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

  <indexterm zone="performance-tips">
   <primary>performance</primary>
  </indexterm>

  <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, so the system includes
    a complex <firstterm>planner</> that tries to select good plans.
    You can use the
    <xref linkend="sql-explain" endterm="sql-explain-title"> command
    to see what query plan the planner 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 structure of a query plan is a tree of <firstterm>plan nodes</>.
    Nodes at the bottom level are table scan nodes: they return raw rows
    from a table.  There are different types of scan nodes for different
    table access methods: sequential scans, index scans, and bitmap index
    scans.  If the query requires joining, aggregation, sorting, or other
    operations on the raw rows, then there will be additional nodes
    <quote>atop</> the scan nodes to perform these operations.  Again,
    there is usually more than one possible way to do these operations,
    so different node types can appear here too.  The output
    of <command>EXPLAIN</command> has one line for each node in the plan
    tree, showing the basic node type plus the cost estimates that the planner
    made for the execution of that plan node.  The first line (topmost node)
    has the estimated total execution cost for the plan; it is this number
    that the planner seeks to minimize.
   </para>

   <para>
    Here is a trivial example, just to show what the output looks like.
    <footnote>
     <para>
      Examples in this section are drawn from the regression test database
      after doing a <command>VACUUM ANALYZE</>, using 8.1 development sources.
      You should be able to get similar results if you try the examples yourself,
      but your estimated costs and row counts will probably vary slightly
      because <command>ANALYZE</>'s statistics are random samples rather
      than being exact.
     </para>
    </footnote>

<programlisting>
EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..458.00 rows=10000 width=244)
</programlisting>
   </para>

   <para>
    The numbers that are 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: for example, a query with a <literal>LIMIT</> clause will stop
       short of paying the total cost of the <literal>Limit</> plan node's
       input node.)
      </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; that is, 1.0
    equals one sequential disk page read, by definition.  (CPU effort
    estimates are made too; they 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-query-constants">.)
   </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 cares about.
    In particular, the cost does not consider the time spent transmitting
    result rows to the client, which could be an important
    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 or scanned by the plan node.  It is usually less,
    reflecting the estimated selectivity of any <literal>WHERE</>-clause
    conditions that are being 
    applied at the node.  Ideally the top-level rows estimate will
    approximate the number of rows actually returned, updated, or deleted
    by the query.
   </para>

   <para>
    Returning to our example:

<programlisting>
EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..458.00 rows=10000 width=244)
</programlisting>
   </para>

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

<programlisting>
SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';
</programlisting>

    you will find out that <classname>tenk1</classname> has 358 disk
    pages and 10000 rows.  So the cost is estimated at 358 page
    reads, defined as costing 1.0 apiece, plus 10000 * <xref
    linkend="guc-cpu-tuple-cost"> which is
    typically 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; 7000;

                         QUERY PLAN
------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..483.00 rows=7033 width=244)
   Filter: (unique1 &lt; 7000)
</programlisting>

    Notice that the <command>EXPLAIN</> output shows the <literal>WHERE</>
    clause being applied as a <quote>filter</> condition; this means that
    the plan node checks the condition for each row it scans, and outputs
    only the ones that pass the condition.
    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 7000, but the rows
    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>
    Now, let's make the condition more restrictive:

<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100;

                                  QUERY PLAN
------------------------------------------------------------------------------
 Bitmap Heap Scan on tenk1  (cost=2.37..232.35 rows=106 width=244)
   Recheck Cond: (unique1 &lt; 100)
   ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0)
         Index Cond: (unique1 &lt; 100)
</programlisting>

    Here the planner has decided to use a two-step plan: the bottom plan
    node visits an index to find the locations of rows matching the index
    condition, and then the upper plan node actually fetches those rows
    from the table itself.  Fetching the rows separately is much more
    expensive than sequentially reading them, but because not all the pages
    of the table have to be visited, this is still cheaper than a sequential
    scan.  (The reason for using two levels of plan is that the upper plan
    node sorts the row locations identified by the index into physical order
    before reading them, so as to minimize the costs of the separate fetches.
    The <quote>bitmap</> mentioned in the node names is the mechanism that
    does the sorting.)
   </para>

   <para>
    If the <literal>WHERE</> condition is selective enough, the planner may
    switch to a <quote>simple</> index scan plan:

<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 3;

                                  QUERY PLAN
------------------------------------------------------------------------------
 Index Scan using tenk1_unique1 on tenk1  (cost=0.00..10.00 rows=2 width=244)
   Index Cond: (unique1 &lt; 3)
</programlisting>

    In this case the table rows are fetched in index order, which makes them
    even more expensive to read, but there are so few that the extra cost
    of sorting the row locations is not worth it.  You'll most often see
    this plan type for queries that fetch just a single row, and for queries
    that request an <literal>ORDER BY</> condition that matches the index
    order.
   </para>

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

<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 3 AND stringu1 = 'xxx';

                                  QUERY PLAN
------------------------------------------------------------------------------
 Index Scan using tenk1_unique1 on tenk1  (cost=0.00..10.01 rows=1 width=244)
   Index Cond: (unique1 &lt; 3)
   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>
    If there are indexes on several columns used in <literal>WHERE</>, the
    planner might choose to use an AND or OR combination of the indexes:

<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100 AND unique2 &gt; 9000;

                                     QUERY PLAN
-------------------------------------------------------------------------------------
 Bitmap Heap Scan on tenk1  (cost=11.27..49.11 rows=11 width=244)
   Recheck Cond: ((unique1 &lt; 100) AND (unique2 &gt; 9000))
   -&gt;  BitmapAnd  (cost=11.27..11.27 rows=11 width=0)
         -&gt;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0)
               Index Cond: (unique1 &lt; 100)
         -&gt;  Bitmap Index Scan on tenk1_unique2  (cost=0.00..8.65 rows=1042 width=0)
               Index Cond: (unique2 &gt; 9000)
</programlisting>

    But this requires visiting both indexes, so it's not necessarily a win
    compared to using just one index and treating the other condition as
    a filter.  If you vary the ranges involved you'll see the plan change
    accordingly.
   </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; 100 AND t1.unique2 = t2.unique2;

                                      QUERY PLAN
--------------------------------------------------------------------------------------
 Nested Loop  (cost=2.37..553.11 rows=106 width=488)
   -&gt;  Bitmap Heap Scan on tenk1 t1  (cost=2.37..232.35 rows=106 width=244)
         Recheck Cond: (unique1 &lt; 100)
         -&gt;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0)
               Index Cond: (unique1 &lt; 100)
   -&gt;  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.00..3.01 rows=1 width=244)
         Index Cond: ("outer".unique2 = t2.unique2)
</programlisting>
   </para>

   <para>
    In this nested-loop join, the outer scan is the same bitmap index scan we
    saw earlier, and so its cost and row count are the same because we are
    applying the <literal>WHERE</> clause <literal>unique1 &lt; 100</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 (106 * 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 described in <xref linkend="runtime-config-query-enable">.
    (This is a crude tool, but useful.  See
    also <xref linkend="explicit-joins">.)

<programlisting>
SET enable_nestloop = off;
EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;

                                        QUERY PLAN
------------------------------------------------------------------------------------------
 Hash Join  (cost=232.61..741.67 rows=106 width=488)
   Hash Cond: ("outer".unique2 = "inner".unique2)
   -&gt;  Seq Scan on tenk2 t2  (cost=0.00..458.00 rows=10000 width=244)
   -&gt;  Hash  (cost=232.35..232.35 rows=106 width=244)
         -&gt;  Bitmap Heap Scan on tenk1 t1  (cost=2.37..232.35 rows=106 width=244)
               Recheck Cond: (unique1 &lt; 100)
               -&gt;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0)
                     Index Cond: (unique1 &lt; 100)
</programlisting>

    This plan proposes to extract the 100 interesting rows of <classname>tenk1</classname>
    using that same old index scan, stash them into an in-memory hash table,
    and then do a sequential scan of <classname>tenk2</classname>, probing into the hash table
    for possible matches of <literal>t1.unique2 = t2.unique2</literal> at each <classname>tenk2</classname> row.
    The cost to read <classname>tenk1</classname> and set up the hash table is entirely start-up
    cost for the hash join, since we won't get any rows out until we can
    start reading <classname>tenk2</classname>.  The total time estimate for the join also
    includes a hefty charge for the CPU time to probe the hash table
    10000 times.  Note, however, that we are <emphasis>not</emphasis> charging 10000 times 232.35;
    the hash table setup is only done once in this plan type.
   </para>

   <para>
    It is possible to check on the accuracy of the planner's estimated costs
    by using <command>EXPLAIN ANALYZE</>.  This command actually executes the query,
    and then displays the true run time accumulated within each plan node
    along with the same estimated costs that a plain <command>EXPLAIN</command> shows.
    For example, we might get a result like this:

<screen>
EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;

                                                            QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------
 Nested Loop  (cost=2.37..553.11 rows=106 width=488) (actual time=1.392..12.700 rows=100 loops=1)
   -&gt;  Bitmap Heap Scan on tenk1 t1  (cost=2.37..232.35 rows=106 width=244) (actual time=0.878..2.367 rows=100 loops=1)
         Recheck Cond: (unique1 &lt; 100)
         -&gt;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0) (actual time=0.546..0.546 rows=100 loops=1)
               Index Cond: (unique1 &lt; 100)
   -&gt;  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.00..3.01 rows=1 width=244) (actual time=0.067..0.078 rows=1 loops=100)
         Index Cond: ("outer".unique2 = t2.unique2)
 Total runtime: 14.452 ms
</screen>

    Note that the <quote>actual time</quote> values are in milliseconds of
    real time, whereas the <quote>cost</quote> estimates are expressed in
    arbitrary units of disk fetches; so they are unlikely to match up.
    The thing to pay attention to is the ratios.
   </para>

   <para>
    In some query plans, it is possible for a subplan node to be executed more
    than once.  For example, the inner index scan is executed once per outer
    row in the above nested-loop plan.  In such cases, the
    <quote>loops</quote> value reports the
    total number of executions of the node, and the actual time and rows
    values shown are averages per-execution.  This is done to make the numbers
    comparable with the way that the cost estimates are shown.  Multiply by
    the <quote>loops</quote> value to get the total time actually spent in
    the node.
   </para>

   <para>
    The <literal>Total runtime</literal> shown by <command>EXPLAIN
    ANALYZE</command> includes executor start-up and shut-down time, as well
    as time spent processing the result rows.  It does not include parsing,
    rewriting, or planning time.  For a <command>SELECT</> query, the total
    run time will normally be just a little larger than the total time
    reported for the top-level plan node.  For <command>INSERT</>,
    <command>UPDATE</>, and <command>DELETE</> commands, the total run time
    may be considerably larger, because it includes the time spent processing
    the result rows.  In these commands, the time for the top plan node
    essentially is the time spent computing the new rows and/or locating the
    old ones, but it doesn't include the time spent making the changes.
    Time spent firing triggers, if any, is also outside the top plan node,
    and is shown separately for each trigger.
   </para>

   <para>
    It is worth noting that <command>EXPLAIN</> results should not be extrapolated
    to situations other than the one you are actually testing; for example,
    results on a toy-sized table can't be assumed to apply to large tables.
    The planner's cost estimates are not linear and so it may well choose
    a different plan for a larger or smaller table.  An extreme example
    is that on a table that only occupies one disk page, you'll nearly
    always get a sequential scan plan whether indexes are available or not.
    The planner realizes that it's going to take one disk page read to
    process the table in any case, so there's no value in expending additional
    page reads to look at an index.
   </para>
  </sect1>

 <sect1 id="planner-stats">
  <title>Statistics Used by the Planner</title>

  <indexterm zone="planner-stats">
   <primary>statistics</primary>
   <secondary>of the planner</secondary>
  </indexterm>

  <para>
   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.
  </para>

  <para>
   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
   <link linkend="catalog-pg-class"><structname>pg_class</structname></link>, in
   the columns <structfield>reltuples</structfield> and
   <structfield>relpages</structfield>.  We can look at it with
   queries similar to this one:

<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)
</screen>

   Here we can see that <structname>tenk1</structname> contains 10000
   rows, as do its indexes, but the indexes are (unsurprisingly) much
   smaller than the table.
  </para>

  <para>
   For efficiency reasons, <structfield>reltuples</structfield> 
   and <structfield>relpages</structfield> are not updated on-the-fly,
   and so they usually contain somewhat out-of-date values.
   They are updated by <command>VACUUM</>, <command>ANALYZE</>, and a
   few DDL commands such as <command>CREATE INDEX</>.  A stand-alone
   <command>ANALYZE</>, that is one not part of <command>VACUUM</>,
   generates an approximate <structfield>reltuples</structfield> value
   since it does not read every row of the table.  The planner
   will scale the values it finds in <structname>pg_class</structname>
   to match the current physical table size, thus obtaining a closer
   approximation.
  </para>

  <indexterm>
   <primary>pg_statistic</primary>
  </indexterm>

  <para>
   Most queries retrieve only a fraction of the rows in a table, due
   to having <literal>WHERE</> clauses that restrict the rows to be
   examined.  The planner thus needs to make an estimate of the
   <firstterm>selectivity</> of <literal>WHERE</> clauses, that is,
   the fraction of rows that match each condition in the
   <literal>WHERE</> clause.  The information used for this task is
   stored in the <link linkend="catalog-pg-statistic"><structname>pg_statistic</structname></link>
   system catalog.  Entries in <structname>pg_statistic</structname>
   are updated by the <command>ANALYZE</> and <command>VACUUM
   ANALYZE</> commands, and are always approximate even when freshly
   updated.
  </para>

  <indexterm>
   <primary>pg_stats</primary>
  </indexterm>

  <para>
   Rather than look at <structname>pg_statistic</structname> directly,
   it's better to look at its view <structname>pg_stats</structname>
   when examining the statistics manually.  <structname>pg_stats</structname>
   is designed to be more easily readable.  Furthermore,
   <structname>pg_stats</structname> is readable by all, whereas
   <structname>pg_statistic</structname> 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
   <structname>pg_stats</structname> view is restricted to show only
   rows about tables that the current user can read.)
   For example, we might do:

<screen>
SELECT attname, n_distinct, most_common_vals FROM pg_stats WHERE tablename = 'road';

 attname | n_distinct |                                                                                                                                                                                  most_common_vals                                                                                                                                                                                   
---------+------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 name    |  -0.467008 | {"I- 580                        Ramp","I- 880                        Ramp","Sp Railroad                       ","I- 580                            ","I- 680                        Ramp","I- 80                         Ramp","14th                          St  ","5th                           St  ","Mission                       Blvd","I- 880                            "}
 thepath |         20 | {"[(-122.089,37.71),(-122.0886,37.711)]"}
(2 rows)
</screen>
  </para>

  <para>
   <structname>pg_stats</structname> is described in detail in
   <xref linkend="view-pg-stats">.
  </para>

  <para>
   The amount of information stored in <structname>pg_statistic</structname>,
   in particular the maximum number of entries in the
   <structfield>most_common_vals</> and <structfield>histogram_bounds</>
   arrays for each column, can be set on a
   column-by-column basis using the <command>ALTER TABLE SET STATISTICS</>
   command, or globally by setting the
   <xref linkend="guc-default-statistics-target"> configuration variable.
   The default limit is presently 10 entries.  Raising the limit
   may allow more accurate planner estimates to be made, particularly for
   columns with irregular data distributions, at the price of consuming
   more space in <structname>pg_statistic</structname> and slightly more
   time to compute the estimates.  Conversely, a lower limit may be
   appropriate for columns with simple data distributions.
  </para>

 </sect1>

 <sect1 id="explicit-joins">
  <title>Controlling the Planner with Explicit <literal>JOIN</> Clauses</title>

  <indexterm zone="explicit-joins">
   <primary>join</primary>
   <secondary>controlling the order</secondary>
  </indexterm>

  <para>
   It is possible
   to control the query planner to some extent by using the explicit <literal>JOIN</>
   syntax.  To see why this matters, we first need some background.
  </para>

  <para>
   In a simple join query, such as
<programlisting>
SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id;
</programlisting>
   the planner is free to join the given tables in any order.  For
   example, it could generate a query plan that joins A to B, using
   the <literal>WHERE</> condition <literal>a.id = b.id</>, and then
   joins C to this joined table, using the other <literal>WHERE</>
   condition.  Or it could join B to C and then join A to that result.
   Or it could join A to C and then join them with B &mdash; but that
   would be inefficient, since the full Cartesian product of A and C
   would have to be formed, there being no applicable condition in the
   <literal>WHERE</> clause to allow optimization of the join.  (All
   joins in the <productname>PostgreSQL</productname> executor happen
   between two input tables, so it's necessary to build up the result
   in one or another of these fashions.)  The important point is that
   these different join possibilities give semantically equivalent
   results but may have hugely different execution costs.  Therefore,
   the planner will explore all of them to try to find the most
   efficient query plan.
  </para>

  <para>
   When a query only involves two or three tables, there aren't many join
   orders to worry about.  But the number of possible join orders grows
   exponentially as the number of tables expands.  Beyond ten or so input
   tables it's no longer practical to do an exhaustive search of all the
   possibilities, and even for six or seven tables planning may take an
   annoyingly long time.  When there are too many input tables, the
   <productname>PostgreSQL</productname> planner will switch from exhaustive
   search to a <firstterm>genetic</firstterm> probabilistic search
   through a limited number of possibilities.  (The switch-over threshold is
   set by the <xref linkend="guc-geqo-threshold"> run-time
   parameter.)
   The genetic search takes less time, but it won't
   necessarily find the best possible plan.
  </para>

  <para>
   When the query involves outer joins, the planner has much less freedom
   than it does for plain (inner) joins. For example, consider
<programlisting>
SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);
</programlisting>
   Although this query's restrictions are superficially similar to the
   previous example, the semantics are different because a row must be
   emitted for each row of A that has no matching row in the join of B and C.
   Therefore the planner has no choice of join order here: it must join
   B to C and then join A to that result.  Accordingly, this query takes
   less time to plan than the previous query.
  </para>

  <para>
   Explicit inner join syntax (<literal>INNER JOIN</>, <literal>CROSS
   JOIN</>, or unadorned <literal>JOIN</>) is semantically the same as
   listing the input relations in <literal>FROM</>, so it does not need to
   constrain the join order.  But it is possible to instruct the
   <productname>PostgreSQL</productname> query planner to treat
   explicit inner <literal>JOIN</>s as constraining the join order anyway.
   For example, these three queries are logically equivalent:
<programlisting>
SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id;
SELECT * FROM a CROSS JOIN b CROSS JOIN c WHERE a.id = b.id AND b.ref = c.id;
SELECT * FROM a JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);
</programlisting>
   But if we tell the planner to honor the <literal>JOIN</> order,
   the second and third take less time to plan than the first.  This effect
   is not worth worrying about for only three tables, but it can be a
   lifesaver with many tables.
  </para>

  <para>
   To force the planner to follow the <literal>JOIN</> order for inner joins,
   set the <xref linkend="guc-join-collapse-limit"> run-time parameter to 1.
   (Other possible values are discussed below.)
  </para>

  <para>
   You do not need to constrain the join order completely in order to
   cut search time, because it's OK to use <literal>JOIN</> operators
   within items of a plain <literal>FROM</> list.  For example, consider
<programlisting>
SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...;
</programlisting>
   With <varname>join_collapse_limit</> = 1, this
   forces the planner to join A to B before joining them to other tables,
   but doesn't constrain its choices otherwise.  In this example, the
   number of possible join orders is reduced by a factor of 5.
  </para>

  <para>
   Constraining the planner's search in this way is a useful technique
   both for reducing planning time and for directing the planner to a
   good query plan.  If the planner chooses a bad join order by default,
   you can force it to choose a better order via <literal>JOIN</> syntax
   &mdash; assuming that you know of a better order, that is.  Experimentation
   is recommended.
  </para>

  <para>
   A closely related issue that affects planning time is collapsing of
   subqueries into their parent query.  For example, consider
<programlisting>
SELECT *
FROM x, y,
    (SELECT * FROM a, b, c WHERE something) AS ss
WHERE somethingelse;
</programlisting>
   This situation might arise from use of a view that contains a join;
   the view's <literal>SELECT</> rule will be inserted in place of the view reference,
   yielding a query much like the above.  Normally, the planner will try
   to collapse the subquery into the parent, yielding
<programlisting>
SELECT * FROM x, y, a, b, c WHERE something AND somethingelse;
</programlisting>
   This usually results in a better plan than planning the subquery
   separately.  (For example, the outer <literal>WHERE</> conditions might be such that
   joining X to A first eliminates many rows of A, thus avoiding the need to
   form the full logical output of the subquery.)  But at the same time,
   we have increased the planning time; here, we have a five-way join
   problem replacing two separate three-way join problems.  Because of the
   exponential growth of the number of possibilities, this makes a big
   difference.  The planner tries to avoid getting stuck in huge join search
   problems by not collapsing a subquery if more than <varname>from_collapse_limit</>
   <literal>FROM</> items would result in the parent
   query.  You can trade off planning time against quality of plan by
   adjusting this run-time parameter up or down.
  </para>

  <para>
   <xref linkend="guc-from-collapse-limit"> and <xref
   linkend="guc-join-collapse-limit">
   are similarly named because they do almost the same thing: one controls
   when the planner will <quote>flatten out</> subselects, and the
   other controls when it will flatten out explicit inner joins.  Typically
   you would either set <varname>join_collapse_limit</> equal to
   <varname>from_collapse_limit</> (so that explicit joins and subselects
   act similarly) or set <varname>join_collapse_limit</> to 1 (if you want
   to control join order with explicit joins).  But you might set them
   differently if you are trying to fine-tune the trade off between planning
   time and run time.
  </para>
 </sect1>

 <sect1 id="populate">
  <title>Populating a Database</title>

  <para>
   One may need to insert a large amount of data when first populating
   a database. This section contains some suggestions on how to make
   this process as efficient as possible.
  </para>

  <sect2 id="disable-autocommit">
   <title>Disable Autocommit</title>

   <indexterm>
    <primary>autocommit</primary>
    <secondary>bulk-loading data</secondary>
   </indexterm>

   <para>
    Turn off autocommit and just do one commit at the end.  (In plain
    SQL, this means issuing <command>BEGIN</command> at the start and
    <command>COMMIT</command> at the end.  Some client libraries may
    do this behind your back, in which case you need to make sure the
    library does it when you want it done.)  If you allow each
    insertion to be committed separately,
    <productname>PostgreSQL</productname> is doing a lot of work for
    each row that is added.  An additional benefit of doing all
    insertions in one transaction is that if the insertion of one row
    were to fail then the insertion of all rows inserted up to that
    point would be rolled back, so you won't be stuck with partially
    loaded data.
   </para>
  </sect2>

  <sect2 id="populate-copy-from">
   <title>Use <command>COPY</command></title>

   <para>
    Use <xref linkend="sql-copy" endterm="sql-copy-title"> to load
    all the rows in one command, instead of using a series of
    <command>INSERT</command> commands.  The <command>COPY</command>
    command is optimized for loading large numbers of rows; it is less
    flexible than <command>INSERT</command>, but incurs significantly
    less overhead for large data loads. Since <command>COPY</command>
    is a single command, there is no need to disable autocommit if you
    use this method to populate a table.
   </para>

   <para>
    If you cannot use <command>COPY</command>, it may help to use <xref
    linkend="sql-prepare" endterm="sql-prepare-title"> to create a
    prepared <command>INSERT</command> statement, and then use
    <command>EXECUTE</command> as many times as required.  This avoids
    some of the overhead of repeatedly parsing and planning
    <command>INSERT</command>.
   </para>

   <para>
    Note that loading a large number of rows using
    <command>COPY</command> is almost always faster than using
    <command>INSERT</command>, even if <command>PREPARE</> is used and
    multiple insertions are batched into a single transaction.
   </para>
  </sect2>

  <sect2 id="populate-rm-indexes">
   <title>Remove Indexes</title>

   <para>
    If you are loading a freshly created table, the fastest way is to
    create the table, bulk load the table's data using
    <command>COPY</command>, then create any indexes needed for the
    table.  Creating an index on pre-existing data is quicker than
    updating it incrementally as each row is loaded.
   </para>

   <para>
    If you are adding large amounts of data to an existing table,
    it may be a win to drop the index,
    load the table, and then recreate the index.  Of course, the
    database performance for other users may be adversely affected
    during the time that the index is missing.  One should also think
    twice before dropping unique indexes, since the error checking
    afforded by the unique constraint will be lost while the index is
    missing.
   </para>
  </sect2>

  <sect2 id="populate-rm-fkeys">
   <title>Remove Foreign Key Constraints</title>

   <para>
    Just as with indexes, a foreign key constraint can be checked
    <quote>in bulk</> more efficiently than row-by-row.  So it may be
    useful to drop foreign key constraints, load data, and re-create
    the constraints.  Again, there is a trade-off between data load
    speed and loss of error checking while the constraint is missing.
   </para>
  </sect2>

  <sect2 id="populate-work-mem">
   <title>Increase <varname>maintenance_work_mem</varname></title>

   <para>
    Temporarily increasing the <xref linkend="guc-maintenance-work-mem">
    configuration variable when loading large amounts of data can
    lead to improved performance.  This will help to speed up <command>CREATE
    INDEX</> commands and <command>ALTER TABLE ADD FOREIGN KEY</> commands.
    It won't do much for <command>COPY</> itself, so this advice is
    only useful when you are using one or both of the above techniques.
   </para>
  </sect2>

  <sect2 id="populate-checkpoint-segments">
   <title>Increase <varname>checkpoint_segments</varname></title>

   <para>
    Temporarily increasing the <xref
    linkend="guc-checkpoint-segments"> configuration variable can also
    make large data loads faster.  This is because loading a large
    amount of data into <productname>PostgreSQL</productname> will
    cause checkpoints to occur more often than the normal checkpoint
    frequency (specified by the <varname>checkpoint_timeout</varname>
    configuration variable). Whenever a checkpoint occurs, all dirty
    pages must be flushed to disk. By increasing
    <varname>checkpoint_segments</varname> temporarily during bulk
    data loads, the number of checkpoints that are required can be
    reduced.
   </para>
  </sect2>

  <sect2 id="populate-analyze">
   <title>Run <command>ANALYZE</command> Afterwards</title>

   <para>
    Whenever you have significantly altered the distribution of data
    within a table, running <xref linkend="sql-analyze"
    endterm="sql-analyze-title"> is strongly recommended. This
    includes bulk loading large amounts of data into the table.  Running
    <command>ANALYZE</command> (or <command>VACUUM ANALYZE</command>)
    ensures that the planner has up-to-date statistics about the
    table.  With no statistics or obsolete statistics, the planner may
    make poor decisions during query planning, leading to poor
    performance on any tables with inaccurate or nonexistent
    statistics.
   </para>
  </sect2>

  <sect2 id="populate-pg-dump">
   <title>Some Notes About <application>pg_dump</></title>

   <para>
    Dump scripts generated by <application>pg_dump</> automatically apply
    several, but not all, of the above guidelines.  To reload a
    <application>pg_dump</> dump as quickly as possible, you need to
    do a few extra things manually.  (Note that these points apply while
    <emphasis>restoring</> a dump, not while <emphasis>creating</> it.
    The same points apply when using <application>pg_restore</> to load
    from a <application>pg_dump</> archive file.)
   </para>

   <para>
    By default, <application>pg_dump</> uses <command>COPY</>, and when
    it is generating a complete schema-and-data dump, it is careful to
    load data before creating indexes and foreign keys.  So in this case
    the first several guidelines are handled automatically.  What is left
    for you to do is to set appropriate (i.e., larger than normal) values
    for <varname>maintenance_work_mem</varname> and
    <varname>checkpoint_segments</varname> before loading the dump script,
    and then to run <command>ANALYZE</> afterwards.
   </para>

   <para>
    A data-only dump will still use <command>COPY</>, but it does not
    drop or recreate indexes, and it does not normally touch foreign
    keys.

     <footnote>
      <para>
       You can get the effect of disabling foreign keys by using
       the <option>-X disable-triggers</> option &mdash; but realize that
       that eliminates, rather than just postponing, foreign key
       validation, and so it is possible to insert bad data if you use it.
      </para>
     </footnote>

    So when loading a data-only dump, it is up to you to drop and recreate
    indexes and foreign keys if you wish to use those techniques.
    It's still useful to increase <varname>checkpoint_segments</varname>
    while loading the data, but don't bother increasing
    <varname>maintenance_work_mem</varname>; rather, you'd do that while
    manually recreating indexes and foreign keys afterwards.
    And don't forget to <command>ANALYZE</> when you're done.
   </para>
  </sect2>
  </sect1>

 </chapter>

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