This section describes additional functions and operators that are useful in connection with text search.
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,
setweight function marks
all lexemes of the given vector the same way, it is necessary to
parse the text and do
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
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.
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 -----------
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
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'
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 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
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.
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.
ts_stat is useful for
checking your configuration and for finding stop-word
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.
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;
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