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>12.9. GIN and GiST Index Types</A
></H1
><P
>   There are two kinds of indexes that can be used to speed up full text
   searches.
   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.

   <P
></P
></P><DIV
CLASS="VARIABLELIST"
><DL
><DT
>&#13;
      <TT
CLASS="LITERAL"
>CREATE INDEX <TT
CLASS="REPLACEABLE"
><I
>name</I
></TT
> ON <TT
CLASS="REPLACEABLE"
><I
>table</I
></TT
> USING GIN (<TT
CLASS="REPLACEABLE"
><I
>column</I
></TT
>);</TT
></DT
><DD
><P
>       Creates a GIN (Generalized Inverted Index)-based index.
       The <TT
CLASS="REPLACEABLE"
><I
>column</I
></TT
> must be of <TT
CLASS="TYPE"
>tsvector</TT
> type.
      </P
></DD
><DT
>&#13;
      <TT
CLASS="LITERAL"
>CREATE INDEX <TT
CLASS="REPLACEABLE"
><I
>name</I
></TT
> ON <TT
CLASS="REPLACEABLE"
><I
>table</I
></TT
> USING GIST (<TT
CLASS="REPLACEABLE"
><I
>column</I
></TT
>);</TT
></DT
><DD
><P
>       Creates a GiST (Generalized Search Tree)-based index.
       The <TT
CLASS="REPLACEABLE"
><I
>column</I
></TT
> can be of <TT
CLASS="TYPE"
>tsvector</TT
> or
       <TT
CLASS="TYPE"
>tsquery</TT
> type.
      </P
></DD
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><P>
  </P
><P
>   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
   <TT
CLASS="TYPE"
>tsvector</TT
> values, and not their weight labels.  Thus a table
   row recheck is needed when using a query that involves weights.
  </P
><P
>   A GiST index is <I
CLASS="FIRSTTERM"
>lossy</I
>, meaning that the index
   might produce false matches, and it is necessary
   to check the actual table row to eliminate such false matches.
   (<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> does this automatically when needed.)
   GiST indexes are lossy because each document is represented in the
   index by a fixed-length signature. 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.
  </P
><P
>   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.
  </P
><P
>   Note that <ACRONYM
CLASS="ACRONYM"
>GIN</ACRONYM
> index build time can often be improved
   by increasing <A
HREF="runtime-config-resource.html#GUC-MAINTENANCE-WORK-MEM"
>maintenance_work_mem</A
>, while
   <ACRONYM
CLASS="ACRONYM"
>GiST</ACRONYM
> index build time is not sensitive to that
   parameter.
  </P
><P
>   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 search results using the <A
HREF="dblink.html"
>dblink</A
>
   module. The latter is possible because ranking functions use
   only local information.
  </P
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