<literal>WHERE a = 5 AND b = 6</literal> could
   use the index, but a query like <literal>WHERE a = 5 OR b = 6</literal> could not
   directly use the index.
  </para>

  <para>
   Fortunately,
   <productname>PostgreSQL</productname> has the ability to combine multiple indexes
   (including multiple uses of the same index) to handle cases that cannot
   be implemented by single index scans.  The system can form <literal>AND</literal>
   and <literal>OR</literal> conditions across several index scans.  For example,
   a query like <literal>WHERE x = 42 OR x = 47 OR x = 53 OR x = 99</literal>
   could be broken down into four separate scans of an index on <literal>x</literal>,
   each scan using one of the query clauses.  The results of these scans are
   then ORed together to produce the result.  Another example is that if we
   have separate indexes on <literal>x</literal> and <literal>y</literal>, one possible
   implementation of a query like <literal>WHERE x = 5 AND y = 6</literal> is to
   use each index with the appropriate query clause and then AND together
   the index results to identify the result rows.
  </para>

  <para>
   To combine multiple indexes, the system scans each needed index and
   prepares a <firstterm>bitmap</firstterm> in memory giving the locations of
   table rows that are reported as matching that index's conditions.
   The bitmaps are then ANDed and ORed together as needed by the query.
   Finally, the actual table rows are visited and returned.  The table rows
   are visited in physical order, because that is how the bitmap is laid
   out; this means that any ordering of the original indexes is lost, and
   so a separate sort step will be needed if the query has an <literal>ORDER
   BY</literal> clause.  For this reason, and because each additional index scan
   adds extra time, the planner will sometimes choose to use a simple index
   scan even though additional indexes are available that could have been
   used as well.
  </para>

  <para>
   In all but the simplest applications, there are various combinations of
   indexes that might be useful, and the database developer must make
   trade-offs to decide which indexes to provide.  Sometimes multicolumn
   indexes are best, but sometimes it's better to create separate indexes
   and rely on the index-combination feature.  For example, if your
   workload includes a mix of queries that sometimes involve only column
   <literal>x</literal>, sometimes only column <literal>y</literal>, and sometimes both
   columns, you might choose to create two separate indexes on
   <literal>x</literal> and <literal>y</literal>, relying on index combination to
   process the queries that use both columns.  You could also create a
   multicolumn index on <literal>(x, y)</literal>.  This index would typically be
   more efficient than index combination for queries involving both
   columns, but as discussed in <xref linkend="indexes-multicolumn"/>, it
   would be less useful for queries involving only <literal>y</literal>.  Just
   how useful will depend on how effective the B-tree index skip scan
   optimization is; if <literal>x</literal> has no more than several hundred
   distinct values, skip scan will make searches for specific
   <literal>y</literal> values execute reasonably efficiently.  A combination
   of a multicolumn index on <literal>(x, y)</literal> and a separate index on
   <literal>y</literal> might also serve reasonably well.  For
   queries involving only <literal>x</literal>, the multicolumn index could be
   used, though it would be larger and hence slower than an index on
   <literal>x</literal> alone.  The last alternative is to create all three
   indexes, but this is probably only reasonable if the table is searched
   much more often than it is updated and all three types of query are
   common.  If one of the types of query is much less common than the
   others, you'd probably settle for creating just the two indexes that
   best match