Summary of Built-in Functions

Summary of Built-in Functions

Greenplum Database 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 Greenplum Database functions see, "Using Functions and Operators" in the Greenplum Database Administrator Guide.

Greenplum Database Function Types

Greenplum Database evaluates functions and operators used in SQL expressions. Some functions and operators are only allowed to execute on the master since they could lead to inconsistencies in Greenplum Database segment instances. This table describes the Greenplum Database Function Types.

Table 1. Functions in Greenplum Database
Function Type Greenplum Support Description Comments
IMMUTABLE Yes Relies only on information directly in its argument list. Given the same argument values, always returns the same result.  
STABLE 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. current_timestamp family of functions is STABLE; values do not change within an execution.
VOLATILE Restricted Function values can change within a single table scan. For example: random(), timeofday(). Any function with side effects is volatile, even if its result is predictable. For example: setval().

In Greenplum Database, data is divided up across segments — each segment is a distinct PostgreSQL database. To prevent inconsistent or unexpected results, do not execute 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 execute on distributed data in Greenplum Database 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 master. For example, the following statements run on the master (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();

Greenplum Database 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 Greenplum Database as in PostgreSQL with the exception of STABLE and VOLATILE functions, which are subject to the restrictions noted in Greenplum Database Function Types. See the Functions and Operators section of the PostgreSQL documentation for more information about these built-in functions and operators.

Table 2. Built-in functions and operators
Operator/Function Category VOLATILE Functions STABLE Functions Restrictions
Logical Operators      
Comparison Operators      
Mathematical Functions and Operators random

setseed

   
String Functions and Operators All built-in conversion functions convert

pg_client_encoding

 
Binary String Functions and Operators      
Bit String Functions and Operators      
Pattern Matching      
Data Type Formatting Functions   to_char

to_timestamp

 
Date/Time Functions and Operators timeofday age

current_date

current_time

current_timestamp

localtime

localtimestamp

now

 
Enum Support Functions      
Geometric Functions and Operators      
Network Address Functions and Operators      
Sequence Manipulation Functions nextval()

setval()

   
Conditional Expressions      
Array Functions and Operators   All array functions  
Aggregate Functions      
Subquery Expressions      
Row and Array Comparisons      
Set Returning Functions generate_series    
System Information Functions   All session information functions

All access privilege inquiry functions

All schema visibility inquiry functions

All system catalog information functions

All comment information functions

All transaction ids and snapshots

 
System Administration Functions set_config

pg_cancel_backend

pg_reload_conf

pg_rotate_logfile

pg_start_backup

pg_stop_backup

pg_size_pretty

pg_ls_dir

pg_read_file

pg_stat_file

current_setting

All database object size functions

Note: The function pg_column_size displays bytes required to store the value, possibly with TOAST compression.
XML Functions and function-like expressions  

cursor_to_xml(cursor refcursor, count int, nulls boolean, tableforest boolean, targetns text)

cursor_to_xmlschema(cursor refcursor, nulls boolean, tableforest boolean, targetns text)

database_to_xml(nulls boolean, tableforest boolean, targetns text)

database_to_xmlschema(nulls boolean, tableforest boolean, targetns text)

database_to_xml_and_xmlschema(nulls boolean, tableforest boolean, targetns text)

query_to_xml(query text, nulls boolean, tableforest boolean, targetns text)

query_to_xmlschema(query text, nulls boolean, tableforest boolean, targetns text)

query_to_xml_and_xmlschema(query text, nulls boolean, tableforest boolean, targetns text)

schema_to_xml(schema name, nulls boolean, tableforest boolean, targetns text)

schema_to_xmlschema(schema name, nulls boolean, tableforest boolean, targetns text)

schema_to_xml_and_xmlschema(schema name, nulls boolean, tableforest boolean, targetns text)

table_to_xml(tbl regclass, nulls boolean, tableforest boolean, targetns text)

table_to_xmlschema(tbl regclass, nulls boolean, tableforest boolean, targetns text)

table_to_xml_and_xmlschema(tbl regclass, nulls boolean, tableforest boolean, targetns text)

xmlagg(xml)

xmlconcat(xml[, ...])

xmlelement(name name [, xmlattributes(value [AS attname] [, ... ])] [, content, ...])

xmlexists(text, xml)

xmlforest(content [AS name] [, ...])

xml_is_well_formed(text)

xml_is_well_formed_document(text)

xml_is_well_formed_content(text)

xmlparse ( { DOCUMENT | CONTENT } value)

xpath(text, xml)

xpath(text, xml, text[])

xpath_exists(text, xml)

xpath_exists(text, xml, text[])

xmlpi(name target [, content])

xmlroot(xml, version text | no value [, standalone yes|no|no value])

xmlserialize ( { DOCUMENT | CONTENT } value AS type )

xml(text)

text(xml)

xmlcomment(xml)

xmlconcat2(xml, xml)

 

JSON Functions and Operators

Greenplum Database includes built-in functions and operators that create and manipulate JSON data.
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 Data.

JSON Operators

This table describes the operators that are available for use with the json and jsonb data types.

Table 3. json and jsonb Operators
Operator Right Operand Type Description Example Example Result
-> int Get the JSON array element (indexed from zero). '[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json->2 {"c":"baz"}
-> text Get the JSON object field by key. '{"a": {"b":"foo"}}'::json->'a' {"b":"foo"}
->> int Get the JSON array element as text. '[1,2,3]'::json->>2 3
->> text Get the JSON object field as text. '{"a":1,"b":2}'::json->>'b' 2
#> text[] Get the JSON object at specified path. '{"a": {"b":{"c": "foo"}}}'::json#>'{a,b}' {"c": "foo"}
#>> text[] Get the JSON object at specified path as text. '{"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 in the following table. 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 Existence. For information about how these operators can be used to effectively index jsonb data, see jsonb Indexing.

Table 4. jsonb Operators
Operator Right Operand Type Description Example
@> jsonb Does the left JSON value contain within it the right value? '{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonb
<@ jsonb Is the left JSON value contained within the right value? '{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonb
? text Does the key/element string exist within the JSON value? '{"a":1, "b":2}'::jsonb ? 'b'
?| text[] Do any of these key/element strings exist? '{"a":1, "b":2, "c":3}'::jsonb ?| array['b', 'c']
?& text[] Do all of these key/element strings exist? '["a", "b"]'::jsonb ?& array['a', 'b']

The standard comparison operators in the following table 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 Indexing.

Table 5. jsonb Comparison Operators
Operator Description
< 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

This table describes the functions that 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.)

Table 6. JSON Creation Functions
Function Description Example Example Result
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 will be used, properly quoted and escaped so that it is a valid JSON string. 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 will be added between dimension-1 elements if pretty_bool is true.

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 will be added between level-1 elements if pretty_bool is true.

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. 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. 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.

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. 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

This table shows the functions aggregate records to an array of JSON objects and pairs of values to a JSON object

Table 7. JSON Aggregate Functions
Function Argument Types Return Type Description
json_agg(record) record json Aggregates records as a JSON array of objects.
json_object_agg(name, value) ("any", "any") json Aggregates name/value pairs as a JSON object.

JSON Processing Functions

This table shows 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.

Table 8. JSON Processing Functions
Function Return Type Description Example Example Result
json_array_length(json)

jsonb_array_length(jsonb)

int Returns the number of elements in the outermost JSON array. json_array_length('[1,2,3,{"f1":1,"f2":[5,6]},4]') 5
json_each(json)

jsonb_each(jsonb)

setof key text, value json

setof key text, value jsonb

Expands the outermost JSON object into a set of key/value pairs. select * from json_each('{"a":"foo", "b":"bar"}')
 key | value
-----+-------
 a   | "foo"
 b   | "bar"
json_each_text(json)

jsonb_each_text(jsonb)

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. select * from json_each_text('{"a":"foo", "b":"bar"}')
 key | value
-----+-------
 a   | foo
 b   | bar
json_extract_path(from_json json, VARIADIC path_elems text[])

jsonb_extract_path(from_json jsonb, VARIADIC path_elems text[])

json

jsonb

Returns the JSON value pointed to by path_elems (equivalent to #> operator). json_extract_path('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}','f4') {"f5":99,"f6":"foo"}
json_extract_path_text(from_json json, VARIADIC path_elems text[])

jsonb_extract_path_text(from_json jsonb, VARIADIC path_elems text[])

text Returns the JSON value pointed to by path_elems as text. Equivalent to #>> operator. json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}','f4', 'f6') foo
json_object_keys(json)

jsonb_object_keys(jsonb)

setof text Returns set of keys in the outermost JSON object. json_object_keys('{"f1":"abc","f2":{"f3":"a", "f4":"b"}}')
 json_object_keys
------------------
 f1
 f2
json_populate_record(base anyelement, from_json json)

jsonb_populate_record(base anyelement, from_json jsonb)

anyelement Expands the object in from_json to a row whose columns match the record type defined by base. See Note 1. select * from json_populate_record(null::myrowtype, '{"a":1,"b":2}')
 a | b
---+---
 1 | 2
json_populate_recordset(base anyelement, from_json json)

jsonb_populate_recordset(base anyelement, from_json jsonb)

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

jsonb_array_elements(jsonb)

setof json

setof jsonb

Expands a JSON array to a set of JSON values. select * from json_array_elements('[1,true, [2,false]]')
   value
-----------
 1
 true
 [2,false]
json_array_elements_text(json)

jsonb_array_elements_text(jsonb)

setof text Expands a JSON array to a set of text values. select * from json_array_elements_text('["foo", "bar"]')
   value
-----------
 foo
 bar
json_typeof(json)

jsonb_typeof(jsonb)

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 2 json_typeof('-123.4') number
json_to_record(json)

jsonb_to_record(jsonb)

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.

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(json)

jsonb_to_recordset(jsonb)

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.

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:
  1. 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.

  2. 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 Greenplum Database built-in window functions. All window functions are immutable. For more information about window functions, see "Window Expressions" in the Greenplum Database Administrator Guide.

Table 9. Window functions
Function Return Type Full Syntax Description
cume_dist() double precision 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() bigint 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) same as input expr type 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]) same as input expr type 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) same as input expr type 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]) same as input expr type LEAD(expr [,offset] [,exprdefault]) 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) bigint 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() double precision 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() bigint 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() bigint 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 Greenplum extensions of the PostgreSQL database. Analytic functions are immutable.

Note: The Greenplum MADlib Extension for Analytics provides additional advanced functions to perform statistical analysis and machine learning with Greenplum Database data. See Greenplum MADlib Extension for Analytics.
Table 10. Advanced Aggregate Functions
Function Return Type Full Syntax Description
MEDIAN (expr) timestamp, timestamptz, interval, float MEDIAN (expression)

Example:

SELECT department_id, MEDIAN(salary) 
FROM employees 
GROUP BY department_id; 
Can take a two-dimensional array as input. Treats such arrays as matrices.
PERCENTILE_CONT (expr) WITHIN GROUP (ORDER BY expr [DESC/ASC]) timestamp, timestamptz, interval, float PERCENTILE_CONT(percentage) WITHIN GROUP (ORDER BY expression)

Example:

SELECT department_id,
PERCENTILE_CONT (0.5) WITHIN GROUP (ORDER BY salary DESC)
"Median_cont"; 
FROM employees GROUP BY department_id;
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.
PERCENTILE_DISC (expr) WITHIN GROUP (ORDER BY expr [DESC/ASC]) timestamp, timestamptz, interval, float PERCENTILE_DISC(percentage) WITHIN GROUP (ORDER BY expression)

Example:

SELECT department_id, 
PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY salary DESC)
"Median_desc"; 
FROM employees GROUP BY department_id;
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.
sum(array[]) smallint[]int[], bigint[], float[] sum(array[[1,2],[3,4]])

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}}
Performs matrix summation. Can take as input a two-dimensional array that is treated as a matrix.
pivot_sum (label[], label, expr) int[], bigint[], float[] pivot_sum( array['A1','A2'], attr, value) A pivot aggregation using sum to resolve duplicate entries.
unnest (array[]) set of anyelement unnest( array['one', 'row', 'per', 'item']) Transforms a one dimensional array into rows. Returns a set of anyelement, a polymorphic pseudotype in PostgreSQL.

Text Search Functions and Operators

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

Table 11. Text Search Operators
Operator Description Example Result
@@ tsvector matches tsquery ? to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat') t
@@@ deprecated synonym for @@ to_tsvector('fat cats ate rats') @@@ to_tsquery('cat & rat') t
|| concatenate tsvectors 'a:1 b:2'::tsvector || 'c:1 d:2 b:3'::tsvector 'a':1 'b':2,5 'c':3 'd':4
&& AND tsquerys together 'fat | rat'::tsquery && 'cat'::tsquery ( 'fat' | 'rat' ) & 'cat'
|| OR tsquerys together 'fat | rat'::tsquery || 'cat'::tsquery ( 'fat' | 'rat' ) | 'cat'
!! negate a tsquery !! 'cat'::tsquery !'cat'
@> tsquery contains another ? 'cat'::tsquery @> 'cat & rat'::tsquery f
<@ tsquery is contained in ? '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 shown in the table, 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.

Table 12. Text Search Functions
Function Return Type Description Example Result
get_current_ts_config() regconfig get default text search configuration get_current_ts_config() english
length(tsvector) integer number of lexemes in tsvector length('fat:2,4 cat:3 rat:5A'::tsvector) 3
numnode(tsquery) integer number of lexemes plus operators in tsquery numnode('(fat & rat) | cat'::tsquery) 5
plainto_tsquery([ config regconfig , ] querytext) tsquery produce tsquery ignoring punctuation plainto_tsquery('english', 'The Fat Rats') 'fat' & 'rat'
querytree(query tsquery) text get indexable part of a tsquery querytree('foo & ! bar'::tsquery) 'foo'
setweight(tsvector, "char") tsvector assign weight to each element of tsvector setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A') 'cat':3A 'fat':2A,4A 'rat':5A
strip(tsvector) tsvector remove positions and weights from tsvector strip('fat:2,4 cat:3 rat:5A'::tsvector) 'cat' 'fat' 'rat'
to_tsquery([ config regconfig , ] query text) tsquery normalize words and convert to tsquery to_tsquery('english', 'The & Fat & Rats') 'fat' & 'rat'
to_tsvector([ config regconfig , ] documenttext) tsvector reduce document text to tsvector to_tsvector('english', 'The Fat Rats') 'fat':2 'rat':3
ts_headline([ config regconfig, ] documenttext, query tsquery [, options text ]) text display a query match ts_headline('x y z', 'z'::tsquery) x y <b>z</b>
ts_rank([ weights float4[], ] vector tsvector,query tsquery [, normalization integer ]) float4 rank document for query ts_rank(textsearch, query) 0.818
ts_rank_cd([ weights float4[], ] vectortsvector, query tsquery [, normalizationinteger ]) float4 rank document for query using cover density ts_rank_cd('{0.1, 0.2, 0.4, 1.0}', textsearch, query) 2.01317
ts_rewrite(query tsquery, target tsquery,substitute tsquery) tsquery replace target with substitute within query ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'foo|bar'::tsquery) 'b' & ( 'foo' | 'bar' )
ts_rewrite(query tsquery, select text) tsquery replace using targets and substitutes from a SELECTcommand SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases') 'b' & ( 'foo' | 'bar' )
tsvector_update_trigger() trigger trigger function for automatic tsvector column update CREATE TRIGGER ... tsvector_update_trigger(tsvcol, 'pg_catalog.swedish', title, body)  
tsvector_update_trigger_column() trigger trigger function for automatic tsvector column update 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 functions in the following table 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.

Table 13. Text Search Debugging Functions
Function Return Type Description Example Result
ts_debug([ config regconfig, ] document text, OUT alias text, OUT description text, OUT token text, OUT dictionaries regdictionary[], OUT dictionary regdictionary, OUT lexemes text[]) setof record test a configuration ts_debug('english', 'The Brightest supernovaes') (asciiword,"Word, all ASCII",The,{english_stem},english_stem,{}) ...
ts_lexize(dict regdictionary, token text) text[] test a dictionary ts_lexize('english_stem', 'stars') {star}
ts_parse(parser_name text, document text, OUT tokid integer, OUT token text) setof record test a parser ts_parse('default', 'foo - bar') (1,foo) ...
ts_parse(parser_oid oid, document text, OUT tokid integer, OUT token text) setof record test a parser ts_parse(3722, 'foo - bar') (1,foo) ...
ts_token_type(parser_name text, OUT tokid integer, OUT alias text, OUT description text) setof record get token types defined by parser ts_token_type('default') (1,asciiword,"Word, all ASCII") ...
ts_token_type(parser_oid oid, OUT tokid integer, OUT alias text, OUT description text) setof record get token types defined by parser ts_token_type(3722) (1,asciiword,"Word, all ASCII") ...
ts_stat(sqlquery text, [ weights text, ] OUT word text, OUT ndocinteger, OUT nentry integer) setof record get statistics of a tsvectorcolumn ts_stat('SELECT vector from apod') (foo,10,15) ...

Range Functions and Operators

See Range Types for an overview of range types.

The following table shows the operators available for range types.

Table 14. Range Operators
Operator Description Example Result
= equal int4range(1,5) = '[1,4]'::int4range t
<> not equal numrange(1.1,2.2) <> numrange(1.1,2.3) t
< less than int4range(1,10) < int4range(2,3) t
> greater than int4range(1,10) > int4range(1,5) t
<= less than or equal numrange(1.1,2.2) <= numrange(1.1,2.2) t
>= greater than or equal numrange(1.1,2.2) >= numrange(1.1,2.0) t
@> contains range int4range(2,4) @> int4range(2,3) t
@> contains element '[2011-01-01,2011-03-01)'::tsrange @> '2011-01-10'::timestamp t
<@ range is contained by int4range(2,4) <@ int4range(1,7) t
<@ element is contained by 42 <@ int4range(1,7) f
&& overlap (have points in common) int8range(3,7) && int8range(4,12) t
<< strictly left of int8range(1,10) << int8range(100,110) t
>> strictly right of int8range(50,60) >> int8range(20,30) t
&< does not extend to the right of int8range(1,20) &< int8range(18,20) t
&> does not extend to the left of int8range(7,20) &> int8range(5,10) t
-|- is adjacent to numrange(1.1,2.2) -|- numrange(2.2,3.3) t
+ union numrange(5,15) + numrange(10,20) [5,20)
* intersection int8range(5,15) * int8range(10,20) [10,15)
- difference 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 table shows the functions available for use with range types.

Table 15. Range Functions
Function Return Type Description Example Result
lower(anyrange) range's element type lower bound of range lower(numrange(1.1,2.2)) 1.1
upper(anyrange) range's element type upper bound of range upper(numrange(1.1,2.2)) 2.2
isempty(anyrange) boolean is the range empty? isempty(numrange(1.1,2.2)) false
lower_inc(anyrange) boolean is the lower bound inclusive? lower_inc(numrange(1.1,2.2)) true
upper_inc(anyrange) boolean is the upper bound inclusive? upper_inc(numrange(1.1,2.2)) false
lower_inf(anyrange) boolean is the lower bound infinite? lower_inf('(,)'::daterange) true
upper_inf(anyrange) boolean is the upper bound infinite? upper_inf('(,)'::daterange) true
range_merge(anyrange, anyrange) anyrange the smallest range which includes both of the given ranges 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.