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Add support for multivariate MCV lists
Introduce a third extended statistic type, supported by the CREATE STATISTICS command - MCV lists, a generalization of the statistic already built and used for individual columns. Compared to the already supported types (n-distinct coefficients and functional dependencies), MCV lists are more complex, include column values and allow estimation of much wider range of common clauses (equality and inequality conditions, IS NULL, IS NOT NULL etc.). Similarly to the other types, a new pseudo-type (pg_mcv_list) is used. Author: Tomas Vondra Reviewed-by: Dean Rasheed, David Rowley, Mark Dilger, Alvaro Herrera Discussion: https://postgr.es/m/dfdac334-9cf2-2597-fb27-f0fb3753f435@2ndquadrant.com
1 parent 333ed24 commit 7300a69

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-134
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‎doc/src/sgml/catalogs.sgml

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@@ -6562,7 +6562,8 @@ SCRAM-SHA-256$<replaceable>&lt;iteration count&gt;</replaceable>:<replaceable>&l
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An array containing codes for the enabled statistic kinds;
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valid values are:
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<literal>d</literal> for n-distinct statistics,
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<literal>f</literal> for functional dependency statistics
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<literal>f</literal> for functional dependency statistics, and
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<literal>m</literal> for most common values (MCV) list statistics
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</entry>
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</row>
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@@ -6585,6 +6586,16 @@ SCRAM-SHA-256$<replaceable>&lt;iteration count&gt;</replaceable>:<replaceable>&l
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</entry>
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</row>
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<row>
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<entry><structfield>stxmcv</structfield></entry>
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<entry><type>pg_mcv_list</type></entry>
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<entry></entry>
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<entry>
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MCV (most-common values) list statistics, serialized as
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<structname>pg_mcv_list</structname> type.
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</entry>
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</row>
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</tbody>
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</tgroup>
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</table>

‎doc/src/sgml/func.sgml

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@@ -22174,4 +22174,86 @@ CREATE EVENT TRIGGER test_table_rewrite_oid
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</sect2>
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</sect1>
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<sect1 id="functions-statistics">
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<title>Statistics Information Functions</title>
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<indexterm zone="functions-statistics">
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<primary>function</primary>
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<secondary>statistics</secondary>
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</indexterm>
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<para>
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<productname>PostgreSQL</productname> provides a function to inspect complex
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statistics defined using the <command>CREATE STATISTICS</command> command.
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</para>
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<sect2 id="functions-statistics-mcv">
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<title>Inspecting MCV lists</title>
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<indexterm>
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<primary>pg_mcv_list_items</primary>
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<secondary>pg_mcv_list</secondary>
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</indexterm>
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<para>
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<function>pg_mcv_list_items</function> returns a list of all items
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stored in a multi-column <acronym>MCV</acronym> list, and returns the
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following columns:
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<informaltable>
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<tgroup cols="3">
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<thead>
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<row>
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<entry>Name</entry>
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<entry>Type</entry>
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<entry>Description</entry>
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</row>
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</thead>
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<tbody>
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<row>
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<entry><literal>index</literal></entry>
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<entry><type>int</type></entry>
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<entry>index of the item in the <acronym>MCV</acronym> list</entry>
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</row>
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<row>
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<entry><literal>values</literal></entry>
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<entry><type>text[]</type></entry>
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<entry>values stored in the MCV item</entry>
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</row>
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<row>
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<entry><literal>nulls</literal></entry>
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<entry><type>boolean[]</type></entry>
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<entry>flags identifying <literal>NULL</literal> values</entry>
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</row>
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<row>
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<entry><literal>frequency</literal></entry>
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<entry><type>double precision</type></entry>
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<entry>frequency of this <acronym>MCV</acronym> item</entry>
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</row>
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<row>
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<entry><literal>base_frequency</literal></entry>
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<entry><type>double precision</type></entry>
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<entry>base frequency of this <acronym>MCV</acronym> item</entry>
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</row>
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</tbody>
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</tgroup>
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</informaltable>
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</para>
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<para>
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The <function>pg_mcv_list_items</function> function can be used like this:
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<programlisting>
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SELECT m.* FROM pg_statistic_ext,
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pg_mcv_list_items(stxmcv) m WHERE stxname = 'stts';
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</programlisting>
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Values of the <type>pg_mcv_list</type> can be obtained only from the
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<literal>pg_statistic_ext.stxmcv</literal> column.
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</para>
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</sect2>
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</sect1>
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</chapter>

‎doc/src/sgml/perform.sgml

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@@ -1285,6 +1285,72 @@ nd | {"1, 2": 33178, "1, 5": 33178, "2, 5": 27435, "1, 2, 5": 33178}
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plans. Otherwise, the <command>ANALYZE</command> cycles are just wasted.
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</para>
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</sect3>
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<sect3>
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<title>Multivariate MCV lists</title>
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<para>
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Another type of statistics stored for each column are most-common value
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lists. This allows very accurate estimates for individual columns, but
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may result in significant misestimates for queries with conditions on
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multiple columns.
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</para>
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<para>
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To improve such estimates, <command>ANALYZE</command> can collect MCV
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lists on combinations of columns. Similarly to functional dependencies
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and n-distinct coefficients, it's impractical to do this for every
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possible column grouping. Even more so in this case, as the MCV list
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(unlike functional dependencies and n-distinct coefficients) does store
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the common column values. So data is collected only for those groups
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of columns appearing together in a statistics object defined with the
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<literal>mcv</literal> option.
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</para>
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<para>
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Continuing the previous example, the MCV list for a table of ZIP codes
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might look like the following (unlike for simpler types of statistics,
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a function is required for inspection of MCV contents):
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<programlisting>
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CREATE STATISTICS stts3 (mcv) ON state, city FROM zipcodes;
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ANALYZE zipcodes;
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SELECT m.* FROM pg_statistic_ext,
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pg_mcv_list_items(stxmcv) m WHERE stxname = 'stts3';
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index | values | nulls | frequency | base_frequency
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-------+------------------------+-------+-----------+----------------
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0 | {Washington, DC} | {f,f} | 0.003467 | 2.7e-05
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1 | {Apo, AE} | {f,f} | 0.003067 | 1.9e-05
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2 | {Houston, TX} | {f,f} | 0.002167 | 0.000133
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3 | {El Paso, TX} | {f,f} | 0.002 | 0.000113
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4 | {New York, NY} | {f,f} | 0.001967 | 0.000114
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5 | {Atlanta, GA} | {f,f} | 0.001633 | 3.3e-05
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6 | {Sacramento, CA} | {f,f} | 0.001433 | 7.8e-05
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7 | {Miami, FL} | {f,f} | 0.0014 | 6e-05
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8 | {Dallas, TX} | {f,f} | 0.001367 | 8.8e-05
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9 | {Chicago, IL} | {f,f} | 0.001333 | 5.1e-05
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...
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(99 rows)
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</programlisting>
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This indicates that the most common combination of city and state is
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Washington in DC, with actual frequency (in the sample) about 0.35%.
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The base frequency of the combination (as computed from the simple
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per-column frequencies) is only 0.0027%, resulting in two orders of
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magnitude under-estimates.
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</para>
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<para>
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It's advisable to create <acronym>MCV</acronym> statistics objects only
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on combinations of columns that are actually used in conditions together,
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and for which misestimation of the number of groups is resulting in bad
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plans. Otherwise, the <command>ANALYZE</command> and planning cycles
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are just wasted.
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</para>
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</sect3>
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</sect2>
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</sect1>
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‎doc/src/sgml/planstats.sgml

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@@ -455,7 +455,7 @@ rows = (outer_cardinality * inner_cardinality) * selectivity
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<secondary>multivariate</secondary>
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</indexterm>
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<sect2>
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<sect2 id="functional-dependencies">
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<title>Functional Dependencies</title>
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<para>
@@ -540,7 +540,7 @@ EXPLAIN (ANALYZE, TIMING OFF) SELECT * FROM t WHERE a = 1 AND b = 1;
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</para>
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</sect2>
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<sect2>
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<sect2 id="multivariate-ndistinct-counts">
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<title>Multivariate N-Distinct Counts</title>
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<para>
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</para>
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587587
</sect2>
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<sect2 id="mcv-lists">
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<title>MCV lists</title>
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<para>
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As explained in <xref linkend="functional-dependencies"/>, functional
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dependencies are very cheap and efficient type of statistics, but their
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main limitation is their global nature (only tracking dependencies at
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the column level, not between individual column values).
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</para>
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<para>
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This section introduces multivariate variant of <acronym>MCV</acronym>
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(most-common values) lists, a straightforward extension of the per-column
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statistics described in <xref linkend="row-estimation-examples"/>. These
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statistics address the limitation by storing individual values, but it is
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naturally more expensive, both in terms of building the statistics in
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<command>ANALYZE</command>, storage and planning time.
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</para>
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<para>
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Let's look at the query from <xref linkend="functional-dependencies"/>
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again, but this time with a <acronym>MCV</acronym> list created on the
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same set of columns (be sure to drop the functional dependencies, to
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make sure the planner uses the newly created statistics).
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<programlisting>
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DROP STATISTICS stts;
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CREATE STATISTICS stts2 (mcv) ON a, b FROM t;
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ANALYZE t;
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EXPLAIN (ANALYZE, TIMING OFF) SELECT * FROM t WHERE a = 1 AND b = 1;
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QUERY PLAN
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-------------------------------------------------------------------------------
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Seq Scan on t (cost=0.00..195.00 rows=100 width=8) (actual rows=100 loops=1)
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Filter: ((a = 1) AND (b = 1))
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Rows Removed by Filter: 9900
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</programlisting>
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The estimate is as accurate as with the functional dependencies, mostly
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thanks to the table being fairly small and having a simple distribution
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with a low number of distinct values. Before looking at the second query,
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which was not handled by functional dependencies particularly well,
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let's inspect the <acronym>MCV</acronym> list a bit.
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</para>
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<para>
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Inspecting the <acronym>MCV</acronym> list is possible using
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<function>pg_mcv_list_items</function> set-returning function.
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<programlisting>
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SELECT m.* FROM pg_statistic_ext,
639+
pg_mcv_list_items(stxmcv) m WHERE stxname = 'stts2';
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index | values | nulls | frequency | base_frequency
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-------+----------+-------+-----------+----------------
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0 | {0, 0} | {f,f} | 0.01 | 0.0001
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1 | {1, 1} | {f,f} | 0.01 | 0.0001
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...
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49 | {49, 49} | {f,f} | 0.01 | 0.0001
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50 | {50, 50} | {f,f} | 0.01 | 0.0001
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...
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97 | {97, 97} | {f,f} | 0.01 | 0.0001
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98 | {98, 98} | {f,f} | 0.01 | 0.0001
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99 | {99, 99} | {f,f} | 0.01 | 0.0001
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(100 rows)
652+
</programlisting>
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This confirms there are 100 distinct combinations in the two columns, and
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all of them are about equally likely (1% frequency for each one). The
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base frequency is the frequency computed from per-column statistics, as if
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there were no multi-column statistics. Had there been any null values in
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either of the columns, this would be identified in the
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<structfield>nulls</structfield> column.
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</para>
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<para>
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When estimating the selectivity, the planner applies all the conditions
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on items in the <acronym>MCV</acronym> list, and then sums the frequencies
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of the matching ones. See <function>mcv_clauselist_selectivity</function>
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in <filename>src/backend/statistics/mcv.c</filename> for details.
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</para>
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<para>
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Compared to functional dependencies, <acronym>MCV</acronym> lists have two
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major advantages. Firstly, the list stores actual values, making it possible
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to decide which combinations are compatible.
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<programlisting>
675+
EXPLAIN (ANALYZE, TIMING OFF) SELECT * FROM t WHERE a = 1 AND b = 10;
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QUERY PLAN
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---------------------------------------------------------------------------
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Seq Scan on t (cost=0.00..195.00 rows=1 width=8) (actual rows=0 loops=1)
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Filter: ((a = 1) AND (b = 10))
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Rows Removed by Filter: 10000
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</programlisting>
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Secondly, <acronym>MCV</acronym> lists handle a wider range of clause types,
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not just equality clauses like functional dependencies. See for example the
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example range query, presented earlier:
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<programlisting>
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EXPLAIN (ANALYZE, TIMING OFF) SELECT * FROM t WHERE a &lt;= 49 AND b &gt; 49;
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QUERY PLAN
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---------------------------------------------------------------------------
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Seq Scan on t (cost=0.00..195.00 rows=1 width=8) (actual rows=0 loops=1)
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Filter: ((a &lt;= 49) AND (b &gt; 49))
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Rows Removed by Filter: 10000
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</programlisting>
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</para>
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</sect2>
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</sect1>
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<sect1 id="planner-stats-security">

‎doc/src/sgml/ref/create_statistics.sgml

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@@ -81,9 +81,10 @@ CREATE STATISTICS [ IF NOT EXISTS ] <replaceable class="parameter">statistics_na
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<para>
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A statistics kind to be computed in this statistics object.
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Currently supported kinds are
84-
<literal>ndistinct</literal>, which enables n-distinct statistics, and
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<literal>ndistinct</literal>, which enables n-distinct statistics,
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<literal>dependencies</literal>, which enables functional
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dependency statistics.
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dependency statistics, and <literal>mcv</literal> which enables
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most-common values lists.
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If this clause is omitted, all supported statistics kinds are
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included in the statistics object.
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For more information, see <xref linkend="planner-stats-extended"/>
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conditions are redundant and does not underestimate the row count.
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</para>
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<para>
169+
Create table <structname>t2</structname> with two perfectly correlated columns
170+
(containing identical data), and a MCV list on those columns:
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<programlisting>
173+
CREATE TABLE t2 (
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a int,
175+
b int
176+
);
177+
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INSERT INTO t2 SELECT mod(i,100), mod(i,100)
179+
FROM generate_series(1,1000000) s(i);
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CREATE STATISTICS s2 (mcv) ON (a, b) FROM t2;
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ANALYZE t2;
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-- valid combination (found in MCV)
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EXPLAIN ANALYZE SELECT * FROM t2 WHERE (a = 1) AND (b = 1);
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-- invalid combination (not found in MCV)
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EXPLAIN ANALYZE SELECT * FROM t2 WHERE (a = 1) AND (b = 2);
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</programlisting>
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The MCV list gives the planner more detailed information about the
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specific values that commonly appear in the table, as well as an upper
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bound on the selectivities of combinations of values that do not appear
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in the table, allowing it to generate better estimates in both cases.
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</para>
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</refsect1>
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<refsect1>

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