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><A
NAME="EXPLICIT-JOINS"
>14.3. Controlling the Planner with Explicit <TT
CLASS="LITERAL"
>JOIN</TT
> Clauses</A
></H1
><A
NAME="AEN21630"
></A
><P
>   It is possible
   to control the query planner to some extent by using the explicit <TT
CLASS="LITERAL"
>JOIN</TT
>
   syntax.  To see why this matters, we first need some background.
  </P
><P
>   In a simple join query, such as:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id;</PRE
><P>
   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 <TT
CLASS="LITERAL"
>WHERE</TT
> condition <TT
CLASS="LITERAL"
>a.id = b.id</TT
>, and then
   joins C to this joined table, using the other <TT
CLASS="LITERAL"
>WHERE</TT
>
   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
   <TT
CLASS="LITERAL"
>WHERE</TT
> clause to allow optimization of the join.  (All
   joins in the <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> 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 might have hugely different execution costs.  Therefore,
   the planner will explore all of them to try to find the most
   efficient query plan.
  </P
><P
>   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 might take an
   annoyingly long time.  When there are too many input tables, the
   <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> planner will switch from exhaustive
   search to a <I
CLASS="FIRSTTERM"
>genetic</I
> probabilistic search
   through a limited number of possibilities.  (The switch-over threshold is
   set by the <A
HREF="runtime-config-query.html#GUC-GEQO-THRESHOLD"
>geqo_threshold</A
> run-time
   parameter.)
   The genetic search takes less time, but it won't
   necessarily find the best possible plan.
  </P
><P
>   When the query involves outer joins, the planner has less freedom
   than it does for plain (inner) joins. For example, consider:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);</PRE
><P>
   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.  In other cases, the planner
   might be able to determine that more than one join order is safe.
   For example, given:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT * FROM a LEFT JOIN b ON (a.bid = b.id) LEFT JOIN c ON (a.cid = c.id);</PRE
><P>
   it is valid to join A to either B or C first.  Currently, only
   <TT
CLASS="LITERAL"
>FULL JOIN</TT
> completely constrains the join order.  Most
   practical cases involving <TT
CLASS="LITERAL"
>LEFT JOIN</TT
> or <TT
CLASS="LITERAL"
>RIGHT JOIN</TT
>
   can be rearranged to some extent.
  </P
><P
>   Explicit inner join syntax (<TT
CLASS="LITERAL"
>INNER JOIN</TT
>, <TT
CLASS="LITERAL"
>CROSS
   JOIN</TT
>, or unadorned <TT
CLASS="LITERAL"
>JOIN</TT
>) is semantically the same as
   listing the input relations in <TT
CLASS="LITERAL"
>FROM</TT
>, so it does not
   constrain the join order.
  </P
><P
>   Even though most kinds of <TT
CLASS="LITERAL"
>JOIN</TT
> don't completely constrain
   the join order, it is possible to instruct the
   <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> query planner to treat all
   <TT
CLASS="LITERAL"
>JOIN</TT
> clauses as constraining the join order anyway.
   For example, these three queries are logically equivalent:
</P><PRE
CLASS="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);</PRE
><P>
   But if we tell the planner to honor the <TT
CLASS="LITERAL"
>JOIN</TT
> 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.
  </P
><P
>   To force the planner to follow the join order laid out by explicit
   <TT
CLASS="LITERAL"
>JOIN</TT
>s,
   set the <A
HREF="runtime-config-query.html#GUC-JOIN-COLLAPSE-LIMIT"
>join_collapse_limit</A
> run-time parameter to 1.
   (Other possible values are discussed below.)
  </P
><P
>   You do not need to constrain the join order completely in order to
   cut search time, because it's OK to use <TT
CLASS="LITERAL"
>JOIN</TT
> operators
   within items of a plain <TT
CLASS="LITERAL"
>FROM</TT
> list.  For example, consider:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...;</PRE
><P>
   With <TT
CLASS="VARNAME"
>join_collapse_limit</TT
> = 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.
  </P
><P
>   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 <TT
CLASS="LITERAL"
>JOIN</TT
> syntax
   &mdash; assuming that you know of a better order, that is.  Experimentation
   is recommended.
  </P
><P
>   A closely related issue that affects planning time is collapsing of
   subqueries into their parent query.  For example, consider:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT *
FROM x, y,
    (SELECT * FROM a, b, c WHERE something) AS ss
WHERE somethingelse;</PRE
><P>
   This situation might arise from use of a view that contains a join;
   the view's <TT
CLASS="LITERAL"
>SELECT</TT
> 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:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT * FROM x, y, a, b, c WHERE something AND somethingelse;</PRE
><P>
   This usually results in a better plan than planning the subquery
   separately.  (For example, the outer <TT
CLASS="LITERAL"
>WHERE</TT
> 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 <TT
CLASS="VARNAME"
>from_collapse_limit</TT
>
   <TT
CLASS="LITERAL"
>FROM</TT
> 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.
  </P
><P
>   <A
HREF="runtime-config-query.html#GUC-FROM-COLLAPSE-LIMIT"
>from_collapse_limit</A
> and <A
HREF="runtime-config-query.html#GUC-JOIN-COLLAPSE-LIMIT"
>join_collapse_limit</A
>
   are similarly named because they do almost the same thing: one controls
   when the planner will <SPAN
CLASS="QUOTE"
>"flatten out"</SPAN
> subqueries, and the
   other controls when it will flatten out explicit joins.  Typically
   you would either set <TT
CLASS="VARNAME"
>join_collapse_limit</TT
> equal to
   <TT
CLASS="VARNAME"
>from_collapse_limit</TT
> (so that explicit joins and subqueries
   act similarly) or set <TT
CLASS="VARNAME"
>join_collapse_limit</TT
> 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.
  </P
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