Summary of Built-in Functions v6.27.4

WarehousePG supports built-in functions and operators including analytic functions and window functions that can be used in window expressions. For information about using built-in WarehousePG functions see, "Using Functions and Operators" in the WarehousePG Administrator Guide.

• WarehousePG Function Types
• Built-in Functions and Operators
• JSON Functions and Operators
• Window Functions
• Advanced Aggregate Functions
• Text Search Functions and Operators
• Range Functions and Operators

Parent topic: WarehousePG Reference Guide

WarehousePG Function Types

WarehousePG evaluates functions and operators used in SQL expressions. Some functions and operators are only allowed to run on the coordinator since they could lead to inconsistencies in WarehousePG segment instances. The following describes the WarehousePG function types.

IMMUTABLE

WarehousePG support: Yes

Relies only on information directly in its argument list. Given the same argument values, always returns the same result.

STABLE

WarehousePG support: Yes, in most cases

Within a single table scan, returns the same result for same argument values, but results change across SQL statements.

Results depend on database lookups or parameter values. The current_timestamp family of functions is STABLE, and values don't change within an execution.

VOLATILE

WarehousePG support: Restricted

Function values can change within a single table scan. For example, random() and timeofday().

Any function with side effects is volatile, even if its result is predictable. For example, setval().

In WarehousePG, data is divided up across segments — each segment is a distinct PostgreSQL database. To prevent inconsistent or unexpected results, do not run functions classified as VOLATILE at the segment level if they contain SQL commands or modify the database in any way. For example, functions such as setval() are not allowed to run on distributed data in WarehousePG because they can cause inconsistent data between segment instances.

To ensure data consistency, you can safely use VOLATILE and STABLE functions in statements that are evaluated on and run from the coordinator. For example, the following statements run on the coordinator (statements without a FROM clause):

SELECT setval('myseq', 201);
SELECT foo();

If a statement has a FROM clause containing a distributed table and the function in the FROM clause returns a set of rows, the statement can run on the segments:

SELECT * from foo();

WarehousePG does not support functions that return a table reference (rangeFuncs) or functions that use the refCursor datatype.

Built-in Functions and Operators

The following table lists the categories of built-in functions and operators supported by PostgreSQL. All functions and operators are supported in WarehousePG as in PostgreSQL with the exception of STABLE and VOLATILE functions, which are subject to the restrictions noted in WarehousePG Function Types. See the Functions and Operators section of the PostgreSQL documentation for more information about these built-in functions and operators.

JSON Functions and Operators

WarehousePG includes built-in functions and operators that create and manipulate JSON data.

• JSON Operators
• JSON Creation Functions
• JSON Aggregate Functions
• JSON Processing Functions

Note For json data type values, all key/value pairs are kept even if a JSON object contains duplicate keys. For duplicate keys, JSON processing functions consider the last value as the operative one. For the jsonb data type, duplicate object keys are not kept. If the input includes duplicate keys, only the last value is kept. See About JSON Datain the WarehousePG Administrator Guide.

JSON Operators

The following operators are available for use with the json and jsonb data types.

-> (int)

Right operand type: int

Get the JSON array element (indexed from zero).

Example:

'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json->2
{"c":"baz"}

-> (text)

Right operand type: text

Get the JSON object field by key.

Example:

'{"a": {"b":"foo"}}'::json->'a'
{"b":"foo"}

->> (int)

Right operand type: int

Get the JSON array element as text.

Example:

'[1,2,3]'::json->>2
3

->> (text)

Right operand type: text

Get the JSON object field as text.

Example:

'{"a":1,"b":2}'::json->>'b'
2

#>

Right operand type: text[]

Get the JSON object at specified path.

Example:

'{"a": {"b":{"c": "foo"}}}'::json#>'{a,b}'
{"c": "foo"}

#>>

Right operand type: text[]

Get the JSON object at specified path as text.

Example:

'{"a":[1,2,3],"b":[4,5,6]}'::json#>>'{a,2}'
3

Note There are parallel variants of these operators for both the json and jsonb data types. The field, element, and path extraction operators return the same data type as their left-hand input (either json or jsonb), except for those specified as returning text, which coerce the value to text. The field, element, and path extraction operators return NULL, rather than failing, if the JSON input does not have the right structure to match the request; for example if no such element exists.

Operators that require the jsonb data type as the left operand are described next. Many of these operators can be indexed by jsonb operator classes. For a full description of jsonb containment and existence semantics, see jsonb Containment and Existencein the WarehousePG Administrator Guide. For information about how these operators can be used to effectively index jsonb data, see jsonb Indexingin the WarehousePG Administrator Guide.

@>

Right operand type: jsonb

Does the left JSON value contain within it the right value?

Example:

'{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonb

<@

Right operand type: jsonb

Is the left JSON value contained within the right value?

Example:

'{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonb

?

Right operand type: text

Does the key/element string exist within the JSON value?

Example:

'{"a":1, "b":2}'::jsonb ? 'b'

?|

Right operand type: text[]

Do any of these key/element strings exist?

Example:

'{"a":1, "b":2, "c":3}'::jsonb ?| array['b', 'c']

?&

Right operand type: text[]

Do all of these key/element strings exist?

Example:

'["a", "b"]'::jsonb ?& array['a', 'b']

The following standard comparison operators are available only for the jsonb data type, not for the json data type. They follow the ordering rules for B-tree operations described in jsonb Indexingin the WarehousePG Administrator Guide.

• < (less than)
• > (greater than)
• <= (less than or equal to)
• >= (greater than or equal to)
• = (equal)
• <> or != (not equal)

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

JSON Creation Functions

The following functions create json data type values. (Currently, there are no equivalent functions for jsonb, but you can cast the result of one of these functions to jsonb.)

to_json(anyelement)

Returns the value as a JSON object. Arrays and composites are processed recursively and are converted to arrays and objects. If the input contains a cast from the type to json, the cast function is used to perform the conversion. Otherwise, a JSON scalar value is produced. For any scalar type other than a number, a Boolean, or a null value, the text representation is used, properly quoted and escaped so that it is a valid JSON string.

Example:

to_json('Fred said "Hi."'::text)
"Fred said \"Hi.\""

array_to_json(anyarray [, pretty_bool])

Returns the array as a JSON array. A multidimensional array becomes a JSON array of arrays. Line feeds are added between dimension-1 elements if pretty_bool is true.

Example:

array_to_json('{ {1,5},{99,100}}'::int[])
[[1,5],[99,100]]

row_to_json(record [, pretty_bool])

Returns the row as a JSON object. Line feeds are added between level-1 elements if pretty_bool is true.

Example:

row_to_json(row(1,'foo'))
{"f1":1,"f2":"foo"}

json_build_array(VARIADIC "any")

Builds a possibly heterogeneously typed JSON array out of a VARIADIC argument list.

Example:

json_build_array(1,2,'3',4,5)
[1, 2, "3", 4, 5]

json_build_object(VARIADIC "any")

Builds a JSON object out of a VARIADIC argument list. The argument list is taken in order and converted to a set of key/value pairs.

Example:

json_build_object('foo',1,'bar',2)
{"foo": 1, "bar": 2}

json_object(text[])

Builds a JSON object out of a text array. The array must be either a one or a two dimensional array.

The one dimensional array must have an even number of elements. The elements are taken as key/value pairs.

For a two dimensional array, each inner array must have exactly two elements, which are taken as a key/value pair.

Example:

json_object('{a, 1, b, "def", c, 3.5}')
json_object('{ {a, 1},{b, "def"},{c, 3.5}}')
{"a": "1", "b": "def", "c": "3.5"}

json_object(keys text[], values text[])

Builds a JSON object out of a text array. This form of json_object takes keys and values pairwise from two separate arrays. In all other respects it is identical to the one-argument form.

Example:

json_object('{a, b}', '{1,2}')
{"a": "1", "b": "2"}

Note array_to_json and row_to_json have the same behavior as to_json except for offering a pretty-printing option. The behavior described for to_json likewise applies to each individual value converted by the other JSON creation functions.

Note The hstore extension has a cast from hstore to json, so that hstore values converted via the JSON creation functions will be represented as JSON objects, not as primitive string values.

JSON Aggregate Functions

The following functions aggregate records to an array of JSON objects and pairs of values to a JSON object.

json_agg(record)

Argument types: record

Return type: json

Aggregates records as a JSON array of objects.

json_object_agg(name, value)

Argument types: ("any", "any")

Return type: json

Aggregates name/value pairs as a JSON object.

JSON Processing Functions

This section describes the functions that are available for processing json and jsonb values.

Many of these processing functions and operators convert Unicode escapes in JSON strings to the appropriate single character. This is a not an issue if the input data type is jsonb, because the conversion was already done. However, for json data type input, this might result in an error being thrown. See About JSON Data.

json_array_length() / jsonb_array_length()

Syntax: json_array_length(json) / jsonb_array_length(jsonb)

Return type: int

Returns the number of elements in the outermost JSON array.

Example:

json_array_length('[1,2,3,{"f1":1,"f2":[5,6]},4]')
5

json_each() / jsonb_each()

Syntax: json_each(json) / jsonb_each(jsonb)

Return type: setof key text, value json / setof key text, value jsonb

Expands the outermost JSON object into a set of key/value pairs.

Example:

select * from json_each('{"a":"foo", "b":"bar"}')
key | value
-----+-------
a   | "foo"
b   | "bar"

json_each_text() / jsonb_each_text()

Syntax: json_each_text(json) / jsonb_each_text(jsonb)

Return type: setof key text, value text

Expands the outermost JSON object into a set of key/value pairs. The returned values will be of type text.

Example:

select * from json_each_text('{"a":"foo", "b":"bar"}')
key | value
-----+-------
a   | foo
b   | bar

json_extract_path() / jsonb_extract_path()

Syntax: json_extract_path(from_json json, VARIADIC path_elems text[]) / jsonb_extract_path(from_json jsonb, VARIADIC path_elems text[])

Return type: json / jsonb

Returns the JSON value pointed to by path_elems (equivalent to #> operator).

Example:

json_extract_path('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}','f4')
{"f5":99,"f6":"foo"}

json_extract_path_text() / jsonb_extract_path_text()

Syntax: json_extract_path_text(from_json json, VARIADIC path_elems text[]) / jsonb_extract_path_text(from_json jsonb, VARIADIC path_elems text[])

Return type: text

Returns the JSON value pointed to by path_elems as text. Equivalent to #>> operator.

Example:

json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}','f4', 'f6')
foo

json_object_keys() / jsonb_object_keys()

Syntax: json_object_keys(json) / jsonb_object_keys(jsonb)

Return type: setof text

Returns set of keys in the outermost JSON object.

Example:

json_object_keys('{"f1":"abc","f2":{"f3":"a", "f4":"b"}}')
json_object_keys
------------------
f1
f2

json_populate_record() / jsonb_populate_record()

Syntax: json_populate_record(base anyelement, from_json json) / jsonb_populate_record(base anyelement, from_json jsonb)

Return type: anyelement

Expands the object in from_json to a row whose columns match the record type defined by base. See Note 1.

Example:

select * from json_populate_record(null::myrowtype, '{"a":1,"b":2}')
a | b
---+---
1 | 2

json_populate_recordset() / jsonb_populate_recordset()

Syntax: json_populate_recordset(base anyelement, from_json json) / jsonb_populate_recordset(base anyelement, from_json jsonb)

Return type: setof anyelement

Expands the outermost array of objects in from_json to a set of rows whose columns match the record type defined by base. See Note 1.

Example:

select * from json_populate_recordset(null::myrowtype, '[{"a":1,"b":2},{"a":3,"b":4}]')
a | b
---+---
1 | 2
3 | 4

json_array_elements() / jsonb_array_elements()

Syntax: json_array_elements(json) / jsonb_array_elements(jsonb)

Return type: setof json / setof jsonb

Expands a JSON array to a set of JSON values.

Example:

select * from json_array_elements('[1,true, [2,false]]')
value
-----------
1
true
[2,false]

json_array_elements_text() / jsonb_array_elements_text()

Syntax: json_array_elements_text(json) / jsonb_array_elements_text(jsonb)

Return type: setof text

Expands a JSON array to a set of text values.

Example:

select * from json_array_elements_text('["foo", "bar"]')
value
-----------
foo
bar

json_typeof() / jsonb_typeof()

Syntax: json_typeof(json) / jsonb_typeof(jsonb)

Return type: text

Returns the type of the outermost JSON value as a text string. Possible types are object, array, string, number, boolean, and null. See Note.

Example:

json_typeof('-123.4')
number

json_to_record() / jsonb_to_record()

Syntax: json_to_record(json) / jsonb_to_record(jsonb)

Return type: record

Builds an arbitrary record from a JSON object. See Note 1.

As with all functions returning record, the caller must explicitly define the structure of the record with an AS clause.

Example:

select * from json_to_record('{"a":1,"b":[1,2,3],"c":"bar"}') as x(a int, b text, d text)
a |    b    | d
---+---------+---
1 | [1,2,3] |

json_to_recordset() / jsonb_to_recordset()

Syntax: json_to_recordset(json) / jsonb_to_recordset(jsonb)

Return type: setof record

Builds an arbitrary set of records from a JSON array of objects See Note 1.

As with all functions returning record, the caller must explicitly define the structure of the record with an AS clause.

Example:

select * from json_to_recordset('[{"a":1,"b":"foo"},{"a":"2","c":"bar"}]') as x(a int, b text);
a |  b
---+-----
1 | foo
2 |

Note on JSON processing functions examples

Note The examples for the functions json_populate_record(), json_populate_recordset(), json_to_record() and json_to_recordset() use constants. However, the typical use would be to reference a table in the FROM clause and use one of its json or jsonb columns as an argument to the function. The extracted key values can then be referenced in other parts of the query. For example the value can be referenced in WHERE clauses and target lists. Extracting multiple values in this way can improve performance over extracting them separately with per-key operators.

JSON keys are matched to identical column names in the target row type. JSON type coercion for these functions might not result in desired values for some types. JSON fields that do not appear in the target row type will be omitted from the output, and target columns that do not match any JSON field will be NULL.

The json_typeof function null return value of null should not be confused with a SQL NULL. While calling json_typeof('null'::json) will return null, calling json_typeof(NULL::json) will return a SQL NULL.

Window Functions

The following are WarehousePG built-in window functions. All window functions are immutable. For more information about window functions, see "Window Expressions" in the WarehousePG Administrator Guide.

cume_dist()

Return type: double precision

Full syntax: CUME_DIST() OVER ( [PARTITION BY expr ] ORDER BY expr )

Calculates the cumulative distribution of a value in a group of values. Rows with equal values always evaluate to the same cumulative distribution value.

dense_rank()

Return type: bigint

Full syntax: DENSE_RANK () OVER ( [PARTITION BY expr ] ORDER BY expr )

Computes the rank of a row in an ordered group of rows without skipping rank values. Rows with equal values are given the same rank value.

first_value(expr)

Return type: same as input expr type

Full syntax: FIRST_VALUE( expr ) OVER ( [PARTITION BY expr ] ORDER BY expr [ROWS | RANGE frame_expr ] )

Returns the first value in an ordered set of values.

lag(expr [,offset] [,default])

Return type: same as input expr type

Full syntax: LAG( expr [, offset ] [, default ]) OVER ( [PARTITION BY expr ] ORDER BY expr )

Provides access to more than one row of the same table without doing a self join. Given a series of rows returned from a query and a position of the cursor, LAG provides access to a row at a given physical offset prior to that position. The default offset is 1. default sets the value that is returned if the offset goes beyond the scope of the window. If default is not specified, the default value is null.

last_value(expr)

Return type: same as input expr type

Full syntax: LAST_VALUE( expr ) OVER ( [PARTITION BY expr ] ORDER BY expr [ROWS | RANGE frame_expr ] )

Returns the last value in an ordered set of values.

lead(expr [,offset] [,default])

Return type: same as input expr type

Full syntax: LEAD( expr [, offset ] [, default ]) OVER ( [PARTITION BY expr ] ORDER BY expr )

Provides access to more than one row of the same table without doing a self join. Given a series of rows returned from a query and a position of the cursor, lead provides access to a row at a given physical offset after that position. If offset is not specified, the default offset is 1. default sets the value that is returned if the offset goes beyond the scope of the window. If default is not specified, the default value is null.

ntile(expr)

Return type: bigint

Full syntax: NTILE(expr) OVER ( [PARTITION BY expr] ORDER BY expr )

Divides an ordered data set into a number of buckets (as defined by expr) and assigns a bucket number to each row.

percent_rank()

Return type: double precision

Full syntax: PERCENT_RANK () OVER ( [PARTITION BY expr] ORDER BY expr)

Calculates the rank of a hypothetical row R minus 1, divided by 1 less than the number of rows being evaluated (within a window partition).

rank()

Return type: bigint

Full syntax: RANK () OVER ( [PARTITION BY expr] ORDER BY expr)

Calculates the rank of a row in an ordered group of values. Rows with equal values for the ranking criteria receive the same rank. The number of tied rows are added to the rank number to calculate the next rank value. Ranks may not be consecutive numbers in this case.

row_number()

Return type: bigint

Full syntax: ROW_NUMBER () OVER ( [PARTITION BY expr] ORDER BY expr)

Assigns a unique number to each row to which it is applied (either each row in a window partition or each row of the query).

Advanced Aggregate Functions

The following built-in advanced analytic functions are WarehousePG extensions of the PostgreSQL database. Analytic functions are immutable.

Note The WarehousePG MADlib Extension for Analytics provides additional advanced functions to perform statistical analysis and machine learning with WarehousePG data. See MADlib Extension for Analytics.

pivot_sum (label[], label, expr)

Return type: int[], bigint[], float[]

Full syntax: pivot_sum( array['A1','A2'], attr, value)

A pivot aggregation using sum to resolve duplicate entries.

unnest (array[])

Return type: set of anyelement

Full syntax: unnest( array['one', 'row', 'per', 'item'])

Transforms a one dimensional array into rows. Returns a set of anyelement, a polymorphic pseudotype in PostgreSQL.

MEDIAN (expr)

Return type: timestamp, timestamptz, interval, float

Full syntax: MEDIAN (expression)

Can take a two-dimensional array as input. Treats such arrays as matrices.

Example:

SELECT department_id, MEDIAN(salary)
FROM employees
GROUP BY department_id;

PERCENTILE_CONT (expr) WITHIN GROUP (ORDER BY expr [DESC/ASC])

Return type: timestamp, timestamptz, interval, float

Full syntax: PERCENTILE_CONT(percentage) WITHIN GROUP (ORDER BY expression)

Performs an inverse distribution function that assumes a continuous distribution model. It takes a percentile value and a sort specification and returns the same datatype as the numeric datatype of the argument. This returned value is a computed result after performing linear interpolation. Null are ignored in this calculation.

Example:

SELECT department_id,
PERCENTILE_CONT (0.5) WITHIN GROUP (ORDER BY salary DESC)
"Median_cont";
FROM employees GROUP BY department_id;

PERCENTILE_DISC (expr) WITHIN GROUP (ORDER BY expr [DESC/ASC])

Return type: timestamp, timestamptz, interval, float

Full syntax: PERCENTILE_DISC(percentage) WITHIN GROUP (ORDER BY expression)

Performs an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort specification. This returned value is an element from the set. Null are ignored in this calculation.

Example:

SELECT department_id,
PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY salary DESC)
"Median_desc";
FROM employees GROUP BY department_id;

sum(array[])

Return type: smallint[]int[], bigint[], float[]

Full syntax: sum(array[[1,2],[3,4]])

Performs matrix summation. Can take as input a two-dimensional array that is treated as a matrix.

Example:

CREATE TABLE mymatrix (myvalue int[]);
INSERT INTO mymatrix VALUES (array[[1,2],[3,4]]);
INSERT INTO mymatrix VALUES (array[[0,1],[1,0]]);
SELECT sum(myvalue) FROM mymatrix;
sum
---------------
{{1,3},{4,4}}

Text Search Functions and Operators

The following sections summarize the functions and operators that are provided for full text searching. See Using Full Text Search for a detailed explanation of WarehousePG's text search facility.

@@

tsvector matches tsquery?

Example:

to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat')
t

@@@

Deprecated synonym for @@.

Example:

to_tsvector('fat cats ate rats') @@@ to_tsquery('cat & rat')
t

|| (tsvector)

Concatenates tsvectors.

Example:

'a:1 b:2'::tsvector || 'c:1 d:2 b:3'::tsvector
'a':1 'b':2,5 'c':3 'd':4

&&

ANDs tsquerys together.

Example:

'fat | rat'::tsquery && 'cat'::tsquery
( 'fat' | 'rat' ) & 'cat'

|| (tsquery)

ORs tsquerys together.

Example:

'fat | rat'::tsquery || 'cat'::tsquery
( 'fat' | 'rat' ) | 'cat'

!!

Negates a tsquery.

Example:

!! 'cat'::tsquery
!'cat'

@> (tsquery)

Does one tsquery contain another?

Example:

'cat'::tsquery @> 'cat & rat'::tsquery
f

<@ (tsquery)

Is one tsquery contained in another?

Example:

'cat'::tsquery <@ 'cat & rat'::tsquery
t

Note The tsquery containment operators consider only the lexemes listed in the two queries, ignoring the combining operators.

In addition to the operators described above, the ordinary B-tree comparison operators (=, <, etc) are defined for types tsvector and tsquery. These are not very useful for text searching but allow, for example, unique indexes to be built on columns of these types.

get_current_ts_config()

Return type: regconfig

Get default text search configuration.

Example:

get_current_ts_config()
english

length(tsvector)

Return type: integer

Number of lexemes in tsvector.

Example:

length('fat:2,4 cat:3 rat:5A'::tsvector)
3

numnode(tsquery)

Return type: integer

Number of lexemes plus operators in tsquery.

Example:

numnode('(fat & rat) | cat'::tsquery)
5

plainto_tsquery([ config regconfig , ] querytext)

Return type: tsquery

Produce tsquery ignoring punctuation.

Example:

plainto_tsquery('english', 'The Fat Rats')
'fat' & 'rat'

querytree(query tsquery)

Return type: text

Get indexable part of a tsquery.

Example:

querytree('foo & ! bar'::tsquery)
'foo'

setweight(tsvector, "char")

Return type: tsvector

Assign weight to each element of tsvector.

Example:

setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A')
'cat':3A 'fat':2A,4A 'rat':5A

strip(tsvector)

Return type: tsvector

Remove positions and weights from tsvector.

Example:

strip('fat:2,4 cat:3 rat:5A'::tsvector)
'cat' 'fat' 'rat'

to_tsquery([ config regconfig , ] query text)

Return type: tsquery

Normalize words and convert to tsquery.

Example:

to_tsquery('english', 'The & Fat & Rats')
'fat' & 'rat'

to_tsvector([ config regconfig , ] documenttext)

Return type: tsvector

Reduce document text to tsvector.

Example:

to_tsvector('english', 'The Fat Rats')
'fat':2 'rat':3

ts_headline([ config regconfig, ] documenttext, query tsquery [, options text ])

Return type: text

Display a query match.

Example:

ts_headline('x y z', 'z'::tsquery)
x y <b>z</b>

ts_rank([ weights float4[], ] vector tsvector,query tsquery [, normalization integer ])

Return type: float4

Rank document for query.

Example:

ts_rank(textsearch, query)
0.818

ts_rank_cd([ weights float4[], ] vectortsvector, query tsquery [, normalizationinteger ])

Return type: float4

Rank document for query using cover density.

Example:

ts_rank_cd('{0.1, 0.2, 0.4, 1.0}', textsearch, query)
2.01317

ts_rewrite(query tsquery, target tsquery,substitute tsquery)

Return type: tsquery

Replace target with substitute within query.

Example:

ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'foo | bar'::tsquery)
'b' & ( 'foo' | 'bar' )

ts_rewrite(query tsquery, select text)

Return type: tsquery

Replace using targets and substitutes from a SELECT command.

Example:

SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases')
'b' & ( 'foo' | 'bar' )

tsvector_update_trigger()

Return type: trigger

Trigger function for automatic tsvector column update.

Example:

CREATE TRIGGER ... tsvector_update_trigger(tsvcol, 'pg_catalog.swedish', title, body)

tsvector_update_trigger_column()

Return type: trigger

Trigger function for automatic tsvector column update.

Example:

CREATE TRIGGER ... tsvector_update_trigger_column(tsvcol, configcol, title, body)

Note All the text search functions that accept an optional regconfig argument will use the configuration specified by default_text_search_config when that argument is omitted.

The following functions are listed separately because they are not usually used in everyday text searching operations. They are helpful for development and debugging of new text search configurations.

ts_debug([ configregconfig, ]documenttext, OUTaliastext, OUTdescriptiontext, OUTtokentext, OUTdictionariesregdictionary[], OUTdictionaryregdictionary, OUTlexemes text[])

Return type: setof record

Test a configuration.

Example:

ts_debug('english', 'The Brightest supernovaes')
(asciiword,"Word, all ASCII",The,{english_stem},english_stem,{}) ...

ts_lexize(dictregdictionary,token text)

Return type: text[]

Test a dictionary.

Example:

ts_lexize('english_stem', 'stars')
{star}

ts_parse(parser_nametext,documenttext, OUTtokidinteger, OUTtoken text)

Return type: setof record

Test a parser.

Example:

ts_parse('default', 'foo - bar')
(1,foo) ...

ts_parse(parser_oidoid,documenttext, OUTtokidinteger, OUTtoken text)

Return type: setof record

Test a parser.

Example:

ts_parse(3722, 'foo - bar')
(1,foo) ...

ts_token_type(parser_nametext, OUTtokidinteger, OUTalias text, OUT description text)

Return type: setof record

Get token types defined by parser.

Example:

ts_token_type('default')
(1,asciiword,"Word, all ASCII") ...

ts_token_type(parser_oidoid, OUTtokidinteger, OUTaliastext, OUTdescription text)

Return type: setof record

Get token types defined by parser.

Example:

ts_token_type(3722)
(1,asciiword,"Word, all ASCII") ...

ts_stat(sqlquerytext, [weightstext, ] OUTwordtext, OUTndocinteger, OUT nentry integer)

Return type: setof record

Get statistics of a tsvector column.

Example:

ts_stat('SELECT vector from apod')
(foo,10,15) ...

Range Functions and Operators

See Range Types for an overview of range types.

The following operators are available for range types.

=

Equal.

Example:

int4range(1,5) = '[1,4]'::int4range
t

<>

Not equal.

Example:

numrange(1.1,2.2) <> numrange(1.1,2.3)
t

<

Less than.

Example:

int4range(1,10) < int4range(2,3)
t

>

Greater than.

Example:

int4range(1,10) > int4range(1,5)
t

<=

Less than or equal.

Example:

numrange(1.1,2.2) <= numrange(1.1,2.2)
t

>=

Greater than or equal.

Example:

numrange(1.1,2.2) >= numrange(1.1,2.0)
t

@> (contains range)

Example:

int4range(2,4) @> int4range(2,3)
t

@> (contains element)

Example:

'[2011-01-01,2011-03-01)'::tsrange @> '2011-01-10'::timestamp
t

<@ (range is contained by)

Example:

int4range(2,4) <@ int4range(1,7)
t

<@ (element is contained by)

Example:

42 <@ int4range(1,7)
f

&& (overlap)

Overlap, meaning the ranges have points in common.

Example:

int8range(3,7) && int8range(4,12)
t

<< (strictly left of)

Example:

int8range(1,10) << int8range(100,110)
t

>> (strictly right of)

Example:

int8range(50,60) >> int8range(20,30)
t

&< (does not extend to the right of)

Example:

int8range(1,20) &< int8range(18,20)
t

&> (does not extend to the left of)

Example:

int8range(7,20) &> int8range(5,10)
t

-|- (is adjacent to)

Example:

numrange(1.1,2.2) -|- numrange(2.2,3.3)
t

+ (union)

Example:

numrange(5,15) + numrange(10,20)
[5,20)

* (intersection)

Example:

int8range(5,15) * int8range(10,20)
[10,15)

- (difference)

Example:

int8range(5,15) - int8range(10,20)
[5,10)

The simple comparison operators <, >, <=, and >= compare the lower bounds first, and only if those are equal, compare the upper bounds. These comparisons are not usually very useful for ranges, but are provided to allow B-tree indexes to be constructed on ranges.

The left-of/right-of/adjacent operators always return false when an empty range is involved; that is, an empty range is not considered to be either before or after any other range.

The union and difference operators will fail if the resulting range would need to contain two disjoint sub-ranges, as such a range cannot be represented.

The following functions are available for use with range types.

lower(anyrange)

Return type: range's element type

Lower bound of range.

Example:

lower(numrange(1.1,2.2))
1.1

upper(anyrange)

Return type: range's element type

Upper bound of range.

Example:

upper(numrange(1.1,2.2))
2.2

isempty(anyrange)

Return type: boolean

Is the range empty?

Example:

isempty(numrange(1.1,2.2))
false

lower_inc(anyrange)

Return type: boolean

Is the lower bound inclusive?

Example:

lower_inc(numrange(1.1,2.2))
true

upper_inc(anyrange)

Return type: boolean

Is the upper bound inclusive?

Example:

upper_inc(numrange(1.1,2.2))
false

lower_inf(anyrange)

Return type: boolean

Is the lower bound infinite?

Example:

lower_inf('(,)'::daterange)
true

upper_inf(anyrange)

Return type: boolean

Is the upper bound infinite?

Example:

upper_inf('(,)'::daterange)
true

range_merge(anyrange, anyrange)

Return type: anyrange

The smallest range which includes both of the given ranges.

Example:

range_merge('[1,2)'::int4range, '[3,4)'::int4range)
[1,4)

The lower and upper functions return null if the range is empty or the requested bound is infinite. The lower_inc, upper_inc, lower_inf, and upper_inf functions all return false for an empty range.