Re: How the planner uses statistics

Patch applied.  Thanks.  Your documentation changes can be viewed in
five minutes using links on the developer's page,
http://www.postgresql.org/developer/testing.


---------------------------------------------------------------------------


Mark Kirkwood wrote:
> Bruce Momjian wrote:
> > Mark Kirkwood wrote:
> >
> >>At Tom's suggestion, I am going to amend the page to fit into the
> >>'internals' chapter as opposed to 'performance tips' one. I might do
> >>this first, and send you the resulting page.
> >
> > That sounds good, that this become part of the developer docs.
> >
>
> Here is the amended version. I have placed it in its own chapter located
> immediately after 'bki Backend Interface', however there is nothing
> special about that location... feel free to move it around :-)
>
> Mark
>
>
>
>
>

> diff -Naur sgml.orig/filelist.sgml sgml/filelist.sgml
> --- sgml.orig/filelist.sgml    Mon Feb 14 15:02:16 2005
> +++ sgml/filelist.sgml    Tue Feb 15 09:52:33 2005
> @@ -77,6 +77,7 @@
>  <!entity catalogs   SYSTEM "catalogs.sgml">
>  <!entity geqo       SYSTEM "geqo.sgml">
>  <!entity gist       SYSTEM "gist.sgml">
> +<!entity howplanstats    SYSTEM "howplanstats.sgml">
>  <!entity indexam    SYSTEM "indexam.sgml">
>  <!entity nls        SYSTEM "nls.sgml">
>  <!entity plhandler  SYSTEM "plhandler.sgml">
> diff -Naur sgml.orig/howplanstats.sgml sgml/howplanstats.sgml
> --- sgml.orig/howplanstats.sgml    Thu Jan  1 12:00:00 1970
> +++ sgml/howplanstats.sgml    Tue Feb 15 17:18:30 2005
> @@ -0,0 +1,370 @@
> +<!--
> +$PostgreSQL$
> +-->
> +
> +<chapter id="how-planner-stats">
> + <title>How the Planner Uses Statistics</title>
> +
> +  <para>
> +   This chapter builds on the material covered in <xref linkend="using-explain">
> +   and <xref linkend="planner-stats">, and shows how the planner uses the
> +   system statistics to estimate the number of rows each stage in a query might
> +   return. This is a significant part of the planning / optimizing process,
> +   providing much of the raw material for cost calculation.
> +  </para>
> +
> +  <para>
> +   The intent of this chapter is not to document the code —
> +   better done in the code itself, but to present an overview of how it works.
> +   This will perhaps ease the learning curve for someone who subsequently
> +   wishes to read the code. As a consequence, the approach chosen is to analyze
> +   a series of incrementally more complex examples.
> +  </para>
> +
> +  <para>
> +   The outputs and algorithms shown below are taken from version 8.0.
> +   The behaviour of earlier (or later) versions may vary.
> +  </para>
> +
> + <sect1 id="row-estimation-examples">
> +  <title>Row Estimation Examples</title>
> +
> +  <indexterm zone="row-estimation-examples">
> +   <primary>row estimation</primary>
> +   <secondary>planner</secondary>
> +  </indexterm>
> +
> +  <para>
> +   Using examples drawn from the regression test database, let's start with a
> +   very simple query:
> +<programlisting>
> +EXPLAIN SELECT * FROM tenk1;
> +
> +                         QUERY PLAN
> +-------------------------------------------------------------
> + Seq Scan on tenk1  (cost=0.00..445.00 rows=10000 width=244)
> +</programlisting>
> +
> +   How the planner determines the cardinality of <classname>tenk1</classname>
> +   is covered in <xref linkend="using-explain">, but is repeated here for
> +   completeness. The number of rows is looked up from
> +   <classname>pg_class</classname>:
> +
> +<programlisting>
> +SELECT reltuples, relpages FROM pg_class WHERE relname = 'tenk1';
> +
> + relpages | reltuples
> +----------+-----------
> +      345 |     10000
> +</programlisting>
> +   The planner will check the <structfield>relpages<structfield> estimate
> +   (this is a cheap operation) and if incorrect may scale
> +   <structfield>reltuples<structfield> to obtain a row estimate. In this case it
> +   does not, thus:
> +
> +<programlisting>
> +rows = 10000
> +</programlisting>
> +
> +  </para>
> +
> +  <para>
> +   let's move on to an example with a range condition in its
> +   <literal>WHERE</literal> clause:
> +
> +<programlisting>
> +EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 1000;
> +
> +                         QUERY PLAN
> +------------------------------------------------------------
> + Seq Scan on tenk1  (cost=0.00..470.00 rows=1031 width=244)
> +   Filter: (unique1 < 1000)
> +</programlisting>
> +
> +   The planner examines the <literal>WHERE</literal> clause condition:
> +
> +<programlisting>
> +unique1 < 1000
> +</programlisting>
> +
> +   and looks up the restriction function for the operator
> +   <literal><</literal> in <classname>pg_operator</classname>.
> +   This is held in the column <structfield>oprrest</structfield>,
> +   and the result in this case is <function>scalarltsel</function>.
> +   The <function>scalarltsel</function> function retrieves the histogram for
> +   <structfield>unique1</structfield> from <classname>pg_statistics</classname>
> +   - we can follow this by using the simpler <classname>pg_stats</classname>
> +   view:
> +
> +<programlisting>
> +SELECT histogram_bounds FROM pg_stats
> +WHERE tablename='tenk1' AND attname='unique1';
> +
> +                   histogram_bounds
> +------------------------------------------------------
> + {1,970,1943,2958,3971,5069,6028,7007,7919,8982,9995}
> +</programlisting>
> +
> +   Next the fraction of the histogram occupied by <quote>< 1000</quote>
> +   is worked out. This is the selectivity. The histogram divides the range
> +   into equal frequency buckets, so all we have to do is locate the bucket
> +   that our value is in and count <emphasis>part</emphasis> of it and
> +   <emphasis>all</emphasis> of the ones before. The value 1000 is clearly in
> +   the second (970 - 1943) bucket, so by assuming a linear distribution of
> +   values inside each bucket we can calculate the selectivity as:
> +
> +<programlisting>
> +selectivity = (1 + (1000 - bckt[2].min)/(bckt[2].max - bckt[2].min))/num_bckts
> +            = (1 + (1000 - 970)/(1943 - 970))/10
> +            = 0.1031
> +</programlisting>
> +
> +   that is, one whole bucket plus a linear fraction of the second, divided by
> +   the number of buckets. The estimated number of rows can now be calculated as
> +   the product of the selectivity and the cardinality of
> +   <classname>tenk1</classname>:
> +
> +<programlisting>
> +rows = rel_cardinality * selectivity
> +     = 10000 * 0.1031
> +     = 1031
> +</programlisting>
> +
> +  </para>
> +
> +  <para>
> +   Next let's consider an example with equality condition in its
> +   <literal>WHERE</literal> clause:
> +
> +<programlisting>
> +EXPLAIN SELECT * FROM tenk1 WHERE stringu1 = 'ATAAAA';
> +
> +                        QUERY PLAN
> +----------------------------------------------------------
> + Seq Scan on tenk1  (cost=0.00..470.00 rows=31 width=244)
> +   Filter: (stringu1 = 'ATAAAA'::name)
> +</programlisting>
> +
> +   Again the planner examines the <literal>WHERE</literal> clause condition:
> +
> +<programlisting>
> +stringu1 = 'ATAAAA'
> +</programlisting>
> +
> +   and looks up the restriction function for <literal>=</literal>, which is
> +   <function>eqsel</function>. This case is a bit different, as the most
> +   common values — <acronym>MCV</acronym>s, are used to determine the
> +   selectivity. Let's have a look at these, with some extra columns that will
> +   be useful later:
> +
> +<programlisting>
> +SELECT null_frac, n_distinct, most_common_vals, most_common_freqs FROM pg_stats
> +WHERE tablename='tenk1' AND attname='stringu1';
> +
> +null_frac         | 0
> +n_distinct        | 672
> +most_common_vals  | {FDAAAA,NHAAAA,ATAAAA,BGAAAA,EBAAAA,MOAAAA,NDAAAA,OWAAAA,BHAAAA,BJAAAA}
> +most_common_freqs | {0.00333333,0.00333333,0.003,0.003,0.003,0.003,0.003,0.003,0.00266667,0.00266667}
> +</programlisting>
> +
> +   The selectivity is merely the most common frequency (<acronym>MCF</acronym>)
> +   corresponding to the third <acronym>MCV</acronym> — 'ATAAAA':
> +
> +<programlisting>
> +selectivity = mcf[3]
> +            = 0.003
> +</programlisting>
> +
> +   The estimated number of rows is just the product of this with the
> +   cardinality of <classname>tenk1</classname> as before:
> +
> +<programlisting>
> +rows = 10000 * 0.003
> +     = 30
> +</programlisting>
> +
> +   The number displayed by <command>EXPLAIN</command> is one more than this,
> +   due to some post estimation checks.
> +  </para>
> +
> +  <para>
> +   Now consider the same query, but with a constant that is not in the
> +   <acronym>MCV</acronym> list:
> +
> +<programlisting>
> +EXPLAIN SELECT * FROM tenk1 WHERE stringu1 = 'xxx';
> +
> +                        QUERY PLAN
> +----------------------------------------------------------
> + Seq Scan on tenk1  (cost=0.00..470.00 rows=15 width=244)
> +   Filter: (stringu1 = 'xxx'::name)
> +</programlisting>
> +
> +   This is quite a different problem, how to estimate the selectivity when the
> +   value is <emphasis>not</emphasis> in the <acronym>MCV</acronym> list.
> +   The approach is to use the fact that the value is not in the list,
> +   combined with the knowledge of the frequencies for all of the
> +   <acronym>MCV</acronym>s:
> +
> +<programlisting>
> +selectivity = (1 - sum(mvf))/(num_distinct - num_mcv)
> +            = (1 - (0.00333333 + 0.00333333 + 0.003 + 0.003 + 0.003
> +            + 0.003 + 0.003 + 0.003 + 0.00266667 + 0.00266667))/(672 - 10)
> +            = 0.001465
> +</programlisting>
> +
> +   That is, add up all the frequencies for the <acronym>MCV</acronym>s and
> +   subtract them from one — because it is <emphasis>not</emphasis> one
> +   of these, and divide by the <emphasis>remaining</emphasis> distinct values.
> +   Notice that there are no null values so we don't have to worry about those.
> +   The estimated number of rows is calculated as usual:
> +
> +<programlisting>
> +rows = 10000 * 0.001465
> +     = 15
> +</programlisting>
> +
> +  </para>
> +
> +  <para>
> +   Let's increase the complexity to consider a case with more than one
> +   condition in the <literal>WHERE</literal> clause:
> +
> +<programlisting>
> +EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 1000 AND stringu1 = 'xxx';
> +
> +                       QUERY PLAN
> +-----------------------------------------------------------
> + Seq Scan on tenk1  (cost=0.00..495.00 rows=2 width=244)
> +   Filter: ((unique1 < 1000) AND (stringu1 = 'xxx'::name))
> +</programlisting>
> +
> +   An assumption of independence is made and the selectivities of the
> +   individual restrictions are multiplied together:
> +
> +<programlisting>
> +selectivity = selectivity(unique1 < 1000) * selectivity(stringu1 = 'xxx')
> +            = 0.1031 * 0.001465
> +            = 0.00015104
> +</programlisting>
> +
> +   The row estimates are calculated as before:
> +
> +<programlisting>
> +rows = 10000 * 0.00015104
> +     = 2
> +</programlisting>
> +  </para>
> +
> +  <para>
> +   Finally we will examine a query that includes a <literal>JOIN</literal>
> +   together with a <literal>WHERE</literal> clause:
> +
> +<programlisting>
> +EXPLAIN SELECT *  FROM tenk1 t1, tenk2 t2
> +WHERE t1.unique1 < 50 AND t1.unique2 = t2.unique2;
> +
> +                                      QUERY PLAN
> +-----------------------------------------------------------------------------------------
> + Nested Loop  (cost=0.00..346.90 rows=51 width=488)
> +   ->  Index Scan using tenk1_unique1 on tenk1 t1  (cost=0.00..192.57 rows=51 width=244)
> +         Index Cond: (unique1 < 50)
> +   ->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.00..3.01 rows=1 width=244)
> +         Index Cond: ("outer".unique2 = t2.unique2)
> +</programlisting>
> +
> +   The restriction on <classname>tenk1</classname>
> +   <quote>unique1 < 50</quote> is evaluated before the nested-loop join.
> +   This is handled analogously to the previous range example. The restriction
> +   operator for <literal><</literal> is <function>scalarlteqsel</function>
> +   as before, but this time the value 50 is in the first bucket of the
> +   <structfield>unique1</structfield> histogram:
> +
> +<programlisting>
> +selectivity = (0 + (50 - bckt[1].min)/(bckt[1].max - bckt[1].min))/num_bckts
> +            = (0 + (50 - 1)/(970 - 1))/10
> +            = 0.005057
> +
> +rows        = 10000 * 0.005057
> +            = 51
> +</programlisting>
> +
> +   The restriction for the join is:
> +
> +<programlisting>
> +t2.unique2 = t1.unique2
> +</programlisting>
> +
> +   This is due to the join method being nested-loop, with
> +   <classname>tenk1</classname> being in the outer loop. The operator is just
> +   our familiar <literal>=<literal>, however the restriction function is
> +   obtained from the <structfield>oprjoin</structfield> column of
> +   <classname>pg_operator</classname> - and is <function>eqjoinsel</function>.
> +   Additionally we use the statistical information for both
> +   <classname>tenk2</classname> and <classname>tenk1</classname>:
> +
> +<programlisting>
> +SELECT tablename, null_frac,n_distinct, most_common_vals FROM pg_stats
> +WHERE tablename IN ('tenk1', 'tenk2') AND attname='unique2';
> +
> +tablename  | null_frac | n_distinct | most_common_vals
> +-----------+-----------+------------+------------------
> + tenk1     |         0 |         -1 |
> + tenk2     |         0 |         -1 |
> +</programlisting>
> +
> +   In this case there is no <acronym>MCV</acronym> information for
> +   <structfield>unique2</structfield> because all the values appear to be
> +   unique, so we can use an algorithm that relies only on the number of
> +   distinct values for both relations together with their null fractions:
> +
> +<programlisting>
> +selectivity = (1 - null_frac1) * (1 - null_frac2) * min(1/num_distinct1, 1/num_distinct2)
> +            = (1 - 0) * (1 - 0) * min(1/10000, 1/1000)
> +            = 0.0001
> +</programlisting>
> +
> +   This is, subtract the null fraction from one for each of the relations,
> +   and divide by the maximum  of the two distinct values. The number of rows
> +   that the join is likely to emit is calculated as the cardinality of
> +   cartesian product of the two nodes in the nested-loop, multiplied by the
> +   selectivity:
> +
> +<programlisting>
> +rows = (outer_cardinality * inner_cardinality) * selectivity
> +     = (51 * 10000) * 0.0001
> +     = 51
> +</programlisting>
> +  </para>
> +
> +  <para>
> +   For those interested in further details, estimation of the number of rows in
> +   a relation is covered in
> +   <filename>src/backend/optimizer/util/plancat.c</filename>. The calculation
> +   logic for clause selectivities is in
> +   <filename>src/backend/optimizer/path/clausesel.c</filename>. The actual
> +   implementations of the operator and join restriction functions can be found
> +   in <filename>src/backend/utils/adt/selfuncs.c</filename>.
> +  </para>
> +
> + </sect1>
> +
> +
> +</chapter>
> +
> +<!-- Keep this comment at the end of the file
> +Local variables:
> +mode:sgml
> +sgml-omittag:nil
> +sgml-shorttag:t
> +sgml-minimize-attributes:nil
> +sgml-always-quote-attributes:t
> +sgml-indent-step:1
> +sgml-indent-data:t
> +sgml-parent-document:nil
> +sgml-default-dtd-file:"./reference.ced"
> +sgml-exposed-tags:nil
> +sgml-local-catalogs:("/usr/lib/sgml/catalog")
> +sgml-local-ecat-files:nil
> +End:
> +-->
> diff -Naur sgml.orig/postgres.sgml sgml/postgres.sgml
> --- sgml.orig/postgres.sgml    Mon Feb 14 14:53:24 2005
> +++ sgml/postgres.sgml    Tue Feb 15 09:51:31 2005
> @@ -239,6 +239,7 @@
>    &gist;
>    &storage;
>    &bki;
> +  &howplanstats;
>
>   </part>
>
>

--
  Bruce Momjian                        |  http://candle.pha.pa.us
  pgman@candle.pha.pa.us               |  (610) 359-1001
  +  If your life is a hard drive,     |  13 Roberts Road
  +  Christ can be your backup.        |  Newtown Square, Pennsylvania 19073