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. PostgreSQL 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 performingto_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 thesetweight
function marks all lexemes of the given vector the same way, it is necessary to parse the text and dosetweight
before concatenating if you want to label different parts of the document with different weights.-
setweight(
vector
tsvector
,weight
"char"
) returnstsvector
setweight
returns a copy of the input vector in which every position has been labeled with the givenweight
, eitherA
,B
,C
, orD
. (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
) returnsinteger
Returns the number of lexemes stored in the vector.
-
strip(
vector
tsvector
) returnstsvector
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 9.41.
12.4.2. Manipulating Queries
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.
-
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'); ?column? ---------------------------- 'fat' <-> ( 'cat' | 'rat' )
-
tsquery_phrase(
query1
tsquery
,query2
tsquery
[,distance
integer
]) returnstsquery
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); tsquery_phrase ------------------ 'fat' <10> 'cat'
-
numnode(
query
tsquery
) returnsinteger
Returns the number of nodes (lexemes plus operators) in a
tsquery
. This function is useful to determine if thequery
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
-
querytree(
query
tsquery
) returnstext
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 -----------
12.4.2.1. 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
) returnstsquery
This form of
ts_rewrite
simply applies a single rewrite rule:target
is replaced bysubstitute
wherever it appears inquery
. For example:SELECT ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'c'::tsquery); ts_rewrite ------------ 'b' & 'c'
-
ts_rewrite (
query
tsquery
,select
text
) returnstsquery
This form of
ts_rewrite
accepts a startingquery
and a SQLselect
command, which is given as a text string. Theselect
must yield two columns oftsquery
type. For each row of theselect
result, occurrences of the first column value (the target) are replaced by the second column value (the substitute) within the currentquery
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'
12.4.3. Triggers for Automatic Updates
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 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.
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
, ] OUTword
text
, OUTndoc
integer
, OUTnentry
integer
) returnssetof 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 lexemendoc
integer
— number of documents (tsvector
s) the word occurred innentry
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;