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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>

   <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 are 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 the
    &cite-admin;.)
   </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 constraints 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 regress test database after a
    <literal>VACUUM ANALYZE</>, and 7.3 development sources):

<programlisting>
regression=# 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>
regression=# 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>
regression=# 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 clause to the <literal>WHERE</> condition:

<programlisting>
regression=# EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 50 AND
regression-# 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 clause <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 fields we have been discussing:

<programlisting>
regression=# EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 &lt; 50
regression-# 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>unique1 &lt; 50</literal> <literal>WHERE</> clause 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 loop'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 relations 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
    also <xref linkend="explicit-joins">.)

<programlisting>
regression=# SET enable_nestloop = off;
SET
regression=# EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 &lt; 50
regression-# AND t1.unique2 = t2.unique2;
                               QUERY PLAN
--------------------------------------------------------------------------
 Hash Join  (cost=179.45..563.06 rows=49 width=296)
   Hash Cond: ("outer".unique2 = "inner".unique2)
   -&gt;  Seq Scan on tenk2 t2  (cost=0.00..333.00 rows=10000 width=148)
   -&gt;  Hash  (cost=179.33..179.33 rows=49 width=148)
         -&gt;  Index Scan using tenk1_unique1 on tenk1 t1
                                    (cost=0.00..179.33 rows=49 width=148)
               Index Cond: (unique1 &lt; 50)
</programlisting>

    This plan proposes to extract the 50 interesting rows of <classname>tenk1</classname>
    using ye same olde 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 179.33;
    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>
regression=# EXPLAIN ANALYZE
regression-# SELECT * FROM tenk1 t1, tenk2 t2
regression-# WHERE t1.unique1 &lt; 50 AND t1.unique2 = t2.unique2;
                                   QUERY PLAN
-------------------------------------------------------------------------------
 Nested Loop  (cost=0.00..327.02 rows=49 width=296)
                                 (actual time=1.18..29.82 rows=50 loops=1)
   -&gt;  Index Scan using tenk1_unique1 on tenk1 t1
                  (cost=0.00..179.33 rows=49 width=148)
                                 (actual time=0.63..8.91 rows=50 loops=1)
         Index Cond: (unique1 &lt; 50)
   -&gt;  Index Scan using tenk2_unique2 on tenk2 t2
                  (cost=0.00..3.01 rows=1 width=148)
                                 (actual time=0.29..0.32 rows=1 loops=50)
         Index Cond: ("outer".unique2 = t2.unique2)
 Total runtime: 31.60 msec
</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.
   </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>

  <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
   <structname>pg_class</structname>'s <structfield>reltuples</structfield>
   and <structfield>relpages</structfield> columns.  We can look at it
   with queries similar to this one:

<screen>
regression=# SELECT relname, relkind, reltuples, relpages FROM pg_class
regression-# WHERE relname LIKE 'tenk1%';
    relname    | relkind | reltuples | relpages
---------------+---------+-----------+----------
 tenk1         | r       |     10000 |      233
 tenk1_hundred | i       |     10000 |       30
 tenk1_unique1 | i       |     10000 |       30
 tenk1_unique2 | i       |     10000 |       30
(4 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 only approximate values (which is good
   enough for the planner's purposes).  They are initialized with dummy
   values (presently 1000 and 10 respectively) when a table is created.
   They are updated by certain commands, presently <command>VACUUM</>,
   <command>ANALYZE</>, and <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.
  </para>

  <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 clause of the <literal>WHERE</> condition.  The information
   used for this task is stored in the <structname>pg_statistic</structname>
   system catalog.  Entries in <structname>pg_statistic</structname> are
   updated by <command>ANALYZE</> and <command>VACUUM ANALYZE</> commands,
   and are always approximate even when freshly updated.
  </para>

  <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 the 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>
regression=# 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)
regression=#
</screen>
  </para>

  <para>
   <xref linkend="planner-pg-stats-table"> shows the columns that
   exist in <structname>pg_stats</structname>.
  </para>

  <table id="planner-pg-stats-table">
   <title><structname>pg_stats</structname> Columns</title>

   <tgroup cols=3>
    <thead>
     <row>
      <entry>Name</entry>
      <entry>Type</entry>
      <entry>Description</entry>
     </row>
    </thead>

    <tbody>
     <row>
      <entry><literal>tablename</literal></entry>
      <entry><type>name</type></entry>
      <entry>Name of the table containing the column</entry>
     </row>

     <row>
      <entry><literal>attname</literal></entry>
      <entry><type>name</type></entry>
      <entry>Column described by this row</entry>
     </row>

     <row>
      <entry><literal>null_frac</literal></entry>
      <entry><type>real</type></entry>
      <entry>Fraction of column's entries that are null</entry>
     </row>

     <row>
      <entry><literal>avg_width</literal></entry>
      <entry><type>integer</type></entry>
      <entry>Average width in bytes of the column's entries</entry>
     </row>

     <row>
      <entry><literal>n_distinct</literal></entry>
      <entry><type>real</type></entry>
      <entry>If greater than zero, the estimated number of distinct values
      in the column.  If less than zero, the negative of the number of
      distinct values divided by the number of rows.  (The negated form
      is used when <command>ANALYZE</> believes that the number of distinct values
      is likely to increase as the table grows; the positive form is used
      when the column seems to have a fixed number of possible values.)
      For example, -1 indicates a unique column in which the number of
      distinct values is the same as the number of rows.
      </entry>
     </row>

     <row>
      <entry><literal>most_common_vals</literal></entry>
      <entry><type>text[]</type></entry>
      <entry>A list of the most common values in the column. (Omitted if
      no values seem to be more common than any others.)</entry>
     </row>

     <row>
      <entry><literal>most_common_freqs</literal></entry>
      <entry><type>real[]</type></entry>
      <entry>A list of the frequencies of the most common values,
      i.e., number of occurrences of each divided by total number of rows.
     </entry>
     </row>

     <row>
      <entry>histogram_bounds</entry>
      <entry><type>text[]</type></entry>
      <entry>A list of values that divide the column's values into
      groups of approximately equal population.  The 
      <structfield>most_common_vals</>, if present, are omitted from the
      histogram calculation.  (Omitted if column data type does not have a
      <literal>&lt;</> operator, or if the <structfield>most_common_vals</>
      list accounts for the entire population.)
      </entry>
     </row>

     <row>
      <entry>correlation</entry>
      <entry><type>real</type></entry>
      <entry>Statistical correlation between physical row ordering and
      logical ordering of the column values.  This ranges from -1 to +1.
      When the value is near -1 or +1, an index scan on the column will
      be estimated to be cheaper than when it is near zero, due to reduction
      of random access to the disk.  (Omitted if column data type does
      not have a <literal>&lt;</> operator.)
      </entry>
     </row>
    </tbody>
   </tgroup>
  </table>

  <para>
   The maximum number of entries in the <structfield>most_common_vals</>
   and <structfield>histogram_bounds</> arrays can be set on a
   column-by-column basis using the <command>ALTER TABLE SET STATISTICS</>
   command.  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>

  <para>
   Beginning with <productname>PostgreSQL</productname> 7.1 it has been 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 --- 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 <varname>GEQO_THRESHOLD</varname> run-time
   parameter described in the &cite-admin;.)
   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>
   The <productname>PostgreSQL</productname> query planner treats all
   explicit <literal>JOIN</> syntaxes as constraining the join order, even though
   it is not logically necessary to make such a constraint for inner
   joins.  Therefore, although all of these queries give the same result:
<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 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>
   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 in a plain
   <literal>FROM</> list.  For example,
<programlisting>
SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...;
</programlisting>
   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>
   If you have a mix of outer and inner joins in a complex query, you
   might not want to constrain the planner's search for a good ordering
   of inner joins inside an outer join.  You can't do that directly in the
   <literal>JOIN</> syntax, but you can get around the syntactic limitation by using
   subselects.  For example,
<programlisting>
SELECT * FROM d LEFT JOIN
        (SELECT * FROM a, b, c WHERE ...) AS ss
        ON (...);
</programlisting>
   Here, joining D must be the last step in the query plan, but the
   planner is free to consider various join orders for A, B, C.
  </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 --- assuming
   that you know of a better order, that is.  Experimentation is recommended.
  </para>
 </sect1>

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

  <para>
   One may need to do a large number of table insertions when first
   populating a database. Here are some tips and techniques for making that as
   efficient as possible.
  </para>

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

   <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
    record added.
    An additional benefit of doing all insertions in one transaction
    is that if the insertion of one record were to fail then the
    insertion of all records 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 COPY FROM</title>

   <para>
    Use <command>COPY FROM STDIN</command> to load all the records in one
    command, instead of using
    a series of <command>INSERT</command> commands.  This reduces parsing,
    planning, etc.
    overhead a great deal. If you do this then it is not necessary to turn
    off autocommit, since it is only one command anyway.
   </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 with <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 record is loaded.
   </para>

   <para>
    If you are augmenting an existing table, you can <command>DROP
    INDEX</command>, load the table, 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-analyze">
   <title>Run ANALYZE Afterwards</title>

   <para>
    It's a good idea to run <command>ANALYZE</command> or <command>VACUUM
    ANALYZE</command> anytime you've added or updated a lot of data,
    including just after initially populating a table.  This ensures that
    the planner has up-to-date statistics about the table.  With no statistics
    or obsolete statistics, the planner may make poor choices of query plans,
    leading to bad performance on queries that use your table.
   </para>
  </sect2>
  </sect1>

 </chapter>

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