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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>12.1. Introduction</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><link rel="prev" href="textsearch.html" title="Chapter 12. Full Text Search" /><link rel="next" href="textsearch-tables.html" title="12.2. Tables and Indexes" /></head><body id="docContent" class="container-fluid col-10"><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">12.1. Introduction</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="textsearch.html" title="Chapter 12. Full Text Search">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="textsearch.html" title="Chapter 12. Full Text Search">Up</a></td><th width="60%" align="center">Chapter 12. Full Text Search</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 15.5 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="textsearch-tables.html" title="12.2. Tables and Indexes">Next</a></td></tr></table><hr /></div><div class="sect1" id="TEXTSEARCH-INTRO"><div class="titlepage"><div><div><h2 class="title" style="clear: both">12.1. Introduction</h2></div></div></div><div class="toc"><dl class="toc"><dt><span class="sect2"><a href="textsearch-intro.html#TEXTSEARCH-DOCUMENT">12.1.1. What Is a Document?</a></span></dt><dt><span class="sect2"><a href="textsearch-intro.html#TEXTSEARCH-MATCHING">12.1.2. Basic Text Matching</a></span></dt><dt><span class="sect2"><a href="textsearch-intro.html#TEXTSEARCH-INTRO-CONFIGURATIONS">12.1.3. Configurations</a></span></dt></dl></div><p>
   Full Text Searching (or just <em class="firstterm">text search</em>) provides
   the capability to identify natural-language <em class="firstterm">documents</em> that
   satisfy a <em class="firstterm">query</em>, and optionally to sort them by
   relevance to the query.  The most common type of search
   is to find all documents containing given <em class="firstterm">query terms</em>
   and return them in order of their <em class="firstterm">similarity</em> to the
   query.  Notions of <code class="varname">query</code> and
   <code class="varname">similarity</code> are very flexible and depend on the specific
   application. The simplest search considers <code class="varname">query</code> as a
   set of words and <code class="varname">similarity</code> as the frequency of query
   words in the document.
  </p><p>
   Textual search operators have existed in databases for years.
   <span class="productname">PostgreSQL</span> has
   <code class="literal">~</code>, <code class="literal">~*</code>, <code class="literal">LIKE</code>, and
   <code class="literal">ILIKE</code> operators for textual data types, but they lack
   many essential properties required by modern information systems:
  </p><div class="itemizedlist"><ul class="itemizedlist compact" style="list-style-type: bullet; "><li class="listitem" style="list-style-type: disc"><p>
     There is no linguistic support, even for English.  Regular expressions
     are not sufficient because they cannot easily handle derived words, e.g.,
     <code class="literal">satisfies</code> and <code class="literal">satisfy</code>. You might
     miss documents that contain <code class="literal">satisfies</code>, although you
     probably would like to find them when searching for
     <code class="literal">satisfy</code>. It is possible to use <code class="literal">OR</code>
     to search for multiple derived forms, but this is tedious and error-prone
     (some words can have several thousand derivatives).
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     They provide no ordering (ranking) of search results, which makes them
     ineffective when thousands of matching documents are found.
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     They tend to be slow because there is no index support, so they must
     process all documents for every search.
    </p></li></ul></div><p>
   Full text indexing allows documents to be <span class="emphasis"><em>preprocessed</em></span>
   and an index saved for later rapid searching. Preprocessing includes:
  </p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: none; "><li class="listitem" style="list-style-type: none"><p>
     <span class="emphasis"><em>Parsing documents into <em class="firstterm">tokens</em></em></span>. 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.
     <span class="productname">PostgreSQL</span> uses a <em class="firstterm">parser</em> to
     perform this step.  A standard parser is provided, and custom parsers
     can be created for specific needs.
    </p></li><li class="listitem" style="list-style-type: none"><p>
     <span class="emphasis"><em>Converting tokens into <em class="firstterm">lexemes</em></em></span>.
     A lexeme is a string, just like a token, but it has been
     <em class="firstterm">normalized</em> 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 <code class="literal">s</code> or <code class="literal">es</code> 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 <em class="firstterm">stop words</em>, 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.)
     <span class="productname">PostgreSQL</span> uses <em class="firstterm">dictionaries</em> to
     perform this step.  Various standard dictionaries are provided, and
     custom ones can be created for specific needs.
    </p></li><li class="listitem" style="list-style-type: none"><p>
     <span class="emphasis"><em>Storing preprocessed documents optimized for
     searching</em></span>.  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
     <em class="firstterm">proximity ranking</em>, so that a document that
     contains a more <span class="quote"><span class="quote">dense</span></span> region of query words is
     assigned a higher rank than one with scattered query words.
    </p></li></ul></div><p>
   Dictionaries allow fine-grained control over how tokens are normalized.
   With appropriate dictionaries, you can:
  </p><div class="itemizedlist"><ul class="itemizedlist compact" style="list-style-type: bullet; "><li class="listitem" style="list-style-type: disc"><p>
     Define stop words that should not be indexed.
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     Map synonyms to a single word using <span class="application">Ispell</span>.
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     Map phrases to a single word using a thesaurus.
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     Map different variations of a word to a canonical form using
     an <span class="application">Ispell</span> dictionary.
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     Map different variations of a word to a canonical form using
     <span class="application">Snowball</span> stemmer rules.
    </p></li></ul></div><p>
   A data type <code class="type">tsvector</code> is provided for storing preprocessed
   documents, along with a type <code class="type">tsquery</code> for representing processed
   queries (<a class="xref" href="datatype-textsearch.html" title="8.11. Text Search Types">Section 8.11</a>).  There are many
   functions and operators available for these data types
   (<a class="xref" href="functions-textsearch.html" title="9.13. Text Search Functions and Operators">Section 9.13</a>), the most important of which is
   the match operator <code class="literal">@@</code>, which we introduce in
   <a class="xref" href="textsearch-intro.html#TEXTSEARCH-MATCHING" title="12.1.2. Basic Text Matching">Section 12.1.2</a>.  Full text searches can be accelerated
   using indexes (<a class="xref" href="textsearch-indexes.html" title="12.9. Preferred Index Types for Text Search">Section 12.9</a>).
  </p><div class="sect2" id="TEXTSEARCH-DOCUMENT"><div class="titlepage"><div><div><h3 class="title">12.1.1. What Is a Document?</h3></div></div></div><a id="id-1.5.11.4.10.2" class="indexterm"></a><p>
    A <em class="firstterm">document</em> 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.
   </p><p>
    For searches within <span class="productname">PostgreSQL</span>,
    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:

</p><pre class="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;
</pre><p>
   </p><div class="note"><h3 class="title">Note</h3><p>
     Actually, in these example queries, <code class="function">coalesce</code>
     should be used to prevent a single <code class="literal">NULL</code> attribute from
     causing a <code class="literal">NULL</code> result for the whole document.
    </p></div><p>
    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 <span class="productname">PostgreSQL</span>.  Also, keeping
    everything inside the database allows easy access
    to document metadata to assist in indexing and display.
   </p><p>
    For text search purposes, each document must be reduced to the
    preprocessed <code class="type">tsvector</code> format.  Searching and ranking
    are performed entirely on the <code class="type">tsvector</code> representation
    of a document — the original text need only be retrieved
    when the document has been selected for display to a user.
    We therefore often speak of the <code class="type">tsvector</code> as being the
    document, but of course it is only a compact representation of
    the full document.
   </p></div><div class="sect2" id="TEXTSEARCH-MATCHING"><div class="titlepage"><div><div><h3 class="title">12.1.2. Basic Text Matching</h3></div></div></div><p>
    Full text searching in <span class="productname">PostgreSQL</span> is based on
    the match operator <code class="literal">@@</code>, which returns
    <code class="literal">true</code> if a <code class="type">tsvector</code>
    (document) matches a <code class="type">tsquery</code> (query).
    It doesn't matter which data type is written first:

</p><pre class="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
</pre><p>
   </p><p>
    As the above example suggests, a <code class="type">tsquery</code> is not just raw
    text, any more than a <code class="type">tsvector</code> is.  A <code class="type">tsquery</code>
    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 <a class="xref" href="datatype-textsearch.html#DATATYPE-TSQUERY" title="8.11.2. tsquery">Section 8.11.2</a>.)  There are
    functions <code class="function">to_tsquery</code>, <code class="function">plainto_tsquery</code>,
    and <code class="function">phraseto_tsquery</code>
    that are helpful in converting user-written text into a proper
    <code class="type">tsquery</code>, primarily by normalizing words appearing in
    the text.  Similarly, <code class="function">to_tsvector</code> is used to parse and
    normalize a document string.  So in practice a text search match would
    look more like this:

</p><pre class="programlisting">
SELECT to_tsvector('fat cats ate fat rats') @@ to_tsquery('fat &amp; rat');
 ?column?
----------
 t
</pre><p>

    Observe that this match would not succeed if written as

</p><pre class="programlisting">
SELECT 'fat cats ate fat rats'::tsvector @@ to_tsquery('fat &amp; rat');
 ?column?
----------
 f
</pre><p>

    since here no normalization of the word <code class="literal">rats</code> will occur.
    The elements of a <code class="type">tsvector</code> are lexemes, which are assumed
    already normalized, so <code class="literal">rats</code> does not match <code class="literal">rat</code>.
   </p><p>
    The <code class="literal">@@</code> operator also
    supports <code class="type">text</code> input, allowing explicit conversion of a text
    string to <code class="type">tsvector</code> or <code class="type">tsquery</code> to be skipped
    in simple cases.  The variants available are:

</p><pre class="programlisting">
tsvector @@ tsquery
tsquery  @@ tsvector
text @@ tsquery
text @@ text
</pre><p>
   </p><p>
    The first two of these we saw already.
    The form <code class="type">text</code> <code class="literal">@@</code> <code class="type">tsquery</code>
    is equivalent to <code class="literal">to_tsvector(x) @@ y</code>.
    The form <code class="type">text</code> <code class="literal">@@</code> <code class="type">text</code>
    is equivalent to <code class="literal">to_tsvector(x) @@ plainto_tsquery(y)</code>.
   </p><p>
    Within a <code class="type">tsquery</code>, the <code class="literal">&amp;</code> (AND) operator
    specifies that both its arguments must appear in the document to have a
    match.  Similarly, the <code class="literal">|</code> (OR) operator specifies that
    at least one of its arguments must appear, while the <code class="literal">!</code> (NOT)
    operator specifies that its argument must <span class="emphasis"><em>not</em></span> appear in
    order to have a match.
    For example, the query <code class="literal">fat &amp; ! rat</code> matches documents that
    contain <code class="literal">fat</code> but not <code class="literal">rat</code>.
   </p><p>
    Searching for phrases is possible with the help of
    the <code class="literal">&lt;-&gt;</code> (FOLLOWED BY) <code class="type">tsquery</code> operator, which
    matches only if its arguments have matches that are adjacent and in the
    given order.  For example:

</p><pre class="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
</pre><p>

    There is a more general version of the FOLLOWED BY operator having the
    form <code class="literal">&lt;<em class="replaceable"><code>N</code></em>&gt;</code>,
    where <em class="replaceable"><code>N</code></em> is an integer standing for the difference between
    the positions of the matching lexemes.  <code class="literal">&lt;1&gt;</code> is
    the same as <code class="literal">&lt;-&gt;</code>, while <code class="literal">&lt;2&gt;</code>
    allows exactly one other lexeme to appear between the matches, and so
    on.  The <code class="literal">phraseto_tsquery</code> function makes use of this
    operator to construct a <code class="literal">tsquery</code> that can match a multi-word
    phrase when some of the words are stop words.  For example:

</p><pre class="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'
</pre><p>
   </p><p>
    A special case that's sometimes useful is that <code class="literal">&lt;0&gt;</code>
    can be used to require that two patterns match the same word.
   </p><p>
    Parentheses can be used to control nesting of the <code class="type">tsquery</code>
    operators.  Without parentheses, <code class="literal">|</code> binds least tightly,
    then <code class="literal">&amp;</code>, then <code class="literal">&lt;-&gt;</code>,
    and <code class="literal">!</code> most tightly.
   </p><p>
    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 <code class="literal">!x</code> matches
    only documents that do not contain <code class="literal">x</code> anywhere.
    But <code class="literal">!x &lt;-&gt; y</code> matches <code class="literal">y</code> if it is not
    immediately after an <code class="literal">x</code>; an occurrence of <code class="literal">x</code>
    elsewhere in the document does not prevent a match.  Another example is
    that <code class="literal">x &amp; y</code> normally only requires that <code class="literal">x</code>
    and <code class="literal">y</code> both appear somewhere in the document, but
    <code class="literal">(x &amp; y) &lt;-&gt; z</code> requires <code class="literal">x</code>
    and <code class="literal">y</code> to match at the same place, immediately before
    a <code class="literal">z</code>.  Thus this query behaves differently from
    <code class="literal">x &lt;-&gt; z &amp; y &lt;-&gt; z</code>, which will match a
    document containing two separate sequences <code class="literal">x z</code> and
    <code class="literal">y z</code>.  (This specific query is useless as written,
    since <code class="literal">x</code> and <code class="literal">y</code> 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.)
   </p></div><div class="sect2" id="TEXTSEARCH-INTRO-CONFIGURATIONS"><div class="titlepage"><div><div><h3 class="title">12.1.3. Configurations</h3></div></div></div><p>
    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 <em class="firstterm">text search
    configurations</em>.  <span class="productname">PostgreSQL</span> comes with predefined
    configurations for many languages, and you can easily create your own
    configurations.  (<span class="application">psql</span>'s <code class="command">\dF</code> command
    shows all available configurations.)
   </p><p>
    During installation an appropriate configuration is selected and
    <a class="xref" href="runtime-config-client.html#GUC-DEFAULT-TEXT-SEARCH-CONFIG">default_text_search_config</a> is set accordingly
    in <code class="filename">postgresql.conf</code>.  If you are using the same text search
    configuration for the entire cluster you can use the value in
    <code class="filename">postgresql.conf</code>.  To use different configurations
    throughout the cluster but the same configuration within any one database,
    use <code class="command">ALTER DATABASE ... SET</code>.  Otherwise, you can set
    <code class="varname">default_text_search_config</code> in each session.
   </p><p>
    Each text search function that depends on a configuration has an optional
    <code class="type">regconfig</code> argument, so that the configuration to use can be
    specified explicitly.  <code class="varname">default_text_search_config</code>
    is used only when this argument is omitted.
   </p><p>
    To make it easier to build custom text search configurations, a
    configuration is built up from simpler database objects.
    <span class="productname">PostgreSQL</span>'s text search facility provides
    four types of configuration-related database objects:
   </p><div class="itemizedlist"><ul class="itemizedlist compact" style="list-style-type: bullet; "><li class="listitem" style="list-style-type: disc"><p>
     <em class="firstterm">Text search parsers</em> break documents into tokens
     and classify each token (for example, as words or numbers).
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     <em class="firstterm">Text search dictionaries</em> convert tokens to normalized
     form and reject stop words.
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     <em class="firstterm">Text search templates</em> provide the functions underlying
     dictionaries.  (A dictionary simply specifies a template and a set
     of parameters for the template.)
    </p></li><li class="listitem" style="list-style-type: disc"><p>
     <em class="firstterm">Text search configurations</em> select a parser and a set
     of dictionaries to use to normalize the tokens produced by the parser.
    </p></li></ul></div><p>
    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 <code class="filename">contrib/</code> area of the
    <span class="productname">PostgreSQL</span> 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.
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