perform.sgml

PostgreSQL7.4.6 for Linux

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 <chapter id="performance-tips">
  <title>Performance Tips</title>

  <para>
   Query performance can be affected by many things. Some of these can 
   be manipulated by the user, while others are fundamental to the underlying
   design of the system.  This chapter provides some hints about understanding
   and tuning <productname>PostgreSQL</productname> performance.
  </para>

  <sect1 id="using-explain">
   <title>Using <command>EXPLAIN</command></title>

   <indexterm zone="using-explain">
    <primary>EXPLAIN</primary>
   </indexterm>

   <indexterm zone="using-explain">
    <primary>query plan</primary>
   </indexterm>

   <para>
    <productname>PostgreSQL</productname> devises a <firstterm>query
    plan</firstterm> for each query it is given.  Choosing the right
    plan to match the query structure and the properties of the data
    is absolutely critical for good performance.  You can use the
    <command>EXPLAIN</command> command to see what query plan the system
    creates for any query.
    Plan-reading is an art that deserves an extensive tutorial, which
    this is not; but here is some basic information.
   </para>

   <para>
    The numbers that are currently quoted by <command>EXPLAIN</command> are:

    <itemizedlist>
     <listitem>
      <para>
       Estimated start-up cost (Time expended before output scan can start,
       e.g., time to do the sorting in a sort node.)
      </para>
     </listitem>

     <listitem>
      <para>
       Estimated total cost (If all rows were to be retrieved, which they may not
       be: a query with a <literal>LIMIT</> clause will stop short of paying the total cost,
       for example.)
      </para>
     </listitem>

     <listitem>
      <para>
       Estimated number of rows output by this plan node (Again, only if
       executed to completion)
      </para>
     </listitem>

     <listitem>
      <para>
       Estimated average width (in bytes) of rows output by this plan
       node
      </para>
     </listitem>
    </itemizedlist>
   </para>

   <para>
    The costs are measured in units of disk page fetches.  (CPU effort
    estimates are converted into disk-page units using some
    fairly arbitrary fudge factors. If you want to experiment with these
    factors, see the list of run-time configuration parameters in
    <xref linkend="runtime-config-resource">.)
   </para>

   <para>
    It's important to note that the cost of an upper-level node includes
    the cost of all its child nodes.  It's also important to realize that
    the cost only reflects things that the planner/optimizer cares about.
    In particular, the cost does not consider the time spent transmitting
    result rows to the frontend, which could be a pretty dominant
    factor in the true elapsed time; but the planner ignores it because
    it cannot change it by altering the plan.  (Every correct plan will
    output the same row set, we trust.)
   </para>

   <para>
    Rows output is a little tricky because it is <emphasis>not</emphasis> the
    number of rows
    processed/scanned by the query, it is usually less, reflecting the
    estimated selectivity of any <literal>WHERE</>-clause conditions that are being
    applied at this node.  Ideally the top-level rows estimate will
    approximate the number of rows actually returned, updated, or deleted
    by the query.
   </para>

   <para>
    Here are some examples (using the regression test database after a
    <literal>VACUUM ANALYZE</>, and 7.3 development sources):

<programlisting>
EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..333.00 rows=10000 width=148)
</programlisting>
   </para>

   <para>
    This is about as straightforward as it gets.  If you do

<programlisting>
SELECT * FROM pg_class WHERE relname = 'tenk1';
</programlisting>

    you will find out that <classname>tenk1</classname> has 233 disk
    pages and 10000 rows.  So the cost is estimated at 233 page
    reads, defined as costing 1.0 apiece, plus 10000 * <varname>cpu_tuple_cost</varname> which is
    currently 0.01 (try <command>SHOW cpu_tuple_cost</command>).
   </para>

   <para>
    Now let's modify the query to add a <literal>WHERE</> condition:

<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 1000;

                         QUERY PLAN
------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..358.00 rows=1033 width=148)
   Filter: (unique1 &lt; 1000)
</programlisting>

    The estimate of output rows has gone down because of the <literal>WHERE</> clause.
    However, the scan will still have to visit all 10000 rows, so the cost
    hasn't decreased; in fact it has gone up a bit to reflect the extra CPU
    time spent checking the <literal>WHERE</> condition.
   </para>

   <para>
    The actual number of rows this query would select is 1000, but the
    estimate is only approximate.  If you try to duplicate this experiment,
    you will probably get a slightly different estimate; moreover, it will
    change after each <command>ANALYZE</command> command, because the
    statistics produced by <command>ANALYZE</command> are taken from a
    randomized sample of the table.
   </para>

   <para>
    Modify the query to restrict the condition even more:

<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 50;

                                   QUERY PLAN
-------------------------------------------------------------------------------
 Index Scan using tenk1_unique1 on tenk1  (cost=0.00..179.33 rows=49 width=148)
   Index Cond: (unique1 &lt; 50)
</programlisting>

    and you will see that if we make the <literal>WHERE</> condition selective
    enough, the planner will
    eventually decide that an index scan is cheaper than a sequential scan.
    This plan will only have to visit 50 rows because of the index,
    so it wins despite the fact that each individual fetch is more expensive
    than reading a whole disk page sequentially.
   </para>

   <para>
    Add another condition to the <literal>WHERE</> clause:

<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 50 AND stringu1 = 'xxx';

                                  QUERY PLAN
-------------------------------------------------------------------------------
 Index Scan using tenk1_unique1 on tenk1  (cost=0.00..179.45 rows=1 width=148)
   Index Cond: (unique1 &lt; 50)
   Filter: (stringu1 = 'xxx'::name)
</programlisting>

    The added condition <literal>stringu1 = 'xxx'</literal> reduces the
    output-rows estimate, but not the cost because we still have to visit the
    same set of rows.  Notice that the <literal>stringu1</> clause
    cannot be applied as an index condition (since this index is only on
    the <literal>unique1</> column).  Instead it is applied as a filter on
    the rows retrieved by the index.  Thus the cost has actually gone up
    a little bit to reflect this extra checking.
   </para>

   <para>
    Let's try joining two tables, using the columns we have been discussing:

<programlisting>
EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 &lt; 50 AND t1.unique2 = t2.unique2;

                               QUERY PLAN
----------------------------------------------------------------------------
 Nested Loop  (cost=0.00..327.02 rows=49 width=296)
   -&gt;  Index Scan using tenk1_unique1 on tenk1 t1
                                      (cost=0.00..179.33 rows=49 width=148)
         Index Cond: (unique1 &lt; 50)
   -&gt;  Index Scan using tenk2_unique2 on tenk2 t2
                                      (cost=0.00..3.01 rows=1 width=148)
         Index Cond: ("outer".unique2 = t2.unique2)
</programlisting>
   </para>

   <para>
    In this nested-loop join, the outer scan is the same index scan we had
    in the example before last, and so its cost and row count are the same
    because we are applying the <literal>WHERE</> clause <literal>unique1 &lt; 50</literal> at that node.
    The <literal>t1.unique2 = t2.unique2</literal> clause is not relevant yet, so it doesn't
    affect row count of the outer scan.  For the inner scan, the <literal>unique2</> value of the
    current
    outer-scan row is plugged into the inner index scan
    to produce an index condition like
    <literal>t2.unique2 = <replaceable>constant</replaceable></literal>.  So we get the
     same inner-scan plan and costs that we'd get from, say, <literal>EXPLAIN SELECT
     * FROM tenk2 WHERE unique2 = 42</literal>.  The costs of the loop node are then set
     on the basis of the cost of the outer scan, plus one repetition of the
     inner scan for each outer row (49 * 3.01, here), plus a little CPU
     time for join processing.
   </para>

   <para>
    In this example the join's output row count is the same as the product
    of the two scans' row counts, but that's not true in general, because
    in general you can have <literal>WHERE</> clauses that mention both tables and
    so can only be applied at the join point, not to either input scan.
    For example, if we added <literal>WHERE ... AND t1.hundred &lt; t2.hundred</literal>,
    that would decrease the output row count of the join node, but not change
    either input scan.
   </para>

   <para>
    One way to look at variant plans is to force the planner to disregard
    whatever strategy it thought was the winner, using the enable/disable
    flags for each plan type.  (This is a crude tool, but useful.  See