subsequent function arguments should be formatted and inserted into the result. Each formatarg argument is converted to text according to the usual output rules for its data type, and then formatted and inserted into the result string according to the format specifier(s). Format specifiers are introduced by a % character and have the form %[position][flags][width]type where the component fields are: position (optional) A string of the form n$ where n is the index of the argument to print. Index 1 means the first argument after formatstr. If the position is omitted, the default is to use the next argument in sequence. flags (optional) Additional options controlling how the format specifier's output is formatted. Currently the only supported flag is a minus sign (-) which will cause the format specifier's output to be left-justified. This has no effect unless the width field is also specified. width (optional) Specifies the minimum number of characters to use to display the format specifier's output. The output is padded on the left or right (depending on the - flag) with spaces as needed to fill the width. A too-small width does not cause truncation of the output, but is simply ignored. The width may be specified using any of the following: a positive integer; an asterisk (*) to use the next function argument as the width; or a string of the form *n$ to use the nth function argument as the width. If the width comes from a function argument, that argument is consumed before the argument that is used for the format specifier's value. If the width argument is negative, the result is left aligned (as if the - flag had been specified) within a field of length abs(width). type (required) The type of format conversion to use to produce the format specifier's output. The following types are supported: s formats the argument value as a simple string. A null value is treated as an empty string. I treats the argument value as an SQL identifier, double-quoting it if necessary. It is an error for the value to be null (equivalent to quote_ident). L quotes the argument value as an SQL literal. A null value is displayed as the string NULL, without quotes (equivalent to quote_nullable). In addition to the format specifiers described above, the special sequence %% may be used to output a literal % character. Here are some examples of the basic format conversions: SELECT format('Hello %s', 'World'); Result: Hello World SELECT format('Testing %s, %s, %s, %%', 'one', 'two', 'three'); Result: Testing one, two, three, % SELECT format('INSERT INTO %I VALUES(%L)', 'Foo bar', E'O\'Reilly'); Result: INSERT INTO "Foo bar" VALUES('O''Reilly') SELECT format('INSERT INTO %I VALUES(%L)', 'locations', 'C:\Program Files'); Result: INSERT INTO locations VALUES('C:\Program Files') Here are examples using width fields and the - flag: SELECT format('|%10s|', 'foo'); Result: | foo| SELECT format('|%-10s|', 'foo'); Result: |foo | SELECT format('|%*s|', 10, 'foo'); Result: | foo| SELECT format('|%*s|', -10, 'foo'); Result: |foo | SELECT format('|%-*s|', 10, 'foo'); Result: |foo | SELECT format('|%-*s|', -10, 'foo'); Result: |foo | These examples show use of position fields: SELECT format('Testing %3$s, %2$s, %1$s', 'one', 'two', 'three'); Result: Testing three, two, one SELECT format('|%*2$s|', 'foo', 10, 'bar'); Result: | bar| SELECT format('|%1$*2$s|', 'foo', 10, 'bar'); Result: | foo| Unlike the standard C function sprintf, PostgreSQL's format function allows format specifiers with and without position fields to be mixed in the same format string. A format specifier without a position field always uses the next argument after the last argument consumed. In addition, the format function does not require all function arguments to be used in the format string. For example: SELECT format('Testing %3$s, %2$s, %s', 'one', 'two', 'three'); Result: Testing three, two, three The %I and %L format specifiers are particularly useful for safely constructing dynamic SQL statements. See . binary data functions This section describes functions and operators for examining and manipulating binary strings, that is values of type bytea. Many of these are equivalent, in purpose and syntax, to the text-string functions described in the previous section. SQL defines some string functions that use key words, rather than commas, to separate arguments. Details are in . PostgreSQL also provides versions of these functions that use the regular function invocation syntax (see ). Function/Operator Description Example(s) binary string concatenation bytea || bytea bytea Concatenates the two binary strings. '\x123456'::bytea || '\x789a00bcde'::bytea \x123456789a00bcde bit_length bit_length ( bytea ) integer Returns number of bits in the binary string (8 times the octet_length). bit_length('\x123456'::bytea) 24 octet_length octet_length ( bytea ) integer Returns number of bytes in the binary string. octet_length('\x123456'::bytea) 3 overlay overlay ( bytes bytea PLACING newsubstring bytea FROM start integer FOR count integer ) bytea Replaces the substring of bytes that starts at the start'th byte and extends for count bytes with newsubstring. If count is omitted, it defaults to the length of newsubstring. overlay('\x1234567890'::bytea placing '\002\003'::bytea from 2 for 3) \x12020390 position position ( substring bytea IN bytes bytea ) integer Returns first starting index of the specified substring within bytes, or zero if it's not present. position('\x5678'::bytea in '\x1234567890'::bytea) 3 substring substring ( bytes bytea FROM start integer FOR count integer ) bytea Extracts the substring of bytes starting at the start'th byte if that is specified, and stopping after count bytes if that is specified. Provide at least one of start and count. substring('\x1234567890'::bytea from 3 for 2) \x5678 trim trim ( LEADING | TRAILING | BOTH bytesremoved bytea FROM bytes bytea ) bytea Removes the longest string containing only bytes appearing in bytesremoved from the start, end, or both ends (BOTH is the default) of bytes. trim('\x9012'::bytea from '\x1234567890'::bytea) \x345678 trim ( LEADING | TRAILING | BOTH FROM bytes bytea, bytesremoved bytea ) bytea This is a non-standard syntax for trim(). trim(both from '\x1234567890'::bytea, '\x9012'::bytea) \x345678 Additional binary string manipulation functions are available and are listed in . Some of them are used internally to implement the SQL-standard string functions listed in . Function Description Example(s) bit_count popcount bit_count bit_count ( bytes bytea ) bigint Returns the number of bits set in the binary string (also known as popcount). bit_count('\x1234567890'::bytea) 15 btrim btrim ( bytes bytea, bytesremoved bytea ) bytea Removes the longest string containing only bytes appearing in bytesremoved from the start and end of bytes. btrim('\x1234567890'::bytea, '\x9012'::bytea) \x345678 get_bit get_bit ( bytes bytea, n bigint ) integer Extracts n'th bit from binary string. get_bit('\x1234567890'::bytea, 30) 1 get_byte get_byte ( bytes bytea, n integer ) integer Extracts n'th byte from binary string. get_byte('\x1234567890'::bytea, 4) 144 length binary string length length of a binary string binary strings, length length ( bytea ) integer Returns the number of bytes in the binary string. length('\x1234567890'::bytea) 5 length ( bytes bytea, encoding name ) integer Returns the number of characters in the binary string, assuming that it is text in the given encoding. length('jose'::bytea, 'UTF8') 4 ltrim ltrim ( bytes bytea, bytesremoved bytea ) bytea Removes the longest string containing only bytes appearing in bytesremoved from the start of bytes. ltrim('\x1234567890'::bytea, '\x9012'::bytea) \x34567890 md5 md5 ( bytea ) text Computes the MD5 hash of the binary string, with the result written in hexadecimal. md5('Th\000omas'::bytea) 8ab2d3c9689aaf18&zwsp;b4958c334c82d8b1 rtrim rtrim ( bytes bytea, bytesremoved bytea ) bytea Removes the longest string containing only bytes appearing in bytesremoved from the end of bytes. rtrim('\x1234567890'::bytea, '\x9012'::bytea) \x12345678 set_bit set_bit ( bytes bytea, n bigint, newvalue integer ) bytea Sets n'th bit in binary string to newvalue. set_bit('\x1234567890'::bytea, 30, 0) \x1234563890 set_byte set_byte ( bytes bytea, n integer, newvalue integer ) bytea Sets n'th byte in binary string to newvalue. set_byte('\x1234567890'::bytea, 4, 64) \x1234567840 sha224 sha224 ( bytea ) bytea Computes the SHA-224 hash of the binary string. sha224('abc'::bytea) \x23097d223405d8228642a477bda2&zwsp;55b32aadbce4bda0b3f7e36c9da7 sha256 sha256 ( bytea ) bytea Computes the SHA-256 hash of the binary string. sha256('abc'::bytea) \xba7816bf8f01cfea414140de5dae2223&zwsp;b00361a396177a9cb410ff61f20015ad sha384 sha384 ( bytea ) bytea Computes the SHA-384 hash of the binary string. sha384('abc'::bytea) \xcb00753f45a35e8bb5a03d699ac65007&zwsp;272c32ab0eded1631a8b605a43ff5bed&zwsp;8086072ba1e7cc2358baeca134c825a7 sha512 sha512 ( bytea ) bytea Computes the SHA-512 hash of the binary string. sha512('abc'::bytea) \xddaf35a193617abacc417349ae204131&zwsp;12e6fa4e89a97ea20a9eeee64b55d39a&zwsp;2192992a274fc1a836ba3c23a3feebbd&zwsp;454d4423643ce80e2a9ac94fa54ca49f substr substr ( bytes bytea, start integer , count integer ) bytea Extracts the substring of bytes starting at the start'th byte, and extending for count bytes if that is specified. (Same as substring(bytes from start for count).) substr('\x1234567890'::bytea, 3, 2) \x5678 Functions get_byte and set_byte number the first byte of a binary string as byte 0. Functions get_bit and set_bit number bits from the right within each byte; for example bit 0 is the least significant bit of the first byte, and bit 15 is the most significant bit of the second byte. For historical reasons, the function md5 returns a hex-encoded value of type text whereas the SHA-2 functions return type bytea. Use the functions encode and decode to convert between the two. For example write encode(sha256('abc'), 'hex') to get a hex-encoded text representation, or decode(md5('abc'), 'hex') to get a bytea value. character string converting to binary string binary string converting to character string Functions for converting strings between different character sets (encodings), and for representing arbitrary binary data in textual form, are shown in . For these functions, an argument or result of type text is expressed in the database's default encoding, while arguments or results of type bytea are in an encoding named by another argument. Function Description Example(s) convert convert ( bytes bytea, src_encoding name, dest_encoding name ) bytea Converts a binary string representing text in encoding src_encoding to a binary string in encoding dest_encoding (see for available conversions). convert('text_in_utf8', 'UTF8', 'LATIN1') \x746578745f696e5f75746638 convert_from convert_from ( bytes bytea, src_encoding name ) text Converts a binary string representing text in encoding src_encoding to text in the database encoding (see for available conversions). convert_from('text_in_utf8', 'UTF8') text_in_utf8 convert_to convert_to ( string text, dest_encoding name ) bytea Converts a text string (in the database encoding) to a binary string encoded in encoding dest_encoding (see for available conversions). convert_to('some_text', 'UTF8') \x736f6d655f74657874 encode encode ( bytes bytea, format text ) text Encodes binary data into a textual representation; supported format values are: base64, escape, hex. encode('123\000\001', 'base64') MTIzAAE= decode decode ( string text, format text ) bytea Decodes binary data from a textual representation; supported format values are the same as for encode. decode('MTIzAAE=', 'base64') \x3132330001 The encode and decode functions support the following textual formats: base64 base64 format The base64 format is that of RFC 2045 Section 6.8. As per the RFC, encoded lines are broken at 76 characters. However instead of the MIME CRLF end-of-line marker, only a newline is used for end-of-line. The decode function ignores carriage-return, newline, space, and tab characters. Otherwise, an error is raised when decode is supplied invalid base64 data — including when trailing padding is incorrect. escape escape format The escape format converts zero bytes and bytes with the high bit set into octal escape sequences (\nnn), and it doubles backslashes. Other byte values are represented literally. The decode function will raise an error if a backslash is not followed by either a second backslash or three octal digits; it accepts other byte values unchanged. hex hex format The hex format represents each 4 bits of data as one hexadecimal digit, 0 through f, writing the higher-order digit of each byte first. The encode function outputs the a-f hex digits in lower case. Because the smallest unit of data is 8 bits, there are always an even number of characters returned by encode. The decode function accepts the a-f characters in either upper or lower case. An error is raised when decode is given invalid hex data — including when given an odd number of characters. See also the aggregate function string_agg in and the large object functions in . bit strings functions This section describes functions and operators for examining and manipulating bit strings, that is values of the types bit and bit varying. (While only type bit is mentioned in these tables, values of type bit varying can be used interchangeably.) Bit strings support the usual comparison operators shown in , as well as the operators shown in . Operator Description Example(s) bit || bit bit Concatenation B'10001' || B'011' 10001011 bit & bit bit Bitwise AND (inputs must be of equal length) B'10001' & B'01101' 00001 bit | bit bit Bitwise OR (inputs must be of equal length) B'10001' | B'01101' 11101 bit # bit bit Bitwise exclusive OR (inputs must be of equal length) B'10001' # B'01101' 11100 ~ bit bit Bitwise NOT ~ B'10001' 01110 bit << integer bit Bitwise shift left (string length is preserved) B'10001' << 3 01000 bit >> integer bit Bitwise shift right (string length is preserved) B'10001' >> 2 00100 Some of the functions available for binary strings are also available for bit strings, as shown in . Function Description Example(s) bit_count bit_count ( bit ) bigint Returns the number of bits set in the bit string (also known as popcount). bit_count(B'10111') 4 bit_length bit_length ( bit ) integer Returns number of bits in the bit string. bit_length(B'10111') 5 length bit string length length ( bit ) integer Returns number of bits in the bit string. length(B'10111') 5 octet_length octet_length ( bit ) integer Returns number of bytes in the bit string. octet_length(B'1011111011') 2 overlay overlay ( bits bit PLACING newsubstring bit FROM start integer FOR count integer ) bit Replaces the substring of bits that starts at the start'th bit and extends for count bits with newsubstring. If count is omitted, it defaults to the length of newsubstring. overlay(B'01010101010101010' placing B'11111' from 2 for 3) 0111110101010101010 position position ( substring bit IN bits bit ) integer Returns first starting index of the specified substring within bits, or zero if it's not present. position(B'010' in B'000001101011') 8 substring substring ( bits bit FROM start integer FOR count integer ) bit Extracts the substring of bits starting at the start'th bit if that is specified, and stopping after count bits if that is specified. Provide at least one of start and count. substring(B'110010111111' from 3 for 2) 00 get_bit get_bit ( bits bit, n integer ) integer Extracts n'th bit from bit string; the first (leftmost) bit is bit 0. get_bit(B'101010101010101010', 6) 1 set_bit set_bit ( bits bit, n integer, newvalue integer ) bit Sets n'th bit in bit string to newvalue; the first (leftmost) bit is bit 0. set_bit(B'101010101010101010', 6, 0) 101010001010101010 In addition, it is possible to cast integral values to and from type bit. Casting an integer to bit(n) copies the rightmost n bits. Casting an integer to a bit string width wider than the integer itself will sign-extend on the left. Some examples: 44::bit(10) 0000101100 44::bit(3) 100 cast(-44 as bit(12)) 111111010100 '1110'::bit(4)::integer 14 Note that casting to just bit means casting to bit(1), and so will deliver only the least significant bit of the integer. pattern matching There are three separate approaches to pattern matching provided by PostgreSQL: the traditional SQL LIKE operator, the more recent SIMILAR TO operator (added in SQL:1999), and POSIX-style regular expressions. Aside from the basic does this string match this pattern? operators, functions are available to extract or replace matching substrings and to split a string at matching locations. If you have pattern matching needs that go beyond this, consider writing a user-defined function in Perl or Tcl. While most regular-expression searches can be executed very quickly, regular expressions can be contrived that take arbitrary amounts of time and memory to process. Be wary of accepting regular-expression search patterns from hostile sources. If you must do so, it is advisable to impose a statement timeout. Searches using SIMILAR TO patterns have the same security hazards, since SIMILAR TO provides many of the same capabilities as POSIX-style regular expressions. LIKE searches, being much simpler than the other two options, are safer to use with possibly-hostile pattern sources. The pattern matching operators of all three kinds do not support nondeterministic collations. If required, apply a different collation to the expression to work around this limitation. LIKE string LIKE pattern ESCAPE escape-character string NOT LIKE pattern ESCAPE escape-character The LIKE expression returns true if the string matches the supplied pattern. (As expected, the NOT LIKE expression returns false if LIKE returns true, and vice versa. An equivalent expression is NOT (string LIKE pattern).) If pattern does not contain percent signs or underscores, then the pattern only represents the string itself; in that case LIKE acts like the equals operator. An underscore (_) in pattern stands for (matches) any single character; a percent sign (%) matches any sequence of zero or more characters. Some examples: 'abc' LIKE 'abc' true 'abc' LIKE 'a%' true 'abc' LIKE '_b_' true 'abc' LIKE 'c' false LIKE pattern matching always covers the entire string. Therefore, if it's desired to match a sequence anywhere within a string, the pattern must start and end with a percent sign. To match a literal underscore or percent sign without matching other characters, the respective character in pattern must be preceded by the escape character. The default escape character is the backslash but a different one can be selected by using the ESCAPE clause. To match the escape character itself, write two escape characters. If you have turned off, any backslashes you write in literal string constants will need to be doubled. See for more information. It's also possible to select no escape character by writing ESCAPE ''. This effectively disables the escape mechanism, which makes it impossible to turn off the special meaning of underscore and percent signs in the pattern. According to the SQL standard, omitting ESCAPE means there is no escape character (rather than defaulting to a backslash), and a zero-length ESCAPE value is disallowed. PostgreSQL's behavior in this regard is therefore slightly nonstandard. The key word ILIKE can be used instead of LIKE to make the match case-insensitive according to the active locale. This is not in the SQL standard but is a PostgreSQL extension. The operator ~~ is equivalent to LIKE, and ~~* corresponds to ILIKE. There are also !~~ and !~~* operators that represent NOT LIKE and NOT ILIKE, respectively. All of these operators are PostgreSQL-specific. You may see these operator names in EXPLAIN output and similar places, since the parser actually translates LIKE et al. to these operators. The phrases LIKE, ILIKE, NOT LIKE, and NOT ILIKE are generally treated as operators in PostgreSQL syntax; for example they can be used in expression operator ANY (subquery) constructs, although an ESCAPE clause cannot be included there. In some obscure cases it may be necessary to use the underlying operator names instead. Also see the starts-with operator ^@ and the corresponding starts_with() function, which are useful in cases where simply matching the beginning of a string is needed. regular expression SIMILAR TO substring string SIMILAR TO pattern ESCAPE escape-character string NOT SIMILAR TO pattern ESCAPE escape-character The SIMILAR TO operator returns true or false depending on whether its pattern matches the given string. It is similar to LIKE, except that it interprets the pattern using the SQL standard's definition of a regular expression. SQL regular expressions are a curious cross between LIKE notation and common (POSIX) regular expression notation. Like LIKE, the SIMILAR TO operator succeeds only if its pattern matches the entire string; this is unlike common regular expression behavior where the pattern can match any part of the string. Also like LIKE, SIMILAR TO uses _ and % as wildcard characters denoting any single character and any string, respectively (these are comparable to . and .* in POSIX regular expressions). In addition to these facilities borrowed from LIKE, SIMILAR TO supports these pattern-matching metacharacters borrowed from POSIX regular expressions: | denotes alternation (either of two alternatives). * denotes repetition of the previous item zero or more times. + denotes repetition of the previous item one or more times. ? denotes repetition of the previous item zero or one time. {m} denotes repetition of the previous item exactly m times. {m,} denotes repetition of the previous item m or more times. {m,n} denotes repetition of the previous item at least m and not more than n times. Parentheses () can be used to group items into a single logical item. A bracket expression [...] specifies a character class, just as in POSIX regular expressions. Notice that the period (.) is not a metacharacter for SIMILAR TO. As with LIKE, a backslash disables the special meaning of any of these metacharacters. A different escape character can be specified with ESCAPE, or the escape capability can be disabled by writing ESCAPE ''. According to the SQL standard, omitting ESCAPE means there is no escape character (rather than defaulting to a backslash), and a zero-length ESCAPE value is disallowed. PostgreSQL's behavior in this regard is therefore slightly nonstandard. Another nonstandard extension is that following the escape character with a letter or digit provides access to the escape sequences defined for POSIX regular expressions; see , , and below. Some examples: 'abc' SIMILAR TO 'abc' true 'abc' SIMILAR TO 'a' false 'abc' SIMILAR TO '%(b|d)%' true 'abc' SIMILAR TO '(b|c)%' false '-abc-' SIMILAR TO '%\mabc\M%' true 'xabcy' SIMILAR TO '%\mabc\M%' false The substring function with three parameters provides extraction of a substring that matches an SQL regular expression pattern. The function can be written according to standard SQL syntax: substring(string similar pattern escape escape-character) or using the now obsolete SQL:1999 syntax: substring(string from pattern for escape-character) or as a plain three-argument function: substring(string, pattern, escape-character) As with SIMILAR TO, the specified pattern must match the entire data string, or else the function fails and returns null. To indicate the part of the pattern for which the matching data sub-string is of interest, the pattern should contain two occurrences of the escape character followed by a double quote ("). The text matching the portion of the pattern between these separators is returned when the match is successful. The escape-double-quote separators actually divide substring's pattern into three independent regular expressions; for example, a vertical bar (|) in any of the three sections affects only that section. Also, the first and third of these regular expressions are defined to match the smallest possible amount of text, not the largest, when there is any ambiguity about how much of the data string matches which pattern. (In POSIX parlance, the first and third regular expressions are forced to be non-greedy.) As an extension to the SQL standard, PostgreSQL allows there to be just one escape-double-quote separator, in which case the third regular expression is taken as empty; or no separators, in which case the first and third regular expressions are taken as empty. Some examples, with #" delimiting the return string: substring('foobar' similar '%#"o_b#"%' escape '#') oob substring('foobar' similar '#"o_b#"%' escape '#') NULL regular expression pattern matching substring regexp_count regexp_instr regexp_like regexp_match regexp_matches regexp_replace regexp_split_to_table regexp_split_to_array regexp_substr lists the available operators for pattern matching using POSIX regular expressions. Operator Description Example(s) text ~ text boolean String matches regular expression, case sensitively 'thomas' ~ 't.*ma' t text ~* text boolean String matches regular expression, case insensitively 'thomas' ~* 'T.*ma' t text !~ text boolean String does not match regular expression, case sensitively 'thomas' !~ 't.*max' t text !~* text boolean String does not match regular expression, case insensitively 'thomas' !~* 'T.*ma' f POSIX regular expressions provide a more powerful means for pattern matching than the LIKE and SIMILAR TO operators. Many Unix tools such as egrep, sed, or awk use a pattern matching language that is similar to the one described here. A regular expression is a character sequence that is an abbreviated definition of a set of strings (a regular set). A string is said to match a regular expression if it is a member of the regular set described by the regular expression. As with LIKE, pattern characters match string characters exactly unless they are special characters in the regular expression language — but regular expressions use different special characters than LIKE does. Unlike LIKE patterns, a regular expression is allowed to match anywhere within a string, unless the regular expression is explicitly anchored to the beginning or end of the string. Some examples: 'abcd' ~ 'bc' true 'abcd' ~ 'a.c' true — dot matches any character 'abcd' ~ 'a.*d' true — * repeats the preceding pattern item 'abcd' ~ '(b|x)' true — | means OR, parentheses group 'abcd' ~ '^a' true — ^ anchors to start of string 'abcd' ~ '^(b|c)' false — would match except for anchoring The POSIX pattern language is described in much greater detail below. The substring function with two parameters, substring(string from pattern), provides extraction of a substring that matches a POSIX regular expression pattern. It returns null if there is no match, otherwise the first portion of the text that matched the pattern. But if the pattern contains any parentheses, the portion of the text that matched the first parenthesized subexpression (the one whose left parenthesis comes first) is returned. You can put parentheses around the whole expression if you want to use parentheses within it without triggering this exception. If you need parentheses in the pattern before the subexpression you want to extract, see the non-capturing parentheses described below. Some examples: substring('foobar' from 'o.b') oob substring('foobar' from 'o(.)b') o The regexp_count function counts the number of places where a POSIX regular expression pattern matches a string. It has the syntax regexp_count(string, pattern , start , flags ). pattern is searched for in string, normally from the beginning of the string, but if the start parameter is provided then beginning from that character index. The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. For example, including i in flags specifies case-insensitive matching. Supported flags are described in . Some examples: regexp_count('ABCABCAXYaxy', 'A.') 3 regexp_count('ABCABCAXYaxy', 'A.', 1, 'i') 4 The regexp_instr function returns the starting or ending position of the N'th match of a POSIX regular expression pattern to a string, or zero if there is no such match. It has the syntax regexp_instr(string, pattern , start , N , endoption , flags , subexpr ). pattern is searched for in string, normally from the beginning of the string, but if the start parameter is provided then beginning from that character index. If N is specified then the N'th match of the pattern is located, otherwise the first match is located. If the endoption parameter is omitted or specified as zero, the function returns the position of the first character of the match. Otherwise, endoption must be one, and the function returns the position of the character following the match. The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in . For a pattern containing parenthesized subexpressions, subexpr is an integer indicating which subexpression is of interest: the result identifies the position of the substring matching that subexpression. Subexpressions are numbered in the order of their leading parentheses. When subexpr is omitted or zero, the result identifies the position of the whole match regardless of parenthesized subexpressions. Some examples: regexp_instr('number of your street, town zip, FR', '[^,]+', 1, 2) 23 regexp_instr('ABCDEFGHI', '(c..)(...)', 1, 1, 0, 'i', 2) 6 The regexp_like function checks whether a match of a POSIX regular expression pattern occurs within a string, returning boolean true or false. It has the syntax regexp_like(string, pattern , flags ). The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in . This function has the same results as the ~ operator if no flags are specified. If only the i flag is specified, it has the same results as the ~* operator. Some examples: regexp_like('Hello World', 'world') false regexp_like('Hello World', 'world', 'i') true The regexp_match function returns a text array of matching substring(s) within the first match of a POSIX regular expression pattern to a string. It has the syntax regexp_match(string, pattern , flags ). If there is no match, the result is NULL. If a match is found, and the pattern contains no parenthesized subexpressions, then the result is a single-element text array containing the substring matching the whole pattern. If a match is found, and the pattern contains parenthesized subexpressions, then the result is a text array whose n'th element is the substring matching the n'th parenthesized subexpression of the pattern (not counting non-capturing parentheses; see below for details). The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in . Some examples: SELECT regexp_match('foobarbequebaz', 'bar.*que'); regexp_match -------------- {barbeque} (1 row) SELECT regexp_match('foobarbequebaz', '(bar)(beque)'); regexp_match -------------- {bar,beque} (1 row) In the common case where you just want the whole matching substring or NULL for no match, the best solution is to use regexp_substr(). However, regexp_substr() only exists in PostgreSQL version 15 and up. When working in older versions, you can extract the first element of regexp_match()'s result, for example: SELECT (regexp_match('foobarbequebaz', 'bar.*que'))[1]; regexp_match -------------- barbeque (1 row) The regexp_matches function returns a set of text arrays of matching substring(s) within matches of a POSIX regular expression pattern to a string. It has the same syntax as regexp_match. This function returns no rows if there is no match, one row if there is a match and the g flag is not given, or N rows if there are N matches and the g flag is given. Each returned row is a text array containing the whole matched substring or the substrings matching parenthesized subexpressions of the pattern, just as described above for regexp_match. regexp_matches accepts all the flags shown in , plus the g flag which commands it to return all matches, not just the first one. Some examples: SELECT regexp_matches('foo', 'not there'); regexp_matches ---------------- (0 rows) SELECT regexp_matches('foobarbequebazilbarfbonk', '(b[^b]+)(b[^b]+)', 'g'); regexp_matches ---------------- {bar,beque} {bazil,barf} (2 rows) In most cases regexp_matches() should be used with the g flag, since if you only want the first match, it's easier and more efficient to use regexp_match(). However, regexp_match() only exists in PostgreSQL version 10 and up. When working in older versions, a common trick is to place a regexp_matches() call in a sub-select, for example: SELECT col1, (SELECT regexp_matches(col2, '(bar)(beque)')) FROM tab; This produces a text array if there's a match, or NULL if not, the same as regexp_match() would do. Without the sub-select, this query would produce no output at all for table rows without a match, which is typically not the desired behavior. The regexp_replace function provides substitution of new text for substrings that match POSIX regular expression patterns. It has the syntax regexp_replace(source, pattern, replacement , start , N , flags ). (Notice that N cannot be specified unless start is, but flags can be given in any case.) The source string is returned unchanged if there is no match to the pattern. If there is a match, the source string is returned with the replacement string substituted for the matching substring. The replacement string can contain \n, where n is 1 through 9, to indicate that the source substring matching the n'th parenthesized subexpression of the pattern should be inserted, and it can contain \& to indicate that the substring matching the entire pattern should be inserted. Write \\ if you need to put a literal backslash in the replacement text. pattern is searched for in string, normally from the beginning of the string, but if the start parameter is provided then beginning from that character index. By default, only the first match of the pattern is replaced. If N is specified and is greater than zero, then the N'th match of the pattern is replaced. If the g flag is given, or if N is specified and is zero, then all matches at or after the start position are replaced. (The g flag is ignored when N is specified.) The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags (though not g) are described in . Some examples: regexp_replace('foobarbaz', 'b..', 'X') fooXbaz regexp_replace('foobarbaz', 'b..', 'X', 'g') fooXX regexp_replace('foobarbaz', 'b(..)', 'X\1Y', 'g') fooXarYXazY regexp_replace('A PostgreSQL function', 'a|e|i|o|u', 'X', 1, 0, 'i') X PXstgrXSQL fXnctXXn regexp_replace('A PostgreSQL function', 'a|e|i|o|u', 'X', 1, 3, 'i') A PostgrXSQL function The regexp_split_to_table function splits a string using a POSIX regular expression pattern as a delimiter. It has the syntax regexp_split_to_table(string, pattern , flags ). If there is no match to the pattern, the function returns the string. If there is at least one match, for each match it returns the text from the end of the last match (or the beginning of the string) to the beginning of the match. When there are no more matches, it returns the text from the end of the last match to the end of the string. The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. regexp_split_to_table supports the flags described in . The regexp_split_to_array function behaves the same as regexp_split_to_table, except that regexp_split_to_array returns its result as an array of text. It has the syntax regexp_split_to_array(string, pattern , flags ). The parameters are the same as for regexp_split_to_table. Some examples: SELECT foo FROM regexp_split_to_table('the quick brown fox jumps over the lazy dog', '\s+') AS foo; foo ------- the quick brown fox jumps over the lazy dog (9 rows) SELECT regexp_split_to_array('the quick brown fox jumps over the lazy dog', '\s+'); regexp_split_to_array ----------------------------------------------- {the,quick,brown,fox,jumps,over,the,lazy,dog} (1 row) SELECT foo FROM regexp_split_to_table('the quick brown fox', '\s*') AS foo; foo ----- t h e q u i c k b r o w n f o x (16 rows) As the last example demonstrates, the regexp split functions ignore zero-length matches that occur at the start or end of the string or immediately after a previous match. This is contrary to the strict definition of regexp matching that is implemented by the other regexp functions, but is usually the most convenient behavior in practice. Other software systems such as Perl use similar definitions. The regexp_substr function returns the substring that matches a POSIX regular expression pattern, or NULL if there is no match. It has the syntax regexp_substr(string, pattern , start , N , flags , subexpr ). pattern is searched for in string, normally from the beginning of the string, but if the start parameter is provided then beginning from that character index. If N is specified then the N'th match of the pattern is returned, otherwise the first match is returned. The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in . For a pattern containing parenthesized subexpressions, subexpr is an integer indicating which subexpression is of interest: the result is the substring matching that subexpression. Subexpressions are numbered in the order of their leading parentheses. When subexpr is omitted or zero, the result is the whole match regardless of parenthesized subexpressions. Some examples: regexp_substr('number of your street, town zip, FR', '[^,]+', 1, 2) town zip regexp_substr('ABCDEFGHI', '(c..)(...)', 1, 1, 'i', 2) FGH PostgreSQL's regular expressions are implemented using a software package written by Henry Spencer. Much of the description of regular expressions below is copied verbatim from his manual. Regular expressions (REs), as defined in POSIX 1003.2, come in two forms: extended REs or EREs (roughly those of egrep), and basic REs or BREs (roughly those of ed). PostgreSQL supports both forms, and also implements some extensions that are not in the POSIX standard, but have become widely used due to their availability in programming languages such as Perl and Tcl. REs using these non-POSIX extensions are called advanced REs or AREs in this documentation. AREs are almost an exact superset of EREs, but BREs have several notational incompatibilities (as well as being much more limited). We first describe the ARE and ERE forms, noting features that apply only to AREs, and then describe how BREs differ. PostgreSQL always initially presumes that a regular expression follows the ARE rules. However, the more limited ERE or BRE rules can be chosen by prepending an embedded option to the RE pattern, as described in . This can be useful for compatibility with applications that expect exactly the POSIX 1003.2 rules. A regular expression is defined as one or more branches, separated by |. It matches anything that matches one of the branches. A branch is zero or more quantified atoms or constraints, concatenated. It matches a match for the first, followed by a match for the second, etc.; an empty branch matches the empty string. A quantified atom is an atom possibly followed by a single quantifier. Without a quantifier, it matches a match for the atom. With a quantifier, it can match some number of matches of the atom. An atom can be any of the possibilities shown in . The possible quantifiers and their meanings are shown in . A constraint matches an empty string, but matches only when specific conditions are met. A constraint can be used where an atom could be used, except it cannot be followed by a quantifier. The simple constraints are shown in ; some more constraints are described later. Atom Description (re) (where re is any regular expression) matches a match for re, with the match noted for possible reporting (?:re) as above, but the match is not noted for reporting (a non-capturing set of parentheses) (AREs only) . matches any single character [chars] a bracket expression, matching any one of the chars (see for more detail) \k (where k is a non-alphanumeric character) matches that character taken as an ordinary character, e.g., \\ matches a backslash character \c where c is alphanumeric (possibly followed by other characters) is an escape, see (AREs only; in EREs and BREs, this matches c) { when followed by a character other than a digit, matches the left-brace character {; when followed by a digit, it is the beginning of a bound (see below) x where x is a single character with no other significance, matches that character An RE cannot end with a backslash (\). If you have turned off, any backslashes you write in literal string constants will need to be doubled. See for more information. Quantifier Matches * a sequence of 0 or more matches of the atom + a sequence of 1 or more matches of the atom ? a sequence of 0 or 1 matches of the atom {m} a sequence of exactly m matches of the atom {m,} a sequence of m or more matches of the atom {m,n} a sequence of m through n (inclusive) matches of the atom; m cannot exceed n *? non-greedy version of * +? non-greedy version of + ?? non-greedy version of ? {m}? non-greedy version of {m} {m,}? non-greedy version of {m,} {m,n}? non-greedy version of {m,n} The forms using {...} are known as bounds. The numbers m and n within a bound are unsigned decimal integers with permissible values from 0 to 255 inclusive. Non-greedy quantifiers (available in AREs only) match the same possibilities as their corresponding normal (greedy) counterparts, but prefer the smallest number rather than the largest number of matches. See for more detail. A quantifier cannot immediately follow another quantifier, e.g., ** is invalid. A quantifier cannot begin an expression or subexpression or follow ^ or |. Constraint Description ^ matches at the beginning of the string $ matches at the end of the string (?=re) positive lookahead matches at any point where a substring matching re begins (AREs only) (?!re) negative lookahead matches at any point where no substring matching re begins (AREs only) (?<=re) positive lookbehind matches at any point where a substring matching re ends (AREs only) (?<!re) negative lookbehind matches at any point where no substring matching re ends (AREs only) Lookahead and lookbehind constraints cannot contain back references (see ), and all parentheses within them are considered non-capturing. A bracket expression is a list of characters enclosed in []. It normally matches any single character from the list (but see below). If the list begins with ^, it matches any single character not from the rest of the list. If two characters in the list are separated by -, this is shorthand for the full range of characters between those two (inclusive) in the collating sequence, e.g., [0-9] in ASCII matches any decimal digit. It is illegal for two ranges to share an endpoint, e.g., a-c-e. Ranges are very collating-sequence-dependent, so portable programs should avoid relying on them. To include a literal ] in the list, make it the first character (after ^, if that is used). To include a literal -, make it the first or last character, or the second endpoint of a range. To use a literal - as the first endpoint of a range, enclose it in [. and .] to make it a collating element (see below). With the exception of these characters, some combinations using [ (see next paragraphs), and escapes (AREs only), all other special characters lose their special significance within a bracket expression. In particular, \ is not special when following ERE or BRE rules, though it is special (as introducing an escape) in AREs. Within a bracket expression, a collating element (a character, a multiple-character sequence that collates as if it were a single character, or a collating-sequence name for either) enclosed in [. and .] stands for the sequence of characters of that collating element. The sequence is treated as a single element of the bracket expression's list. This allows a bracket expression containing a multiple-character collating element to match more than one character, e.g., if the collating sequence includes a ch collating element, then the RE [[.ch.]]*c matches the first five characters of chchcc. PostgreSQL currently does not support multi-character collating elements. This information describes possible future behavior. Within a bracket expression, a collating element enclosed in [= and =] is an equivalence class, standing for the sequences of characters of all collating elements equivalent to that one, including itself. (If there are no other equivalent collating elements, the treatment is as if the enclosing delimiters were [. and .].) For example, if o and ^ are the members of an equivalence class, then [[=o=]], [[=^=]], and [o^] are all synonymous. An equivalence class cannot be an endpoint of a range. Within a bracket expression, the name of a character class enclosed in [: and :] stands for the list of all characters belonging to that class. A character class cannot be used as an endpoint of a range. The POSIX standard defines these character class names: alnum (letters and numeric digits), alpha (letters), blank (space and tab), cntrl (control characters), digit (numeric digits), graph (printable characters except space), lower (lower-case letters), print (printable characters including space), punct (punctuation), space (any white space), upper (upper-case letters), and xdigit (hexadecimal digits). The behavior of these standard character classes is generally consistent across platforms for characters in the 7-bit ASCII set. Whether a given non-ASCII character is considered to belong to one of these classes depends on the collation that is used for the regular-expression function or operator (see ), or by default on the database's LC_CTYPE locale setting (see ). The classification of non-ASCII characters can vary across platforms even in similarly-named locales. (But the C locale never considers any non-ASCII characters to belong to any of these classes.) In addition to these standard character classes, PostgreSQL defines the word character class, which is the same as alnum plus the underscore (_) character, and the ascii character class, which contains exactly the 7-bit ASCII set. There are two special cases of bracket expressions: the bracket expressions [[:<:]] and [[:>:]] are constraints, matching empty strings at the beginning and end of a word respectively. A word is defined as a sequence of word characters that is neither preceded nor followed by word characters. A word character is any character belonging to the word character class, that is, any letter, digit, or underscore. This is an extension, compatible with but not specified by POSIX 1003.2, and should be used with caution in software intended to be portable to other systems. The constraint escapes described below are usually preferable; they are no more standard, but are easier to type. Escapes are special sequences beginning with \ followed by an alphanumeric character. Escapes come in several varieties: character entry, class shorthands, constraint escapes, and back references. A \ followed by an alphanumeric character but not constituting a valid escape is illegal in AREs. In EREs, there are no escapes: outside a bracket expression, a \ followed by an alphanumeric character merely stands for that character as an ordinary character, and inside a bracket expression, \ is an ordinary character. (The latter is the one actual incompatibility between EREs and AREs.) Character-entry escapes exist to make it easier to specify non-printing and other inconvenient characters in REs. They are shown in . Class-shorthand escapes provide shorthands for certain commonly-used character classes. They are shown in . A constraint escape is a constraint, matching the empty string if specific conditions are met, written as an escape. They are shown in . A back reference (\n) matches the same string matched by the previous parenthesized subexpression specified by the number n (see ). For example, ([bc])\1 matches bb or cc but not bc or cb. The subexpression must entirely precede the back reference in the RE. Subexpressions are numbered in the order of their leading parentheses. Non-capturing parentheses do not define subexpressions. The back reference considers only the string characters matched by the referenced subexpression, not any constraints contained in it. For example, (^\d)\1 will match 22. Escape Description \a alert (bell) character, as in C \b backspace, as in C \B synonym for backslash (\) to help reduce the need for backslash doubling \cX (where X is any character) the character whose low-order 5 bits are the same as those of X, and whose other bits are all zero \e the character whose collating-sequence name is ESC, or failing that, the character with octal value 033 \f form feed, as in C \n newline, as in C \r carriage return, as in C \t horizontal tab, as in C \uwxyz (where wxyz is exactly four hexadecimal digits) the character whose hexadecimal value is 0xwxyz \Ustuvwxyz (where stuvwxyz is exactly eight hexadecimal digits) the character whose hexadecimal value is 0xstuvwxyz \v vertical tab, as in C \xhhh (where hhh is any sequence of hexadecimal digits) the character whose hexadecimal value is 0xhhh (a single character no matter how many hexadecimal digits are used) \0 the character whose value is 0 (the null byte) \xy (where xy is exactly two octal digits, and is not a back reference) the character whose octal value is 0xy \xyz (where xyz is exactly three octal digits, and is not a back reference) the character whose octal value is 0xyz Hexadecimal digits are 0-9, a-f, and A-F. Octal digits are 0-7. Numeric character-entry escapes specifying values outside the ASCII range (0–127) have meanings dependent on the database encoding. When the encoding is UTF-8, escape values are equivalent to Unicode code points, for example \u1234 means the character U+1234. For other multibyte encodings, character-entry escapes usually just specify the concatenation of the byte values for the character. If the escape value does not correspond to any legal character in the database encoding, no error will be raised, but it will never match any data. The character-entry escapes are always taken as ordinary characters. For example, \135 is ] in ASCII, but \135 does not terminate a bracket expression. Escape Description \d matches any digit, like [[:digit:]] \s matches any whitespace character, like [[:space:]] \w matches any word character, like [[:word:]] \D matches any non-digit, like [^[:digit:]] \S matches any non-whitespace character, like [^[:space:]] \W matches any non-word character, like [^[:word:]] The class-shorthand escapes also work within bracket expressions, although the definitions shown above are not quite syntactically valid in that context. For example, [a-c\d] is equivalent to [a-c[:digit:]]. Escape Description \A matches only at the beginning of the string (see for how this differs from ^) \m matches only at the beginning of a word \M matches only at the end of a word \y matches only at the beginning or end of a word \Y matches only at a point that is not the beginning or end of a word \Z matches only at the end of the string (see for how this differs from $) A word is defined as in the specification of [[:<:]] and [[:>:]] above. Constraint escapes are illegal within bracket expressions. Escape Description \m (where m is a nonzero digit) a back reference to the m'th subexpression \mnn (where m is a nonzero digit, and nn is some more digits, and the decimal value mnn is not greater than the number of closing capturing parentheses seen so far) a back reference to the mnn'th subexpression There is an inherent ambiguity between octal character-entry escapes and back references, which is resolved by the following heuristics, as hinted at above. A leading zero always indicates an octal escape. A single non-zero digit, not followed by another digit, is always taken as a back reference. A multi-digit sequence not starting with a zero is taken as a back reference if it comes after a suitable subexpression (i.e., the number is in the legal range for a back reference), and otherwise is taken as octal. In addition to the main syntax described above, there are some special forms and miscellaneous syntactic facilities available. An RE can begin with one of two special director prefixes. If an RE begins with ***:, the rest of the RE is taken as an ARE. (This normally has no effect in PostgreSQL, since REs are assumed to be AREs; but it does have an effect if ERE or BRE mode had been specified by the flags parameter to a regex function.) If an RE begins with ***=, the rest of the RE is taken to be a literal string, with all characters considered ordinary characters. An ARE can begin with embedded options: a sequence (?xyz) (where xyz is one or more alphabetic characters) specifies options affecting the rest of the RE. These options override any previously determined options — in particular, they can override the case-sensitivity behavior implied by a regex operator, or the flags parameter to a regex function. The available option letters are shown in . Note that these same option letters are used in the flags parameters of regex functions. Option Description b rest of RE is a BRE c case-sensitive matching (overrides operator type) e rest of RE is an ERE i case-insensitive matching (see ) (overrides operator type) m historical synonym for n n newline-sensitive matching (see ) p partial newline-sensitive matching (see ) q rest of RE is a literal (quoted) string, all ordinary characters s non-newline-sensitive matching (default) t tight syntax (default; see below) w inverse partial newline-sensitive (weird) matching (see ) x expanded syntax (see below) Embedded options take effect at the ) terminating the sequence. They can appear only at the start of an ARE (after the ***: director if any). In addition to the usual (tight) RE syntax, in which all characters are significant, there is an expanded syntax, available by specifying the embedded x option. In the expanded syntax, white-space characters in the RE are ignored, as are all characters between a # and the following newline (or the end of the RE). This permits paragraphing and commenting a complex RE. There are three exceptions to that basic rule: a white-space character or # preceded by \ is retained white space or # within a bracket expression is retained white space and comments cannot appear within multi-character symbols, such as (?: For this purpose, white-space characters are blank, tab, newline, and any character that belongs to the space character class. Finally, in an ARE, outside bracket expressions, the sequence (?#ttt) (where ttt is any text not containing a )) is a comment, completely ignored. Again, this is not allowed between the characters of multi-character symbols, like (?:. Such comments are more a historical artifact than a useful facility, and their use is deprecated; use the expanded syntax instead. None of these metasyntax extensions is available if an initial ***= director has specified that the user's input be treated as a literal string rather than as an RE. In the event that an RE could match more than one substring of a given string, the RE matches the one starting earliest in the string. If the RE could match more than one substring starting at that point, either the longest possible match or the shortest possible match will be taken, depending on whether the RE is greedy or non-greedy. Whether an RE is greedy or not is determined by the following rules: Most atoms, and all constraints, have no greediness attribute (because they cannot match variable amounts of text anyway). Adding parentheses around an RE does not change its greediness. A quantified atom with a fixed-repetition quantifier ({m} or {m}?) has the same greediness (possibly none) as the atom itself. A quantified atom with other normal quantifiers (including {m,n} with m equal to n) is greedy (prefers longest match). A quantified atom with a non-greedy quantifier (including {m,n}? with m equal to n) is non-greedy (prefers shortest match). A branch — that is, an RE that has no top-level | operator — has the same greediness as the first quantified atom in it that has a greediness attribute. An RE consisting of two or more branches connected by the | operator is always greedy. The above rules associate greediness attributes not only with individual quantified atoms, but with branches and entire REs that contain quantified atoms. What that means is that the matching is done in such a way that the branch, or whole RE, matches the longest or shortest possible substring as a whole. Once the length of the entire match is determined, the part of it that matches any particular subexpression is determined on the basis of the greediness attribute of that subexpression, with subexpressions starting earlier in the RE taking priority over ones starting later. An example of what this means: SELECT SUBSTRING('XY1234Z', 'Y*([0-9]{1,3})'); Result: 123 SELECT SUBSTRING('XY1234Z', 'Y*?([0-9]{1,3})'); Result: 1 In the first case, the RE as a whole is greedy because Y* is greedy. It can match beginning at the Y, and it matches the longest possible string starting there, i.e., Y123. The output is the parenthesized part of that, or 123. In the second case, the RE as a whole is non-greedy because Y*? is non-greedy. It can match beginning at the Y, and it matches the shortest possible string starting there, i.e., Y1. The subexpression [0-9]{1,3} is greedy but it cannot change the decision as to the overall match length; so it is forced to match just 1. In short, when an RE contains both greedy and non-greedy subexpressions, the total match length is either as long as possible or as short as possible, according to the attribute assigned to the whole RE. The attributes assigned to the subexpressions only affect how much of that match they are allowed to eat relative to each other. The quantifiers {1,1} and {1,1}? can be used to force greediness or non-greediness, respectively, on a subexpression or a whole RE. This is useful when you need the whole RE to have a greediness attribute different from what's deduced from its elements. As an example, suppose that we are trying to separate a string containing some digits into the digits and the parts before and after them. We might try to do that like this: SELECT regexp_match('abc01234xyz', '(.*)(\d+)(.*)'); Result: {abc0123,4,xyz} That didn't work: the first .* is greedy so it eats as much as it can, leaving the \d+ to match at the last possible place, the last digit. We might try to fix that by making it non-greedy: SELECT regexp_match('abc01234xyz', '(.*?)(\d+)(.*)'); Result: {abc,0,""} That didn't work either, because now the RE as a whole is non-greedy and so it ends the overall match as soon as possible. We can get what we want by forcing the RE as a whole to be greedy: SELECT regexp_match('abc01234xyz', '(?:(.*?)(\d+)(.*)){1,1}'); Result: {abc,01234,xyz} Controlling the RE's overall greediness separately from its components' greediness allows great flexibility in handling variable-length patterns. When deciding what is a longer or shorter match, match lengths are measured in characters, not collating elements. An empty string is considered longer than no match at all. For example: bb* matches the three middle characters of abbbc; (week|wee)(night|knights) matches all ten characters of weeknights; when (.*).* is matched against abc the parenthesized subexpression matches all three characters; and when (a*)* is matched against bc both the whole RE and the parenthesized subexpression match an empty string. If case-independent matching is specified, the effect is much as if all case distinctions had vanished from the alphabet. When an alphabetic that exists in multiple cases appears as an ordinary character outside a bracket expression, it is effectively transformed into a bracket expression containing both cases, e.g., x becomes [xX]. When it appears inside a bracket expression, all case counterparts of it are added to the bracket expression, e.g., [x] becomes [xX] and [^x] becomes [^xX]. If newline-sensitive matching is specified, . and bracket expressions using ^ will never match the newline character (so that matches will not cross lines unless the RE explicitly includes a newline) and ^ and $ will match the empty string after and before a newline respectively, in addition to matching at beginning and end of string respectively. But the ARE escapes \A and \Z continue to match beginning or end of string only. Also, the character class shorthands \D and \W will match a newline regardless of this mode. (Before PostgreSQL 14, they did not match newlines when in newline-sensitive mode. Write [^[:digit:]] or [^[:word:]] to get the old behavior.) If partial newline-sensitive matching is specified, this affects . and bracket expressions as with newline-sensitive matching, but not ^ and $. If inverse partial newline-sensitive matching is specified, this affects ^ and $ as with newline-sensitive matching, but not . and bracket expressions. This isn't very useful but is provided for symmetry. No particular limit is imposed on the length of REs in this implementation. However, programs intended to be highly portable should not employ REs longer than 256 bytes, as a POSIX-compliant implementation can refuse to accept such REs. The only feature of AREs that is actually incompatible with POSIX EREs is that \ does not lose its special significance inside bracket expressions. All other ARE features use syntax which is illegal or has undefined or unspecified effects in POSIX EREs; the *** syntax of directors likewise is outside the POSIX syntax for both BREs and EREs. Many of the ARE extensions are borrowed from Perl, but some have been changed to clean them up, and a few Perl extensions are not present. Incompatibilities of note include \b, \B, the lack of special treatment for a trailing newline, the addition of complemented bracket expressions to the things affected by newline-sensitive matching, the restrictions on parentheses and back references in lookahead/lookbehind constraints, and the longest/shortest-match (rather than first-match) matching semantics. BREs differ from EREs in several respects. In BREs, |, +, and ? are ordinary characters and there is no equivalent for their functionality. The delimiters for bounds are \{ and \}, with { and } by themselves ordinary characters. The parentheses for nested subexpressions are \( and \), with ( and ) by themselves ordinary characters. ^ is an ordinary character except at the beginning of the RE or the beginning of a parenthesized subexpression, $ is an ordinary character except at the end of the RE or the end of a parenthesized subexpression, and * is an ordinary character if it appears at the beginning of the RE or the beginning of a parenthesized subexpression (after a possible leading ^). Finally, single-digit back references are available, and \< and \> are synonyms for [[:<:]] and [[:>:]] respectively; no other escapes are available in BREs. LIKE_REGEX OCCURRENCES_REGEX POSITION_REGEX SUBSTRING_REGEX TRANSLATE_REGEX XQuery regular expressions Since SQL:2008, the SQL standard includes regular expression operators and functions that performs pattern matching according to the XQuery regular expression standard: LIKE_REGEX OCCURRENCES_REGEX POSITION_REGEX SUBSTRING_REGEX TRANSLATE_REGEX PostgreSQL does not currently implement these operators and functions. You can get approximately equivalent functionality in each case as shown in . (Various optional clauses on both sides have been omitted in this table.) SQL standard PostgreSQL string LIKE_REGEX pattern regexp_like(string, pattern) or string ~ pattern OCCURRENCES_REGEX(pattern IN string) regexp_count(string, pattern) POSITION_REGEX(pattern IN string) regexp_instr(string, pattern) SUBSTRING_REGEX(pattern IN string) regexp_substr(string, pattern) TRANSLATE_REGEX(pattern IN string WITH replacement) regexp_replace(string, pattern, replacement) Regular expression functions similar to those provided by PostgreSQL are also available in a number of other SQL implementations, whereas the SQL-standard functions are not as widely implemented. Some of the details of the regular expression syntax will likely differ in each implementation. The SQL-standard operators and functions use XQuery regular expressions, which are quite close to the ARE syntax described above. Notable differences between the existing POSIX-based regular-expression feature and XQuery regular expressions include: XQuery character class subtraction is not supported. An example of this feature is using the following to match only English consonants: [a-z-[aeiou]]. XQuery character class shorthands \c, \C, \i, and \I are not supported. XQuery character class elements using \p{UnicodeProperty} or the inverse \P{UnicodeProperty} are not supported. POSIX interprets character classes such as \w (see ) according to the prevailing locale (which you can control by attaching a COLLATE clause to the operator or function). XQuery specifies these classes by reference to Unicode character properties, so equivalent behavior is obtained only with a locale that follows the Unicode rules. The SQL standard (not XQuery itself) attempts to cater for more variants of newline than POSIX does. The newline-sensitive matching options described above consider only ASCII NL (\n) to be a newline, but SQL would have us treat CR (\r), CRLF (\r\n) (a Windows-style newline), and some Unicode-only characters like LINE SEPARATOR (U+2028) as newlines as well. Notably, . and \s should count \r\n as one character not two according to SQL. Of the character-entry escapes described in , XQuery supports only \n, \r, and \t. XQuery does not support the [:name:] syntax for character classes within bracket expressions. XQuery does not have lookahead or lookbehind constraints, nor any of the constraint escapes described in . The metasyntax forms described in do not exist in XQuery. The regular expression flag letters defined by XQuery are related to but not the same as the option letters for POSIX (). While the i and q options behave the same, others do not: XQuery's s (allow dot to match newline) and m (allow ^ and $ to match at newlines) flags provide access to the same behaviors as POSIX's n, p and w flags, but they do not match the behavior of POSIX's s and m flags. Note in particular that dot-matches-newline is the default behavior in POSIX but not XQuery. XQuery's x (ignore whitespace in pattern) flag is noticeably different from POSIX's expanded-mode flag. POSIX's x flag also allows # to begin a comment in the pattern, and POSIX will not ignore a whitespace character after a backslash. formatting The PostgreSQL formatting functions provide a powerful set of tools for converting various data types (date/time, integer, floating point, numeric) to formatted strings and for converting from formatted strings to specific data types. lists them. These functions all follow a common calling convention: the first argument is the value to be formatted and the second argument is a template that defines the output or input format. Function Description Example(s) to_char to_char ( timestamp, text ) text to_char ( timestamp with time zone, text ) text Converts time stamp to string according to the given format. to_char(timestamp '2002-04-20 17:31:12.66', 'HH12:MI:SS') 05:31:12 to_char ( interval, text ) text Converts interval to string according to the given format. to_char(interval '15h 2m 12s', 'HH24:MI:SS') 15:02:12 to_char ( numeric_type, text ) text Converts number to string according to the given format; available for integer, bigint, numeric, real, double precision. to_char(125, '999') 125 to_char(125.8::real, '999D9') 125.8 to_char(-125.8, '999D99S') 125.80- to_date to_date ( text, text ) date Converts string to date according to the given format. to_date('05 Dec 2000', 'DD Mon YYYY') 2000-12-05 to_number to_number ( text, text ) numeric Converts string to numeric according to the given format. to_number('12,454.8-', '99G999D9S') -12454.8 to_timestamp to_timestamp ( text, text ) timestamp with time zone Converts string to time stamp according to the given format. (See also to_timestamp(double precision) in .) to_timestamp('05 Dec 2000', 'DD Mon YYYY') 2000-12-05 00:00:00-05 to_timestamp and to_date exist to handle input formats that cannot be converted by simple casting. For most standard date/time formats, simply casting the source string to the required data type works, and is much easier. Similarly, to_number is unnecessary for standard numeric representations. In a to_char output template string, there are certain patterns that are recognized and replaced with appropriately-formatted data based on the given value. Any text that is not a template pattern is simply copied verbatim. Similarly, in an input template string (for the other functions), template patterns identify the values to be supplied by the input data string. If there are characters in the template string that are not template patterns, the corresponding characters in the input data string are simply skipped over (whether or not they are equal to the template string characters). shows the template patterns available for formatting date and time values. Pattern Description HH hour of day (01–12) HH12 hour of day (01–12) HH24 hour of day (00–23) MI minute (00–59) SS second (00–59) MS millisecond (000–999) US microsecond (000000–999999) FF1 tenth of second (0–9) FF2 hundredth of second (00–99) FF3 millisecond (000–999) FF4 tenth of a millisecond (0000–9999) FF5 hundredth of a millisecond (00000–99999) FF6 microsecond (000000–999999) SSSS, SSSSS seconds past midnight (0–86399) AM, am, PM or pm meridiem indicator (without periods) A.M., a.m., P.M. or p.m. meridiem indicator (with periods) Y,YYY year (4 or more digits) with comma YYYY year (4 or more digits) YYY last 3 digits of year YY last 2 digits of year Y last digit of year IYYY ISO 8601 week-numbering year (4 or more digits) IYY last 3 digits of ISO 8601 week-numbering year IY last 2 digits of ISO 8601 week-numbering year I last digit of ISO 8601 week-numbering year BC, bc, AD or ad era indicator (without periods) B.C., b.c., A.D. or a.d. era indicator (with periods) MONTH full upper case month name (blank-padded to 9 chars) Month full capitalized month name (blank-padded to 9 chars) month full lower case month name (blank-padded to 9 chars) MON abbreviated upper case month name (3 chars in English, localized lengths vary) Mon abbreviated capitalized month name (3 chars in English, localized lengths vary) mon abbreviated lower case month name (3 chars in English, localized lengths vary) MM month number (01–12) DAY full upper case day name (blank-padded to 9 chars) Day full capitalized day name (blank-padded to 9 chars) day full lower case day name (blank-padded to 9 chars) DY abbreviated upper case day name (3 chars in English, localized lengths vary) Dy abbreviated capitalized day name (3 chars in English, localized lengths vary) dy abbreviated lower case day name (3 chars in English, localized lengths vary) DDD day of year (001–366) IDDD day of ISO 8601 week-numbering year (001–371; day 1 of the year is Monday of the first ISO week) DD day of month (01–31) D day of the week, Sunday (1) to Saturday (7) ID ISO 8601 day of the week, Monday (1) to Sunday (7) W week of month (1–5) (the first week starts on the first day of the month) WW week number of year (1–53) (the first week starts on the first day of the year) IW week number of ISO 8601 week-numbering year (01–53; the first Thursday of the year is in week 1) CC century (2 digits) (the twenty-first century starts on 2001-01-01) J Julian Date (integer days since November 24, 4714 BC at local midnight; see ) Q quarter RM month in upper case Roman numerals (I–XII; I=January) rm month in lower case Roman numerals (i–xii; i=January) TZ upper case time-zone abbreviation (only supported in to_char) tz lower case time-zone abbreviation (only supported in to_char) TZH time-zone hours TZM time-zone minutes OF time-zone offset from UTC (only supported in to_char) Modifiers can be applied to any template pattern to alter its behavior. For example, FMMonth is the Month pattern with the FM modifier. shows the modifier patterns for date/time formatting. Modifier Description Example FM prefix fill mode (suppress leading zeroes and padding blanks) FMMonth TH suffix upper case ordinal number suffix DDTH, e.g., 12TH th suffix lower case ordinal number suffix DDth, e.g., 12th FX prefix fixed format global option (see usage notes) FX Month DD Day TM prefix translation mode (use localized day and month names based on ) TMMonth SP suffix spell mode (not implemented) DDSP Usage notes for date/time formatting: FM suppresses leading zeroes and trailing blanks that would otherwise be added to make the output of a pattern be fixed-width. In PostgreSQL, FM modifies only the next specification, while in Oracle FM affects all subsequent specifications, and repeated FM modifiers toggle fill mode on and off. TM suppresses trailing blanks whether or not FM is specified. to_timestamp and to_date ignore letter case in the input; so for example MON, Mon, and mon all accept the same strings. When using the TM modifier, case-folding is done according to the rules of the function's input collation (see ). to_timestamp and to_date skip multiple blank spaces at the beginning of the input string and around date and time values unless the FX option is used. For example, to_timestamp(' 2000    JUN', 'YYYY MON') and to_timestamp('2000 - JUN', 'YYYY-MON') work, but to_timestamp('2000    JUN', 'FXYYYY MON') returns an error because to_timestamp expects only a single space. FX must be specified as the first item in the template. A separator (a space or non-letter/non-digit character) in the template string of to_timestamp and to_date matches any single separator in the input string or is skipped, unless the FX option is used. For example, to_timestamp('2000JUN', 'YYYY///MON') and to_timestamp('2000/JUN', 'YYYY MON') work, but to_timestamp('2000//JUN', 'YYYY/MON') returns an error because the number of separators in the input string exceeds the number of separators in the template. If FX is specified, a separator in the template string matches exactly one character in the input string. But note that the input string character is not required to be the same as the separator from the template string. For example, to_timestamp('2000/JUN', 'FXYYYY MON') works, but to_timestamp('2000/JUN', 'FXYYYY  MON') returns an error because the second space in the template string consumes the letter J from the input string. A TZH template pattern can match a signed number. Without the FX option, minus signs may be ambiguous, and could be interpreted as a separator. This ambiguity is resolved as follows: If the number of separators before TZH in the template string is less than the number of separators before the minus sign in the input string, the minus sign is interpreted as part of TZH. Otherwise, the minus sign is considered to be a separator between values. For example, to_timestamp('2000 -10', 'YYYY TZH') matches -10 to TZH, but to_timestamp('2000 -10', 'YYYY  TZH') matches 10 to TZH. Ordinary text is allowed in to_char templates and will be output literally. You can put a substring in double quotes to force it to be interpreted as literal text even if it contains template patterns. For example, in '"Hello Year "YYYY', the YYYY will be replaced by the year data, but the single Y in Year will not be. In to_date, to_number, and to_timestamp, literal text and double-quoted strings result in skipping the number of characters contained in the string; for example "XX" skips two input characters (whether or not they are XX). Prior to PostgreSQL 12, it was possible to skip arbitrary text in the input string using non-letter or non-digit characters. For example, to_timestamp('2000y6m1d', 'yyyy-MM-DD') used to work. Now you can only use letter characters for this purpose. For example, to_timestamp('2000y6m1d', 'yyyytMMtDDt') and to_timestamp('2000y6m1d', 'yyyy"y"MM"m"DD"d"') skip y, m, and d. If you want to have a double quote in the output you must precede it with a backslash, for example '\"YYYY Month\"'. Backslashes are not otherwise special outside of double-quoted strings. Within a double-quoted string, a backslash causes the next character to be taken literally, whatever it is (but this has no special effect unless the next character is a double quote or another backslash). In to_timestamp and to_date, if the year format specification is less than four digits, e.g., YYY, and the supplied year is less than four digits, the year will be adjusted to be nearest to the year 2020, e.g., 95 becomes 1995. In to_timestamp and to_date, negative years are treated as signifying BC. If you write both a negative year and an explicit BC field, you get AD again. An input of year zero is treated as 1 BC. In to_timestamp and to_date, the YYYY conversion has a restriction when processing years with more than 4 digits. You must use some non-digit character or template after YYYY, otherwise the year is always interpreted as 4 digits. For example (with the year 20000): to_date('200001130', 'YYYYMMDD') will be interpreted as a 4-digit year; instead use a non-digit separator after the year, like to_date('20000-1130', 'YYYY-MMDD') or to_date('20000Nov30', 'YYYYMonDD'). In to_timestamp and to_date, the CC (century) field is accepted but ignored if there is a YYY, YYYY or Y,YYY field. If CC is used with YY or Y then the result is computed as that year in the specified century. If the century is specified but the year is not, the first year of the century is assumed. In to_timestamp and to_date, weekday names or numbers (DAY, D, and related field types) are accepted but are ignored for purposes of computing the result. The same is true for quarter (Q) fields. In to_timestamp and to_date, an ISO 8601 week-numbering date (as distinct from a Gregorian date) can be specified in one of two ways: Year, week number, and weekday: for example to_date('2006-42-4', 'IYYY-IW-ID') returns the date 2006-10-19. If you omit the weekday it is assumed to be 1 (Monday). Year and day of year: for example to_date('2006-291', 'IYYY-IDDD') also returns 2006-10-19. Attempting to enter a date using a mixture of ISO 8601 week-numbering fields and Gregorian date fields is nonsensical, and will cause an error. In the context of an ISO 8601 week-numbering year, the concept of a month or day of month has no meaning. In the context of a Gregorian year, the ISO week has no meaning. While to_date will reject a mixture of Gregorian and ISO week-numbering date fields, to_char will not, since output format specifications like YYYY-MM-DD (IYYY-IDDD) can be useful. But avoid writing something like IYYY-MM-DD; that would yield surprising results near the start of the year. (See for more information.) In to_timestamp, millisecond (MS) or microsecond (US) fields are used as the seconds digits after the decimal point. For example to_timestamp('12.3', 'SS.MS') is not 3 milliseconds, but 300, because the conversion treats it as 12 + 0.3 seconds. So, for the format SS.MS, the input values 12.3, 12.30, and 12.300 specify the same number of milliseconds. To get three milliseconds, one must write 12.003, which the conversion treats as 12 + 0.003 = 12.003 seconds. Here is a more complex example: to_timestamp('15:12:02.020.001230', 'HH24:MI:SS.MS.US') is 15 hours, 12 minutes, and 2 seconds + 20 milliseconds + 1230 microseconds = 2.021230 seconds. to_char(..., 'ID')'s day of the week numbering matches the extract(isodow from ...) function, but to_char(..., 'D')'s does not match extract(dow from ...)'s day numbering. to_char(interval) formats HH and HH12 as shown on a 12-hour clock, for example zero hours and 36 hours both output as 12, while HH24 outputs the full hour value, which can exceed 23 in an interval value. shows the template patterns available for formatting numeric values. Pattern Description 9 digit position (can be dropped if insignificant) 0 digit position (will not be dropped, even if insignificant) . (period) decimal point , (comma) group (thousands) separator PR negative value in angle brackets S sign anchored to number (uses locale) L currency symbol (uses locale) D decimal point (uses locale) G group separator (uses locale) MI minus sign in specified position (if number < 0) PL plus sign in specified position (if number > 0) SG plus/minus sign in specified position RN Roman numeral (input between 1 and 3999) TH or th ordinal number suffix V shift specified number of digits (see notes) EEEE exponent for scientific notation Usage notes for numeric formatting: 0 specifies a digit position that will always be printed, even if it contains a leading/trailing zero. 9 also specifies a digit position, but if it is a leading zero then it will be replaced by a space, while if it is a trailing zero and fill mode is specified then it will be deleted. (For to_number(), these two pattern characters are equivalent.) If the format provides fewer fractional digits than the number being formatted, to_char() will round the number to the specified number of fractional digits. The pattern characters S, L, D, and G represent the sign, currency symbol, decimal point, and thousands separator characters defined by the current locale (see and ). The pattern characters period and comma represent those exact characters, with the meanings of decimal point and thousands separator, regardless of locale. If no explicit provision is made for a sign in to_char()'s pattern, one column will be reserved for the sign, and it will be anchored to (appear just left of) the number. If S appears just left of some 9's, it will likewise be anchored to the number. A sign formatted using SG, PL, or MI is not anchored to the number; for example, to_char(-12, 'MI9999') produces '-  12' but to_char(-12, 'S9999') produces '  -12'. (The Oracle implementation does not allow the use of MI before 9, but rather requires that 9 precede MI.) TH does not convert values less than zero and does not convert fractional numbers. PL, SG, and TH are PostgreSQL extensions. In to_number, if non-data template patterns such as L or TH are used, the corresponding number of input characters are skipped, whether or not they match the template pattern, unless they are data characters (that is, digits, sign, decimal point, or comma). For example, TH would skip two non-data characters. V with to_char multiplies the input values by 10^n, where n is the number of digits following V. V with to_number divides in a similar manner. to_char and to_number do not support the use of V combined with a decimal point (e.g., 99.9V99 is not allowed). EEEE (scientific notation) cannot be used in combination with any of the other formatting patterns or modifiers other than digit and decimal point patterns, and must be at the end of the format string (e.g., 9.99EEEE is a valid pattern). Certain modifiers can be applied to any template pattern to alter its behavior. For example, FM99.99 is the 99.99 pattern with the FM modifier. shows the modifier patterns for numeric formatting. Modifier Description Example FM prefix fill mode (suppress trailing zeroes and padding blanks) FM99.99 TH suffix upper case ordinal number suffix 999TH th suffix lower case ordinal number suffix 999th shows some examples of the use of the to_char function. Expression Result to_char(current_timestamp, 'Day, DD  HH12:MI:SS') 'Tuesday  , 06  05:39:18' to_char(current_timestamp, 'FMDay, FMDD  HH12:MI:SS') 'Tuesday, 6  05:39:18' to_char(-0.1, '99.99') '  -.10' to_char(-0.1, 'FM9.99') '-.1' to_char(-0.1, 'FM90.99') '-0.1' to_char(0.1, '0.9') ' 0.1' to_char(12, '9990999.9') '    0012.0' to_char(12, 'FM9990999.9') '0012.' to_char(485, '999') ' 485' to_char(-485, '999') '-485' to_char(485, '9 9 9') ' 4 8 5' to_char(1485, '9,999') ' 1,485' to_char(1485, '9G999') ' 1 485' to_char(148.5, '999.999') ' 148.500' to_char(148.5, 'FM999.999') '148.5' to_char(148.5, 'FM999.990') '148.500' to_char(148.5, '999D999') ' 148,500' to_char(3148.5, '9G999D999') ' 3 148,500' to_char(-485, '999S') '485-' to_char(-485, '999MI') '485-' to_char(485, '999MI') '485 ' to_char(485, 'FM999MI') '485' to_char(485, 'PL999') '+485' to_char(485, 'SG999') '+485' to_char(-485, 'SG999') '-485' to_char(-485, '9SG99') '4-85' to_char(-485, '999PR') '<485>' to_char(485, 'L999') 'DM 485' to_char(485, 'RN') '        CDLXXXV' to_char(485, 'FMRN') 'CDLXXXV' to_char(5.2, 'FMRN') 'V' to_char(482, '999th') ' 482nd' to_char(485, '"Good number:"999') 'Good number: 485' to_char(485.8, '"Pre:"999" Post:" .999') 'Pre: 485 Post: .800' to_char(12, '99V999') ' 12000' to_char(12.4, '99V999') ' 12400' to_char(12.45, '99V9') ' 125' to_char(0.0004859, '9.99EEEE') ' 4.86e-04' shows the available functions for date/time value processing, with details appearing in the following subsections. illustrates the behaviors of the basic arithmetic operators (+, *, etc.). For formatting functions, refer to . You should be familiar with the background information on date/time data types from . In addition, the usual comparison operators shown in are available for the date/time types. Dates and timestamps (with or without time zone) are all comparable, while times (with or without time zone) and intervals can only be compared to other values of the same data type. When comparing a timestamp without time zone to a timestamp with time zone, the former value is assumed to be given in the time zone specified by the configuration parameter, and is rotated to UTC for comparison to the latter value (which is already in UTC internally). Similarly, a date value is assumed to represent midnight in the TimeZone zone when comparing it to a timestamp. All the functions and operators described below that take time or timestamp inputs actually come in two variants: one that takes time with time zone or timestamp with time zone, and one that takes time without time zone or timestamp without time zone. For brevity, these variants are not shown separately. Also, the + and * operators come in commutative pairs (for example both date + integer and integer + date); we show only one of each such pair. Operator Description Example(s) date + integer date Add a number of days to a date date '2001-09-28' + 7 2001-10-05 date + interval timestamp Add an interval to a date date '2001-09-28' + interval '1 hour' 2001-09-28 01:00:00 date + time timestamp Add a time-of-day to a date date '2001-09-28' + time '03:00' 2001-09-28 03:00:00 interval + interval interval Add intervals interval '1 day' + interval '1 hour' 1 day 01:00:00 timestamp + interval timestamp Add an interval to a timestamp timestamp '2001-09-28 01:00' + interval '23 hours' 2001-09-29 00:00:00 time + interval time Add an interval to a time time '01:00' + interval '3 hours' 04:00:00 - interval interval Negate an interval - interval '23 hours' -23:00:00 date - date integer Subtract dates, producing the number of days elapsed date '2001-10-01' - date '2001-09-28' 3 date - integer date Subtract a number of days from a date date '2001-10-01' - 7 2001-09-24 date - interval timestamp Subtract an interval from a date date '2001-09-28' - interval '1 hour' 2001-09-27 23:00:00 time - time interval Subtract times time '05:00' - time '03:00' 02:00:00 time - interval time Subtract an interval from a time time '05:00' - interval '2 hours' 03:00:00 timestamp - interval timestamp Subtract an interval from a timestamp timestamp '2001-09-28 23:00' - interval '23 hours' 2001-09-28 00:00:00 interval - interval interval Subtract intervals interval '1 day' - interval '1 hour' 1 day -01:00:00 timestamp - timestamp interval Subtract timestamps (converting 24-hour intervals into days, similarly to justify_hours()) timestamp '2001-09-29 03:00' - timestamp '2001-07-27 12:00' 63 days 15:00:00 interval * double precision interval Multiply an interval by a scalar interval '1 second' * 900 00:15:00 interval '1 day' * 21 21 days interval '1 hour' * 3.5 03:30:00 interval / double precision interval Divide an interval by a scalar interval '1 hour' / 1.5 00:40:00 Function Description Example(s) age age ( timestamp, timestamp ) interval Subtract arguments, producing a symbolic result that uses years and months, rather than just days age(timestamp '2001-04-10', timestamp '1957-06-13') 43 years 9 mons 27 days age ( timestamp ) interval Subtract argument from current_date (at midnight) age(timestamp '1957-06-13') 62 years 6 mons 10 days clock_timestamp clock_timestamp ( ) timestamp with time zone Current date and time (changes during statement execution); see clock_timestamp() 2019-12-23 14:39:53.662522-05 current_date current_date date Current date; see current_date 2019-12-23 current_time current_time time with time zone Current time of day; see current_time 14:39:53.662522-05 current_time ( integer ) time with time zone Current time of day, with limited precision; see current_time(2) 14:39:53.66-05 current_timestamp current_timestamp timestamp with time zone Current date and time (start of current transaction); see current_timestamp 2019-12-23 14:39:53.662522-05 current_timestamp ( integer ) timestamp with time zone Current date and time (start of current transaction), with limited precision; see current_timestamp(0) 2019-12-23 14:39:53-05 date_bin ( interval, timestamp, timestamp ) timestamp Bin input into specified interval aligned with specified origin; see date_bin('15 minutes', timestamp '2001-02-16 20:38:40', timestamp '2001-02-16 20:05:00') 2001-02-16 20:35:00 date_part date_part ( text, timestamp ) double precision Get timestamp subfield (equivalent to extract); see date_part('hour', timestamp '2001-02-16 20:38:40') 20 date_part ( text, interval ) double precision Get interval subfield (equivalent to extract); see date_part('month', interval '2 years 3 months') 3 date_trunc date_trunc ( text, timestamp ) timestamp Truncate to specified precision; see date_trunc('hour', timestamp '2001-02-16 20:38:40') 2001-02-16 20:00:00 date_trunc ( text, timestamp with time zone, text ) timestamp with time zone Truncate to specified precision in the specified time zone; see date_trunc('day', timestamptz '2001-02-16 20:38:40+00', 'Australia/Sydney') 2001-02-16 13:00:00+00 date_trunc ( text, interval ) interval Truncate to specified precision; see date_trunc('hour', interval '2 days 3 hours 40 minutes') 2 days 03:00:00 extract extract ( field from timestamp ) numeric Get timestamp subfield; see extract(hour from timestamp '2001-02-16 20:38:40') 20 extract ( field from interval ) numeric Get interval subfield; see extract(month from interval '2 years 3 months') 3 isfinite isfinite ( date ) boolean Test for finite date (not +/-infinity) isfinite(date '2001-02-16') true isfinite ( timestamp ) boolean Test for finite timestamp (not +/-infinity) isfinite(timestamp 'infinity') false isfinite ( interval ) boolean Test for finite interval (currently always true) isfinite(interval '4 hours') true justify_days justify_days ( interval ) interval Adjust interval, converting 30-day time periods to months justify_days(interval '1 year 65 days') 1 year 2 mons 5 days justify_hours justify_hours ( interval ) interval Adjust interval, converting 24-hour time periods to days justify_hours(interval '50 hours 10 minutes') 2 days 02:10:00 justify_interval justify_interval ( interval ) interval Adjust interval using justify_days and justify_hours, with additional sign adjustments justify_interval(interval '1 mon -1 hour') 29 days 23:00:00 localtime localtime time Current time of day; see localtime 14:39:53.662522 localtime ( integer ) time Current time of day, with limited precision; see localtime(0) 14:39:53 localtimestamp localtimestamp timestamp Current date and time (start of current transaction); see localtimestamp 2019-12-23 14:39:53.662522 localtimestamp ( integer ) timestamp Current date and time (start of current transaction), with limited precision; see localtimestamp(2) 2019-12-23 14:39:53.66 make_date make_date ( year int, month int, day int ) date Create date from year, month and day fields (negative years signify BC) make_date(2013, 7, 15) 2013-07-15 make_interval make_interval ( years int , months int , weeks int , days int , hours int , mins int , secs double precision ) interval Create interval from years, months, weeks, days, hours, minutes and seconds fields, each of which can default to zero make_interval(days => 10) 10 days make_time make_time ( hour int, min int, sec double precision ) time Create time from hour, minute and seconds fields make_time(8, 15, 23.5) 08:15:23.5 make_timestamp make_timestamp ( year int, month int, day int, hour int, min int, sec double precision ) timestamp Create timestamp from year, month, day, hour, minute and seconds fields (negative years signify BC) make_timestamp(2013, 7, 15, 8, 15, 23.5) 2013-07-15 08:15:23.5 make_timestamptz make_timestamptz ( year int, month int, day int, hour int, min int, sec double precision , timezone text ) timestamp with time zone Create timestamp with time zone from year, month, day, hour, minute and seconds fields (negative years signify BC). If timezone is not specified, the current time zone is used; the examples assume the session time zone is Europe/London make_timestamptz(2013, 7, 15, 8, 15, 23.5) 2013-07-15 08:15:23.5+01 make_timestamptz(2013, 7, 15, 8, 15, 23.5, 'America/New_York') 2013-07-15 13:15:23.5+01 now now ( ) timestamp with time zone Current date and time (start of current transaction); see now() 2019-12-23 14:39:53.662522-05 statement_timestamp statement_timestamp ( ) timestamp with time zone Current date and time (start of current statement); see statement_timestamp() 2019-12-23 14:39:53.662522-05 timeofday timeofday ( ) text Current date and time (like clock_timestamp, but as a text string); see timeofday() Mon Dec 23 14:39:53.662522 2019 EST transaction_timestamp transaction_timestamp ( ) timestamp with time zone Current date and time (start of current transaction); see transaction_timestamp() 2019-12-23 14:39:53.662522-05 to_timestamp to_timestamp ( double precision ) timestamp with time zone Convert Unix epoch (seconds since 1970-01-01 00:00:00+00) to timestamp with time zone to_timestamp(1284352323) 2010-09-13 04:32:03+00 OVERLAPS In addition to these functions, the SQL OVERLAPS operator is supported: (start1, end1) OVERLAPS (start2, end2) (start1, length1) OVERLAPS (start2, length2) This expression yields true when two time periods (defined by their endpoints) overlap, false when they do not overlap. The endpoints can be specified as pairs of dates, times, or time stamps; or as a date, time, or time stamp followed by an interval. When a pair of values is provided, either the start or the end can be written first; OVERLAPS automatically takes the earlier value of the pair as the start. Each time period is considered to represent the half-open interval start <= time < end, unless start and end are equal in which case it represents that single time instant. This means for instance that two time periods with only an endpoint in common do not overlap. SELECT (DATE '2001-02-16', DATE '2001-12-21') OVERLAPS (DATE '2001-10-30', DATE '2002-10-30'); Result: true SELECT (DATE '2001-02-16', INTERVAL '100 days') OVERLAPS (DATE '2001-10-30', DATE '2002-10-30'); Result: false SELECT (DATE '2001-10-29', DATE '2001-10-30') OVERLAPS (DATE '2001-10-30', DATE '2001-10-31'); Result: false SELECT (DATE '2001-10-30', DATE '2001-10-30') OVERLAPS (DATE '2001-10-30', DATE '2001-10-31'); Result: true When adding an interval value to (or subtracting an interval value from) a timestamp with time zone value, the days component advances or decrements the date of the timestamp with time zone by the indicated number of days, keeping the time of day the same. Across daylight saving time changes (when the session time zone is set to a time zone that recognizes DST), this means interval '1 day' does not necessarily equal interval '24 hours'. For example, with the session time zone set to America/Denver: SELECT timestamp with time zone '2005-04-02 12:00:00-07' + interval '1 day'; Result: 2005-04-03 12:00:00-06 SELECT timestamp with time zone '2005-04-02 12:00:00-07' + interval '24 hours'; Result: 2005-04-03 13:00:00-06 This happens because an hour was skipped due to a change in daylight saving time at 2005-04-03 02:00:00 in time zone America/Denver. Note there can be ambiguity in the months field returned by age because different months have different numbers of days. PostgreSQL's approach uses the month from the earlier of the two dates when calculating partial months. For example, age('2004-06-01', '2004-04-30') uses April to yield 1 mon 1 day, while using May would yield 1 mon 2 days because May has 31 days, while April has only 30. Subtraction of dates and timestamps can also be complex. One conceptually simple way to perform subtraction is to convert each value to a number of seconds using EXTRACT(EPOCH FROM ...), then subtract the results; this produces the number of seconds between the two values. This will adjust for the number of days in each month, timezone changes, and daylight saving time adjustments. Subtraction of date or timestamp values with the - operator returns the number of days (24-hours) and hours/minutes/seconds between the values, making the same adjustments. The age function returns years, months, days, and hours/minutes/seconds, performing field-by-field subtraction and then adjusting for negative field values. The following queries illustrate the differences in these approaches. The sample results were produced with timezone = 'US/Eastern'; there is a daylight saving time change between the two dates used: SELECT EXTRACT(EPOCH FROM timestamptz '2013-07-01 12:00:00') - EXTRACT(EPOCH FROM timestamptz '2013-03-01 12:00:00'); Result: 10537200.000000 SELECT (EXTRACT(EPOCH FROM timestamptz '2013-07-01 12:00:00') - EXTRACT(EPOCH FROM timestamptz '2013-03-01 12:00:00')) / 60 / 60 / 24; Result: 121.9583333333333333 SELECT timestamptz '2013-07-01 12:00:00' - timestamptz '2013-03-01 12:00:00'; Result: 121 days 23:00:00 SELECT age(timestamptz '2013-07-01 12:00:00', timestamptz '2013-03-01 12:00:00'); Result: 4 mons date_part extract EXTRACT(field FROM source) The extract function retrieves subfields such as year or hour from date/time values. source must be a value expression of type timestamp, date, time, or interval. (Timestamps and times can be with or without time zone.) field is an identifier or string that selects what field to extract from the source value. Not all fields are valid for every input data type; for example, fields smaller than a day cannot be extracted from a date, while fields of a day or more cannot be extracted from a time. The extract function returns values of type numeric. The following are valid field names: century The century; for interval values, the year field divided by 100 SELECT EXTRACT(CENTURY FROM TIMESTAMP '2000-12-16 12:21:13'); Result: 20 SELECT EXTRACT(CENTURY FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 21 SELECT EXTRACT(CENTURY FROM DATE '0001-01-01 AD'); Result: 1 SELECT EXTRACT(CENTURY FROM DATE '0001-12-31 BC'); Result: -1 SELECT EXTRACT(CENTURY FROM INTERVAL '2001 years'); Result: 20 day The day of the month (1–31); for interval values, the number of days SELECT EXTRACT(DAY FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 16 SELECT EXTRACT(DAY FROM INTERVAL '40 days 1 minute'); Result: 40 decade The year field divided by 10 SELECT EXTRACT(DECADE FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 200 dow The day of the week as Sunday (0) to Saturday (6) SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 5 Note that extract's day of the week numbering differs from that of the to_char(..., 'D') function. doy The day of the year (1–365/366) SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 47 epoch For timestamp with time zone values, the number of seconds since 1970-01-01 00:00:00 UTC (negative for timestamps before that); for date and timestamp values, the nominal number of seconds since 1970-01-01 00:00:00, without regard to timezone or daylight-savings rules; for interval values, the total number of seconds in the interval SELECT EXTRACT(EPOCH FROM TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40.12-08'); Result: 982384720.120000 SELECT EXTRACT(EPOCH FROM TIMESTAMP '2001-02-16 20:38:40.12'); Result: 982355920.120000 SELECT EXTRACT(EPOCH FROM INTERVAL '5 days 3 hours'); Result: 442800.000000 You can convert an epoch value back to a timestamp with time zone with to_timestamp: SELECT to_timestamp(982384720.12); Result: 2001-02-17 04:38:40.12+00 Beware that applying to_timestamp to an epoch extracted from a date or timestamp value could produce a misleading result: the result will effectively assume that the original value had been given in UTC, which might not be the case. hour The hour field (0–23 in timestamps, unrestricted in intervals) SELECT EXTRACT(HOUR FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 20 isodow The day of the week as Monday (1) to Sunday (7) SELECT EXTRACT(ISODOW FROM TIMESTAMP '2001-02-18 20:38:40'); Result: 7 This is identical to dow except for Sunday. This matches the ISO 8601 day of the week numbering. isoyear The ISO 8601 week-numbering year that the date falls in SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-01'); Result: 2005 SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-02'); Result: 2006 Each ISO 8601 week-numbering year begins with the Monday of the week containing the 4th of January, so in early January or late December the ISO year may be different from the Gregorian year. See the week field for more information. julian The Julian Date corresponding to the date or timestamp. Timestamps that are not local midnight result in a fractional value. See for more information. SELECT EXTRACT(JULIAN FROM DATE '2006-01-01'); Result: 2453737 SELECT EXTRACT(JULIAN FROM TIMESTAMP '2006-01-01 12:00'); Result: 2453737.50000000000000000000 microseconds The seconds field, including fractional parts, multiplied by 1 000 000; note that this includes full seconds SELECT EXTRACT(MICROSECONDS FROM TIME '17:12:28.5'); Result: 28500000 millennium The millennium; for interval values, the year field divided by 1000 SELECT EXTRACT(MILLENNIUM FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 3 SELECT EXTRACT(MILLENNIUM FROM INTERVAL '2001 years'); Result: 2 Years in the 1900s are in the second millennium. The third millennium started January 1, 2001. milliseconds The seconds field, including fractional parts, multiplied by 1000. Note that this includes full seconds. SELECT EXTRACT(MILLISECONDS FROM TIME '17:12:28.5'); Result: 28500.000 minute The minutes field (0–59) SELECT EXTRACT(MINUTE FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 38 month The number of the month within the year (1–12); for interval values, the number of months modulo 12 (0–11) SELECT EXTRACT(MONTH FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 2 SELECT EXTRACT(MONTH FROM INTERVAL '2 years 3 months'); Result: 3 SELECT EXTRACT(MONTH FROM INTERVAL '2 years 13 months'); Result: 1 quarter The quarter of the year (1–4) that the date is in SELECT EXTRACT(QUARTER FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 1 second The seconds field, including any fractional seconds SELECT EXTRACT(SECOND FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 40.000000 SELECT EXTRACT(SECOND FROM TIME '17:12:28.5'); Result: 28.500000 timezone The time zone offset from UTC, measured in seconds. Positive values correspond to time zones east of UTC, negative values to zones west of UTC. (Technically, PostgreSQL does not use UTC because leap seconds are not handled.) timezone_hour The hour component of the time zone offset timezone_minute The minute component of the time zone offset week The number of the ISO 8601 week-numbering week of the year. By definition, ISO weeks start on Mondays and the first week of a year contains January 4 of that year. In other words, the first Thursday of a year is in week 1 of that year. In the ISO week-numbering system, it is possible for early-January dates to be part of the 52nd or 53rd week of the previous year, and for late-December dates to be part of the first week of the next year. For example, 2005-01-01 is part of the 53rd week of year 2004, and 2006-01-01 is part of the 52nd week of year 2005, while 2012-12-31 is part of the first week of 2013. It's recommended to use the isoyear field together with week to get consistent results. SELECT EXTRACT(WEEK FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 7 year The year field. Keep in mind there is no 0 AD, so subtracting BC years from AD years should be done with care. SELECT EXTRACT(YEAR FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 2001 When processing an interval value, the extract function produces field values that match the interpretation used by the interval output function. This can produce surprising results if one starts with a non-normalized interval representation, for example: SELECT INTERVAL '80 minutes'; Result: 01:20:00 SELECT EXTRACT(MINUTES FROM INTERVAL '80 minutes'); Result: 20 When the input value is +/-Infinity, extract returns +/-Infinity for monotonically-increasing fields (epoch, julian, year, isoyear, decade, century, and millennium). For other fields, NULL is returned. PostgreSQL versions before 9.6 returned zero for all cases of infinite input. The extract function is primarily intended for computational processing. For formatting date/time values for display, see . The date_part function is modeled on the traditional Ingres equivalent to the SQL-standard function extract: date_part('field', source) Note that here the field parameter needs to be a string value, not a name. The valid field names for date_part are the same as for extract. For historical reasons, the date_part function returns values of type double precision. This can result in a loss of precision in certain uses. Using extract is recommended instead. SELECT date_part('day', TIMESTAMP '2001-02-16 20:38:40'); Result: 16 SELECT date_part('hour', INTERVAL '4 hours 3 minutes'); Result: 4 date_trunc The function date_trunc is conceptually similar to the trunc function for numbers. date_trunc(field, source [, time_zone ]) source is a value expression of type timestamp, timestamp with time zone, or interval. (Values of type date and time are cast automatically to timestamp or interval, respectively.) field selects to which precision to truncate the input value. The return value is likewise of type timestamp, timestamp with time zone, or interval, and it has all fields that are less significant than the selected one set to zero (or one, for day and month). Valid values for field are: microseconds milliseconds second minute hour day week month quarter year decade century millennium When the input value is of type timestamp with time zone, the truncation is performed with respect to a particular time zone; for example, truncation to day produces a value that is midnight in that zone. By default, truncation is done with respect to the current setting, but the optional time_zone argument can be provided to specify a different time zone. The time zone name can be specified in any of the ways described in . A time zone cannot be specified when processing timestamp without time zone or interval inputs. These are always taken at face value. Examples (assuming the local time zone is America/New_York): SELECT date_trunc('hour', TIMESTAMP '2001-02-16 20:38:40'); Result: 2001-02-16 20:00:00 SELECT date_trunc('year', TIMESTAMP '2001-02-16 20:38:40'); Result: 2001-01-01 00:00:00 SELECT date_trunc('day', TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40+00'); Result: 2001-02-16 00:00:00-05 SELECT date_trunc('day', TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40+00', 'Australia/Sydney'); Result: 2001-02-16 08:00:00-05 SELECT date_trunc('hour', INTERVAL '3 days 02:47:33'); Result: 3 days 02:00:00 date_bin The function date_bin bins the input timestamp into the specified interval (the stride) aligned with a specified origin. date_bin(stride, source, origin) source is a value expression of type timestamp or timestamp with time zone. (Values of type date are cast automatically to timestamp.) stride is a value expression of type interval. The return value is likewise of type timestamp or timestamp with time zone, and it marks the beginning of the bin into which the source is placed. Examples: SELECT date_bin('15 minutes', TIMESTAMP '2020-02-11 15:44:17', TIMESTAMP '2001-01-01'); Result: 2020-02-11 15:30:00 SELECT date_bin('15 minutes', TIMESTAMP '2020-02-11 15:44:17', TIMESTAMP '2001-01-01 00:02:30'); Result: 2020-02-11 15:32:30 In the case of full units (1 minute, 1 hour, etc.), it gives the same result as the analogous date_trunc call, but the difference is that date_bin can truncate to an arbitrary interval. The stride interval must be greater than zero and cannot contain units of month or larger. time zone conversion AT TIME ZONE The AT TIME ZONE operator converts time stamp without time zone to/from time stamp with time zone, and time with time zone values to different time zones. shows its variants. Operator Description Example(s) timestamp without time zone AT TIME ZONE zone timestamp with time zone Converts given time stamp without time zone to time stamp with time zone, assuming the given value is in the named time zone. timestamp '2001-02-16 20:38:40' at time zone 'America/Denver' 2001-02-17 03:38:40+00 timestamp with time zone AT TIME ZONE zone timestamp without time zone Converts given time stamp with time zone to time stamp without time zone, as the time would appear in that zone. timestamp with time zone '2001-02-16 20:38:40-05' at time zone 'America/Denver' 2001-02-16 18:38:40 time with time zone AT TIME ZONE zone time with time zone Converts given time with time zone to a new time zone. Since no date is supplied, this uses the currently active UTC offset for the named destination zone. time with time zone '05:34:17-05' at time zone 'UTC' 10:34:17+00 In these expressions, the desired time zone zone can be specified either as a text value (e.g., 'America/Los_Angeles') or as an interval (e.g., INTERVAL '-08:00'). In the text case, a time zone name can be specified in any of the ways described in . The interval case is only useful for zones that have fixed offsets from UTC, so it is not very common in practice. Examples (assuming the current setting is America/Los_Angeles): SELECT TIMESTAMP '2001-02-16 20:38:40' AT TIME ZONE 'America/Denver'; Result: 2001-02-16 19:38:40-08 SELECT TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-05' AT TIME ZONE 'America/Denver'; Result: 2001-02-16 18:38:40 SELECT TIMESTAMP '2001-02-16 20:38:40' AT TIME ZONE 'Asia/Tokyo' AT TIME ZONE 'America/Chicago'; Result: 2001-02-16 05:38:40 The first example adds a time zone to a value that lacks it, and displays the value using the current TimeZone setting. The second example shifts the time stamp with time zone value to the specified time zone, and returns the value without a time zone. This allows storage and display of values different from the current TimeZone setting. The third example converts Tokyo time to Chicago time. The function timezone(zone, timestamp) is equivalent to the SQL-conforming construct timestamp AT TIME ZONE zone. date current time current PostgreSQL provides a number of functions that return values related to the current date and time. These SQL-standard functions all return values based on the start time of the current transaction: CURRENT_DATE CURRENT_TIME CURRENT_TIMESTAMP CURRENT_TIME(precision) CURRENT_TIMESTAMP(precision) LOCALTIME LOCALTIMESTAMP LOCALTIME(precision) LOCALTIMESTAMP(precision) CURRENT_TIME and CURRENT_TIMESTAMP deliver values with time zone; LOCALTIME and LOCALTIMESTAMP deliver values without time zone. CURRENT_TIME, CURRENT_TIMESTAMP, LOCALTIME, and LOCALTIMESTAMP can optionally take a precision parameter, which causes the result to be rounded to that many fractional digits in the seconds field. Without a precision parameter, the result is given to the full available precision. Some examples: SELECT CURRENT_TIME; Result: 14:39:53.662522-05 SELECT CURRENT_DATE; Result: 2019-12-23 SELECT CURRENT_TIMESTAMP; Result: 2019-12-23 14:39:53.662522-05 SELECT CURRENT_TIMESTAMP(2); Result: 2019-12-23 14:39:53.66-05 SELECT LOCALTIMESTAMP; Result: 2019-12-23 14:39:53.662522 Since these functions return the start time of the current transaction, their values do not change during the transaction. This is considered a feature: the intent is to allow a single transaction to have a consistent notion of the current time, so that multiple modifications within the same transaction bear the same time stamp. Other database systems might advance these values more frequently. PostgreSQL also provides functions that return the start time of the current statement, as well as the actual current time at the instant the function is called. The complete list of non-SQL-standard time functions is: transaction_timestamp() statement_timestamp() clock_timestamp() timeofday() now() transaction_timestamp() is equivalent to CURRENT_TIMESTAMP, but is named to clearly reflect what it returns. statement_timestamp() returns the start time of the current statement (more specifically, the time of receipt of the latest command message from the client). statement_timestamp() and transaction_timestamp() return the same value during the first command of a transaction, but might differ during subsequent commands. clock_timestamp() returns the actual current time, and therefore its value changes even within a single SQL command. timeofday() is a historical PostgreSQL function. Like clock_timestamp(), it returns the actual current time, but as a formatted text string rather than a timestamp with time zone value. now() is a traditional PostgreSQL equivalent to transaction_timestamp(). All the date/time data types also accept the special literal value now to specify the current date and time (again, interpreted as the transaction start time). Thus, the following three all return the same result: SELECT CURRENT_TIMESTAMP; SELECT now(); SELECT TIMESTAMP 'now'; -- but see tip below Do not use the third form when specifying a value to be evaluated later, for example in a DEFAULT clause for a table column. The system will convert now to a timestamp as soon as the constant is parsed, so that when the default value is needed, the time of the table creation would be used! The first two forms will not be evaluated until the default value is used, because they are function calls. Thus they will give the desired behavior of defaulting to the time of row insertion. (See also .) pg_sleep pg_sleep_for pg_sleep_until sleep delay The following functions are available to delay execution of the server process: pg_sleep ( double precision ) pg_sleep_for ( interval ) pg_sleep_until ( timestamp with time zone ) pg_sleep makes the current session's process sleep until the given number of seconds have elapsed. Fractional-second delays can be specified. pg_sleep_for is a convenience function to allow the sleep time to be specified as an interval. pg_sleep_until is a convenience function for when a specific wake-up time is desired. For example: SELECT pg_sleep(1.5); SELECT pg_sleep_for('5 minutes'); SELECT pg_sleep_until('tomorrow 03:00'); The effective resolution of the sleep interval is platform-specific; 0.01 seconds is a common value. The sleep delay will be at least as long as specified. It might be longer depending on factors such as server load. In particular, pg_sleep_until is not guaranteed to wake up exactly at the specified time, but it will not wake up any earlier. Make sure that your session does not hold more locks than necessary when calling pg_sleep or its variants. Otherwise other sessions might have to wait for your sleeping process, slowing down the entire system. For enum types (described in ), there are several functions that allow cleaner programming without hard-coding particular values of an enum type. These are listed in . The examples assume an enum type created as: CREATE TYPE rainbow AS ENUM ('red', 'orange', 'yellow', 'green', 'blue', 'purple'); Function Description Example(s) enum_first enum_first ( anyenum ) anyenum Returns the first value of the input enum type. enum_first(null::rainbow) red enum_last enum_last ( anyenum ) anyenum Returns the last value of the input enum type. enum_last(null::rainbow) purple enum_range enum_range ( anyenum ) anyarray Returns all values of the input enum type in an ordered array. enum_range(null::rainbow) {red,orange,yellow,&zwsp;green,blue,purple} enum_range ( anyenum, anyenum ) anyarray Returns the range between the two given enum values, as an ordered array. The values must be from the same enum type. If the first parameter is null, the result will start with the first value of the enum type. If the second parameter is null, the result will end with the last value of the enum type. enum_range('orange'::rainbow, 'green'::rainbow) {orange,yellow,green} enum_range(NULL, 'green'::rainbow) {red,orange,&zwsp;yellow,green} enum_range('orange'::rainbow, NULL) {orange,yellow,green,&zwsp;blue,purple} Notice that except for the two-argument form of enum_range, these functions disregard the specific value passed to them; they care only about its declared data type. Either null or a specific value of the type can be passed, with the same result. It is more common to apply these functions to a table column or function argument than to a hardwired type name as used in the examples. The geometric types point, box, lseg, line, path, polygon, and circle have a large set of native support functions and operators, shown in , , and . Operator Description Example(s) geometric_type + point geometric_type Adds the coordinates of the second point to those of each point of the first argument, thus performing translation. Available for point, box, path, circle. box '(1,1),(0,0)' + point '(2,0)' (3,1),(2,0) path + path path Concatenates two open paths (returns NULL if either path is closed). path '[(0,0),(1,1)]' + path '[(2,2),(3,3),(4,4)]' [(0,0),(1,1),(2,2),(3,3),(4,4)] geometric_type - point geometric_type Subtracts the coordinates of the second point from those of each point of the first argument, thus performing translation. Available for point, box, path, circle. box '(1,1),(0,0)' - point '(2,0)' (-1,1),(-2,0) geometric_type * point geometric_type Multiplies each point of the first argument by the second point (treating a point as being a complex number represented by real and imaginary parts, and performing standard complex multiplication). If one interprets the second point as a vector, this is equivalent to scaling the object's size and distance from the origin by the length of the vector, and rotating it counterclockwise around the origin by the vector's angle from the x axis. Available for point, box,Rotating a box with these operators only moves its corner points: the box is still considered to have sides parallel to the axes. Hence the box's size is not preserved, as a true rotation would do. path, circle. path '((0,0),(1,0),(1,1))' * point '(3.0,0)' ((0,0),(3,0),(3,3)) path '((0,0),(1,0),(1,1))' * point(cosd(45), sind(45)) ((0,0),&zwsp;(0.7071067811865475,0.7071067811865475),&zwsp;(0,1.414213562373095)) geometric_type / point geometric_type Divides each point of the first argument by the second point (treating a point as being a complex number represented by real and imaginary parts, and performing standard complex division). If one interprets the second point as a vector, this is equivalent to scaling the object's size and distance from the origin down by the length of the vector, and rotating it clockwise around the origin by the vector's angle from the x axis. Available for point, box, path, circle. path '((0,0),(1,0),(1,1))' / point '(2.0,0)' ((0,0),(0.5,0),(0.5,0.5)) path '((0,0),(1,0),(1,1))' / point(cosd(45), sind(45)) ((0,0),&zwsp;(0.7071067811865476,-0.7071067811865476),&zwsp;(1.4142135623730951,0)) @-@ geometric_type double precision Computes the total length. Available for lseg, path. @-@ path '[(0,0),(1,0),(1,1)]' 2 @@ geometric_type point Computes the center point. Available for box, lseg, polygon, circle. @@ box '(2,2),(0,0)' (1,1) # geometric_type integer Returns the number of points. Available for path, polygon. # path '((1,0),(0,1),(-1,0))' 3 geometric_type # geometric_type point Computes the point of intersection, or NULL if there is none. Available for lseg, line. lseg '[(0,0),(1,1)]' # lseg '[(1,0),(0,1)]' (0.5,0.5) box # box box Computes the intersection of two boxes, or NULL if there is none. box '(2,2),(-1,-1)' # box '(1,1),(-2,-2)' (1,1),(-1,-1) geometric_type ## geometric_type point Computes the closest point to the first object on the second object. Available for these pairs of types: (point, box), (point, lseg), (point, line), (lseg, box), (lseg, lseg), (line, lseg). point '(0,0)' ## lseg '[(2,0),(0,2)]' (1,1) geometric_type <-> geometric_type double precision Computes the distance between the objects. Available for all seven geometric types, for all combinations of point with another geometric type, and for these additional pairs of types: (box, lseg), (lseg, line), (polygon, circle) (and the commutator cases). circle '<(0,0),1>' <-> circle '<(5,0),1>' 3 geometric_type @> geometric_type boolean Does first object contain second? Available for these pairs of types: (box, point), (box, box), (path, point), (polygon, point), (polygon, polygon), (circle, point), (circle, circle). circle '<(0,0),2>' @> point '(1,1)' t geometric_type <@ geometric_type boolean Is first object contained in or on second? Available for these pairs of types: (point, box), (point, lseg), (point, line), (point, path), (point, polygon), (point, circle), (box, box), (lseg, box), (lseg, line), (polygon, polygon), (circle, circle). point '(1,1)' <@ circle '<(0,0),2>' t geometric_type && geometric_type boolean Do these objects overlap? (One point in common makes this true.) Available for box, polygon, circle. box '(1,1),(0,0)' && box '(2,2),(0,0)' t geometric_type << geometric_type boolean Is first object strictly left of second? Available for point, box, polygon, circle. circle '<(0,0),1>' << circle '<(5,0),1>' t geometric_type >> geometric_type boolean Is first object strictly right of second? Available for point, box, polygon, circle. circle '<(5,0),1>' >> circle '<(0,0),1>' t geometric_type &< geometric_type boolean Does first object not extend to the right of second? Available for box, polygon, circle. box '(1,1),(0,0)' &< box '(2,2),(0,0)' t geometric_type &> geometric_type boolean Does first object not extend to the left of second? Available for box, polygon, circle. box '(3,3),(0,0)' &> box '(2,2),(0,0)' t geometric_type <<| geometric_type boolean Is first object strictly below second? Available for point, box, polygon, circle. box '(3,3),(0,0)' <<| box '(5,5),(3,4)' t geometric_type |>> geometric_type boolean Is first object strictly above second? Available for point, box, polygon, circle. box '(5,5),(3,4)' |>> box '(3,3),(0,0)' t geometric_type &<| geometric_type boolean Does first object not extend above second? Available for box, polygon, circle. box '(1,1),(0,0)' &<| box '(2,2),(0,0)' t geometric_type |&> geometric_type boolean Does first object not extend below second? Available for box, polygon, circle. box '(3,3),(0,0)' |&> box '(2,2),(0,0)' t box <^ box boolean Is first object below second (allows edges to touch)? box '((1,1),(0,0))' <^ box '((2,2),(1,1))' t box >^ box boolean Is first object above second (allows edges to touch)? box '((2,2),(1,1))' >^ box '((1,1),(0,0))' t geometric_type ?# geometric_type boolean Do these objects intersect? Available for these pairs of types: (box, box), (lseg, box), (lseg, lseg), (lseg, line), (line, box), (line, line), (path, path). lseg '[(-1,0),(1,0)]' ?# box '(2,2),(-2,-2)' t ?- line boolean ?- lseg boolean Is line horizontal? ?- lseg '[(-1,0),(1,0)]' t point ?- point boolean Are points horizontally aligned (that is, have same y coordinate)? point '(1,0)' ?- point '(0,0)' t ?| line boolean ?| lseg boolean Is line vertical? ?| lseg '[(-1,0),(1,0)]' f point ?| point boolean Are points vertically aligned (that is, have same x coordinate)? point '(0,1)' ?| point '(0,0)' t line ?-| line boolean lseg ?-| lseg boolean Are lines perpendicular? lseg '[(0,0),(0,1)]' ?-| lseg '[(0,0),(1,0)]' t line ?|| line boolean lseg ?|| lseg boolean Are lines parallel? lseg '[(-1,0),(1,0)]' ?|| lseg '[(-1,2),(1,2)]' t geometric_type ~= geometric_type boolean Are these objects the same? Available for point, box, polygon, circle. polygon '((0,0),(1,1))' ~= polygon '((1,1),(0,0))' t Note that the same as operator, ~=, represents the usual notion of equality for the point, box, polygon, and circle types. Some of the geometric types also have an = operator, but = compares for equal areas only. The other scalar comparison operators (<= and so on), where available for these types, likewise compare areas. Before PostgreSQL 14, the point is strictly below/above comparison operators point <<| point and point |>> point were respectively called <^ and >^. These names are still available, but are deprecated and will eventually be removed. Function Description Example(s) area area ( geometric_type ) double precision Computes area. Available for box, path, circle. A path input must be closed, else NULL is returned. Also, if the path is self-intersecting, the result may be meaningless. area(box '(2,2),(0,0)') 4 center center ( geometric_type ) point Computes center point. Available for box, circle. center(box '(1,2),(0,0)') (0.5,1) diagonal diagonal ( box ) lseg Extracts box's diagonal as a line segment (same as lseg(box)). diagonal(box '(1,2),(0,0)') [(1,2),(0,0)] diameter diameter ( circle ) double precision Computes diameter of circle. diameter(circle '<(0,0),2>') 4 height height ( box ) double precision Computes vertical size of box. height(box '(1,2),(0,0)') 2 isclosed isclosed ( path ) boolean Is path closed? isclosed(path '((0,0),(1,1),(2,0))') t isopen isopen ( path ) boolean Is path open? isopen(path '[(0,0),(1,1),(2,0)]') t length length ( geometric_type ) double precision Computes the total length. Available for lseg, path. length(path '((-1,0),(1,0))') 4 npoints npoints ( geometric_type ) integer Returns the number of points. Available for path, polygon. npoints(path '[(0,0),(1,1),(2,0)]') 3 pclose pclose ( path ) path Converts path to closed form. pclose(path '[(0,0),(1,1),(2,0)]') ((0,0),(1,1),(2,0)) popen popen ( path ) path Converts path to open form. popen(path '((0,0),(1,1),(2,0))') [(0,0),(1,1),(2,0)] radius radius ( circle ) double precision Computes radius of circle. radius(circle '<(0,0),2>') 2 slope slope ( point, point ) double precision Computes slope of a line drawn through the two points. slope(point '(0,0)', point '(2,1)') 0.5 width width ( box ) double precision Computes horizontal size of box. width(box '(1,2),(0,0)') 1 Function Description Example(s) box box ( circle ) box Computes box inscribed within the circle. box(circle '<(0,0),2>') (1.414213562373095,1.414213562373095),&zwsp;(-1.414213562373095,-1.414213562373095) box ( point ) box Converts point to empty box. box(point '(1,0)') (1,0),(1,0) box ( point, point ) box Converts any two corner points to box. box(point '(0,1)', point '(1,0)') (1,1),(0,0) box ( polygon ) box Computes bounding box of polygon. box(polygon '((0,0),(1,1),(2,0))') (2,1),(0,0) bound_box bound_box ( box, box ) box Computes bounding box of two boxes. bound_box(box '(1,1),(0,0)', box '(4,4),(3,3)') (4,4),(0,0) circle circle ( box ) circle Computes smallest circle enclosing box. circle(box '(1,1),(0,0)') <(0.5,0.5),0.7071067811865476> circle ( point, double precision ) circle Constructs circle from center and radius. circle(point '(0,0)', 2.0) <(0,0),2> circle ( polygon ) circle Converts polygon to circle. The circle's center is the mean of the positions of the polygon's points, and the radius is the average distance of the polygon's points from that center. circle(polygon '((0,0),(1,3),(2,0))') <(1,1),1.6094757082487299> line line ( point, point ) line Converts two points to the line through them. line(point '(-1,0)', point '(1,0)') {0,-1,0} lseg lseg ( box ) lseg Extracts box's diagonal as a line segment. lseg(box '(1,0),(-1,0)') [(1,0),(-1,0)] lseg ( point, point ) lseg Constructs line segment from two endpoints. lseg(point '(-1,0)', point '(1,0)') [(-1,0),(1,0)] path path ( polygon ) path Converts polygon to a closed path with the same list of points. path(polygon '((0,0),(1,1),(2,0))') ((0,0),(1,1),(2,0)) point point ( double precision, double precision ) point Constructs point from its coordinates. point(23.4, -44.5) (23.4,-44.5) point ( box ) point Computes center of box. point(box '(1,0),(-1,0)') (0,0) point ( circle ) point Computes center of circle. point(circle '<(0,0),2>') (0,0) point ( lseg ) point Computes center of line segment. point(lseg '[(-1,0),(1,0)]') (0,0) point ( polygon ) point Computes center of polygon (the mean of the positions of the polygon's points). point(polygon '((0,0),(1,1),(2,0))') (1,0.3333333333333333) polygon polygon ( box ) polygon Converts box to a 4-point polygon. polygon(box '(1,1),(0,0)') ((0,0),(0,1),(1,1),(1,0)) polygon ( circle ) polygon Converts circle to a 12-point polygon. polygon(circle '<(0,0),2>') ((-2,0),&zwsp;(-1.7320508075688774,0.9999999999999999),&zwsp;(-1.0000000000000002,1.7320508075688772),&zwsp;(-1.2246063538223773e-16,2),&zwsp;(0.9999999999999996,1.7320508075688774),&zwsp;(1.732050807568877,1.0000000000000007),&zwsp;(2,2.4492127076447545e-16),&zwsp;(1.7320508075688776,-0.9999999999999994),&zwsp;(1.0000000000000009,-1.7320508075688767),&zwsp;(3.673819061467132e-16,-2),&zwsp;(-0.9999999999999987,-1.732050807568878),&zwsp;(-1.7320508075688767,-1.0000000000000009)) polygon ( integer, circle ) polygon Converts circle to an n-point polygon. polygon(4, circle '<(3,0),1>') ((2,0),&zwsp;(3,1),&zwsp;(4,1.2246063538223773e-16),&zwsp;(3,-1)) polygon ( path ) polygon Converts closed path to a polygon with the same list of points. polygon(path '((0,0),(1,1),(2,0))') ((0,0),(1,1),(2,0)) It is possible to access the two component numbers of a point as though the point were an array with indexes 0 and 1. For example, if t.p is a point column then SELECT p[0] FROM t retrieves the X coordinate and UPDATE t SET p[1] = ... changes the Y coordinate. In the same way, a value of type box or lseg can be treated as an array of two point values. The IP network address types, cidr and inet, support the usual comparison operators shown in as well as the specialized operators and functions shown in and . Any cidr value can be cast to inet implicitly; therefore, the operators and functions shown below as operating on inet also work on cidr values. (Where there are separate functions for inet and cidr, it is because the behavior should be different for the two cases.) Also, it is permitted to cast an inet value to cidr. When this is done, any bits to the right of the netmask are silently zeroed to create a valid cidr value. Operator Description Example(s) inet << inet boolean Is subnet strictly contained by subnet? This operator, and the next four, test for subnet inclusion. They consider only the network parts of the two addresses (ignoring any bits to the right of the netmasks) and determine whether one network is identical to or a subnet of the other. inet '192.168.1.5' << inet '192.168.1/24' t inet '192.168.0.5' << inet '192.168.1/24' f inet '192.168.1/24' << inet '192.168.1/24' f inet <<= inet boolean Is subnet contained by or equal to subnet? inet '192.168.1/24' <<= inet '192.168.1/24' t inet >> inet boolean Does subnet strictly contain subnet? inet '192.168.1/24' >> inet '192.168.1.5' t inet >>= inet boolean Does subnet contain or equal subnet? inet '192.168.1/24' >>= inet '192.168.1/24' t inet && inet boolean Does either subnet contain or equal the other? inet '192.168.1/24' && inet '192.168.1.80/28' t inet '192.168.1/24' && inet '192.168.2.0/28' f ~ inet inet Computes bitwise NOT. ~ inet '192.168.1.6' 63.87.254.249 inet & inet inet Computes bitwise AND. inet '192.168.1.6' & inet '0.0.0.255' 0.0.0.6 inet | inet inet Computes bitwise OR. inet '192.168.1.6' | inet '0.0.0.255' 192.168.1.255 inet + bigint inet Adds an offset to an address. inet '192.168.1.6' + 25 192.168.1.31 bigint + inet inet Adds an offset to an address. 200 + inet '::ffff:fff0:1' ::ffff:255.240.0.201 inet - bigint inet Subtracts an offset from an address. inet '192.168.1.43' - 36 192.168.1.7 inet - inet bigint Computes the difference of two addresses. inet '192.168.1.43' - inet '192.168.1.19' 24 inet '::1' - inet '::ffff:1' -4294901760 Function Description Example(s) abbrev abbrev ( inet ) text Creates an abbreviated display format as text. (The result is the same as the inet output function produces; it is abbreviated only in comparison to the result of an explicit cast to text, which for historical reasons will never suppress the netmask part.) abbrev(inet '10.1.0.0/32') 10.1.0.0 abbrev ( cidr ) text Creates an abbreviated display format as text. (The abbreviation consists of dropping all-zero octets to the right of the netmask; more examples are in .) abbrev(cidr '10.1.0.0/16') 10.1/16 broadcast broadcast ( inet ) inet Computes the broadcast address for the address's network. broadcast(inet '192.168.1.5/24') 192.168.1.255/24 family family ( inet ) integer Returns the address's family: 4 for IPv4, 6 for IPv6. family(inet '::1') 6 host host ( inet ) text Returns the IP address as text, ignoring the netmask. host(inet '192.168.1.0/24') 192.168.1.0 hostmask hostmask ( inet ) inet Computes the host mask for the address's network. hostmask(inet '192.168.23.20/30') 0.0.0.3 inet_merge inet_merge ( inet, inet ) cidr Computes the smallest network that includes both of the given networks. inet_merge(inet '192.168.1.5/24', inet '192.168.2.5/24') 192.168.0.0/22 inet_same_family inet_same_family ( inet, inet ) boolean Tests whether the addresses belong to the same IP family. inet_same_family(inet '192.168.1.5/24', inet '::1') f masklen masklen ( inet ) integer Returns the netmask length in bits. masklen(inet '192.168.1.5/24') 24 netmask netmask ( inet ) inet Computes the network mask for the address's network. netmask(inet '192.168.1.5/24') 255.255.255.0 network network ( inet ) cidr Returns the network part of the address, zeroing out whatever is to the right of the netmask. (This is equivalent to casting the value to cidr.) network(inet '192.168.1.5/24') 192.168.1.0/24 set_masklen set_masklen ( inet, integer ) inet Sets the netmask length for an inet value. The address part does not change. set_masklen(inet '192.168.1.5/24', 16) 192.168.1.5/16 set_masklen ( cidr, integer ) cidr Sets the netmask length for a cidr value. Address bits to the right of the new netmask are set to zero. set_masklen(cidr '192.168.1.0/24', 16) 192.168.0.0/16 text text ( inet ) text Returns the unabbreviated IP address and netmask length as text. (This has the same result as an explicit cast to text.) text(inet '192.168.1.5') 192.168.1.5/32 The abbrev, host, and text functions are primarily intended to offer alternative display formats for IP addresses. The MAC address types, macaddr and macaddr8, support the usual comparison operators shown in as well as the specialized functions shown in . In addition, they support the bitwise logical operators ~, & and | (NOT, AND and OR), just as shown above for IP addresses. Function Description Example(s) trunc trunc ( macaddr ) macaddr Sets the last 3 bytes of the address to zero. The remaining prefix can be associated with a particular manufacturer (using data not included in PostgreSQL). trunc(macaddr '12:34:56:78:90:ab') 12:34:56:00:00:00 trunc ( macaddr8 ) macaddr8 Sets the last 5 bytes of the address to zero. The remaining prefix can be associated with a particular manufacturer (using data not included in PostgreSQL). trunc(macaddr8 '12:34:56:78:90:ab:cd:ef') 12:34:56:00:00:00:00:00 macaddr8_set7bit macaddr8_set7bit ( macaddr8 ) macaddr8 Sets the 7th bit of the address to one, creating what is known as modified EUI-64, for inclusion in an IPv6 address. macaddr8_set7bit(macaddr8 '00:34:56:ab:cd:ef') 02:34:56:ff:fe:ab:cd:ef full text search functions and operators text search functions and operators , and summarize the functions and operators that are provided for full text searching. See for a detailed explanation of PostgreSQL's text search facility. Operator Description Example(s) tsvector @@ tsquery boolean tsquery @@ tsvector boolean Does tsvector match tsquery? (The arguments can be given in either order.) to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat') t text @@ tsquery boolean Does text string, after implicit invocation of to_tsvector(), match tsquery? 'fat cats ate rats' @@ to_tsquery('cat & rat') t tsvector @@@ tsquery boolean tsquery @@@ tsvector boolean This is a deprecated synonym for @@. to_tsvector('fat cats ate rats') @@@ to_tsquery('cat & rat') t tsvector || tsvector tsvector Concatenates two tsvectors. If both inputs contain lexeme positions, the second input's positions are adjusted accordingly. 'a:1 b:2'::tsvector || 'c:1 d:2 b:3'::tsvector 'a':1 'b':2,5 'c':3 'd':4 tsquery && tsquery tsquery ANDs two tsquerys together, producing a query that matches documents that match both input queries. 'fat | rat'::tsquery && 'cat'::tsquery ( 'fat' | 'rat' ) & 'cat' tsquery || tsquery tsquery ORs two tsquerys together, producing a query that matches documents that match either input query. 'fat | rat'::tsquery || 'cat'::tsquery 'fat' | 'rat' | 'cat' !! tsquery tsquery Negates a tsquery, producing a query that matches documents that do not match the input query. !! 'cat'::tsquery !'cat' tsquery <-> tsquery tsquery Constructs a phrase query, which matches if the two input queries match at successive lexemes. to_tsquery('fat') <-> to_tsquery('rat') 'fat' <-> 'rat' tsquery @> tsquery boolean Does first tsquery contain the second? (This considers only whether all the lexemes appearing in one query appear in the other, ignoring the combining operators.) 'cat'::tsquery @> 'cat & rat'::tsquery f tsquery <@ tsquery boolean Is first tsquery contained in the second? (This considers only whether all the lexemes appearing in one query appear in the other, ignoring the combining operators.) 'cat'::tsquery <@ 'cat & rat'::tsquery t 'cat'::tsquery <@ '!cat & rat'::tsquery t In addition to these specialized operators, the usual comparison operators shown in are available for types tsvector and tsquery. These are not very useful for text searching but allow, for example, unique indexes to be built on columns of these types. Function Description Example(s) array_to_tsvector array_to_tsvector ( text[] ) tsvector Converts an array of text strings to a tsvector. The given strings are used as lexemes as-is, without further processing. Array elements must not be empty strings or NULL. array_to_tsvector('{fat,cat,rat}'::text[]) 'cat' 'fat' 'rat' get_current_ts_config get_current_ts_config ( ) regconfig Returns the OID of the current default text search configuration (as set by ). get_current_ts_config() english length length ( tsvector ) integer Returns the number of lexemes in the tsvector. length('fat:2,4 cat:3 rat:5A'::tsvector) 3 numnode numnode ( tsquery ) integer Returns the number of lexemes plus operators in the tsquery. numnode('(fat & rat) | cat'::tsquery) 5 plainto_tsquery plainto_tsquery ( config regconfig, query text ) tsquery Converts text to a tsquery, normalizing words according to the specified or default configuration. Any punctuation in the string is ignored (it does not determine query operators). The resulting query matches documents containing all non-stopwords in the text. plainto_tsquery('english', 'The Fat Rats') 'fat' & 'rat' phraseto_tsquery phraseto_tsquery ( config regconfig, query text ) tsquery Converts text to a tsquery, normalizing words according to the specified or default configuration. Any punctuation in the string is ignored (it does not determine query operators). The resulting query matches phrases containing all non-stopwords in the text. phraseto_tsquery('english', 'The Fat Rats') 'fat' <-> 'rat' phraseto_tsquery('english', 'The Cat and Rats') 'cat' <2> 'rat' websearch_to_tsquery websearch_to_tsquery ( config regconfig, query text ) tsquery Converts text to a tsquery, normalizing words according to the specified or default configuration. Quoted word sequences are converted to phrase tests. The word or is understood as producing an OR operator, and a dash produces a NOT operator; other punctuation is ignored. This approximates the behavior of some common web search tools. websearch_to_tsquery('english', '"fat rat" or cat dog') 'fat' <-> 'rat' | 'cat' & 'dog' querytree querytree ( tsquery ) text Produces a representation of the indexable portion of a tsquery. A result that is empty or just T indicates a non-indexable query. querytree('foo & ! bar'::tsquery) 'foo' setweight setweight ( vector tsvector, weight "char" ) tsvector Assigns the specified weight to each element of the vector. setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A') 'cat':3A 'fat':2A,4A 'rat':5A setweight setweight for specific lexeme(s) setweight ( vector tsvector, weight "char", lexemes text[] ) tsvector Assigns the specified weight to elements of the vector that are listed in lexemes. The strings in lexemes are taken as lexemes as-is, without further processing. Strings that do not match any lexeme in vector are ignored. setweight('fat:2,4 cat:3 rat:5,6B'::tsvector, 'A', '{cat,rat}') 'cat':3A 'fat':2,4 'rat':5A,6A strip strip ( tsvector ) tsvector Removes positions and weights from the tsvector. strip('fat:2,4 cat:3 rat:5A'::tsvector) 'cat' 'fat' 'rat' to_tsquery to_tsquery ( config regconfig, query text ) tsquery Converts text to a tsquery, normalizing words according to the specified or default configuration. The words must be combined by valid tsquery operators. to_tsquery('english', 'The & Fat & Rats') 'fat' & 'rat' to_tsvector to_tsvector ( config regconfig, document text ) tsvector Converts text to a tsvector, normalizing words according to the specified or default configuration. Position information is included in the result. to_tsvector('english', 'The Fat Rats') 'fat':2 'rat':3 to_tsvector ( config regconfig, document json ) tsvector to_tsvector ( config regconfig, document jsonb ) tsvector Converts each string value in the JSON document to a tsvector, normalizing words according to the specified or default configuration. The results are then concatenated in document order to produce the output. Position information is generated as though one stopword exists between each pair of string values. (Beware that document order of the fields of a JSON object is implementation-dependent when the input is jsonb; observe the difference in the examples.) to_tsvector('english', '{"aa": "The Fat Rats", "b": "dog"}'::json) 'dog':5 'fat':2 'rat':3 to_tsvector('english', '{"aa": "The Fat Rats", "b": "dog"}'::jsonb) 'dog':1 'fat':4 'rat':5 json_to_tsvector json_to_tsvector ( config regconfig, document json, filter jsonb ) tsvector jsonb_to_tsvector jsonb_to_tsvector ( config regconfig, document jsonb, filter jsonb ) tsvector Selects each item in the JSON document that is requested by the filter and converts each one to a tsvector, normalizing words according to the specified or default configuration. The results are then concatenated in document order to produce the output. Position information is generated as though one stopword exists between each pair of selected items. (Beware that document order of the fields of a JSON object is implementation-dependent when the input is jsonb.) The filter must be a jsonb array containing zero or more of these keywords: "string" (to include all string values), "numeric" (to include all numeric values), "boolean" (to include all boolean values), "key" (to include all keys), or "all" (to include all the above). As a special case, the filter can also be a simple JSON value that is one of these keywords. json_to_tsvector('english', '{"a": "The Fat Rats", "b": 123}'::json, '["string", "numeric"]') '123':5 'fat':2 'rat':3 json_to_tsvector('english', '{"cat": "The Fat Rats", "dog": 123}'::json, '"all"') '123':9 'cat':1 'dog':7 'fat':4 'rat':5 ts_delete ts_delete ( vector tsvector, lexeme text ) tsvector Removes any occurrence of the given lexeme from the vector. The lexeme string is treated as a lexeme as-is, without further processing. ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, 'fat') 'cat':3 'rat':5A ts_delete ( vector tsvector, lexemes text[] ) tsvector Removes any occurrences of the lexemes in lexemes from the vector. The strings in lexemes are taken as lexemes as-is, without further processing. Strings that do not match any lexeme in vector are ignored. ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, ARRAY['fat','rat']) 'cat':3 ts_filter ts_filter ( vector tsvector, weights "char"[] ) tsvector Selects only elements with the given weights from the vector. ts_filter('fat:2,4 cat:3b,7c rat:5A'::tsvector, '{a,b}') 'cat':3B 'rat':5A ts_headline ts_headline ( config regconfig, document text, query tsquery , options text ) text Displays, in an abbreviated form, the match(es) for the query in the document, which must be raw text not a tsvector. Words in the document are normalized according to the specified or default configuration before matching to the query. Use of this function is discussed in , which also describes the available options. ts_headline('The fat cat ate the rat.', 'cat') The fat <b>cat</b> ate the rat. ts_headline ( config regconfig, document json, query tsquery , options text ) text ts_headline ( config regconfig, document jsonb, query tsquery , options text ) text Displays, in an abbreviated form, match(es) for the query that occur in string values within the JSON document. See for more details. ts_headline('{"cat":"raining cats and dogs"}'::jsonb, 'cat') {"cat": "raining <b>cats</b> and dogs"} ts_rank ts_rank ( weights real[], vector tsvector, query tsquery , normalization integer ) real Computes a score showing how well the vector matches the query. See for details. ts_rank(to_tsvector('raining cats and dogs'), 'cat') 0.06079271 ts_rank_cd ts_rank_cd ( weights real[], vector tsvector, query tsquery , normalization integer ) real Computes a score showing how well the vector matches the query, using a cover density algorithm. See for details. ts_rank_cd(to_tsvector('raining cats and dogs'), 'cat') 0.1 ts_rewrite ts_rewrite ( query tsquery, target tsquery, substitute tsquery ) tsquery Replaces occurrences of target with substitute within the query. See for details. ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'foo|bar'::tsquery) 'b' & ( 'foo' | 'bar' ) ts_rewrite ( query tsquery, select text ) tsquery Replaces portions of the query according to target(s) and substitute(s) obtained by executing a SELECT command. See for details. SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases') 'b' & ( 'foo' | 'bar' ) tsquery_phrase tsquery_phrase ( query1 tsquery, query2 tsquery ) tsquery Constructs a phrase query that searches for matches of query1 and query2 at successive lexemes (same as <-> operator). tsquery_phrase(to_tsquery('fat'), to_tsquery('cat')) 'fat' <-> 'cat' tsquery_phrase ( query1 tsquery, query2 tsquery, distance integer ) tsquery Constructs a phrase query that searches for matches of query1 and query2 that occur exactly distance lexemes apart. tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10) 'fat' <10> 'cat' tsvector_to_array tsvector_to_array ( tsvector ) text[] Converts a tsvector to an array of lexemes. tsvector_to_array('fat:2,4 cat:3 rat:5A'::tsvector) {cat,fat,rat} unnest for tsvector unnest ( tsvector ) setof record ( lexeme text, positions smallint[], weights text ) Expands a tsvector into a set of rows, one per lexeme. select * from unnest('cat:3 fat:2,4 rat:5A'::tsvector) lexeme | positions | weights --------+-----------+--------- cat | {3} | {D} fat | {2,4} | {D,D} rat | {5} | {A} All the text search functions that accept an optional regconfig argument will use the configuration specified by when that argument is omitted. The functions in are listed separately because they are not usually used in everyday text searching operations. They are primarily helpful for development and debugging of new text search configurations. Function Description Example(s) ts_debug ts_debug ( config regconfig, document text ) setof record ( alias text, description text, token text, dictionaries regdictionary[], dictionary regdictionary, lexemes text[] ) Extracts and normalizes tokens from the document according to the specified or default text search configuration, and returns information about how each token was processed. See for details. ts_debug('english', 'The Brightest supernovaes') (asciiword,"Word, all ASCII",The,{english_stem},english_stem,{}) ... ts_lexize ts_lexize ( dict regdictionary, token text ) text[] Returns an array of replacement lexemes if the input token is known to the dictionary, or an empty array if the token is known to the dictionary but it is a stop word, or NULL if it is not a known word. See for details. ts_lexize('english_stem', 'stars') {star} ts_parse ts_parse ( parser_name text, document text ) setof record ( tokid integer, token text ) Extracts tokens from the document using the named parser. See for details. ts_parse('default', 'foo - bar') (1,foo) ... ts_parse ( parser_oid oid, document text ) setof record ( tokid integer, token text ) Extracts tokens from the document using a parser specified by OID. See for details. ts_parse(3722, 'foo - bar') (1,foo) ... ts_token_type ts_token_type ( parser_name text ) setof record ( tokid integer, alias text, description text ) Returns a table that describes each type of token the named parser can recognize. See for details. ts_token_type('default') (1,asciiword,"Word, all ASCII") ... ts_token_type ( parser_oid oid ) setof record ( tokid integer, alias text, description text ) Returns a table that describes each type of token a parser specified by OID can recognize. See for details. ts_token_type(3722) (1,asciiword,"Word, all ASCII") ... ts_stat ts_stat ( sqlquery text , weights text ) setof record ( word text, ndoc integer, nentry integer ) Executes the sqlquery, which must return a single tsvector column, and returns statistics about each distinct lexeme contained in the data. See for details. ts_stat('SELECT vector FROM apod') (foo,10,15) ... UUID generating gen_random_uuid PostgreSQL includes one function to generate a UUID: gen_random_uuid () uuid This function returns a version 4 (random) UUID. This is the most commonly used type of UUID and is appropriate for most applications. The module provides additional functions that implement other standard algorithms for generating UUIDs. PostgreSQL also provides the usual comparison operators shown in for UUIDs. XML Functions The functions and function-like expressions described in this section operate on values of type xml. See for information about the xml type. The function-like expressions xmlparse and xmlserialize for converting to and from type xml are documented there, not in this section. Use of most of these functions requires PostgreSQL to have been built with configure --with-libxml. A set of functions and function-like expressions is available for producing XML content from SQL data. As such, they are particularly suitable for formatting query results into XML documents for processing in client applications. xmlcomment xmlcomment ( text ) xml The function xmlcomment creates an XML value containing an XML comment with the specified text as content. The text cannot contain -- or end with a -, otherwise the resulting construct would not be a valid XML comment. If the argument is null, the result is null. Example: ]]> xmlconcat xmlconcat ( xml , ... ) xml The function xmlconcat concatenates a list of individual XML values to create a single value containing an XML content fragment. Null values are omitted; the result is only null if there are no nonnull arguments. Example: ', 'foo'); xmlconcat ---------------------- foo ]]> XML declarations, if present, are combined as follows. If all argument values have the same XML version declaration, that version is used in the result, else no version is used. If all argument values have the standalone declaration value yes, then that value is used in the result. If all argument values have a standalone declaration value and at least one is no, then that is used in the result. Else the result will have no standalone declaration. If the result is determined to require a standalone declaration but no version declaration, a version declaration with version 1.0 will be used because XML requires an XML declaration to contain a version declaration. Encoding declarations are ignored and removed in all cases. Example: ', ''); xmlconcat ----------------------------------- ]]> xmlelement xmlelement ( NAME name , XMLATTRIBUTES ( attvalue AS attname , ... ) , content , ... ) xml The xmlelement expression produces an XML element with the given name, attributes, and content. The name and attname items shown in the syntax are simple identifiers, not values. The attvalue and content items are expressions, which can yield any PostgreSQL data type. The argument(s) within XMLATTRIBUTES generate attributes of the XML element; the content value(s) are concatenated to form its content. Examples: SELECT xmlelement(name foo, xmlattributes('xyz' as bar)); xmlelement ------------------ SELECT xmlelement(name foo, xmlattributes(current_date as bar), 'cont', 'ent'); xmlelement ------------------------------------- content ]]> Element and attribute names that are not valid XML names are escaped by replacing the offending characters by the sequence _xHHHH_, where HHHH is the character's Unicode codepoint in hexadecimal notation. For example: ]]> An explicit attribute name need not be specified if the attribute value is a column reference, in which case the column's name will be used as the attribute name by default. In other cases, the attribute must be given an explicit name. So this example is valid: CREATE TABLE test (a xml, b xml); SELECT xmlelement(name test, xmlattributes(a, b)) FROM test; But these are not: SELECT xmlelement(name test, xmlattributes('constant'), a, b) FROM test; SELECT xmlelement(name test, xmlattributes(func(a, b))) FROM test; Element content, if specified, will be formatted according to its data type. If the content is itself of type xml, complex XML documents can be constructed. For example: ]]> Content of other types will be formatted into valid XML character data. This means in particular that the characters <, >, and & will be converted to entities. Binary data (data type bytea) will be represented in base64 or hex encoding, depending on the setting of the configuration parameter . The particular behavior for individual data types is expected to evolve in order to align the PostgreSQL mappings with those specified in SQL:2006 and later, as discussed in . xmlforest xmlforest ( content AS name , ... ) xml The xmlforest expression produces an XML forest (sequence) of elements using the given names and content. As for xmlelement, each name must be a simple identifier, while the content expressions can have any data type. Examples: SELECT xmlforest('abc' AS foo, 123 AS bar); xmlforest ------------------------------ <foo>abc</foo><bar>123</bar> SELECT xmlforest(table_name, column_name) FROM information_schema.columns WHERE table_schema = 'pg_catalog'; xmlforest ------------------------------------&zwsp;----------------------------------- <table_name>pg_authid</table_name>&zwsp;<column_name>rolname</column_name> <table_name>pg_authid</table_name>&zwsp;<column_name>rolsuper</column_name> ... As seen in the second example, the element name can be omitted if the content value is a column reference, in which case the column name is used by default. Otherwise, a name must be specified. Element names that are not valid XML names are escaped as shown for xmlelement above. Similarly, content data is escaped to make valid XML content, unless it is already of type xml. Note that XML forests are not valid XML documents if they consist of more than one element, so it might be useful to wrap xmlforest expressions in xmlelement. xmlpi xmlpi ( NAME name , content ) xml The xmlpi expression creates an XML processing instruction. As for xmlelement, the name must be a simple identifier, while the content expression can have any data type. The content, if present, must not contain the character sequence ?>. Example: ]]> xmlroot xmlroot ( xml, VERSION {text|NO VALUE} , STANDALONE {YES|NO|NO VALUE} ) xml The xmlroot expression alters the properties of the root node of an XML value. If a version is specified, it replaces the value in the root node's version declaration; if a standalone setting is specified, it replaces the value in the root node's standalone declaration. abc'), version '1.0', standalone yes); xmlroot ---------------------------------------- abc ]]> xmlagg xmlagg ( xml ) xml The function xmlagg is, unlike the other functions described here, an aggregate function. It concatenates the input values to the aggregate function call, much like xmlconcat does, except that concatenation occurs across rows rather than across expressions in a single row. See for additional information about aggregate functions. Example: abc'); INSERT INTO test VALUES (2, ''); SELECT xmlagg(x) FROM test; xmlagg ---------------------- abc ]]> To determine the order of the concatenation, an ORDER BY clause may be added to the aggregate call as described in . For example: abc ]]> The following non-standard approach used to be recommended in previous versions, and may still be useful in specific cases: abc ]]> The expressions described in this section check properties of xml values. IS DOCUMENT xml IS DOCUMENT boolean The expression IS DOCUMENT returns true if the argument XML value is a proper XML document, false if it is not (that is, it is a content fragment), or null if the argument is null. See about the difference between documents and content fragments. IS NOT DOCUMENT xml IS NOT DOCUMENT boolean The expression IS NOT DOCUMENT returns false if the argument XML value is a proper XML document, true if it is not (that is, it is a content fragment), or null if the argument is null. XMLEXISTS XMLEXISTS ( text PASSING BY {REF|VALUE} xml BY {REF|VALUE} ) boolean The function xmlexists evaluates an XPath 1.0 expression (the first argument), with the passed XML value as its context item. The function returns false if the result of that evaluation yields an empty node-set, true if it yields any other value. The function returns null if any argument is null. A nonnull value passed as the context item must be an XML document, not a content fragment or any non-XML value. Example: TorontoOttawa'); xmlexists ------------ t (1 row) ]]> The BY REF and BY VALUE clauses are accepted in PostgreSQL, but are ignored, as discussed in . In the SQL standard, the xmlexists function evaluates an expression in the XML Query language, but PostgreSQL allows only an XPath 1.0 expression, as discussed in . xml_is_well_formed xml_is_well_formed_document xml_is_well_formed_content xml_is_well_formed ( text ) boolean xml_is_well_formed_document ( text ) boolean xml_is_well_formed_content ( text ) boolean These functions check whether a text string represents well-formed XML, returning a Boolean result. xml_is_well_formed_document checks for a well-formed document, while xml_is_well_formed_content checks for well-formed content. xml_is_well_formed does the former if the configuration parameter is set to DOCUMENT, or the latter if it is set to CONTENT. This means that xml_is_well_formed is useful for seeing whether a simple cast to type xml will succeed, whereas the other two functions are useful for seeing whether the corresponding variants of XMLPARSE will succeed. Examples: '); xml_is_well_formed -------------------- f (1 row) SELECT xml_is_well_formed(''); xml_is_well_formed -------------------- t (1 row) SET xmloption TO CONTENT; SELECT xml_is_well_formed('abc'); xml_is_well_formed -------------------- t (1 row) SELECT xml_is_well_formed_document('bar'); xml_is_well_formed_document ----------------------------- t (1 row) SELECT xml_is_well_formed_document('bar'); xml_is_well_formed_document ----------------------------- f (1 row) ]]> The last example shows that the checks include whether namespaces are correctly matched. To process values of data type xml, PostgreSQL offers the functions xpath and xpath_exists, which evaluate XPath 1.0 expressions, and the XMLTABLE table function. XPath xpath ( xpath text, xml xml , nsarray text[] ) xml[] The function xpath evaluates the XPath 1.0 expression xpath (given as text) against the XML value xml. It returns an array of XML values corresponding to the node-set produced by the XPath expression. If the XPath expression returns a scalar value rather than a node-set, a single-element array is returned. The second argument must be a well formed XML document. In particular, it must have a single root node element. The optional third argument of the function is an array of namespace mappings. This array should be a two-dimensional text array with the length of the second axis being equal to 2 (i.e., it should be an array of arrays, each of which consists of exactly 2 elements). The first element of each array entry is the namespace name (alias), the second the namespace URI. It is not required that aliases provided in this array be the same as those being used in the XML document itself (in other words, both in the XML document and in the xpath function context, aliases are local). Example: test', ARRAY[ARRAY['my', 'http://example.com']]); xpath -------- {test} (1 row) ]]> To deal with default (anonymous) namespaces, do something like this: test', ARRAY[ARRAY['mydefns', 'http://example.com']]); xpath -------- {test} (1 row) ]]> xpath_exists xpath_exists ( xpath text, xml xml , nsarray text[] ) boolean The function xpath_exists is a specialized form of the xpath function. Instead of returning the individual XML values that satisfy the XPath 1.0 expression, this function returns a Boolean indicating whether the query was satisfied or not (specifically, whether it produced any value other than an empty node-set). This function is equivalent to the XMLEXISTS predicate, except that it also offers support for a namespace mapping argument. Example: test', ARRAY[ARRAY['my', 'http://example.com']]); xpath_exists -------------- t (1 row) ]]> xmltable table function XMLTABLE XMLTABLE ( XMLNAMESPACES ( namespace_uri AS namespace_name , ... ), row_expression PASSING BY {REF|VALUE} document_expression BY {REF|VALUE} COLUMNS name { type PATH column_expression DEFAULT default_expression NOT NULL | NULL | FOR ORDINALITY } , ... ) setof record The xmltable expression produces a table based on an XML value, an XPath filter to extract rows, and a set of column definitions. Although it syntactically resembles a function, it can only appear as a table in a query's FROM clause. The optional XMLNAMESPACES clause gives a comma-separated list of namespace definitions, where each namespace_uri is a text expression and each namespace_name is a simple identifier. It specifies the XML namespaces used in the document and their aliases. A default namespace specification is not currently supported. The required row_expression argument is an XPath 1.0 expression (given as text) that is evaluated, passing the XML value document_expression as its context item, to obtain a set of XML nodes. These nodes are what xmltable transforms into output rows. No rows will be produced if the document_expression is null, nor if the row_expression produces an empty node-set or any value other than a node-set. document_expression provides the context item for the row_expression. It must be a well-formed XML document; fragments/forests are not accepted. The BY REF and BY VALUE clauses are accepted but ignored, as discussed in . In the SQL standard, the xmltable function evaluates expressions in the XML Query language, but PostgreSQL allows only XPath 1.0 expressions, as discussed in . The required COLUMNS clause specifies the column(s) that will be produced in the output table. See the syntax summary above for the format. A name is required for each column, as is a data type (unless FOR ORDINALITY is specified, in which case type integer is implicit). The path, default and nullability clauses are optional. A column marked FOR ORDINALITY will be populated with row numbers, starting with 1, in the order of nodes retrieved from the row_expression's result node-set. At most one column may be marked FOR ORDINALITY. XPath 1.0 does not specify an order for nodes in a node-set, so code that relies on a particular order of the results will be implementation-dependent. Details can be found in . The column_expression for a column is an XPath 1.0 expression that is evaluated for each row, with the current node from the row_expression result as its context item, to find the value of the column. If no column_expression is given, then the column name is used as an implicit path. If a column's XPath expression returns a non-XML value (which is limited to string, boolean, or double in XPath 1.0) and the column has a PostgreSQL type other than xml, the column will be set as if by assigning the value's string representation to the PostgreSQL type. (If the value is a boolean, its string representation is taken to be 1 or 0 if the output column's type category is numeric, otherwise true or false.) If a column's XPath expression returns a non-empty set of XML nodes and the column's PostgreSQL type is xml, the column will be assigned the expression result exactly, if it is of document or content form. A result containing more than one element node at the top level, or non-whitespace text outside of an element, is an example of content form. An XPath result can be of neither form, for example if it returns an attribute node selected from the element that contains it. Such a result will be put into content form with each such disallowed node replaced by its string value, as defined for the XPath 1.0 string function. A non-XML result assigned to an xml output column produces content, a single text node with the string value of the result. An XML result assigned to a column of any other type may not have more than one node, or an error is raised. If there is exactly one node, the column will be set as if by assigning the node's string value (as defined for the XPath 1.0 string function) to the PostgreSQL type. The string value of an XML element is the concatenation, in document order, of all text nodes contained in that element and its descendants. The string value of an element with no descendant text nodes is an empty string (not NULL). Any xsi:nil attributes are ignored. Note that the whitespace-only text() node between two non-text elements is preserved, and that leading whitespace on a text() node is not flattened. The XPath 1.0 string function may be consulted for the rules defining the string value of other XML node types and non-XML values. The conversion rules presented here are not exactly those of the SQL standard, as discussed in . If the path expression returns an empty node-set (typically, when it does not match) for a given row, the column will be set to NULL, unless a default_expression is specified; then the value resulting from evaluating that expression is used. A default_expression, rather than being evaluated immediately when xmltable is called, is evaluated each time a default is needed for the column. If the expression qualifies as stable or immutable, the repeat evaluation may be skipped. This means that you can usefully use volatile functions like nextval in default_expression. Columns may be marked NOT NULL. If the column_expression for a NOT NULL column does not match anything and there is no DEFAULT or the default_expression also evaluates to null, an error is reported. Examples: AU Australia JP Japan Shinzo Abe 145935 SG Singapore 697 $$ AS data; SELECT xmltable.* FROM xmldata, XMLTABLE('//ROWS/ROW' PASSING data COLUMNS id int PATH '@id', ordinality FOR ORDINALITY, "COUNTRY_NAME" text, country_id text PATH 'COUNTRY_ID', size_sq_km float PATH 'SIZE[@unit = "sq_km"]', size_other text PATH 'concat(SIZE[@unit!="sq_km"], " ", SIZE[@unit!="sq_km"]/@unit)', premier_name text PATH 'PREMIER_NAME' DEFAULT 'not specified'); id | ordinality | COUNTRY_NAME | country_id | size_sq_km | size_other | premier_name ----+------------+--------------+------------+------------+--------------+--------------- 1 | 1 | Australia | AU | | | not specified 5 | 2 | Japan | JP | | 145935 sq_mi | Shinzo Abe 6 | 3 | Singapore | SG | 697 | | not specified ]]> The following example shows concatenation of multiple text() nodes, usage of the column name as XPath filter, and the treatment of whitespace, XML comments and processing instructions: Hello2a2 bbbxxxCC $$ AS data; SELECT xmltable.* FROM xmlelements, XMLTABLE('/root' PASSING data COLUMNS element text); element ------------------------- Hello2a2 bbbxxxCC ]]> The following example illustrates how the XMLNAMESPACES clause can be used to specify a list of namespaces used in the XML document as well as in the XPath expressions: '::xml) ) SELECT xmltable.* FROM XMLTABLE(XMLNAMESPACES('http://example.com/myns' AS x, 'http://example.com/b' AS "B"), '/x:example/x:item' PASSING (SELECT data FROM xmldata) COLUMNS foo int PATH '@foo', bar int PATH '@B:bar'); foo | bar -----+----- 1 | 2 3 | 4 4 | 5 (3 rows) ]]> XML export The following functions map the contents of relational tables to XML values. They can be thought of as XML export functionality: table_to_xml ( table regclass, nulls boolean, tableforest boolean, targetns text ) xml query_to_xml ( query text, nulls boolean, tableforest boolean, targetns text ) xml cursor_to_xml ( cursor refcursor, count integer, nulls boolean, tableforest boolean, targetns text ) xml table_to_xml maps the content of the named table, passed as parameter table. The regclass type accepts strings identifying tables using the usual notation, including optional schema qualification and double quotes (see for details). query_to_xml executes the query whose text is passed as parameter query and maps the result set. cursor_to_xml fetches the indicated number of rows from the cursor specified by the parameter cursor. This variant is recommended if large tables have to be mapped, because the result value is built up in memory by each function. If tableforest is false, then the resulting XML document looks like this: data data ... ... ]]> If tableforest is true, the result is an XML content fragment that looks like this: data data ... ... ]]> If no table name is available, that is, when mapping a query or a cursor, the string table is used in the first format, row in the second format. The choice between these formats is up to the user. The first format is a proper XML document, which will be important in many applications. The second format tends to be more useful in the cursor_to_xml function if the result values are to be reassembled into one document later on. The functions for producing XML content discussed above, in particular xmlelement, can be used to alter the results to taste. The data values are mapped in the same way as described for the function xmlelement above. The parameter nulls determines whether null values should be included in the output. If true, null values in columns are represented as: ]]> where xsi is the XML namespace prefix for XML Schema Instance. An appropriate namespace declaration will be added to the result value. If false, columns containing null values are simply omitted from the output. The parameter targetns specifies the desired XML namespace of the result. If no particular namespace is wanted, an empty string should be passed. The following functions return XML Schema documents describing the mappings performed by the corresponding functions above: table_to_xmlschema ( table regclass, nulls boolean, tableforest boolean, targetns text ) xml query_to_xmlschema ( query text, nulls boolean, tableforest boolean, targetns text ) xml cursor_to_xmlschema ( cursor refcursor, nulls boolean, tableforest boolean, targetns text ) xml It is essential that the same parameters are passed in order to obtain matching XML data mappings and XML Schema documents. The following functions produce XML data mappings and the corresponding XML Schema in one document (or forest), linked together. They can be useful where self-contained and self-describing results are wanted: table_to_xml_and_xmlschema ( table regclass, nulls boolean, tableforest boolean, targetns text ) xml query_to_xml_and_xmlschema ( query text, nulls boolean, tableforest boolean, targetns text ) xml In addition, the following functions are available to produce analogous mappings of entire schemas or the entire current database: schema_to_xml ( schema name, nulls boolean, tableforest boolean, targetns text ) xml schema_to_xmlschema ( schema name, nulls boolean, tableforest boolean, targetns text ) xml schema_to_xml_and_xmlschema ( schema name, nulls boolean, tableforest boolean, targetns text ) xml database_to_xml ( nulls boolean, tableforest boolean, targetns text ) xml database_to_xmlschema ( nulls boolean, tableforest boolean, targetns text ) xml database_to_xml_and_xmlschema ( nulls boolean, tableforest boolean, targetns text ) xml These functions ignore tables that are not readable by the current user. The database-wide functions additionally ignore schemas that the current user does not have USAGE (lookup) privilege for. Note that these potentially produce a lot of data, which needs to be built up in memory. When requesting content mappings of large schemas or databases, it might be worthwhile to consider mapping the tables separately instead, possibly even through a cursor. The result of a schema content mapping looks like this: table1-mapping table2-mapping ... ]]> where the format of a table mapping depends on the tableforest parameter as explained above. The result of a database content mapping looks like this: ... ... ... ]]> where the schema mapping is as above. As an example of using the output produced by these functions, shows an XSLT stylesheet that converts the output of table_to_xml_and_xmlschema to an HTML document containing a tabular rendition of the table data. In a similar manner, the results from these functions can be converted into other XML-based formats. ]]> JSON functions and operators This section describes: functions and operators for processing and creating JSON data the SQL/JSON path language To learn more about the SQL/JSON standard, see . For details on JSON types supported in PostgreSQL, see . shows the operators that are available for use with JSON data types (see ). In addition, the usual comparison operators shown in are available for jsonb, though not for json. The comparison operators follow the ordering rules for B-tree operations outlined in . See also for the aggregate function json_agg which aggregates record values as JSON, the aggregate function json_object_agg which aggregates pairs of values into a JSON object, and their jsonb equivalents, jsonb_agg and jsonb_object_agg. Operator Description Example(s) json -> integer json jsonb -> integer jsonb Extracts n'th element of JSON array (array elements are indexed from zero, but negative integers count from the end). '[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json -> 2 {"c":"baz"} '[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json -> -3 {"a":"foo"} json -> text json jsonb -> text jsonb Extracts JSON object field with the given key. '{"a": {"b":"foo"}}'::json -> 'a' {"b":"foo"} json ->> integer text jsonb ->> integer text Extracts n'th element of JSON array, as text. '[1,2,3]'::json ->> 2 3 json ->> text text jsonb ->> text text Extracts JSON object field with the given key, as text. '{"a":1,"b":2}'::json ->> 'b' 2 json #> text[] json jsonb #> text[] jsonb Extracts JSON sub-object at the specified path, where path elements can be either field keys or array indexes. '{"a": {"b": ["foo","bar"]}}'::json #> '{a,b,1}' "bar" json #>> text[] text jsonb #>> text[] text Extracts JSON sub-object at the specified path as text. '{"a": {"b": ["foo","bar"]}}'::json #>> '{a,b,1}' bar The field/element/path extraction operators return NULL, rather than failing, if the JSON input does not have the right structure to match the request; for example if no such key or array element exists. Some further operators exist only for jsonb, as shown in . describes how these operators can be used to effectively search indexed jsonb data. Operator Description Example(s) jsonb @> jsonb boolean Does the first JSON value contain the second? (See for details about containment.) '{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonb t jsonb <@ jsonb boolean Is the first JSON value contained in the second? '{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonb t jsonb ? text boolean Does the text string exist as a top-level key or array element within the JSON value? '{"a":1, "b":2}'::jsonb ? 'b' t '["a", "b", "c"]'::jsonb ? 'b' t jsonb ?| text[] boolean Do any of the strings in the text array exist as top-level keys or array elements? '{"a":1, "b":2, "c":3}'::jsonb ?| array['b', 'd'] t jsonb ?& text[] boolean Do all of the strings in the text array exist as top-level keys or array elements? '["a", "b", "c"]'::jsonb ?& array['a', 'b'] t jsonb || jsonb jsonb Concatenates two jsonb values. Concatenating two arrays generates an array containing all the elements of each input. Concatenating two objects generates an object containing the union of their keys, taking the second object's value when there are duplicate keys. All other cases are treated by converting a non-array input into a single-element array, and then proceeding as for two arrays. Does not operate recursively: only the top-level array or object structure is merged. '["a", "b"]'::jsonb || '["a", "d"]'::jsonb ["a", "b", "a", "d"] '{"a": "b"}'::jsonb || '{"c": "d"}'::jsonb {"a": "b", "c": "d"} '[1, 2]'::jsonb || '3'::jsonb [1, 2, 3] '{"a": "b"}'::jsonb || '42'::jsonb [{"a": "b"}, 42] To append an array to another array as a single entry, wrap it in an additional layer of array, for example: '[1, 2]'::jsonb || jsonb_build_array('[3, 4]'::jsonb) [1, 2, [3, 4]] jsonb - text jsonb Deletes a key (and its value) from a JSON object, or matching string value(s) from a JSON array. '{"a": "b", "c": "d"}'::jsonb - 'a' {"c": "d"} '["a", "b", "c", "b"]'::jsonb - 'b' ["a", "c"] jsonb - text[] jsonb Deletes all matching keys or array elements from the left operand. '{"a": "b", "c": "d"}'::jsonb - '{a,c}'::text[] {} jsonb - integer jsonb Deletes the array element with specified index (negative integers count from the end). Throws an error if JSON value is not an array. '["a", "b"]'::jsonb - 1 ["a"] jsonb #- text[] jsonb Deletes the field or array element at the specified path, where path elements can be either field keys or array indexes. '["a", {"b":1}]'::jsonb #- '{1,b}' ["a", {}] jsonb @? jsonpath boolean Does JSON path return any item for the specified JSON value? '{"a":[1,2,3,4,5]}'::jsonb @? '$.a[*] ? (@ > 2)' t jsonb @@ jsonpath boolean Returns the result of a JSON path predicate check for the specified JSON value. Only the first item of the result is taken into account. If the result is not Boolean, then NULL is returned. '{"a":[1,2,3,4,5]}'::jsonb @@ '$.a[*] > 2' t The jsonpath operators @? and @@ suppress the following errors: missing object field or array element, unexpected JSON item type, datetime and numeric errors. The jsonpath-related functions described below can also be told to suppress these types of errors. This behavior might be helpful when searching JSON document collections of varying structure. shows the functions that are available for constructing json and jsonb values. Function Description Example(s) to_json to_json ( anyelement ) json to_jsonb to_jsonb ( anyelement ) jsonb Converts any SQL value to json or jsonb. Arrays and composites are converted recursively to arrays and objects (multidimensional arrays become arrays of arrays in JSON). Otherwise, if there is a cast from the SQL data type to json, the cast function will be used to perform the conversion; For example, the extension has a cast from hstore to json, so that hstore values converted via the JSON creation functions will be represented as JSON objects, not as primitive string values. otherwise, a scalar JSON value is produced. For any scalar other than a number, a Boolean, or a null value, the text representation will be used, with escaping as necessary to make it a valid JSON string value. to_json('Fred said "Hi."'::text) "Fred said \"Hi.\"" to_jsonb(row(42, 'Fred said "Hi."'::text)) {"f1": 42, "f2": "Fred said \"Hi.\""} array_to_json array_to_json ( anyarray , boolean ) json Converts an SQL array to a JSON array. The behavior is the same as to_json except that line feeds will be added between top-level array elements if the optional boolean parameter is true. array_to_json('{{1,5},{99,100}}'::int[]) [[1,5],[99,100]] row_to_json row_to_json ( record , boolean ) json Converts an SQL composite value to a JSON object. The behavior is the same as to_json except that line feeds will be added between top-level elements if the optional boolean parameter is true. row_to_json(row(1,'foo')) {"f1":1,"f2":"foo"} json_build_array json_build_array ( VARIADIC "any" ) json jsonb_build_array jsonb_build_array ( VARIADIC "any" ) jsonb Builds a possibly-heterogeneously-typed JSON array out of a variadic argument list. Each argument is converted as per to_json or to_jsonb. json_build_array(1, 2, 'foo', 4, 5) [1, 2, "foo", 4, 5] json_build_object json_build_object ( VARIADIC "any" ) json jsonb_build_object jsonb_build_object ( VARIADIC "any" ) jsonb Builds a JSON object out of a variadic argument list. By convention, the argument list consists of alternating keys and values. Key arguments are coerced to text; value arguments are converted as per to_json or to_jsonb. json_build_object('foo', 1, 2, row(3,'bar')) {"foo" : 1, "2" : {"f1":3,"f2":"bar"}} json_object json_object ( text[] ) json jsonb_object jsonb_object ( text[] ) jsonb Builds a JSON object out of a text array. The array must have either exactly one dimension with an even number of members, in which case they are taken as alternating key/value pairs, or two dimensions such that each inner array has exactly two elements, which are taken as a key/value pair. All values are converted to JSON strings. json_object('{a, 1, b, "def", c, 3.5}') {"a" : "1", "b" : "def", "c" : "3.5"} json_object('{{a, 1}, {b, "def"}, {c, 3.5}}') {"a" : "1", "b" : "def", "c" : "3.5"} json_object ( keys text[], values text[] ) json jsonb_object ( keys text[], values text[] ) jsonb This form of json_object takes keys and values pairwise from separate text arrays. Otherwise it is identical to the one-argument form. json_object('{a,b}', '{1,2}') {"a": "1", "b": "2"} shows the functions that are available for processing json and jsonb values. Function Description Example(s) json_array_elements json_array_elements ( json ) setof json jsonb_array_elements jsonb_array_elements ( jsonb ) setof jsonb Expands the top-level JSON array into a set of JSON values. select * from json_array_elements('[1,true, [2,false]]') value ----------- 1 true [2,false] json_array_elements_text json_array_elements_text ( json ) setof text jsonb_array_elements_text jsonb_array_elements_text ( jsonb ) setof text Expands the top-level JSON array into a set of text values. select * from json_array_elements_text('["foo", "bar"]') value ----------- foo bar json_array_length json_array_length ( json ) integer jsonb_array_length jsonb_array_length ( jsonb ) integer Returns the number of elements in the top-level JSON array. json_array_length('[1,2,3,{"f1":1,"f2":[5,6]},4]') 5 jsonb_array_length('[]') 0 json_each json_each ( json ) setof record ( key text, value json ) jsonb_each jsonb_each ( jsonb ) setof record ( key text, value jsonb ) Expands the top-level JSON object into a set of key/value pairs. select * from json_each('{"a":"foo", "b":"bar"}') key | value -----+------- a | "foo" b | "bar" json_each_text json_each_text ( json ) setof record ( key text, value text ) jsonb_each_text jsonb_each_text ( jsonb ) setof record ( key text, value text ) Expands the top-level JSON object into a set of key/value pairs. The returned values will be of type text. select * from json_each_text('{"a":"foo", "b":"bar"}') key | value -----+------- a | foo b | bar json_extract_path json_extract_path ( from_json json, VARIADIC path_elems text[] ) json jsonb_extract_path jsonb_extract_path ( from_json jsonb, VARIADIC path_elems text[] ) jsonb Extracts JSON sub-object at the specified path. (This is functionally equivalent to the #> operator, but writing the path out as a variadic list can be more convenient in some cases.) json_extract_path('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}', 'f4', 'f6') "foo" json_extract_path_text json_extract_path_text ( from_json json, VARIADIC path_elems text[] ) text jsonb_extract_path_text jsonb_extract_path_text ( from_json jsonb, VARIADIC path_elems text[] ) text Extracts JSON sub-object at the specified path as text. (This is functionally equivalent to the #>> operator.) json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}', 'f4', 'f6') foo json_object_keys json_object_keys ( json ) setof text jsonb_object_keys jsonb_object_keys ( jsonb ) setof text Returns the set of keys in the top-level JSON object. select * from json_object_keys('{"f1":"abc","f2":{"f3":"a", "f4":"b"}}') json_object_keys ------------------ f1 f2 json_populate_record json_populate_record ( base anyelement, from_json json ) anyelement jsonb_populate_record jsonb_populate_record ( base anyelement, from_json jsonb ) anyelement Expands the top-level JSON object to a row having the composite type of the base argument. The JSON object is scanned for fields whose names match column names of the output row type, and their values are inserted into those columns of the output. (Fields that do not correspond to any output column name are ignored.) In typical use, the value of base is just NULL, which means that any output columns that do not match any object field will be filled with nulls. However, if base isn't NULL then the values it contains will be used for unmatched columns. To convert a JSON value to the SQL type of an output column, the following rules are applied in sequence: A JSON null value is converted to an SQL null in all cases. If the output column is of type json or jsonb, the JSON value is just reproduced exactly. If the output column is a composite (row) type, and the JSON value is a JSON object, the fields of the object are converted to columns of the output row type by recursive application of these rules. Likewise, if the output column is an array type and the JSON value is a JSON array, the elements of the JSON array are converted to elements of the output array by recursive application of these rules. Otherwise, if the JSON value is a string, the contents of the string are fed to the input conversion function for the column's data type. Otherwise, the ordinary text representation of the JSON value is fed to the input conversion function for the column's data type. While the example below uses a constant JSON value, typical use would be to reference a json or jsonb column laterally from another table in the query's FROM clause. Writing json_populate_record in the FROM clause is good practice, since all of the extracted columns are available for use without duplicate function calls. create type subrowtype as (d int, e text); create type myrowtype as (a int, b text[], c subrowtype); select * from json_populate_record(null::myrowtype, '{"a": 1, "b": ["2", "a b"], "c": {"d": 4, "e": "a b c"}, "x": "foo"}') a | b | c ---+-----------+------------- 1 | {2,"a b"} | (4,"a b c") json_populate_recordset json_populate_recordset ( base anyelement, from_json json ) setof anyelement jsonb_populate_recordset jsonb_populate_recordset ( base anyelement, from_json jsonb ) setof anyelement Expands the top-level JSON array of objects to a set of rows having the composite type of the base argument. Each element of the JSON array is processed as described above for json[b]_populate_record. create type twoints as (a int, b int); select * from json_populate_recordset(null::twoints, '[{"a":1,"b":2}, {"a":3,"b":4}]') a | b ---+--- 1 | 2 3 | 4 json_to_record json_to_record ( json ) record jsonb_to_record jsonb_to_record ( jsonb ) record Expands the top-level JSON object to a row having the composite type defined by an AS clause. (As with all functions returning record, the calling query must explicitly define the structure of the record with an AS clause.) The output record is filled from fields of the JSON object, in the same way as described above for json[b]_populate_record. Since there is no input record value, unmatched columns are always filled with nulls. create type myrowtype as (a int, b text); select * from json_to_record('{"a":1,"b":[1,2,3],"c":[1,2,3],"e":"bar","r": {"a": 123, "b": "a b c"}}') as x(a int, b text, c int[], d text, r myrowtype) a | b | c | d | r ---+---------+---------+---+--------------- 1 | [1,2,3] | {1,2,3} | | (123,"a b c") json_to_recordset json_to_recordset ( json ) setof record jsonb_to_recordset jsonb_to_recordset ( jsonb ) setof record Expands the top-level JSON array of objects to a set of rows having the composite type defined by an AS clause. (As with all functions returning record, the calling query must explicitly define the structure of the record with an AS clause.) Each element of the JSON array is processed as described above for json[b]_populate_record. select * from json_to_recordset('[{"a":1,"b":"foo"}, {"a":"2","c":"bar"}]') as x(a int, b text) a | b ---+----- 1 | foo 2 | jsonb_set jsonb_set ( target jsonb, path text[], new_value jsonb , create_if_missing boolean ) jsonb Returns target with the item designated by path replaced by new_value, or with new_value added if create_if_missing is true (which is the default) and the item designated by path does not exist. All earlier steps in the path must exist, or the target is returned unchanged. As with the path oriented operators, negative integers that appear in the path count from the end of JSON arrays. If the last path step is an array index that is out of range, and create_if_missing is true, the new value is added at the beginning of the array if the index is negative, or at the end of the array if it is positive. jsonb_set('[{"f1":1,"f2":null},2,null,3]', '{0,f1}', '[2,3,4]', false) [{"f1": [2, 3, 4], "f2": null}, 2, null, 3] jsonb_set('[{"f1":1,"f2":null},2]', '{0,f3}', '[2,3,4]') [{"f1": 1, "f2": null, "f3": [2, 3, 4]}, 2] jsonb_set_lax jsonb_set_lax ( target jsonb, path text[], new_value jsonb , create_if_missing boolean , null_value_treatment text ) jsonb If new_value is not NULL, behaves identically to jsonb_set. Otherwise behaves according to the value of null_value_treatment which must be one of 'raise_exception', 'use_json_null', 'delete_key', or 'return_target'. The default is 'use_json_null'. jsonb_set_lax('[{"f1":1,"f2":null},2,null,3]', '{0,f1}', null) [{"f1": null, "f2": null}, 2, null, 3] jsonb_set_lax('[{"f1":99,"f2":null},2]', '{0,f3}', null, true, 'return_target') [{"f1": 99, "f2": null}, 2] jsonb_insert jsonb_insert ( target jsonb, path text[], new_value jsonb , insert_after boolean ) jsonb Returns target with new_value inserted. If the item designated by the path is an array element, new_value will be inserted before that item if insert_after is false (which is the default), or after it if insert_after is true. If the item designated by the path is an object field, new_value will be inserted only if the object does not already contain that key. All earlier steps in the path must exist, or the target is returned unchanged. As with the path oriented operators, negative integers that appear in the path count from the end of JSON arrays. If the last path step is an array index that is out of range, the new value is added at the beginning of the array if the index is negative, or at the end of the array if it is positive. jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"') {"a": [0, "new_value", 1, 2]} jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"', true) {"a": [0, 1, "new_value", 2]} json_strip_nulls json_strip_nulls ( json ) json jsonb_strip_nulls jsonb_strip_nulls ( jsonb ) jsonb Deletes all object fields that have null values from the given JSON value, recursively. Null values that are not object fields are untouched. json_strip_nulls('[{"f1":1, "f2":null}, 2, null, 3]') [{"f1":1},2,null,3] jsonb_path_exists jsonb_path_exists ( target jsonb, path jsonpath , vars jsonb , silent boolean ) boolean Checks whether the JSON path returns any item for the specified JSON value. If the vars argument is specified, it must be a JSON object, and its fields provide named values to be substituted into the jsonpath expression. If the silent argument is specified and is true, the function suppresses the same errors as the @? and @@ operators do. jsonb_path_exists('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}') t jsonb_path_match jsonb_path_match ( target jsonb, path jsonpath , vars jsonb , silent boolean ) boolean Returns the result of a JSON path predicate check for the specified JSON value. Only the first item of the result is taken into account. If the result is not Boolean, then NULL is returned. The optional vars and silent arguments act the same as for jsonb_path_exists. jsonb_path_match('{"a":[1,2,3,4,5]}', 'exists($.a[*] ? (@ >= $min && @ <= $max))', '{"min":2, "max":4}') t jsonb_path_query jsonb_path_query ( target jsonb, path jsonpath , vars jsonb , silent boolean ) setof jsonb Returns all JSON items returned by the JSON path for the specified JSON value. The optional vars and silent arguments act the same as for jsonb_path_exists. select * from jsonb_path_query('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}') jsonb_path_query ------------------ 2 3 4 jsonb_path_query_array jsonb_path_query_array ( target jsonb, path jsonpath , vars jsonb , silent boolean ) jsonb Returns all JSON items returned by the JSON path for the specified JSON value, as a JSON array. The optional vars and silent arguments act the same as for jsonb_path_exists. jsonb_path_query_array('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}') [2, 3, 4] jsonb_path_query_first jsonb_path_query_first ( target jsonb, path jsonpath , vars jsonb , silent boolean ) jsonb Returns the first JSON item returned by the JSON path for the specified JSON value. Returns NULL if there are no results. The optional vars and silent arguments act the same as for jsonb_path_exists. jsonb_path_query_first('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}') 2 jsonb_path_exists_tz jsonb_path_exists_tz ( target jsonb, path jsonpath , vars jsonb , silent boolean ) boolean jsonb_path_match_tz jsonb_path_match_tz ( target jsonb, path jsonpath , vars jsonb , silent boolean ) boolean jsonb_path_query_tz jsonb_path_query_tz ( target jsonb, path jsonpath , vars jsonb , silent boolean ) setof jsonb jsonb_path_query_array_tz jsonb_path_query_array_tz ( target jsonb, path jsonpath , vars jsonb , silent boolean ) jsonb jsonb_path_query_first_tz jsonb_path_query_first_tz ( target jsonb, path jsonpath , vars jsonb , silent boolean ) jsonb These functions act like their counterparts described above without the _tz suffix, except that these functions support comparisons of date/time values that require timezone-aware conversions. The example below requires interpretation of the date-only value 2015-08-02 as a timestamp with time zone, so the result depends on the current setting. Due to this dependency, these functions are marked as stable, which means these functions cannot be used in indexes. Their counterparts are immutable, and so can be used in indexes; but they will throw errors if asked to make such comparisons. jsonb_path_exists_tz('["2015-08-01 12:00:00-05"]', '$[*] ? (@.datetime() < "2015-08-02".datetime())') t jsonb_pretty jsonb_pretty ( jsonb ) text Converts the given JSON value to pretty-printed, indented text. jsonb_pretty('[{"f1":1,"f2":null}, 2]') [ { "f1": 1, "f2": null }, 2 ] json_typeof json_typeof ( json ) text jsonb_typeof jsonb_typeof ( jsonb ) text Returns the type of the top-level JSON value as a text string. Possible types are object, array, string, number, boolean, and null. (The null result should not be confused with an SQL NULL; see the examples.) json_typeof('-123.4') number json_typeof('null'::json) null json_typeof(NULL::json) IS NULL t SQL/JSON path language SQL/JSON path expressions specify the items to be retrieved from the JSON data, similar to XPath expressions used for SQL access to XML. In PostgreSQL, path expressions are implemented as the jsonpath data type and can use any elements described in . JSON query functions and operators pass the provided path expression to the path engine for evaluation. If the expression matches the queried JSON data, the corresponding JSON item, or set of items, is returned. Path expressions are written in the SQL/JSON path language and can include arithmetic expressions and functions. A path expression consists of a sequence of elements allowed by the jsonpath data type. The path expression is normally evaluated from left to right, but you can use parentheses to change the order of operations. If the evaluation is successful, a sequence of JSON items is produced, and the evaluation result is returned to the JSON query function that completes the specified computation. To refer to the JSON value being queried (the context item), use the $ variable in the path expression. It can be followed by one or more accessor operators, which go down the JSON structure level by level to retrieve sub-items of the context item. Each operator that follows deals with the result of the previous evaluation step. For example, suppose you have some JSON data from a GPS tracker that you would like to parse, such as: { "track": { "segments": [ { "location": [ 47.763, 13.4034 ], "start time": "2018-10-14 10:05:14", "HR": 73 }, { "location": [ 47.706, 13.2635 ], "start time": "2018-10-14 10:39:21", "HR": 135 } ] } } To retrieve the available track segments, you need to use the .key accessor operator to descend through surrounding JSON objects: $.track.segments To retrieve the contents of an array, you typically use the [*] operator. For example, the following path will return the location coordinates for all the available track segments: $.track.segments[*].location To return the coordinates of the first segment only, you can specify the corresponding subscript in the [] accessor operator. Recall that JSON array indexes are 0-relative: $.track.segments[0].location The result of each path evaluation step can be processed by one or more jsonpath operators and methods listed in . Each method name must be preceded by a dot. For example, you can get the size of an array: $.track.segments.size() More examples of using jsonpath operators and methods within path expressions appear below in . When defining a path, you can also use one or more filter expressions that work similarly to the WHERE clause in SQL. A filter expression begins with a question mark and provides a condition in parentheses: ? (condition) Filter expressions must be written just after the path evaluation step to which they should apply. The result of that step is filtered to include only those items that satisfy the provided condition. SQL/JSON defines three-valued logic, so the condition can be true, false, or unknown. The unknown value plays the same role as SQL NULL and can be tested for with the is unknown predicate. Further path evaluation steps use only those items for which the filter expression returned true. The functions and operators that can be used in filter expressions are listed in . Within a filter expression, the @ variable denotes the value being filtered (i.e., one result of the preceding path step). You can write accessor operators after @ to retrieve component items. For example, suppose you would like to retrieve all heart rate values higher than 130. You can achieve this using the following expression: $.track.segments[*].HR ? (@ > 130) To get the start times of segments with such values, you have to filter out irrelevant segments before returning the start times, so the filter expression is applied to the previous step, and the path used in the condition is different: $.track.segments[*] ? (@.HR > 130)."start time" You can use several filter expressions in sequence, if required. For example, the following expression selects start times of all segments that contain locations with relevant coordinates and high heart rate values: $.track.segments[*] ? (@.location[1] < 13.4) ? (@.HR > 130)."start time" Using filter expressions at different nesting levels is also allowed. The following example first filters all segments by location, and then returns high heart rate values for these segments, if available: $.track.segments[*] ? (@.location[1] < 13.4).HR ? (@ > 130) You can also nest filter expressions within each other: $.track ? (exists(@.segments[*] ? (@.HR > 130))).segments.size() This expression returns the size of the track if it contains any segments with high heart rate values, or an empty sequence otherwise. PostgreSQL's implementation of the SQL/JSON path language has the following deviations from the SQL/JSON standard: A path expression can be a Boolean predicate, although the SQL/JSON standard allows predicates only in filters. This is necessary for implementation of the @@ operator. For example, the following jsonpath expression is valid in PostgreSQL: $.track.segments[*].HR < 70 There are minor differences in the interpretation of regular expression patterns used in like_regex filters, as described in . When you query JSON data, the path expression may not match the actual JSON data structure. An attempt to access a non-existent member of an object or element of an array results in a structural error. SQL/JSON path expressions have two modes of handling structural errors: lax (default) — the path engine implicitly adapts the queried data to the specified path. Any remaining structural errors are suppressed and converted to empty SQL/JSON sequences. strict — if a structural error occurs, an error is raised. The lax mode facilitates matching of a JSON document structure and path expression if the JSON data does not conform to the expected schema. If an operand does not match the requirements of a particular operation, it can be automatically wrapped as an SQL/JSON array or unwrapped by converting its elements into an SQL/JSON sequence before performing this operation. Besides, comparison operators automatically unwrap their operands in the lax mode, so you can compare SQL/JSON arrays out-of-the-box. An array of size 1 is considered equal to its sole element. Automatic unwrapping is not performed only when: The path expression contains type() or size() methods that return the type and the number of elements in the array, respectively. The queried JSON data contain nested arrays. In this case, only the outermost array is unwrapped, while all the inner arrays remain unchanged. Thus, implicit unwrapping can only go one level down within each path evaluation step. For example, when querying the GPS data listed above, you can abstract from the fact that it stores an array of segments when using the lax mode: lax $.track.segments.location In the strict mode, the specified path must exactly match the structure of the queried JSON document to return an SQL/JSON item, so using this path expression will cause an error. To get the same result as in the lax mode, you have to explicitly unwrap the segments array: strict $.track.segments[*].location The .** accessor can lead to surprising results when using the lax mode. For instance, the following query selects every HR value twice: lax $.**.HR This happens because the .** accessor selects both the segments array and each of its elements, while the .HR accessor automatically unwraps arrays when using the lax mode. To avoid surprising results, we recommend using the .** accessor only in the strict mode. The following query selects each HR value just once: strict $.**.HR shows the operators and methods available in jsonpath. Note that while the unary operators and methods can be applied to multiple values resulting from a preceding path step, the binary operators (addition etc.) can only be applied to single values. Operator/Method Description Example(s) number + number number Addition jsonb_path_query('[2]', '$[0] + 3') 5 + number number Unary plus (no operation); unlike addition, this can iterate over multiple values jsonb_path_query_array('{"x": [2,3,4]}', '+ $.x') [2, 3, 4] number - number number Subtraction jsonb_path_query('[2]', '7 - $[0]') 5 - number number Negation; unlike subtraction, this can iterate over multiple values jsonb_path_query_array('{"x": [2,3,4]}', '- $.x') [-2, -3, -4] number * number number Multiplication jsonb_path_query('[4]', '2 * $[0]') 8 number / number number Division jsonb_path_query('[8.5]', '$[0] / 2') 4.2500000000000000 number % number number Modulo (remainder) jsonb_path_query('[32]', '$[0] % 10') 2 value . type() string Type of the JSON item (see json_typeof) jsonb_path_query_array('[1, "2", {}]', '$[*].type()') ["number", "string", "object"] value . size() number Size of the JSON item (number of array elements, or 1 if not an array) jsonb_path_query('{"m": [11, 15]}', '$.m.size()') 2 value . double() number Approximate floating-point number converted from a JSON number or string jsonb_path_query('{"len": "1.9"}', '$.len.double() * 2') 3.8 number . ceiling() number Nearest integer greater than or equal to the given number jsonb_path_query('{"h": 1.3}', '$.h.ceiling()') 2 number . floor() number Nearest integer less than or equal to the given number jsonb_path_query('{"h": 1.7}', '$.h.floor()') 1 number . abs() number Absolute value of the given number jsonb_path_query('{"z": -0.3}', '$.z.abs()') 0.3 string . datetime() datetime_type (see note) Date/time value converted from a string jsonb_path_query('["2015-8-1", "2015-08-12"]', '$[*] ? (@.datetime() < "2015-08-2".datetime())') "2015-8-1" string . datetime(template) datetime_type (see note) Date/time value converted from a string using the specified to_timestamp template jsonb_path_query_array('["12:30", "18:40"]', '$[*].datetime("HH24:MI")') ["12:30:00", "18:40:00"] object . keyvalue() array The object's key-value pairs, represented as an array of objects containing three fields: "key", "value", and "id"; "id" is a unique identifier of the object the key-value pair belongs to jsonb_path_query_array('{"x": "20", "y": 32}', '$.keyvalue()') [{"id": 0, "key": "x", "value": "20"}, {"id": 0, "key": "y", "value": 32}] The result type of the datetime() and datetime(template) methods can be date, timetz, time, timestamptz, or timestamp. Both methods determine their result type dynamically. The datetime() method sequentially tries to match its input string to the ISO formats for date, timetz, time, timestamptz, and timestamp. It stops on the first matching format and emits the corresponding data type. The datetime(template) method determines the result type according to the fields used in the provided template string. The datetime() and datetime(template) methods use the same parsing rules as the to_timestamp SQL function does (see ), with three exceptions. First, these methods don't allow unmatched template patterns. Second, only the following separators are allowed in the template string: minus sign, period, solidus (slash), comma, apostrophe, semicolon, colon and space. Third, separators in the template string must exactly match the input string. If different date/time types need to be compared, an implicit cast is applied. A date value can be cast to timestamp or timestamptz, timestamp can be cast to timestamptz, and time to timetz. However, all but the first of these conversions depend on the current setting, and thus can only be performed within timezone-aware jsonpath functions. shows the available filter expression elements. Predicate/Value Description Example(s) value == value boolean Equality comparison (this, and the other comparison operators, work on all JSON scalar values) jsonb_path_query_array('[1, "a", 1, 3]', '$[*] ? (@ == 1)') [1, 1] jsonb_path_query_array('[1, "a", 1, 3]', '$[*] ? (@ == "a")') ["a"] value != value boolean value <> value boolean Non-equality comparison jsonb_path_query_array('[1, 2, 1, 3]', '$[*] ? (@ != 1)') [2, 3] jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <> "b")') ["a", "c"] value < value boolean Less-than comparison jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ < 2)') [1] value <= value boolean Less-than-or-equal-to comparison jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <= "b")') ["a", "b"] value > value boolean Greater-than comparison jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ > 2)') [3] value >= value boolean Greater-than-or-equal-to comparison jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ >= 2)') [2, 3] true boolean JSON constant true jsonb_path_query('[{"name": "John", "parent": false}, {"name": "Chris", "parent": true}]', '$[*] ? (@.parent == true)') {"name": "Chris", "parent": true} false boolean JSON constant false jsonb_path_query('[{"name": "John", "parent": false}, {"name": "Chris", "parent": true}]', '$[*] ? (@.parent == false)') {"name": "John", "parent": false} null value JSON constant null (note that, unlike in SQL, comparison to null works normally) jsonb_path_query('[{"name": "Mary", "job": null}, {"name": "Michael", "job": "driver"}]', '$[*] ? (@.job == null) .name') "Mary" boolean && boolean boolean Boolean AND jsonb_path_query('[1, 3, 7]', '$[*] ? (@ > 1 && @ < 5)') 3 boolean || boolean boolean Boolean OR jsonb_path_query('[1, 3, 7]', '$[*] ? (@ < 1 || @ > 5)') 7 ! boolean boolean Boolean NOT jsonb_path_query('[1, 3, 7]', '$[*] ? (!(@ < 5))') 7 boolean is unknown boolean Tests whether a Boolean condition is unknown. jsonb_path_query('[-1, 2, 7, "foo"]', '$[*] ? ((@ > 0) is unknown)') "foo" string like_regex string flag string boolean Tests whether the first operand matches the regular expression given by the second operand, optionally with modifications described by a string of flag characters (see ). jsonb_path_query_array('["abc", "abd", "aBdC", "abdacb", "babc"]', '$[*] ? (@ like_regex "^ab.*c")') ["abc", "abdacb"] jsonb_path_query_array('["abc", "abd", "aBdC", "abdacb", "babc"]', '$[*] ? (@ like_regex "^ab.*c" flag "i")') ["abc", "aBdC", "abdacb"] string starts with string boolean Tests whether the second operand is an initial substring of the first operand. jsonb_path_query('["John Smith", "Mary Stone", "Bob Johnson"]', '$[*] ? (@ starts with "John")') "John Smith" exists ( path_expression ) boolean Tests whether a path expression matches at least one SQL/JSON item. Returns unknown if the path expression would result in an error; the second example uses this to avoid a no-such-key error in strict mode. jsonb_path_query('{"x": [1, 2], "y": [2, 4]}', 'strict $.* ? (exists (@ ? (@[*] > 2)))') [2, 4] jsonb_path_query_array('{"value": 41}', 'strict $ ? (exists (@.name)) .name') [] LIKE_REGEX in SQL/JSON SQL/JSON path expressions allow matching text to a regular expression with the like_regex filter. For example, the following SQL/JSON path query would case-insensitively match all strings in an array that start with an English vowel: $[*] ? (@ like_regex "^[aeiou]" flag "i") The optional flag string may include one or more of the characters i for case-insensitive match, m to allow ^ and $ to match at newlines, s to allow . to match a newline, and q to quote the whole pattern (reducing the behavior to a simple substring match). The SQL/JSON standard borrows its definition for regular expressions from the LIKE_REGEX operator, which in turn uses the XQuery standard. PostgreSQL does not currently support the LIKE_REGEX operator. Therefore, the like_regex filter is implemented using the POSIX regular expression engine described in . This leads to various minor discrepancies from standard SQL/JSON behavior, which are cataloged in . Note, however, that the flag-letter incompatibilities described there do not apply to SQL/JSON, as it translates the XQuery flag letters to match what the POSIX engine expects. Keep in mind that the pattern argument of like_regex is a JSON path string literal, written according to the rules given in . This means in particular that any backslashes you want to use in the regular expression must be doubled. For example, to match string values of the root document that contain only digits: $.* ? (@ like_regex "^\\d+$") sequence This section describes functions for operating on sequence objects, also called sequence generators or just sequences. Sequence objects are special single-row tables created with . Sequence objects are commonly used to generate unique identifiers for rows of a table. The sequence functions, listed in , provide simple, multiuser-safe methods for obtaining successive sequence values from sequence objects. Function Description nextval nextval ( regclass ) bigint Advances the sequence object to its next value and returns that value. This is done atomically: even if multiple sessions execute nextval concurrently, each will safely receive a distinct sequence value. If the sequence object has been created with default parameters, successive nextval calls will return successive values beginning with 1. Other behaviors can be obtained by using appropriate parameters in the command. This function requires USAGE or UPDATE privilege on the sequence. setval setval ( regclass, bigint , boolean ) bigint Sets the sequence object's current value, and optionally its is_called flag. The two-parameter form sets the sequence's last_value field to the specified value and sets its is_called field to true, meaning that the next nextval will advance the sequence before returning a value. The value that will be reported by currval is also set to the specified value. In the three-parameter form, is_called can be set to either true or false. true has the same effect as the two-parameter form. If it is set to false, the next nextval will return exactly the specified value, and sequence advancement commences with the following nextval. Furthermore, the value reported by currval is not changed in this case. For example, SELECT setval('myseq', 42); Next nextval will return 43 SELECT setval('myseq', 42, true); Same as above SELECT setval('myseq', 42, false); Next nextval will return 42 The result returned by setval is just the value of its second argument. This function requires UPDATE privilege on the sequence. currval currval ( regclass ) bigint Returns the value most recently obtained by nextval for this sequence in the current session. (An error is reported if nextval has never been called for this sequence in this session.) Because this is returning a session-local value, it gives a predictable answer whether or not other sessions have executed nextval since the current session did. This function requires USAGE or SELECT privilege on the sequence. lastval lastval () bigint Returns the value most recently returned by nextval in the current session. This function is identical to currval, except that instead of taking the sequence name as an argument it refers to whichever sequence nextval was most recently applied to in the current session. It is an error to call lastval if nextval has not yet been called in the current session. This function requires USAGE or SELECT privilege on the last used sequence. To avoid blocking concurrent transactions that obtain numbers from the same sequence, the value obtained by nextval is not reclaimed for re-use if the calling transaction later aborts. This means that transaction aborts or database crashes can result in gaps in the sequence of assigned values. That can happen without a transaction abort, too. For example an INSERT with an ON CONFLICT clause will compute the to-be-inserted tuple, including doing any required nextval calls, before detecting any conflict that would cause it to follow the ON CONFLICT rule instead. Thus, PostgreSQL sequence objects cannot be used to obtain gapless sequences. Likewise, sequence state changes made by setval are immediately visible to other transactions, and are not undone if the calling transaction rolls back. If the database cluster crashes before committing a transaction containing a nextval or setval call, the sequence state change might not have made its way to persistent storage, so that it is uncertain whether the sequence will have its original or updated state after the cluster restarts. This is harmless for usage of the sequence within the database, since other effects of uncommitted transactions will not be visible either. However, if you wish to use a sequence value for persistent outside-the-database purposes, make sure that the nextval call has been committed before doing so. The sequence to be operated on by a sequence function is specified by a regclass argument, which is simply the OID of the sequence in the pg_class system catalog. You do not have to look up the OID by hand, however, since the regclass data type's input converter will do the work for you. See for details. CASE conditional expression This section describes the SQL-compliant conditional expressions available in PostgreSQL. If your needs go beyond the capabilities of these conditional expressions, you might want to consider writing a server-side function in a more expressive programming language. Although COALESCE, GREATEST, and LEAST are syntactically similar to functions, they are not ordinary functions, and thus cannot be used with explicit VARIADIC array arguments. The SQL CASE expression is a generic conditional expression, similar to if/else statements in other programming languages: CASE WHEN condition THEN result WHEN ... ELSE result END CASE clauses can be used wherever an expression is valid. Each condition is an expression that returns a boolean result. If the condition's result is true, the value of the CASE expression is the result that follows the condition, and the remainder of the CASE expression is not processed. If the condition's result is not true, any subsequent WHEN clauses are examined in the same manner. If no WHEN condition yields true, the value of the CASE expression is the result of the ELSE clause. If the ELSE clause is omitted and no condition is true, the result is null. An example: SELECT * FROM test; a --- 1 2 3 SELECT a, CASE WHEN a=1 THEN 'one' WHEN a=2 THEN 'two' ELSE 'other' END FROM test; a | case ---+------- 1 | one 2 | two 3 | other The data types of all the result expressions must be convertible to a single output type. See for more details. There is a simple form of CASE expression that is a variant of the general form above: CASE expression WHEN value THEN result WHEN ... ELSE result END The first expression is computed, then compared to each of the value expressions in the WHEN clauses until one is found that is equal to it. If no match is found, the result of the ELSE clause (or a null value) is returned. This is similar to the switch statement in C. The example above can be written using the simple CASE syntax: SELECT a, CASE a WHEN 1 THEN 'one' WHEN 2 THEN 'two' ELSE 'other' END FROM test; a | case ---+------- 1 | one 2 | two 3 | other A CASE expression does not evaluate any subexpressions that are not needed to determine the result. For example, this is a possible way of avoiding a division-by-zero failure: SELECT ... WHERE CASE WHEN x <> 0 THEN y/x > 1.5 ELSE false END; As described in , there are various situations in which subexpressions of an expression are evaluated at different times, so that the principle that CASE evaluates only necessary subexpressions is not ironclad. For example a constant 1/0 subexpression will usually result in a division-by-zero failure at planning time, even if it's within a CASE arm that would never be entered at run time. COALESCE NVL IFNULL COALESCE(value , ...) The COALESCE function returns the first of its arguments that is not null. Null is returned only if all arguments are null. It is often used to substitute a default value for null values when data is retrieved for display, for example: SELECT COALESCE(description, short_description, '(none)') ... This returns description if it is not null, otherwise short_description if it is not null, otherwise (none). The arguments must all be convertible to a common data type, which will be the type of the result (see for details). Like a CASE expression, COALESCE only evaluates the arguments that are needed to determine the result; that is, arguments to the right of the first non-null argument are not evaluated. This SQL-standard function provides capabilities similar to NVL and IFNULL, which are used in some other database systems. NULLIF NULLIF(value1, value2) The NULLIF function returns a null value if value1 equals value2; otherwise it returns value1. This can be used to perform the inverse operation of the COALESCE example given above: SELECT NULLIF(value, '(none)') ... In this example, if value is (none), null is returned, otherwise the value of value is returned. The two arguments must be of comparable types. To be specific, they are compared exactly as if you had written value1 = value2, so there must be a suitable = operator available. The result has the same type as the first argument — but there is a subtlety. What is actually returned is the first argument of the implied = operator, and in some cases that will have been promoted to match the second argument's type. For example, NULLIF(1, 2.2) yields numeric, because there is no integer = numeric operator, only numeric = numeric. GREATEST LEAST GREATEST(value , ...) LEAST(value , ...) The GREATEST and LEAST functions select the largest or smallest value from a list of any number of expressions. The expressions must all be convertible to a common data type, which will be the type of the result (see for details). NULL values in the list are ignored. The result will be NULL only if all the expressions evaluate to NULL. Note that GREATEST and LEAST are not in the SQL standard, but are a common extension. Some other databases make them return NULL if any argument is NULL, rather than only when all are NULL. shows the specialized operators available for array types. In addition to those, the usual comparison operators shown in are available for arrays. The comparison operators compare the array contents element-by-element, using the default B-tree comparison function for the element data type, and sort based on the first difference. In multidimensional arrays the elements are visited in row-major order (last subscript varies most rapidly). If the contents of two arrays are equal but the dimensionality is different, the first difference in the dimensionality information determines the sort order. Operator Description Example(s) anyarray @> anyarray boolean Does the first array contain the second, that is, does each element appearing in the second array equal some element of the first array? (Duplicates are not treated specially, thus ARRAY[1] and ARRAY[1,1] are each considered to contain the other.) ARRAY[1,4,3] @> ARRAY[3,1,3] t anyarray <@ anyarray boolean Is the first array contained by the second? ARRAY[2,2,7] <@ ARRAY[1,7,4,2,6] t anyarray && anyarray boolean Do the arrays overlap, that is, have any elements in common? ARRAY[1,4,3] && ARRAY[2,1] t anycompatiblearray || anycompatiblearray anycompatiblearray Concatenates the two arrays. Concatenating a null or empty array is a no-op; otherwise the arrays must have the same number of dimensions (as illustrated by the first example) or differ in number of dimensions by one (as illustrated by the second). If the arrays are not of identical element types, they will be coerced to a common type (see ). ARRAY[1,2,3] || ARRAY[4,5,6,7] {1,2,3,4,5,6,7} ARRAY[1,2,3] || ARRAY[[4,5,6],[7,8,9.9]] {{1,2,3},{4,5,6},{7,8,9.9}} anycompatible || anycompatiblearray anycompatiblearray Concatenates an element onto the front of an array (which must be empty or one-dimensional). 3 || ARRAY[4,5,6] {3,4,5,6} anycompatiblearray || anycompatible anycompatiblearray Concatenates an element onto the end of an array (which must be empty or one-dimensional). ARRAY[4,5,6] || 7 {4,5,6,7} See for more details about array operator behavior. See for more details about which operators support indexed operations. shows the functions available for use with array types. See for more information and examples of the use of these functions. Function Description Example(s) array_append array_append ( anycompatiblearray, anycompatible ) anycompatiblearray Appends an element to the end of an array (same as the anycompatiblearray || anycompatible operator). array_append(ARRAY[1,2], 3) {1,2,3} array_cat array_cat ( anycompatiblearray, anycompatiblearray ) anycompatiblearray Concatenates two arrays (same as the anycompatiblearray || anycompatiblearray operator). array_cat(ARRAY[1,2,3], ARRAY[4,5]) {1,2,3,4,5} array_dims array_dims ( anyarray ) text Returns a text representation of the array's dimensions. array_dims(ARRAY[[1,2,3], [4,5,6]]) [1:2][1:3] array_fill array_fill ( anyelement, integer[] , integer[] ) anyarray Returns an array filled with copies of the given value, having dimensions of the lengths specified by the second argument. The optional third argument supplies lower-bound values for each dimension (which default to all 1). array_fill(11, ARRAY[2,3]) {{11,11,11},{11,11,11}} array_fill(7, ARRAY[3], ARRAY[2]) [2:4]={7,7,7} array_length array_length ( anyarray, integer ) integer Returns the length of the requested array dimension. (Produces NULL instead of 0 for empty or missing array dimensions.) array_length(array[1,2,3], 1) 3 array_length(array[]::int[], 1) NULL array_length(array['text'], 2) NULL array_lower array_lower ( anyarray, integer ) integer Returns the lower bound of the requested array dimension. array_lower('[0:2]={1,2,3}'::integer[], 1) 0 array_ndims array_ndims ( anyarray ) integer Returns the number of dimensions of the array. array_ndims(ARRAY[[1,2,3], [4,5,6]]) 2 array_position array_position ( anycompatiblearray, anycompatible , integer ) integer Returns the subscript of the first occurrence of the second argument in the array, or NULL if it's not present. If the third argument is given, the search begins at that subscript. The array must be one-dimensional. Comparisons are done using IS NOT DISTINCT FROM semantics, so it is possible to search for NULL. array_position(ARRAY['sun', 'mon', 'tue', 'wed', 'thu', 'fri', 'sat'], 'mon') 2 array_positions array_positions ( anycompatiblearray, anycompatible ) integer[] Returns an array of the subscripts of all occurrences of the second argument in the array given as first argument. The array must be one-dimensional. Comparisons are done using IS NOT DISTINCT FROM semantics, so it is possible to search for NULL. NULL is returned only if the array is NULL; if the value is not found in the array, an empty array is returned. array_positions(ARRAY['A','A','B','A'], 'A') {1,2,4} array_prepend array_prepend ( anycompatible, anycompatiblearray ) anycompatiblearray Prepends an element to the beginning of an array (same as the anycompatible || anycompatiblearray operator). array_prepend(1, ARRAY[2,3]) {1,2,3} array_remove array_remove ( anycompatiblearray, anycompatible ) anycompatiblearray Removes all elements equal to the given value from the array. The array must be one-dimensional. Comparisons are done using IS NOT DISTINCT FROM semantics, so it is possible to remove NULLs. array_remove(ARRAY[1,2,3,2], 2) {1,3} array_replace array_replace ( anycompatiblearray, anycompatible, anycompatible ) anycompatiblearray Replaces each array element equal to the second argument with the third argument. array_replace(ARRAY[1,2,5,4], 5, 3) {1,2,3,4} array_to_string array_to_string ( array anyarray, delimiter text , null_string text ) text Converts each array element to its text representation, and concatenates those separated by the delimiter string. If null_string is given and is not NULL, then NULL array entries are represented by that string; otherwise, they are omitted. See also string_to_array. array_to_string(ARRAY[1, 2, 3, NULL, 5], ',', '*') 1,2,3,*,5 array_upper array_upper ( anyarray, integer ) integer Returns the upper bound of the requested array dimension. array_upper(ARRAY[1,8,3,7], 1) 4 cardinality cardinality ( anyarray ) integer Returns the total number of elements in the array, or 0 if the array is empty. cardinality(ARRAY[[1,2],[3,4]]) 4 trim_array trim_array ( array anyarray, n integer ) anyarray Trims an array by removing the last n elements. If the array is multidimensional, only the first dimension is trimmed. trim_array(ARRAY[1,2,3,4,5,6], 2) {1,2,3,4} unnest unnest ( anyarray ) setof anyelement Expands an array into a set of rows. The array's elements are read out in storage order. unnest(ARRAY[1,2]) 1 2 unnest(ARRAY[['foo','bar'],['baz','quux']]) foo bar baz quux unnest ( anyarray, anyarray , ... ) setof anyelement, anyelement [, ... ] Expands multiple arrays (possibly of different data types) into a set of rows. If the arrays are not all the same length then the shorter ones are padded with NULLs. This form is only allowed in a query's FROM clause; see . select * from unnest(ARRAY[1,2], ARRAY['foo','bar','baz']) as x(a,b) a | b ---+----- 1 | foo 2 | bar | baz See also about the aggregate function array_agg for use with arrays. See for an overview of range types. shows the specialized operators available for range types. shows the specialized operators available for multirange types. In addition to those, the usual comparison operators shown in are available for range and multirange types. The comparison operators order first by the range lower bounds, and only if those are equal do they compare the upper bounds. The multirange operators compare each range until one is unequal. This does not usually result in a useful overall ordering, but the operators are provided to allow unique indexes to be constructed on ranges. Operator Description Example(s) anyrange @> anyrange boolean Does the first range contain the second? int4range(2,4) @> int4range(2,3) t anyrange @> anyelement boolean Does the range contain the element? '[2011-01-01,2011-03-01)'::tsrange @> '2011-01-10'::timestamp t anyrange <@ anyrange boolean Is the first range contained by the second? int4range(2,4) <@ int4range(1,7) t anyelement <@ anyrange boolean Is the element contained in the range? 42 <@ int4range(1,7) f anyrange && anyrange boolean Do the ranges overlap, that is, have any elements in common? int8range(3,7) && int8range(4,12) t anyrange << anyrange boolean Is the first range strictly left of the second? int8range(1,10) << int8range(100,110) t anyrange >> anyrange boolean Is the first range strictly right of the second? int8range(50,60) >> int8range(20,30) t anyrange &< anyrange boolean Does the first range not extend to the right of the second? int8range(1,20) &< int8range(18,20) t anyrange &> anyrange boolean Does the first range not extend to the left of the second? int8range(7,20) &> int8range(5,10) t anyrange -|- anyrange boolean Are the ranges adjacent? numrange(1.1,2.2) -|- numrange(2.2,3.3) t anyrange + anyrange anyrange Computes the union of the ranges. The ranges must overlap or be adjacent, so that the union is a single range (but see range_merge()). numrange(5,15) + numrange(10,20) [5,20) anyrange * anyrange anyrange Computes the intersection of the ranges. int8range(5,15) * int8range(10,20) [10,15) anyrange - anyrange anyrange Computes the difference of the ranges. The second range must not be contained in the first in such a way that the difference would not be a single range. int8range(5,15) - int8range(10,20) [5,10) Operator Description Example(s) anymultirange @> anymultirange boolean Does the first multirange contain the second? '{[2,4)}'::int4multirange @> '{[2,3)}'::int4multirange t anymultirange @> anyrange boolean Does the multirange contain the range? '{[2,4)}'::int4multirange @> int4range(2,3) t anymultirange @> anyelement boolean Does the multirange contain the element? '{[2011-01-01,2011-03-01)}'::tsmultirange @> '2011-01-10'::timestamp t anyrange @> anymultirange boolean Does the range contain the multirange? '[2,4)'::int4range @> '{[2,3)}'::int4multirange t anymultirange <@ anymultirange boolean Is the first multirange contained by the second? '{[2,4)}'::int4multirange <@ '{[1,7)}'::int4multirange t anymultirange <@ anyrange boolean Is the multirange contained by the range? '{[2,4)}'::int4multirange <@ int4range(1,7) t anyrange <@ anymultirange boolean Is the range contained by the multirange? int4range(2,4) <@ '{[1,7)}'::int4multirange t anyelement <@ anymultirange boolean Is the element contained by the multirange? 4 <@ '{[1,7)}'::int4multirange t anymultirange && anymultirange boolean Do the multiranges overlap, that is, have any elements in common? '{[3,7)}'::int8multirange && '{[4,12)}'::int8multirange t anymultirange && anyrange boolean Does the multirange overlap the range? '{[3,7)}'::int8multirange && int8range(4,12) t anyrange && anymultirange boolean Does the range overlap the multirange? int8range(3,7) && '{[4,12)}'::int8multirange t anymultirange << anymultirange boolean Is the first multirange strictly left of the second? '{[1,10)}'::int8multirange << '{[100,110)}'::int8multirange t anymultirange << anyrange boolean Is the multirange strictly left of the range? '{[1,10)}'::int8multirange << int8range(100,110) t anyrange << anymultirange boolean Is the range strictly left of the multirange? int8range(1,10) << '{[100,110)}'::int8multirange t anymultirange >> anymultirange boolean Is the first multirange strictly right of the second? '{[50,60)}'::int8multirange >> '{[20,30)}'::int8multirange t anymultirange >> anyrange boolean Is the multirange strictly right of the range? '{[50,60)}'::int8multirange >> int8range(20,30) t anyrange >> anymultirange boolean Is the range strictly right of the multirange? int8range(50,60) >> '{[20,30)}'::int8multirange t anymultirange &< anymultirange boolean Does the first multirange not extend to the right of the second? '{[1,20)}'::int8multirange &< '{[18,20)}'::int8multirange t anymultirange &< anyrange boolean Does the multirange not extend to the right of the range? '{[1,20)}'::int8multirange &< int8range(18,20) t anyrange &< anymultirange boolean Does the range not extend to the right of the multirange? int8range(1,20) &< '{[18,20)}'::int8multirange t anymultirange &> anymultirange boolean Does the first multirange not extend to the left of the second? '{[7,20)}'::int8multirange &> '{[5,10)}'::int8multirange t anymultirange &> anyrange boolean Does the multirange not extend to the left of the range? '{[7,20)}'::int8multirange &> int8range(5,10) t anyrange &> anymultirange boolean Does the range not extend to the left of the multirange? int8range(7,20) &> '{[5,10)}'::int8multirange t anymultirange -|- anymultirange boolean Are the multiranges adjacent? '{[1.1,2.2)}'::nummultirange -|- '{[2.2,3.3)}'::nummultirange t anymultirange -|- anyrange boolean Is the multirange adjacent to the range? '{[1.1,2.2)}'::nummultirange -|- numrange(2.2,3.3) t anyrange -|- anymultirange boolean Is the range adjacent to the multirange? numrange(1.1,2.2) -|- '{[2.2,3.3)}'::nummultirange t anymultirange + anymultirange anymultirange Computes the union of the multiranges. The multiranges need not overlap or be adjacent. '{[5,10)}'::nummultirange + '{[15,20)}'::nummultirange {[5,10), [15,20)} anymultirange * anymultirange anymultirange Computes the intersection of the multiranges. '{[5,15)}'::int8multirange * '{[10,20)}'::int8multirange {[10,15)} anymultirange - anymultirange anymultirange Computes the difference of the multiranges. '{[5,20)}'::int8multirange - '{[10,15)}'::int8multirange {[5,10), [15,20)} The left-of/right-of/adjacent operators always return false when an empty range or multirange is involved; that is, an empty range is not considered to be either before or after any other range. Elsewhere empty ranges and multiranges are treated as the additive identity: anything unioned with an empty value is itself. Anything minus an empty value is itself. An empty multirange has exactly the same points as an empty range. Every range contains the empty range. Every multirange contains as many empty ranges as you like. The range union and difference operators will fail if the resulting range would need to contain two disjoint sub-ranges, as such a range cannot be represented. There are separate operators for union and difference that take multirange parameters and return a multirange, and they do not fail even if their arguments are disjoint. So if you need a union or difference operation for ranges that may be disjoint, you can avoid errors by first casting your ranges to multiranges. shows the functions available for use with range types. shows the functions available for use with multirange types. Function Description Example(s) lower lower ( anyrange ) anyelement Extracts the lower bound of the range (NULL if the range is empty or the lower bound is infinite). lower(numrange(1.1,2.2)) 1.1 upper upper ( anyrange ) anyelement Extracts the upper bound of the range (NULL if the range is empty or the upper bound is infinite). upper(numrange(1.1,2.2)) 2.2 isempty isempty ( anyrange ) boolean Is the range empty? isempty(numrange(1.1,2.2)) f lower_inc lower_inc ( anyrange ) boolean Is the range's lower bound inclusive? lower_inc(numrange(1.1,2.2)) t upper_inc upper_inc ( anyrange ) boolean Is the range's upper bound inclusive? upper_inc(numrange(1.1,2.2)) f lower_inf lower_inf ( anyrange ) boolean Is the range's lower bound infinite? lower_inf('(,)'::daterange) t upper_inf upper_inf ( anyrange ) boolean Is the range's upper bound infinite? upper_inf('(,)'::daterange) t range_merge range_merge ( anyrange, anyrange ) anyrange Computes the smallest range that includes both of the given ranges. range_merge('[1,2)'::int4range, '[3,4)'::int4range) [1,4) Function Description Example(s) lower lower ( anymultirange ) anyelement Extracts the lower bound of the multirange (NULL if the multirange is empty or the lower bound is infinite). lower('{[1.1,2.2)}'::nummultirange) 1.1 upper upper ( anymultirange ) anyelement Extracts the upper bound of the multirange (NULL if the multirange is empty or the upper bound is infinite). upper('{[1.1,2.2)}'::nummultirange) 2.2 isempty isempty ( anymultirange ) boolean Is the multirange empty? isempty('{[1.1,2.2)}'::nummultirange) f lower_inc lower_inc ( anymultirange ) boolean Is the multirange's lower bound inclusive? lower_inc('{[1.1,2.2)}'::nummultirange) t upper_inc upper_inc ( anymultirange ) boolean Is the multirange's upper bound inclusive? upper_inc('{[1.1,2.2)}'::nummultirange) f lower_inf lower_inf ( anymultirange ) boolean Is the multirange's lower bound infinite? lower_inf('{(,)}'::datemultirange) t upper_inf upper_inf ( anymultirange ) boolean Is the multirange's upper bound infinite? upper_inf('{(,)}'::datemultirange) t range_merge range_merge ( anymultirange ) anyrange Computes the smallest range that includes the entire multirange. range_merge('{[1,2), [3,4)}'::int4multirange) [1,4) multirange (function) multirange ( anyrange ) anymultirange Returns a multirange containing just the given range. multirange('[1,2)'::int4range) {[1,2)} unnest for multirange unnest ( anymultirange ) setof anyrange Expands a multirange into a set of ranges. The ranges are read out in storage order (ascending). unnest('{[1,2), [3,4)}'::int4multirange) [1,2) [3,4) The lower_inc, upper_inc, lower_inf, and upper_inf functions all return false for an empty range or multirange. aggregate function built-in Aggregate functions compute a single result from a set of input values. The built-in general-purpose aggregate functions are listed in while statistical aggregates are in . The built-in within-group ordered-set aggregate functions are listed in while the built-in within-group hypothetical-set ones are in . Grouping operations, which are closely related to aggregate functions, are listed in . The special syntax considerations for aggregate functions are explained in . Consult for additional introductory information. Aggregate functions that support Partial Mode are eligible to participate in various optimizations, such as parallel aggregation. Function Description Partial Mode array_agg array_agg ( anynonarray ) anyarray Collects all the input values, including nulls, into an array. No array_agg ( anyarray ) anyarray Concatenates all the input arrays into an array of one higher dimension. (The inputs must all have the same dimensionality, and cannot be empty or null.) No average avg avg ( smallint ) numeric avg ( integer ) numeric avg ( bigint ) numeric avg ( numeric ) numeric avg ( real ) double precision avg ( double precision ) double precision avg ( interval ) interval Computes the average (arithmetic mean) of all the non-null input values. Yes bit_and bit_and ( smallint ) smallint bit_and ( integer ) integer bit_and ( bigint ) bigint bit_and ( bit ) bit Computes the bitwise AND of all non-null input values. Yes bit_or bit_or ( smallint ) smallint bit_or ( integer ) integer bit_or ( bigint ) bigint bit_or ( bit ) bit Computes the bitwise OR of all non-null input values. Yes bit_xor bit_xor ( smallint ) smallint bit_xor ( integer ) integer bit_xor ( bigint ) bigint bit_xor ( bit ) bit Computes the bitwise exclusive OR of all non-null input values. Can be useful as a checksum for an unordered set of values. Yes bool_and bool_and ( boolean ) boolean Returns true if all non-null input values are true, otherwise false. Yes bool_or bool_or ( boolean ) boolean Returns true if any non-null input value is true, otherwise false. Yes count count ( * ) bigint Computes the number of input rows. Yes count ( "any" ) bigint Computes the number of input rows in which the input value is not null. Yes every every ( boolean ) boolean This is the SQL standard's equivalent to bool_and. Yes json_agg json_agg ( anyelement ) json jsonb_agg jsonb_agg ( anyelement ) jsonb Collects all the input values, including nulls, into a JSON array. Values are converted to JSON as per to_json or to_jsonb. No json_object_agg json_object_agg ( key "any", value "any" ) json jsonb_object_agg jsonb_object_agg ( key "any", value "any" ) jsonb Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as per to_json or to_jsonb. Values can be null, but not keys. No max max ( see text ) same as input type Computes the maximum of the non-null input values. Available for any numeric, string, date/time, or enum type, as well as inet, interval, money, oid, pg_lsn, tid, xid8, and arrays of any of these types. Yes min min ( see text ) same as input type Computes the minimum of the non-null input values. Available for any numeric, string, date/time, or enum type, as well as inet, interval, money, oid, pg_lsn, tid, xid8, and arrays of any of these types. Yes range_agg range_agg ( value anyrange ) anymultirange range_agg ( value anymultirange ) anymultirange Computes the union of the non-null input values. No range_intersect_agg range_intersect_agg ( value anyrange ) anyrange range_intersect_agg ( value anymultirange ) anymultirange Computes the intersection of the non-null input values. No string_agg string_agg ( value text, delimiter text ) text string_agg ( value bytea, delimiter bytea ) bytea Concatenates the non-null input values into a string. Each value after the first is preceded by the corresponding delimiter (if it's not null). No sum sum ( smallint ) bigint sum ( integer ) bigint sum ( bigint ) numeric sum ( numeric ) numeric sum ( real ) real sum ( double precision ) double precision sum ( interval ) interval sum ( money ) money Computes the sum of the non-null input values. Yes xmlagg xmlagg ( xml ) xml Concatenates the non-null XML input values (see ). No It should be noted that except for count, these functions return a null value when no rows are selected. In particular, sum of no rows returns null, not zero as one might expect, and array_agg returns null rather than an empty array when there are no input rows. The coalesce function can be used to substitute zero or an empty array for null when necessary. The aggregate functions array_agg, json_agg, jsonb_agg, json_object_agg, jsonb_object_agg, string_agg, and xmlagg, as well as similar user-defined aggregate functions, produce meaningfully different result values depending on the order of the input values. This ordering is unspecified by default, but can be controlled by writing an ORDER BY clause within the aggregate call, as shown in . Alternatively, supplying the input values from a sorted subquery will usually work. For example: Beware that this approach can fail if the outer query level contains additional processing, such as a join, because that might cause the subquery's output to be reordered before the aggregate is computed. ANY SOME The boolean aggregates bool_and and bool_or correspond to the standard SQL aggregates every and any or some. PostgreSQL supports every, but not any or some, because there is an ambiguity built into the standard syntax: SELECT b1 = ANY((SELECT b2 FROM t2 ...)) FROM t1 ...; Here ANY can be considered either as introducing a subquery, or as being an aggregate function, if the subquery returns one row with a Boolean value. Thus the standard name cannot be given to these aggregates. Users accustomed to working with other SQL database management systems might be disappointed by the performance of the count aggregate when it is applied to the entire table. A query like: SELECT count(*) FROM sometable; will require effort proportional to the size of the table: PostgreSQL will need to scan either the entire table or the entirety of an index that includes all rows in the table. shows aggregate functions typically used in statistical analysis. (These are separated out merely to avoid cluttering the listing of more-commonly-used aggregates.) Functions shown as accepting numeric_type are available for all the types smallint, integer, bigint, numeric, real, and double precision. Where the description mentions N, it means the number of input rows for which all the input expressions are non-null. In all cases, null is returned if the computation is meaningless, for example when N is zero. statistics linear regression Function Description Partial Mode correlation corr corr ( Y double precision, X double precision ) double precision Computes the correlation coefficient. Yes covariance population covar_pop covar_pop ( Y double precision, X double precision ) double precision Computes the population covariance. Yes covariance sample covar_samp covar_samp ( Y double precision, X double precision ) double precision Computes the sample covariance. Yes regr_avgx regr_avgx ( Y double precision, X double precision ) double precision Computes the average of the independent variable, sum(X)/N. Yes regr_avgy regr_avgy ( Y double precision, X double precision ) double precision Computes the average of the dependent variable, sum(Y)/N. Yes regr_count regr_count ( Y double precision, X double precision ) bigint Computes the number of rows in which both inputs are non-null. Yes regression intercept regr_intercept regr_intercept ( Y double precision, X double precision ) double precision Computes the y-intercept of the least-squares-fit linear equation determined by the (X, Y) pairs. Yes regr_r2 regr_r2 ( Y double precision, X double precision ) double precision Computes the square of the correlation coefficient. Yes regression slope regr_slope regr_slope ( Y double precision, X double precision ) double precision Computes the slope of the least-squares-fit linear equation determined by the (X, Y) pairs. Yes regr_sxx regr_sxx ( Y double precision, X double precision ) double precision Computes the sum of squares of the independent variable, sum(X^2) - sum(X)^2/N. Yes regr_sxy regr_sxy ( Y double precision, X double precision ) double precision Computes the sum of products of independent times dependent variables, sum(X*Y) - sum(X) * sum(Y)/N. Yes regr_syy regr_syy ( Y double precision, X double precision ) double precision Computes the sum of squares of the dependent variable, sum(Y^2) - sum(Y)^2/N. Yes standard deviation stddev stddev ( numeric_type ) double precision for real or double precision, otherwise numeric This is a historical alias for stddev_samp. Yes standard deviation population stddev_pop stddev_pop ( numeric_type ) double precision for real or double precision, otherwise numeric Computes the population standard deviation of the input values. Yes standard deviation sample stddev_samp stddev_samp ( numeric_type ) double precision for real or double precision, otherwise numeric Computes the sample standard deviation of the input values. Yes variance variance ( numeric_type ) double precision for real or double precision, otherwise numeric This is a historical alias for var_samp. Yes variance population var_pop var_pop ( numeric_type ) double precision for real or double precision, otherwise numeric Computes the population variance of the input values (square of the population standard deviation). Yes variance sample var_samp var_samp ( numeric_type ) double precision for real or double precision, otherwise numeric Computes the sample variance of the input values (square of the sample standard deviation). Yes shows some aggregate functions that use the ordered-set aggregate syntax. These functions are sometimes referred to as inverse distribution functions. Their aggregated input is introduced by ORDER BY, and they may also take a direct argument that is not aggregated, but is computed only once. All these functions ignore null values in their aggregated input. For those that take a fraction parameter, the fraction value must be between 0 and 1; an error is thrown if not. However, a null fraction value simply produces a null result. ordered-set aggregate built-in inverse distribution Function Description Partial Mode mode statistical mode () WITHIN GROUP ( ORDER BY anyelement ) anyelement Computes the mode, the most frequent value of the aggregated argument (arbitrarily choosing the first one if there are multiple equally-frequent values). The aggregated argument must be of a sortable type. No percentile continuous percentile_cont ( fraction double precision ) WITHIN GROUP ( ORDER BY double precision ) double precision percentile_cont ( fraction double precision ) WITHIN GROUP ( ORDER BY interval ) interval Computes the continuous percentile, a value corresponding to the specified fraction within the ordered set of aggregated argument values. This will interpolate between adjacent input items if needed. No percentile_cont ( fractions double precision[] ) WITHIN GROUP ( ORDER BY double precision ) double precision[] percentile_cont ( fractions double precision[] ) WITHIN GROUP ( ORDER BY interval ) interval[] Computes multiple continuous percentiles. The result is an array of the same dimensions as the fractions parameter, with each non-null element replaced by the (possibly interpolated) value corresponding to that percentile. No percentile discrete percentile_disc ( fraction double precision ) WITHIN GROUP ( ORDER BY anyelement ) anyelement Computes the discrete percentile, the first value within the ordered set of aggregated argument values whose position in the ordering equals or exceeds the specified fraction. The aggregated argument must be of a sortable type. No percentile_disc ( fractions double precision[] ) WITHIN GROUP ( ORDER BY anyelement ) anyarray Computes multiple discrete percentiles. The result is an array of the same dimensions as the fractions parameter, with each non-null element replaced by the input value corresponding to that percentile. The aggregated argument must be of a sortable type. No hypothetical-set aggregate built-in Each of the hypothetical-set aggregates listed in is associated with a window function of the same name defined in . In each case, the aggregate's result is the value that the associated window function would have returned for the hypothetical row constructed from args, if such a row had been added to the sorted group of rows represented by the sorted_args. For each of these functions, the list of direct arguments given in args must match the number and types of the aggregated arguments given in sorted_args. Unlike most built-in aggregates, these aggregates are not strict, that is they do not drop input rows containing nulls. Null values sort according to the rule specified in the ORDER BY clause. Function Description Partial Mode rank hypothetical rank ( args ) WITHIN GROUP ( ORDER BY sorted_args ) bigint Computes the rank of the hypothetical row, with gaps; that is, the row number of the first row in its peer group. No dense_rank hypothetical dense_rank ( args ) WITHIN GROUP ( ORDER BY sorted_args ) bigint Computes the rank of the hypothetical row, without gaps; this function effectively counts peer groups. No percent_rank hypothetical percent_rank ( args ) WITHIN GROUP ( ORDER BY sorted_args ) double precision Computes the relative rank of the hypothetical row, that is (rank - 1) / (total rows - 1). The value thus ranges from 0 to 1 inclusive. No cume_dist hypothetical cume_dist ( args ) WITHIN GROUP ( ORDER BY sorted_args ) double precision Computes the cumulative distribution, that is (number of rows preceding or peers with hypothetical row) / (total rows). The value thus ranges from 1/N to 1. No Function Description GROUPING GROUPING ( group_by_expression(s) ) integer Returns a bit mask indicating which GROUP BY expressions are not included in the current grouping set. Bits are assigned with the rightmost argument corresponding to the least-significant bit; each bit is 0 if the corresponding expression is included in the grouping criteria of the grouping set generating the current result row, and 1 if it is not included. The grouping operations shown in are used in conjunction with grouping sets (see ) to distinguish result rows. The arguments to the GROUPING function are not actually evaluated, but they must exactly match expressions given in the GROUP BY clause of the associated query level. For example: => SELECT * FROM items_sold; make | model | sales -------+-------+------- Foo | GT | 10 Foo | Tour | 20 Bar | City | 15 Bar | Sport | 5 (4 rows) => SELECT make, model, GROUPING(make,model), sum(sales) FROM items_sold GROUP BY ROLLUP(make,model); make | model | grouping | sum -------+-------+----------+----- Foo | GT | 0 | 10 Foo | Tour | 0 | 20 Bar | City | 0 | 15 Bar | Sport | 0 | 5 Foo | | 1 | 30 Bar | | 1 | 20 | | 3 | 50 (7 rows) Here, the grouping value 0 in the first four rows shows that those have been grouped normally, over both the grouping columns. The value 1 indicates that model was not grouped by in the next-to-last two rows, and the value 3 indicates that neither make nor model was grouped by in the last row (which therefore is an aggregate over all the input rows). window function built-in Window functions provide the ability to perform calculations across sets of rows that are related to the current query row. See for an introduction to this feature, and for syntax details. The built-in window functions are listed in . Note that these functions must be invoked using window function syntax, i.e., an OVER clause is required. In addition to these functions, any built-in or user-defined ordinary aggregate (i.e., not ordered-set or hypothetical-set aggregates) can be used as a window function; see for a list of the built-in aggregates. Aggregate functions act as window functions only when an OVER clause follows the call; otherwise they act as plain aggregates and return a single row for the entire set. Function Description row_number row_number () bigint Returns the number of the current row within its partition, counting from 1. rank rank () bigint Returns the rank of the current row, with gaps; that is, the row_number of the first row in its peer group. dense_rank dense_rank () bigint Returns the rank of the current row, without gaps; this function effectively counts peer groups. percent_rank percent_rank () double precision Returns the relative rank of the current row, that is (rank - 1) / (total partition rows - 1). The value thus ranges from 0 to 1 inclusive. cume_dist cume_dist () double precision Returns the cumulative distribution, that is (number of partition rows preceding or peers with current row) / (total partition rows). The value thus ranges from 1/N to 1. ntile ntile ( num_buckets integer ) integer Returns an integer ranging from 1 to the argument value, dividing the partition as equally as possible. lag lag ( value anycompatible , offset integer , default anycompatible ) anycompatible Returns value evaluated at the row that is offset rows before the current row within the partition; if there is no such row, instead returns default (which must be of a type compatible with value). Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to 1 and default to NULL. lead lead ( value anycompatible , offset integer , default anycompatible ) anycompatible Returns value evaluated at the row that is offset rows after the current row within the partition; if there is no such row, instead returns default (which must be of a type compatible with value). Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to 1 and default to NULL. first_value first_value ( value anyelement ) anyelement Returns value evaluated at the row that is the first row of the window frame. last_value last_value ( value anyelement ) anyelement Returns value evaluated at the row that is the last row of the window frame. nth_value nth_value ( value anyelement, n integer ) anyelement Returns value evaluated at the row that is the n'th row of the window frame (counting from 1); returns NULL if there is no such row. All of the functions listed in depend on the sort ordering specified by the ORDER BY clause of the associated window definition. Rows that are not distinct when considering only the ORDER BY columns are said to be peers. The four ranking functions (including cume_dist) are defined so that they give the same answer for all rows of a peer group. Note that first_value, last_value, and nth_value consider only the rows within the window frame, which by default contains the rows from the start of the partition through the last peer of the current row. This is likely to give unhelpful results for last_value and sometimes also nth_value. You can redefine the frame by adding a suitable frame specification (RANGE, ROWS or GROUPS) to the OVER clause. See for more information about frame specifications. When an aggregate function is used as a window function, it aggregates over the rows within the current row's window frame. An aggregate used with ORDER BY and the default window frame definition produces a running sum type of behavior, which may or may not be what's wanted. To obtain aggregation over the whole partition, omit ORDER BY or use ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING. Other frame specifications can be used to obtain other effects. The SQL standard defines a RESPECT NULLS or IGNORE NULLS option for lead, lag, first_value, last_value, and nth_value. This is not implemented in PostgreSQL: the behavior is always the same as the standard's default, namely RESPECT NULLS. Likewise, the standard's FROM FIRST or FROM LAST option for nth_value is not implemented: only the default FROM FIRST behavior is supported. (You can achieve the result of FROM LAST by reversing the ORDER BY ordering.) EXISTS IN NOT IN ANY ALL SOME subquery This section describes the SQL-compliant subquery expressions available in PostgreSQL. All of the expression forms documented in this section return Boolean (true/false) results. EXISTS (subquery) The argument of EXISTS is an arbitrary SELECT statement, or subquery. The subquery is evaluated to determine whether it returns any rows. If it returns at least one row, the result of EXISTS is true; if the subquery returns no rows, the result of EXISTS is false. The subquery can refer to variables from the surrounding query, which will act as constants during any one evaluation of the subquery. The subquery will generally only be executed long enough to determine whether at least one row is returned, not all the way to completion. It is unwise to write a subquery that has side effects (such as calling sequence functions); whether the side effects occur might be unpredictable. Since the result depends only on whether any rows are returned, and not on the contents of those rows, the output list of the subquery is normally unimportant. A common coding convention is to write all EXISTS tests in the form EXISTS(SELECT 1 WHERE ...). There are exceptions to this rule however, such as subqueries that use INTERSECT. This simple example is like an inner join on col2, but it produces at most one output row for each tab1 row, even if there are several matching tab2 rows: SELECT col1 FROM tab1 WHERE EXISTS (SELECT 1 FROM tab2 WHERE col2 = tab1.col2); expression IN (subquery) The right-hand side is a parenthesized subquery, which must return exactly one column. The left-hand expression is evaluated and compared to each row of the subquery result. The result of IN is true if any equal subquery row is found. The result is false if no equal row is found (including the case where the subquery returns no rows). Note that if the left-hand expression yields null, or if there are no equal right-hand values and at least one right-hand row yields null, the result of the IN construct will be null, not false. This is in accordance with SQL's normal rules for Boolean combinations of null values. As with EXISTS, it's unwise to assume that the subquery will be evaluated completely. row_constructor IN (subquery) The left-hand side of this form of IN is a row constructor, as described in . The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result. The result of IN is true if any equal subquery row is found. The result is false if no equal row is found (including the case where the subquery returns no rows). As usual, null values in the rows are combined per the normal rules of SQL Boolean expressions. Two rows are considered equal if all their corresponding members are non-null and equal; the rows are unequal if any corresponding members are non-null and unequal; otherwise the result of that row comparison is unknown (null). If all the per-row results are either unequal or null, with at least one null, then the result of IN is null. expression NOT IN (subquery) The right-hand side is a parenthesized subquery, which must return exactly one column. The left-hand expression is evaluated and compared to each row of the subquery result. The result of NOT IN is true if only unequal subquery rows are found (including the case where the subquery returns no rows). The result is false if any equal row is found. Note that if the left-hand expression yields null, or if there are no equal right-hand values and at least one right-hand row yields null, the result of the NOT IN construct will be null, not true. This is in accordance with SQL's normal rules for Boolean combinations of null values. As with EXISTS, it's unwise to assume that the subquery will be evaluated completely. row_constructor NOT IN (subquery) The left-hand side of this form of NOT IN is a row constructor, as described in . The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result. The result of NOT IN is true if only unequal subquery rows are found (including the case where the subquery returns no rows). The result is false if any equal row is found. As usual, null values in the rows are combined per the normal rules of SQL Boolean expressions. Two rows are considered equal if all their corresponding members are non-null and equal; the rows are unequal if any corresponding members are non-null and unequal; otherwise the result of that row comparison is unknown (null). If all the per-row results are either unequal or null, with at least one null, then the result of NOT IN is null. expression operator ANY (subquery) expression operator SOME (subquery) The right-hand side is a parenthesized subquery, which must return exactly one column. The left-hand expression is evaluated and compared to each row of the subquery result using the given operator, which must yield a Boolean result. The result of ANY is true if any true result is obtained. The result is false if no true result is found (including the case where the subquery returns no rows). SOME is a synonym for ANY. IN is equivalent to = ANY. Note that if there are no successes and at least one right-hand row yields null for the operator's result, the result of the ANY construct will be null, not false. This is in accordance with SQL's normal rules for Boolean combinations of null values. As with EXISTS, it's unwise to assume that the subquery will be evaluated completely. row_constructor operator ANY (subquery) row_constructor operator SOME (subquery) The left-hand side of this form of ANY is a row constructor, as described in . The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result, using the given operator. The result of ANY is true if the comparison returns true for any subquery row. The result is false if the comparison returns false for every subquery row (including the case where the subquery returns no rows). The result is NULL if no comparison with a subquery row returns true, and at least one comparison returns NULL. See for details about the meaning of a row constructor comparison. expression operator ALL (subquery) The right-hand side is a parenthesized subquery, which must return exactly one column. The left-hand expression is evaluated and compared to each row of the subquery result using the given operator, which must yield a Boolean result. The result of ALL is true if all rows yield true (including the case where the subquery returns no rows). The result is false if any false result is found. The result is NULL if no comparison with a subquery row returns false, and at least one comparison returns NULL. NOT IN is equivalent to <> ALL. As with EXISTS, it's unwise to assume that the subquery will be evaluated completely. row_constructor operator ALL (subquery) The left-hand side of this form of ALL is a row constructor, as described in . The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result, using the given operator. The result of ALL is true if the comparison returns true for all subquery rows (including the case where the subquery returns no rows). The result is false if the comparison returns false for any subquery row. The result is NULL if no comparison with a subquery row returns false, and at least one comparison returns NULL. See for details about the meaning of a row constructor comparison. comparison subquery result row row_constructor operator (subquery) The left-hand side is a row constructor, as described in . The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. Furthermore, the subquery cannot return more than one row. (If it returns zero rows, the result is taken to be null.) The left-hand side is evaluated and compared row-wise to the single subquery result row. See for details about the meaning of a row constructor comparison. IN NOT IN ANY ALL SOME composite type comparison row-wise comparison comparison composite type comparison row constructor IS DISTINCT FROM IS NOT DISTINCT FROM This section describes several specialized constructs for making multiple comparisons between groups of values. These forms are syntactically related to the subquery forms of the previous section, but do not involve subqueries. The forms involving array subexpressions are PostgreSQL extensions; the rest are SQL-compliant. All of the expression forms documented in this section return Boolean (true/false) results. expression IN (value , ...) The right-hand side is a parenthesized list of expressions. The result is true if the left-hand expression's result is equal to any of the right-hand expressions. This is a shorthand notation for expression = value1 OR expression = value2 OR ... Note that if the left-hand expression yields null, or if there are no equal right-hand values and at least one right-hand expression yields null, the result of the IN construct will be null, not false. This is in accordance with SQL's normal rules for Boolean combinations of null values. expression NOT IN (value , ...) The right-hand side is a parenthesized list of expressions. The result is true if the left-hand expression's result is unequal to all of the right-hand expressions. This is a shorthand notation for expression <> value1 AND expression <> value2 AND ... Note that if the left-hand expression yields null, or if there are no equal right-hand values and at least one right-hand expression yields null, the result of the NOT IN construct will be null, not true as one might naively expect. This is in accordance with SQL's normal rules for Boolean combinations of null values. x NOT IN y is equivalent to NOT (x IN y) in all cases. However, null values are much more likely to trip up the novice when working with NOT IN than when working with IN. It is best to express your condition positively if possible. expression operator ANY (array expression) expression operator SOME (array expression) The right-hand side is a parenthesized expression, which must yield an array value. The left-hand expression is evaluated and compared to each element of the array using the given operator, which must yield a Boolean result. The result of ANY is true if any true result is obtained. The result is false if no true result is found (including the case where the array has zero elements). If the array expression yields a null array, the result of ANY will be null. If the left-hand expression yields null, the result of ANY is ordinarily null (though a non-strict comparison operator could possibly yield a different result). Also, if the right-hand array contains any null elements and no true comparison result is obtained, the result of ANY will be null, not false (again, assuming a strict comparison operator). This is in accordance with SQL's normal rules for Boolean combinations of null values. SOME is a synonym for ANY. expression operator ALL (array expression) The right-hand side is a parenthesized expression, which must yield an array value. The left-hand expression is evaluated and compared to each element of the array using the given operator, which must yield a Boolean result. The result of ALL is true if all comparisons yield true (including the case where the array has zero elements). The result is false if any false result is found. If the array expression yields a null array, the result of ALL will be null. If the left-hand expression yields null, the result of ALL is ordinarily null (though a non-strict comparison operator could possibly yield a different result). Also, if the right-hand array contains any null elements and no false comparison result is obtained, the result of ALL will be null, not true (again, assuming a strict comparison operator). This is in accordance with SQL's normal rules for Boolean combinations of null values. row_constructor operator row_constructor Each side is a row constructor, as described in . The two row constructors must have the same number of fields. The given operator is applied to each pair of corresponding fields. (Since the fields could be of different types, this means that a different specific operator could be selected for each pair.) All the selected operators must be members of some B-tree operator class, or be the negator of an = member of a B-tree operator class, meaning that row constructor comparison is only possible when the operator is =, <>, <, <=, >, or >=, or has semantics similar to one of these. The = and <> cases work slightly differently from the others. Two rows are considered equal if all their corresponding members are non-null and equal; the rows are unequal if any corresponding members are non-null and unequal; otherwise the result of the row comparison is unknown (null). For the <, <=, > and >= cases, the row elements are compared left-to-right, stopping as soon as an unequal or null pair of elements is found. If either of this pair of elements is null, the result of the row comparison is unknown (null); otherwise comparison of this pair of elements determines the result. For example, ROW(1,2,NULL) < ROW(1,3,0) yields true, not null, because the third pair of elements are not considered. Prior to PostgreSQL 8.2, the <, <=, > and >= cases were not handled per SQL specification. A comparison like ROW(a,b) < ROW(c,d) was implemented as a < c AND b < d whereas the correct behavior is equivalent to a < c OR (a = c AND b < d). row_constructor IS DISTINCT FROM row_constructor This construct is similar to a <> row comparison, but it does not yield null for null inputs. Instead, any null value is considered unequal to (distinct from) any non-null value, and any two nulls are considered equal (not distinct). Thus the result will either be true or false, never null. row_constructor IS NOT DISTINCT FROM row_constructor This construct is similar to a = row comparison, but it does not yield null for null inputs. Instead, any null value is considered unequal to (distinct from) any non-null value, and any two nulls are considered equal (not distinct). Thus the result will always be either true or false, never null. record operator record The SQL specification requires row-wise comparison to return NULL if the result depends on comparing two NULL values or a NULL and a non-NULL. PostgreSQL does this only when comparing the results of two row constructors (as in ) or comparing a row constructor to the output of a subquery (as in ). In other contexts where two composite-type values are compared, two NULL field values are considered equal, and a NULL is considered larger than a non-NULL. This is necessary in order to have consistent sorting and indexing behavior for composite types. Each side is evaluated and they are compared row-wise. Composite type comparisons are allowed when the operator is =, <>, <, <=, > or >=, or has semantics similar to one of these. (To be specific, an operator can be a row comparison operator if it is a member of a B-tree operator class, or is the negator of the = member of a B-tree operator class.) The default behavior of the above operators is the same as for IS [ NOT ] DISTINCT FROM for row constructors (see ). To support matching of rows which include elements without a default B-tree operator class, the following operators are defined for composite type comparison: *=, *<>, *<, *<=, *>, and *>=. These operators compare the internal binary representation of the two rows. Two rows might have a different binary representation even though comparisons of the two rows with the equality operator is true. The ordering of rows under these comparison operators is deterministic but not otherwise meaningful. These operators are used internally for materialized views and might be useful for other specialized purposes such as replication and B-Tree deduplication (see ). They are not intended to be generally useful for writing queries, though. set returning functions functions This section describes functions that possibly return more than one row. The most widely used functions in this class are series generating functions, as detailed in and . Other, more specialized set-returning functions are described elsewhere in this manual. See for ways to combine multiple set-returning functions. Function Description generate_series generate_series ( start integer, stop integer , step integer ) setof integer generate_series ( start bigint, stop bigint , step bigint ) setof bigint generate_series ( start numeric, stop numeric , step numeric ) setof numeric Generates a series of values from start to stop, with a step size of step. step defaults to 1. generate_series ( start timestamp, stop timestamp, step interval ) setof timestamp generate_series ( start timestamp with time zone, stop timestamp with time zone, step interval ) setof timestamp with time zone Generates a series of values from start to stop, with a step size of step. When step is positive, zero rows are returned if start is greater than stop. Conversely, when step is negative, zero rows are returned if start is less than stop. Zero rows are also returned if any input is NULL. It is an error for step to be zero. Some examples follow: SELECT * FROM generate_series(2,4); generate_series ----------------- 2 3 4 (3 rows) SELECT * FROM generate_series(5,1,-2); generate_series ----------------- 5 3 1 (3 rows) SELECT * FROM generate_series(4,3); generate_series ----------------- (0 rows) SELECT generate_series(1.1, 4, 1.3); generate_series ----------------- 1.1 2.4 3.7 (3 rows) -- this example relies on the date-plus-integer operator: SELECT current_date + s.a AS dates FROM generate_series(0,14,7) AS s(a); dates ------------ 2004-02-05 2004-02-12 2004-02-19 (3 rows) SELECT * FROM generate_series('2008-03-01 00:00'::timestamp, '2008-03-04 12:00', '10 hours'); generate_series --------------------- 2008-03-01 00:00:00 2008-03-01 10:00:00 2008-03-01 20:00:00 2008-03-02 06:00:00 2008-03-02 16:00:00 2008-03-03 02:00:00 2008-03-03 12:00:00 2008-03-03 22:00:00 2008-03-04 08:00:00 (9 rows) Function Description generate_subscripts generate_subscripts ( array anyarray, dim integer ) setof integer Generates a series comprising the valid subscripts of the dim'th dimension of the given array. generate_subscripts ( array anyarray, dim integer, reverse boolean ) setof integer Generates a series comprising the valid subscripts of the dim'th dimension of the given array. When reverse is true, returns the series in reverse order. generate_subscripts is a convenience function that generates the set of valid subscripts for the specified dimension of the given array. Zero rows are returned for arrays that do not have the requested dimension, or if any input is NULL. Some examples follow: -- basic usage: SELECT generate_subscripts('{NULL,1,NULL,2}'::int[], 1) AS s; s --- 1 2 3 4 (4 rows) -- presenting an array, the subscript and the subscripted -- value requires a subquery: SELECT * FROM arrays; a -------------------- {-1,-2} {100,200,300} (2 rows) SELECT a AS array, s AS subscript, a[s] AS value FROM (SELECT generate_subscripts(a, 1) AS s, a FROM arrays) foo; array | subscript | value ---------------+-----------+------- {-1,-2} | 1 | -1 {-1,-2} | 2 | -2 {100,200,300} | 1 | 100 {100,200,300} | 2 | 200 {100,200,300} | 3 | 300 (5 rows) -- unnest a 2D array: CREATE OR REPLACE FUNCTION unnest2(anyarray) RETURNS SETOF anyelement AS $$ select $1[i][j] from generate_subscripts($1,1) g1(i), generate_subscripts($1,2) g2(j); $$ LANGUAGE sql IMMUTABLE; CREATE FUNCTION SELECT * FROM unnest2(ARRAY[[1,2],[3,4]]); unnest2 --------- 1 2 3 4 (4 rows) ordinality When a function in the FROM clause is suffixed by WITH ORDINALITY, a bigint column is appended to the function's output column(s), which starts from 1 and increments by 1 for each row of the function's output. This is most useful in the case of set returning functions such as unnest(). -- set returning function WITH ORDINALITY: SELECT * FROM pg_ls_dir('.') WITH ORDINALITY AS t(ls,n); ls | n -----------------+---- pg_serial | 1 pg_twophase | 2 postmaster.opts | 3 pg_notify | 4 postgresql.conf | 5 pg_tblspc | 6 logfile | 7 base | 8 postmaster.pid | 9 pg_ident.conf | 10 global | 11 pg_xact | 12 pg_snapshots | 13 pg_multixact | 14 PG_VERSION | 15 pg_wal | 16 pg_hba.conf | 17 pg_stat_tmp | 18 pg_subtrans | 19 (19 rows) shows several functions that extract session and system information. In addition to the functions listed in this section, there are a number of functions related to the statistics system that also provide system information. See for more information. Function Description current_catalog current_catalog name current_database current_database () name Returns the name of the current database. (Databases are called catalogs in the SQL standard, so current_catalog is the standard's spelling.) current_query current_query () text Returns the text of the currently executing query, as submitted by the client (which might contain more than one statement). current_role current_role name This is equivalent to current_user. current_schema schema current current_schema name current_schema () name Returns the name of the schema that is first in the search path (or a null value if the search path is empty). This is the schema that will be used for any tables or other named objects that are created without specifying a target schema. current_schemas search path current current_schemas ( include_implicit boolean ) name[] Returns an array of the names of all schemas presently in the effective search path, in their priority order. (Items in the current setting that do not correspond to existing, searchable schemas are omitted.) If the Boolean argument is true, then implicitly-searched system schemas such as pg_catalog are included in the result. current_user user current current_user name Returns the user name of the current execution context. inet_client_addr inet_client_addr () inet Returns the IP address of the current client, or NULL if the current connection is via a Unix-domain socket. inet_client_port inet_client_port () integer Returns the IP port number of the current client, or NULL if the current connection is via a Unix-domain socket. inet_server_addr inet_server_addr () inet Returns the IP address on which the server accepted the current connection, or NULL if the current connection is via a Unix-domain socket. inet_server_port inet_server_port () integer Returns the IP port number on which the server accepted the current connection, or NULL if the current connection is via a Unix-domain socket. pg_backend_pid pg_backend_pid () integer Returns the process ID of the server process attached to the current session. pg_blocking_pids pg_blocking_pids ( integer ) integer[] Returns an array of the process ID(s) of the sessions that are blocking the server process with the specified process ID from acquiring a lock, or an empty array if there is no such server process or it is not blocked. One server process blocks another if it either holds a lock that conflicts with the blocked process's lock request (hard block), or is waiting for a lock that would conflict with the blocked process's lock request and is ahead of it in the wait queue (soft block). When using parallel queries the result always lists client-visible process IDs (that is, pg_backend_pid results) even if the actual lock is held or awaited by a child worker process. As a result of that, there may be duplicated PIDs in the result. Also note that when a prepared transaction holds a conflicting lock, it will be represented by a zero process ID. Frequent calls to this function could have some impact on database performance, because it needs exclusive access to the lock manager's shared state for a short time. pg_conf_load_time pg_conf_load_time () timestamp with time zone Returns the time when the server configuration files were last loaded. If the current session was alive at the time, this will be the time when the session itself re-read the configuration files (so the reading will vary a little in different sessions). Otherwise it is the time when the postmaster process re-read the configuration files. pg_current_logfile Logging pg_current_logfile function current_logfiles and the pg_current_logfile function Logging current_logfiles file and the pg_current_logfile function pg_current_logfile ( text ) text Returns the path name of the log file currently in use by the logging collector. The path includes the directory and the individual log file name. The result is NULL if the logging collector is disabled. When multiple log files exist, each in a different format, pg_current_logfile without an argument returns the path of the file having the first format found in the ordered list: stderr, csvlog, jsonlog. NULL is returned if no log file has any of these formats. To request information about a specific log file format, supply either csvlog, jsonlog or stderr as the value of the optional parameter. The result is NULL if the log format requested is not configured in . The result reflects the contents of the current_logfiles file. pg_my_temp_schema pg_my_temp_schema () oid Returns the OID of the current session's temporary schema, or zero if it has none (because it has not created any temporary tables). pg_is_other_temp_schema pg_is_other_temp_schema ( oid ) boolean Returns true if the given OID is the OID of another session's temporary schema. (This can be useful, for example, to exclude other sessions' temporary tables from a catalog display.) pg_jit_available pg_jit_available () boolean Returns true if a JIT compiler extension is available (see ) and the configuration parameter is set to on. pg_listening_channels pg_listening_channels () setof text Returns the set of names of asynchronous notification channels that the current session is listening to. pg_notification_queue_usage pg_notification_queue_usage () double precision Returns the fraction (0–1) of the asynchronous notification queue's maximum size that is currently occupied by notifications that are waiting to be processed. See and for more information. pg_postmaster_start_time pg_postmaster_start_time () timestamp with time zone Returns the time when the server started. pg_safe_snapshot_blocking_pids pg_safe_snapshot_blocking_pids ( integer ) integer[] Returns an array of the process ID(s) of the sessions that are blocking the server process with the specified process ID from acquiring a safe snapshot, or an empty array if there is no such server process or it is not blocked. A session running a SERIALIZABLE transaction blocks a SERIALIZABLE READ ONLY DEFERRABLE transaction from acquiring a snapshot until the latter determines that it is safe to avoid taking any predicate locks. See for more information about serializable and deferrable transactions. Frequent calls to this function could have some impact on database performance, because it needs access to the predicate lock manager's shared state for a short time. pg_trigger_depth pg_trigger_depth () integer Returns the current nesting level of PostgreSQL triggers (0 if not called, directly or indirectly, from inside a trigger). session_user session_user name Returns the session user's name. user user name This is equivalent to current_user. version version () text Returns a string describing the PostgreSQL server's version. You can also get this information from , or for a machine-readable version use . Software developers should use server_version_num (available since 8.2) or instead of parsing the text version. current_catalog, current_role, current_schema, current_user, session_user, and user have special syntactic status in SQL: they must be called without trailing parentheses. In PostgreSQL, parentheses can optionally be used with current_schema, but not with the others. The session_user is normally the user who initiated the current database connection; but superusers can change this setting with . The current_user is the user identifier that is applicable for permission checking. Normally it is equal to the session user, but it can be changed with . It also changes during the execution of functions with the attribute SECURITY DEFINER. In Unix parlance, the session user is the real user and the current user is the effective user. current_role and user are synonyms for current_user. (The SQL standard draws a distinction between current_role and current_user, but PostgreSQL does not, since it unifies users and roles into a single kind of entity.) privilege querying lists functions that allow querying object access privileges programmatically. (See for more information about privileges.) In these functions, the user whose privileges are being inquired about can be specified by name or by OID (pg_authid.oid), or if the name is given as public then the privileges of the PUBLIC pseudo-role are checked. Also, the user argument can be omitted entirely, in which case the current_user is assumed. The object that is being inquired about can be specified either by name or by OID, too. When specifying by name, a schema name can be included if relevant. The access privilege of interest is specified by a text string, which must evaluate to one of the appropriate privilege keywords for the object's type (e.g., SELECT). Optionally, WITH GRANT OPTION can be added to a privilege type to test whether the privilege is held with grant option. Also, multiple privilege types can be listed separated by commas, in which case the result will be true if any of the listed privileges is held. (Case of the privilege string is not significant, and extra whitespace is allowed between but not within privilege names.) Some examples: SELECT has_table_privilege('myschema.mytable', 'select'); SELECT has_table_privilege('joe', 'mytable', 'INSERT, SELECT WITH GRANT OPTION'); Function Description has_any_column_privilege has_any_column_privilege ( user name or oid, table text or oid, privilege text ) boolean Does user have privilege for any column of table? This succeeds either if the privilege is held for the whole table, or if there is a column-level grant of the privilege for at least one column. Allowable privilege types are SELECT, INSERT, UPDATE, and REFERENCES. has_column_privilege has_column_privilege ( user name or oid, table text or oid, column text or smallint, privilege text ) boolean Does user have privilege for the specified table column? This succeeds either if the privilege is held for the whole table, or if there is a column-level grant of the privilege for the column. The column can be specified by name or by attribute number (pg_attribute.attnum). Allowable privilege types are SELECT, INSERT, UPDATE, and REFERENCES. has_database_privilege has_database_privilege ( user name or oid, database text or oid, privilege text ) boolean Does user have privilege for database? Allowable privilege types are CREATE, CONNECT, TEMPORARY, and TEMP (which is equivalent to TEMPORARY). has_foreign_data_wrapper_privilege has_foreign_data_wrapper_privilege ( user name or oid, fdw text or oid, privilege text ) boolean Does user have privilege for foreign-data wrapper? The only allowable privilege type is USAGE. has_function_privilege has_function_privilege ( user name or oid, function text or oid, privilege text ) boolean Does user have privilege for function? The only allowable privilege type is EXECUTE. When specifying a function by name rather than by OID, the allowed input is the same as for the regprocedure data type (see ). An example is: SELECT has_function_privilege('joeuser', 'myfunc(int, text)', 'execute'); has_language_privilege has_language_privilege ( user name or oid, language text or oid, privilege text ) boolean Does user have privilege for language? The only allowable privilege type is USAGE. has_parameter_privilege has_parameter_privilege ( user name or oid, parameter text, privilege text ) boolean Does user have privilege for configuration parameter? The parameter name is case-insensitive. Allowable privilege types are SET and ALTER SYSTEM. has_schema_privilege has_schema_privilege ( user name or oid, schema text or oid, privilege text ) boolean Does user have privilege for schema? Allowable privilege types are CREATE and USAGE. has_sequence_privilege has_sequence_privilege ( user name or oid, sequence text or oid, privilege text ) boolean Does user have privilege for sequence? Allowable privilege types are USAGE, SELECT, and UPDATE. has_server_privilege has_server_privilege ( user name or oid, server text or oid, privilege text ) boolean Does user have privilege for foreign server? The only allowable privilege type is USAGE. has_table_privilege has_table_privilege ( user name or oid, table text or oid, privilege text ) boolean Does user have privilege for table? Allowable privilege types are SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, and TRIGGER. has_tablespace_privilege has_tablespace_privilege ( user name or oid, tablespace text or oid, privilege text ) boolean Does user have privilege for tablespace? The only allowable privilege type is CREATE. has_type_privilege has_type_privilege ( user name or oid, type text or oid, privilege text ) boolean Does user have privilege for data type? The only allowable privilege type is USAGE. When specifying a type by name rather than by OID, the allowed input is the same as for the regtype data type (see ). pg_has_role pg_has_role ( user name or oid, role text or oid, privilege text ) boolean Does user have privilege for role? Allowable privilege types are MEMBER and USAGE. MEMBER denotes direct or indirect membership in the role (that is, the right to do SET ROLE), while USAGE denotes whether the privileges of the role are immediately available without doing SET ROLE. This function does not allow the special case of setting user to public, because the PUBLIC pseudo-role can never be a member of real roles. row_security_active row_security_active ( table text or oid ) boolean Is row-level security active for the specified table in the context of the current user and current environment? shows the operators available for the aclitem type, which is the catalog representation of access privileges. See for information about how to read access privilege values. Operator Description Example(s) aclitemeq aclitem = aclitem boolean Are aclitems equal? (Notice that type aclitem lacks the usual set of comparison operators; it has only equality. In turn, aclitem arrays can only be compared for equality.) 'calvin=r*w/hobbes'::aclitem = 'calvin=r*w*/hobbes'::aclitem f aclcontains aclitem[] @> aclitem boolean Does array contain the specified privileges? (This is true if there is an array entry that matches the aclitem's grantee and grantor, and has at least the specified set of privileges.) '{calvin=r*w/hobbes,hobbes=r*w*/postgres}'::aclitem[] @> 'calvin=r*/hobbes'::aclitem t aclitem[] ~ aclitem boolean This is a deprecated alias for @>. '{calvin=r*w/hobbes,hobbes=r*w*/postgres}'::aclitem[] ~ 'calvin=r*/hobbes'::aclitem t shows some additional functions to manage the aclitem type. Function Description acldefault acldefault ( type "char", ownerId oid ) aclitem[] Constructs an aclitem array holding the default access privileges for an object of type type belonging to the role with OID ownerId. This represents the access privileges that will be assumed when an object's ACL entry is null. (The default access privileges are described in .) The type parameter must be one of 'c' for COLUMN, 'r' for TABLE and table-like objects, 's' for SEQUENCE, 'd' for DATABASE, 'f' for FUNCTION or PROCEDURE, 'l' for LANGUAGE, 'L' for LARGE OBJECT, 'n' for SCHEMA, 'p' for PARAMETER, 't' for TABLESPACE, 'F' for FOREIGN DATA WRAPPER, 'S' for FOREIGN SERVER, or 'T' for TYPE or DOMAIN. aclexplode aclexplode ( aclitem[] ) setof record ( grantor oid, grantee oid, privilege_type text, is_grantable boolean ) Returns the aclitem array as a set of rows. If the grantee is the pseudo-role PUBLIC, it is represented by zero in the grantee column. Each granted privilege is represented as SELECT, INSERT, etc. Note that each privilege is broken out as a separate row, so only one keyword appears in the privilege_type column. makeaclitem makeaclitem ( grantee oid, grantor oid, privileges text, is_grantable boolean ) aclitem Constructs an aclitem with the given properties. shows functions that determine whether a certain object is visible in the current schema search path. For example, a table is said to be visible if its containing schema is in the search path and no table of the same name appears earlier in the search path. This is equivalent to the statement that the table can be referenced by name without explicit schema qualification. Thus, to list the names of all visible tables: SELECT relname FROM pg_class WHERE pg_table_is_visible(oid); For functions and operators, an object in the search path is said to be visible if there is no object of the same name and argument data type(s) earlier in the path. For operator classes and families, both the name and the associated index access method are considered. search path object visibility Function Description pg_collation_is_visible pg_collation_is_visible ( collation oid ) boolean Is collation visible in search path? pg_conversion_is_visible pg_conversion_is_visible ( conversion oid ) boolean Is conversion visible in search path? pg_function_is_visible pg_function_is_visible ( function oid ) boolean Is function visible in search path? (This also works for procedures and aggregates.) pg_opclass_is_visible pg_opclass_is_visible ( opclass oid ) boolean Is operator class visible in search path? pg_operator_is_visible pg_operator_is_visible ( operator oid ) boolean Is operator visible in search path? pg_opfamily_is_visible pg_opfamily_is_visible ( opclass oid ) boolean Is operator family visible in search path? pg_statistics_obj_is_visible pg_statistics_obj_is_visible ( stat oid ) boolean Is statistics object visible in search path? pg_table_is_visible pg_table_is_visible ( table oid ) boolean Is table visible in search path? (This works for all types of relations, including views, materialized views, indexes, sequences and foreign tables.) pg_ts_config_is_visible pg_ts_config_is_visible ( config oid ) boolean Is text search configuration visible in search path? pg_ts_dict_is_visible pg_ts_dict_is_visible ( dict oid ) boolean Is text search dictionary visible in search path? pg_ts_parser_is_visible pg_ts_parser_is_visible ( parser oid ) boolean Is text search parser visible in search path? pg_ts_template_is_visible pg_ts_template_is_visible ( template oid ) boolean Is text search template visible in search path? pg_type_is_visible pg_type_is_visible ( type oid ) boolean Is type (or domain) visible in search path? All these functions require object OIDs to identify the object to be checked. If you want to test an object by name, it is convenient to use the OID alias types (regclass, regtype, regprocedure, regoperator, regconfig, or regdictionary), for example: SELECT pg_type_is_visible('myschema.widget'::regtype); Note that it would not make much sense to test a non-schema-qualified type name in this way — if the name can be recognized at all, it must be visible. lists functions that extract information from the system catalogs. Function Description format_type format_type ( type oid, typemod integer ) text Returns the SQL name for a data type that is identified by its type OID and possibly a type modifier. Pass NULL for the type modifier if no specific modifier is known. pg_char_to_encoding pg_char_to_encoding ( encoding name ) integer Converts the supplied encoding name into an integer representing the internal identifier used in some system catalog tables. Returns -1 if an unknown encoding name is provided. pg_encoding_to_char pg_encoding_to_char ( encoding integer ) name Converts the integer used as the internal identifier of an encoding in some system catalog tables into a human-readable string. Returns an empty string if an invalid encoding number is provided. pg_get_catalog_foreign_keys pg_get_catalog_foreign_keys () setof record ( fktable regclass, fkcols text[], pktable regclass, pkcols text[], is_array boolean, is_opt boolean ) Returns a set of records describing the foreign key relationships that exist within the PostgreSQL system catalogs. The fktable column contains the name of the referencing catalog, and the fkcols column contains the name(s) of the referencing column(s). Similarly, the pktable column contains the name of the referenced catalog, and the pkcols column contains the name(s) of the referenced column(s). If is_array is true, the last referencing column is an array, each of whose elements should match some entry in the referenced catalog. If is_opt is true, the referencing column(s) are allowed to contain zeroes instead of a valid reference. pg_get_constraintdef pg_get_constraintdef ( constraint oid , pretty boolean ) text Reconstructs the creating command for a constraint. (This is a decompiled reconstruction, not the original text of the command.) pg_get_expr pg_get_expr ( expr pg_node_tree, relation oid , pretty boolean ) text Decompiles the internal form of an expression stored in the system catalogs, such as the default value for a column. If the expression might contain Vars, specify the OID of the relation they refer to as the second parameter; if no Vars are expected, passing zero is sufficient. pg_get_functiondef pg_get_functiondef ( func oid ) text Reconstructs the creating command for a function or procedure. (This is a decompiled reconstruction, not the original text of the command.) The result is a complete CREATE OR REPLACE FUNCTION or CREATE OR REPLACE PROCEDURE statement. pg_get_function_arguments pg_get_function_arguments ( func oid ) text Reconstructs the argument list of a function or procedure, in the form it would need to appear in within CREATE FUNCTION (including default values). pg_get_function_identity_arguments pg_get_function_identity_arguments ( func oid ) text Reconstructs the argument list necessary to identify a function or procedure, in the form it would need to appear in within commands such as ALTER FUNCTION. This form omits default values. pg_get_function_result pg_get_function_result ( func oid ) text Reconstructs the RETURNS clause of a function, in the form it would need to appear in within CREATE FUNCTION. Returns NULL for a procedure. pg_get_indexdef pg_get_indexdef ( index oid , column integer, pretty boolean ) text Reconstructs the creating command for an index. (This is a decompiled reconstruction, not the original text of the command.) If column is supplied and is not zero, only the definition of that column is reconstructed. pg_get_keywords pg_get_keywords () setof record ( word text, catcode "char", barelabel boolean, catdesc text, baredesc text ) Returns a set of records describing the SQL keywords recognized by the server. The word column contains the keyword. The catcode column contains a category code: U for an unreserved keyword, C for a keyword that can be a column name, T for a keyword that can be a type or function name, or R for a fully reserved keyword. The barelabel column contains true if the keyword can be used as a bare column label in SELECT lists, or false if it can only be used after AS. The catdesc column contains a possibly-localized string describing the keyword's category. The baredesc column contains a possibly-localized string describing the keyword's column label status. pg_get_ruledef pg_get_ruledef ( rule oid , pretty boolean ) text Reconstructs the creating command for a rule. (This is a decompiled reconstruction, not the original text of the command.) pg_get_serial_sequence pg_get_serial_sequence ( table text, column text ) text Returns the name of the sequence associated with a column, or NULL if no sequence is associated with the column. If the column is an identity column, the associated sequence is the sequence internally created for that column. For columns created using one of the serial types (serial, smallserial, bigserial), it is the sequence created for that serial column definition. In the latter case, the association can be modified or removed with ALTER SEQUENCE OWNED BY. (This function probably should have been called pg_get_owned_sequence; its current name reflects the fact that it has historically been used with serial-type columns.) The first parameter is a table name with optional schema, and the second parameter is a column name. Because the first parameter potentially contains both schema and table names, it is parsed per usual SQL rules, meaning it is lower-cased by default. The second parameter, being just a column name, is treated literally and so has its case preserved. The result is suitably formatted for passing to the sequence functions (see ). A typical use is in reading the current value of the sequence for an identity or serial column, for example: SELECT currval(pg_get_serial_sequence('sometable', 'id')); pg_get_statisticsobjdef pg_get_statisticsobjdef ( statobj oid ) text Reconstructs the creating command for an extended statistics object. (This is a decompiled reconstruction, not the original text of the command.) pg_get_triggerdef pg_get_triggerdef ( trigger oid , pretty boolean ) text Reconstructs the creating command for a trigger. (This is a decompiled reconstruction, not the original text of the command.) pg_get_userbyid pg_get_userbyid ( role oid ) name Returns a role's name given its OID. pg_get_viewdef pg_get_viewdef ( view oid , pretty boolean ) text Reconstructs the underlying SELECT command for a view or materialized view. (This is a decompiled reconstruction, not the original text of the command.) pg_get_viewdef ( view oid, wrap_column integer ) text Reconstructs the underlying SELECT command for a view or materialized view. (This is a decompiled reconstruction, not the original text of the command.) In this form of the function, pretty-printing is always enabled, and long lines are wrapped to try to keep them shorter than the specified number of columns. pg_get_viewdef ( view text , pretty boolean ) text Reconstructs the underlying SELECT command for a view or materialized view, working from a textual name for the view rather than its OID. (This is deprecated; use the OID variant instead.) pg_index_column_has_property pg_index_column_has_property ( index regclass, column integer, property text ) boolean Tests whether an index column has the named property. Common index column properties are listed in . (Note that extension access methods can define additional property names for their indexes.) NULL is returned if the property name is not known or does not apply to the particular object, or if the OID or column number does not identify a valid object. pg_index_has_property pg_index_has_property ( index regclass, property text ) boolean Tests whether an index has the named property. Common index properties are listed in . (Note that extension access methods can define additional property names for their indexes.) NULL is returned if the property name is not known or does not apply to the particular object, or if the OID does not identify a valid object. pg_indexam_has_property pg_indexam_has_property ( am oid, property text ) boolean Tests whether an index access method has the named property. Access method properties are listed in . NULL is returned if the property name is not known or does not apply to the particular object, or if the OID does not identify a valid object. pg_options_to_table pg_options_to_table ( options_array text[] ) setof record ( option_name text, option_value text ) Returns the set of storage options represented by a value from pg_class.reloptions or pg_attribute.attoptions. pg_settings_get_flags pg_settings_get_flags ( guc text ) text[] Returns an array of the flags associated with the given GUC, or NULL if it does not exist. The result is an empty array if the GUC exists but there are no flags to show. Only the most useful flags listed in are exposed. pg_tablespace_databases pg_tablespace_databases ( tablespace oid ) setof oid Returns the set of OIDs of databases that have objects stored in the specified tablespace. If this function returns any rows, the tablespace is not empty and cannot be dropped. To identify the specific objects populating the tablespace, you will need to connect to the database(s) identified by pg_tablespace_databases and query their pg_class catalogs. pg_tablespace_location pg_tablespace_location ( tablespace oid ) text Returns the file system path that this tablespace is located in. pg_typeof pg_typeof ( "any" ) regtype Returns the OID of the data type of the value that is passed to it. This can be helpful for troubleshooting or dynamically constructing SQL queries. The function is declared as returning regtype, which is an OID alias type (see ); this means that it is the same as an OID for comparison purposes but displays as a type name. For example: SELECT pg_typeof(33); pg_typeof ----------- integer SELECT typlen FROM pg_type WHERE oid = pg_typeof(33); typlen -------- 4 COLLATION FOR COLLATION FOR ( "any" ) text Returns the name of the collation of the value that is passed to it. The value is quoted and schema-qualified if necessary. If no collation was derived for the argument expression, then NULL is returned. If the argument is not of a collatable data type, then an error is raised. For example: SELECT collation for (description) FROM pg_description LIMIT 1; pg_collation_for ------------------ "default" SELECT collation for ('foo' COLLATE "de_DE"); pg_collation_for ------------------ "de_DE" to_regclass to_regclass ( text ) regclass Translates a textual relation name to its OID. A similar result is obtained by casting the string to type regclass (see ); however, this function will return NULL rather than throwing an error if the name is not found. Also unlike the cast, this does not accept a numeric OID as input. to_regcollation to_regcollation ( text ) regcollation Translates a textual collation name to its OID. A similar result is obtained by casting the string to type regcollation (see ); however, this function will return NULL rather than throwing an error if the name is not found. Also unlike the cast, this does not accept a numeric OID as input. to_regnamespace to_regnamespace ( text ) regnamespace Translates a textual schema name to its OID. A similar result is obtained by casting the string to type regnamespace (see ); however, this function will return NULL rather than throwing an error if the name is not found. Also unlike the cast, this does not accept a numeric OID as input. to_regoper to_regoper ( text ) regoper Translates a textual operator name to its OID. A similar result is obtained by casting the string to type regoper (see ); however, this function will return NULL rather than throwing an error if the name is not found or is ambiguous. Also unlike the cast, this does not accept a numeric OID as input. to_regoperator to_regoperator ( text ) regoperator Translates a textual operator name (with parameter types) to its OID. A similar result is obtained by casting the string to type regoperator (see ); however, this function will return NULL rather than throwing an error if the name is not found. Also unlike the cast, this does not accept a numeric OID as input. to_regproc to_regproc ( text ) regproc Translates a textual function or procedure name to its OID. A similar result is obtained by casting the string to type regproc (see ); however, this function will return NULL rather than throwing an error if the name is not found or is ambiguous. Also unlike the cast, this does not accept a numeric OID as input. to_regprocedure to_regprocedure ( text ) regprocedure Translates a textual function or procedure name (with argument types) to its OID. A similar result is obtained by casting the string to type regprocedure (see ); however, this function will return NULL rather than throwing an error if the name is not found. Also unlike the cast, this does not accept a numeric OID as input. to_regrole to_regrole ( text ) regrole Translates a textual role name to its OID. A similar result is obtained by casting the string to type regrole (see ); however, this function will return NULL rather than throwing an error if the name is not found. Also unlike the cast, this does not accept a numeric OID as input. to_regtype to_regtype ( text ) regtype Translates a textual type name to its OID. A similar result is obtained by casting the string to type regtype (see ); however, this function will return NULL rather than throwing an error if the name is not found. Also unlike the cast, this does not accept a numeric OID as input. Most of the functions that reconstruct (decompile) database objects have an optional pretty flag, which if true causes the result to be pretty-printed. Pretty-printing suppresses unnecessary parentheses and adds whitespace for legibility. The pretty-printed format is more readable, but the default format is more likely to be interpreted the same way by future versions of PostgreSQL; so avoid using pretty-printed output for dump purposes. Passing false for the pretty parameter yields the same result as omitting the parameter. NameDescription asc Does the column sort in ascending order on a forward scan? desc Does the column sort in descending order on a forward scan? nulls_first Does the column sort with nulls first on a forward scan? nulls_last Does the column sort with nulls last on a forward scan? orderable Does the column possess any defined sort ordering? distance_orderable Can the column be scanned in order by a distance operator, for example ORDER BY col <-> constant ? returnable Can the column value be returned by an index-only scan? search_array Does the column natively support col = ANY(array) searches? search_nulls Does the column support IS NULL and IS NOT NULL searches? NameDescription clusterable Can the index be used in a CLUSTER command? index_scan Does the index support plain (non-bitmap) scans? bitmap_scan Does the index support bitmap scans? backward_scan Can the scan direction be changed in mid-scan (to support FETCH BACKWARD on a cursor without needing materialization)? NameDescription can_order Does the access method support ASC, DESC and related keywords in CREATE INDEX? can_unique Does the access method support unique indexes? can_multi_col Does the access method support indexes with multiple columns? can_exclude Does the access method support exclusion constraints? can_include Does the access method support the INCLUDE clause of CREATE INDEX? FlagDescription EXPLAIN Parameters with this flag are included in EXPLAIN (SETTINGS) commands. NO_SHOW_ALL Parameters with this flag are excluded from SHOW ALL commands. NO_RESET_ALL Parameters with this flag are excluded from RESET ALL commands. NOT_IN_SAMPLE Parameters with this flag are not included in postgresql.conf by default. RUNTIME_COMPUTED Parameters with this flag are runtime-computed ones. lists functions related to database object identification and addressing. Function Description pg_describe_object pg_describe_object ( classid oid, objid oid, objsubid integer ) text Returns a textual description of a database object identified by catalog OID, object OID, and sub-object ID (such as a column number within a table; the sub-object ID is zero when referring to a whole object). This description is intended to be human-readable, and might be translated, depending on server configuration. This is especially useful to determine the identity of an object referenced in the pg_depend catalog. This function returns NULL values for undefined objects. pg_identify_object pg_identify_object ( classid oid, objid oid, objsubid integer ) record ( type text, schema text, name text, identity text ) Returns a row containing enough information to uniquely identify the database object specified by catalog OID, object OID and sub-object ID. This information is intended to be machine-readable, and is never translated. type identifies the type of database object; schema is the schema name that the object belongs in, or NULL for object types that do not belong to schemas; name is the name of the object, quoted if necessary, if the name (along with schema name, if pertinent) is sufficient to uniquely identify the object, otherwise NULL; identity is the complete object identity, with the precise format depending on object type, and each name within the format being schema-qualified and quoted as necessary. Undefined objects are identified with NULL values. pg_identify_object_as_address pg_identify_object_as_address ( classid oid, objid oid, objsubid integer ) record ( type text, object_names text[], object_args text[] ) Returns a row containing enough information to uniquely identify the database object specified by catalog OID, object OID and sub-object ID. The returned information is independent of the current server, that is, it could be used to identify an identically named object in another server. type identifies the type of database object; object_names and object_args are text arrays that together form a reference to the object. These three values can be passed to pg_get_object_address to obtain the internal address of the object. pg_get_object_address pg_get_object_address ( type text, object_names text[], object_args text[] ) record ( classid oid, objid oid, objsubid integer ) Returns a row containing enough information to uniquely identify the database object specified by a type code and object name and argument arrays. The returned values are the ones that would be used in system catalogs such as pg_depend; they can be passed to other system functions such as pg_describe_object or pg_identify_object. classid is the OID of the system catalog containing the object; objid is the OID of the object itself, and objsubid is the sub-object ID, or zero if none. This function is the inverse of pg_identify_object_as_address. Undefined objects are identified with NULL values. comment about database objects The functions shown in extract comments previously stored with the command. A null value is returned if no comment could be found for the specified parameters. Function Description col_description col_description ( table oid, column integer ) text Returns the comment for a table column, which is specified by the OID of its table and its column number. (obj_description cannot be used for table columns, since columns do not have OIDs of their own.) obj_description obj_description ( object oid, catalog name ) text Returns the comment for a database object specified by its OID and the name of the containing system catalog. For example, obj_description(123456, 'pg_class') would retrieve the comment for the table with OID 123456. obj_description ( object oid ) text Returns the comment for a database object specified by its OID alone. This is deprecated since there is no guarantee that OIDs are unique across different system catalogs; therefore, the wrong comment might be returned. shobj_description shobj_description ( object oid, catalog name ) text Returns the comment for a shared database object specified by its OID and the name of the containing system catalog. This is just like obj_description except that it is used for retrieving comments on shared objects (that is, databases, roles, and tablespaces). Some system catalogs are global to all databases within each cluster, and the descriptions for objects in them are stored globally as well. The functions shown in provide server transaction information in an exportable form. The main use of these functions is to determine which transactions were committed between two snapshots. Function Description pg_current_xact_id pg_current_xact_id () xid8 Returns the current transaction's ID. It will assign a new one if the current transaction does not have one already (because it has not performed any database updates). pg_current_xact_id_if_assigned pg_current_xact_id_if_assigned () xid8 Returns the current transaction's ID, or NULL if no ID is assigned yet. (It's best to use this variant if the transaction might otherwise be read-only, to avoid unnecessary consumption of an XID.) pg_xact_status pg_xact_status ( xid8 ) text Reports the commit status of a recent transaction. The result is one of in progress, committed, or aborted, provided that the transaction is recent enough that the system retains the commit status of that transaction. If it is old enough that no references to the transaction survive in the system and the commit status information has been discarded, the result is NULL. Applications might use this function, for example, to determine whether their transaction committed or aborted after the application and database server become disconnected while a COMMIT is in progress. Note that prepared transactions are reported as in progress; applications must check pg_prepared_xacts if they need to determine whether a transaction ID belongs to a prepared transaction. pg_current_snapshot pg_current_snapshot () pg_snapshot Returns a current snapshot, a data structure showing which transaction IDs are now in-progress. pg_snapshot_xip pg_snapshot_xip ( pg_snapshot ) setof xid8 Returns the set of in-progress transaction IDs contained in a snapshot. pg_snapshot_xmax pg_snapshot_xmax ( pg_snapshot ) xid8 Returns the xmax of a snapshot. pg_snapshot_xmin pg_snapshot_xmin ( pg_snapshot ) xid8 Returns the xmin of a snapshot. pg_visible_in_snapshot pg_visible_in_snapshot ( xid8, pg_snapshot ) boolean Is the given transaction ID visible according to this snapshot (that is, was it completed before the snapshot was taken)? Note that this function will not give the correct answer for a subtransaction ID. The internal transaction ID type xid is 32 bits wide and wraps around every 4 billion transactions. However, the functions shown in use a 64-bit type xid8 that does not wrap around during the life of an installation, and can be converted to xid by casting if required. The data type pg_snapshot stores information about transaction ID visibility at a particular moment in time. Its components are described in . pg_snapshot's textual representation is xmin:xmax:xip_list. For example 10:20:10,14,15 means xmin=10, xmax=20, xip_list=10, 14, 15. Name Description xmin Lowest transaction ID that was still active. All transaction IDs less than xmin are either committed and visible, or rolled back and dead. xmax One past the highest completed transaction ID. All transaction IDs greater than or equal to xmax had not yet completed as of the time of the snapshot, and thus are invisible. xip_list Transactions in progress at the time of the snapshot. A transaction ID that is xmin <= X < xmax and not in this list was already completed at the time of the snapshot, and thus is either visible or dead according to its commit status. This list does not include the transaction IDs of subtransactions. In releases of PostgreSQL before 13 there was no xid8 type, so variants of these functions were provided that used bigint to represent a 64-bit XID, with a correspondingly distinct snapshot data type txid_snapshot. These older functions have txid in their names. They are still supported for backward compatibility, but may be removed from a future release. See . Function Description txid_current txid_current () bigint See pg_current_xact_id(). txid_current_if_assigned txid_current_if_assigned () bigint See pg_current_xact_id_if_assigned(). txid_current_snapshot txid_current_snapshot () txid_snapshot See pg_current_snapshot(). txid_snapshot_xip txid_snapshot_xip ( txid_snapshot ) setof bigint See pg_snapshot_xip(). txid_snapshot_xmax txid_snapshot_xmax ( txid_snapshot ) bigint See pg_snapshot_xmax(). txid_snapshot_xmin txid_snapshot_xmin ( txid_snapshot ) bigint See pg_snapshot_xmin(). txid_visible_in_snapshot txid_visible_in_snapshot ( bigint, txid_snapshot ) boolean See pg_visible_in_snapshot(). txid_status txid_status ( bigint ) text See pg_xact_status(). The functions shown in provide information about when past transactions were committed. They only provide useful data when the configuration option is enabled, and only for transactions that were committed after it was enabled. Function Description pg_xact_commit_timestamp pg_xact_commit_timestamp ( xid ) timestamp with time zone Returns the commit timestamp of a transaction. pg_xact_commit_timestamp_origin pg_xact_commit_timestamp_origin ( xid ) record ( timestamp timestamp with time zone, roident oid) Returns the commit timestamp and replication origin of a transaction. pg_last_committed_xact pg_last_committed_xact () record ( xid xid, timestamp timestamp with time zone, roident oid ) Returns the transaction ID, commit timestamp and replication origin of the latest committed transaction. The functions shown in print information initialized during initdb, such as the catalog version. They also show information about write-ahead logging and checkpoint processing. This information is cluster-wide, not specific to any one database. These functions provide most of the same information, from the same source, as the application. Function Description pg_control_checkpoint pg_control_checkpoint () record Returns information about current checkpoint state, as shown in . pg_control_system pg_control_system () record Returns information about current control file state, as shown in . pg_control_init pg_control_init () record Returns information about cluster initialization state, as shown in . pg_control_recovery pg_control_recovery () record Returns information about recovery state, as shown in . Column Name Data Type checkpoint_lsn pg_lsn redo_lsn pg_lsn redo_wal_file text timeline_id integer prev_timeline_id integer full_page_writes boolean next_xid text next_oid oid next_multixact_id xid next_multi_offset xid oldest_xid xid oldest_xid_dbid oid oldest_active_xid xid oldest_multi_xid xid oldest_multi_dbid oid oldest_commit_ts_xid xid newest_commit_ts_xid xid checkpoint_time timestamp with time zone Column Name Data Type pg_control_version integer catalog_version_no integer system_identifier bigint pg_control_last_modified timestamp with time zone Column Name Data Type max_data_alignment integer database_block_size integer blocks_per_segment integer wal_block_size integer bytes_per_wal_segment integer max_identifier_length integer max_index_columns integer max_toast_chunk_size integer large_object_chunk_size integer float8_pass_by_value boolean data_page_checksum_version integer Column Name Data Type min_recovery_end_lsn pg_lsn min_recovery_end_timeline integer backup_start_lsn pg_lsn backup_end_lsn pg_lsn end_of_backup_record_required boolean The functions described in this section are used to control and monitor a PostgreSQL installation. SET SHOW configuration of the server functions shows the functions available to query and alter run-time configuration parameters. Function Description Example(s) current_setting current_setting ( setting_name text , missing_ok boolean ) text Returns the current value of the setting setting_name. If there is no such setting, current_setting throws an error unless missing_ok is supplied and is true (in which case NULL is returned). This function corresponds to the SQL command . current_setting('datestyle') ISO, MDY set_config set_config ( setting_name text, new_value text, is_local boolean ) text Sets the parameter setting_name to new_value, and returns that value. If is_local is true, the new value will only apply during the current transaction. If you want the new value to apply for the rest of the current session, use false instead. This function corresponds to the SQL command . set_config('log_statement_stats', 'off', false) off signal backend processes The functions shown in send control signals to other server processes. Use of these functions is restricted to superusers by default but access may be granted to others using GRANT, with noted exceptions. Each of these functions returns true if the signal was successfully sent and false if sending the signal failed. Function Description pg_cancel_backend pg_cancel_backend ( pid integer ) boolean Cancels the current query of the session whose backend process has the specified process ID. This is also allowed if the calling role is a member of the role whose backend is being canceled or the calling role has privileges of pg_signal_backend, however only superusers can cancel superuser backends. pg_log_backend_memory_contexts pg_log_backend_memory_contexts ( pid integer ) boolean Requests to log the memory contexts of the backend with the specified process ID. This function can send the request to backends and auxiliary processes except logger. These memory contexts will be logged at LOG message level. They will appear in the server log based on the log configuration set (see for more information), but will not be sent to the client regardless of . pg_reload_conf pg_reload_conf () boolean Causes all processes of the PostgreSQL server to reload their configuration files. (This is initiated by sending a SIGHUP signal to the postmaster process, which in turn sends SIGHUP to each of its children.) You can use the pg_file_settings, pg_hba_file_rules and pg_ident_file_mappings views to check the configuration files for possible errors, before reloading. pg_rotate_logfile pg_rotate_logfile () boolean Signals the log-file manager to switch to a new output file immediately. This works only when the built-in log collector is running, since otherwise there is no log-file manager subprocess. pg_terminate_backend pg_terminate_backend ( pid integer, timeout bigint DEFAULT 0 ) boolean Terminates the session whose backend process has the specified process ID. This is also allowed if the calling role is a member of the role whose backend is being terminated or the calling role has privileges of pg_signal_backend, however only superusers can terminate superuser backends. If timeout is not specified or zero, this function returns true whether the process actually terminates or not, indicating only that the sending of the signal was successful. If the timeout is specified (in milliseconds) and greater than zero, the function waits until the process is actually terminated or until the given time has passed. If the process is terminated, the function returns true. On timeout, a warning is emitted and false is returned. pg_cancel_backend and pg_terminate_backend send signals (SIGINT or SIGTERM respectively) to backend processes identified by process ID. The process ID of an active backend can be found from the pid column of the pg_stat_activity view, or by listing the postgres processes on the server (using ps on Unix or the Task Manager on Windows). The role of an active backend can be found from the usename column of the pg_stat_activity view. pg_log_backend_memory_contexts can be used to log the memory contexts of a backend process. For example: postgres=# SELECT pg_log_backend_memory_contexts(pg_backend_pid()); pg_log_backend_memory_contexts -------------------------------- t (1 row) One message for each memory context will be logged. For example: LOG: logging memory contexts of PID 10377 STATEMENT: SELECT pg_log_backend_memory_contexts(pg_backend_pid()); LOG: level: 0; TopMemoryContext: 80800 total in 6 blocks; 14432 free (5 chunks); 66368 used LOG: level: 1; pgstat TabStatusArray lookup hash table: 8192 total in 1 blocks; 1408 free (0 chunks); 6784 used LOG: level: 1; TopTransactionContext: 8192 total in 1 blocks; 7720 free (1 chunks); 472 used LOG: level: 1; RowDescriptionContext: 8192 total in 1 blocks; 6880 free (0 chunks); 1312 used LOG: level: 1; MessageContext: 16384 total in 2 blocks; 5152 free (0 chunks); 11232 used LOG: level: 1; Operator class cache: 8192 total in 1 blocks; 512 free (0 chunks); 7680 used LOG: level: 1; smgr relation table: 16384 total in 2 blocks; 4544 free (3 chunks); 11840 used LOG: level: 1; TransactionAbortContext: 32768 total in 1 blocks; 32504 free (0 chunks); 264 used ... LOG: level: 1; ErrorContext: 8192 total in 1 blocks; 7928 free (3 chunks); 264 used LOG: Grand total: 1651920 bytes in 201 blocks; 622360 free (88 chunks); 1029560 used If there are more than 100 child contexts under the same parent, the first 100 child contexts are logged, along with a summary of the remaining contexts. Note that frequent calls to this function could incur significant overhead, because it may generate a large number of log messages. backup The functions shown in assist in making on-line backups. These functions cannot be executed during recovery (except pg_backup_start, pg_backup_stop, and pg_wal_lsn_diff). For details about proper usage of these functions, see . Function Description pg_create_restore_point pg_create_restore_point ( name text ) pg_lsn Creates a named marker record in the write-ahead log that can later be used as a recovery target, and returns the corresponding write-ahead log location. The given name can then be used with to specify the point up to which recovery will proceed. Avoid creating multiple restore points with the same name, since recovery will stop at the first one whose name matches the recovery target. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_current_wal_flush_lsn pg_current_wal_flush_lsn () pg_lsn Returns the current write-ahead log flush location (see notes below). pg_current_wal_insert_lsn pg_current_wal_insert_lsn () pg_lsn Returns the current write-ahead log insert location (see notes below). pg_current_wal_lsn pg_current_wal_lsn () pg_lsn Returns the current write-ahead log write location (see notes below). pg_backup_start pg_backup_start ( label text , fast boolean ) pg_lsn Prepares the server to begin an on-line backup. The only required parameter is an arbitrary user-defined label for the backup. (Typically this would be the name under which the backup dump file will be stored.) If the optional second parameter is given as true, it specifies executing pg_backup_start as quickly as possible. This forces an immediate checkpoint which will cause a spike in I/O operations, slowing any concurrently executing queries. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_backup_stop pg_backup_stop ( wait_for_archive boolean ) record ( lsn pg_lsn, labelfile text, spcmapfile text ) Finishes performing an on-line backup. The desired contents of the backup label file and the tablespace map file are returned as part of the result of the function and must be written to files in the backup area. These files must not be written to the live data directory (doing so will cause PostgreSQL to fail to restart in the event of a crash). There is an optional parameter of type boolean. If false, the function will return immediately after the backup is completed, without waiting for WAL to be archived. This behavior is only useful with backup software that independently monitors WAL archiving. Otherwise, WAL required to make the backup consistent might be missing and make the backup useless. By default or when this parameter is true, pg_backup_stop will wait for WAL to be archived when archiving is enabled. (On a standby, this means that it will wait only when archive_mode = always. If write activity on the primary is low, it may be useful to run pg_switch_wal on the primary in order to trigger an immediate segment switch.) When executed on a primary, this function also creates a backup history file in the write-ahead log archive area. The history file includes the label given to pg_backup_start, the starting and ending write-ahead log locations for the backup, and the starting and ending times of the backup. After recording the ending location, the current write-ahead log insertion point is automatically advanced to the next write-ahead log file, so that the ending write-ahead log file can be archived immediately to complete the backup. The result of the function is a single record. The lsn column holds the backup's ending write-ahead log location (which again can be ignored). The second column returns the contents of the backup label file, and the third column returns the contents of the tablespace map file. These must be stored as part of the backup and are required as part of the restore process. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_switch_wal pg_switch_wal () pg_lsn Forces the server to switch to a new write-ahead log file, which allows the current file to be archived (assuming you are using continuous archiving). The result is the ending write-ahead log location plus 1 within the just-completed write-ahead log file. If there has been no write-ahead log activity since the last write-ahead log switch, pg_switch_wal does nothing and returns the start location of the write-ahead log file currently in use. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_walfile_name pg_walfile_name ( lsn pg_lsn ) text Converts a write-ahead log location to the name of the WAL file holding that location. pg_walfile_name_offset pg_walfile_name_offset ( lsn pg_lsn ) record ( file_name text, file_offset integer ) Converts a write-ahead log location to a WAL file name and byte offset within that file. pg_wal_lsn_diff pg_wal_lsn_diff ( lsn1 pg_lsn, lsn2 pg_lsn ) numeric Calculates the difference in bytes (lsn1 - lsn2) between two write-ahead log locations. This can be used with pg_stat_replication or some of the functions shown in to get the replication lag. pg_current_wal_lsn displays the current write-ahead log write location in the same format used by the above functions. Similarly, pg_current_wal_insert_lsn displays the current write-ahead log insertion location and pg_current_wal_flush_lsn displays the current write-ahead log flush location. The insertion location is the logical end of the write-ahead log at any instant, while the write location is the end of what has actually been written out from the server's internal buffers, and the flush location is the last location known to be written to durable storage. The write location is the end of what can be examined from outside the server, and is usually what you want if you are interested in archiving partially-complete write-ahead log files. The insertion and flush locations are made available primarily for server debugging purposes. These are all read-only operations and do not require superuser permissions. You can use pg_walfile_name_offset to extract the corresponding write-ahead log file name and byte offset from a pg_lsn value. For example: postgres=# SELECT * FROM pg_walfile_name_offset((pg_backup_stop()).lsn); file_name | file_offset --------------------------+------------- 00000001000000000000000D | 4039624 (1 row) Similarly, pg_walfile_name extracts just the write-ahead log file name. When the given write-ahead log location is exactly at a write-ahead log file boundary, both these functions return the name of the preceding write-ahead log file. This is usually the desired behavior for managing write-ahead log archiving behavior, since the preceding file is the last one that currently needs to be archived. The functions shown in provide information about the current status of a standby server. These functions may be executed both during recovery and in normal running. Function Description pg_is_in_recovery pg_is_in_recovery () boolean Returns true if recovery is still in progress. pg_last_wal_receive_lsn pg_last_wal_receive_lsn () pg_lsn Returns the last write-ahead log location that has been received and synced to disk by streaming replication. While streaming replication is in progress this will increase monotonically. If recovery has completed then this will remain static at the location of the last WAL record received and synced to disk during recovery. If streaming replication is disabled, or if it has not yet started, the function returns NULL. pg_last_wal_replay_lsn pg_last_wal_replay_lsn () pg_lsn Returns the last write-ahead log location that has been replayed during recovery. If recovery is still in progress this will increase monotonically. If recovery has completed then this will remain static at the location of the last WAL record applied during recovery. When the server has been started normally without recovery, the function returns NULL. pg_last_xact_replay_timestamp pg_last_xact_replay_timestamp () timestamp with time zone Returns the time stamp of the last transaction replayed during recovery. This is the time at which the commit or abort WAL record for that transaction was generated on the primary. If no transactions have been replayed during recovery, the function returns NULL. Otherwise, if recovery is still in progress this will increase monotonically. If recovery has completed then this will remain static at the time of the last transaction applied during recovery. When the server has been started normally without recovery, the function returns NULL. pg_get_wal_resource_managers pg_get_wal_resource_managers () setof record ( rm_id integer, rm_name text, rm_builtin boolean ) Returns the currently-loaded WAL resource managers in the system. The column rm_builtin indicates whether it's a built-in resource manager, or a custom resource manager loaded by an extension. The functions shown in control the progress of recovery. These functions may be executed only during recovery. Function Description pg_is_wal_replay_paused pg_is_wal_replay_paused () boolean Returns true if recovery pause is requested. pg_get_wal_replay_pause_state pg_get_wal_replay_pause_state () text Returns recovery pause state. The return values are not paused if pause is not requested, pause requested if pause is requested but recovery is not yet paused, and paused if the recovery is actually paused. pg_promote pg_promote ( wait boolean DEFAULT true, wait_seconds integer DEFAULT 60 ) boolean Promotes a standby server to primary status. With wait set to true (the default), the function waits until promotion is completed or wait_seconds seconds have passed, and returns true if promotion is successful and false otherwise. If wait is set to false, the function returns true immediately after sending a SIGUSR1 signal to the postmaster to trigger promotion. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_wal_replay_pause pg_wal_replay_pause () void Request to pause recovery. A request doesn't mean that recovery stops right away. If you want a guarantee that recovery is actually paused, you need to check for the recovery pause state returned by pg_get_wal_replay_pause_state(). Note that pg_is_wal_replay_paused() returns whether a request is made. While recovery is paused, no further database changes are applied. If hot standby is active, all new queries will see the same consistent snapshot of the database, and no further query conflicts will be generated until recovery is resumed. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_wal_replay_resume pg_wal_replay_resume () void Restarts recovery if it was paused. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_wal_replay_pause and pg_wal_replay_resume cannot be executed while a promotion is ongoing. If a promotion is triggered while recovery is paused, the paused state ends and promotion continues. If streaming replication is disabled, the paused state may continue indefinitely without a problem. If streaming replication is in progress then WAL records will continue to be received, which will eventually fill available disk space, depending upon the duration of the pause, the rate of WAL generation and available disk space. PostgreSQL allows database sessions to synchronize their snapshots. A snapshot determines which data is visible to the transaction that is using the snapshot. Synchronized snapshots are necessary when two or more sessions need to see identical content in the database. If two sessions just start their transactions independently, there is always a possibility that some third transaction commits between the executions of the two START TRANSACTION commands, so that one session sees the effects of that transaction and the other does not. To solve this problem, PostgreSQL allows a transaction to export the snapshot it is using. As long as the exporting transaction remains open, other transactions can import its snapshot, and thereby be guaranteed that they see exactly the same view of the database that the first transaction sees. But note that any database changes made by any one of these transactions remain invisible to the other transactions, as is usual for changes made by uncommitted transactions. So the transactions are synchronized with respect to pre-existing data, but act normally for changes they make themselves. Snapshots are exported with the pg_export_snapshot function, shown in , and imported with the command. Function Description pg_export_snapshot pg_export_snapshot () text Saves the transaction's current snapshot and returns a text string identifying the snapshot. This string must be passed (outside the database) to clients that want to import the snapshot. The snapshot is available for import only until the end of the transaction that exported it. A transaction can export more than one snapshot, if needed. Note that doing so is only useful in READ COMMITTED transactions, since in REPEATABLE READ and higher isolation levels, transactions use the same snapshot throughout their lifetime. Once a transaction has exported any snapshots, it cannot be prepared with . The functions shown in are for controlling and interacting with replication features. See , , and for information about the underlying features. Use of functions for replication origin is only allowed to the superuser by default, but may be allowed to other users by using the GRANT command. Use of functions for replication slots is restricted to superusers and users having REPLICATION privilege. Many of these functions have equivalent commands in the replication protocol; see . The functions described in , , and are also relevant for replication. Function Description pg_create_physical_replication_slot pg_create_physical_replication_slot ( slot_name name , immediately_reserve boolean, temporary boolean ) record ( slot_name name, lsn pg_lsn ) Creates a new physical replication slot named slot_name. The optional second parameter, when true, specifies that the LSN for this replication slot be reserved immediately; otherwise the LSN is reserved on first connection from a streaming replication client. Streaming changes from a physical slot is only possible with the streaming-replication protocol — see . The optional third parameter, temporary, when set to true, specifies that the slot should not be permanently stored to disk and is only meant for use by the current session. Temporary slots are also released upon any error. This function corresponds to the replication protocol command CREATE_REPLICATION_SLOT ... PHYSICAL. pg_drop_replication_slot pg_drop_replication_slot ( slot_name name ) void Drops the physical or logical replication slot named slot_name. Same as replication protocol command DROP_REPLICATION_SLOT. For logical slots, this must be called while connected to the same database the slot was created on. pg_create_logical_replication_slot pg_create_logical_replication_slot ( slot_name name, plugin name , temporary boolean, twophase boolean ) record ( slot_name name, lsn pg_lsn ) Creates a new logical (decoding) replication slot named slot_name using the output plugin plugin. The optional third parameter, temporary, when set to true, specifies that the slot should not be permanently stored to disk and is only meant for use by the current session. Temporary slots are also released upon any error. The optional fourth parameter, twophase, when set to true, specifies that the decoding of prepared transactions is enabled for this slot. A call to this function has the same effect as the replication protocol command CREATE_REPLICATION_SLOT ... LOGICAL. pg_copy_physical_replication_slot pg_copy_physical_replication_slot ( src_slot_name name, dst_slot_name name , temporary boolean ) record ( slot_name name, lsn pg_lsn ) Copies an existing physical replication slot named src_slot_name to a physical replication slot named dst_slot_name. The copied physical slot starts to reserve WAL from the same LSN as the source slot. temporary is optional. If temporary is omitted, the same value as the source slot is used. pg_copy_logical_replication_slot pg_copy_logical_replication_slot ( src_slot_name name, dst_slot_name name , temporary boolean , plugin name ) record ( slot_name name, lsn pg_lsn ) Copies an existing logical replication slot named src_slot_name to a logical replication slot named dst_slot_name, optionally changing the output plugin and persistence. The copied logical slot starts from the same LSN as the source logical slot. Both temporary and plugin are optional; if they are omitted, the values of the source slot are used. pg_logical_slot_get_changes pg_logical_slot_get_changes ( slot_name name, upto_lsn pg_lsn, upto_nchanges integer, VARIADIC options text[] ) setof record ( lsn pg_lsn, xid xid, data text ) Returns changes in the slot slot_name, starting from the point from which changes have been consumed last. If upto_lsn and upto_nchanges are NULL, logical decoding will continue until end of WAL. If upto_lsn is non-NULL, decoding will include only those transactions which commit prior to the specified LSN. If upto_nchanges is non-NULL, decoding will stop when the number of rows produced by decoding exceeds the specified value. Note, however, that the actual number of rows returned may be larger, since this limit is only checked after adding the rows produced when decoding each new transaction commit. pg_logical_slot_peek_changes pg_logical_slot_peek_changes ( slot_name name, upto_lsn pg_lsn, upto_nchanges integer, VARIADIC options text[] ) setof record ( lsn pg_lsn, xid xid, data text ) Behaves just like the pg_logical_slot_get_changes() function, except that changes are not consumed; that is, they will be returned again on future calls. pg_logical_slot_get_binary_changes pg_logical_slot_get_binary_changes ( slot_name name, upto_lsn pg_lsn, upto_nchanges integer, VARIADIC options text[] ) setof record ( lsn pg_lsn, xid xid, data bytea ) Behaves just like the pg_logical_slot_get_changes() function, except that changes are returned as bytea. pg_logical_slot_peek_binary_changes pg_logical_slot_peek_binary_changes ( slot_name name, upto_lsn pg_lsn, upto_nchanges integer, VARIADIC options text[] ) setof record ( lsn pg_lsn, xid xid, data bytea ) Behaves just like the pg_logical_slot_peek_changes() function, except that changes are returned as bytea. pg_replication_slot_advance pg_replication_slot_advance ( slot_name name, upto_lsn pg_lsn ) record ( slot_name name, end_lsn pg_lsn ) Advances the current confirmed position of a replication slot named slot_name. The slot will not be moved backwards, and it will not be moved beyond the current insert location. Returns the name of the slot and the actual position that it was advanced to. The updated slot position information is written out at the next checkpoint if any advancing is done. So in the event of a crash, the slot may return to an earlier position. pg_replication_origin_create pg_replication_origin_create ( node_name text ) oid Creates a replication origin with the given external name, and returns the internal ID assigned to it. pg_replication_origin_drop pg_replication_origin_drop ( node_name text ) void Deletes a previously-created replication origin, including any associated replay progress. pg_replication_origin_oid pg_replication_origin_oid ( node_name text ) oid Looks up a replication origin by name and returns the internal ID. If no such replication origin is found, NULL is returned. pg_replication_origin_session_setup pg_replication_origin_session_setup ( node_name text ) void Marks the current session as replaying from the given origin, allowing replay progress to be tracked. Can only be used if no origin is currently selected. Use pg_replication_origin_session_reset to undo. pg_replication_origin_session_reset pg_replication_origin_session_reset () void Cancels the effects of pg_replication_origin_session_setup(). pg_replication_origin_session_is_setup pg_replication_origin_session_is_setup () boolean Returns true if a replication origin has been selected in the current session. pg_replication_origin_session_progress pg_replication_origin_session_progress ( flush boolean ) pg_lsn Returns the replay location for the replication origin selected in the current session. The parameter flush determines whether the corresponding local transaction will be guaranteed to have been flushed to disk or not. pg_replication_origin_xact_setup pg_replication_origin_xact_setup ( origin_lsn pg_lsn, origin_timestamp timestamp with time zone ) void Marks the current transaction as replaying a transaction that has committed at the given LSN and timestamp. Can only be called when a replication origin has been selected using pg_replication_origin_session_setup. pg_replication_origin_xact_reset pg_replication_origin_xact_reset () void Cancels the effects of pg_replication_origin_xact_setup(). pg_replication_origin_advance pg_replication_origin_advance ( node_name text, lsn pg_lsn ) void Sets replication progress for the given node to the given location. This is primarily useful for setting up the initial location, or setting a new location after configuration changes and similar. Be aware that careless use of this function can lead to inconsistently replicated data. pg_replication_origin_progress pg_replication_origin_progress ( node_name text, flush boolean ) pg_lsn Returns the replay location for the given replication origin. The parameter flush determines whether the corresponding local transaction will be guaranteed to have been flushed to disk or not. pg_logical_emit_message pg_logical_emit_message ( transactional boolean, prefix text, content text ) pg_lsn pg_logical_emit_message ( transactional boolean, prefix text, content bytea ) pg_lsn Emits a logical decoding message. This can be used to pass generic messages to logical decoding plugins through WAL. The transactional parameter specifies if the message should be part of the current transaction, or if it should be written immediately and decoded as soon as the logical decoder reads the record. The prefix parameter is a textual prefix that can be used by logical decoding plugins to easily recognize messages that are interesting for them. The content parameter is the content of the message, given either in text or binary form. The functions shown in calculate the disk space usage of database objects, or assist in presentation or understanding of usage results. bigint results are measured in bytes. If an OID that does not represent an existing object is passed to one of these functions, NULL is returned. Function Description pg_column_size pg_column_size ( "any" ) integer Shows the number of bytes used to store any individual data value. If applied directly to a table column value, this reflects any compression that was done. pg_column_compression pg_column_compression ( "any" ) text Shows the compression algorithm that was used to compress an individual variable-length value. Returns NULL if the value is not compressed. pg_database_size pg_database_size ( name ) bigint pg_database_size ( oid ) bigint Computes the total disk space used by the database with the specified name or OID. To use this function, you must have CONNECT privilege on the specified database (which is granted by default) or have privileges of the pg_read_all_stats role. pg_indexes_size pg_indexes_size ( regclass ) bigint Computes the total disk space used by indexes attached to the specified table. pg_relation_size pg_relation_size ( relation regclass , fork text ) bigint Computes the disk space used by one fork of the specified relation. (Note that for most purposes it is more convenient to use the higher-level functions pg_total_relation_size or pg_table_size, which sum the sizes of all forks.) With one argument, this returns the size of the main data fork of the relation. The second argument can be provided to specify which fork to examine: main returns the size of the main data fork of the relation. fsm returns the size of the Free Space Map (see ) associated with the relation. vm returns the size of the Visibility Map (see ) associated with the relation. init returns the size of the initialization fork, if any, associated with the relation. pg_size_bytes pg_size_bytes ( text ) bigint Converts a size in human-readable format (as returned by pg_size_pretty) into bytes. pg_size_pretty pg_size_pretty ( bigint ) text pg_size_pretty ( numeric ) text Converts a size in bytes into a more easily human-readable format with size units (bytes, kB, MB, GB, TB, or PB as appropriate). Note that the units are powers of 2 rather than powers of 10, so 1kB is 1024 bytes, 1MB is 10242 = 1048576 bytes, and so on. pg_table_size pg_table_size ( regclass ) bigint Computes the disk space used by the specified table, excluding indexes (but including its TOAST table if any, free space map, and visibility map). pg_tablespace_size pg_tablespace_size ( name ) bigint pg_tablespace_size ( oid ) bigint Computes the total disk space used in the tablespace with the specified name or OID. To use this function, you must have CREATE privilege on the specified tablespace or have privileges of the pg_read_all_stats role, unless it is the default tablespace for the current database. pg_total_relation_size pg_total_relation_size ( regclass ) bigint Computes the total disk space used by the specified table, including all indexes and TOAST data. The result is equivalent to pg_table_size + pg_indexes_size. The functions above that operate on tables or indexes accept a regclass argument, which is simply the OID of the table or index in the pg_class system catalog. You do not have to look up the OID by hand, however, since the regclass data type's input converter will do the work for you. See for details. The functions shown in assist in identifying the specific disk files associated with database objects. Function Description pg_relation_filenode pg_relation_filenode ( relation regclass ) oid Returns the filenode number currently assigned to the specified relation. The filenode is the base component of the file name(s) used for the relation (see for more information). For most relations the result is the same as pg_class.relfilenode, but for certain system catalogs relfilenode is zero and this function must be used to get the correct value. The function returns NULL if passed a relation that does not have storage, such as a view. pg_relation_filepath pg_relation_filepath ( relation regclass ) text Returns the entire file path name (relative to the database cluster's data directory, PGDATA) of the relation. pg_filenode_relation pg_filenode_relation ( tablespace oid, filenode oid ) regclass Returns a relation's OID given the tablespace OID and filenode it is stored under. This is essentially the inverse mapping of pg_relation_filepath. For a relation in the database's default tablespace, the tablespace can be specified as zero. Returns NULL if no relation in the current database is associated with the given values. lists functions used to manage collations. Function Description pg_collation_actual_version pg_collation_actual_version ( oid ) text Returns the actual version of the collation object as it is currently installed in the operating system. If this is different from the value in pg_collation.collversion, then objects depending on the collation might need to be rebuilt. See also . pg_database_collation_actual_version pg_database_collation_actual_version ( oid ) text Returns the actual version of the database's collation as it is currently installed in the operating system. If this is different from the value in pg_database.datcollversion, then objects depending on the collation might need to be rebuilt. See also . pg_import_system_collations pg_import_system_collations ( schema regnamespace ) integer Adds collations to the system catalog pg_collation based on all the locales it finds in the operating system. This is what initdb uses; see for more details. If additional locales are installed into the operating system later on, this function can be run again to add collations for the new locales. Locales that match existing entries in pg_collation will be skipped. (But collation objects based on locales that are no longer present in the operating system are not removed by this function.) The schema parameter would typically be pg_catalog, but that is not a requirement; the collations could be installed into some other schema as well. The function returns the number of new collation objects it created. Use of this function is restricted to superusers. lists functions that provide information about the structure of partitioned tables. Function Description pg_partition_tree pg_partition_tree ( regclass ) setof record ( relid regclass, parentrelid regclass, isleaf boolean, level integer ) Lists the tables or indexes in the partition tree of the given partitioned table or partitioned index, with one row for each partition. Information provided includes the OID of the partition, the OID of its immediate parent, a boolean value telling if the partition is a leaf, and an integer telling its level in the hierarchy. The level value is 0 for the input table or index, 1 for its immediate child partitions, 2 for their partitions, and so on. Returns no rows if the relation does not exist or is not a partition or partitioned table. pg_partition_ancestors pg_partition_ancestors ( regclass ) setof regclass Lists the ancestor relations of the given partition, including the relation itself. Returns no rows if the relation does not exist or is not a partition or partitioned table. pg_partition_root pg_partition_root ( regclass ) regclass Returns the top-most parent of the partition tree to which the given relation belongs. Returns NULL if the relation does not exist or is not a partition or partitioned table. For example, to check the total size of the data contained in a partitioned table measurement, one could use the following query: SELECT pg_size_pretty(sum(pg_relation_size(relid))) AS total_size FROM pg_partition_tree('measurement'); shows the functions available for index maintenance tasks. (Note that these maintenance tasks are normally done automatically by autovacuum; use of these functions is only required in special cases.) These functions cannot be executed during recovery. Use of these functions is restricted to superusers and the owner of the given index. Function Description brin_summarize_new_values brin_summarize_new_values ( index regclass ) integer Scans the specified BRIN index to find page ranges in the base table that are not currently summarized by the index; for any such range it creates a new summary index tuple by scanning those table pages. Returns the number of new page range summaries that were inserted into the index. brin_summarize_range brin_summarize_range ( index regclass, blockNumber bigint ) integer Summarizes the page range covering the given block, if not already summarized. This is like brin_summarize_new_values except that it only processes the page range that covers the given table block number. brin_desummarize_range brin_desummarize_range ( index regclass, blockNumber bigint ) void Removes the BRIN index tuple that summarizes the page range covering the given table block, if there is one. gin_clean_pending_list gin_clean_pending_list ( index regclass ) bigint Cleans up the pending list of the specified GIN index by moving entries in it, in bulk, to the main GIN data structure. Returns the number of pages removed from the pending list. If the argument is a GIN index built with the fastupdate option disabled, no cleanup happens and the result is zero, because the index doesn't have a pending list. See and for details about the pending list and fastupdate option. The functions shown in provide native access to files on the machine hosting the server. Only files within the database cluster directory and the log_directory can be accessed, unless the user is a superuser or is granted the role pg_read_server_files. Use a relative path for files in the cluster directory, and a path matching the log_directory configuration setting for log files. Note that granting users the EXECUTE privilege on pg_read_file(), or related functions, allows them the ability to read any file on the server that the database server process can read; these functions bypass all in-database privilege checks. This means that, for example, a user with such access is able to read the contents of the pg_authid table where authentication information is stored, as well as read any table data in the database. Therefore, granting access to these functions should be carefully considered. Some of these functions take an optional missing_ok parameter, which specifies the behavior when the file or directory does not exist. If true, the function returns NULL or an empty result set, as appropriate. If false, an error is raised. The default is false. Function Description pg_ls_dir pg_ls_dir ( dirname text , missing_ok boolean, include_dot_dirs boolean ) setof text Returns the names of all files (and directories and other special files) in the specified directory. The include_dot_dirs parameter indicates whether . and .. are to be included in the result set; the default is to exclude them. Including them can be useful when missing_ok is true, to distinguish an empty directory from a non-existent directory. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_ls_logdir pg_ls_logdir () setof record ( name text, size bigint, modification timestamp with time zone ) Returns the name, size, and last modification time (mtime) of each ordinary file in the server's log directory. Filenames beginning with a dot, directories, and other special files are excluded. This function is restricted to superusers and roles with privileges of the pg_monitor role by default, but other users can be granted EXECUTE to run the function. pg_ls_waldir pg_ls_waldir () setof record ( name text, size bigint, modification timestamp with time zone ) Returns the name, size, and last modification time (mtime) of each ordinary file in the server's write-ahead log (WAL) directory. Filenames beginning with a dot, directories, and other special files are excluded. This function is restricted to superusers and roles with privileges of the pg_monitor role by default, but other users can be granted EXECUTE to run the function. pg_ls_logicalmapdir pg_ls_logicalmapdir () setof record ( name text, size bigint, modification timestamp with time zone ) Returns the name, size, and last modification time (mtime) of each ordinary file in the server's pg_logical/mappings directory. Filenames beginning with a dot, directories, and other special files are excluded. This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function. pg_ls_logicalsnapdir pg_ls_logicalsnapdir () setof record ( name text, size bigint, modification timestamp with time zone ) Returns the name, size, and last modification time (mtime) of each ordinary file in the server's pg_logical/snapshots directory. Filenames beginning with a dot, directories, and other special files are excluded. This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function. pg_ls_replslotdir pg_ls_replslotdir ( slot_name text ) setof record ( name text, size bigint, modification timestamp with time zone ) Returns the name, size, and last modification time (mtime) of each ordinary file in the server's pg_replslot/slot_name directory, where slot_name is the name of the replication slot provided as input of the function. Filenames beginning with a dot, directories, and other special files are excluded. This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function. pg_ls_archive_statusdir pg_ls_archive_statusdir () setof record ( name text, size bigint, modification timestamp with time zone ) Returns the name, size, and last modification time (mtime) of each ordinary file in the server's WAL archive status directory (pg_wal/archive_status). Filenames beginning with a dot, directories, and other special files are excluded. This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function. pg_ls_tmpdir pg_ls_tmpdir ( tablespace oid ) setof record ( name text, size bigint, modification timestamp with time zone ) Returns the name, size, and last modification time (mtime) of each ordinary file in the temporary file directory for the specified tablespace. If tablespace is not provided, the pg_default tablespace is examined. Filenames beginning with a dot, directories, and other special files are excluded. This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function. pg_read_file pg_read_file ( filename text , offset bigint, length bigint , missing_ok boolean ) text Returns all or part of a text file, starting at the given byte offset, returning at most length bytes (less if the end of file is reached first). If offset is negative, it is relative to the end of the file. If offset and length are omitted, the entire file is returned. The bytes read from the file are interpreted as a string in the database's encoding; an error is thrown if they are not valid in that encoding. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_read_binary_file pg_read_binary_file ( filename text , offset bigint, length bigint , missing_ok boolean ) bytea Returns all or part of a file. This function is identical to pg_read_file except that it can read arbitrary binary data, returning the result as bytea not text; accordingly, no encoding checks are performed. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. In combination with the convert_from function, this function can be used to read a text file in a specified encoding and convert to the database's encoding: SELECT convert_from(pg_read_binary_file('file_in_utf8.txt'), 'UTF8'); pg_stat_file pg_stat_file ( filename text , missing_ok boolean ) record ( size bigint, access timestamp with time zone, modification timestamp with time zone, change timestamp with time zone, creation timestamp with time zone, isdir boolean ) Returns a record containing the file's size, last access time stamp, last modification time stamp, last file status change time stamp (Unix platforms only), file creation time stamp (Windows only), and a flag indicating if it is a directory. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. The functions shown in manage advisory locks. For details about proper use of these functions, see . All these functions are intended to be used to lock application-defined resources, which can be identified either by a single 64-bit key value or two 32-bit key values (note that these two key spaces do not overlap). If another session already holds a conflicting lock on the same resource identifier, the functions will either wait until the resource becomes available, or return a false result, as appropriate for the function. Locks can be either shared or exclusive: a shared lock does not conflict with other shared locks on the same resource, only with exclusive locks. Locks can be taken at session level (so that they are held until released or the session ends) or at transaction level (so that they are held until the current transaction ends; there is no provision for manual release). Multiple session-level lock requests stack, so that if the same resource identifier is locked three times there must then be three unlock requests to release the resource in advance of session end. Function Description pg_advisory_lock pg_advisory_lock ( key bigint ) void pg_advisory_lock ( key1 integer, key2 integer ) void Obtains an exclusive session-level advisory lock, waiting if necessary. pg_advisory_lock_shared pg_advisory_lock_shared ( key bigint ) void pg_advisory_lock_shared ( key1 integer, key2 integer ) void Obtains a shared session-level advisory lock, waiting if necessary. pg_advisory_unlock pg_advisory_unlock ( key bigint ) boolean pg_advisory_unlock ( key1 integer, key2 integer ) boolean Releases a previously-acquired exclusive session-level advisory lock. Returns true if the lock is successfully released. If the lock was not held, false is returned, and in addition, an SQL warning will be reported by the server. pg_advisory_unlock_all pg_advisory_unlock_all () void Releases all session-level advisory locks held by the current session. (This function is implicitly invoked at session end, even if the client disconnects ungracefully.) pg_advisory_unlock_shared pg_advisory_unlock_shared ( key bigint ) boolean pg_advisory_unlock_shared ( key1 integer, key2 integer ) boolean Releases a previously-acquired shared session-level advisory lock. Returns true if the lock is successfully released. If the lock was not held, false is returned, and in addition, an SQL warning will be reported by the server. pg_advisory_xact_lock pg_advisory_xact_lock ( key bigint ) void pg_advisory_xact_lock ( key1 integer, key2 integer ) void Obtains an exclusive transaction-level advisory lock, waiting if necessary. pg_advisory_xact_lock_shared pg_advisory_xact_lock_shared ( key bigint ) void pg_advisory_xact_lock_shared ( key1 integer, key2 integer ) void Obtains a shared transaction-level advisory lock, waiting if necessary. pg_try_advisory_lock pg_try_advisory_lock ( key bigint ) boolean pg_try_advisory_lock ( key1 integer, key2 integer ) boolean Obtains an exclusive session-level advisory lock if available. This will either obtain the lock immediately and return true, or return false without waiting if the lock cannot be acquired immediately. pg_try_advisory_lock_shared pg_try_advisory_lock_shared ( key bigint ) boolean pg_try_advisory_lock_shared ( key1 integer, key2 integer ) boolean Obtains a shared session-level advisory lock if available. This will either obtain the lock immediately and return true, or return false without waiting if the lock cannot be acquired immediately. pg_try_advisory_xact_lock pg_try_advisory_xact_lock ( key bigint ) boolean pg_try_advisory_xact_lock ( key1 integer, key2 integer ) boolean Obtains an exclusive transaction-level advisory lock if available. This will either obtain the lock immediately and return true, or return false without waiting if the lock cannot be acquired immediately. pg_try_advisory_xact_lock_shared pg_try_advisory_xact_lock_shared ( key bigint ) boolean pg_try_advisory_xact_lock_shared ( key1 integer, key2 integer ) boolean Obtains a shared transaction-level advisory lock if available. This will either obtain the lock immediately and return true, or return false without waiting if the lock cannot be acquired immediately. While many uses of triggers involve user-written trigger functions, PostgreSQL provides a few built-in trigger functions that can be used directly in user-defined triggers. These are summarized in . (Additional built-in trigger functions exist, which implement foreign key constraints and deferred index constraints. Those are not documented here since users need not use them directly.) For more information about creating triggers, see . Function Description Example Usage suppress_redundant_updates_trigger suppress_redundant_updates_trigger ( ) trigger Suppresses do-nothing update operations. See below for details. CREATE TRIGGER ... suppress_redundant_updates_trigger() tsvector_update_trigger tsvector_update_trigger ( ) trigger Automatically updates a tsvector column from associated plain-text document column(s). The text search configuration to use is specified by name as a trigger argument. See for details. CREATE TRIGGER ... tsvector_update_trigger(tsvcol, 'pg_catalog.swedish', title, body) tsvector_update_trigger_column tsvector_update_trigger_column ( ) trigger Automatically updates a tsvector column from associated plain-text document column(s). The text search configuration to use is taken from a regconfig column of the table. See for details. CREATE TRIGGER ... tsvector_update_trigger_column(tsvcol, tsconfigcol, title, body) The suppress_redundant_updates_trigger function, when applied as a row-level BEFORE UPDATE trigger, will prevent any update that does not actually change the data in the row from taking place. This overrides the normal behavior which always performs a physical row update regardless of whether or not the data has changed. (This normal behavior makes updates run faster, since no checking is required, and is also useful in certain cases.) Ideally, you should avoid running updates that don't actually change the data in the record. Redundant updates can cost considerable unnecessary time, especially if there are lots of indexes to alter, and space in dead rows that will eventually have to be vacuumed. However, detecting such situations in client code is not always easy, or even possible, and writing expressions to detect them can be error-prone. An alternative is to use suppress_redundant_updates_trigger, which will skip updates that don't change the data. You should use this with care, however. The trigger takes a small but non-trivial time for each record, so if most of the records affected by updates do actually change, use of this trigger will make updates run slower on average. The suppress_redundant_updates_trigger function can be added to a table like this: CREATE TRIGGER z_min_update BEFORE UPDATE ON tablename FOR EACH ROW EXECUTE FUNCTION suppress_redundant_updates_trigger(); In most cases, you need to fire this trigger last for each row, so that it does not override other triggers that might wish to alter the row. Bearing in mind that triggers fire in name order, you would therefore choose a trigger name that comes after the name of any other trigger you might have on the table. (Hence the z prefix in the example.) PostgreSQL provides these helper functions to retrieve information from event triggers. For more information about event triggers, see . pg_event_trigger_ddl_commands pg_event_trigger_ddl_commands () setof record pg_event_trigger_ddl_commands returns a list of DDL commands executed by each user action, when invoked in a function attached to a ddl_command_end event trigger. If called in any other context, an error is raised. pg_event_trigger_ddl_commands returns one row for each base command executed; some commands that are a single SQL sentence may return more than one row. This function returns the following columns: Name Type Description classid oid OID of catalog the object belongs in objid oid OID of the object itself objsubid integer Sub-object ID (e.g., attribute number for a column) command_tag text Command tag object_type text Type of the object schema_name text Name of the schema the object belongs in, if any; otherwise NULL. No quoting is applied. object_identity text Text rendering of the object identity, schema-qualified. Each identifier included in the identity is quoted if necessary. in_extension boolean True if the command is part of an extension script command pg_ddl_command A complete representation of the command, in internal format. This cannot be output directly, but it can be passed to other functions to obtain different pieces of information about the command. pg_event_trigger_dropped_objects pg_event_trigger_dropped_objects () setof record pg_event_trigger_dropped_objects returns a list of all objects dropped by the command in whose sql_drop event it is called. If called in any other context, an error is raised. This function returns the following columns: Name Type Description classid oid OID of catalog the object belonged in objid oid OID of the object itself objsubid integer Sub-object ID (e.g., attribute number for a column) original boolean True if this was one of the root object(s) of the deletion normal boolean True if there was a normal dependency relationship in the dependency graph leading to this object is_temporary boolean True if this was a temporary object object_type text Type of the object schema_name text Name of the schema the object belonged in, if any; otherwise NULL. No quoting is applied. object_name text Name of the object, if the combination of schema and name can be used as a unique identifier for the object; otherwise NULL. No quoting is applied, and name is never schema-qualified. object_identity text Text rendering of the object identity, schema-qualified. Each identifier included in the identity is quoted if necessary. address_names text[] An array that, together with object_type and address_args, can be used by the pg_get_object_address function to recreate the object address in a remote server containing an identically named object of the same kind. address_args text[] Complement for address_names The pg_event_trigger_dropped_objects function can be used in an event trigger like this: CREATE FUNCTION test_event_trigger_for_drops() RETURNS event_trigger LANGUAGE plpgsql AS $$ DECLARE obj record; BEGIN FOR obj IN SELECT * FROM pg_event_trigger_dropped_objects() LOOP RAISE NOTICE '% dropped object: % %.% %', tg_tag, obj.object_type, obj.schema_name, obj.object_name, obj.object_identity; END LOOP; END; $$; CREATE EVENT TRIGGER test_event_trigger_for_drops ON sql_drop EXECUTE FUNCTION test_event_trigger_for_drops(); The functions shown in provide information about a table for which a table_rewrite event has just been called. If called in any other context, an error is raised. Function Description pg_event_trigger_table_rewrite_oid pg_event_trigger_table_rewrite_oid () oid Returns the OID of the table about to be rewritten. pg_event_trigger_table_rewrite_reason pg_event_trigger_table_rewrite_reason () integer Returns a code explaining the reason(s) for rewriting. The exact meaning of the codes is release dependent. These functions can be used in an event trigger like this: CREATE FUNCTION test_event_trigger_table_rewrite_oid() RETURNS event_trigger LANGUAGE plpgsql AS $$ BEGIN RAISE NOTICE 'rewriting table % for reason %', pg_event_trigger_table_rewrite_oid()::regclass, pg_event_trigger_table_rewrite_reason(); END; $$; CREATE EVENT TRIGGER test_table_rewrite_oid ON table_rewrite EXECUTE FUNCTION test_event_trigger_table_rewrite_oid(); function statistics PostgreSQL provides a function to inspect complex statistics defined using the CREATE STATISTICS command. pg_mcv_list_items pg_mcv_list_items ( pg_mcv_list ) setof record pg_mcv_list_items returns a set of records describing all items stored in a multi-column MCV list. It returns the following columns: Name Type Description index integer index of the item in the MCV list values text[] values stored in the MCV item nulls boolean[] flags identifying NULL values frequency double precision frequency of this MCV item base_frequency double precision base frequency of this MCV item The pg_mcv_list_items function can be used like this: SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid), pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts'; Values of the pg_mcv_list type can be obtained only from the pg_statistic_ext_data.stxdmcv column.