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>PostgreSQL 9.2.24 Documentation</A
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><DIV
CLASS="SECT1"
><H1
CLASS="SECT1"
><A
NAME="TEXTSEARCH-FEATURES"
>12.4. Additional Features</A
></H1
><P
> This section describes additional functions and operators that are
useful in connection with text search.
</P
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-MANIPULATE-TSVECTOR"
>12.4.1. Manipulating Documents</A
></H2
><P
> <A
HREF="textsearch-controls.html#TEXTSEARCH-PARSING-DOCUMENTS"
>Section 12.3.1</A
> showed how raw textual
documents can be converted into <TT
CLASS="TYPE"
>tsvector</TT
> values.
<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> also provides functions and
operators that can be used to manipulate documents that are already
in <TT
CLASS="TYPE"
>tsvector</TT
> form.
</P
><P
></P
><DIV
CLASS="VARIABLELIST"
><DL
><DT
><PRE
CLASS="SYNOPSIS"
><TT
CLASS="TYPE"
>tsvector</TT
> || <TT
CLASS="TYPE"
>tsvector</TT
></PRE
></DT
><DD
><P
> The <TT
CLASS="TYPE"
>tsvector</TT
> 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 <CODE
CLASS="FUNCTION"
>to_tsvector</CODE
>
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.)
</P
><P
> One advantage of using concatenation in the vector form, rather than
concatenating text before applying <CODE
CLASS="FUNCTION"
>to_tsvector</CODE
>, is that
you can use different configurations to parse different sections
of the document. Also, because the <CODE
CLASS="FUNCTION"
>setweight</CODE
> function
marks all lexemes of the given vector the same way, it is necessary
to parse the text and do <CODE
CLASS="FUNCTION"
>setweight</CODE
> before concatenating
if you want to label different parts of the document with different
weights.
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
>setweight(<TT
CLASS="REPLACEABLE"
><I
>vector</I
></TT
> <TT
CLASS="TYPE"
>tsvector</TT
>, <TT
CLASS="REPLACEABLE"
><I
>weight</I
></TT
> <TT
CLASS="TYPE"
>"char"</TT
>) returns <TT
CLASS="TYPE"
>tsvector</TT
></PRE
></DT
><DD
><P
> <CODE
CLASS="FUNCTION"
>setweight</CODE
> returns a copy of the input vector in which every
position has been labeled with the given <TT
CLASS="REPLACEABLE"
><I
>weight</I
></TT
>, either
<TT
CLASS="LITERAL"
>A</TT
>, <TT
CLASS="LITERAL"
>B</TT
>, <TT
CLASS="LITERAL"
>C</TT
>, or
<TT
CLASS="LITERAL"
>D</TT
>. (<TT
CLASS="LITERAL"
>D</TT
> 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.
</P
><P
> Note that weight labels apply to <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>positions</I
></SPAN
>, not
<SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>lexemes</I
></SPAN
>. If the input vector has been stripped of
positions then <CODE
CLASS="FUNCTION"
>setweight</CODE
> does nothing.
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
>length(<TT
CLASS="REPLACEABLE"
><I
>vector</I
></TT
> <TT
CLASS="TYPE"
>tsvector</TT
>) returns <TT
CLASS="TYPE"
>integer</TT
></PRE
></DT
><DD
><P
> Returns the number of lexemes stored in the vector.
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
>strip(<TT
CLASS="REPLACEABLE"
><I
>vector</I
></TT
> <TT
CLASS="TYPE"
>tsvector</TT
>) returns <TT
CLASS="TYPE"
>tsvector</TT
></PRE
></DT
><DD
><P
> 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.
</P
></DD
></DL
></DIV
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-MANIPULATE-TSQUERY"
>12.4.2. Manipulating Queries</A
></H2
><P
> <A
HREF="textsearch-controls.html#TEXTSEARCH-PARSING-QUERIES"
>Section 12.3.2</A
> showed how raw textual
queries can be converted into <TT
CLASS="TYPE"
>tsquery</TT
> values.
<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> also provides functions and
operators that can be used to manipulate queries that are already
in <TT
CLASS="TYPE"
>tsquery</TT
> form.
</P
><P
></P
><DIV
CLASS="VARIABLELIST"
><DL
><DT
><PRE
CLASS="SYNOPSIS"
><TT
CLASS="TYPE"
>tsquery</TT
> && <TT
CLASS="TYPE"
>tsquery</TT
></PRE
></DT
><DD
><P
> Returns the AND-combination of the two given queries.
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
><TT
CLASS="TYPE"
>tsquery</TT
> || <TT
CLASS="TYPE"
>tsquery</TT
></PRE
></DT
><DD
><P
> Returns the OR-combination of the two given queries.
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
>!! <TT
CLASS="TYPE"
>tsquery</TT
></PRE
></DT
><DD
><P
> Returns the negation (NOT) of the given query.
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
>numnode(<TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
>) returns <TT
CLASS="TYPE"
>integer</TT
></PRE
></DT
><DD
><P
> Returns the number of nodes (lexemes plus operators) in a
<TT
CLASS="TYPE"
>tsquery</TT
>. This function is useful
to determine if the <TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> is meaningful
(returns > 0), or contains only stop words (returns 0).
Examples:
</P><PRE
CLASS="SCREEN"
>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</PRE
><P>
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
>querytree(<TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
>) returns <TT
CLASS="TYPE"
>text</TT
></PRE
></DT
><DD
><P
> Returns the portion of a <TT
CLASS="TYPE"
>tsquery</TT
> 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:
</P><PRE
CLASS="SCREEN"
>SELECT querytree(to_tsquery('!defined'));
querytree
----------- </PRE
><P>
</P
></DD
></DL
></DIV
><DIV
CLASS="SECT3"
><H3
CLASS="SECT3"
><A
NAME="TEXTSEARCH-QUERY-REWRITING"
>12.4.2.1. Query Rewriting</A
></H3
><P
> The <CODE
CLASS="FUNCTION"
>ts_rewrite</CODE
> family of functions search a
given <TT
CLASS="TYPE"
>tsquery</TT
> for occurrences of a target
subquery, and replace each occurrence with a
substitute subquery. In essence this operation is a
<TT
CLASS="TYPE"
>tsquery</TT
>-specific version of substring replacement.
A target and substitute combination can be
thought of as a <I
CLASS="FIRSTTERM"
>query rewrite rule</I
>. A collection
of such rewrite rules can be a powerful search aid.
For example, you can expand the search using synonyms
(e.g., <TT
CLASS="LITERAL"
>new york</TT
>, <TT
CLASS="LITERAL"
>big apple</TT
>, <TT
CLASS="LITERAL"
>nyc</TT
>,
<TT
CLASS="LITERAL"
>gotham</TT
>) or narrow the search to direct the user to some hot
topic. There is some overlap in functionality between this feature
and thesaurus dictionaries (<A
HREF="textsearch-dictionaries.html#TEXTSEARCH-THESAURUS"
>Section 12.6.4</A
>).
However, you can modify a set of rewrite rules on-the-fly without
reindexing, whereas updating a thesaurus requires reindexing to be
effective.
</P
><P
></P
><DIV
CLASS="VARIABLELIST"
><DL
><DT
><PRE
CLASS="SYNOPSIS"
>ts_rewrite (<TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
>, <TT
CLASS="REPLACEABLE"
><I
>target</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
>, <TT
CLASS="REPLACEABLE"
><I
>substitute</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
>) returns <TT
CLASS="TYPE"
>tsquery</TT
></PRE
></DT
><DD
><P
> This form of <CODE
CLASS="FUNCTION"
>ts_rewrite</CODE
> simply applies a single
rewrite rule: <TT
CLASS="REPLACEABLE"
><I
>target</I
></TT
>
is replaced by <TT
CLASS="REPLACEABLE"
><I
>substitute</I
></TT
>
wherever it appears in <TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
>. For example:
</P><PRE
CLASS="SCREEN"
>SELECT ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'c'::tsquery);
ts_rewrite
------------
'b' & 'c'</PRE
><P>
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
>ts_rewrite (<TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
>, <TT
CLASS="REPLACEABLE"
><I
>select</I
></TT
> <TT
CLASS="TYPE"
>text</TT
>) returns <TT
CLASS="TYPE"
>tsquery</TT
></PRE
></DT
><DD
><P
> This form of <CODE
CLASS="FUNCTION"
>ts_rewrite</CODE
> accepts a starting
<TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> and a SQL <TT
CLASS="REPLACEABLE"
><I
>select</I
></TT
> command, which
is given as a text string. The <TT
CLASS="REPLACEABLE"
><I
>select</I
></TT
> must yield two
columns of <TT
CLASS="TYPE"
>tsquery</TT
> type. For each row of the
<TT
CLASS="REPLACEABLE"
><I
>select</I
></TT
> result, occurrences of the first column value
(the target) are replaced by the second column value (the substitute)
within the current <TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> value. For example:
</P><PRE
CLASS="SCREEN"
>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'</PRE
><P>
</P
><P
> 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 <TT
CLASS="LITERAL"
>ORDER BY</TT
> some ordering key.
</P
></DD
></DL
></DIV
><P
> Let's consider a real-life astronomical example. We'll expand query
<TT
CLASS="LITERAL"
>supernovae</TT
> using table-driven rewriting rules:
</P><PRE
CLASS="SCREEN"
>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' )</PRE
><P>
We can change the rewriting rules just by updating the table:
</P><PRE
CLASS="SCREEN"
>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' )</PRE
><P>
</P
><P
> 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 <TT
CLASS="TYPE"
>tsquery</TT
>
type. In the example below, we select only those rules which might match
the original query:
</P><PRE
CLASS="SCREEN"
>SELECT ts_rewrite('a & b'::tsquery,
'SELECT t,s FROM aliases WHERE ''a & b''::tsquery @> t');
ts_rewrite
------------
'b' & 'c'</PRE
><P>
</P
></DIV
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-UPDATE-TRIGGERS"
>12.4.3. Triggers for Automatic Updates</A
></H2
><P
> When using a separate column to store the <TT
CLASS="TYPE"
>tsvector</TT
> representation
of your documents, it is necessary to create a trigger to update the
<TT
CLASS="TYPE"
>tsvector</TT
> column when the document content columns change.
Two built-in trigger functions are available for this, or you can write
your own.
</P
><PRE
CLASS="SYNOPSIS"
>tsvector_update_trigger(<TT
CLASS="REPLACEABLE"
><I
>tsvector_column_name</I
></TT
>, <TT
CLASS="REPLACEABLE"
><I
>config_name</I
></TT
>, <TT
CLASS="REPLACEABLE"
><I
>text_column_name</I
></TT
> [<SPAN
CLASS="OPTIONAL"
>, ... </SPAN
>])
tsvector_update_trigger_column(<TT
CLASS="REPLACEABLE"
><I
>tsvector_column_name</I
></TT
>, <TT
CLASS="REPLACEABLE"
><I
>config_column_name</I
></TT
>, <TT
CLASS="REPLACEABLE"
><I
>text_column_name</I
></TT
> [<SPAN
CLASS="OPTIONAL"
>, ... </SPAN
>])</PRE
><P
> These trigger functions automatically compute a <TT
CLASS="TYPE"
>tsvector</TT
>
column from one or more textual columns, under the control of
parameters specified in the <TT
CLASS="COMMAND"
>CREATE TRIGGER</TT
> command.
An example of their use is:
</P><PRE
CLASS="SCREEN"
>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</PRE
><P>
Having created this trigger, any change in <TT
CLASS="STRUCTFIELD"
>title</TT
> or
<TT
CLASS="STRUCTFIELD"
>body</TT
> will automatically be reflected into
<TT
CLASS="STRUCTFIELD"
>tsv</TT
>, without the application having to worry about it.
</P
><P
> The first trigger argument must be the name of the <TT
CLASS="TYPE"
>tsvector</TT
>
column to be updated. The second argument specifies the text search
configuration to be used to perform the conversion. For
<CODE
CLASS="FUNCTION"
>tsvector_update_trigger</CODE
>, 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 <TT
CLASS="VARNAME"
>search_path</TT
>. For
<CODE
CLASS="FUNCTION"
>tsvector_update_trigger_column</CODE
>, the second trigger argument
is the name of another table column, which must be of type
<TT
CLASS="TYPE"
>regconfig</TT
>. This allows a per-row selection of configuration
to be made. The remaining argument(s) are the names of textual columns
(of type <TT
CLASS="TYPE"
>text</TT
>, <TT
CLASS="TYPE"
>varchar</TT
>, or <TT
CLASS="TYPE"
>char</TT
>). These
will be included in the document in the order given. NULL values will
be skipped (but the other columns will still be indexed).
</P
><P
> 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
<SPAN
CLASS="APPLICATION"
>PL/pgSQL</SPAN
> as the trigger language:
</P><PRE
CLASS="PROGRAMLISTING"
>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();</PRE
><P>
</P
><P
> Keep in mind that it is important to specify the configuration name
explicitly when creating <TT
CLASS="TYPE"
>tsvector</TT
> values inside triggers,
so that the column's contents will not be affected by changes to
<TT
CLASS="VARNAME"
>default_text_search_config</TT
>. Failure to do this is likely to
lead to problems such as search results changing after a dump and reload.
</P
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-STATISTICS"
>12.4.4. Gathering Document Statistics</A
></H2
><P
> The function <CODE
CLASS="FUNCTION"
>ts_stat</CODE
> is useful for checking your
configuration and for finding stop-word candidates.
</P
><PRE
CLASS="SYNOPSIS"
>ts_stat(<TT
CLASS="REPLACEABLE"
><I
>sqlquery</I
></TT
> <TT
CLASS="TYPE"
>text</TT
>, [<SPAN
CLASS="OPTIONAL"
> <TT
CLASS="REPLACEABLE"
><I
>weights</I
></TT
> <TT
CLASS="TYPE"
>text</TT
>, </SPAN
>]
OUT <TT
CLASS="REPLACEABLE"
><I
>word</I
></TT
> <TT
CLASS="TYPE"
>text</TT
>, OUT <TT
CLASS="REPLACEABLE"
><I
>ndoc</I
></TT
> <TT
CLASS="TYPE"
>integer</TT
>,
OUT <TT
CLASS="REPLACEABLE"
><I
>nentry</I
></TT
> <TT
CLASS="TYPE"
>integer</TT
>) returns <TT
CLASS="TYPE"
>setof record</TT
></PRE
><P
> <TT
CLASS="REPLACEABLE"
><I
>sqlquery</I
></TT
> is a text value containing an SQL
query which must return a single <TT
CLASS="TYPE"
>tsvector</TT
> column.
<CODE
CLASS="FUNCTION"
>ts_stat</CODE
> executes the query and returns statistics about
each distinct lexeme (word) contained in the <TT
CLASS="TYPE"
>tsvector</TT
>
data. The columns returned are
<P
></P
></P><UL
COMPACT="COMPACT"
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="REPLACEABLE"
><I
>word</I
></TT
> <TT
CLASS="TYPE"
>text</TT
> — the value of a lexeme
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="REPLACEABLE"
><I
>ndoc</I
></TT
> <TT
CLASS="TYPE"
>integer</TT
> — number of documents
(<TT
CLASS="TYPE"
>tsvector</TT
>s) the word occurred in
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="REPLACEABLE"
><I
>nentry</I
></TT
> <TT
CLASS="TYPE"
>integer</TT
> — total number of
occurrences of the word
</P
></LI
></UL
><P>
If <TT
CLASS="REPLACEABLE"
><I
>weights</I
></TT
> is supplied, only occurrences
having one of those weights are counted.
</P
><P
> For example, to find the ten most frequent words in a document collection:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT * FROM ts_stat('SELECT vector FROM apod')
ORDER BY nentry DESC, ndoc DESC, word
LIMIT 10;</PRE
><P>
The same, but counting only word occurrences with weight <TT
CLASS="LITERAL"
>A</TT
>
or <TT
CLASS="LITERAL"
>B</TT
>:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT * FROM ts_stat('SELECT vector FROM apod', 'ab')
ORDER BY nentry DESC, ndoc DESC, word
LIMIT 10;</PRE
><P>
</P
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