12.4. Additional Features
This section describes additional functions and operators that are useful in connection with text search.
12.4.1. Manipulating Documents
showed how raw textual
documents can be converted into
also provides functions and
operators that can be used to manipulate documents that are already
tsvectorconcatenation 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_tsvectoron 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
setweightfunction marks all lexemes of the given vector the same way, it is necessary to parse the text and do
setweightbefore concatenating if you want to label different parts of the document with different weights.
setweightreturns a copy of the input vector in which every position has been labeled with the given
Dis 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
Returns the number of lexemes stored in the vector.
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
tsqueryoperator will never match stripped input, since it cannot determine the distance between lexeme occurrences.
A full list of
-related functions is available
12.4.2. Manipulating Queries
showed how raw textual
queries can be converted into
also provides functions and
operators that can be used to manipulate queries that are already
Returns the AND-combination of the two given queries.
Returns the OR-combination of the two given queries.
Returns the negation (NOT) of the given query.
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
tsqueryoperator. For example:
SELECT to_tsquery('fat') <-> to_tsquery('cat | rat'); ?column? ---------------------------- 'fat' <-> ( 'cat' | 'rat' )
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
distancelexemes, using the
tsqueryoperator. For example:
SELECT tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10); tsquery_phrase ------------------ 'fat' <10> 'cat'
Returns the number of nodes (lexemes plus operators) in a
tsquery. This function is useful to determine if the
queryis 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 numnode --------- 0 SELECT numnode('foo & bar'::tsquery); numnode --------- 3
Returns the portion of a
tsquerythat 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')); querytree ----------- 'defin' SELECT querytree(to_tsquery('!defined')); querytree ----------- T
126.96.36.199. Query Rewriting
family of functions search a
for occurrences of a target
subquery, and replace each occurrence with a
substitute subquery. In essence this operation is a
-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
) or narrow the search to direct the user to some hot
topic. There is some overlap in functionality between this feature
and thesaurus dictionaries (
However, you can modify a set of rewrite rules on-the-fly without
reindexing, whereas updating a thesaurus requires reindexing to be
This form of
ts_rewritesimply applies a single rewrite rule:
targetis replaced by
substitutewherever it appears in
query. For example:
SELECT ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'c'::tsquery); ts_rewrite ------------ 'b' & 'c'
This form of
ts_rewriteaccepts a starting
queryand a SQL
selectcommand, which is given as a text string. The
selectmust yield two columns of
tsquerytype. For each row of the
selectresult, occurrences of the first column value (the target) are replaced by the second column value (the substitute) within the current
queryvalue. 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'); ts_rewrite ------------ '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 BYsome ordering key.
Let's consider a real-life astronomical example. We'll expand query
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'); ts_rewrite --------------------------------- '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'); ts_rewrite --------------------------------------------- '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
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'); ts_rewrite ------------ '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
of your documents, it is necessary to create a trigger to update the
column when the document content columns change.
Two built-in trigger functions are available for this, or you can write
text_column_name[, ... ]) tsvector_update_trigger_column(
text_column_name[, ... ])
These trigger functions automatically compute a
column from one or more textual columns, under the control of
parameters specified in the
An example of their use is:
CREATE TABLE messages ( title text, body text, tsv tsvector ); CREATE TRIGGER tsvectorupdate BEFORE INSERT OR UPDATE ON messages FOR EACH ROW EXECUTE FUNCTION 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
will automatically be reflected into
, without the application having to worry about it.
The first trigger argument must be the name of the
column to be updated. The second argument specifies the text search
configuration to be used to perform the conversion. For
, 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
, the second trigger argument
is the name of another table column, which must be of type
. This allows a per-row selection of configuration
to be made. The remaining argument(s) are the names of textual columns
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 $$ begin new.tsv := setweight(to_tsvector('pg_catalog.english', coalesce(new.title,'')), 'A') || setweight(to_tsvector('pg_catalog.english', coalesce(new.body,'')), 'D'); return new; end $$ LANGUAGE plpgsql; CREATE TRIGGER tsvectorupdate BEFORE INSERT OR UPDATE ON messages FOR EACH ROW EXECUTE FUNCTION messages_trigger();
Keep in mind that it is important to specify the configuration name
explicitly when creating
values inside triggers,
so that the column's contents will not be affected by changes to
. 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
is useful for checking your
configuration and for finding stop-word candidates.
text, ] OUT
is a text value containing an SQL
query which must return a single
executes the query and returns statistics about
each distinct lexeme (word) contained in the
data. The columns returned are
text- the value of a lexeme
integer- number of documents (
tsvectors) the word occurred in
integer- total number of occurrences of the word
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 LIMIT 10;
The same, but counting only word occurrences with weight
SELECT * FROM ts_stat('SELECT vector FROM apod', 'ab') ORDER BY nentry DESC, ndoc DESC, word LIMIT 10;