9.13. Text Search Functions and Operators
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## 9.13. Text Search Functions and Operators #

Table 9.42, Table 9.43 and Table 9.44 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 9.42. Text Search Operators

Operator Description Example(s)
tsvector `@@` tsquery boolean tsquery `@@` tsvector boolean Does tsvector match tsquery? (The arguments can be given in either order.) `to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat')` t
text `@@` tsquery boolean Does text string, after implicit invocation of `to_tsvector()`, match tsquery? `'fat cats ate rats' @@ to_tsquery('cat & rat')` t
tsvector `||` tsvector tsvector Concatenates two tsvectors. If both inputs contain lexeme positions, the second input's positions are adjusted accordingly. `'a:1 b:2'::tsvector || 'c:1 d:2 b:3'::tsvector` 'a':1 'b':2,5 'c':3 'd':4
tsquery `&&` tsquery tsquery ANDs two tsquerys together, producing a query that matches documents that match both input queries. `'fat | rat'::tsquery && 'cat'::tsquery` ( 'fat' | 'rat' ) & 'cat'
tsquery `||` tsquery tsquery ORs two tsquerys together, producing a query that matches documents that match either input query. `'fat | rat'::tsquery || 'cat'::tsquery` 'fat' | 'rat' | 'cat'
`!!` tsquery tsquery Negates a tsquery, producing a query that matches documents that do not match the input query. `!! 'cat'::tsquery` !'cat'
tsquery `<->` tsquery tsquery Constructs a phrase query, which matches if the two input queries match at successive lexemes. `to_tsquery('fat') <-> to_tsquery('rat')` 'fat' <-> 'rat'
tsquery `@>` tsquery boolean Does first tsquery contain the second? (This considers only whether all the lexemes appearing in one query appear in the other, ignoring the combining operators.) `'cat'::tsquery @> 'cat & rat'::tsquery` f
tsquery `<@` tsquery boolean Is first tsquery contained in the second? (This considers only whether all the lexemes appearing in one query appear in the other, ignoring the combining operators.) `'cat'::tsquery <@ 'cat & rat'::tsquery` t `'cat'::tsquery <@ '!cat & rat'::tsquery` t

In addition to these specialized operators, the usual comparison operators shown in Table 9.1 are available for types tsvector and tsquery. These are not very useful for text searching but allow, for example, unique indexes to be built on columns of these types.

Table 9.43. Text Search Functions

Function Description Example(s)
`array_to_tsvector` ( text[] ) tsvector Converts an array of text strings to a tsvector. The given strings are used as lexemes as-is, without further processing. Array elements must not be empty strings or `NULL`. `array_to_tsvector('{fat,cat,rat}'::text[])` 'cat' 'fat' 'rat'
`get_current_ts_config` ( ) regconfig Returns the OID of the current default text search configuration (as set by default_text_search_config). `get_current_ts_config()` english
`length` ( tsvector ) integer Returns the number of lexemes in the tsvector. `length('fat:2,4 cat:3 rat:5A'::tsvector)` 3
`numnode` ( tsquery ) integer Returns the number of lexemes plus operators in the tsquery. `numnode('(fat & rat) | cat'::tsquery)` 5
`plainto_tsquery` ( [ `config` regconfig, ] `query` text ) tsquery Converts text to a tsquery, normalizing words according to the specified or default configuration. Any punctuation in the string is ignored (it does not determine query operators). The resulting query matches documents containing all non-stopwords in the text. `plainto_tsquery('english', 'The Fat Rats')` 'fat' & 'rat'
`phraseto_tsquery` ( [ `config` regconfig, ] `query` text ) tsquery Converts text to a tsquery, normalizing words according to the specified or default configuration. Any punctuation in the string is ignored (it does not determine query operators). The resulting query matches phrases containing all non-stopwords in the text. `phraseto_tsquery('english', 'The Fat Rats')` 'fat' <-> 'rat' `phraseto_tsquery('english', 'The Cat and Rats')` 'cat' <2> 'rat'
`websearch_to_tsquery` ( [ `config` regconfig, ] `query` text ) tsquery Converts text to a tsquery, normalizing words according to the specified or default configuration. Quoted word sequences are converted to phrase tests. The word “or” is understood as producing an OR operator, and a dash produces a NOT operator; other punctuation is ignored. This approximates the behavior of some common web search tools. `websearch_to_tsquery('english', '"fat rat" or cat dog')` 'fat' <-> 'rat' | 'cat' & 'dog'
`querytree` ( tsquery ) text Produces a representation of the indexable portion of a tsquery. A result that is empty or just `T` indicates a non-indexable query. `querytree('foo & ! bar'::tsquery)` 'foo'
`setweight` ( `vector` tsvector, `weight` "char" ) tsvector Assigns the specified `weight` to each element of the `vector`. `setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A')` 'cat':3A 'fat':2A,4A 'rat':5A
`setweight` ( `vector` tsvector, `weight` "char", `lexemes` text[] ) tsvector Assigns the specified `weight` to elements of the `vector` that are listed in `lexemes`. The strings in `lexemes` are taken as lexemes as-is, without further processing. Strings that do not match any lexeme in `vector` are ignored. `setweight('fat:2,4 cat:3 rat:5,6B'::tsvector, 'A', '{cat,rat}')` 'cat':3A 'fat':2,4 'rat':5A,6A
`strip` ( tsvector ) tsvector Removes positions and weights from the tsvector. `strip('fat:2,4 cat:3 rat:5A'::tsvector)` 'cat' 'fat' 'rat'
`to_tsquery` ( [ `config` regconfig, ] `query` text ) tsquery Converts text to a tsquery, normalizing words according to the specified or default configuration. The words must be combined by valid tsquery operators. `to_tsquery('english', 'The & Fat & Rats')` 'fat' & 'rat'
`to_tsvector` ( [ `config` regconfig, ] `document` text ) tsvector Converts text to a tsvector, normalizing words according to the specified or default configuration. Position information is included in the result. `to_tsvector('english', 'The Fat Rats')` 'fat':2 'rat':3
`to_tsvector` ( [ `config` regconfig, ] `document` json ) tsvector `to_tsvector` ( [ `config` regconfig, ] `document` jsonb ) tsvector Converts each string value in the JSON document to a tsvector, normalizing words according to the specified or default configuration. The results are then concatenated in document order to produce the output. Position information is generated as though one stopword exists between each pair of string values. (Beware that “document order” of the fields of a JSON object is implementation-dependent when the input is jsonb; observe the difference in the examples.) `to_tsvector('english', '{"aa": "The Fat Rats", "b": "dog"}'::json)` 'dog':5 'fat':2 'rat':3 `to_tsvector('english', '{"aa": "The Fat Rats", "b": "dog"}'::jsonb)` 'dog':1 'fat':4 'rat':5
`json_to_tsvector` ( [ `config` regconfig, ] `document` json, `filter` jsonb ) tsvector `jsonb_to_tsvector` ( [ `config` regconfig, ] `document` jsonb, `filter` jsonb ) tsvector Selects each item in the JSON document that is requested by the `filter` and converts each one to a tsvector, normalizing words according to the specified or default configuration. The results are then concatenated in document order to produce the output. Position information is generated as though one stopword exists between each pair of selected items. (Beware that “document order” of the fields of a JSON object is implementation-dependent when the input is jsonb.) The `filter` must be a jsonb array containing zero or more of these keywords: `"string"` (to include all string values), `"numeric"` (to include all numeric values), `"boolean"` (to include all boolean values), `"key"` (to include all keys), or `"all"` (to include all the above). As a special case, the `filter` can also be a simple JSON value that is one of these keywords. `json_to_tsvector('english', '{"a": "The Fat Rats", "b": 123}'::json, '["string", "numeric"]')` '123':5 'fat':2 'rat':3 `json_to_tsvector('english', '{"cat": "The Fat Rats", "dog": 123}'::json, '"all"')` '123':9 'cat':1 'dog':7 'fat':4 'rat':5
`ts_delete` ( `vector` tsvector, `lexeme` text ) tsvector Removes any occurrence of the given `lexeme` from the `vector`. The `lexeme` string is treated as a lexeme as-is, without further processing. `ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, 'fat')` 'cat':3 'rat':5A
`ts_delete` ( `vector` tsvector, `lexemes` text[] ) tsvector Removes any occurrences of the lexemes in `lexemes` from the `vector`. The strings in `lexemes` are taken as lexemes as-is, without further processing. Strings that do not match any lexeme in `vector` are ignored. `ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, ARRAY['fat','rat'])` 'cat':3
`ts_filter` ( `vector` tsvector, `weights` "char"[] ) tsvector Selects only elements with the given `weights` from the `vector`. `ts_filter('fat:2,4 cat:3b,7c rat:5A'::tsvector, '{a,b}')` 'cat':3B 'rat':5A
`ts_headline` ( [ `config` regconfig, ] `document` text, `query` tsquery [, `options` text ] ) text Displays, in an abbreviated form, the match(es) for the `query` in the `document`, which must be raw text not a tsvector. Words in the document are normalized according to the specified or default configuration before matching to the query. Use of this function is discussed in Section 12.3.4, which also describes the available `options`. `ts_headline('The fat cat ate the rat.', 'cat')` The fat <b>cat</b> ate the rat.
`ts_headline` ( [ `config` regconfig, ] `document` json, `query` tsquery [, `options` text ] ) text `ts_headline` ( [ `config` regconfig, ] `document` jsonb, `query` tsquery [, `options` text ] ) text Displays, in an abbreviated form, match(es) for the `query` that occur in string values within the JSON `document`. See Section 12.3.4 for more details. `ts_headline('{"cat":"raining cats and dogs"}'::jsonb, 'cat')` {"cat": "raining <b>cats</b> and dogs"}
`ts_rank` ( [ `weights` real[], ] `vector` tsvector, `query` tsquery [, `normalization` integer ] ) real Computes a score showing how well the `vector` matches the `query`. See Section 12.3.3 for details. `ts_rank(to_tsvector('raining cats and dogs'), 'cat')` 0.06079271
`ts_rank_cd` ( [ `weights` real[], ] `vector` tsvector, `query` tsquery [, `normalization` integer ] ) real Computes a score showing how well the `vector` matches the `query`, using a cover density algorithm. See Section 12.3.3 for details. `ts_rank_cd(to_tsvector('raining cats and dogs'), 'cat')` 0.1
`ts_rewrite` ( `query` tsquery, `target` tsquery, `substitute` tsquery ) tsquery Replaces occurrences of `target` with `substitute` within the `query`. See Section 12.4.2.1 for details. `ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'foo|bar'::tsquery)` 'b' & ( 'foo' | 'bar' )
`ts_rewrite` ( `query` tsquery, `select` text ) tsquery Replaces portions of the `query` according to target(s) and substitute(s) obtained by executing a SELECT command. See Section 12.4.2.1 for details. `SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases')` 'b' & ( 'foo' | 'bar' )
`tsquery_phrase` ( `query1` tsquery, `query2` tsquery ) tsquery Constructs a phrase query that searches for matches of `query1` and `query2` at successive lexemes (same as `<->` operator). `tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'))` 'fat' <-> 'cat'
`tsquery_phrase` ( `query1` tsquery, `query2` tsquery, `distance` integer ) tsquery Constructs a phrase query that searches for matches of `query1` and `query2` that occur exactly `distance` lexemes apart. `tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10)` 'fat' <10> 'cat'
`tsvector_to_array` ( tsvector ) text[] Converts a tsvector to an array of lexemes. `tsvector_to_array('fat:2,4 cat:3 rat:5A'::tsvector)` {cat,fat,rat}
`unnest` ( tsvector ) setof record ( `lexeme` text, `positions` smallint[], `weights` text ) Expands a tsvector into a set of rows, one per lexeme. `select * from unnest('cat:3 fat:2,4 rat:5A'::tsvector)`
     lexeme | positions | weights
    --------+-----------+---------
     cat    | {3}       | {D}
     fat    | {2,4}     | {D,D}
     rat    | {5}       | {A}

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

Table 9.44. Text Search Debugging Functions

Function Description Example(s)
`ts_debug` ( [ `config` regconfig, ] `document` text ) setof record ( `alias` text, `description` text, `token` text, `dictionaries` regdictionary[], `dictionary` regdictionary, `lexemes` text[] ) Extracts and normalizes tokens from the `document` according to the specified or default text search configuration, and returns information about how each token was processed. See Section 12.8.1 for details. `ts_debug('english', 'The Brightest supernovaes')` (asciiword,"Word, all ASCII",The,{english_stem},english_stem,{}) ...
`ts_lexize` ( `dict` regdictionary, `token` text ) text[] Returns an array of replacement lexemes if the input token is known to the dictionary, or an empty array if the token is known to the dictionary but it is a stop word, or NULL if it is not a known word. See Section 12.8.3 for details. `ts_lexize('english_stem', 'stars')` {star}
`ts_parse` ( `parser_name` text, `document` text ) setof record ( `tokid` integer, `token` text ) Extracts tokens from the `document` using the named parser. See Section 12.8.2 for details. `ts_parse('default', 'foo - bar')` (1,foo) ...
`ts_parse` ( `parser_oid` oid, `document` text ) setof record ( `tokid` integer, `token` text ) Extracts tokens from the `document` using a parser specified by OID. See Section 12.8.2 for details. `ts_parse(3722, 'foo - bar')` (1,foo) ...
`ts_token_type` ( `parser_name` text ) setof record ( `tokid` integer, `alias` text, `description` text ) Returns a table that describes each type of token the named parser can recognize. See Section 12.8.2 for details. `ts_token_type('default')` (1,asciiword,"Word, all ASCII") ...
`ts_token_type` ( `parser_oid` oid ) setof record ( `tokid` integer, `alias` text, `description` text ) Returns a table that describes each type of token a parser specified by OID can recognize. See Section 12.8.2 for details. `ts_token_type(3722)` (1,asciiword,"Word, all ASCII") ...
`ts_stat` ( `sqlquery` text [, `weights` text ] ) setof record ( `word` text, `ndoc` integer, `nentry` integer ) Executes the `sqlquery`, which must return a single tsvector column, and returns statistics about each distinct lexeme contained in the data. See Section 12.4.4 for details. `ts_stat('SELECT vector FROM apod')` (foo,10,15) ...

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