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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>14.2. Statistics Used by the Planner</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><link rel="prev" href="using-explain.html" title="14.1. Using EXPLAIN" /><link rel="next" href="explicit-joins.html" title="14.3. Controlling the Planner with Explicit JOIN Clauses" /></head><body id="docContent" class="container-fluid col-10"><div xmlns="http://www.w3.org/TR/xhtml1/transitional" class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">14.2. Statistics Used by the Planner</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="using-explain.html" title="14.1. Using EXPLAIN">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="performance-tips.html" title="Chapter 14. Performance Tips">Up</a></td><th width="60%" align="center">Chapter 14. Performance Tips</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 14.5 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="explicit-joins.html" title="14.3. Controlling the Planner with Explicit JOIN Clauses">Next</a></td></tr></table><hr></hr></div><div class="sect1" id="PLANNER-STATS"><div class="titlepage"><div><div><h2 class="title" style="clear: both">14.2. Statistics Used by the Planner</h2></div></div></div><div class="toc"><dl class="toc"><dt><span class="sect2"><a href="planner-stats.html#id-1.5.13.5.3">14.2.1. Single-Column Statistics</a></span></dt><dt><span class="sect2"><a href="planner-stats.html#PLANNER-STATS-EXTENDED">14.2.2. Extended Statistics</a></span></dt></dl></div><a id="id-1.5.13.5.2" class="indexterm"></a><div class="sect2" id="id-1.5.13.5.3"><div class="titlepage"><div><div><h3 class="title">14.2.1. Single-Column Statistics</h3></div></div></div><p>
As we saw in the previous section, the query planner needs to estimate
the number of rows retrieved by a query in order to make good choices
of query plans. This section provides a quick look at the statistics
that the system uses for these estimates.
</p><p>
One component of the statistics is the total number of entries in
each table and index, as well as the number of disk blocks occupied
by each table and index. This information is kept in the table
<a class="link" href="catalog-pg-class.html" title="52.11. pg_class"><code class="structname">pg_class</code></a>,
in the columns <code class="structfield">reltuples</code> and
<code class="structfield">relpages</code>. We can look at it with
queries similar to this one:
</p><pre class="screen">
SELECT relname, relkind, reltuples, relpages
FROM pg_class
WHERE relname LIKE 'tenk1%';
relname | relkind | reltuples | relpages
----------------------+---------+-----------+----------
tenk1 | r | 10000 | 358
tenk1_hundred | i | 10000 | 30
tenk1_thous_tenthous | i | 10000 | 30
tenk1_unique1 | i | 10000 | 30
tenk1_unique2 | i | 10000 | 30
(5 rows)
</pre><p>
Here we can see that <code class="structname">tenk1</code> contains 10000
rows, as do its indexes, but the indexes are (unsurprisingly) much
smaller than the table.
</p><p>
For efficiency reasons, <code class="structfield">reltuples</code>
and <code class="structfield">relpages</code> are not updated on-the-fly,
and so they usually contain somewhat out-of-date values.
They are updated by <code class="command">VACUUM</code>, <code class="command">ANALYZE</code>, and a
few DDL commands such as <code class="command">CREATE INDEX</code>. A <code class="command">VACUUM</code>
or <code class="command">ANALYZE</code> operation that does not scan the entire table
(which is commonly the case) will incrementally update the
<code class="structfield">reltuples</code> count on the basis of the part
of the table it did scan, resulting in an approximate value.
In any case, the planner
will scale the values it finds in <code class="structname">pg_class</code>
to match the current physical table size, thus obtaining a closer
approximation.
</p><a id="id-1.5.13.5.3.5" class="indexterm"></a><p>
Most queries retrieve only a fraction of the rows in a table, due
to <code class="literal">WHERE</code> clauses that restrict the rows to be
examined. The planner thus needs to make an estimate of the
<em class="firstterm">selectivity</em> of <code class="literal">WHERE</code> clauses, that is,
the fraction of rows that match each condition in the
<code class="literal">WHERE</code> clause. The information used for this task is
stored in the
<a class="link" href="catalog-pg-statistic.html" title="52.49. pg_statistic"><code class="structname">pg_statistic</code></a>
system catalog. Entries in <code class="structname">pg_statistic</code>
are updated by the <code class="command">ANALYZE</code> and <code class="command">VACUUM
ANALYZE</code> commands, and are always approximate even when freshly
updated.
</p><a id="id-1.5.13.5.3.7" class="indexterm"></a><p>
Rather than look at <code class="structname">pg_statistic</code> directly,
it's better to look at its view
<a class="link" href="view-pg-stats.html" title="52.89. pg_stats"><code class="structname">pg_stats</code></a>
when examining the statistics manually. <code class="structname">pg_stats</code>
is designed to be more easily readable. Furthermore,
<code class="structname">pg_stats</code> is readable by all, whereas
<code class="structname">pg_statistic</code> is only readable by a superuser.
(This prevents unprivileged users from learning something about
the contents of other people's tables from the statistics. The
<code class="structname">pg_stats</code> view is restricted to show only
rows about tables that the current user can read.)
For example, we might do:
</p><pre class="screen">
SELECT attname, inherited, n_distinct,
array_to_string(most_common_vals, E'\n') as most_common_vals
FROM pg_stats
WHERE tablename = 'road';
attname | inherited | n_distinct | most_common_vals
---------+-----------+------------+------------------------------------
name | f | -0.363388 | I- 580 Ramp+
| | | I- 880 Ramp+
| | | Sp Railroad +
| | | I- 580 +
| | | I- 680 Ramp
name | t | -0.284859 | I- 880 Ramp+
| | | I- 580 Ramp+
| | | I- 680 Ramp+
| | | I- 580 +
| | | State Hwy 13 Ramp
(2 rows)
</pre><p>
Note that two rows are displayed for the same column, one corresponding
to the complete inheritance hierarchy starting at the
<code class="literal">road</code> table (<code class="literal">inherited</code>=<code class="literal">t</code>),
and another one including only the <code class="literal">road</code> table itself
(<code class="literal">inherited</code>=<code class="literal">f</code>).
</p><p>
The amount of information stored in <code class="structname">pg_statistic</code>
by <code class="command">ANALYZE</code>, in particular the maximum number of entries in the
<code class="structfield">most_common_vals</code> and <code class="structfield">histogram_bounds</code>
arrays for each column, can be set on a
column-by-column basis using the <code class="command">ALTER TABLE SET STATISTICS</code>
command, or globally by setting the
<a class="xref" href="runtime-config-query.html#GUC-DEFAULT-STATISTICS-TARGET">default_statistics_target</a> configuration variable.
The default limit is presently 100 entries. Raising the limit
might allow more accurate planner estimates to be made, particularly for
columns with irregular data distributions, at the price of consuming
more space in <code class="structname">pg_statistic</code> and slightly more
time to compute the estimates. Conversely, a lower limit might be
sufficient for columns with simple data distributions.
</p><p>
Further details about the planner's use of statistics can be found in
<a class="xref" href="planner-stats-details.html" title="Chapter 72. How the Planner Uses Statistics">Chapter 72</a>.
</p></div><div class="sect2" id="PLANNER-STATS-EXTENDED"><div class="titlepage"><div><div><h3 class="title">14.2.2. Extended Statistics</h3></div></div></div><a id="id-1.5.13.5.4.2" class="indexterm"></a><a id="id-1.5.13.5.4.3" class="indexterm"></a><a id="id-1.5.13.5.4.4" class="indexterm"></a><a id="id-1.5.13.5.4.5" class="indexterm"></a><p>
It is common to see slow queries running bad execution plans because
multiple columns used in the query clauses are correlated.
The planner normally assumes that multiple conditions
are independent of each other,
an assumption that does not hold when column values are correlated.
Regular statistics, because of their per-individual-column nature,
cannot capture any knowledge about cross-column correlation.
However, <span class="productname">PostgreSQL</span> has the ability to compute
<em class="firstterm">multivariate statistics</em>, which can capture
such information.
</p><p>
Because the number of possible column combinations is very large,
it's impractical to compute multivariate statistics automatically.
Instead, <em class="firstterm">extended statistics objects</em>, more often
called just <em class="firstterm">statistics objects</em>, can be created to instruct
the server to obtain statistics across interesting sets of columns.
</p><p>
Statistics objects are created using the
<a class="link" href="sql-createstatistics.html" title="CREATE STATISTICS"><code class="command">CREATE STATISTICS</code></a> command.
Creation of such an object merely creates a catalog entry expressing
interest in the statistics. Actual data collection is performed
by <code class="command">ANALYZE</code> (either a manual command, or background
auto-analyze). The collected values can be examined in the
<a class="link" href="catalog-pg-statistic-ext-data.html" title="52.51. pg_statistic_ext_data"><code class="structname">pg_statistic_ext_data</code></a>
catalog.
</p><p>
<code class="command">ANALYZE</code> computes extended statistics based on the same
sample of table rows that it takes for computing regular single-column
statistics. Since the sample size is increased by increasing the
statistics target for the table or any of its columns (as described in
the previous section), a larger statistics target will normally result in
more accurate extended statistics, as well as more time spent calculating
them.
</p><p>
The following subsections describe the kinds of extended statistics
that are currently supported.
</p><div class="sect3" id="id-1.5.13.5.4.11"><div class="titlepage"><div><div><h4 class="title">14.2.2.1. Functional Dependencies</h4></div></div></div><p>
The simplest kind of extended statistics tracks <em class="firstterm">functional
dependencies</em>, a concept used in definitions of database normal forms.
We say that column <code class="structfield">b</code> is functionally dependent on
column <code class="structfield">a</code> if knowledge of the value of
<code class="structfield">a</code> is sufficient to determine the value
of <code class="structfield">b</code>, that is there are no two rows having the same value
of <code class="structfield">a</code> but different values of <code class="structfield">b</code>.
In a fully normalized database, functional dependencies should exist
only on primary keys and superkeys. However, in practice many data sets
are not fully normalized for various reasons; intentional
denormalization for performance reasons is a common example.
Even in a fully normalized database, there may be partial correlation
between some columns, which can be expressed as partial functional
dependency.
</p><p>
The existence of functional dependencies directly affects the accuracy
of estimates in certain queries. If a query contains conditions on
both the independent and the dependent column(s), the
conditions on the dependent columns do not further reduce the result
size; but without knowledge of the functional dependency, the query
planner will assume that the conditions are independent, resulting
in underestimating the result size.
</p><p>
To inform the planner about functional dependencies, <code class="command">ANALYZE</code>
can collect measurements of cross-column dependency. Assessing the
degree of dependency between all sets of columns would be prohibitively
expensive, so data collection is limited to those groups of columns
appearing together in a statistics object defined with
the <code class="literal">dependencies</code> option. It is advisable to create
<code class="literal">dependencies</code> statistics only for column groups that are
strongly correlated, to avoid unnecessary overhead in both
<code class="command">ANALYZE</code> and later query planning.
</p><p>
Here is an example of collecting functional-dependency statistics:
</p><pre class="programlisting">
CREATE STATISTICS stts (dependencies) ON city, zip FROM zipcodes;
ANALYZE zipcodes;
SELECT stxname, stxkeys, stxddependencies
FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid)
WHERE stxname = 'stts';
stxname | stxkeys | stxddependencies
---------+---------+------------------------------------------
stts | 1 5 | {"1 => 5": 1.000000, "5 => 1": 0.423130}
(1 row)
</pre><p>
Here it can be seen that column 1 (zip code) fully determines column
5 (city) so the coefficient is 1.0, while city only determines zip code
about 42% of the time, meaning that there are many cities (58%) that are
represented by more than a single ZIP code.
</p><p>
When computing the selectivity for a query involving functionally
dependent columns, the planner adjusts the per-condition selectivity
estimates using the dependency coefficients so as not to produce
an underestimate.
</p><div class="sect4" id="id-1.5.13.5.4.11.7"><div class="titlepage"><div><div><h5 class="title">14.2.2.1.1. Limitations of Functional Dependencies</h5></div></div></div><p>
Functional dependencies are currently only applied when considering
simple equality conditions that compare columns to constant values,
and <code class="literal">IN</code> clauses with constant values.
They are not used to improve estimates for equality conditions
comparing two columns or comparing a column to an expression, nor for
range clauses, <code class="literal">LIKE</code> or any other type of condition.
</p><p>
When estimating with functional dependencies, the planner assumes that
conditions on the involved columns are compatible and hence redundant.
If they are incompatible, the correct estimate would be zero rows, but
that possibility is not considered. For example, given a query like
</p><pre class="programlisting">
SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '94105';
</pre><p>
the planner will disregard the <code class="structfield">city</code> clause as not
changing the selectivity, which is correct. However, it will make
the same assumption about
</p><pre class="programlisting">
SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '90210';
</pre><p>
even though there will really be zero rows satisfying this query.
Functional dependency statistics do not provide enough information
to conclude that, however.
</p><p>
In many practical situations, this assumption is usually satisfied;
for example, there might be a GUI in the application that only allows
selecting compatible city and ZIP code values to use in a query.
But if that's not the case, functional dependencies may not be a viable
option.
</p></div></div><div class="sect3" id="id-1.5.13.5.4.12"><div class="titlepage"><div><div><h4 class="title">14.2.2.2. Multivariate N-Distinct Counts</h4></div></div></div><p>
Single-column statistics store the number of distinct values in each
column. Estimates of the number of distinct values when combining more
than one column (for example, for <code class="literal">GROUP BY a, b</code>) are
frequently wrong when the planner only has single-column statistical
data, causing it to select bad plans.
</p><p>
To improve such estimates, <code class="command">ANALYZE</code> can collect n-distinct
statistics for groups of columns. As before, it's impractical to do
this for every possible column grouping, so data is collected only for
those groups of columns appearing together in a statistics object
defined with the <code class="literal">ndistinct</code> option. Data will be collected
for each possible combination of two or more columns from the set of
listed columns.
</p><p>
Continuing the previous example, the n-distinct counts in a
table of ZIP codes might look like the following:
</p><pre class="programlisting">
CREATE STATISTICS stts2 (ndistinct) ON city, state, zip FROM zipcodes;
ANALYZE zipcodes;
SELECT stxkeys AS k, stxdndistinct AS nd
FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid)
WHERE stxname = 'stts2';
-[ RECORD 1 ]--------------------------------------------------------
k | 1 2 5
nd | {"1, 2": 33178, "1, 5": 33178, "2, 5": 27435, "1, 2, 5": 33178}
(1 row)
</pre><p>
This indicates that there are three combinations of columns that
have 33178 distinct values: ZIP code and state; ZIP code and city;
and ZIP code, city and state (the fact that they are all equal is
expected given that ZIP code alone is unique in this table). On the
other hand, the combination of city and state has only 27435 distinct
values.
</p><p>
It's advisable to create <code class="literal">ndistinct</code> statistics objects only
on combinations of columns that are actually used for grouping, and
for which misestimation of the number of groups is resulting in bad
plans. Otherwise, the <code class="command">ANALYZE</code> cycles are just wasted.
</p></div><div class="sect3" id="id-1.5.13.5.4.13"><div class="titlepage"><div><div><h4 class="title">14.2.2.3. Multivariate MCV Lists</h4></div></div></div><p>
Another type of statistic stored for each column are most-common value
lists. This allows very accurate estimates for individual columns, but
may result in significant misestimates for queries with conditions on
multiple columns.
</p><p>
To improve such estimates, <code class="command">ANALYZE</code> can collect MCV
lists on combinations of columns. Similarly to functional dependencies
and n-distinct coefficients, it's impractical to do this for every
possible column grouping. Even more so in this case, as the MCV list
(unlike functional dependencies and n-distinct coefficients) does store
the common column values. So data is collected only for those groups
of columns appearing together in a statistics object defined with the
<code class="literal">mcv</code> option.
</p><p>
Continuing the previous example, the MCV list for a table of ZIP codes
might look like the following (unlike for simpler types of statistics,
a function is required for inspection of MCV contents):
</p><pre class="programlisting">
CREATE STATISTICS stts3 (mcv) ON city, state FROM zipcodes;
ANALYZE zipcodes;
SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid),
pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts3';
index | values | nulls | frequency | base_frequency
-------+------------------------+-------+-----------+----------------
0 | {Washington, DC} | {f,f} | 0.003467 | 2.7e-05
1 | {Apo, AE} | {f,f} | 0.003067 | 1.9e-05
2 | {Houston, TX} | {f,f} | 0.002167 | 0.000133
3 | {El Paso, TX} | {f,f} | 0.002 | 0.000113
4 | {New York, NY} | {f,f} | 0.001967 | 0.000114
5 | {Atlanta, GA} | {f,f} | 0.001633 | 3.3e-05
6 | {Sacramento, CA} | {f,f} | 0.001433 | 7.8e-05
7 | {Miami, FL} | {f,f} | 0.0014 | 6e-05
8 | {Dallas, TX} | {f,f} | 0.001367 | 8.8e-05
9 | {Chicago, IL} | {f,f} | 0.001333 | 5.1e-05
...
(99 rows)
</pre><p>
This indicates that the most common combination of city and state is
Washington in DC, with actual frequency (in the sample) about 0.35%.
The base frequency of the combination (as computed from the simple
per-column frequencies) is only 0.0027%, resulting in two orders of
magnitude under-estimates.
</p><p>
It's advisable to create <acronym class="acronym">MCV</acronym> statistics objects only
on combinations of columns that are actually used in conditions together,
and for which misestimation of the number of groups is resulting in bad
plans. Otherwise, the <code class="command">ANALYZE</code> and planning cycles
are just wasted.
</p></div></div></div><div xmlns="http://www.w3.org/TR/xhtml1/transitional" class="navfooter"><hr></hr><table width="100%" summary="Navigation footer"><tr><td width="40%" align="left"><a accesskey="p" href="using-explain.html" title="14.1. Using EXPLAIN">Prev</a> </td><td width="20%" align="center"><a accesskey="u" href="performance-tips.html" title="Chapter 14. Performance Tips">Up</a></td><td width="40%" align="right"> <a accesskey="n" href="explicit-joins.html" title="14.3. Controlling the Planner with Explicit JOIN Clauses">Next</a></td></tr><tr><td width="40%" align="left" valign="top">14.1. Using <code xmlns="http://www.w3.org/1999/xhtml" class="command">EXPLAIN</code> </td><td width="20%" align="center"><a accesskey="h" href="index.html" title="PostgreSQL 14.5 Documentation">Home</a></td><td width="40%" align="right" valign="top"> 14.3. Controlling the Planner with Explicit <code xmlns="http://www.w3.org/1999/xhtml" class="literal">JOIN</code> Clauses</td></tr></table></div></body></html>
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