<!-- doc/src/sgml/pgtrgm.sgml -->

<sect1 id="pgtrgm" xreflabel="pg_trgm">
 <title>pg_trgm</title>

 <indexterm zone="pgtrgm">
  <primary>pg_trgm</primary>
 </indexterm>

 <para>
  The <filename>pg_trgm</filename> module provides functions and operators
  for determining the similarity of
  alphanumeric text based on trigram matching, as
  well as index operator classes that support fast searching for similar
  strings.
 </para>

 <sect2>
  <title>Trigram (or Trigraph) Concepts</title>

  <para>
   A trigram is a group of three consecutive characters taken
   from a string.  We can measure the similarity of two strings by
   counting the number of trigrams they share.  This simple idea
   turns out to be very effective for measuring the similarity of
   words in many natural languages.
  </para>

  <note>
   <para>
    <filename>pg_trgm</filename> ignores non-word characters
    (non-alphanumerics) when extracting trigrams from a string.
    Each word is considered to have two spaces
    prefixed and one space suffixed when determining the set
    of trigrams contained in the string.
    For example, the set of trigrams in the string
    <quote><literal>cat</literal></quote> is
    <quote><literal>  c</literal></quote>,
    <quote><literal> ca</literal></quote>,
    <quote><literal>cat</literal></quote>, and
    <quote><literal>at </literal></quote>.
    The set of trigrams in the string
    <quote><literal>foo|bar</literal></quote> is
    <quote><literal>  f</literal></quote>,
    <quote><literal> fo</literal></quote>,
    <quote><literal>foo</literal></quote>,
    <quote><literal>oo </literal></quote>,
    <quote><literal>  b</literal></quote>,
    <quote><literal> ba</literal></quote>,
    <quote><literal>bar</literal></quote>, and
    <quote><literal>ar </literal></quote>.
   </para>
  </note>
 </sect2>

 <sect2>
  <title>Functions and Operators</title>

  <para>
   The functions provided by the <filename>pg_trgm</filename> module
   are shown in <xref linkend="pgtrgm-func-table">, the operators
   in <xref linkend="pgtrgm-op-table">.
  </para>

  <table id="pgtrgm-func-table">
   <title><filename>pg_trgm</filename> Functions</title>
   <tgroup cols="3">
    <thead>
     <row>
      <entry>Function</entry>
      <entry>Returns</entry>
      <entry>Description</entry>
     </row>
    </thead>

    <tbody>
     <row>
      <entry><function>similarity(text, text)</function></entry>
      <entry><type>real</type></entry>
      <entry>
       Returns a number that indicates how similar the two arguments are.
       The range of the result is zero (indicating that the two strings are
       completely dissimilar) to one (indicating that the two strings are
       identical).
      </entry>
     </row>
     <row>
      <entry><function>show_trgm(text)</function></entry>
      <entry><type>text[]</type></entry>
      <entry>
       Returns an array of all the trigrams in the given string.
       (In practice this is seldom useful except for debugging.)
      </entry>
     </row>
     <row>
      <entry><function>show_limit()</function></entry>
      <entry><type>real</type></entry>
      <entry>
       Returns the current similarity threshold used by the <literal>%</>
       operator.  This sets the minimum similarity between
       two words for them to be considered similar enough to
       be misspellings of each other, for example.
      </entry>
     </row>
     <row>
      <entry><function>set_limit(real)</function></entry>
      <entry><type>real</type></entry>
      <entry>
       Sets the current similarity threshold that is used by the <literal>%</>
       operator.  The threshold must be between 0 and 1 (default is 0.3).
       Returns the same value passed in.
      </entry>
     </row>
    </tbody>
   </tgroup>
  </table>

  <table id="pgtrgm-op-table">
   <title><filename>pg_trgm</filename> Operators</title>
   <tgroup cols="3">
    <thead>
     <row>
      <entry>Operator</entry>
      <entry>Returns</entry>
      <entry>Description</entry>
     </row>
    </thead>

    <tbody>
     <row>
      <entry><type>text</> <literal>%</literal> <type>text</></entry>
      <entry><type>boolean</type></entry>
      <entry>
       Returns <literal>true</> if its arguments have a similarity that is
       greater than the current similarity threshold set by
       <function>set_limit</>.
      </entry>
     </row>
     <row>
      <entry><type>text</> <literal>&lt;-&gt;</literal> <type>text</></entry>
      <entry><type>real</type></entry>
      <entry>
       Returns the <quote>distance</> between the arguments, that is
       one minus the <function>similarity()</> value.
      </entry>
     </row>
    </tbody>
   </tgroup>
  </table>
 </sect2>

 <sect2>
  <title>Index Support</title>

  <para>
   The <filename>pg_trgm</filename> module provides GiST and GIN index
   operator classes that allow you to create an index over a text column for
   the purpose of very fast similarity searches.  These index types support
   the above-described similarity operators, and additionally support
   trigram-based index searches for <literal>LIKE</>, <literal>ILIKE</>,
   <literal>~</> and <literal>~*</> queries.  (These indexes do not
   support equality nor simple comparison operators, so you may need a
   regular B-tree index too.)
  </para>

  <para>
   Example:

<programlisting>
CREATE TABLE test_trgm (t text);
CREATE INDEX trgm_idx ON test_trgm USING gist (t gist_trgm_ops);
</programlisting>
or
<programlisting>
CREATE INDEX trgm_idx ON test_trgm USING gin (t gin_trgm_ops);
</programlisting>
  </para>

  <para>
   At this point, you will have an index on the <structfield>t</> column that
   you can use for similarity searching.  A typical query is
<programlisting>
SELECT t, similarity(t, '<replaceable>word</>') AS sml
  FROM test_trgm
  WHERE t % '<replaceable>word</>'
  ORDER BY sml DESC, t;
</programlisting>
   This will return all values in the text column that are sufficiently
   similar to <replaceable>word</>, sorted from best match to worst.  The
   index will be used to make this a fast operation even over very large data
   sets.
  </para>

  <para>
   A variant of the above query is
<programlisting>
SELECT t, t &lt;-&gt; '<replaceable>word</>' AS dist
  FROM test_trgm
  ORDER BY dist LIMIT 10;
</programlisting>
   This can be implemented quite efficiently by GiST indexes, but not
   by GIN indexes.  It will usually beat the first formulation when only
   a small number of the closest matches is wanted.
  </para>

  <para>
   Beginning in <productname>PostgreSQL</> 9.1, these index types also support
   index searches for <literal>LIKE</> and <literal>ILIKE</>, for example
<programlisting>
SELECT * FROM test_trgm WHERE t LIKE '%foo%bar';
</programlisting>
   The index search works by extracting trigrams from the search string
   and then looking these up in the index.  The more trigrams in the search
   string, the more effective the index search is.  Unlike B-tree based
   searches, the search string need not be left-anchored.
  </para>

  <para>
   Beginning in <productname>PostgreSQL</> 9.3, these index types also support
   index searches for regular-expression matches
   (<literal>~</> and <literal>~*</> operators), for example
<programlisting>
SELECT * FROM test_trgm WHERE t ~ '(foo|bar)';
</programlisting>
   The index search works by extracting trigrams from the regular expression
   and then looking these up in the index.  The more trigrams that can be
   extracted from the regular expression, the more effective the index search
   is.  Unlike B-tree based searches, the search string need not be
   left-anchored.
  </para>

  <para>
   For both <literal>LIKE</> and regular-expression searches, keep in mind
   that a pattern with no extractable trigrams will degenerate to a full-index
   scan.
  </para>

  <para>
   The choice between GiST and GIN indexing depends on the relative
   performance characteristics of GiST and GIN, which are discussed elsewhere.
   As a rule of thumb, a GIN index is faster to search than a GiST index, but
   slower to build or update; so GIN is better suited for static data and GiST
   for often-updated data.
  </para>
 </sect2>

 <sect2>
  <title>Text Search Integration</title>

  <para>
   Trigram matching is a very useful tool when used in conjunction
   with a full text index.  In particular it can help to recognize
   misspelled input words that will not be matched directly by the
   full text search mechanism.
  </para>

  <para>
   The first step is to generate an auxiliary table containing all
   the unique words in the documents:

<programlisting>
CREATE TABLE words AS SELECT word FROM
        ts_stat('SELECT to_tsvector(''simple'', bodytext) FROM documents');
</programlisting>

   where <structname>documents</> is a table that has a text field
   <structfield>bodytext</> that we wish to search.  The reason for using
   the <literal>simple</> configuration with the <function>to_tsvector</>
   function, instead of using a language-specific configuration,
   is that we want a list of the original (unstemmed) words.
  </para>

  <para>
   Next, create a trigram index on the word column:

<programlisting>
CREATE INDEX words_idx ON words USING gin(word gin_trgm_ops);
</programlisting>

   Now, a <command>SELECT</command> query similar to the previous example can
   be used to suggest spellings for misspelled words in user search terms.
   A useful extra test is to require that the selected words are also of
   similar length to the misspelled word.
  </para>

  <note>
   <para>
    Since the <structname>words</> table has been generated as a separate,
    static table, it will need to be periodically regenerated so that
    it remains reasonably up-to-date with the document collection.
    Keeping it exactly current is usually unnecessary.
   </para>
  </note>
 </sect2>

 <sect2>
  <title>References</title>

  <para>
   GiST Development Site
   <ulink url="http://www.sai.msu.su/~megera/postgres/gist/"></ulink>
  </para>
  <para>
   Tsearch2 Development Site
   <ulink url="http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/"></ulink>
  </para>
 </sect2>

 <sect2>
  <title>Authors</title>

  <para>
   Oleg Bartunov <email>oleg@sai.msu.su</email>, Moscow, Moscow University, Russia
  </para>
  <para>
   Teodor Sigaev <email>teodor@sigaev.ru</email>, Moscow, Delta-Soft Ltd.,Russia
  </para>
  <para>
   Documentation: Christopher Kings-Lynne
  </para>
  <para>
   This module is sponsored by Delta-Soft Ltd., Moscow, Russia.
  </para>
 </sect2>

</sect1>
