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<chapter id="textsearch">
<title>Full Text Search</title>
<indexterm zone="textsearch">
<primary>full text search</primary>
</indexterm>
<indexterm zone="textsearch">
<primary>text search</primary>
</indexterm>
<sect1 id="textsearch-intro">
<title>Introduction</title>
<para>
Full Text Searching (or just <firstterm>text search</firstterm>) provides
the capability to identify natural-language <firstterm>documents</firstterm> that
satisfy a <firstterm>query</firstterm>, and optionally to sort them by
relevance to the query. The most common type of search
is to find all documents containing given <firstterm>query terms</firstterm>
and return them in order of their <firstterm>similarity</firstterm> to the
query. Notions of <varname>query</varname> and
<varname>similarity</varname> are very flexible and depend on the specific
application. The simplest search considers <varname>query</varname> as a
set of words and <varname>similarity</varname> as the frequency of query
words in the document.
</para>
<para>
Textual search operators have existed in databases for years.
<productname>PostgreSQL</productname> has
<literal>~</literal>, <literal>~*</literal>, <literal>LIKE</literal>, and
<literal>ILIKE</literal> operators for textual data types, but they lack
many essential properties required by modern information systems:
</para>
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
There is no linguistic support, even for English. Regular expressions
are not sufficient because they cannot easily handle derived words, e.g.,
<literal>satisfies</literal> and <literal>satisfy</literal>. You might
miss documents that contain <literal>satisfies</literal>, although you
probably would like to find them when searching for
<literal>satisfy</literal>. It is possible to use <literal>OR</literal>
to search for multiple derived forms, but this is tedious and error-prone
(some words can have several thousand derivatives).
</para>
</listitem>
<listitem>
<para>
They provide no ordering (ranking) of search results, which makes them
ineffective when thousands of matching documents are found.
</para>
</listitem>
<listitem>
<para>
They tend to be slow because there is no index support, so they must
process all documents for every search.
</para>
</listitem>
</itemizedlist>
<para>
Full text indexing allows documents to be <emphasis>preprocessed</emphasis>
and an index saved for later rapid searching. Preprocessing includes:
</para>
<itemizedlist mark="none">
<listitem>
<para>
<emphasis>Parsing documents into <firstterm>tokens</firstterm></emphasis>. It is
useful to identify various classes of tokens, e.g., numbers, words,
complex words, email addresses, so that they can be processed
differently. In principle token classes depend on the specific
application, but for most purposes it is adequate to use a predefined
set of classes.
<productname>PostgreSQL</productname> uses a <firstterm>parser</firstterm> to
perform this step. A standard parser is provided, and custom parsers
can be created for specific needs.
</para>
</listitem>
<listitem>
<para>
<emphasis>Converting tokens into <firstterm>lexemes</firstterm></emphasis>.
A lexeme is a string, just like a token, but it has been
<firstterm>normalized</firstterm> so that different forms of the same word
are made alike. For example, normalization almost always includes
folding upper-case letters to lower-case, and often involves removal
of suffixes (such as <literal>s</literal> or <literal>es</literal> in English).
This allows searches to find variant forms of the
same word, without tediously entering all the possible variants.
Also, this step typically eliminates <firstterm>stop words</firstterm>, which
are words that are so common that they are useless for searching.
(In short, then, tokens are raw fragments of the document text, while
lexemes are words that are believed useful for indexing and searching.)
<productname>PostgreSQL</productname> uses <firstterm>dictionaries</firstterm> to
perform this step. Various standard dictionaries are provided, and
custom ones can be created for specific needs.
</para>
</listitem>
<listitem>
<para>
<emphasis>Storing preprocessed documents optimized for
searching</emphasis>. For example, each document can be represented
as a sorted array of normalized lexemes. Along with the lexemes it is
often desirable to store positional information to use for
<firstterm>proximity ranking</firstterm>, so that a document that
contains a more <quote>dense</quote> region of query words is
assigned a higher rank than one with scattered query words.
</para>
</listitem>
</itemizedlist>
<para>
Dictionaries allow fine-grained control over how tokens are normalized.
With appropriate dictionaries, you can:
</para>
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
Define stop words that should not be indexed.
</para>
</listitem>
<listitem>
<para>
Map synonyms to a single word using <application>Ispell</application>.
</para>
</listitem>
<listitem>
<para>
Map phrases to a single word using a thesaurus.
</para>
</listitem>
<listitem>
<para>
Map different variations of a word to a canonical form using
an <application>Ispell</application> dictionary.
</para>
</listitem>
<listitem>
<para>
Map different variations of a word to a canonical form using
<application>Snowball</application> stemmer rules.
</para>
</listitem>
</itemizedlist>
<para>
A data type <type>tsvector</type> is provided for storing preprocessed
documents, along with a type <type>tsquery</type> for representing processed
queries (<xref linkend="datatype-textsearch"/>). There are many
functions and operators available for these data types
(<xref linkend="functions-textsearch"/>), the most important of which is
the match operator <literal>@@</literal>, which we introduce in
<xref linkend="textsearch-matching"/>. Full text searches can be accelerated
using indexes (<xref linkend="textsearch-indexes"/>).
</para>
<sect2 id="textsearch-document">
<title>What Is a Document?</title>
<indexterm zone="textsearch-document">
<primary>document</primary>
<secondary>text search</secondary>
</indexterm>
<para>
A <firstterm>document</firstterm> is the unit of searching in a full text search
system; for example, a magazine article or email message. The text search
engine must be able to parse documents and store associations of lexemes
(key words) with their parent document. Later, these associations are
used to search for documents that contain query words.
</para>
<para>
For searches within <productname>PostgreSQL</productname>,
a document is normally a textual field within a row of a database table,
or possibly a combination (concatenation) of such fields, perhaps stored
in several tables or obtained dynamically. In other words, a document can
be constructed from different parts for indexing and it might not be
stored anywhere as a whole. For example:
<programlisting>
SELECT title || ' ' || author || ' ' || abstract || ' ' || body AS document
FROM messages
WHERE mid = 12;
SELECT m.title || ' ' || m.author || ' ' || m.abstract || ' ' || d.body AS document
FROM messages m, docs d
WHERE m.mid = d.did AND m.mid = 12;
</programlisting>
</para>
<note>
<para>
Actually, in these example queries, <function>coalesce</function>
should be used to prevent a single <literal>NULL</literal> attribute from
causing a <literal>NULL</literal> result for the whole document.
</para>
</note>
<para>
Another possibility is to store the documents as simple text files in the
file system. In this case, the database can be used to store the full text
index and to execute searches, and some unique identifier can be used to
retrieve the document from the file system. However, retrieving files
from outside the database requires superuser permissions or special
function support, so this is usually less convenient than keeping all
the data inside <productname>PostgreSQL</productname>. Also, keeping
everything inside the database allows easy access
to document metadata to assist in indexing and display.
</para>
<para>
For text search purposes, each document must be reduced to the
preprocessed <type>tsvector</type> format. Searching and ranking
are performed entirely on the <type>tsvector</type> representation
of a document &mdash; the original text need only be retrieved
when the document has been selected for display to a user.
We therefore often speak of the <type>tsvector</type> as being the
document, but of course it is only a compact representation of
the full document.
</para>
</sect2>
<sect2 id="textsearch-matching">
<title>Basic Text Matching</title>
<para>
Full text searching in <productname>PostgreSQL</productname> is based on
the match operator <literal>@@</literal>, which returns
<literal>true</literal> if a <type>tsvector</type>
(document) matches a <type>tsquery</type> (query).
It doesn't matter which data type is written first:
<programlisting>
SELECT 'a fat cat sat on a mat and ate a fat rat'::tsvector @@ 'cat &amp; rat'::tsquery;
?column?
----------
t
SELECT 'fat &amp; cow'::tsquery @@ 'a fat cat sat on a mat and ate a fat rat'::tsvector;
?column?
----------
f
</programlisting>
</para>
<para>
As the above example suggests, a <type>tsquery</type> is not just raw
text, any more than a <type>tsvector</type> is. A <type>tsquery</type>
contains search terms, which must be already-normalized lexemes, and
may combine multiple terms using AND, OR, NOT, and FOLLOWED BY operators.
(For syntax details see <xref linkend="datatype-tsquery"/>.) There are
functions <function>to_tsquery</function>, <function>plainto_tsquery</function>,
and <function>phraseto_tsquery</function>
that are helpful in converting user-written text into a proper
<type>tsquery</type>, primarily by normalizing words appearing in
the text. Similarly, <function>to_tsvector</function> is used to parse and
normalize a document string. So in practice a text search match would
look more like this:
<programlisting>
SELECT to_tsvector('fat cats ate fat rats') @@ to_tsquery('fat &amp; rat');
?column?
----------
t
</programlisting>
Observe that this match would not succeed if written as
<programlisting>
SELECT 'fat cats ate fat rats'::tsvector @@ to_tsquery('fat &amp; rat');
?column?
----------
f
</programlisting>
since here no normalization of the word <literal>rats</literal> will occur.
The elements of a <type>tsvector</type> are lexemes, which are assumed
already normalized, so <literal>rats</literal> does not match <literal>rat</literal>.
</para>
<para>
The <literal>@@</literal> operator also
supports <type>text</type> input, allowing explicit conversion of a text
string to <type>tsvector</type> or <type>tsquery</type> to be skipped
in simple cases. The variants available are:
<programlisting>
tsvector @@ tsquery
tsquery @@ tsvector
text @@ tsquery
text @@ text
</programlisting>
</para>
<para>
The first two of these we saw already.
The form <type>text</type> <literal>@@</literal> <type>tsquery</type>
is equivalent to <literal>to_tsvector(x) @@ y</literal>.
The form <type>text</type> <literal>@@</literal> <type>text</type>
is equivalent to <literal>to_tsvector(x) @@ plainto_tsquery(y)</literal>.
</para>
<para>
Within a <type>tsquery</type>, the <literal>&amp;</literal> (AND) operator
specifies that both its arguments must appear in the document to have a
match. Similarly, the <literal>|</literal> (OR) operator specifies that
at least one of its arguments must appear, while the <literal>!</literal> (NOT)
operator specifies that its argument must <emphasis>not</emphasis> appear in
order to have a match.
For example, the query <literal>fat &amp; ! rat</literal> matches documents that
contain <literal>fat</literal> but not <literal>rat</literal>.
</para>
<para>
Searching for phrases is possible with the help of
the <literal>&lt;-&gt;</literal> (FOLLOWED BY) <type>tsquery</type> operator, which
matches only if its arguments have matches that are adjacent and in the
given order. For example:
<programlisting>
SELECT to_tsvector('fatal error') @@ to_tsquery('fatal &lt;-&gt; error');
?column?
----------
t
SELECT to_tsvector('error is not fatal') @@ to_tsquery('fatal &lt;-&gt; error');
?column?
----------
f
</programlisting>
There is a more general version of the FOLLOWED BY operator having the
form <literal>&lt;<replaceable>N</replaceable>&gt;</literal>,
where <replaceable>N</replaceable> is an integer standing for the difference between
the positions of the matching lexemes. <literal>&lt;1&gt;</literal> is
the same as <literal>&lt;-&gt;</literal>, while <literal>&lt;2&gt;</literal>
allows exactly one other lexeme to appear between the matches, and so
on. The <literal>phraseto_tsquery</literal> function makes use of this
operator to construct a <literal>tsquery</literal> that can match a multi-word
phrase when some of the words are stop words. For example:
<programlisting>
SELECT phraseto_tsquery('cats ate rats');
phraseto_tsquery
-------------------------------
'cat' &lt;-&gt; 'ate' &lt;-&gt; 'rat'
SELECT phraseto_tsquery('the cats ate the rats');
phraseto_tsquery
-------------------------------
'cat' &lt;-&gt; 'ate' &lt;2&gt; 'rat'
</programlisting>
</para>
<para>
A special case that's sometimes useful is that <literal>&lt;0&gt;</literal>
can be used to require that two patterns match the same word.
</para>
<para>
Parentheses can be used to control nesting of the <type>tsquery</type>
operators. Without parentheses, <literal>|</literal> binds least tightly,
then <literal>&amp;</literal>, then <literal>&lt;-&gt;</literal>,
and <literal>!</literal> most tightly.
</para>
<para>
It's worth noticing that the AND/OR/NOT operators mean something subtly
different when they are within the arguments of a FOLLOWED BY operator
than when they are not, because within FOLLOWED BY the exact position of
the match is significant. For example, normally <literal>!x</literal> matches
only documents that do not contain <literal>x</literal> anywhere.
But <literal>!x &lt;-&gt; y</literal> matches <literal>y</literal> if it is not
immediately after an <literal>x</literal>; an occurrence of <literal>x</literal>
elsewhere in the document does not prevent a match. Another example is
that <literal>x &amp; y</literal> normally only requires that <literal>x</literal>
and <literal>y</literal> both appear somewhere in the document, but
<literal>(x &amp; y) &lt;-&gt; z</literal> requires <literal>x</literal>
and <literal>y</literal> to match at the same place, immediately before
a <literal>z</literal>. Thus this query behaves differently from
<literal>x &lt;-&gt; z &amp; y &lt;-&gt; z</literal>, which will match a
document containing two separate sequences <literal>x z</literal> and
<literal>y z</literal>. (This specific query is useless as written,
since <literal>x</literal> and <literal>y</literal> could not match at the same place;
but with more complex situations such as prefix-match patterns, a query
of this form could be useful.)
</para>
</sect2>
<sect2 id="textsearch-intro-configurations">
<title>Configurations</title>
<para>
The above are all simple text search examples. As mentioned before, full
text search functionality includes the ability to do many more things:
skip indexing certain words (stop words), process synonyms, and use
sophisticated parsing, e.g., parse based on more than just white space.
This functionality is controlled by <firstterm>text search
configurations</firstterm>. <productname>PostgreSQL</productname> comes with predefined
configurations for many languages, and you can easily create your own
configurations. (<application>psql</application>'s <command>\dF</command> command
shows all available configurations.)
</para>
<para>
During installation an appropriate configuration is selected and
<xref linkend="guc-default-text-search-config"/> is set accordingly
in <filename>postgresql.conf</filename>. If you are using the same text search
configuration for the entire cluster you can use the value in
<filename>postgresql.conf</filename>. To use different configurations
throughout the cluster but the same configuration within any one database,
use <command>ALTER DATABASE ... SET</command>. Otherwise, you can set
<varname>default_text_search_config</varname> in each session.
</para>
<para>
Each text search function that depends on a configuration has an optional
<type>regconfig</type> argument, so that the configuration to use can be
specified explicitly. <varname>default_text_search_config</varname>
is used only when this argument is omitted.
</para>
<para>
To make it easier to build custom text search configurations, a
configuration is built up from simpler database objects.
<productname>PostgreSQL</productname>'s text search facility provides
four types of configuration-related database objects:
</para>
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
<firstterm>Text search parsers</firstterm> break documents into tokens
and classify each token (for example, as words or numbers).
</para>
</listitem>
<listitem>
<para>
<firstterm>Text search dictionaries</firstterm> convert tokens to normalized
form and reject stop words.
</para>
</listitem>
<listitem>
<para>
<firstterm>Text search templates</firstterm> provide the functions underlying
dictionaries. (A dictionary simply specifies a template and a set
of parameters for the template.)
</para>
</listitem>
<listitem>
<para>
<firstterm>Text search configurations</firstterm> select a parser and a set
of dictionaries to use to normalize the tokens produced by the parser.
</para>
</listitem>
</itemizedlist>
<para>
Text search parsers and templates are built from low-level C functions;
therefore it requires C programming ability to develop new ones, and
superuser privileges to install one into a database. (There are examples
of add-on parsers and templates in the <filename>contrib/</filename> area of the
<productname>PostgreSQL</productname> distribution.) Since dictionaries and
configurations just parameterize and connect together some underlying
parsers and templates, no special privilege is needed to create a new
dictionary or configuration. Examples of creating custom dictionaries and
configurations appear later in this chapter.
</para>
</sect2>
</sect1>
<sect1 id="textsearch-tables">
<title>Tables and Indexes</title>
<para>
The examples in the previous section illustrated full text matching using
simple constant strings. This section shows how to search table data,
optionally using indexes.
</para>
<sect2 id="textsearch-tables-search">
<title>Searching a Table</title>
<para>
It is possible to do a full text search without an index. A simple query
to print the <structname>title</structname> of each row that contains the word
<literal>friend</literal> in its <structfield>body</structfield> field is:
<programlisting>
SELECT title
FROM pgweb
WHERE to_tsvector('english', body) @@ to_tsquery('english', 'friend');
</programlisting>
This will also find related words such as <literal>friends</literal>
and <literal>friendly</literal>, since all these are reduced to the same
normalized lexeme.
</para>
<para>
The query above specifies that the <literal>english</literal> configuration
is to be used to parse and normalize the strings. Alternatively we
could omit the configuration parameters:
<programlisting>
SELECT title
FROM pgweb
WHERE to_tsvector(body) @@ to_tsquery('friend');
</programlisting>
This query will use the configuration set by <xref
linkend="guc-default-text-search-config"/>.
</para>
<para>
A more complex example is to
select the ten most recent documents that contain <literal>create</literal> and
<literal>table</literal> in the <structname>title</structname> or <structname>body</structname>:
<programlisting>
SELECT title
FROM pgweb
WHERE to_tsvector(title || ' ' || body) @@ to_tsquery('create &amp; table')
ORDER BY last_mod_date DESC
LIMIT 10;
</programlisting>
For clarity we omitted the <function>coalesce</function> function calls
which would be needed to find rows that contain <literal>NULL</literal>
in one of the two fields.
</para>
<para>
Although these queries will work without an index, most applications
will find this approach too slow, except perhaps for occasional ad-hoc
searches. Practical use of text searching usually requires creating
an index.
</para>
</sect2>
<sect2 id="textsearch-tables-index">
<title>Creating Indexes</title>
<para>
We can create a <acronym>GIN</acronym> index (<xref
linkend="textsearch-indexes"/>) to speed up text searches:
<programlisting>
CREATE INDEX pgweb_idx ON pgweb USING GIN (to_tsvector('english', body));
</programlisting>
Notice that the 2-argument version of <function>to_tsvector</function> is
used. Only text search functions that specify a configuration name can
be used in expression indexes (<xref linkend="indexes-expressional"/>).
This is because the index contents must be unaffected by <xref
linkend="guc-default-text-search-config"/>. If they were affected, the
index contents might be inconsistent because different entries could
contain <type>tsvector</type>s that were created with different text search
configurations, and there would be no way to guess which was which. It
would be impossible to dump and restore such an index correctly.
</para>
<para>
Because the two-argument version of <function>to_tsvector</function> was
used in the index above, only a query reference that uses the 2-argument
version of <function>to_tsvector</function> with the same configuration
name will use that index. That is, <literal>WHERE
to_tsvector('english', body) @@ 'a &amp; b'</literal> can use the index,
but <literal>WHERE to_tsvector(body) @@ 'a &amp; b'</literal> cannot.
This ensures that an index will be used only with the same configuration
used to create the index entries.
</para>
<para>
It is possible to set up more complex expression indexes wherein the
configuration name is specified by another column, e.g.:
<programlisting>
CREATE INDEX pgweb_idx ON pgweb USING GIN (to_tsvector(config_name, body));
</programlisting>
where <literal>config_name</literal> is a column in the <literal>pgweb</literal>
table. This allows mixed configurations in the same index while
recording which configuration was used for each index entry. This
would be useful, for example, if the document collection contained
documents in different languages. Again,
queries that are meant to use the index must be phrased to match, e.g.,
<literal>WHERE to_tsvector(config_name, body) @@ 'a &amp; b'</literal>.
</para>
<para>
Indexes can even concatenate columns:
<programlisting>
CREATE INDEX pgweb_idx ON pgweb USING GIN (to_tsvector('english', title || ' ' || body));
</programlisting>
</para>
<para>
Another approach is to create a separate <type>tsvector</type> column
to hold the output of <function>to_tsvector</function>. To keep this
column automatically up to date with its source data, use a stored
generated column. This example is a
concatenation of <literal>title</literal> and <literal>body</literal>,
using <function>coalesce</function> to ensure that one field will still be
indexed when the other is <literal>NULL</literal>:
<programlisting>
ALTER TABLE pgweb
ADD COLUMN textsearchable_index_col tsvector
GENERATED ALWAYS AS (to_tsvector('english', coalesce(title, '') || ' ' || coalesce(body, ''))) STORED;
</programlisting>
Then we create a <acronym>GIN</acronym> index to speed up the search:
<programlisting>
CREATE INDEX textsearch_idx ON pgweb USING GIN (textsearchable_index_col);
</programlisting>
Now we are ready to perform a fast full text search:
<programlisting>
SELECT title
FROM pgweb
WHERE textsearchable_index_col @@ to_tsquery('create &amp; table')
ORDER BY last_mod_date DESC
LIMIT 10;
</programlisting>
</para>
<para>
One advantage of the separate-column approach over an expression index
is that it is not necessary to explicitly specify the text search
configuration in queries in order to make use of the index. As shown
in the example above, the query can depend on
<varname>default_text_search_config</varname>. Another advantage is that
searches will be faster, since it will not be necessary to redo the
<function>to_tsvector</function> calls to verify index matches. (This is more
important when using a GiST index than a GIN index; see <xref
linkend="textsearch-indexes"/>.) The expression-index approach is
simpler to set up, however, and it requires less disk space since the
<type>tsvector</type> representation is not stored explicitly.
</para>
</sect2>
</sect1>
<sect1 id="textsearch-controls">
<title>Controlling Text Search</title>
<para>
To implement full text searching there must be a function to create a
<type>tsvector</type> from a document and a <type>tsquery</type> from a
user query. Also, we need to return results in a useful order, so we need
a function that compares documents with respect to their relevance to
the query. It's also important to be able to display the results nicely.
<productname>PostgreSQL</productname> provides support for all of these
functions.
</para>
<sect2 id="textsearch-parsing-documents">
<title>Parsing Documents</title>
<para>
<productname>PostgreSQL</productname> provides the
function <function>to_tsvector</function> for converting a document to
the <type>tsvector</type> data type.
</para>
<indexterm>
<primary>to_tsvector</primary>
</indexterm>
<synopsis>
to_tsvector(<optional> <replaceable class="parameter">config</replaceable> <type>regconfig</type>, </optional> <replaceable class="parameter">document</replaceable> <type>text</type>) returns <type>tsvector</type>
</synopsis>
<para>
<function>to_tsvector</function> parses a textual document into tokens,
reduces the tokens to lexemes, and returns a <type>tsvector</type> which
lists the lexemes together with their positions in the document.
The document is processed according to the specified or default
text search configuration.
Here is a simple example:
<screen>
SELECT to_tsvector('english', 'a fat cat sat on a mat - it ate a fat rats');
to_tsvector
-----------------------------------------------------
'ate':9 'cat':3 'fat':2,11 'mat':7 'rat':12 'sat':4
</screen>
</para>
<para>
In the example above we see that the resulting <type>tsvector</type> does not
contain the words <literal>a</literal>, <literal>on</literal>, or
<literal>it</literal>, the word <literal>rats</literal> became
<literal>rat</literal>, and the punctuation sign <literal>-</literal> was
ignored.
</para>
<para>
The <function>to_tsvector</function> function internally calls a parser
which breaks the document text into tokens and assigns a type to
each token. For each token, a list of
dictionaries (<xref linkend="textsearch-dictionaries"/>) is consulted,
where the list can vary depending on the token type. The first dictionary
that <firstterm>recognizes</firstterm> the token emits one or more normalized
<firstterm>lexemes</firstterm> to represent the token. For example,
<literal>rats</literal> became <literal>rat</literal> because one of the
dictionaries recognized that the word <literal>rats</literal> is a plural
form of <literal>rat</literal>. Some words are recognized as
<firstterm>stop words</firstterm> (<xref linkend="textsearch-stopwords"/>), which
causes them to be ignored since they occur too frequently to be useful in
searching. In our example these are
<literal>a</literal>, <literal>on</literal>, and <literal>it</literal>.
If no dictionary in the list recognizes the token then it is also ignored.
In this example that happened to the punctuation sign <literal>-</literal>
because there are in fact no dictionaries assigned for its token type
(<literal>Space symbols</literal>), meaning space tokens will never be
indexed. The choices of parser, dictionaries and which types of tokens to
index are determined by the selected text search configuration (<xref
linkend="textsearch-configuration"/>). It is possible to have
many different configurations in the same database, and predefined
configurations are available for various languages. In our example
we used the default configuration <literal>english</literal> for the
English language.
</para>
<para>
The function <function>setweight</function> can be used to label the
entries of a <type>tsvector</type> with a given <firstterm>weight</firstterm>,
where a weight is one of the letters <literal>A</literal>, <literal>B</literal>,
<literal>C</literal>, or <literal>D</literal>.
This is typically used to mark entries coming from
different parts of a document, such as title versus body. Later, this
information can be used for ranking of search results.
</para>
<para>
Because <function>to_tsvector</function>(<literal>NULL</literal>) will
return <literal>NULL</literal>, it is recommended to use
<function>coalesce</function> whenever a field might be null.
Here is the recommended method for creating
a <type>tsvector</type> from a structured document:
<programlisting>
UPDATE tt SET ti =
setweight(to_tsvector(coalesce(title,'')), 'A') ||
setweight(to_tsvector(coalesce(keyword,'')), 'B') ||
setweight(to_tsvector(coalesce(abstract,'')), 'C') ||
setweight(to_tsvector(coalesce(body,'')), 'D');
</programlisting>
Here we have used <function>setweight</function> to label the source
of each lexeme in the finished <type>tsvector</type>, and then merged
the labeled <type>tsvector</type> values using the <type>tsvector</type>
concatenation operator <literal>||</literal>. (<xref
linkend="textsearch-manipulate-tsvector"/> gives details about these
operations.)
</para>
</sect2>
<sect2 id="textsearch-parsing-queries">
<title>Parsing Queries</title>
<para>
<productname>PostgreSQL</productname> provides the
functions <function>to_tsquery</function>,
<function>plainto_tsquery</function>,
<function>phraseto_tsquery</function> and
<function>websearch_to_tsquery</function>
for converting a query to the <type>tsquery</type> data type.
<function>to_tsquery</function> offers access to more features
than either <function>plainto_tsquery</function> or
<function>phraseto_tsquery</function>, but it is less forgiving about its
input. <function>websearch_to_tsquery</function> is a simplified version
of <function>to_tsquery</function> with an alternative syntax, similar
to the one used by web search engines.
</para>
<indexterm>
<primary>to_tsquery</primary>
</indexterm>
<synopsis>
to_tsquery(<optional> <replaceable class="parameter">config</replaceable> <type>regconfig</type>, </optional> <replaceable class="parameter">querytext</replaceable> <type>text</type>) returns <type>tsquery</type>
</synopsis>
<para>
<function>to_tsquery</function> creates a <type>tsquery</type> value from
<replaceable>querytext</replaceable>, which must consist of single tokens
separated by the <type>tsquery</type> operators <literal>&amp;</literal> (AND),
<literal>|</literal> (OR), <literal>!</literal> (NOT), and
<literal>&lt;-&gt;</literal> (FOLLOWED BY), possibly grouped
using parentheses. In other words, the input to
<function>to_tsquery</function> must already follow the general rules for
<type>tsquery</type> input, as described in <xref
linkend="datatype-tsquery"/>. The difference is that while basic
<type>tsquery</type> input takes the tokens at face value,
<function>to_tsquery</function> normalizes each token into a lexeme using
the specified or default configuration, and discards any tokens that are
stop words according to the configuration. For example:
<screen>
SELECT to_tsquery('english', 'The &amp; Fat &amp; Rats');
to_tsquery
---------------
'fat' &amp; 'rat'
</screen>
As in basic <type>tsquery</type> input, weight(s) can be attached to each
lexeme to restrict it to match only <type>tsvector</type> lexemes of those
weight(s). For example:
<screen>
SELECT to_tsquery('english', 'Fat | Rats:AB');
to_tsquery
------------------
'fat' | 'rat':AB
</screen>
Also, <literal>*</literal> can be attached to a lexeme to specify prefix matching:
<screen>
SELECT to_tsquery('supern:*A &amp; star:A*B');
to_tsquery
--------------------------
'supern':*A &amp; 'star':*AB
</screen>
Such a lexeme will match any word in a <type>tsvector</type> that begins
with the given string.
</para>
<para>
<function>to_tsquery</function> can also accept single-quoted
phrases. This is primarily useful when the configuration includes a
thesaurus dictionary that may trigger on such phrases.
In the example below, a thesaurus contains the rule <literal>supernovae
stars : sn</literal>:
<screen>
SELECT to_tsquery('''supernovae stars'' &amp; !crab');
to_tsquery
---------------
'sn' &amp; !'crab'
</screen>
Without quotes, <function>to_tsquery</function> will generate a syntax
error for tokens that are not separated by an AND, OR, or FOLLOWED BY
operator.
</para>
<indexterm>
<primary>plainto_tsquery</primary>
</indexterm>
<synopsis>
plainto_tsquery(<optional> <replaceable class="parameter">config</replaceable> <type>regconfig</type>, </optional> <replaceable class="parameter">querytext</replaceable> <type>text</type>) returns <type>tsquery</type>
</synopsis>
<para>
<function>plainto_tsquery</function> transforms the unformatted text
<replaceable>querytext</replaceable> to a <type>tsquery</type> value.
The text is parsed and normalized much as for <function>to_tsvector</function>,
then the <literal>&amp;</literal> (AND) <type>tsquery</type> operator is
inserted between surviving words.
</para>
<para>
Example:
<screen>
SELECT plainto_tsquery('english', 'The Fat Rats');
plainto_tsquery
-----------------
'fat' &amp; 'rat'
</screen>
Note that <function>plainto_tsquery</function> will not
recognize <type>tsquery</type> operators, weight labels,
or prefix-match labels in its input:
<screen>
SELECT plainto_tsquery('english', 'The Fat &amp; Rats:C');
plainto_tsquery
---------------------
'fat' &amp; 'rat' &amp; 'c'
</screen>
Here, all the input punctuation was discarded.
</para>
<indexterm>
<primary>phraseto_tsquery</primary>
</indexterm>
<synopsis>
phraseto_tsquery(<optional> <replaceable class="parameter">config</replaceable> <type>regconfig</type>, </optional> <replaceable class="parameter">querytext</replaceable> <type>text</type>) returns <type>tsquery</type>
</synopsis>
<para>
<function>phraseto_tsquery</function> behaves much like
<function>plainto_tsquery</function>, except that it inserts
the <literal>&lt;-&gt;</literal> (FOLLOWED BY) operator between
surviving words instead of the <literal>&amp;</literal> (AND) operator.
Also, stop words are not simply discarded, but are accounted for by
inserting <literal>&lt;<replaceable>N</replaceable>&gt;</literal> operators rather
than <literal>&lt;-&gt;</literal> operators. This function is useful
when searching for exact lexeme sequences, since the FOLLOWED BY
operators check lexeme order not just the presence of all the lexemes.
</para>
<para>
Example:
<screen>
SELECT phraseto_tsquery('english', 'The Fat Rats');
phraseto_tsquery
------------------
'fat' &lt;-&gt; 'rat'
</screen>
Like <function>plainto_tsquery</function>, the
<function>phraseto_tsquery</function> function will not
recognize <type>tsquery</type> operators, weight labels,
or prefix-match labels in its input:
<screen>
SELECT phraseto_tsquery('english', 'The Fat &amp; Rats:C');
phraseto_tsquery
-----------------------------
'fat' &lt;-&gt; 'rat' &lt;-&gt; 'c'
</screen>
</para>
<synopsis>
websearch_to_tsquery(<optional> <replaceable class="parameter">config</replaceable> <type>regconfig</type>, </optional> <replaceable class="parameter">querytext</replaceable> <type>text</type>) returns <type>tsquery</type>
</synopsis>
<para>
<function>websearch_to_tsquery</function> creates a <type>tsquery</type>
value from <replaceable>querytext</replaceable> using an alternative
syntax in which simple unformatted text is a valid query.
Unlike <function>plainto_tsquery</function>
and <function>phraseto_tsquery</function>, it also recognizes certain
operators. Moreover, this function will never raise syntax errors,
which makes it possible to use raw user-supplied input for search.
The following syntax is supported:
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
<literal>unquoted text</literal>: text not inside quote marks will be
converted to terms separated by <literal>&amp;</literal> operators, as
if processed by <function>plainto_tsquery</function>.
</para>
</listitem>
<listitem>
<para>
<literal>"quoted text"</literal>: text inside quote marks will be
converted to terms separated by <literal>&lt;-&gt;</literal>
operators, as if processed by <function>phraseto_tsquery</function>.
</para>
</listitem>
<listitem>
<para>
<literal>OR</literal>: the word <quote>or</quote> will be converted to
the <literal>|</literal> operator.
</para>
</listitem>
<listitem>
<para>
<literal>-</literal>: a dash will be converted to
the <literal>!</literal> operator.
</para>
</listitem>
</itemizedlist>
Other punctuation is ignored. So
like <function>plainto_tsquery</function>
and <function>phraseto_tsquery</function>,
the <function>websearch_to_tsquery</function> function will not
recognize <type>tsquery</type> operators, weight labels, or prefix-match
labels in its input.
</para>
<para>
Examples:
<screen>
SELECT websearch_to_tsquery('english', 'The fat rats');
websearch_to_tsquery
----------------------
'fat' &amp; 'rat'
(1 row)
SELECT websearch_to_tsquery('english', '"supernovae stars" -crab');
websearch_to_tsquery
----------------------------------
'supernova' &lt;-&gt; 'star' &amp; !'crab'
(1 row)
SELECT websearch_to_tsquery('english', '"sad cat" or "fat rat"');
websearch_to_tsquery
-----------------------------------
'sad' &lt;-&gt; 'cat' | 'fat' &lt;-&gt; 'rat'
(1 row)
SELECT websearch_to_tsquery('english', 'signal -"segmentation fault"');
websearch_to_tsquery
---------------------------------------
'signal' &amp; !( 'segment' &lt;-&gt; 'fault' )
(1 row)
SELECT websearch_to_tsquery('english', '""" )( dummy \\ query &lt;-&gt;');
websearch_to_tsquery
----------------------
'dummi' &amp; 'queri'
(1 row)
</screen>
</para>
</sect2>
<sect2 id="textsearch-ranking">
<title>Ranking Search Results</title>
<para>
Ranking attempts to measure how relevant documents are to a particular
query, so that when there are many matches the most relevant ones can be
shown first. <productname>PostgreSQL</productname> provides two
predefined ranking functions, which take into account lexical, proximity,
and structural information; that is, they consider how often the query
terms appear in the document, how close together the terms are in the
document, and how important is the part of the document where they occur.
However, the concept of relevancy is vague and very application-specific.
Different applications might require additional information for ranking,
e.g., document modification time. The built-in ranking functions are only
examples. You can write your own ranking functions and/or combine their
results with additional factors to fit your specific needs.
</para>
<para>
The two ranking functions currently available are:
<variablelist>
<varlistentry>
<term>
<indexterm>
<primary>ts_rank</primary>
</indexterm>
<literal>ts_rank(<optional> <replaceable class="parameter">weights</replaceable> <type>float4[]</type>, </optional> <replaceable class="parameter">vector</replaceable> <type>tsvector</type>, <replaceable class="parameter">query</replaceable> <type>tsquery</type> <optional>, <replaceable class="parameter">normalization</replaceable> <type>integer</type> </optional>) returns <type>float4</type></literal>
</term>
<listitem>
<para>
Ranks vectors based on the frequency of their matching lexemes.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<indexterm>
<primary>ts_rank_cd</primary>
</indexterm>
<literal>ts_rank_cd(<optional> <replaceable class="parameter">weights</replaceable> <type>float4[]</type>, </optional> <replaceable class="parameter">vector</replaceable> <type>tsvector</type>, <replaceable class="parameter">query</replaceable> <type>tsquery</type> <optional>, <replaceable class="parameter">normalization</replaceable> <type>integer</type> </optional>) returns <type>float4</type></literal>
</term>
<listitem>
<para>
This function computes the <firstterm>cover density</firstterm>
ranking for the given document vector and query, as described in
Clarke, Cormack, and Tudhope's "Relevance Ranking for One to Three
Term Queries" in the journal "Information Processing and Management",
1999. Cover density is similar to <function>ts_rank</function> ranking
except that the proximity of matching lexemes to each other is
taken into consideration.
</para>
<para>
This function requires lexeme positional information to perform
its calculation. Therefore, it ignores any <quote>stripped</quote>
lexemes in the <type>tsvector</type>. If there are no unstripped
lexemes in the input, the result will be zero. (See <xref
linkend="textsearch-manipulate-tsvector"/> for more information
about the <function>strip</function> function and positional information
in <type>tsvector</type>s.)
</para>
</listitem>
</varlistentry>
</variablelist>
</para>
<para>
For both these functions,
the optional <replaceable class="parameter">weights</replaceable>
argument offers the ability to weigh word instances more or less
heavily depending on how they are labeled. The weight arrays specify
how heavily to weigh each category of word, in the order:
<synopsis>
{D-weight, C-weight, B-weight, A-weight}
</synopsis>
If no <replaceable class="parameter">weights</replaceable> are provided,
then these defaults are used:
<programlisting>
{0.1, 0.2, 0.4, 1.0}
</programlisting>
Typically weights are used to mark words from special areas of the
document, like the title or an initial abstract, so they can be
treated with more or less importance than words in the document body.
</para>
<para>
Since a longer document has a greater chance of containing a query term
it is reasonable to take into account document size, e.g., a hundred-word
document with five instances of a search word is probably more relevant
than a thousand-word document with five instances. Both ranking functions
take an integer <replaceable>normalization</replaceable> option that
specifies whether and how a document's length should impact its rank.
The integer option controls several behaviors, so it is a bit mask:
you can specify one or more behaviors using
<literal>|</literal> (for example, <literal>2|4</literal>).
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
0 (the default) ignores the document length
</para>
</listitem>
<listitem>
<para>
1 divides the rank by 1 + the logarithm of the document length
</para>
</listitem>
<listitem>
<para>
2 divides the rank by the document length
</para>
</listitem>
<listitem>
<para>
4 divides the rank by the mean harmonic distance between extents
(this is implemented only by <function>ts_rank_cd</function>)
</para>
</listitem>
<listitem>
<para>
8 divides the rank by the number of unique words in document
</para>
</listitem>
<listitem>
<para>
16 divides the rank by 1 + the logarithm of the number
of unique words in document
</para>
</listitem>
<listitem>
<para>
32 divides the rank by itself + 1
</para>
</listitem>
</itemizedlist>
If more than one flag bit is specified, the transformations are
applied in the order listed.
</para>
<para>
It is important to note that the ranking functions do not use any global
information, so it is impossible to produce a fair normalization to 1% or
100% as sometimes desired. Normalization option 32
(<literal>rank/(rank+1)</literal>) can be applied to scale all ranks
into the range zero to one, but of course this is just a cosmetic change;
it will not affect the ordering of the search results.
</para>
<para>
Here is an example that selects only the ten highest-ranked matches:
<screen>
SELECT title, ts_rank_cd(textsearch, query) AS rank
FROM apod, to_tsquery('neutrino|(dark &amp; matter)') query
WHERE query @@ textsearch
ORDER BY rank DESC
LIMIT 10;
title | rank
-----------------------------------------------+----------
Neutrinos in the Sun | 3.1
The Sudbury Neutrino Detector | 2.4
A MACHO View of Galactic Dark Matter | 2.01317
Hot Gas and Dark Matter | 1.91171
The Virgo Cluster: Hot Plasma and Dark Matter | 1.90953
Rafting for Solar Neutrinos | 1.9
NGC 4650A: Strange Galaxy and Dark Matter | 1.85774
Hot Gas and Dark Matter | 1.6123
Ice Fishing for Cosmic Neutrinos | 1.6
Weak Lensing Distorts the Universe | 0.818218
</screen>
This is the same example using normalized ranking:
<screen>
SELECT title, ts_rank_cd(textsearch, query, 32 /* rank/(rank+1) */ ) AS rank
FROM apod, to_tsquery('neutrino|(dark &amp; matter)') query
WHERE query @@ textsearch
ORDER BY rank DESC
LIMIT 10;
title | rank
-----------------------------------------------+-------------------
Neutrinos in the Sun | 0.756097569485493
The Sudbury Neutrino Detector | 0.705882361190954
A MACHO View of Galactic Dark Matter | 0.668123210574724
Hot Gas and Dark Matter | 0.65655958650282
The Virgo Cluster: Hot Plasma and Dark Matter | 0.656301290640973
Rafting for Solar Neutrinos | 0.655172410958162
NGC 4650A: Strange Galaxy and Dark Matter | 0.650072921219637
Hot Gas and Dark Matter | 0.617195790024749
Ice Fishing for Cosmic Neutrinos | 0.615384618911517
Weak Lensing Distorts the Universe | 0.450010798361481
</screen>
</para>
<para>
Ranking can be expensive since it requires consulting the
<type>tsvector</type> of each matching document, which can be I/O bound and
therefore slow. Unfortunately, it is almost impossible to avoid since
practical queries often result in large numbers of matches.
</para>
</sect2>
<sect2 id="textsearch-headline">
<title>Highlighting Results</title>
<para>
To present search results it is ideal to show a part of each document and
how it is related to the query. Usually, search engines show fragments of
the document with marked search terms. <productname>PostgreSQL</productname>
provides a function <function>ts_headline</function> that
implements this functionality.
</para>
<indexterm>
<primary>ts_headline</primary>
</indexterm>
<synopsis>
ts_headline(<optional> <replaceable class="parameter">config</replaceable> <type>regconfig</type>, </optional> <replaceable class="parameter">document</replaceable> <type>text</type>, <replaceable class="parameter">query</replaceable> <type>tsquery</type> <optional>, <replaceable class="parameter">options</replaceable> <type>text</type> </optional>) returns <type>text</type>
</synopsis>
<para>
<function>ts_headline</function> accepts a document along
with a query, and returns an excerpt from
the document in which terms from the query are highlighted. The
configuration to be used to parse the document can be specified by
<replaceable>config</replaceable>; if <replaceable>config</replaceable>
is omitted, the
<varname>default_text_search_config</varname> configuration is used.
</para>
<para>
If an <replaceable>options</replaceable> string is specified it must
consist of a comma-separated list of one or more
<replaceable>option</replaceable><literal>=</literal><replaceable>value</replaceable> pairs.
The available options are:
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
<literal>MaxWords</literal>, <literal>MinWords</literal> (integers):
these numbers determine the longest and shortest headlines to output.
The default values are 35 and 15.
</para>
</listitem>
<listitem>
<para>
<literal>ShortWord</literal> (integer): words of this length or less
will be dropped at the start and end of a headline, unless they are
query terms. The default value of three eliminates common English
articles.
</para>
</listitem>
<listitem>
<para>
<literal>HighlightAll</literal> (boolean): if
<literal>true</literal> the whole document will be used as the
headline, ignoring the preceding three parameters. The default
is <literal>false</literal>.
</para>
</listitem>
<listitem>
<para>
<literal>MaxFragments</literal> (integer): maximum number of text
fragments to display. The default value of zero selects a
non-fragment-based headline generation method. A value greater
than zero selects fragment-based headline generation (see below).
</para>
</listitem>
<listitem>
<para>
<literal>StartSel</literal>, <literal>StopSel</literal> (strings):
the strings with which to delimit query words appearing in the
document, to distinguish them from other excerpted words. The
default values are <quote><literal>&lt;b&gt;</literal></quote> and
<quote><literal>&lt;/b&gt;</literal></quote>, which can be suitable
for HTML output.
</para>
</listitem>
<listitem>
<para>
<literal>FragmentDelimiter</literal> (string): When more than one
fragment is displayed, the fragments will be separated by this string.
The default is <quote><literal> ... </literal></quote>.
</para>
</listitem>
</itemizedlist>
These option names are recognized case-insensitively.
You must double-quote string values if they contain spaces or commas.
</para>
<para>
In non-fragment-based headline
generation, <function>ts_headline</function> locates matches for the
given <replaceable class="parameter">query</replaceable> and chooses a
single one to display, preferring matches that have more query words
within the allowed headline length.
In fragment-based headline generation, <function>ts_headline</function>
locates the query matches and splits each match
into <quote>fragments</quote> of no more than <literal>MaxWords</literal>
words each, preferring fragments with more query words, and when
possible <quote>stretching</quote> fragments to include surrounding
words. The fragment-based mode is thus more useful when the query
matches span large sections of the document, or when it's desirable to
display multiple matches.
In either mode, if no query matches can be identified, then a single
fragment of the first <literal>MinWords</literal> words in the document
will be displayed.
</para>
<para>
For example:
<screen>
SELECT ts_headline('english',
'The most common type of search
is to find all documents containing given query terms
and return them in order of their similarity to the
query.',
to_tsquery('english', 'query &amp; similarity'));
ts_headline
------------------------------------------------------------
containing given &lt;b&gt;query&lt;/b&gt; terms +
and return them in order of their &lt;b&gt;similarity&lt;/b&gt; to the+
&lt;b&gt;query&lt;/b&gt;.
SELECT ts_headline('english',
'Search terms may occur
many times in a document,
requiring ranking of the search matches to decide which
occurrences to display in the result.',
to_tsquery('english', 'search &amp; term'),
'MaxFragments=10, MaxWords=7, MinWords=3, StartSel=&lt;&lt;, StopSel=&gt;&gt;');
ts_headline
------------------------------------------------------------
&lt;&lt;Search&gt;&gt; &lt;&lt;terms&gt;&gt; may occur +
many times ... ranking of the &lt;&lt;search&gt;&gt; matches to decide
</screen>
</para>
<para>
<function>ts_headline</function> uses the original document, not a
<type>tsvector</type> summary, so it can be slow and should be used with
care.
</para>
</sect2>
</sect1>
<sect1 id="textsearch-features">
<title>Additional Features</title>
<para>
This section describes additional functions and operators that are
useful in connection with text search.
</para>
<sect2 id="textsearch-manipulate-tsvector">
<title>Manipulating Documents</title>
<para>
<xref linkend="textsearch-parsing-documents"/> showed how raw textual
documents can be converted into <type>tsvector</type> values.
<productname>PostgreSQL</productname> also provides functions and
operators that can be used to manipulate documents that are already
in <type>tsvector</type> form.
</para>
<variablelist>
<varlistentry>
<term>
<indexterm>
<primary>tsvector concatenation</primary>
</indexterm>
<literal><type>tsvector</type> || <type>tsvector</type></literal>
</term>
<listitem>
<para>
The <type>tsvector</type> 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 <function>to_tsvector</function>
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.)
</para>
<para>
One advantage of using concatenation in the vector form, rather than
concatenating text before applying <function>to_tsvector</function>, is that
you can use different configurations to parse different sections
of the document. Also, because the <function>setweight</function> function
marks all lexemes of the given vector the same way, it is necessary
to parse the text and do <function>setweight</function> before concatenating
if you want to label different parts of the document with different
weights.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<indexterm>
<primary>setweight</primary>
</indexterm>
<literal>setweight(<replaceable class="parameter">vector</replaceable> <type>tsvector</type>, <replaceable class="parameter">weight</replaceable> <type>"char"</type>) returns <type>tsvector</type></literal>
</term>
<listitem>
<para>
<function>setweight</function> returns a copy of the input vector in which every
position has been labeled with the given <replaceable>weight</replaceable>, either
<literal>A</literal>, <literal>B</literal>, <literal>C</literal>, or
<literal>D</literal>. (<literal>D</literal> 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.
</para>
<para>
Note that weight labels apply to <emphasis>positions</emphasis>, not
<emphasis>lexemes</emphasis>. If the input vector has been stripped of
positions then <function>setweight</function> does nothing.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<indexterm>
<primary>length(tsvector)</primary>
</indexterm>
<literal>length(<replaceable class="parameter">vector</replaceable> <type>tsvector</type>) returns <type>integer</type></literal>
</term>
<listitem>
<para>
Returns the number of lexemes stored in the vector.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<indexterm>
<primary>strip</primary>
</indexterm>
<literal>strip(<replaceable class="parameter">vector</replaceable> <type>tsvector</type>) returns <type>tsvector</type></literal>
</term>
<listitem>
<para>
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 <literal>&lt;-&gt;</literal> (FOLLOWED BY) <type>tsquery</type> operator
will never match stripped input, since it cannot determine the
distance between lexeme occurrences.
</para>
</listitem>
</varlistentry>
</variablelist>
<para>
A full list of <type>tsvector</type>-related functions is available
in <xref linkend="textsearch-functions-table"/>.
</para>
</sect2>
<sect2 id="textsearch-manipulate-tsquery">
<title>Manipulating Queries</title>
<para>
<xref linkend="textsearch-parsing-queries"/> showed how raw textual
queries can be converted into <type>tsquery</type> values.
<productname>PostgreSQL</productname> also provides functions and
operators that can be used to manipulate queries that are already
in <type>tsquery</type> form.
</para>
<variablelist>
<varlistentry>
<term>
<literal><type>tsquery</type> &amp;&amp; <type>tsquery</type></literal>
</term>
<listitem>
<para>
Returns the AND-combination of the two given queries.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal><type>tsquery</type> || <type>tsquery</type></literal>
</term>
<listitem>
<para>
Returns the OR-combination of the two given queries.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>!! <type>tsquery</type></literal>
</term>
<listitem>
<para>
Returns the negation (NOT) of the given query.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal><type>tsquery</type> &lt;-&gt; <type>tsquery</type></literal>
</term>
<listitem>
<para>
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 <literal>&lt;-&gt;</literal> (FOLLOWED BY)
<type>tsquery</type> operator. For example:
<screen>
SELECT to_tsquery('fat') &lt;-&gt; to_tsquery('cat | rat');
?column?
----------------------------
'fat' &lt;-&gt; ( 'cat' | 'rat' )
</screen>
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<indexterm>
<primary>tsquery_phrase</primary>
</indexterm>
<literal>tsquery_phrase(<replaceable class="parameter">query1</replaceable> <type>tsquery</type>, <replaceable class="parameter">query2</replaceable> <type>tsquery</type> [, <replaceable class="parameter">distance</replaceable> <type>integer</type> ]) returns <type>tsquery</type></literal>
</term>
<listitem>
<para>
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
<replaceable>distance</replaceable> lexemes, using
the <literal>&lt;<replaceable>N</replaceable>&gt;</literal>
<type>tsquery</type> operator. For example:
<screen>
SELECT tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10);
tsquery_phrase
------------------
'fat' &lt;10&gt; 'cat'
</screen>
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<indexterm>
<primary>numnode</primary>
</indexterm>
<literal>numnode(<replaceable class="parameter">query</replaceable> <type>tsquery</type>) returns <type>integer</type></literal>
</term>
<listitem>
<para>
Returns the number of nodes (lexemes plus operators) in a
<type>tsquery</type>. This function is useful
to determine if the <replaceable>query</replaceable> is meaningful
(returns &gt; 0), or contains only stop words (returns 0).
Examples:
<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 &amp; bar'::tsquery);
numnode
---------
3
</screen>
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<indexterm>
<primary>querytree</primary>
</indexterm>
<literal>querytree(<replaceable class="parameter">query</replaceable> <type>tsquery</type>) returns <type>text</type></literal>
</term>
<listitem>
<para>
Returns the portion of a <type>tsquery</type> 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:
<screen>
SELECT querytree(to_tsquery('defined'));
querytree
-----------
'defin'
SELECT querytree(to_tsquery('!defined'));
querytree
-----------
T
</screen>
</para>
</listitem>
</varlistentry>
</variablelist>
<sect3 id="textsearch-query-rewriting">
<title>Query Rewriting</title>
<indexterm zone="textsearch-query-rewriting">
<primary>ts_rewrite</primary>
</indexterm>
<para>
The <function>ts_rewrite</function> family of functions search a
given <type>tsquery</type> for occurrences of a target
subquery, and replace each occurrence with a
substitute subquery. In essence this operation is a
<type>tsquery</type>-specific version of substring replacement.
A target and substitute combination can be
thought of as a <firstterm>query rewrite rule</firstterm>. A collection
of such rewrite rules can be a powerful search aid.
For example, you can expand the search using synonyms
(e.g., <literal>new york</literal>, <literal>big apple</literal>, <literal>nyc</literal>,
<literal>gotham</literal>) or narrow the search to direct the user to some hot
topic. There is some overlap in functionality between this feature
and thesaurus dictionaries (<xref linkend="textsearch-thesaurus"/>).
However, you can modify a set of rewrite rules on-the-fly without
reindexing, whereas updating a thesaurus requires reindexing to be
effective.
</para>
<variablelist>
<varlistentry>
<term>
<literal>ts_rewrite (<replaceable class="parameter">query</replaceable> <type>tsquery</type>, <replaceable class="parameter">target</replaceable> <type>tsquery</type>, <replaceable class="parameter">substitute</replaceable> <type>tsquery</type>) returns <type>tsquery</type></literal>
</term>
<listitem>
<para>
This form of <function>ts_rewrite</function> simply applies a single
rewrite rule: <replaceable class="parameter">target</replaceable>
is replaced by <replaceable class="parameter">substitute</replaceable>
wherever it appears in <replaceable
class="parameter">query</replaceable>. For example:
<screen>
SELECT ts_rewrite('a &amp; b'::tsquery, 'a'::tsquery, 'c'::tsquery);
ts_rewrite
------------
'b' &amp; 'c'
</screen>
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>ts_rewrite (<replaceable class="parameter">query</replaceable> <type>tsquery</type>, <replaceable class="parameter">select</replaceable> <type>text</type>) returns <type>tsquery</type></literal>
</term>
<listitem>
<para>
This form of <function>ts_rewrite</function> accepts a starting
<replaceable>query</replaceable> and an SQL <replaceable>select</replaceable> command, which
is given as a text string. The <replaceable>select</replaceable> must yield two
columns of <type>tsquery</type> type. For each row of the
<replaceable>select</replaceable> result, occurrences of the first column value
(the target) are replaced by the second column value (the substitute)
within the current <replaceable>query</replaceable> value. For example:
<screen>
CREATE TABLE aliases (t tsquery PRIMARY KEY, s tsquery);
INSERT INTO aliases VALUES('a', 'c');
SELECT ts_rewrite('a &amp; b'::tsquery, 'SELECT t,s FROM aliases');
ts_rewrite
------------
'b' &amp; 'c'
</screen>
</para>
<para>
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 <literal>ORDER BY</literal> some ordering key.
</para>
</listitem>
</varlistentry>
</variablelist>
<para>
Let's consider a real-life astronomical example. We'll expand query
<literal>supernovae</literal> using table-driven rewriting rules:
<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 &amp; crab'), 'SELECT * FROM aliases');
ts_rewrite
---------------------------------
'crab' &amp; ( 'supernova' | 'sn' )
</screen>
We can change the rewriting rules just by updating the table:
<screen>
UPDATE aliases
SET s = to_tsquery('supernovae|sn &amp; !nebulae')
WHERE t = to_tsquery('supernovae');
SELECT ts_rewrite(to_tsquery('supernovae &amp; crab'), 'SELECT * FROM aliases');
ts_rewrite
---------------------------------------------
'crab' &amp; ( 'supernova' | 'sn' &amp; !'nebula' )
</screen>
</para>
<para>
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>tsquery</type>
type. In the example below, we select only those rules which might match
the original query:
<screen>
SELECT ts_rewrite('a &amp; b'::tsquery,
'SELECT t,s FROM aliases WHERE ''a &amp; b''::tsquery @&gt; t');
ts_rewrite
------------
'b' &amp; 'c'
</screen>
</para>
</sect3>
</sect2>
<sect2 id="textsearch-update-triggers">
<title>Triggers for Automatic Updates</title>
<indexterm>
<primary>trigger</primary>
<secondary>for updating a derived tsvector column</secondary>
</indexterm>
<note>
<para>
The method described in this section has been obsoleted by the use of
stored generated columns, as described in <xref
linkend="textsearch-tables-index"/>.
</para>
</note>
<para>
When using a separate column to store the <type>tsvector</type> representation
of your documents, it is necessary to create a trigger to update the
<type>tsvector</type> column when the document content columns change.
Two built-in trigger functions are available for this, or you can write
your own.
</para>
<synopsis>
tsvector_update_trigger(<replaceable class="parameter">tsvector_column_name</replaceable>,&zwsp; <replaceable class="parameter">config_name</replaceable>, <replaceable class="parameter">text_column_name</replaceable> <optional>, ... </optional>)
tsvector_update_trigger_column(<replaceable class="parameter">tsvector_column_name</replaceable>,&zwsp; <replaceable class="parameter">config_column_name</replaceable>, <replaceable class="parameter">text_column_name</replaceable> <optional>, ... </optional>)
</synopsis>
<para>
These trigger functions automatically compute a <type>tsvector</type>
column from one or more textual columns, under the control of
parameters specified in the <command>CREATE TRIGGER</command> command.
An example of their use is:
<screen>
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 &amp; body');
title | body
------------+-----------------------
title here | the body text is here
</screen>
Having created this trigger, any change in <structfield>title</structfield> or
<structfield>body</structfield> will automatically be reflected into
<structfield>tsv</structfield>, without the application having to worry about it.
</para>
<para>
The first trigger argument must be the name of the <type>tsvector</type>
column to be updated. The second argument specifies the text search
configuration to be used to perform the conversion. For
<function>tsvector_update_trigger</function>, 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 <varname>search_path</varname>. For
<function>tsvector_update_trigger_column</function>, the second trigger argument
is the name of another table column, which must be of type
<type>regconfig</type>. This allows a per-row selection of configuration
to be made. The remaining argument(s) are the names of textual columns
(of type <type>text</type>, <type>varchar</type>, or <type>char</type>). These
will be included in the document in the order given. NULL values will
be skipped (but the other columns will still be indexed).
</para>
<para>
A limitation of these built-in triggers is that they treat all the
input columns alike. To process columns differently &mdash; for
example, to weight title differently from body &mdash; it is necessary
to write a custom trigger. Here is an example using
<application>PL/pgSQL</application> as the trigger language:
<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 FUNCTION messages_trigger();
</programlisting>
</para>
<para>
Keep in mind that it is important to specify the configuration name
explicitly when creating <type>tsvector</type> values inside triggers,
so that the column's contents will not be affected by changes to
<varname>default_text_search_config</varname>. Failure to do this is likely to
lead to problems such as search results changing after a dump and restore.
</para>
</sect2>
<sect2 id="textsearch-statistics">
<title>Gathering Document Statistics</title>
<indexterm>
<primary>ts_stat</primary>
</indexterm>
<para>
The function <function>ts_stat</function> is useful for checking your
configuration and for finding stop-word candidates.
</para>
<synopsis>
ts_stat(<replaceable class="parameter">sqlquery</replaceable> <type>text</type>, <optional> <replaceable class="parameter">weights</replaceable> <type>text</type>, </optional>
OUT <replaceable class="parameter">word</replaceable> <type>text</type>, OUT <replaceable class="parameter">ndoc</replaceable> <type>integer</type>,
OUT <replaceable class="parameter">nentry</replaceable> <type>integer</type>) returns <type>setof record</type>
</synopsis>
<para>
<replaceable>sqlquery</replaceable> is a text value containing an SQL
query which must return a single <type>tsvector</type> column.
<function>ts_stat</function> executes the query and returns statistics about
each distinct lexeme (word) contained in the <type>tsvector</type>
data. The columns returned are
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
<replaceable>word</replaceable> <type>text</type> &mdash; the value of a lexeme
</para>
</listitem>
<listitem>
<para>
<replaceable>ndoc</replaceable> <type>integer</type> &mdash; number of documents
(<type>tsvector</type>s) the word occurred in
</para>
</listitem>
<listitem>
<para>
<replaceable>nentry</replaceable> <type>integer</type> &mdash; total number of
occurrences of the word
</para>
</listitem>
</itemizedlist>
If <replaceable>weights</replaceable> is supplied, only occurrences
having one of those weights are counted.
</para>
<para>
For example, to find the ten most frequent words in a document collection:
<programlisting>
SELECT * FROM ts_stat('SELECT vector FROM apod')
ORDER BY nentry DESC, ndoc DESC, word
LIMIT 10;
</programlisting>
The same, but counting only word occurrences with weight <literal>A</literal>
or <literal>B</literal>:
<programlisting>
SELECT * FROM ts_stat('SELECT vector FROM apod', 'ab')
ORDER BY nentry DESC, ndoc DESC, word
LIMIT 10;
</programlisting>
</para>
</sect2>
</sect1>
<sect1 id="textsearch-parsers">
<title>Parsers</title>
<para>
Text search parsers are responsible for splitting raw document text
into <firstterm>tokens</firstterm> and identifying each token's type, where
the set of possible types is defined by the parser itself.
Note that a parser does not modify the text at all &mdash; it simply
identifies plausible word boundaries. Because of this limited scope,
there is less need for application-specific custom parsers than there is
for custom dictionaries. At present <productname>PostgreSQL</productname>
provides just one built-in parser, which has been found to be useful for a
wide range of applications.
</para>
<para>
The built-in parser is named <literal>pg_catalog.default</literal>.
It recognizes 23 token types, shown in <xref linkend="textsearch-default-parser"/>.
</para>
<table id="textsearch-default-parser">
<title>Default Parser's Token Types</title>
<tgroup cols="3">
<colspec colname="col1" colwidth="2*"/>
<colspec colname="col2" colwidth="2*"/>
<colspec colname="col3" colwidth="3*"/>
<thead>
<row>
<entry>Alias</entry>
<entry>Description</entry>
<entry>Example</entry>
</row>
</thead>
<tbody>
<row>
<entry><literal>asciiword</literal></entry>
<entry>Word, all ASCII letters</entry>
<entry><literal>elephant</literal></entry>
</row>
<row>
<entry><literal>word</literal></entry>
<entry>Word, all letters</entry>
<entry><literal>ma&ntilde;ana</literal></entry>
</row>
<row>
<entry><literal>numword</literal></entry>
<entry>Word, letters and digits</entry>
<entry><literal>beta1</literal></entry>
</row>
<row>
<entry><literal>asciihword</literal></entry>
<entry>Hyphenated word, all ASCII</entry>
<entry><literal>up-to-date</literal></entry>
</row>
<row>
<entry><literal>hword</literal></entry>
<entry>Hyphenated word, all letters</entry>
<entry><literal>l&oacute;gico-matem&aacute;tica</literal></entry>
</row>
<row>
<entry><literal>numhword</literal></entry>
<entry>Hyphenated word, letters and digits</entry>
<entry><literal>postgresql-beta1</literal></entry>
</row>
<row>
<entry><literal>hword_asciipart</literal></entry>
<entry>Hyphenated word part, all ASCII</entry>
<entry><literal>postgresql</literal> in the context <literal>postgresql-beta1</literal></entry>
</row>
<row>
<entry><literal>hword_part</literal></entry>
<entry>Hyphenated word part, all letters</entry>
<entry><literal>l&oacute;gico</literal> or <literal>matem&aacute;tica</literal>
in the context <literal>l&oacute;gico-matem&aacute;tica</literal></entry>
</row>
<row>
<entry><literal>hword_numpart</literal></entry>
<entry>Hyphenated word part, letters and digits</entry>
<entry><literal>beta1</literal> in the context
<literal>postgresql-beta1</literal></entry>
</row>
<row>
<entry><literal>email</literal></entry>
<entry>Email address</entry>
<entry><literal>foo@example.com</literal></entry>
</row>
<row>
<entry><literal>protocol</literal></entry>
<entry>Protocol head</entry>
<entry><literal>http://</literal></entry>
</row>
<row>
<entry><literal>url</literal></entry>
<entry>URL</entry>
<entry><literal>example.com/stuff/index.html</literal></entry>
</row>
<row>
<entry><literal>host</literal></entry>
<entry>Host</entry>
<entry><literal>example.com</literal></entry>
</row>
<row>
<entry><literal>url_path</literal></entry>
<entry>URL path</entry>
<entry><literal>/stuff/index.html</literal>, in the context of a URL</entry>
</row>
<row>
<entry><literal>file</literal></entry>
<entry>File or path name</entry>
<entry><literal>/usr/local/foo.txt</literal>, if not within a URL</entry>
</row>
<row>
<entry><literal>sfloat</literal></entry>
<entry>Scientific notation</entry>
<entry><literal>-1.234e56</literal></entry>
</row>
<row>
<entry><literal>float</literal></entry>
<entry>Decimal notation</entry>
<entry><literal>-1.234</literal></entry>
</row>
<row>
<entry><literal>int</literal></entry>
<entry>Signed integer</entry>
<entry><literal>-1234</literal></entry>
</row>
<row>
<entry><literal>uint</literal></entry>
<entry>Unsigned integer</entry>
<entry><literal>1234</literal></entry>
</row>
<row>
<entry><literal>version</literal></entry>
<entry>Version number</entry>
<entry><literal>8.3.0</literal></entry>
</row>
<row>
<entry><literal>tag</literal></entry>
<entry>XML tag</entry>
<entry><literal>&lt;a href="dictionaries.html"&gt;</literal></entry>
</row>
<row>
<entry><literal>entity</literal></entry>
<entry>XML entity</entry>
<entry><literal>&amp;amp;</literal></entry>
</row>
<row>
<entry><literal>blank</literal></entry>
<entry>Space symbols</entry>
<entry>(any whitespace or punctuation not otherwise recognized)</entry>
</row>
</tbody>
</tgroup>
</table>
<note>
<para>
The parser's notion of a <quote>letter</quote> is determined by the database's
locale setting, specifically <varname>lc_ctype</varname>. Words containing
only the basic ASCII letters are reported as a separate token type,
since it is sometimes useful to distinguish them. In most European
languages, token types <literal>word</literal> and <literal>asciiword</literal>
should be treated alike.
</para>
<para>
<literal>email</literal> does not support all valid email characters as
defined by <ulink url="https://tools.ietf.org/html/rfc5322">RFC 5322</ulink>.
Specifically, the only non-alphanumeric characters supported for
email user names are period, dash, and underscore.
</para>
</note>
<para>
It is possible for the parser to produce overlapping tokens from the same
piece of text. As an example, a hyphenated word will be reported both
as the entire word and as each component:
<screen>
SELECT alias, description, token FROM ts_debug('foo-bar-beta1');
alias | description | token
-----------------+------------------------------------------+---------------
numhword | Hyphenated word, letters and digits | foo-bar-beta1
hword_asciipart | Hyphenated word part, all ASCII | foo
blank | Space symbols | -
hword_asciipart | Hyphenated word part, all ASCII | bar
blank | Space symbols | -
hword_numpart | Hyphenated word part, letters and digits | beta1
</screen>
This behavior is desirable since it allows searches to work for both
the whole compound word and for components. Here is another
instructive example:
<screen>
SELECT alias, description, token FROM ts_debug('http://example.com/stuff/index.html');
alias | description | token
----------+---------------+------------------------------
protocol | Protocol head | http://
url | URL | example.com/stuff/index.html
host | Host | example.com
url_path | URL path | /stuff/index.html
</screen>
</para>
</sect1>
<sect1 id="textsearch-dictionaries">
<title>Dictionaries</title>
<para>
Dictionaries are used to eliminate words that should not be considered in a
search (<firstterm>stop words</firstterm>), and to <firstterm>normalize</firstterm> words so
that different derived forms of the same word will match. A successfully
normalized word is called a <firstterm>lexeme</firstterm>. Aside from
improving search quality, normalization and removal of stop words reduce the
size of the <type>tsvector</type> representation of a document, thereby
improving performance. Normalization does not always have linguistic meaning
and usually depends on application semantics.
</para>
<para>
Some examples of normalization:
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
Linguistic &mdash; Ispell dictionaries try to reduce input words to a
normalized form; stemmer dictionaries remove word endings
</para>
</listitem>
<listitem>
<para>
<acronym>URL</acronym> locations can be canonicalized to make
equivalent URLs match:
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
http://www.pgsql.ru/db/mw/index.html
</para>
</listitem>
<listitem>
<para>
http://www.pgsql.ru/db/mw/
</para>
</listitem>
<listitem>
<para>
http://www.pgsql.ru/db/../db/mw/index.html
</para>
</listitem>
</itemizedlist>
</para>
</listitem>
<listitem>
<para>
Color names can be replaced by their hexadecimal values, e.g.,
<literal>red, green, blue, magenta -> FF0000, 00FF00, 0000FF, FF00FF</literal>
</para>
</listitem>
<listitem>
<para>
If indexing numbers, we can
remove some fractional digits to reduce the range of possible
numbers, so for example <emphasis>3.14</emphasis>159265359,
<emphasis>3.14</emphasis>15926, <emphasis>3.14</emphasis> will be the same
after normalization if only two digits are kept after the decimal point.
</para>
</listitem>
</itemizedlist>
</para>
<para>
A dictionary is a program that accepts a token as
input and returns:
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
an array of lexemes if the input token is known to the dictionary
(notice that one token can produce more than one lexeme)
</para>
</listitem>
<listitem>
<para>
a single lexeme with the <literal>TSL_FILTER</literal> flag set, to replace
the original token with a new token to be passed to subsequent
dictionaries (a dictionary that does this is called a
<firstterm>filtering dictionary</firstterm>)
</para>
</listitem>
<listitem>
<para>
an empty array if the dictionary knows the token, but it is a stop word
</para>
</listitem>
<listitem>
<para>
<literal>NULL</literal> if the dictionary does not recognize the input token
</para>
</listitem>
</itemizedlist>
</para>
<para>
<productname>PostgreSQL</productname> provides predefined dictionaries for
many languages. There are also several predefined templates that can be
used to create new dictionaries with custom parameters. Each predefined
dictionary template is described below. If no existing
template is suitable, it is possible to create new ones; see the
<filename>contrib/</filename> area of the <productname>PostgreSQL</productname> distribution
for examples.
</para>
<para>
A text search configuration binds a parser together with a set of
dictionaries to process the parser's output tokens. For each token
type that the parser can return, a separate list of dictionaries is
specified by the configuration. When a token of that type is found
by the parser, each dictionary in the list is consulted in turn,
until some dictionary recognizes it as a known word. If it is identified
as a stop word, or if no dictionary recognizes the token, it will be
discarded and not indexed or searched for.
Normally, the first dictionary that returns a non-<literal>NULL</literal>
output determines the result, and any remaining dictionaries are not
consulted; but a filtering dictionary can replace the given word
with a modified word, which is then passed to subsequent dictionaries.
</para>
<para>
The general rule for configuring a list of dictionaries
is to place first the most narrow, most specific dictionary, then the more
general dictionaries, finishing with a very general dictionary, like
a <application>Snowball</application> stemmer or <literal>simple</literal>, which
recognizes everything. For example, for an astronomy-specific search
(<literal>astro_en</literal> configuration) one could bind token type
<type>asciiword</type> (ASCII word) to a synonym dictionary of astronomical
terms, a general English dictionary and a <application>Snowball</application> English
stemmer:
<programlisting>
ALTER TEXT SEARCH CONFIGURATION astro_en
ADD MAPPING FOR asciiword WITH astrosyn, english_ispell, english_stem;
</programlisting>
</para>
<para>
A filtering dictionary can be placed anywhere in the list, except at the
end where it'd be useless. Filtering dictionaries are useful to partially
normalize words to simplify the task of later dictionaries. For example,
a filtering dictionary could be used to remove accents from accented
letters, as is done by the <xref linkend="unaccent"/> module.
</para>
<sect2 id="textsearch-stopwords">
<title>Stop Words</title>
<para>
Stop words are words that are very common, appear in almost every
document, and have no discrimination value. Therefore, they can be ignored
in the context of full text searching. For example, every English text
contains words like <literal>a</literal> and <literal>the</literal>, so it is
useless to store them in an index. However, stop words do affect the
positions in <type>tsvector</type>, which in turn affect ranking:
<screen>
SELECT to_tsvector('english', 'in the list of stop words');
to_tsvector
----------------------------
'list':3 'stop':5 'word':6
</screen>
The missing positions 1,2,4 are because of stop words. Ranks
calculated for documents with and without stop words are quite different:
<screen>
SELECT ts_rank_cd (to_tsvector('english', 'in the list of stop words'), to_tsquery('list &amp; stop'));
ts_rank_cd
------------
0.05
SELECT ts_rank_cd (to_tsvector('english', 'list stop words'), to_tsquery('list &amp; stop'));
ts_rank_cd
------------
0.1
</screen>
</para>
<para>
It is up to the specific dictionary how it treats stop words. For example,
<literal>ispell</literal> dictionaries first normalize words and then
look at the list of stop words, while <literal>Snowball</literal> stemmers
first check the list of stop words. The reason for the different
behavior is an attempt to decrease noise.
</para>
</sect2>
<sect2 id="textsearch-simple-dictionary">
<title>Simple Dictionary</title>
<para>
The <literal>simple</literal> dictionary template operates by converting the
input token to lower case and checking it against a file of stop words.
If it is found in the file then an empty array is returned, causing
the token to be discarded. If not, the lower-cased form of the word
is returned as the normalized lexeme. Alternatively, the dictionary
can be configured to report non-stop-words as unrecognized, allowing
them to be passed on to the next dictionary in the list.
</para>
<para>
Here is an example of a dictionary definition using the <literal>simple</literal>
template:
<programlisting>
CREATE TEXT SEARCH DICTIONARY public.simple_dict (
TEMPLATE = pg_catalog.simple,
STOPWORDS = english
);
</programlisting>
Here, <literal>english</literal> is the base name of a file of stop words.
The file's full name will be
<filename>$SHAREDIR/tsearch_data/english.stop</filename>,
where <literal>$SHAREDIR</literal> means the
<productname>PostgreSQL</productname> installation's shared-data directory,
often <filename>/usr/local/share/postgresql</filename> (use <command>pg_config
--sharedir</command> to determine it if you're not sure).
The file format is simply a list
of words, one per line. Blank lines and trailing spaces are ignored,
and upper case is folded to lower case, but no other processing is done
on the file contents.
</para>
<para>
Now we can test our dictionary:
<screen>
SELECT ts_lexize('public.simple_dict', 'YeS');
ts_lexize
-----------
{yes}
SELECT ts_lexize('public.simple_dict', 'The');
ts_lexize
-----------
{}
</screen>
</para>
<para>
We can also choose to return <literal>NULL</literal>, instead of the lower-cased
word, if it is not found in the stop words file. This behavior is
selected by setting the dictionary's <literal>Accept</literal> parameter to
<literal>false</literal>. Continuing the example:
<screen>
ALTER TEXT SEARCH DICTIONARY public.simple_dict ( Accept = false );
SELECT ts_lexize('public.simple_dict', 'YeS');
ts_lexize
-----------
SELECT ts_lexize('public.simple_dict', 'The');
ts_lexize
-----------
{}
</screen>
</para>
<para>
With the default setting of <literal>Accept</literal> = <literal>true</literal>,
it is only useful to place a <literal>simple</literal> dictionary at the end
of a list of dictionaries, since it will never pass on any token to
a following dictionary. Conversely, <literal>Accept</literal> = <literal>false</literal>
is only useful when there is at least one following dictionary.
</para>
<caution>
<para>
Most types of dictionaries rely on configuration files, such as files of
stop words. These files <emphasis>must</emphasis> be stored in UTF-8 encoding.
They will be translated to the actual database encoding, if that is
different, when they are read into the server.
</para>
</caution>
<caution>
<para>
Normally, a database session will read a dictionary configuration file
only once, when it is first used within the session. If you modify a
configuration file and want to force existing sessions to pick up the
new contents, issue an <command>ALTER TEXT SEARCH DICTIONARY</command> command
on the dictionary. This can be a <quote>dummy</quote> update that doesn't
actually change any parameter values.
</para>
</caution>
</sect2>
<sect2 id="textsearch-synonym-dictionary">
<title>Synonym Dictionary</title>
<para>
This dictionary template is used to create dictionaries that replace a
word with a synonym. Phrases are not supported (use the thesaurus
template (<xref linkend="textsearch-thesaurus"/>) for that). A synonym
dictionary can be used to overcome linguistic problems, for example, to
prevent an English stemmer dictionary from reducing the word <quote>Paris</quote> to
<quote>pari</quote>. It is enough to have a <literal>Paris paris</literal> line in the
synonym dictionary and put it before the <literal>english_stem</literal>
dictionary. For example:
<screen>
SELECT * FROM ts_debug('english', 'Paris');
alias | description | token | dictionaries | dictionary | lexemes
-----------+-----------------+-------+----------------+--------------+---------
asciiword | Word, all ASCII | Paris | {english_stem} | english_stem | {pari}
CREATE TEXT SEARCH DICTIONARY my_synonym (
TEMPLATE = synonym,
SYNONYMS = my_synonyms
);
ALTER TEXT SEARCH CONFIGURATION english
ALTER MAPPING FOR asciiword
WITH my_synonym, english_stem;
SELECT * FROM ts_debug('english', 'Paris');
alias | description | token | dictionaries | dictionary | lexemes
-----------+-----------------+-------+---------------------------+------------+---------
asciiword | Word, all ASCII | Paris | {my_synonym,english_stem} | my_synonym | {paris}
</screen>
</para>
<para>
The only parameter required by the <literal>synonym</literal> template is
<literal>SYNONYMS</literal>, which is the base name of its configuration file
&mdash; <literal>my_synonyms</literal> in the above example.
The file's full name will be
<filename>$SHAREDIR/tsearch_data/my_synonyms.syn</filename>
(where <literal>$SHAREDIR</literal> means the
<productname>PostgreSQL</productname> installation's shared-data directory).
The file format is just one line
per word to be substituted, with the word followed by its synonym,
separated by white space. Blank lines and trailing spaces are ignored.
</para>
<para>
The <literal>synonym</literal> template also has an optional parameter
<literal>CaseSensitive</literal>, which defaults to <literal>false</literal>. When
<literal>CaseSensitive</literal> is <literal>false</literal>, words in the synonym file
are folded to lower case, as are input tokens. When it is
<literal>true</literal>, words and tokens are not folded to lower case,
but are compared as-is.
</para>
<para>
An asterisk (<literal>*</literal>) can be placed at the end of a synonym
in the configuration file. This indicates that the synonym is a prefix.
The asterisk is ignored when the entry is used in
<function>to_tsvector()</function>, but when it is used in
<function>to_tsquery()</function>, the result will be a query item with
the prefix match marker (see
<xref linkend="textsearch-parsing-queries"/>).
For example, suppose we have these entries in
<filename>$SHAREDIR/tsearch_data/synonym_sample.syn</filename>:
<programlisting>
postgres pgsql
postgresql pgsql
postgre pgsql
gogle googl
indices index*
</programlisting>
Then we will get these results:
<screen>
mydb=# CREATE TEXT SEARCH DICTIONARY syn (template=synonym, synonyms='synonym_sample');
mydb=# SELECT ts_lexize('syn', 'indices');
ts_lexize
-----------
{index}
(1 row)
mydb=# CREATE TEXT SEARCH CONFIGURATION tst (copy=simple);
mydb=# ALTER TEXT SEARCH CONFIGURATION tst ALTER MAPPING FOR asciiword WITH syn;
mydb=# SELECT to_tsvector('tst', 'indices');
to_tsvector
-------------
'index':1
(1 row)
mydb=# SELECT to_tsquery('tst', 'indices');
to_tsquery
------------
'index':*
(1 row)
mydb=# SELECT 'indexes are very useful'::tsvector;
tsvector
---------------------------------
'are' 'indexes' 'useful' 'very'
(1 row)
mydb=# SELECT 'indexes are very useful'::tsvector @@ to_tsquery('tst', 'indices');
?column?
----------
t
(1 row)
</screen>
</para>
</sect2>
<sect2 id="textsearch-thesaurus">
<title>Thesaurus Dictionary</title>
<para>
A thesaurus dictionary (sometimes abbreviated as <acronym>TZ</acronym>) is
a collection of words that includes information about the relationships
of words and phrases, i.e., broader terms (<acronym>BT</acronym>), narrower
terms (<acronym>NT</acronym>), preferred terms, non-preferred terms, related
terms, etc.
</para>
<para>
Basically a thesaurus dictionary replaces all non-preferred terms by one
preferred term and, optionally, preserves the original terms for indexing
as well. <productname>PostgreSQL</productname>'s current implementation of the
thesaurus dictionary is an extension of the synonym dictionary with added
<firstterm>phrase</firstterm> support. A thesaurus dictionary requires
a configuration file of the following format:
<programlisting>
# this is a comment
sample word(s) : indexed word(s)
more sample word(s) : more indexed word(s)
...
</programlisting>
where the colon (<symbol>:</symbol>) symbol acts as a delimiter between a
phrase and its replacement.
</para>
<para>
A thesaurus dictionary uses a <firstterm>subdictionary</firstterm> (which
is specified in the dictionary's configuration) to normalize the input
text before checking for phrase matches. It is only possible to select one
subdictionary. An error is reported if the subdictionary fails to
recognize a word. In that case, you should remove the use of the word or
teach the subdictionary about it. You can place an asterisk
(<symbol>*</symbol>) at the beginning of an indexed word to skip applying
the subdictionary to it, but all sample words <emphasis>must</emphasis> be known
to the subdictionary.
</para>
<para>
The thesaurus dictionary chooses the longest match if there are multiple
phrases matching the input, and ties are broken by using the last
definition.
</para>
<para>
Specific stop words recognized by the subdictionary cannot be
specified; instead use <literal>?</literal> to mark the location where any
stop word can appear. For example, assuming that <literal>a</literal> and
<literal>the</literal> are stop words according to the subdictionary:
<programlisting>
? one ? two : swsw
</programlisting>
matches <literal>a one the two</literal> and <literal>the one a two</literal>;
both would be replaced by <literal>swsw</literal>.
</para>
<para>
Since a thesaurus dictionary has the capability to recognize phrases it
must remember its state and interact with the parser. A thesaurus dictionary
uses these assignments to check if it should handle the next word or stop
accumulation. The thesaurus dictionary must be configured
carefully. For example, if the thesaurus dictionary is assigned to handle
only the <literal>asciiword</literal> token, then a thesaurus dictionary
definition like <literal>one 7</literal> will not work since token type
<literal>uint</literal> is not assigned to the thesaurus dictionary.
</para>
<caution>
<para>
Thesauruses are used during indexing so any change in the thesaurus
dictionary's parameters <emphasis>requires</emphasis> reindexing.
For most other dictionary types, small changes such as adding or
removing stopwords does not force reindexing.
</para>
</caution>
<sect3 id="textsearch-thesaurus-config">
<title>Thesaurus Configuration</title>
<para>
To define a new thesaurus dictionary, use the <literal>thesaurus</literal>
template. For example:
<programlisting>
CREATE TEXT SEARCH DICTIONARY thesaurus_simple (
TEMPLATE = thesaurus,
DictFile = mythesaurus,
Dictionary = pg_catalog.english_stem
);
</programlisting>
Here:
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
<literal>thesaurus_simple</literal> is the new dictionary's name
</para>
</listitem>
<listitem>
<para>
<literal>mythesaurus</literal> is the base name of the thesaurus
configuration file.
(Its full name will be <filename>$SHAREDIR/tsearch_data/mythesaurus.ths</filename>,
where <literal>$SHAREDIR</literal> means the installation shared-data
directory.)
</para>
</listitem>
<listitem>
<para>
<literal>pg_catalog.english_stem</literal> is the subdictionary (here,
a Snowball English stemmer) to use for thesaurus normalization.
Notice that the subdictionary will have its own
configuration (for example, stop words), which is not shown here.
</para>
</listitem>
</itemizedlist>
Now it is possible to bind the thesaurus dictionary <literal>thesaurus_simple</literal>
to the desired token types in a configuration, for example:
<programlisting>
ALTER TEXT SEARCH CONFIGURATION russian
ALTER MAPPING FOR asciiword, asciihword, hword_asciipart
WITH thesaurus_simple;
</programlisting>
</para>
</sect3>
<sect3 id="textsearch-thesaurus-examples">
<title>Thesaurus Example</title>
<para>
Consider a simple astronomical thesaurus <literal>thesaurus_astro</literal>,
which contains some astronomical word combinations:
<programlisting>
supernovae stars : sn
crab nebulae : crab
</programlisting>
Below we create a dictionary and bind some token types to
an astronomical thesaurus and English stemmer:
<programlisting>
CREATE TEXT SEARCH DICTIONARY thesaurus_astro (
TEMPLATE = thesaurus,
DictFile = thesaurus_astro,
Dictionary = english_stem
);
ALTER TEXT SEARCH CONFIGURATION russian
ALTER MAPPING FOR asciiword, asciihword, hword_asciipart
WITH thesaurus_astro, english_stem;
</programlisting>
Now we can see how it works.
<function>ts_lexize</function> is not very useful for testing a thesaurus,
because it treats its input as a single token. Instead we can use
<function>plainto_tsquery</function> and <function>to_tsvector</function>
which will break their input strings into multiple tokens:
<screen>
SELECT plainto_tsquery('supernova star');
plainto_tsquery
-----------------
'sn'
SELECT to_tsvector('supernova star');
to_tsvector
-------------
'sn':1
</screen>
In principle, one can use <function>to_tsquery</function> if you quote
the argument:
<screen>
SELECT to_tsquery('''supernova star''');
to_tsquery
------------
'sn'
</screen>
Notice that <literal>supernova star</literal> matches <literal>supernovae
stars</literal> in <literal>thesaurus_astro</literal> because we specified
the <literal>english_stem</literal> stemmer in the thesaurus definition.
The stemmer removed the <literal>e</literal> and <literal>s</literal>.
</para>
<para>
To index the original phrase as well as the substitute, just include it
in the right-hand part of the definition:
<screen>
supernovae stars : sn supernovae stars
SELECT plainto_tsquery('supernova star');
plainto_tsquery
-----------------------------
'sn' &amp; 'supernova' &amp; 'star'
</screen>
</para>
</sect3>
</sect2>
<sect2 id="textsearch-ispell-dictionary">
<title><application>Ispell</application> Dictionary</title>
<para>
The <application>Ispell</application> dictionary template supports
<firstterm>morphological dictionaries</firstterm>, which can normalize many
different linguistic forms of a word into the same lexeme. For example,
an English <application>Ispell</application> dictionary can match all declensions and
conjugations of the search term <literal>bank</literal>, e.g.,
<literal>banking</literal>, <literal>banked</literal>, <literal>banks</literal>,
<literal>banks'</literal>, and <literal>bank's</literal>.
</para>
<para>
The standard <productname>PostgreSQL</productname> distribution does
not include any <application>Ispell</application> configuration files.
Dictionaries for a large number of languages are available from <ulink
url="https://www.cs.hmc.edu/~geoff/ispell.html">Ispell</ulink>.
Also, some more modern dictionary file formats are supported &mdash; <ulink
url="https://en.wikipedia.org/wiki/MySpell">MySpell</ulink> (OO &lt; 2.0.1)
and <ulink url="https://sourceforge.net/projects/hunspell/">Hunspell</ulink>
(OO &gt;= 2.0.2). A large list of dictionaries is available on the <ulink
url="https://wiki.openoffice.org/wiki/Dictionaries">OpenOffice
Wiki</ulink>.
</para>
<para>
To create an <application>Ispell</application> dictionary perform these steps:
</para>
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
download dictionary configuration files. <productname>OpenOffice</productname>
extension files have the <filename>.oxt</filename> extension. It is necessary
to extract <filename>.aff</filename> and <filename>.dic</filename> files, change
extensions to <filename>.affix</filename> and <filename>.dict</filename>. For some
dictionary files it is also needed to convert characters to the UTF-8
encoding with commands (for example, for a Norwegian language dictionary):
<programlisting>
iconv -f ISO_8859-1 -t UTF-8 -o nn_no.affix nn_NO.aff
iconv -f ISO_8859-1 -t UTF-8 -o nn_no.dict nn_NO.dic
</programlisting>
</para>
</listitem>
<listitem>
<para>
copy files to the <filename>$SHAREDIR/tsearch_data</filename> directory
</para>
</listitem>
<listitem>
<para>
load files into PostgreSQL with the following command:
<programlisting>
CREATE TEXT SEARCH DICTIONARY english_hunspell (
TEMPLATE = ispell,
DictFile = en_us,
AffFile = en_us,
Stopwords = english);
</programlisting>
</para>
</listitem>
</itemizedlist>
<para>
Here, <literal>DictFile</literal>, <literal>AffFile</literal>, and <literal>StopWords</literal>
specify the base names of the dictionary, affixes, and stop-words files.
The stop-words file has the same format explained above for the
<literal>simple</literal> dictionary type. The format of the other files is
not specified here but is available from the above-mentioned web sites.
</para>
<para>
Ispell dictionaries usually recognize a limited set of words, so they
should be followed by another broader dictionary; for
example, a Snowball dictionary, which recognizes everything.
</para>
<para>
The <filename>.affix</filename> file of <application>Ispell</application> has the following
structure:
<programlisting>
prefixes
flag *A:
. > RE # As in enter > reenter
suffixes
flag T:
E > ST # As in late > latest
[^AEIOU]Y > -Y,IEST # As in dirty > dirtiest
[AEIOU]Y > EST # As in gray > grayest
[^EY] > EST # As in small > smallest
</programlisting>
</para>
<para>
And the <filename>.dict</filename> file has the following structure:
<programlisting>
lapse/ADGRS
lard/DGRS
large/PRTY
lark/MRS
</programlisting>
</para>
<para>
Format of the <filename>.dict</filename> file is:
<programlisting>
basic_form/affix_class_name
</programlisting>
</para>
<para>
In the <filename>.affix</filename> file every affix flag is described in the
following format:
<programlisting>
condition > [-stripping_letters,] adding_affix
</programlisting>
</para>
<para>
Here, condition has a format similar to the format of regular expressions.
It can use groupings <literal>[...]</literal> and <literal>[^...]</literal>.
For example, <literal>[AEIOU]Y</literal> means that the last letter of the word
is <literal>"y"</literal> and the penultimate letter is <literal>"a"</literal>,
<literal>"e"</literal>, <literal>"i"</literal>, <literal>"o"</literal> or <literal>"u"</literal>.
<literal>[^EY]</literal> means that the last letter is neither <literal>"e"</literal>
nor <literal>"y"</literal>.
</para>
<para>
Ispell dictionaries support splitting compound words;
a useful feature.
Notice that the affix file should specify a special flag using the
<literal>compoundwords controlled</literal> statement that marks dictionary
words that can participate in compound formation:
<programlisting>
compoundwords controlled z
</programlisting>
Here are some examples for the Norwegian language:
<programlisting>
SELECT ts_lexize('norwegian_ispell', 'overbuljongterningpakkmesterassistent');
{over,buljong,terning,pakk,mester,assistent}
SELECT ts_lexize('norwegian_ispell', 'sjokoladefabrikk');
{sjokoladefabrikk,sjokolade,fabrikk}
</programlisting>
</para>
<para>
<application>MySpell</application> format is a subset of <application>Hunspell</application>.
The <filename>.affix</filename> file of <application>Hunspell</application> has the following
structure:
<programlisting>
PFX A Y 1
PFX A 0 re .
SFX T N 4
SFX T 0 st e
SFX T y iest [^aeiou]y
SFX T 0 est [aeiou]y
SFX T 0 est [^ey]
</programlisting>
</para>
<para>
The first line of an affix class is the header. Fields of an affix rules are
listed after the header:
</para>
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
parameter name (PFX or SFX)
</para>
</listitem>
<listitem>
<para>
flag (name of the affix class)
</para>
</listitem>
<listitem>
<para>
stripping characters from beginning (at prefix) or end (at suffix) of the
word
</para>
</listitem>
<listitem>
<para>
adding affix
</para>
</listitem>
<listitem>
<para>
condition that has a format similar to the format of regular expressions.
</para>
</listitem>
</itemizedlist>
<para>
The <filename>.dict</filename> file looks like the <filename>.dict</filename> file of
<application>Ispell</application>:
<programlisting>
larder/M
lardy/RT
large/RSPMYT
largehearted
</programlisting>
</para>
<note>
<para>
<application>MySpell</application> does not support compound words.
<application>Hunspell</application> has sophisticated support for compound words. At
present, <productname>PostgreSQL</productname> implements only the basic
compound word operations of Hunspell.
</para>
</note>
</sect2>
<sect2 id="textsearch-snowball-dictionary">
<title><application>Snowball</application> Dictionary</title>
<para>
The <application>Snowball</application> dictionary template is based on a project
by Martin Porter, inventor of the popular Porter's stemming algorithm
for the English language. Snowball now provides stemming algorithms for
many languages (see the <ulink url="https://snowballstem.org/">Snowball
site</ulink> for more information). Each algorithm understands how to
reduce common variant forms of words to a base, or stem, spelling within
its language. A Snowball dictionary requires a <literal>language</literal>
parameter to identify which stemmer to use, and optionally can specify a
<literal>stopword</literal> file name that gives a list of words to eliminate.
(<productname>PostgreSQL</productname>'s standard stopword lists are also
provided by the Snowball project.)
For example, there is a built-in definition equivalent to
<programlisting>
CREATE TEXT SEARCH DICTIONARY english_stem (
TEMPLATE = snowball,
Language = english,
StopWords = english
);
</programlisting>
The stopword file format is the same as already explained.
</para>
<para>
A <application>Snowball</application> dictionary recognizes everything, whether
or not it is able to simplify the word, so it should be placed
at the end of the dictionary list. It is useless to have it
before any other dictionary because a token will never pass through it to
the next dictionary.
</para>
</sect2>
</sect1>
<sect1 id="textsearch-configuration">
<title>Configuration Example</title>
<para>
A text search configuration specifies all options necessary to transform a
document into a <type>tsvector</type>: the parser to use to break text
into tokens, and the dictionaries to use to transform each token into a
lexeme. Every call of
<function>to_tsvector</function> or <function>to_tsquery</function>
needs a text search configuration to perform its processing.
The configuration parameter
<xref linkend="guc-default-text-search-config"/>
specifies the name of the default configuration, which is the
one used by text search functions if an explicit configuration
parameter is omitted.
It can be set in <filename>postgresql.conf</filename>, or set for an
individual session using the <command>SET</command> command.
</para>
<para>
Several predefined text search configurations are available, and
you can create custom configurations easily. To facilitate management
of text search objects, a set of <acronym>SQL</acronym> commands
is available, and there are several <application>psql</application> commands that display information
about text search objects (<xref linkend="textsearch-psql"/>).
</para>
<para>
As an example we will create a configuration
<literal>pg</literal>, starting by duplicating the built-in
<literal>english</literal> configuration:
<programlisting>
CREATE TEXT SEARCH CONFIGURATION public.pg ( COPY = pg_catalog.english );
</programlisting>
</para>
<para>
We will use a PostgreSQL-specific synonym list
and store it in <filename>$SHAREDIR/tsearch_data/pg_dict.syn</filename>.
The file contents look like:
<programlisting>
postgres pg
pgsql pg
postgresql pg
</programlisting>
We define the synonym dictionary like this:
<programlisting>
CREATE TEXT SEARCH DICTIONARY pg_dict (
TEMPLATE = synonym,
SYNONYMS = pg_dict
);
</programlisting>
Next we register the <productname>Ispell</productname> dictionary
<literal>english_ispell</literal>, which has its own configuration files:
<programlisting>
CREATE TEXT SEARCH DICTIONARY english_ispell (
TEMPLATE = ispell,
DictFile = english,
AffFile = english,
StopWords = english
);
</programlisting>
Now we can set up the mappings for words in configuration
<literal>pg</literal>:
<programlisting>
ALTER TEXT SEARCH CONFIGURATION pg
ALTER MAPPING FOR asciiword, asciihword, hword_asciipart,
word, hword, hword_part
WITH pg_dict, english_ispell, english_stem;
</programlisting>
We choose not to index or search some token types that the built-in
configuration does handle:
<programlisting>
ALTER TEXT SEARCH CONFIGURATION pg
DROP MAPPING FOR email, url, url_path, sfloat, float;
</programlisting>
</para>
<para>
Now we can test our configuration:
<programlisting>
SELECT * FROM ts_debug('public.pg', '
PostgreSQL, the highly scalable, SQL compliant, open source object-relational
database management system, is now undergoing beta testing of the next
version of our software.
');
</programlisting>
</para>
<para>
The next step is to set the session to use the new configuration, which was
created in the <literal>public</literal> schema:
<screen>
=&gt; \dF
List of text search configurations
Schema | Name | Description
---------+------+-------------
public | pg |
SET default_text_search_config = 'public.pg';
SET
SHOW default_text_search_config;
default_text_search_config
----------------------------
public.pg
</screen>
</para>
</sect1>
<sect1 id="textsearch-debugging">
<title>Testing and Debugging Text Search</title>
<para>
The behavior of a custom text search configuration can easily become
confusing. The functions described
in this section are useful for testing text search objects. You can
test a complete configuration, or test parsers and dictionaries separately.
</para>
<sect2 id="textsearch-configuration-testing">
<title>Configuration Testing</title>
<para>
The function <function>ts_debug</function> allows easy testing of a
text search configuration.
</para>
<indexterm>
<primary>ts_debug</primary>
</indexterm>
<synopsis>
ts_debug(<optional> <replaceable class="parameter">config</replaceable> <type>regconfig</type>, </optional> <replaceable class="parameter">document</replaceable> <type>text</type>,
OUT <replaceable class="parameter">alias</replaceable> <type>text</type>,
OUT <replaceable class="parameter">description</replaceable> <type>text</type>,
OUT <replaceable class="parameter">token</replaceable> <type>text</type>,
OUT <replaceable class="parameter">dictionaries</replaceable> <type>regdictionary[]</type>,
OUT <replaceable class="parameter">dictionary</replaceable> <type>regdictionary</type>,
OUT <replaceable class="parameter">lexemes</replaceable> <type>text[]</type>)
returns setof record
</synopsis>
<para>
<function>ts_debug</function> displays information about every token of
<replaceable class="parameter">document</replaceable> as produced by the
parser and processed by the configured dictionaries. It uses the
configuration specified by <replaceable
class="parameter">config</replaceable>,
or <varname>default_text_search_config</varname> if that argument is
omitted.
</para>
<para>
<function>ts_debug</function> returns one row for each token identified in the text
by the parser. The columns returned are
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>
<replaceable>alias</replaceable> <type>text</type> &mdash; short name of the token type
</para>
</listitem>
<listitem>
<para>
<replaceable>description</replaceable> <type>text</type> &mdash; description of the
token type
</para>
</listitem>
<listitem>
<para>
<replaceable>token</replaceable> <type>text</type> &mdash; text of the token
</para>
</listitem>
<listitem>
<para>
<replaceable>dictionaries</replaceable> <type>regdictionary[]</type> &mdash; the
dictionaries selected by the configuration for this token type
</para>
</listitem>
<listitem>
<para>
<replaceable>dictionary</replaceable> <type>regdictionary</type> &mdash; the dictionary
that recognized the token, or <literal>NULL</literal> if none did
</para>
</listitem>
<listitem>
<para>
<replaceable>lexemes</replaceable> <type>text[]</type> &mdash; the lexeme(s) produced
by the dictionary that recognized the token, or <literal>NULL</literal> if
none did; an empty array (<literal>{}</literal>) means it was recognized as a
stop word
</para>
</listitem>
</itemizedlist>
</para>
<para>
Here is a simple example:
<screen>
SELECT * FROM ts_debug('english', 'a fat cat sat on a mat - it ate a fat rats');
alias | description | token | dictionaries | dictionary | lexemes
-----------+-----------------+-------+----------------+--------------+---------
asciiword | Word, all ASCII | a | {english_stem} | english_stem | {}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | fat | {english_stem} | english_stem | {fat}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | cat | {english_stem} | english_stem | {cat}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | sat | {english_stem} | english_stem | {sat}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | on | {english_stem} | english_stem | {}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | a | {english_stem} | english_stem | {}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | mat | {english_stem} | english_stem | {mat}
blank | Space symbols | | {} | |
blank | Space symbols | - | {} | |
asciiword | Word, all ASCII | it | {english_stem} | english_stem | {}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | ate | {english_stem} | english_stem | {ate}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | a | {english_stem} | english_stem | {}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | fat | {english_stem} | english_stem | {fat}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | rats | {english_stem} | english_stem | {rat}
</screen>
</para>
<para>
For a more extensive demonstration, we
first create a <literal>public.english</literal> configuration and
Ispell dictionary for the English language:
</para>
<programlisting>
CREATE TEXT SEARCH CONFIGURATION public.english ( COPY = pg_catalog.english );
CREATE TEXT SEARCH DICTIONARY english_ispell (
TEMPLATE = ispell,
DictFile = english,
AffFile = english,
StopWords = english
);
ALTER TEXT SEARCH CONFIGURATION public.english
ALTER MAPPING FOR asciiword WITH english_ispell, english_stem;
</programlisting>
<screen>
SELECT * FROM ts_debug('public.english', 'The Brightest supernovaes');
alias | description | token | dictionaries | dictionary | lexemes
-----------+-----------------+-------------+-------------------------------+----------------+-------------
asciiword | Word, all ASCII | The | {english_ispell,english_stem} | english_ispell | {}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | Brightest | {english_ispell,english_stem} | english_ispell | {bright}
blank | Space symbols | | {} | |
asciiword | Word, all ASCII | supernovaes | {english_ispell,english_stem} | english_stem | {supernova}
</screen>
<para>
In this example, the word <literal>Brightest</literal> was recognized by the
parser as an <literal>ASCII word</literal> (alias <literal>asciiword</literal>).
For this token type the dictionary list is
<literal>english_ispell</literal> and
<literal>english_stem</literal>. The word was recognized by
<literal>english_ispell</literal>, which reduced it to the noun
<literal>bright</literal>. The word <literal>supernovaes</literal> is
unknown to the <literal>english_ispell</literal> dictionary so it
was passed to the next dictionary, and, fortunately, was recognized (in
fact, <literal>english_stem</literal> is a Snowball dictionary which
recognizes everything; that is why it was placed at the end of the
dictionary list).
</para>
<para>
The word <literal>The</literal> was recognized by the
<literal>english_ispell</literal> dictionary as a stop word (<xref
linkend="textsearch-stopwords"/>) and will not be indexed.
The spaces are discarded too, since the configuration provides no
dictionaries at all for them.
</para>
<para>
You can reduce the width of the output by explicitly specifying which columns
you want to see:
<screen>
SELECT alias, token, dictionary, lexemes
FROM ts_debug('public.english', 'The Brightest supernovaes');
alias | token | dictionary | lexemes
-----------+-------------+----------------+-------------
asciiword | The | english_ispell | {}
blank | | |
asciiword | Brightest | english_ispell | {bright}
blank | | |
asciiword | supernovaes | english_stem | {supernova}
</screen>
</para>
</sect2>
<sect2 id="textsearch-parser-testing">
<title>Parser Testing</title>
<para>
The following functions allow direct testing of a text search parser.
</para>
<indexterm>
<primary>ts_parse</primary>
</indexterm>
<synopsis>
ts_parse(<replaceable class="parameter">parser_name</replaceable> <type>text</type>, <replaceable class="parameter">document</replaceable> <type>text</type>,
OUT <replaceable class="parameter">tokid</replaceable> <type>integer</type>, OUT <replaceable class="parameter">token</replaceable> <type>text</type>) returns <type>setof record</type>
ts_parse(<replaceable class="parameter">parser_oid</replaceable> <type>oid</type>, <replaceable class="parameter">document</replaceable> <type>text</type>,
OUT <replaceable class="parameter">tokid</replaceable> <type>integer</type>, OUT <replaceable class="parameter">token</replaceable> <type>text</type>) returns <type>setof record</type>
</synopsis>
<para>
<function>ts_parse</function> parses the given <replaceable>document</replaceable>
and returns a series of records, one for each token produced by
parsing. Each record includes a <varname>tokid</varname> showing the
assigned token type and a <varname>token</varname> which is the text of the
token. For example:
<screen>
SELECT * FROM ts_parse('default', '123 - a number');
tokid | token
-------+--------
22 | 123
12 |
12 | -
1 | a
12 |
1 | number
</screen>
</para>
<indexterm>
<primary>ts_token_type</primary>
</indexterm>
<synopsis>
ts_token_type(<replaceable class="parameter">parser_name</replaceable> <type>text</type>, OUT <replaceable class="parameter">tokid</replaceable> <type>integer</type>,
OUT <replaceable class="parameter">alias</replaceable> <type>text</type>, OUT <replaceable class="parameter">description</replaceable> <type>text</type>) returns <type>setof record</type>
ts_token_type(<replaceable class="parameter">parser_oid</replaceable> <type>oid</type>, OUT <replaceable class="parameter">tokid</replaceable> <type>integer</type>,
OUT <replaceable class="parameter">alias</replaceable> <type>text</type>, OUT <replaceable class="parameter">description</replaceable> <type>text</type>) returns <type>setof record</type>
</synopsis>
<para>
<function>ts_token_type</function> returns a table which describes each type of
token the specified parser can recognize. For each token type, the table
gives the integer <varname>tokid</varname> that the parser uses to label a
token of that type, the <varname>alias</varname> that names the token type
in configuration commands, and a short <varname>description</varname>. For
example:
<screen>
SELECT * FROM ts_token_type('default');
tokid | alias | description
-------+-----------------+------------------------------------------
1 | asciiword | Word, all ASCII
2 | word | Word, all letters
3 | numword | Word, letters and digits
4 | email | Email address
5 | url | URL
6 | host | Host
7 | sfloat | Scientific notation
8 | version | Version number
9 | hword_numpart | Hyphenated word part, letters and digits
10 | hword_part | Hyphenated word part, all letters
11 | hword_asciipart | Hyphenated word part, all ASCII
12 | blank | Space symbols
13 | tag | XML tag
14 | protocol | Protocol head
15 | numhword | Hyphenated word, letters and digits
16 | asciihword | Hyphenated word, all ASCII
17 | hword | Hyphenated word, all letters
18 | url_path | URL path
19 | file | File or path name
20 | float | Decimal notation
21 | int | Signed integer
22 | uint | Unsigned integer
23 | entity | XML entity
</screen>
</para>
</sect2>
<sect2 id="textsearch-dictionary-testing">
<title>Dictionary Testing</title>
<para>
The <function>ts_lexize</function> function facilitates dictionary testing.
</para>
<indexterm>
<primary>ts_lexize</primary>
</indexterm>
<synopsis>
ts_lexize(<replaceable class="parameter">dict</replaceable> <type>regdictionary</type>, <replaceable class="parameter">token</replaceable> <type>text</type>) returns <type>text[]</type>
</synopsis>
<para>
<function>ts_lexize</function> returns an array of lexemes if the input
<replaceable>token</replaceable> is known to the dictionary,
or an empty array if the token
is known to the dictionary but it is a stop word, or
<literal>NULL</literal> if it is an unknown word.
</para>
<para>
Examples:
<screen>
SELECT ts_lexize('english_stem', 'stars');
ts_lexize
-----------
{star}
SELECT ts_lexize('english_stem', 'a');
ts_lexize
-----------
{}
</screen>
</para>
<note>
<para>
The <function>ts_lexize</function> function expects a single
<emphasis>token</emphasis>, not text. Here is a case
where this can be confusing:
<screen>
SELECT ts_lexize('thesaurus_astro', 'supernovae stars') is null;
?column?
----------
t
</screen>
The thesaurus dictionary <literal>thesaurus_astro</literal> does know the
phrase <literal>supernovae stars</literal>, but <function>ts_lexize</function>
fails since it does not parse the input text but treats it as a single
token. Use <function>plainto_tsquery</function> or <function>to_tsvector</function> to
test thesaurus dictionaries, for example:
<screen>
SELECT plainto_tsquery('supernovae stars');
plainto_tsquery
-----------------
'sn'
</screen>
</para>
</note>
</sect2>
</sect1>
<sect1 id="textsearch-indexes">
<title>Preferred Index Types for Text Search</title>
<indexterm zone="textsearch-indexes">
<primary>text search</primary>
<secondary>indexes</secondary>
</indexterm>
<para>
There are two kinds of indexes that can be used to speed up full text
searches:
<link linkend="gin"><acronym>GIN</acronym></link> and
<link linkend="gist"><acronym>GiST</acronym></link>.
Note that indexes are not mandatory for full text searching, but in
cases where a column is searched on a regular basis, an index is
usually desirable.
</para>
<para>
To create such an index, do one of:
<variablelist>
<varlistentry>
<term>
<indexterm zone="textsearch-indexes">
<primary>index</primary>
<secondary>GIN</secondary>
<tertiary>text search</tertiary>
</indexterm>
<literal>CREATE INDEX <replaceable>name</replaceable> ON <replaceable>table</replaceable> USING GIN (<replaceable>column</replaceable>);</literal>
</term>
<listitem>
<para>
Creates a GIN (Generalized Inverted Index)-based index.
The <replaceable>column</replaceable> must be of <type>tsvector</type> type.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<indexterm zone="textsearch-indexes">
<primary>index</primary>
<secondary>GiST</secondary>
<tertiary>text search</tertiary>
</indexterm>
<literal>CREATE INDEX <replaceable>name</replaceable> ON <replaceable>table</replaceable> USING GIST (<replaceable>column</replaceable> [ { DEFAULT | tsvector_ops } (siglen = <replaceable>number</replaceable>) ] );</literal>
</term>
<listitem>
<para>
Creates a GiST (Generalized Search Tree)-based index.
The <replaceable>column</replaceable> can be of <type>tsvector</type> or
<type>tsquery</type> type.
Optional integer parameter <literal>siglen</literal> determines
signature length in bytes (see below for details).
</para>
</listitem>
</varlistentry>
</variablelist>
</para>
<para>
GIN indexes are the preferred text search index type. As inverted
indexes, they contain an index entry for each word (lexeme), with a
compressed list of matching locations. Multi-word searches can find
the first match, then use the index to remove rows that are lacking
additional words. GIN indexes store only the words (lexemes) of
<type>tsvector</type> values, and not their weight labels. Thus a table
row recheck is needed when using a query that involves weights.
</para>
<para>
A GiST index is <firstterm>lossy</firstterm>, meaning that the index
might produce false matches, and it is necessary
to check the actual table row to eliminate such false matches.
(<productname>PostgreSQL</productname> does this automatically when needed.)
GiST indexes are lossy because each document is represented in the
index by a fixed-length signature. The signature length in bytes is determined
by the value of the optional integer parameter <literal>siglen</literal>.
The default signature length (when <literal>siglen</literal> is not specified) is
124 bytes, the maximum signature length is 2024 bytes. The signature is generated by hashing
each word into a single bit in an n-bit string, with all these bits OR-ed
together to produce an n-bit document signature. When two words hash to
the same bit position there will be a false match. If all words in
the query have matches (real or false) then the table row must be
retrieved to see if the match is correct. Longer signatures lead to a more
precise search (scanning a smaller fraction of the index and fewer heap
pages), at the cost of a larger index.
</para>
<para>
A GiST index can be covering, i.e., use the <literal>INCLUDE</literal>
clause. Included columns can have data types without any GiST operator
class. Included attributes will be stored uncompressed.
</para>
<para>
Lossiness causes performance degradation due to unnecessary fetches of table
records that turn out to be false matches. Since random access to table
records is slow, this limits the usefulness of GiST indexes. The
likelihood of false matches depends on several factors, in particular the
number of unique words, so using dictionaries to reduce this number is
recommended.
</para>
<para>
Note that <acronym>GIN</acronym> index build time can often be improved
by increasing <xref linkend="guc-maintenance-work-mem"/>, while
<acronym>GiST</acronym> index build time is not sensitive to that
parameter.
</para>
<para>
Partitioning of big collections and the proper use of GIN and GiST indexes
allows the implementation of very fast searches with online update.
Partitioning can be done at the database level using table inheritance,
or by distributing documents over
servers and collecting external search results, e.g., via <link
linkend="ddl-foreign-data">Foreign Data</link> access.
The latter is possible because ranking functions use
only local information.
</para>
</sect1>
<sect1 id="textsearch-psql">
<title><application>psql</application> Support</title>
<para>
Information about text search configuration objects can be obtained
in <application>psql</application> using a set of commands:
<synopsis>
\dF{d,p,t}<optional>+</optional> <optional>PATTERN</optional>
</synopsis>
An optional <literal>+</literal> produces more details.
</para>
<para>
The optional parameter <replaceable>PATTERN</replaceable> can be the name of
a text search object, optionally schema-qualified. If
<replaceable>PATTERN</replaceable> is omitted then information about all
visible objects will be displayed. <replaceable>PATTERN</replaceable> can be a
regular expression and can provide <emphasis>separate</emphasis> patterns
for the schema and object names. The following examples illustrate this:
<screen>
=&gt; \dF *fulltext*
List of text search configurations
Schema | Name | Description
--------+--------------+-------------
public | fulltext_cfg |
</screen>
<screen>
=&gt; \dF *.fulltext*
List of text search configurations
Schema | Name | Description
----------+----------------------------
fulltext | fulltext_cfg |
public | fulltext_cfg |
</screen>
The available commands are:
</para>
<variablelist>
<varlistentry>
<term><literal>\dF<optional>+</optional> <optional>PATTERN</optional></literal></term>
<listitem>
<para>
List text search configurations (add <literal>+</literal> for more detail).
<screen>
=&gt; \dF russian
List of text search configurations
Schema | Name | Description
------------+---------+------------------------------------
pg_catalog | russian | configuration for russian language
=&gt; \dF+ russian
Text search configuration "pg_catalog.russian"
Parser: "pg_catalog.default"
Token | Dictionaries
-----------------+--------------
asciihword | english_stem
asciiword | english_stem
email | simple
file | simple
float | simple
host | simple
hword | russian_stem
hword_asciipart | english_stem
hword_numpart | simple
hword_part | russian_stem
int | simple
numhword | simple
numword | simple
sfloat | simple
uint | simple
url | simple
url_path | simple
version | simple
word | russian_stem
</screen>
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><literal>\dFd<optional>+</optional> <optional>PATTERN</optional></literal></term>
<listitem>
<para>
List text search dictionaries (add <literal>+</literal> for more detail).
<screen>
=&gt; \dFd
List of text search dictionaries
Schema | Name | Description
------------+-----------------+-----------------------------------------------------------
pg_catalog | arabic_stem | snowball stemmer for arabic language
pg_catalog | armenian_stem | snowball stemmer for armenian language
pg_catalog | basque_stem | snowball stemmer for basque language
pg_catalog | catalan_stem | snowball stemmer for catalan language
pg_catalog | danish_stem | snowball stemmer for danish language
pg_catalog | dutch_stem | snowball stemmer for dutch language
pg_catalog | english_stem | snowball stemmer for english language
pg_catalog | finnish_stem | snowball stemmer for finnish language
pg_catalog | french_stem | snowball stemmer for french language
pg_catalog | german_stem | snowball stemmer for german language
pg_catalog | greek_stem | snowball stemmer for greek language
pg_catalog | hindi_stem | snowball stemmer for hindi language
pg_catalog | hungarian_stem | snowball stemmer for hungarian language
pg_catalog | indonesian_stem | snowball stemmer for indonesian language
pg_catalog | irish_stem | snowball stemmer for irish language
pg_catalog | italian_stem | snowball stemmer for italian language
pg_catalog | lithuanian_stem | snowball stemmer for lithuanian language
pg_catalog | nepali_stem | snowball stemmer for nepali language
pg_catalog | norwegian_stem | snowball stemmer for norwegian language
pg_catalog | portuguese_stem | snowball stemmer for portuguese language
pg_catalog | romanian_stem | snowball stemmer for romanian language
pg_catalog | russian_stem | snowball stemmer for russian language
pg_catalog | serbian_stem | snowball stemmer for serbian language
pg_catalog | simple | simple dictionary: just lower case and check for stopword
pg_catalog | spanish_stem | snowball stemmer for spanish language
pg_catalog | swedish_stem | snowball stemmer for swedish language
pg_catalog | tamil_stem | snowball stemmer for tamil language
pg_catalog | turkish_stem | snowball stemmer for turkish language
pg_catalog | yiddish_stem | snowball stemmer for yiddish language
</screen>
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><literal>\dFp<optional>+</optional> <optional>PATTERN</optional></literal></term>
<listitem>
<para>
List text search parsers (add <literal>+</literal> for more detail).
<screen>
=&gt; \dFp
List of text search parsers
Schema | Name | Description
------------+---------+---------------------
pg_catalog | default | default word parser
=&gt; \dFp+
Text search parser "pg_catalog.default"
Method | Function | Description
-----------------+----------------+-------------
Start parse | prsd_start |
Get next token | prsd_nexttoken |
End parse | prsd_end |
Get headline | prsd_headline |
Get token types | prsd_lextype |
Token types for parser "pg_catalog.default"
Token name | Description
-----------------+------------------------------------------
asciihword | Hyphenated word, all ASCII
asciiword | Word, all ASCII
blank | Space symbols
email | Email address
entity | XML entity
file | File or path name
float | Decimal notation
host | Host
hword | Hyphenated word, all letters
hword_asciipart | Hyphenated word part, all ASCII
hword_numpart | Hyphenated word part, letters and digits
hword_part | Hyphenated word part, all letters
int | Signed integer
numhword | Hyphenated word, letters and digits
numword | Word, letters and digits
protocol | Protocol head
sfloat | Scientific notation
tag | XML tag
uint | Unsigned integer
url | URL
url_path | URL path
version | Version number
word | Word, all letters
(23 rows)
</screen>
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><literal>\dFt<optional>+</optional> <optional>PATTERN</optional></literal></term>
<listitem>
<para>
List text search templates (add <literal>+</literal> for more detail).
<screen>
=&gt; \dFt
List of text search templates
Schema | Name | Description
------------+-----------+-----------------------------------------------------------
pg_catalog | ispell | ispell dictionary
pg_catalog | simple | simple dictionary: just lower case and check for stopword
pg_catalog | snowball | snowball stemmer
pg_catalog | synonym | synonym dictionary: replace word by its synonym
pg_catalog | thesaurus | thesaurus dictionary: phrase by phrase substitution
</screen>
</para>
</listitem>
</varlistentry>
</variablelist>
</sect1>
<sect1 id="textsearch-limitations">
<title>Limitations</title>
<para>
The current limitations of <productname>PostgreSQL</productname>'s
text search features are:
<itemizedlist spacing="compact" mark="bullet">
<listitem>
<para>The length of each lexeme must be less than 2 kilobytes</para>
</listitem>
<listitem>
<para>The length of a <type>tsvector</type> (lexemes + positions) must be
less than 1 megabyte</para>
</listitem>
<listitem>
<!-- TODO: number of lexemes in what? This is unclear -->
<para>The number of lexemes must be less than
2<superscript>64</superscript></para>
</listitem>
<listitem>
<para>Position values in <type>tsvector</type> must be greater than 0 and
no more than 16,383</para>
</listitem>
<listitem>
<para>The match distance in a <literal>&lt;<replaceable>N</replaceable>&gt;</literal>
(FOLLOWED BY) <type>tsquery</type> operator cannot be more than
16,384</para>
</listitem>
<listitem>
<para>No more than 256 positions per lexeme</para>
</listitem>
<listitem>
<para>The number of nodes (lexemes + operators) in a <type>tsquery</type>
must be less than 32,768</para>
</listitem>
</itemizedlist>
</para>
<para>
For comparison, the <productname>PostgreSQL</productname> 8.1 documentation
contained 10,441 unique words, a total of 335,420 words, and the most
frequent word <quote>postgresql</quote> was mentioned 6,127 times in 655
documents.
</para>
<!-- TODO we need to put a date on these numbers? -->
<para>
Another example &mdash; the <productname>PostgreSQL</productname> mailing
list archives contained 910,989 unique words with 57,491,343 lexemes in
461,020 messages.
</para>
</sect1>
</chapter>