12.4. Additional Features #

This section describes additional functions and operators that are useful in connection with text search.

12.4.1. Manipulating Documents #

Section 12.3.1 showed how raw textual documents can be converted into tsvector values. Postgres Pro also provides functions and operators that can be used to manipulate documents that are already in tsvector form.

tsvector || tsvector

The tsvector concatenation operator returns a vector which combines the lexemes and positional information of the two vectors given as arguments. Positions and weight labels are retained during the concatenation. Positions appearing in the right-hand vector are offset by the largest position mentioned in the left-hand vector, so that the result is nearly equivalent to the result of performing to_tsvector on the concatenation of the two original document strings. (The equivalence is not exact, because any stop-words removed from the end of the left-hand argument will not affect the result, whereas they would have affected the positions of the lexemes in the right-hand argument if textual concatenation were used.)

One advantage of using concatenation in the vector form, rather than concatenating text before applying to_tsvector, is that you can use different configurations to parse different sections of the document. Also, because the setweight function marks all lexemes of the given vector the same way, it is necessary to parse the text and do setweight before concatenating if you want to label different parts of the document with different weights.

setweight(vector tsvector, weight "char") returns tsvector

setweight returns a copy of the input vector in which every position has been labeled with the given weight, either A, B, C, or D. (D is the default for new vectors and as such is not displayed on output.) These labels are retained when vectors are concatenated, allowing words from different parts of a document to be weighted differently by ranking functions.

Note that weight labels apply to positions, not lexemes. If the input vector has been stripped of positions then setweight does nothing.

length(vector tsvector) returns integer

Returns the number of lexemes stored in the vector.

strip(vector tsvector) returns tsvector

Returns a vector that lists the same lexemes as the given vector, but lacks any position or weight information. The result is usually much smaller than an unstripped vector, but it is also less useful. Relevance ranking does not work as well on stripped vectors as unstripped ones. Also, the <-> (FOLLOWED BY) tsquery operator will never match stripped input, since it cannot determine the distance between lexeme occurrences.

A full list of tsvector-related functions is available in Table 9.43.

12.4.2. Manipulating Queries #

Section 12.3.2 showed how raw textual queries can be converted into tsquery values. Postgres Pro also provides functions and operators that can be used to manipulate queries that are already in tsquery form.

tsquery && tsquery

Returns the AND-combination of the two given queries.

tsquery || tsquery

Returns the OR-combination of the two given queries.

!! tsquery

Returns the negation (NOT) of the given query.

tsquery <-> tsquery

Returns a query that searches for a match to the first given query immediately followed by a match to the second given query, using the <-> (FOLLOWED BY) tsquery operator. For example:

SELECT to_tsquery('fat') <-> to_tsquery('cat | rat');
 'fat' <-> ( 'cat' | 'rat' )

tsquery_phrase(query1 tsquery, query2 tsquery [, distance integer ]) returns tsquery

Returns a query that searches for a match to the first given query followed by a match to the second given query at a distance of exactly distance lexemes, using the <N> tsquery operator. For example:

SELECT tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10);
 'fat' <10> 'cat'

numnode(query tsquery) returns integer

Returns the number of nodes (lexemes plus operators) in a tsquery. This function is useful to determine if the query is meaningful (returns > 0), or contains only stop words (returns 0). Examples:

SELECT numnode(plainto_tsquery('the any'));
NOTICE:  query contains only stopword(s) or doesn't contain lexeme(s), ignored

SELECT numnode('foo & bar'::tsquery);

querytree(query tsquery) returns text

Returns the portion of a tsquery that can be used for searching an index. This function is useful for detecting unindexable queries, for example those containing only stop words or only negated terms. For example:

SELECT querytree(to_tsquery('defined'));

SELECT querytree(to_tsquery('!defined'));
 T Query Rewriting #

The ts_rewrite family of functions search a given tsquery for occurrences of a target subquery, and replace each occurrence with a substitute subquery. In essence this operation is a tsquery-specific version of substring replacement. A target and substitute combination can be thought of as a query rewrite rule. A collection of such rewrite rules can be a powerful search aid. For example, you can expand the search using synonyms (e.g., new york, big apple, nyc, gotham) or narrow the search to direct the user to some hot topic. There is some overlap in functionality between this feature and thesaurus dictionaries (Section 12.6.4). However, you can modify a set of rewrite rules on-the-fly without reindexing, whereas updating a thesaurus requires reindexing to be effective.

ts_rewrite (query tsquery, target tsquery, substitute tsquery) returns tsquery

This form of ts_rewrite simply applies a single rewrite rule: target is replaced by substitute wherever it appears in query. For example:

SELECT ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'c'::tsquery);
 'b' & 'c'

ts_rewrite (query tsquery, select text) returns tsquery

This form of ts_rewrite accepts a starting query and an SQL select command, which is given as a text string. The select must yield two columns of tsquery type. For each row of the select result, occurrences of the first column value (the target) are replaced by the second column value (the substitute) within the current query value. For example:

CREATE TABLE aliases (t tsquery PRIMARY KEY, s tsquery);
INSERT INTO aliases VALUES('a', 'c');

SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases');
 'b' & 'c'

Note that when multiple rewrite rules are applied in this way, the order of application can be important; so in practice you will want the source query to ORDER BY some ordering key.

Let's consider a real-life astronomical example. We'll expand query supernovae using table-driven rewriting rules:

CREATE TABLE aliases (t tsquery primary key, s tsquery);
INSERT INTO aliases VALUES(to_tsquery('supernovae'), to_tsquery('supernovae|sn'));

SELECT ts_rewrite(to_tsquery('supernovae & crab'), 'SELECT * FROM aliases');
 'crab' & ( 'supernova' | 'sn' )

We can change the rewriting rules just by updating the table:

UPDATE aliases
SET s = to_tsquery('supernovae|sn & !nebulae')
WHERE t = to_tsquery('supernovae');

SELECT ts_rewrite(to_tsquery('supernovae & crab'), 'SELECT * FROM aliases');
 'crab' & ( 'supernova' | 'sn' & !'nebula' )

Rewriting can be slow when there are many rewriting rules, since it checks every rule for a possible match. To filter out obvious non-candidate rules we can use the containment operators for the tsquery type. In the example below, we select only those rules which might match the original query:

SELECT ts_rewrite('a & b'::tsquery,
                  'SELECT t,s FROM aliases WHERE ''a & b''::tsquery @> t');
 'b' & 'c'

12.4.3. Triggers for Automatic Updates #


The method described in this section has been obsoleted by the use of stored generated columns, as described in Section 12.2.2.

When using a separate column to store the tsvector representation of your documents, it is necessary to create a trigger to update the tsvector column when the document content columns change. Two built-in trigger functions are available for this, or you can write your own.

tsvector_update_trigger(tsvector_column_name,​ config_name, text_column_name [, ... ])
tsvector_update_trigger_column(tsvector_column_name,​ config_column_name, text_column_name [, ... ])

These trigger functions automatically compute a tsvector column from one or more textual columns, under the control of parameters specified in the CREATE TRIGGER command. An example of their use is:

CREATE TABLE messages (
    title       text,
    body        text,
    tsv         tsvector

tsvector_update_trigger(tsv, 'pg_catalog.english', title, body);

INSERT INTO messages VALUES('title here', 'the body text is here');

SELECT * FROM messages;
   title    |         body          |            tsv
 title here | the body text is here | 'bodi':4 'text':5 'titl':1

SELECT title, body FROM messages WHERE tsv @@ to_tsquery('title & body');
   title    |         body
 title here | the body text is here

Having created this trigger, any change in title or body will automatically be reflected into tsv, without the application having to worry about it.

The first trigger argument must be the name of the tsvector column to be updated. The second argument specifies the text search configuration to be used to perform the conversion. For tsvector_update_trigger, the configuration name is simply given as the second trigger argument. It must be schema-qualified as shown above, so that the trigger behavior will not change with changes in search_path. For tsvector_update_trigger_column, the second trigger argument is the name of another table column, which must be of type regconfig. This allows a per-row selection of configuration to be made. The remaining argument(s) are the names of textual columns (of type text, varchar, or char). These will be included in the document in the order given. NULL values will be skipped (but the other columns will still be indexed).

A limitation of these built-in triggers is that they treat all the input columns alike. To process columns differently — for example, to weight title differently from body — it is necessary to write a custom trigger. Here is an example using PL/pgSQL as the trigger language:

CREATE FUNCTION messages_trigger() RETURNS trigger AS $$
  new.tsv :=
     setweight(to_tsvector('pg_catalog.english', coalesce(new.title,'')), 'A') ||
     setweight(to_tsvector('pg_catalog.english', coalesce(new.body,'')), 'D');
  return new;
$$ LANGUAGE plpgsql;

    ON messages FOR EACH ROW EXECUTE FUNCTION messages_trigger();

Keep in mind that it is important to specify the configuration name explicitly when creating tsvector values inside triggers, so that the column's contents will not be affected by changes to default_text_search_config. Failure to do this is likely to lead to problems such as search results changing after a dump and restore.

12.4.4. Gathering Document Statistics #

The function ts_stat is useful for checking your configuration and for finding stop-word candidates.

ts_stat(sqlquery text, [ weights text, ]
        OUT word text, OUT ndoc integer,
        OUT nentry integer) returns setof record

sqlquery is a text value containing an SQL query which must return a single tsvector column. ts_stat executes the query and returns statistics about each distinct lexeme (word) contained in the tsvector data. The columns returned are

  • word text — the value of a lexeme

  • ndoc integer — number of documents (tsvectors) the word occurred in

  • nentry integer — total number of occurrences of the word

If weights is supplied, only occurrences having one of those weights are counted.

For example, to find the ten most frequent words in a document collection:

SELECT * FROM ts_stat('SELECT vector FROM apod')
ORDER BY nentry DESC, ndoc DESC, word

The same, but counting only word occurrences with weight A or B:

SELECT * FROM ts_stat('SELECT vector FROM apod', 'ab')
ORDER BY nentry DESC, ndoc DESC, word