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Note: The following description applies both to Postgres-XC and PostgreSQL if not described explicitly. You can read PostgreSQL as Postgres-XC except for version number, which is specific to each product.
This section describes additional functions and operators that are useful in connection with text search.
Note: The following description applies both to Postgres-XC and PostgreSQL if not described explicitly. You can read PostgreSQL as Postgres-XC except for version number, which is specific to each product.
Section 12.3.1 showed how raw textual documents can be converted into tsvector values. PostgreSQL also provides functions and operators that can be used to manipulate documents that are already in tsvector form.
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 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.
Returns the number of lexemes stored in the vector.
Returns a vector which lists the same lexemes as the given vector, but which lacks any position or weight information. While the returned vector is much less useful than an unstripped vector for relevance ranking, it will usually be much smaller.
Note: The following description applies both to Postgres-XC and PostgreSQL if not described explicitly. You can read PostgreSQL as Postgres-XC except for version number, which is specific to each product.
Section 12.3.2 showed how raw textual queries can be converted into tsquery values. PostgreSQL also provides functions and operators that can be used to manipulate queries that are already in tsquery form.
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 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
numnode
---------
0
SELECT numnode('foo & bar'::tsquery);
numnode
---------
3
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'));
querytree
-----------
Note: The following description applies both to Postgres-XC and PostgreSQL if not described explicitly. You can read PostgreSQL as Postgres-XC except for version number, which is specific to each product.
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.
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);
ts_rewrite
------------
'b' & 'c'
This form of ts_rewrite accepts a starting
query and a 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');
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 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');
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 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');
ts_rewrite
------------
'b' & 'c'
Note: The following description applies both to Postgres-XC and PostgreSQL if not described explicitly. You can read PostgreSQL as Postgres-XC except for version number, which is specific to each product.
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
);
CREATE TRIGGER tsvectorupdate BEFORE INSERT OR UPDATE
ON messages FOR EACH ROW EXECUTE PROCEDURE
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 hereHaving 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 $$
begin
new.tsv :=
setweight(to_tsvector('pg_catalog.english', coalesce(new.title,'')), 'A') ||
setweight(to_tsvector('pg_catalog.english', coalesce(new.body,'')), 'D');
return new;
end
$$ LANGUAGE plpgsql;
CREATE TRIGGER tsvectorupdate BEFORE INSERT OR UPDATE
ON messages FOR EACH ROW EXECUTE PROCEDURE 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 reload.
Note: The following description applies both to Postgres-XC and PostgreSQL if not described explicitly. You can read PostgreSQL as Postgres-XC except for version number, which is specific to each product.
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
LIMIT 10;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
LIMIT 10;