-------------------------------------------------------------------&zwsp;-------------------------
 HashAggregate  (cost=220.00..221.00 rows=100 width=16) (actual rows=100.00 loops=1)
   Group Key: a, b
   ->  Seq Scan on t  (cost=0.00..145.00 rows=10000 width=8) (actual rows=10000.00 loops=1)
</programlisting>
   </para>

  </sect2>

  <sect2 id="mcv-lists">
   <title>MCV Lists</title>

   <para>
    As explained in <xref linkend="functional-dependencies"/>, functional
    dependencies are very cheap and efficient type of statistics, but their
    main limitation is their global nature (only tracking dependencies at
    the column level, not between individual column values).
   </para>

   <para>
    This section introduces multivariate variant of <acronym>MCV</acronym>
    (most-common values) lists, a straightforward extension of the per-column
    statistics described in <xref linkend="row-estimation-examples"/>.  These
    statistics address the limitation by storing individual values, but it is
    naturally more expensive, both in terms of building the statistics in
    <command>ANALYZE</command>, storage and planning time.
   </para>

   <para>
    Let's look at the query from <xref linkend="functional-dependencies"/>
    again, but this time with a <acronym>MCV</acronym> list created on the
    same set of columns (be sure to drop the functional dependencies, to
    make sure the planner uses the newly created statistics).

<programlisting>
DROP STATISTICS stts;
CREATE STATISTICS stts2 (mcv) ON a, b FROM t;
ANALYZE t;
EXPLAIN (ANALYZE, TIMING OFF, BUFFERS OFF) SELECT * FROM t WHERE a = 1 AND b = 1;
                                   QUERY PLAN
-------------------------------------------------------------------&zwsp;------------
 Seq Scan on t  (cost=0.00..195.00 rows=100 width=8) (actual rows=100.00 loops=1)
   Filter: ((a = 1) AND (b = 1))
   Rows Removed by Filter: 9900
</programlisting>

    The estimate is as accurate as with the functional dependencies, mostly
    thanks to the table being fairly small and having a simple distribution
    with a low number of distinct values. Before looking at the second query,
    which was not handled by functional dependencies particularly well,
    let's inspect the <acronym>MCV</acronym> list a bit.
   </para>

   <para>
    Inspecting the <acronym>MCV</acronym> list is possible using
    <function>pg_mcv_list_items</function> set-returning function.

<programlisting>
SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid),
                pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts2';
 index |  values  | nulls | frequency | base_frequency
-------+----------+-------+-----------+----------------
     0 | {0, 0}   | {f,f} |      0.01 |         0.0001
     1 | {1, 1}   | {f,f} |      0.01 |         0.0001
   ...
    49 | {49, 49} | {f,f} |      0.01 |         0.0001
    50 | {50, 50} | {f,f} |      0.01 |         0.0001
   ...
    97 | {97, 97} | {f,f} |      0.01 |         0.0001
    98 | {98, 98} | {f,f} |      0.01 |         0.0001
    99 | {99, 99} | {f,f} |      0.01 |         0.0001
(100 rows)
</programlisting>

    This confirms there are 100 distinct combinations in the two columns, and
    all of them are about equally likely (1% frequency for each one).  The
    base frequency is the frequency computed from per-column statistics, as if
    there were no multi-column statistics. Had there been any null values in
    either of the columns, this would be identified in the
    <structfield>nulls</structfield> column.
   </para>

   <para>
    When estimating the selectivity, the planner applies all the conditions
    on items in the <acronym>MCV</acronym> list, and then sums the frequencies
    of the matching ones. See <function>mcv_clauselist_selectivity</function>
    in <filename>src/backend/statistics/mcv.c</filename> for details.
   </para>

   <para>
    Compared to functional dependencies, <acronym>MCV</acronym> lists have two