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>Controlling the Planner with Explicit JOIN Clauses</TITLE
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><A
NAME="EXPLICIT-JOINS"
>13.3. Controlling the Planner with Explicit <TT
CLASS="LITERAL"
>JOIN</TT
> Clauses</A
></H1
><A
NAME="AEN13898"
></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, 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 may 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 may 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 <VAR
CLASS="VARNAME"
>geqo_threshold</VAR
> 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 much 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.
</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 need to
constrain the join order. But it is possible to instruct the
<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> query planner to treat
explicit inner <TT
CLASS="LITERAL"
>JOIN</TT
>s 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 <TT
CLASS="LITERAL"
>JOIN</TT
> order for inner joins,
set the <VAR
CLASS="VARNAME"
>join_collapse_limit</VAR
> 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 <VAR
CLASS="VARNAME"
>join_collapse_limit</VAR
> = 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
--- 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
<VAR
CLASS="VARNAME"
>from_collapse_limit</VAR
> <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
> <VAR
CLASS="VARNAME"
>from_collapse_limit</VAR
> and <VAR
CLASS="VARNAME"
>join_collapse_limit</VAR
>
are similarly named because they do almost the same thing: one controls
when the planner will <SPAN
CLASS="QUOTE"
>"flatten out"</SPAN
> subselects, and the
other controls when it will flatten out explicit inner joins. Typically
you would either set <VAR
CLASS="VARNAME"
>join_collapse_limit</VAR
> equal to
<VAR
CLASS="VARNAME"
>from_collapse_limit</VAR
> (so that explicit joins and subselects
act similarly) or set <VAR
CLASS="VARNAME"
>join_collapse_limit</VAR
> 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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