# Functions and Operators

PostgreSQL provides a large number of functions and operators for the built-in data types. Users can also define their own functions and operators, as described in the &cite-programmer;. The psql commands `\df` and `\do` can be used to show the list of all actually available functions and operators, respectively.

If you are concerned about portability then take 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 implementations, and in many cases this functionality is compatible and consistent between various products.

## Logical Operators

The usual logical operators are available:

- AND
- OR
- NOT

SQL uses a three-valued Boolean logic where the null value 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

## Comparison Operators

The usual comparison operators are available, shown in functions-comparison-table.

## Comparison Operators

Operator | Description
`<` | less than
`>` | greater than
`<=` | less than or equal to
`>=` | greater than or equal to
`=` | equal
`<>` or `!=` | not equal

The `!=` operator is converted to `<>` in the parser stage. It is not possible to implement `!=` and `<>` operators that do different things.

Comparison operators are available for all data types where this makes sense. All comparison operators are binary operators that return values of type `boolean`; expressions like `1 < 2 < 3` are not valid (because there is no `<` operator to compare a Boolean value with `3`).

In addition to the comparison operators, the special BETWEEN construct is available.

    a BETWEEN x AND y

is equivalent to

    a >= x AND a <= y

Similarly,

    a NOT BETWEEN x AND y

is equivalent to

    a < x OR a > y

There is no difference between the two respective forms apart from the CPU cycles required to rewrite the first one into the second one internally.

To check whether a value is or is not null, use the constructs

    expression IS NULL
    expression IS NOT NULL

or the equivalent, but nonstandard, constructs

    expression ISNULL
    expression NOTNULL

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 may (incorrectly) require that ``expression` = NULL` returns true if `expression` evaluates to the null value. To support these applications, the run-time option `transform_null_equals` can be turned on (e.g., `SET transform_null_equals TO ON;`). PostgreSQL will then convert `x = NULL` clauses to `x IS NULL`. This was the default behavior in releases 6.5 through 7.1.

Boolean values can also be tested using the constructs

    expression IS TRUE
    expression IS NOT TRUE
    expression IS FALSE
    expression IS NOT FALSE
    expression IS UNKNOWN
    expression IS NOT UNKNOWN

These are similar to `IS NULL` in that they 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.

## Mathematical Functions and Operators

Mathematical operators are provided for many PostgreSQL types. For types without common mathematical conventions for all possible permutations (e.g., date/time types) we describe the actual behavior in subsequent sections.

functions-math-op-table shows the available mathematical operators.

## Mathematical Operators

Name | Description | Example | Result
`+` | addition | 2 + 3 | 5
`-` | subtraction | 2 - 3 | -1
`*` | multiplication | 2 * 3 | 6
`/` | division (integer division truncates results) | 4 / 2 | 2
`%` | modulo (remainder) | 5 % 4 | 1
`^` | exponentiation | 2.0 ^ 3.0 | 8
`|/` | square root | |/ 25.0 | 5
`||/` | cube root | ||/ 27.0 | 3
`!` | factorial | 5 ! | 120
`!!` | factorial (prefix operator) | !! 5 | 120
`@` | absolute value | @ -5.0 | 5
`&` | binary AND | 91 & 15 | 11
`|` | binary OR | 32 | 3 | 35
`#` | binary XOR | 17 # 5 | 20
`~` | binary NOT | ~1 | -2
<< | binary shift left | 1 << 4 | 16
>> | binary shift right | 8 >> 2 | 2

The binary operators are also available for the bit string types `BIT` and `BIT VARYING`, as shown in functions-math-bit-table. Bit string arguments to `&`, `|`, and `#` must be of equal length. When bit shifting, the original length of the string is preserved, as shown in the table.

## Bit String Binary Operators

Example | Result
B'10001' & B'01101' | 00001
B'10001' | B'01101' | 11101
B'10001' # B'01101' | 11110
~ B'10001' | 01110
B'10001' << 3 | 01000
B'10001' >> 2 | 00100

functions-math-func-table shows the available mathematical functions. In the table, `dp` indicates `double precision`. 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 datatype as its argument. 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 may therefore vary depending on the host system.

## Mathematical Functions

Function | Return Type | Description | Example | Result
`abs`(`x`) | (same as `x`) | absolute value | `abs(-17.4)` | 17.4
`cbrt`(`dp`) | `dp` | cube root | `cbrt(27.0)` | 3
`ceil`(`dp` or `numeric`) | (same as input) | smallest integer not less than argument | `ceil(-42.8)` | -42
`degrees`(`dp`) | `dp` | radians to degrees | `degrees(0.5)` | 28.6478897565412
`exp`(`dp` or `numeric`) | (same as input) | exponential | `exp(1.0)` | 2.71828182845905
`floor`(`dp` or `numeric`) | (same as input) | largest integer not greater than argument | `floor(-42.8)` | -43
`ln`(`dp` or `numeric`) | (same as input) | natural logarithm | `ln(2.0)` | 0.693147180559945
`log`(`dp` or `numeric`) | (same as input) | base 10 logarithm | `log(100.0)` | 2
`log`(`b` `numeric`, `x` `numeric`) | `numeric` | logarithm to base `b` | `log(2.0, 64.0)` | 6.0000000000
`mod`(`y`, `x`) | (same as argument types) | remainder of `y`/`x` | `mod(9,4)` | 1
`pi`() | `dp` | Pi constant | `pi()` | 3.14159265358979
`pow`(`x` `dp`, `e` `dp`) | `dp` | raise a number to exponent `e` | `pow(9.0, 3.0)` | 729
`pow`(`x` `numeric`, `e` `numeric`) | `numeric` | raise a number to exponent `e` | `pow(9.0, 3.0)` | 729
`radians`(`dp`) | `dp` | degrees to radians | `radians(45.0)` | 0.785398163397448
`random`() | `dp` | random value between 0.0 and 1.0 | `random()` |
`round`(`dp` or `numeric`) | (same as input) | round to nearest integer | `round(42.4)` | 42
`round`(`v` `numeric`, `s` `integer`) | `numeric` | round to `s` decimal places | `round(42.4382, 2)` | 42.44
`sign`(`dp` or `numeric`) | (same as input) | sign of the argument (-1, 0, +1) | `sign(-8.4)` | -1
`sqrt`(`dp` or `numeric`) | (same as input) | square root | `sqrt(2.0)` | 1.4142135623731
`trunc`(`dp` or `numeric`) | (same as input) | truncate toward zero | `trunc(42.8)` | 42
`trunc`(`v` `numeric`, `s` `integer`) | `numeric` | truncate to `s` decimal places | `trunc(42.4382, 2)` | 42.43

Finally, functions-math-trig-table shows the available trigonometric functions. All trigonometric functions take arguments and return values of type `double precision`.

## Trigonometric Functions

Function | Description
`acos`(`x`) | inverse cosine
`asin`(`x`) | inverse sine
`atan`(`x`) | inverse tangent
`atan2`(`x`, `y`) | inverse tangent of `x`/`y`
`cos`(`x`) | cosine
`cot`(`x`) | cotangent
`sin`(`x`) | sine
`tan`(`x`) | tangent

## String Functions and Operators

This section describes functions and operators for examining and manipulating string values. Strings in this context include values of all the types `CHARACTER`, `CHARACTER VARYING`, and `TEXT`. Unless otherwise noted, all of the functions listed below work on all of these types, but be wary of potential effects of the automatic padding when using the `CHARACTER` type. Generally, the functions described here also work on data of non-string types by converting that data to a string representation first. Some functions also exist natively for bit-string types.

SQL defines some string functions with a special syntax where certain key words rather than commas are used to separate the arguments. Details are in functions-string-sql. These functions are also implemented using the regular syntax for function invocation. (See functions-string-other.)

## SQL String Functions and Operators

Function | Return Type | Description | Example | Result
`string` `||` `string` | `text` | String concatenation | `'Post' || 'greSQL'` | `PostgreSQL`
`bit_length`(`string`) | `integer` | Number of bits in string | `bit_length('jose')` | `32`
`char_length`(`string`) or `character_length`(`string`) | `integer` | Number of characters in string | `char_length('jose')` | `4`
`convert`(`string` using `conversion_name`) | `text` | Change encoding using specified conversion name. Conversions can be defined by `CREATE CONVERSION`. Also there are some pre-defined conversion names. See conversion-names for available conversion names. | `convert('PostgreSQL' using iso_8859_1_to_utf_8)` | `'PostgreSQL'` in Unicode (UTF-8) encoding
`lower`(`string`) | `text` | Convert string to lower case | `lower('TOM')` | `tom`
`octet_length`(`string`) | `integer` | Number of bytes in string | `octet_length('jose')` | `4`
`overlay`(`string` placing `string` from `integer` for `integer`) | `text` | Insert substring | `overlay('Txxxxas' placing 'hom' from 2 for 4)` | `Thomas`
`position`(`substring` in `string`) | `integer` | Location of specified substring | `position('om' in 'Thomas')` | `3`
`substring`(`string` from `integer` for `integer`) | `text` | Extract substring | `substring('Thomas' from 2 for 3)` | `hom`
`substring`(`string` from `pattern`) | `text` | Extract substring matching POSIX regular expression | `substring('Thomas' from '...$')` | `mas`
`substring`(`string` from `pattern` for `escape`) | `text` | Extract substring matching SQL regular expression | `substring('Thomas' from '%#"o_a#"_' for '#')` | `oma`
`trim`(leading | trailing | both `characters` from `string`) | `text` | Remove the longest string containing only the `characters` (a space by default) from the beginning/end/both ends of the `string` | `trim(both 'x' from 'xTomxx')` | `Tom`
`upper`(`string`) | `text` | Convert string to upper case | `upper('tom')` | `TOM`

Additional string manipulation functions are available and are listed in functions-string-other. Some of them are used internally to implement the SQL-standard string functions listed in functions-string-sql.

## Other String Functions

Function | Return Type | Description | Example | Result
`ascii`(`text`) | integer | ASCII code of the first character of the argument. | `ascii('x')` | `120`
`btrim`(`string` `text`, `trim` `text`) | `text` | Remove (trim) the longest string consisting only of characters in `trim` from the start and end of `string` | `btrim('xyxtrimyyx','xy')` | `trim`
`chr`(`integer`) | `text` | Character with the given ASCII code | `chr(65)` | `A`
`convert`(`string` `text`, `src_encoding` `name`, `dest_encoding` `name`) | `text` | Convert string to `dest_encoding`. The original encoding is specified by `src_encoding`. If `src_encoding` is omitted, database encoding is assumed. | `convert('text_in_unicode', 'UNICODE', 'LATIN1')` | `text_in_unicode` represented in ISO 8859-1
`decode`(`string` `text`, `type` `text`) | `bytea` | Decode binary data from `string` previously encoded with `encode()`. Parameter type is same as in `encode()`. | `decode('MTIzAAE=', 'base64')` | `123\000\001`
`encode`(`data` `bytea`, `type` `text`) | `text` | Encode binary data to ASCII-only representation. Supported types are: base64, hex, escape. | `encode('123\\000\\001', 'base64')` | `MTIzAAE=`
`initcap`(`text`) | `text` | Convert first letter of each word (whitespace separated) to upper case | `initcap('hi thomas')` | `Hi Thomas`
`length`(`string`) | `integer` | Length of string | `length('jose')` | `4`
`lpad`(`string` `text`, `length` `integer` , `fill` `text`) | text | Fill up 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`(`string` `text`, `text` `text`) | `text` | Remove the longest string containing only characters from `trim` from the start of the string. | `ltrim('zzzytrim','xyz')` | `trim`
`pg_client_encoding`() | `name` | Current client encoding name. | `pg_client_encoding()` | `SQL_ASCII`
`quote_ident`(`string` text) | `text` | Return the given string suitably quoted to be used as an identifier in an SQL query 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. | `quote_ident('Foo')` | `"Foo"`
`quote_literal`(`string` text) | `text` | Return the given string suitably quoted to be used as a literal in an SQL query string. Embedded quotes and backslashes are properly doubled. | `quote_literal('O\'Reilly')` | `'O''Reilly'`
`repeat`(`text`, `integer`) | `text` | Repeat text a number of times | `repeat('Pg', 4)` | `PgPgPgPg`
`replace`(`string` `text`, `from` `text`, `to` `text`) | `text` | Replace all occurrences in `string` of substring `from` with substring `to` | `replace('abcdefabcdef', 'cd', 'XX')` | `abXXefabXXef`
`rpad`(`string` `text`, `length` `integer` , `fill` `text`) | `text` | Fill up 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`(`string` text, `trim` text) | `text` | Remove the longest string containing only characters from `trim` from the end of the string. | `rtrim('trimxxxx','x')` | `trim`
`split_part`(`string` `text`, `delimiter` `text`, `column` `integer`) | `text` | Split `string` on `delimiter` returning the resulting (one based) `column` number. | `split_part('abc~@~def~@~ghi','~@~',2)` | `def`
`strpos`(`string`, `substring`) | `text` | Locate specified substring (same as `position(`substring` in `string`)`, but note the reversed argument order) | `strpos('high','ig')` | `2`
`substr`(`string`, `from` , `count`) | `text` | Extract specified substring (same as `substring(`string` from `from` for `count`)`) | `substr('alphabet', 3, 2)` | `ph`
`to_ascii`(`text` , `encoding`) | `text` | Convert text to ASCII from other encoding The `to_ascii` function supports conversion from `LATIN1`, `LATIN2`, and `WIN1250` only. | `to_ascii('Karel')` | `Karel`
`to_hex`(`number` `integer` or `bigint`) | `text` | Convert `number` to its equivalent hexadecimal representation | `to_hex(9223372036854775807::bigint)` | `7fffffffffffffff`
`translate`(`string` `text`, `from` `text`, `to` `text`) | `text` | Any character in `string` that matches a character in the `from` set is replaced by the corresponding character in the `to` set. | `translate('12345', '14', 'ax')` | `a23x5`

## Built-in Conversions

Conversion Name The conversion names follow a standard naming scheme: The official name of the source encoding with all non-alphanumeric characters replaced by underscores followed by `_to_` followed by the equally processed destination encoding name. Therefore the names might deviate from the customary encoding names. | Source Encoding | Destination Encoding
`ascii_to_mic` | `SQL_ASCII` | `MULE_INTERNAL`
`ascii_to_utf_8` | `SQL_ASCII` | `UNICODE`
`big5_to_euc_tw` | `BIG5` | `EUC_TW`
`big5_to_mic` | `BIG5` | `MULE_INTERNAL`
`big5_to_utf_8` | `BIG5` | `UNICODE`
`euc_cn_to_mic` | `EUC_CN` | `MULE_INTERNAL`
`euc_cn_to_utf_8` | `EUC_CN` | `UNICODE`
`euc_jp_to_mic` | `EUC_JP` | `MULE_INTERNAL`
`euc_jp_to_sjis` | `EUC_JP` | `SJIS`
`euc_jp_to_utf_8` | `EUC_JP` | `UNICODE`
`euc_kr_to_mic` | `EUC_KR` | `MULE_INTERNAL`
`euc_kr_to_utf_8` | `EUC_KR` | `UNICODE`
`euc_tw_to_big5` | `EUC_TW` | `BIG5`
`euc_tw_to_mic` | `EUC_TW` | `MULE_INTERNAL`
`euc_tw_to_utf_8` | `EUC_TW` | `UNICODE`
`gb18030_to_utf_8` | `GB18030` | `UNICODE`
`gbk_to_utf_8` | `GBK` | `UNICODE`
`iso_8859_10_to_utf_8` | `LATIN6` | `UNICODE`
`iso_8859_13_to_utf_8` | `LATIN7` | `UNICODE`
`iso_8859_14_to_utf_8` | `LATIN8` | `UNICODE`
`iso_8859_15_to_utf_8` | `LATIN9` | `UNICODE`
`iso_8859_16_to_utf_8` | `LATIN10` | `UNICODE`
`iso_8859_1_to_mic` | `LATIN1` | `MULE_INTERNAL`
`iso_8859_1_to_utf_8` | `LATIN1` | `UNICODE`
`iso_8859_2_to_mic` | `LATIN2` | `MULE_INTERNAL`
`iso_8859_2_to_utf_8` | `LATIN2` | `UNICODE`
`iso_8859_2_to_windows_1250` | `LATIN2` | `WIN1250`
`iso_8859_3_to_mic` | `LATIN3` | `MULE_INTERNAL`
`iso_8859_3_to_utf_8` | `LATIN3` | `UNICODE`
`iso_8859_4_to_mic` | `LATIN4` | `MULE_INTERNAL`
`iso_8859_4_to_utf_8` | `LATIN4` | `UNICODE`
`iso_8859_5_to_koi8_r` | `ISO_8859_5` | `KOI8`
`iso_8859_5_to_mic` | `ISO_8859_5` | `MULE_INTERNAL`
`iso_8859_5_to_utf_8` | `ISO_8859_5` | `UNICODE`
`iso_8859_5_to_windows_1251` | `ISO_8859_5` | `WIN`
`iso_8859_5_to_windows_866` | `ISO_8859_5` | `ALT`
`iso_8859_6_to_utf_8` | `ISO_8859_6` | `UNICODE`
`iso_8859_7_to_utf_8` | `ISO_8859_7` | `UNICODE`
`iso_8859_8_to_utf_8` | `ISO_8859_8` | `UNICODE`
`iso_8859_9_to_utf_8` | `LATIN5` | `UNICODE`
`johab_to_utf_8` | `JOHAB` | `UNICODE`
`koi8_r_to_iso_8859_5` | `KOI8` | `ISO_8859_5`
`koi8_r_to_mic` | `KOI8` | `MULE_INTERNAL`
`koi8_r_to_utf_8` | `KOI8` | `UNICODE`
`koi8_r_to_windows_1251` | `KOI8` | `WIN`
`koi8_r_to_windows_866` | `KOI8` | `ALT`
`mic_to_ascii` | `MULE_INTERNAL` | `SQL_ASCII`
`mic_to_big5` | `MULE_INTERNAL` | `BIG5`
`mic_to_euc_cn` | `MULE_INTERNAL` | `EUC_CN`
`mic_to_euc_jp` | `MULE_INTERNAL` | `EUC_JP`
`mic_to_euc_kr` | `MULE_INTERNAL` | `EUC_KR`
`mic_to_euc_tw` | `MULE_INTERNAL` | `EUC_TW`
`mic_to_iso_8859_1` | `MULE_INTERNAL` | `LATIN1`
`mic_to_iso_8859_2` | `MULE_INTERNAL` | `LATIN2`
`mic_to_iso_8859_3` | `MULE_INTERNAL` | `LATIN3`
`mic_to_iso_8859_4` | `MULE_INTERNAL` | `LATIN4`
`mic_to_iso_8859_5` | `MULE_INTERNAL` | `ISO_8859_5`
`mic_to_koi8_r` | `MULE_INTERNAL` | `KOI8`
`mic_to_sjis` | `MULE_INTERNAL` | `SJIS`
`mic_to_windows_1250` | `MULE_INTERNAL` | `WIN1250`
`mic_to_windows_1251` | `MULE_INTERNAL` | `WIN`
`mic_to_windows_866` | `MULE_INTERNAL` | `ALT`
`sjis_to_euc_jp` | `SJIS` | `EUC_JP`
`sjis_to_mic` | `SJIS` | `MULE_INTERNAL`
`sjis_to_utf_8` | `SJIS` | `UNICODE`
`tcvn_to_utf_8` | `TCVN` | `UNICODE`
`uhc_to_utf_8` | `UHC` | `UNICODE`
`utf_8_to_ascii` | `UNICODE` | `SQL_ASCII`
`utf_8_to_big5` | `UNICODE` | `BIG5`
`utf_8_to_euc_cn` | `UNICODE` | `EUC_CN`
`utf_8_to_euc_jp` | `UNICODE` | `EUC_JP`
`utf_8_to_euc_kr` | `UNICODE` | `EUC_KR`
`utf_8_to_euc_tw` | `UNICODE` | `EUC_TW`
`utf_8_to_gb18030` | `UNICODE` | `GB18030`
`utf_8_to_gbk` | `UNICODE` | `GBK`
`utf_8_to_iso_8859_1` | `UNICODE` | `LATIN1`
`utf_8_to_iso_8859_10` | `UNICODE` | `LATIN6`
`utf_8_to_iso_8859_13` | `UNICODE` | `LATIN7`
`utf_8_to_iso_8859_14` | `UNICODE` | `LATIN8`
`utf_8_to_iso_8859_15` | `UNICODE` | `LATIN9`
`utf_8_to_iso_8859_16` | `UNICODE` | `LATIN10`
`utf_8_to_iso_8859_2` | `UNICODE` | `LATIN2`
`utf_8_to_iso_8859_3` | `UNICODE` | `LATIN3`
`utf_8_to_iso_8859_4` | `UNICODE` | `LATIN4`
`utf_8_to_iso_8859_5` | `UNICODE` | `ISO_8859_5`
`utf_8_to_iso_8859_6` | `UNICODE` | `ISO_8859_6`
`utf_8_to_iso_8859_7` | `UNICODE` | `ISO_8859_7`
`utf_8_to_iso_8859_8` | `UNICODE` | `ISO_8859_8`
`utf_8_to_iso_8859_9` | `UNICODE` | `LATIN5`
`utf_8_to_johab` | `UNICODE` | `JOHAB`
`utf_8_to_koi8_r` | `UNICODE` | `KOI8`
`utf_8_to_sjis` | `UNICODE` | `SJIS`
`utf_8_to_tcvn` | `UNICODE` | `TCVN`
`utf_8_to_uhc` | `UNICODE` | `UHC`
`utf_8_to_windows_1250` | `UNICODE` | `WIN1250`
`utf_8_to_windows_1251` | `UNICODE` | `WIN`
`utf_8_to_windows_1256` | `UNICODE` | `WIN1256`
`utf_8_to_windows_866` | `UNICODE` | `ALT`
`utf_8_to_windows_874` | `UNICODE` | `WIN874`
`windows_1250_to_iso_8859_2` | `WIN1250` | `LATIN2`
`windows_1250_to_mic` | `WIN1250` | `MULE_INTERNAL`
`windows_1250_to_utf_8` | `WIN1250` | `UNICODE`
`windows_1251_to_iso_8859_5` | `WIN` | `ISO_8859_5`
`windows_1251_to_koi8_r` | `WIN` | `KOI8`
`windows_1251_to_mic` | `WIN` | `MULE_INTERNAL`
`windows_1251_to_utf_8` | `WIN` | `UNICODE`
`windows_1251_to_windows_866` | `WIN` | `ALT`
`windows_1256_to_utf_8` | `WIN1256` | `UNICODE`
`windows_866_to_iso_8859_5` | `ALT` | `ISO_8859_5`
`windows_866_to_koi8_r` | `ALT` | `KOI8`
`windows_866_to_mic` | `ALT` | `MULE_INTERNAL`
`windows_866_to_utf_8` | `ALT` | `UNICODE`
`windows_866_to_windows_1251` | `ALT` | `WIN`
`windows_874_to_utf_8` | `WIN874` | `UNICODE`

## Binary String Functions and Operators

This section describes functions and operators for examining and manipulating binary string values. Strings in this context mean values of the type `BYTEA`.

SQL defines some string functions with a special syntax where certain key words rather than commas are used to separate the arguments. Details are in functions-binarystring-sql. Some functions are also implemented using the regular syntax for function invocation. (See functions-binarystring-other.)

## SQL Binary String Functions and Operators

Function | Return Type | Description | Example | Result
`string` `||` `string` | `bytea` | String concatenation | `'\\\\Post'::bytea || '\\047greSQL\\000'::bytea` | `\\Post'greSQL\000`
`octet_length`(`string`) | `integer` | Number of bytes in binary string | `octet_length('jo\\000se'::bytea)` | `5`
`position`(`substring` in `string`) | `integer` | Location of specified substring | `position('\\000om'::bytea in 'Th\\000omas'::bytea)` | `3`
`substring`(`string` from `integer` for `integer`) | `bytea` | Extract substring | `substring('Th\\000omas'::bytea from 2 for 3)` | `h\000o`
`trim`(both `characters` from `string`) | `bytea` | Remove the longest string containing only the `characters` from the beginning/end/both ends of the `string` | `trim('\\000'::bytea from '\\000Tom\\000'::bytea)` | `Tom`
`get_byte`(`string`, `offset`) | `integer` | Extract byte from string. | `get_byte('Th\\000omas'::bytea, 4)` | `109`
`set_byte`(`string`, `offset`, `newvalue`) | `bytea` | Set byte in string. | `set_byte('Th\\000omas'::bytea, 4, 64)` | `Th\000o@as`
`get_bit`(`string`, `offset`) | `integer` | Extract bit from string. | `get_bit('Th\\000omas'::bytea, 45)` | `1`
`set_bit`(`string`, `offset`, `newvalue`) | `bytea` | Set bit in string. | `set_bit('Th\\000omas'::bytea, 45, 0)` | `Th\000omAs`

Additional binary string manipulation functions are available and are listed in functions-binarystring-other. Some of them are used internally to implement the SQL-standard string functions listed in functions-binarystring-sql.

## Other Binary String Functions

Function | Return Type | Description | Example | Result
`btrim`(`string` `bytea` `trim` `bytea`) | `bytea` | Remove (trim) the longest string consisting only of characters in `trim` from the start and end of `string`. | `btrim('\\000trim\\000'::bytea,'\\000'::bytea)` | `trim`
`length`(`string`) | `integer` | Length of binary string | `length('jo\\000se'::bytea)` | `5`
`encode`(`string` `bytea`, `type` `text`) | `text` | Encode binary string to ASCII-only representation. Supported types are: base64, hex, escape. | `encode('123\\000456'::bytea, 'escape')` | `123\000456`
`decode`(`string` `text`, `type` `text`) | `bytea` | Decode binary string from `string` previously encoded with `encode()`. Parameter type is same as in `encode()`. | `decode('123\\000456', 'escape')` | `123\000456`

## Pattern Matching

There are three separate approaches to pattern matching provided by PostgreSQL: the traditional SQL `LIKE` operator, the more recent SQL99 `SIMILAR TO` operator, and POSIX-style regular expressions. Additionally, a pattern matching function, `SUBSTRING`, is available, using either SQL99-style or POSIX-style regular expressions.

If you have pattern matching needs that go beyond this, consider writing a user-defined function in Perl or Tcl.

### `LIKE`

    string LIKE pattern ESCAPE escape-character
    string NOT LIKE pattern ESCAPE escape-character

Every `pattern` defines a set of strings. The `LIKE` expression returns true if the `string` is contained in the set of strings represented by `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 underscore, 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 string 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 matches always cover the entire string. To match a pattern anywhere within a string, the pattern must therefore 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 may be selected by using the `ESCAPE` clause. To match the escape character itself, write two escape characters.

Note that the backslash already has a special meaning in string literals, so to write a pattern constant that contains a backslash you must write two backslashes in the query. Thus, writing a pattern that actually matches a literal backslash means writing four backslashes in the query. You can avoid this by selecting a different escape character with `ESCAPE`; then backslash is not special to `LIKE` anymore. (But it is still special to the string literal parser, so you still need two of them.)

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.

The keyword 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`. All of these operators are PostgreSQL-specific.

### `SIMILAR TO` and SQL99 Regular Expressions

    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 much like `LIKE`, except that it interprets the pattern using SQL99's definition of a regular expression. SQL99's regular expressions are a curious cross between `LIKE` notation and common regular expression notation.

Like `LIKE`, the `SIMILAR TO` operator succeeds only if its pattern matches the entire string; this is unlike common regular expression practice, wherein the pattern may match any part of the string. Also like `LIKE`, `SIMILAR TO` uses `%` and `_` as wildcard characters denoting any string and any single character, 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.

- Parentheses `()` may 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 bounded repetition (`?` and `{...}`) are not provided, though they exist in POSIX. Also, dot (`.`) is not a metacharacter.

As with `LIKE`, a backslash disables the special meaning of any of these metacharacters; or a different escape character can be specified with `ESCAPE`.

Some examples:

    'abc' SIMILAR TO 'abc'      true
    'abc' SIMILAR TO 'a'        false
    'abc' SIMILAR TO '%(b|d)%'  true
    'abc' SIMILAR TO '(b|c)%'   false

The `SUBSTRING` function with three parameters, `SUBSTRING(`string` FROM `pattern` FOR `escape`)`, provides extraction of a substring that matches a SQL99 regular expression pattern. As with `SIMILAR TO`, the specified pattern must match to the entire data string, else the function fails and returns null. To indicate the part of the pattern that should be returned on success, SQL99 specifies that the pattern must contain two occurrences of the escape character followed by double quote (`"`). The text matching the portion of the pattern between these markers is returned.

Some examples:

    SUBSTRING('foobar' FROM '%#"o_b#"%' FOR '#')   oob
    SUBSTRING('foobar' FROM '#"o_b#"%' FOR '#')    NULL

### POSIX Regular Expressions

functions-posix-table lists the available operators for pattern matching using POSIX regular expressions.

### Regular Expression Match Operators

Operator | Description | Example
`~` | Matches regular expression, case sensitive | `'thomas' ~ '.*thomas.*'`
`~*` | Matches regular expression, case insensitive | `'thomas' ~* '.*Thomas.*'`
`!~` | Does not match regular expression, case sensitive | `'thomas' !~ '.*Thomas.*'`
`!~*` | Does not match regular expression, case insensitive | `'thomas' !~* '.*vadim.*'`

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:

    'abc' ~ 'abc'    true
    'abc' ~ '^a'     true
    'abc' ~ '(b|d)'  true
    'abc' ~ '^(b|c)' false

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 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 always put parentheses around the whole expression if you want to use parentheses within it without triggering this exception.

Some examples:

    SUBSTRING('foobar' FROM 'o.b')     oob
    SUBSTRING('foobar' FROM 'o(.)b')   o

Regular expressions (REs), as defined in POSIX 1003.2, come in two forms: modern REs (roughly those of `egrep`; 1003.2 calls these extended REs) and obsolete REs (roughly those of `ed`; 1003.2 basic REs). PostgreSQL implements the modern form.

A (modern) RE is one or more non-empty branches, separated by `|`. It matches anything that matches one of the branches.

A branch is one or more pieces, concatenated. It matches a match for the first, followed by a match for the second, etc.

A piece is an atom possibly followed by a single `*`, `+`, `?`, or bound. An atom followed by `*` matches a sequence of 0 or more matches of the atom. An atom followed by `+` matches a sequence of 1 or more matches of the atom. An atom followed by `?` matches a sequence of 0 or 1 matches of the atom.

A bound is `{` followed by an unsigned decimal integer, possibly followed by `,` possibly followed by another unsigned decimal integer, always followed by `}`. The integers must lie between 0 and RE_DUP_MAX (255) inclusive, and if there are two of them, the first may not exceed the second. An atom followed by a bound containing one integer `i` and no comma matches a sequence of exactly `i` matches of the atom. An atom followed by a bound containing one integer `i` and a comma matches a sequence of `i` or more matches of the atom. An atom followed by a bound containing two integers `i` and `j` matches a sequence of `i` through `j` (inclusive) matches of the atom.

A repetition operator (`?`, `*`, `+`, or bounds) cannot follow another repetition operator. A repetition operator cannot begin an expression or subexpression or follow `^` or `|`.

An atom is a regular expression enclosed in `()` (matching a match for the regular expression), an empty set of `()` (matching the null string), a bracket expression (see below), `.` (matching any single character), `^` (matching the null string at the beginning of the input string), `$` (matching the null string at the end of the input string), a `\` followed by one of the characters `^.[$()|*+?{\` (matching that character taken as an ordinary character), a `\` followed by any other character (matching that character taken as an ordinary character, as if the `\` had not been present), or a single character with no other significance (matching that character). A `{` followed by a character other than a digit is an ordinary character, not the beginning of a bound. It is illegal to end an RE with `\`.

Note that the backslash (`\`) already has a special meaning in string literals, so to write a pattern constant that contains a backslash you must write two backslashes in the query.

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 (but see below) 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, and portable programs should avoid relying on them.

To include a literal `]` in the list, make it the first character (following a possible `^`). 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 and some combinations using `[` (see next paragraphs), all other special characters, including `\`, lose their special significance within a bracket expression.

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 a single element of the bracket expression's list. A bracket expression containing a multiple-character collating element can thus 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`.

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 may not 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. Standard character class names are: `alnum`, `alpha`, `blank`, `cntrl`, `digit`, `graph`, `lower`, `print`, `punct`, `space`, `upper`, `xdigit`. These stand for the character classes defined in

ctype

3. A locale may provide others. A character class may not be used as an endpoint of a range.

There are two special cases of bracket expressions: the bracket expressions `[[:<:]]` and `[[:>:]]` match the null string at the beginning and end of a word respectively. A word is defined as a sequence of word characters which is neither preceded nor followed by word characters. A word character is an alnum character (as defined by

ctype

3) or an 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.

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, it matches the longest. Subexpressions also match the longest possible substrings, subject to the constraint that the whole match be as long as possible, with subexpressions starting earlier in the RE taking priority over ones starting later. Note that higher-level subexpressions thus take priority over their lower-level component subexpressions.

Match lengths are measured in characters, not collating elements. A null string is considered longer than no match at all. For example, `bb*` matches the three middle characters of `abbbc`, `(wee|week)(knights|nights)` 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 the null 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, so that (e.g.) `[x]` becomes `[xX]` and `[^x]` becomes `[^xX]`.

There is no particular limit on the length of REs, except insofar as memory is limited. Memory usage is approximately linear in RE size, and largely insensitive to RE complexity, except for bounded repetitions. Bounded repetitions are implemented by macro expansion, which is costly in time and space if counts are large or bounded repetitions are nested. An RE like, say, `((((a{1,100}){1,100}){1,100}){1,100}){1,100}` will (eventually) run almost any existing machine out of swap space.

This was written in 1994, mind you. The numbers have probably changed, but the problem persists.

## Data Type Formatting Functions

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. functions-formatting-table 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.

## Formatting Functions

Function | Returns | Description | Example
`to_char`(`timestamp`, `text`) | `text` | convert time stamp to string | `to_char(timestamp 'now','HH12:MI:SS')`
`to_char`(`interval`, `text`) | `text` | convert interval to string | `to_char(interval '15h 2m 12s','HH24:MI:SS')`
`to_char`(`int`, `text`) | `text` | convert integer to string | `to_char(125, '999')`
`to_char`(`double precision`, `text`) | `text` | convert real/double precision to string | `to_char(125.8, '999D9')`
`to_char`(`numeric`, `text`) | `text` | convert numeric to string | `to_char(numeric '-125.8', '999D99S')`
`to_date`(`text`, `text`) | `date` | convert string to date | `to_date('05 Dec 2000', 'DD Mon YYYY')`
`to_timestamp`(`text`, `text`) | `timestamp` | convert string to time stamp | `to_timestamp('05 Dec 2000', 'DD Mon YYYY')`
`to_number`(`text`, `text`) | `numeric` | convert string to numeric | `to_number('12,454.8-', '99G999D9S')`

In an output template string, there are certain patterns that are recognized and replaced with appropriately-formatted data from the value to be formatted. Any text that is not a template pattern is simply copied verbatim. Similarly, in an input template string, template patterns identify the parts of the input data string to be looked at and the values to be found there.

functions-formatting-datetime-table shows the template patterns available for formatting date and time values.

## Template patterns for date/time conversions

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)
`SSSS` | seconds past midnight (0-86399)
`AM` or `A.M.` or `PM` or `P.M.` | meridian indicator (upper case)
`am` or `a.m.` or `pm` or `p.m.` | meridian indicator (lower case)
`Y,YYY` | year (4 and more digits) with comma
`YYYY` | year (4 and more digits)
`YYY` | last 3 digits of year
`YY` | last 2 digits of year
`Y` | last digit of year
`BC` or `B.C.` or `AD` or `A.D.` | era indicator (upper case)
`bc` or `b.c.` or `ad` or `a.d.` | era indicator (lower case)
`MONTH` | full upper case month name (blank-padded to 9 chars)
`Month` | full mixed case 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)
`Mon` | abbreviated mixed case month name (3 chars)
`mon` | abbreviated lower case month name (3 chars)
`MM` | month number (01-12)
`DAY` | full upper case day name (blank-padded to 9 chars)
`Day` | full mixed case 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)
`Dy` | abbreviated mixed case day name (3 chars)
`dy` | abbreviated lower case day name (3 chars)
`DDD` | day of year (001-366)
`DD` | day of month (01-31)
`D` | day of week (1-7; SUN=1)
`W` | week of month (1-5) where first week start on the first day of the month
`WW` | week number of year (1-53) where first week start on the first day of the year
`IW` | ISO week number of year (The first Thursday of the new year is in week 1.)
`CC` | century (2 digits)
`J` | Julian Day (days since January 1, 4712 BC)
`Q` | quarter
`RM` | month in Roman Numerals (I-XII; I=January) - upper case
`rm` | month in Roman Numerals (I-XII; I=January) - lower case
`TZ` | time-zone name - upper case
`tz` | time-zone name - lower case

Certain modifiers may be applied to any template pattern to alter its behavior. For example, `FMMonth` is the `Month` pattern with the `FM` prefix. functions-formatting-datetimemod-table shows the modifier patterns for date/time formatting.

## Template pattern modifiers for date/time conversions

Modifier | Description | Example
`FM` prefix | fill mode (suppress padding blanks and zeroes) | `FMMonth`
`TH` suffix | add upper-case ordinal number suffix | `DDTH`
`th` suffix | add lower-case ordinal number suffix | `DDth`
`FX` prefix | fixed format global option (see usage notes) | `FX Month DD Day`
`SP` suffix | spell mode (not yet implemented) | `DDSP`

Usage notes for the 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.

- `to_timestamp` and `to_date` skip multiple blank spaces in the input string if the `FX` option is not used. `FX` must be specified as the first item in the template; for example `to_timestamp('2000 JUN','YYYY MON')` is right, but `to_timestamp('2000 JUN','FXYYYY MON')` returns an error, because `to_timestamp` expects one blank space only.

- If a backslash (`\`) is desired in a string constant, a double backslash (`\\`) must be entered; for example `'\\HH\\MI\\SS'`. This is true for any string constant in PostgreSQL.

- 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 pattern keywords. For example, in `'"Hello Year "YYYY'`, the `YYYY` will be replaced by the year data, but the single `Y` in Year will not be.

- If you want to have a double quote in the output you must precede it with a backslash, for example `'\\"YYYY Month\\"'`.

- `YYYY` conversion from string to `timestamp` or `date` is restricted if you use a year 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 year 20000): `to_date('200001131', 'YYYYMMDD')` will be interpreted as a 4-digit year; better is to use a non-digit separator after the year, like `to_date('20000-1131', 'YYYY-MMDD')` or `to_date('20000Nov31', 'YYYYMonDD')`.

- Millisecond `MS` and microsecond `US` values in a conversion from string to time stamp are used as part of the seconds after the decimal point. For example `to_timestamp('12:3', 'SS:MS')` is not 3 milliseconds, but 300, because the conversion counts it as 12 + 0.3. This means 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 use `12:003`, which the conversion counts as 12 + 0.003 = 12.003 seconds.

  Here is a more complex example: `to_timestamp('15:12:02.020.001230','HH:MI:SS.MS.US')` is 15 hours, 12 minutes, and 2 seconds + 20 milliseconds + 1230 microseconds = 2.021230 seconds.

functions-formatting-numeric-table shows the template patterns available for formatting numeric values.

## Template patterns for numeric conversions

Pattern | Description
`9` | value with the specified number of digits
`0` | value with leading zeros
`.` (period) | decimal point
`,` (comma) | group (thousand) separator
`PR` | negative value in angle brackets
`S` | negative value with minus sign (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` | convert to ordinal number
`V` | shift `n` digits (see notes)
`EEEE` | scientific notation (not implemented yet)

Usage notes for the numeric formatting:

- A sign formatted using `SG`, `PL`, or `MI` is not an anchor in the number; for example, `to_char(-12, 'S9999')` produces `' -12'`, but `to_char(-12, 'MI9999')` produces `'- 12'`. The Oracle implementation does not allow the use of `MI` ahead of `9`, but rather requires that `9` precede `MI`.

- `9` specifies a value with the same number of digits as there are `9`s. If a digit is not available it outputs a space.

- `TH` does not convert values less than zero and does not convert decimal numbers.

- `PL`, `SG`, and `TH` are PostgreSQL extensions.

- `V` effectively multiplies the input values by `10^`n``, where `n` is the number of digits following `V`. `to_char` does not support the use of `V` combined with a decimal point. (E.g., `99.9V99` is not allowed.)

functions-formatting-examples-table shows some examples of the use of the `to_char` function.

## `to_char` Examples

Input | Output
`to_char(now(),'Day, DD HH12:MI:SS')` | `'Tuesday , 06 05:39:18'`
`to_char(now(),'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,'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,'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,'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'`

## Date/Time Functions and Operators

functions-datetime-table shows the available functions for date/time value processing, with details appearing in the following subsections. operators-datetime-table illustrates the behaviors of the basic arithmetic operators (`+`, `*`, etc.). For formatting functions, refer to functions-formatting. You should be familiar with the background information on date/time data types (see datatype-datetime).

All the functions and operators described below that take time or timestamp inputs actually come in two variants: one that takes time or timestamp with time zone, and one that takes time or timestamp without time zone. For brevity, these variants are not shown separately.

## Date/Time Operators

Name | Example | Result
`+` | `timestamp '2001-09-28 01:00' + interval '23 hours'` | `timestamp '2001-09-29 00:00'`
`+` | `date '2001-09-28' + interval '1 hour'` | `timestamp '2001-09-28 01:00'`
`+` | `time '01:00' + interval '3 hours'` | `time '04:00'`
`-` | `timestamp '2001-09-28 23:00' - interval '23 hours'` | `timestamp '2001-09-28'`
`-` | `date '2001-09-28' - interval '1 hour'` | `timestamp '2001-09-27 23:00'`
`-` | `time '05:00' - interval '2 hours'` | `time '03:00'`
`-` | `interval '2 hours' - time '05:00'` | `time '03:00:00'`
`*` | `interval '1 hour' * int '3'` | `interval '03:00'`
`/` | `interval '1 hour' / int '3'` | `interval '00:20'`

## Date/Time Functions

Name | Return Type | Description | Example | Result
`age`(`timestamp`) | `interval` | Subtract from today | `age(timestamp '1957-06-13')` | `43 years 8 mons 3 days`
`age`(`timestamp`, `timestamp`) | `interval` | Subtract arguments | `age('2001-04-10', timestamp '1957-06-13')` | `43 years 9 mons 27 days`
`current_date` | `date` | Today's date; see functions-datetime-current |  |
`current_time` | `time with time zone` | Time of day; see functions-datetime-current |  |
`current_timestamp` | `timestamp with time zone` | Date and time; see functions-datetime-current |  |
`date_part`(`text`, `timestamp`) | `double precision` | Get subfield (equivalent to `extract`); see also below | `date_part('hour', timestamp '2001-02-16 20:38:40')` | `20`
`date_part`(`text`, `interval`) | `double precision` | Get subfield (equivalent to `extract`); see also below | `date_part('month', interval '2 years 3 months')` | `3`
`date_trunc`(`text`, `timestamp`) | `timestamp` | Truncate to specified precision; see also functions-datetime-trunc | `date_trunc('hour', timestamp '2001-02-16 20:38:40')` | `2001-02-16 20:00:00+00`
`extract`(`field` from `timestamp`) | `double precision` | Get subfield; see also functions-datetime-extract | `extract(hour from timestamp '2001-02-16 20:38:40')` | `20`
`extract`(`field` from `interval`) | `double precision` | Get subfield; see also functions-datetime-extract | `extract(month from interval '2 years 3 months')` | `3`
`isfinite`(`timestamp`) | `boolean` | Test for finite time stamp (neither invalid nor infinity) | `isfinite(timestamp '2001-02-16 21:28:30')` | `true`
`isfinite`(`interval`) | `boolean` | Test for finite interval | `isfinite(interval '4 hours')` | `true`
`localtime` | `time` | Time of day; see functions-datetime-current |  |
`localtimestamp` | `timestamp` | Date and time; see functions-datetime-current |  |
`now`() | `timestamp with time zone` | Current date and time (equivalent to `current_timestamp`); see functions-datetime-current |  |
`timeofday()` | `text` | Current date and time; see functions-datetime-current | `timeofday()` | `Wed Feb 21 17:01:13.000126 2001 EST`

### `EXTRACT`, `date_part`

    EXTRACT (field FROM source)

The `extract` function retrieves subfields from date/time values, such as year or hour. `source` is a value expression that evaluates to type `timestamp` or `interval`. (Expressions of type `date` or `time` will be cast to `timestamp` and can therefore be used as well.) `field` is an identifier or string that selects what field to extract from the source value. The `extract` function returns values of type `double precision`. The following are valid values:

`century`

- The year field divided by 100

      SELECT EXTRACT(CENTURY FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 20

  Note that the result for the century field is simply the year field divided by 100, and not the conventional definition which puts most years in the 1900's in the twentieth century.

`day`

- The day (of the month) field (1 - 31)

      SELECT EXTRACT(DAY FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 16

`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 (0 - 6; Sunday is 0) (for `timestamp` values only)

      SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 5

`doy`

- The day of the year (1 - 365/366) (for `timestamp` values only)

      SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 47

`epoch`

- For `date` and `timestamp` values, the number of seconds since 1970-01-01 00:00:00-00 (can be negative); for `interval` values, the total number of seconds in the interval

      SELECT EXTRACT(EPOCH FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 982352320

      SELECT EXTRACT(EPOCH FROM INTERVAL '5 days 3 hours');
      Result: 442800

`hour`

- The hour field (0 - 23)

      SELECT EXTRACT(HOUR FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 20

`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 year field divided by 1000

      SELECT EXTRACT(MILLENNIUM FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 2

  Note that the result for the millennium field is simply the year field divided by 1000, and not the conventional definition which puts years in the 1900's in the second millennium.

`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

`minute`

- The minutes field (0 - 59)

      SELECT EXTRACT(MINUTE FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 38

`month`

- For `timestamp` values, 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 day is in (for `timestamp` values only)

      SELECT EXTRACT(QUARTER FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 1

`second`

- The seconds field, including fractional parts (0 - 59

  60 if leap seconds are implemented by the operating system

  )

      SELECT EXTRACT(SECOND FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 40

      SELECT EXTRACT(SECOND FROM TIME '17:12:28.5');
      Result: 28.5

`timezone_hour`

- The hour component of the time zone offset.

`timezone_minute`

- The minute component of the time zone offset.

`week`

- From a `timestamp` value, calculate the number of the week of the year that the day is in. By definition (ISO 8601), the first week of a year contains January 4 of that year. (The ISO week starts on Monday.) In other words, the first Thursday of a year is in week 1 of that year.

      SELECT EXTRACT(WEEK FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 7

`year`

- The year field

      SELECT EXTRACT(YEAR FROM TIMESTAMP '2001-02-16 20:38:40');
      Result: 2001

The `extract` function is primarily intended for computational processing. For formatting date/time values for display, see functions-formatting.

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 values for `date_part` are the same as for `extract`.

    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)

`source` is a value expression of type `timestamp` (values of type `date` and `time` are cast automatically). `field` selects to which precision to truncate the time stamp value. The return value is of type `timestamp` with all fields that are less than the selected one set to zero (or one, for day and month).

Valid values for `field` are:

- microseconds
- milliseconds
- second
- minute
- hour
- day
- month
- year
- decade
- century
- millennium

Examples:

    SELECT date_trunc('hour', TIMESTAMP '2001-02-16 20:38:40');
    Result: 2001-02-16 20:00:00+00

    SELECT date_trunc('year', TIMESTAMP '2001-02-16 20:38:40');
    Result: 2001-01-01 00:00:00+00

### `AT TIME ZONE`

The `AT TIME ZONE` construct allows conversions of timestamps to different timezones.

### AT TIME ZONE Variants

Expression | Returns | Description
`timestamp without time zone` `AT TIME ZONE` `zone` | `timestamp with time zone` | Convert local time in given timezone to UTC
`timestamp with time zone` `AT TIME ZONE` `zone` | `timestamp without time zone` | Convert UTC to local time in given timezone
`time with time zone` `AT TIME ZONE` `zone` | `time with time zone` | Convert local time across timezones

In these expressions, the desired time `zone` can be specified either as a text string (e.g., `'PST'`) or as an interval (e.g., `INTERVAL '-08:00'`).

Examples (supposing that `TimeZone` is `PST8PDT`):

    SELECT TIMESTAMP '2001-02-16 20:38:40' AT TIME ZONE 'MST';
    Result: 2001-02-16 19:38:40-08

    SELECT TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-05' AT TIME ZONE 'MST';
    Result: 2001-02-16 18:38:40

The first example takes a zone-less timestamp and interprets it as MST time (GMT-7) to produce a UTC timestamp, which is then rotated to PST (GMT-8) for display. The second example takes a timestamp specified in EST (GMT-5) and converts it to local time in MST (GMT-7).

The function `timezone`(`zone`, `timestamp`) is equivalent to the SQL-compliant construct `timestamp` `AT TIME ZONE` `zone`.

### Current Date/Time

The following functions are available to obtain the current date and/or time:

    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 be given a precision parameter, which causes the result to be rounded to that many fractional digits. Without a precision parameter, the result is given to the full available precision.

Prior to PostgreSQL 7.2, the precision parameters were unimplemented, and the result was always given in integer seconds.

Some examples:

    SELECT CURRENT_TIME;
    14:39:53.662522-05

    SELECT CURRENT_DATE;
    2001-12-23

    SELECT CURRENT_TIMESTAMP;
    2001-12-23 14:39:53.662522-05

    SELECT CURRENT_TIMESTAMP(2);
    2001-12-23 14:39:53.66-05

    SELECT LOCALTIMESTAMP;
    2001-12-23 14:39:53.662522

The function `now()` is the traditional PostgreSQL equivalent to `CURRENT_TIMESTAMP`.

There is also `timeofday()`, which for historical reasons returns a text string rather than a `timestamp` value:

    SELECT timeofday();
     Sat Feb 17 19:07:32.000126 2001 EST

It is important to realize that `CURRENT_TIMESTAMP` and related functions return the start time of the current transaction; their values do not change during the transaction. `timeofday()` returns the wall clock time and does advance during transactions.

Many other database systems advance these values more frequently.

All the date/time data types also accept the special literal value `now` to specify the current date and time. Thus, the following three all return the same result:

    SELECT CURRENT_TIMESTAMP;
    SELECT now();
    SELECT TIMESTAMP 'now';

You do not want to use the third form when specifying a `DEFAULT` clause while creating a table. 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.

## Geometric Functions and Operators

The geometric types `point`, `box`, `lseg`, `line`, `path`, `polygon`, and `circle` have a large set of native support functions and operators, shown in functions-geometry-op-table, functions-geometry-func-table, and functions-geometry-conv-table.

## Geometric Operators

Operator | Description | Usage
+ | Translation | `box '((0,0),(1,1))' + point '(2.0,0)'`
- | Translation | `box '((0,0),(1,1))' - point '(2.0,0)'`
* | Scaling/rotation | `box '((0,0),(1,1))' * point '(2.0,0)'`
/ | Scaling/rotation | `box '((0,0),(2,2))' / point '(2.0,0)'`
# | Intersection | `'((1,-1),(-1,1))' # '((1,1),(-1,-1))'`
# | Number of points in path or polygon | `# '((1,0),(0,1),(-1,0))'`
## | Point of closest proximity | `point '(0,0)' ## lseg '((2,0),(0,2))'`
&& | Overlaps? | `box '((0,0),(1,1))' && box '((0,0),(2,2))'`
&< | Overlaps to left? | `box '((0,0),(1,1))' &< box '((0,0),(2,2))'`
&> | Overlaps to right? | `box '((0,0),(3,3))' &> box '((0,0),(2,2))'`
<-> | Distance between | `circle '((0,0),1)' <-> circle '((5,0),1)'`
<< | Left of? | `circle '((0,0),1)' << circle '((5,0),1)'`
<^ | Is below? | `circle '((0,0),1)' <^ circle '((0,5),1)'`
>> | Is right of? | `circle '((5,0),1)' >> circle '((0,0),1)'`
>^ | Is above? | `circle '((0,5),1)' >^ circle '((0,0),1)'`
?# | Intersects or overlaps | `lseg '((-1,0),(1,0))' ?# box '((-2,-2),(2,2))'`
?- | Is horizontal? | `point '(1,0)' ?- point '(0,0)'`
?-| | Is perpendicular? | `lseg '((0,0),(0,1))' ?-| lseg '((0,0),(1,0))'`
@-@ | Length or circumference | `@-@ path '((0,0),(1,0))'`
?| | Is vertical? | `point '(0,1)' ?| point '(0,0)'`
?|| | Is parallel? | `lseg '((-1,0),(1,0))' ?|| lseg '((-1,2),(1,2))'`
@ | Contained or on | `point '(1,1)' @ circle '((0,0),2)'`
@@ | Center of | `@@ circle '((0,0),10)'`
~= | Same as | `polygon '((0,0),(1,1))' ~= polygon '((1,1),(0,0))'`

## Geometric Functions

Function | Returns | Description | Example
`area`(object) | `double precision` | area of item | `area(box '((0,0),(1,1))')`
`box`(box, box) | `box` | intersection box | `box(box '((0,0),(1,1))',box '((0.5,0.5),(2,2))')`
`center`(object) | `point` | center of item | `center(box '((0,0),(1,2))')`
`diameter`(circle) | `double precision` | diameter of circle | `diameter(circle '((0,0),2.0)')`
`height`(box) | `double precision` | vertical size of box | `height(box '((0,0),(1,1))')`
`isclosed`(path) | `boolean` | a closed path? | `isclosed(path '((0,0),(1,1),(2,0))')`
`isopen`(path) | `boolean` | an open path? | `isopen(path '[(0,0),(1,1),(2,0)]')`
`length`(object) | `double precision` | length of item | `length(path '((-1,0),(1,0))')`
`npoints`(path) | `integer` | number of points | `npoints(path '[(0,0),(1,1),(2,0)]')`
`npoints`(polygon) | `integer` | number of points | `npoints(polygon '((1,1),(0,0))')`
`pclose`(path) | `path` | convert path to closed | `popen(path '[(0,0),(1,1),(2,0)]')`
`point`(lseg,lseg) | `point` | intersection | `point(lseg '((-1,0),(1,0))',lseg '((-2,-2),(2,2))')`
]]>
`popen`(path) | `path` | convert path to open path | `popen(path '((0,0),(1,1),(2,0))')`
`radius`(circle) | `double precision` | radius of circle | `radius(circle '((0,0),2.0)')`
`width`(box) | `double precision` | horizontal size | `width(box '((0,0),(1,1))')`

## Geometric Type Conversion Functions

Function | Returns | Description | Example
`box`(`circle`) | `box` | circle to box | `box(circle '((0,0),2.0)')`
`box`(`point`, `point`) | `box` | points to box | `box(point '(0,0)', point '(1,1)')`
`box`(`polygon`) | `box` | polygon to box | `box(polygon '((0,0),(1,1),(2,0))')`
`circle`(`box`) | `circle` | to circle | `circle(box '((0,0),(1,1))')`
`circle`(`point`, `double precision`) | `circle` | point to circle | `circle(point '(0,0)', 2.0)`
`lseg`(`box`) | `lseg` | box diagonal to `lseg` | `lseg(box '((-1,0),(1,0))')`
`lseg`(`point`, `point`) | `lseg` | points to `lseg` | `lseg(point '(-1,0)', point '(1,0)')`
`path`(`polygon`) | `point` | polygon to path | `path(polygon '((0,0),(1,1),(2,0))')`
`point`(`circle`) | `point` | center | `point(circle '((0,0),2.0)')`
`point`(`lseg`, `lseg`) | `point` | intersection | `point(lseg '((-1,0),(1,0))', lseg '((-2,-2),(2,2))')`
`point`(`polygon`) | `point` | center | `point(polygon '((0,0),(1,1),(2,0))')`
`polygon`(`box`) | `polygon` | 4-point polygon | `polygon(box '((0,0),(1,1))')`
`polygon`(`circle`) | `polygon` | 12-point polygon | `polygon(circle '((0,0),2.0)')`
`polygon`(`npts`, `circle`) | `polygon` | `npts` polygon | `polygon(12, circle '((0,0),2.0)')`
`polygon`(`path`) | `polygon` | path to polygon | `polygon(path '((0,0),(1,1),(2,0))')`

It is possible to access the two component numbers of a `point` as though it were an array with subscripts 0, 1. For example, if `t.p` is a `point` column then `SELECT p[0] FROM t` retrieves the X coordinate; `UPDATE t SET p[1] = ...` changes the Y coordinate. In the same way, a `box` or an `lseg` may be treated as an array of two `point`s.

## Network Address Type Functions

cidr-inet-operators-table shows the operators available for the `inet` and `cidr` types. The operators `<<`, `<<=`, `>>`, `>>=` test for subnet inclusion: they consider only the network parts of the two addresses, ignoring any host part, and determine whether one network part is identical to or a subnet of the other.

## `cidr` and `inet` Operators

Operator | Description | Usage
< | Less than | `inet '192.168.1.5' < inet '192.168.1.6'`
<= | Less than or equal | `inet '192.168.1.5' <= inet '192.168.1.5'`
= | Equals | `inet '192.168.1.5' = inet '192.168.1.5'`
>= | Greater or equal | `inet '192.168.1.5' >= inet '192.168.1.5'`
> | Greater | `inet '192.168.1.5' > inet '192.168.1.4'`
<> | Not equal | `inet '192.168.1.5' <> inet '192.168.1.4'`
<< | is contained within | `inet '192.168.1.5' << inet '192.168.1/24'`
<<= | is contained within or equals | `inet '192.168.1/24' <<= inet '192.168.1/24'`
>> | contains | `inet'192.168.1/24' >> inet '192.168.1.5'`
>>= | contains or equals | `inet '192.168.1/24' >>= inet '192.168.1/24'`

cidr-inet-functions-table shows the functions available for use with the `inet` and `cidr` types. The `host()`, `text()`, and `abbrev()` functions are primarily intended to offer alternative display formats. You can cast a text field to inet using normal casting syntax: `inet(expression)` or `colname::inet`.

## `cidr` and `inet` Functions

Function | Returns | Description | Example | Result
`broadcast`(`inet`) | `inet` | broadcast address for network | `broadcast('192.168.1.5/24')` | `192.168.1.255/24`
`host`(`inet`) | `text` | extract IP address as text | `host('192.168.1.5/24')` | `192.168.1.5`
`masklen`(`inet`) | `integer` | extract netmask length | `masklen('192.168.1.5/24')` | `24`
`set_masklen`(`inet`,`integer`) | `inet` | set netmask length for `inet` value | `set_masklen('192.168.1.5/24',16)` | `192.168.1.5/16`
`netmask`(`inet`) | `inet` | construct netmask for network | `netmask('192.168.1.5/24')` | `255.255.255.0`
`network`(`inet`) | `cidr` | extract network part of address | `network('192.168.1.5/24')` | `192.168.1.0/24`
`text`(`inet`) | `text` | extract IP address and masklen as text | `text(inet '192.168.1.5')` | `192.168.1.5/32`
`abbrev`(`inet`) | `text` | extract abbreviated display as text | `abbrev(cidr '10.1.0.0/16')` | `10.1/16`

macaddr-functions-table shows the functions available for use with the `mac` type. The function `trunc`(`macaddr`) returns a MAC address with the last 3 bytes set to 0. This can be used to associate the remaining prefix with a manufacturer. The directory `contrib/mac` in the source distribution contains some utilities to create and maintain such an association table.

## `macaddr` Functions

Function | Returns | Description | Example | Result
`trunc`(`macaddr`) | `macaddr` | set last 3 bytes to zero | `trunc(macaddr '12:34:56:78:90:ab')` | `12:34:56:00:00:00`

The `macaddr` type also supports the standard relational operators (`>`, `<=`, etc.) for lexicographical ordering.

## Sequence-Manipulation Functions

This section describes PostgreSQL's functions for operating on sequence objects. Sequence objects (also called sequence generators or just sequences) are special single-row tables created with `CREATE SEQUENCE`. A sequence object is usually used to generate unique identifiers for rows of a table. The sequence functions, listed in functions-sequence-table, provide simple, multiuser-safe methods for obtaining successive sequence values from sequence objects.

## Sequence Functions

Function | Returns | Description
`nextval`(`text`) | `bigint` | Advance sequence and return new value
`currval`(`text`) | `bigint` | Return value most recently obtained with `nextval`
`setval`(`text`,`bigint`) | `bigint` | Set sequence's current value
`setval`(`text`,`bigint`,`boolean`) | `bigint` | Set sequence's current value and `is_called` flag

For largely historical reasons, the sequence to be operated on by a sequence-function call is specified by a text-string argument. To achieve some compatibility with the handling of ordinary SQL names, the sequence functions convert their argument to lower case unless the string is double-quoted. Thus

    nextval('foo')      operates on sequence foo
    nextval('FOO')      operates on sequence foo
    nextval('"Foo"')    operates on sequence Foo

The sequence name can be schema-qualified if necessary:

    nextval('myschema.foo')     operates on myschema.foo
    nextval('"myschema".foo')   same as above
    nextval('foo')              searches search path for foo

Of course, the text argument can be the result of an expression, not only a simple literal, which is occasionally useful.

The available sequence functions are:

`nextval`

- Advance the sequence object to its next value and return that value. This is done atomically: even if multiple sessions execute `nextval` concurrently, each will safely receive a distinct sequence value.

`currval`

- Return 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.) Notice that because this is returning a session-local value, it gives a predictable answer even if other sessions are executing `nextval` meanwhile.

`setval`

- Reset the sequence object's counter value. 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. In the three-parameter form, `is_called` may be set either `true` or `false`. If it's set to `false`, the next `nextval` will return exactly the specified value, and sequence advancement commences with the following `nextval`. For example,

      SELECT setval('foo', 42);           Next nextval() will return 43
      SELECT setval('foo', 42, true);     Same as above
      SELECT setval('foo', 42, false);    Next nextval() will return 42

  The result returned by `setval` is just the value of its second argument.

To avoid blocking of concurrent transactions that obtain numbers from the same sequence, a `nextval` operation is never rolled back; that is, once a value has been fetched it is considered used, even if the transaction that did the `nextval` later aborts. This means that aborted transactions may leave unused holes in the sequence of assigned values. `setval` operations are never rolled back, either.

If a sequence object has been created with default parameters, `nextval()` calls on it will return successive values beginning with one. Other behaviors can be obtained by using special parameters in the `CREATE SEQUENCE` command; see its command reference page for more information.

## Conditional Expressions

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 stored procedure in a more expressive programming language.

### CASE

    CASE WHEN condition THEN result
         WHEN ...
         ELSE result
    END

The SQL CASE expression is a generic conditional expression, similar to if/else statements in other languages. CASE clauses can be used wherever an expression is valid. `condition` is an expression that returns a `boolean` result. If the result is true then the value of the CASE expression is `result`. If the result is false any subsequent WHEN clauses are searched in the same manner. If no WHEN `condition` is true then the value of the case expression is the `result` in the ELSE clause. If the ELSE clause is omitted and no condition matches, 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 coercible to a single output type. See typeconv-union-case for more detail.

    CASE expression
        WHEN value THEN result
        WHEN ...
        ELSE result
    END

This simple CASE expression is a specialized variant of the general form above. The `expression` is computed and compared to all the `value`s in the WHEN clauses until one is found that is equal. If no match is found, the `result` in 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

### COALESCE

    COALESCE(value , ...)

The `COALESCE` function returns the first of its arguments that is not null. This is often useful to substitute a default value for null values when data is retrieved for display, for example:

    SELECT COALESCE(description, short_description, '(none)') ...

### NULLIF

    NULLIF(value1, value2)

The `NULLIF` function returns a null value if and only if `value1` and `value2` are equal. Otherwise it returns `value1`. This can be used to perform the inverse operation of the `COALESCE` example given above:

    SELECT NULLIF(value, '(none)') ...

`COALESCE` and `NULLIF` are just shorthand for CASE expressions. They are actually converted into CASE expressions at a very early stage of processing, and subsequent processing thinks it is dealing with CASE. Thus an incorrect `COALESCE` or `NULLIF` usage may draw an error message that refers to CASE.

## Miscellaneous Functions

functions-misc-session-table shows several functions that extract session and system information.

## Session Information Functions

Name | Return Type | Description
`current_database()` | `name` | name of current database
`current_schema()` | `name` | name of current schema
`current_schemas(boolean)` | `name[]` | names of schemas in search path optionally including implicit schemas
`current_user` | `name` | user name of current execution context
`session_user` | `name` | session user name
`user` | `name` | equivalent to `current_user`
`version()` | `text` | PostgreSQL version information

The `session_user` is the user that initiated a database connection; it is fixed for the duration of that connection. The `current_user` is the user identifier that is applicable for permission checking. Normally, it is equal to the session user, but it 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_user`, `session_user`, and `user` have special syntactic status in SQL: they must be called without trailing parentheses.

`current_schema` returns the name of the schema that is at the front of 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(boolean)` returns an array of the names of all schemas presently in the search path. The boolean option determines whether or not implicitly included system schemas such as pg_catalog are included in the search path returned.

The search path may be altered by a run-time setting. The command to use is `SET SEARCH_PATH '`schema`'[,'`schema`']...`

`version()` returns a string describing the PostgreSQL server's version.

functions-misc-set-table shows the functions available to query and alter run-time configuration parameters.

## Configuration Settings Information Functions

Name | Return Type | Description
`current_setting`(`setting_name`) | `text` | value of current setting
`set_config(`setting_name`, `new_value`, `is_local`)` | `text` | new value of current setting

The `current_setting` is used to obtain the current value of the `setting_name` setting, as a query result. It is the equivalent to the SQL `SHOW` command. For example:

    select current_setting('DateStyle');
                current_setting
    ---------------------------------------
     ISO with US (NonEuropean) conventions
    (1 row)

`set_config` allows the `setting_name` setting to be changed to `new_value`. If `is_local` is set to `true`, the new value will only apply to the current transaction. If you want the new value to apply for the current session, use `false` instead. It is the equivalent to the SQL `SET` command. For example:

    select set_config('show_statement_stats','off','f');
     set_config
    ------------
     off
    (1 row)

functions-misc-access-table lists functions that allow the user to query object access privileges programmatically. See ddl-priv for more information about privileges.

## Access Privilege Inquiry Functions

Name | Return Type | Description
`has_table_privilege`(`user`, `table`, `access`) | `boolean` | does user have access to table
`has_table_privilege`(`table`, `access`) | `boolean` | does current user have access to table
`has_database_privilege`(`user`, `database`, `access`) | `boolean` | does user have access to database
`has_database_privilege`(`database`, `access`) | `boolean` | does current user have access to database
`has_function_privilege`(`user`, `function`, `access`) | `boolean` | does user have access to function
`has_function_privilege`(`function`, `access`) | `boolean` | does current user have access to function
`has_language_privilege`(`user`, `language`, `access`) | `boolean` | does user have access to language
`has_language_privilege`(`language`, `access`) | `boolean` | does current user have access to language
`has_schema_privilege`(`user`, `schema`, `access`) | `boolean` | does user have access to schema
`has_schema_privilege`(`schema`, `access`) | `boolean` | does current user have access to schema

`has_table_privilege` checks whether a user can access a table in a particular way. The user can be specified by name or by ID (`pg_user`.usesysid), or if the argument is omitted `current_user` is assumed. The table can be specified by name or by OID. (Thus, there are actually six variants of `has_table_privilege`, which can be distinguished by the number and types of their arguments.) When specifying by name, the name can be schema-qualified if necessary. The desired access type is specified by a text string, which must evaluate to one of the values `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `RULE`, `REFERENCES`, or `TRIGGER`. (Case of the string is not significant, however.) An example is:

    SELECT has_table_privilege('myschema.mytable', 'select');

`has_database_privilege` checks whether a user can access a database in a particular way. The possibilities for its arguments are analogous to `has_table_privilege`. The desired access type must evaluate to `CREATE`, `TEMPORARY`, or `TEMP` (which is equivalent to `TEMPORARY`).

`has_function_privilege` checks whether a user can access a function in a particular way. The possibilities for its arguments are analogous to `has_table_privilege`. When specifying a function by a text string rather than by OID, the allowed input is the same as for the `regprocedure` data type. The desired access type must currently evaluate to `EXECUTE`.

`has_language_privilege` checks whether a user can access a procedural language in a particular way. The possibilities for its arguments are analogous to `has_table_privilege`. The desired access type must currently evaluate to `USAGE`.

`has_schema_privilege` checks whether a user can access a schema in a particular way. The possibilities for its arguments are analogous to `has_table_privilege`. The desired access type must evaluate to `CREATE` or `USAGE`.

functions-misc-schema-table shows functions that determine whether a certain object is visible in the current schema search path. 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. For example, to list the names of all visible tables:

    SELECT relname FROM pg_class WHERE pg_table_is_visible(oid);

## Schema Visibility Inquiry Functions

Name | Return Type | Description
`pg_table_is_visible`(`tableOID`) | `boolean` | is table visible in search path
`pg_type_is_visible`(`typeOID`) | `boolean` | is type visible in search path
`pg_function_is_visible`(`functionOID`) | `boolean` | is function visible in search path
`pg_operator_is_visible`(`operatorOID`) | `boolean` | is operator visible in search path
`pg_opclass_is_visible`(`opclassOID`) | `boolean` | is operator class visible in search path

`pg_table_is_visible` performs the check for tables (or views, or any other kind of `pg_class` entry). `pg_type_is_visible`, `pg_function_is_visible`, `pg_operator_is_visible`, and `pg_opclass_is_visible` perform the same sort of visibility check for types, functions, operators, and operator classes, respectively. For functions and operators, an object in the search path is visible if there is no object of the same name and argument data type(s) earlier in the path. For operator classes, both name and associated index access method are considered.

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`, or `regoperator`), for example

    SELECT pg_type_is_visible('myschema.widget'::regtype);

Note that it would not make much sense to test an unqualified name in this way --- if the name can be recognized at all, it must be visible.

functions-misc-catalog-table lists functions that extract information from the system catalogs. `pg_get_viewdef()`, `pg_get_ruledef()`, `pg_get_indexdef()`, and `pg_get_constraintdef()` respectively reconstruct the creating command for a view, rule, index, or constraint. (Note that this is a decompiled reconstruction, not the verbatim text of the command.) At present `pg_get_constraintdef()` only works for foreign-key constraints. `pg_get_userbyid()` extracts a user's name given a usesysid value.

## Catalog Information Functions

Name | Return Type | Description
`pg_get_viewdef`(`viewname`) | `text` | Get `CREATE VIEW` command for view (deprecated)
`pg_get_viewdef`(`viewOID`) | `text` | Get `CREATE VIEW` command for view
`pg_get_ruledef`(`ruleOID`) | `text` | Get `CREATE RULE` command for rule
`pg_get_indexdef`(`indexOID`) | `text` | Get `CREATE INDEX` command for index
`pg_get_constraintdef`(`constraintOID`) | `text` | Get definition of a constraint
`pg_get_userbyid`(`userid`) | `name` | Get user name with given ID

The function shown in functions-misc-comment-table extract comments previously stored with the `COMMENT` command. A null value is returned if no comment can be found matching the specified parameters.

## Comment Information Functions

Name | Return Type | Description
`obj_description`(`objectOID`, `tablename`) | `text` | Get comment for a database object
`obj_description`(`objectOID`) | `text` | Get comment for a database object (deprecated)
`col_description`(`tableOID`, `columnnumber`) | `text` | Get comment for a table column

The two-parameter form of `obj_description()` 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 a table with OID 123456. The one-parameter form of `obj_description()` requires only the object OID. It is now deprecated since there is no guarantee that OIDs are unique across different system catalogs; therefore, the wrong comment could be returned.

`col_description()` 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.

## Aggregate Functions

Aggregate functions compute a single result value from a set of input values. functions-aggregate-table show the built-in aggregate functions. The special syntax considerations for aggregate functions are explained in syntax-aggregates. Consult the &cite-tutorial; for additional introductory information.

## Aggregate Functions

Function | Argument Type | Return Type | Description
`avg(`expression`)` | `smallint`, `integer`, `bigint`, `real`, `double precision`, `numeric`, or `interval`. | `numeric` for any integer type argument, `double precision` for a floating-point argument, otherwise the same as the argument data type | the average (arithmetic mean) of all input values
`count(*)` |  | `bigint` | number of input values
`count(`expression`)` | any | `bigint` | number of input values for which the value of `expression` is not null
`max(`expression`)` | any numeric, string, or date/time type | same as argument type | maximum value of `expression` across all input values
`min(`expression`)` | any numeric, string, or date/time type | same as argument type | minimum value of `expression` across all input values
`stddev(`expression`)` | `smallint`, `integer`, `bigint`, `real`, `double precision`, or `numeric`. | `double precision` for floating-point arguments, otherwise `numeric`. | sample standard deviation of the input values
`sum(`expression`)` | `smallint`, `integer`, `bigint`, `real`, `double precision`, `numeric`, or `interval` | `bigint` for `smallint` or `integer` arguments, `numeric` for `bigint` arguments, `double precision` for floating-point arguments, otherwise the same as the argument data type | sum of `expression` across all input values
`variance`(`expression`) | `smallint`, `integer`, `bigint`, `real`, `double precision`, or `numeric`. | `double precision` for floating-point arguments, otherwise `numeric`. | sample variance of the input values (square of the sample standard deviation)

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. The function `coalesce` may be used to substitute zero for null when necessary.

## Subquery Expressions

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

    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 far 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 any side effects (such as calling sequence functions); whether the side effects occur or not may be difficult to predict.

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 uninteresting. 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 multiple matching `tab2` rows:

    SELECT col1 FROM tab1
        WHERE EXISTS(SELECT 1 FROM tab2 WHERE col2 = tab1.col2);

### IN (scalar form)

    expression IN (value, ...)

The right-hand side of this form of IN is a parenthesized list of scalar 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.

This form of IN is not truly a subquery expression, but it seems best to document it in the same place as subquery IN.

### IN (subquery form)

    expression IN (subquery)

The right-hand side of this form of IN 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 special 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.

    (expression , expression ...) IN (subquery)

The right-hand side of this form of IN is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand list. 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 special case where the subquery returns no rows).

As usual, null values in the expressions or subquery 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 row results are either unequal or null, with at least one null, then the result of IN is null.

### NOT IN (scalar form)

    expression NOT IN (value, ...)

The right-hand side of this form of NOT IN is a parenthesized list of scalar 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's best to express your condition positively if possible.

### NOT IN (subquery form)

    expression NOT IN (subquery)

The right-hand side of this form of NOT IN 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 special 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.

    (expression , expression ...) NOT IN (subquery)

The right-hand side of this form of NOT IN is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand list. 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 special case where the subquery returns no rows). The result is false if any equal row is found.

As usual, null values in the expressions or subquery 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 row results are either unequal or null, with at least one null, then the result of NOT IN is null.

### ANY/SOME

    expression operator ANY (subquery)
    expression operator SOME (subquery)

The right-hand side of this form of ANY 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 special 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.

    (expression , expression ...) operator ANY (subquery)
    (expression , expression ...) operator SOME (subquery)

The right-hand side of this form of ANY is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand list. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result, using the given `operator`. Presently, only `=` and `<>` operators are allowed in row-wise ANY queries. The result of ANY is true if any equal or unequal row is found, respectively. The result is false if no such row is found (including the special case where the subquery returns no rows).

As usual, null values in the expressions or subquery 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 there is at least one null row result, then the result of ANY cannot be false; it will be true or null.

### ALL

    expression operator ALL (subquery)

The right-hand side of this form of ALL 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 special case where the subquery returns no rows). The result is false if any false result is found.

NOT IN is equivalent to `<> ALL`.

Note that if there are no failures but at least one right-hand row yields null for the operator's result, the result of the ALL 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.

    (expression , expression ...) operator ALL (subquery)

The right-hand side of this form of ALL is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand list. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result, using the given `operator`. Presently, only `=` and `<>` operators are allowed in row-wise ALL queries. The result of ALL is true if all subquery rows are equal or unequal, respectively (including the special case where the subquery returns no rows). The result is false if any row is found to be unequal or equal, respectively.

As usual, null values in the expressions or subquery 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 there is at least one null row result, then the result of ALL cannot be true; it will be false or null.

### Row-wise Comparison

    (expression , expression ...) operator (subquery)
    (expression , expression ...) operator (expression , expression ...)

The left-hand side is a list of scalar expressions. The right-hand side can be either a list of scalar expressions of the same length, or a parenthesized subquery, which must return exactly as many columns as there are expressions on the left-hand side. 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, or to the right-hand expression list. Presently, only `=` and `<>` operators are allowed in row-wise comparisons. The result is true if the two rows are equal or unequal, respectively.

As usual, null values in the expressions or subquery 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 the row comparison is unknown (null).