Text Search Functions and Operators

Table 9-39, Table 9-40 and Table 9-41 summarize the functions and operators that are provided for full text searching. See Chapter 12 for a detailed explanation of PostgreSQL's text search facility.

Table 9-39. Text Search Operators

Operator	Return Type	Description	Example	Result
`@@`	`boolean`	`tsvector` matches `tsquery` ?	`to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat')`	`t`
`@@@`	`boolean`	deprecated synonym for `@@`	`to_tsvector('fat cats ate rats') @@@ to_tsquery('cat & rat')`	`t`
`\|\|`	`tsvector`	concatenate `tsvector`s	`'a:1 b:2'::tsvector \|\| 'c:1 d:2 b:3'::tsvector`	`'a':1 'b':2,5 'c':3 'd':4`
`&&`	`tsquery`	AND `tsquery`s together	`'fat \| rat'::tsquery && 'cat'::tsquery`	`( 'fat' \| 'rat' ) & 'cat'`
`\|\|`	`tsquery`	OR `tsquery`s together	`'fat \| rat'::tsquery \|\| 'cat'::tsquery`	`( 'fat' \| 'rat' ) \| 'cat'`
`!!`	`tsquery`	negate a `tsquery`	`!! 'cat'::tsquery`	`!'cat'`
`<->`	`tsquery`	`tsquery` followed by `tsquery`	`to_tsquery('fat') <-> to_tsquery('rat')`	`'fat' <-> 'rat'`
`@>`	`boolean`	`tsquery` contains another ?	`'cat'::tsquery @> 'cat & rat'::tsquery`	`f`
`<@`	`boolean`	`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 9-40. Text Search Functions

Function	Return Type	Description	Example	Result
`array_to_tsvector(text[])`	`tsvector`	convert array of lexemes to `tsvector`	`array_to_tsvector('{fat,cat,rat}'::text[])`	`'cat' 'fat' 'rat'`
`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 , ] query text)`	`tsquery`	produce `tsquery` ignoring punctuation	`plainto_tsquery('english', 'The Fat Rats')`	`'fat' & 'rat'`
`phraseto_tsquery([ config regconfig , ] query text)`	`tsquery`	produce `tsquery` that searches for a phrase, ignoring punctuation	`phraseto_tsquery('english', 'The Fat Rats')`	`'fat' <-> 'rat'`
`querytree(query tsquery)`	`text`	get indexable part of a `tsquery`	`querytree('foo & ! bar'::tsquery)`	`'foo'`
`setweight(vector tsvector, weight "char")`	`tsvector`	assign `weight` to each element of `vector`	`setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A')`	`'cat':3A 'fat':2A,4A 'rat':5A`
`setweight(vector tsvector, weight "char", lexemes text[])`	`tsvector`	assign `weight` to elements of `vector` that are listed in `lexemes`	`setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A', '{cat,rat}')`	`'cat':3A 'fat':2,4 '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 , ] document text)`	`tsvector`	reduce document text to `tsvector`	`to_tsvector('english', 'The Fat Rats')`	`'fat':2 'rat':3`
`ts_delete(vector tsvector, lexeme text)`	`tsvector`	remove given `lexeme` from `vector`	`ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, 'fat')`	`'cat':3 'rat':5A`
`ts_delete(vector tsvector, lexemes text[])`	`tsvector`	remove any occurrence of lexemes in `lexemes` from `vector`	`ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, ARRAY['fat','rat'])`	`'cat':3`
`ts_filter(vector tsvector, weights "char"[])`	`tsvector`	select only elements with given `weights` from `vector`	`ts_filter('fat:2,4 cat:3b rat:5A'::tsvector, '{a,b}')`	`'cat':3B 'rat':5A`
`ts_headline([ config regconfig, ] document text, 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[], ] vector tsvector, query tsquery [, normalization integer ])`	`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 `SELECT` command	`SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases')`	`'b' & ( 'foo' \| 'bar' )`
`tsquery_phrase(query1 tsquery, query2 tsquery)`	`tsquery`	make query that searches for `query1` followed by `query2` (same as `<->` operator)	`tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'))`	`'fat' <-> 'cat'`
`tsquery_phrase(query1 tsquery, query2 tsquery, distance integer)`	`tsquery`	make query that searches for `query1` followed by `query2` at maximum distance `distance`	`tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10)`	`'fat' <10> 'cat'`
`tsvector_to_array(tsvector)`	`text[]`	convert `tsvector` to array of lexemes	`tsvector_to_array('fat:2,4 cat:3 rat:5A'::tsvector)`	`{cat,fat,rat}`
`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)`
`unnest(tsvector, OUT lexeme text, OUT positions smallint[], OUT weights text)`	`setof record`	expand a tsvector to a set of rows	`unnest('fat:2,4 cat:3 rat:5A'::tsvector)`	`(cat,{3},{D}) ...`

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 Table 9-41 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 9-41. 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 ndoc integer, OUT nentry integer)`	`setof record`	get statistics of a `tsvector` column	`ts_stat('SELECT vector from apod')`	`(foo,10,15) ...`

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Links:

https://www.postgresql.org/docs/9.6/static/functions-textsearch.html

doc_PostgreSQL

2016-10-12 11:34:23

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