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><H1
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><A
NAME="PLANNER-OPTIMIZER"
>44.5. Planner/Optimizer</A
></H1
><P
> The task of the <I
CLASS="FIRSTTERM"
>planner/optimizer</I
> is to
create an optimal execution plan. A given SQL query (and hence, a
query tree) can be actually executed in a wide variety of
different ways, each of which will produce the same set of
results. If it is computationally feasible, the query optimizer
will examine each of these possible execution plans, ultimately
selecting the execution plan that is expected to run the fastest.
</P
><DIV
CLASS="NOTE"
><BLOCKQUOTE
CLASS="NOTE"
><P
><B
>Note: </B
> In some situations, examining each possible way in which a query
can be executed would take an excessive amount of time and memory
space. In particular, this occurs when executing queries
involving large numbers of join operations. In order to determine
a reasonable (not necessarily optimal) query plan in a reasonable amount
of time, <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> uses a <I
CLASS="FIRSTTERM"
>Genetic
Query Optimizer</I
> (see <A
HREF="geqo.html"
>Chapter 51</A
>) when the number of joins
exceeds a threshold (see <A
HREF="runtime-config-query.html#GUC-GEQO-THRESHOLD"
>geqo_threshold</A
>).
</P
></BLOCKQUOTE
></DIV
><P
> The planner's search procedure actually works with data structures
called <I
CLASS="FIRSTTERM"
>paths</I
>, which are simply cut-down representations of
plans containing only as much information as the planner needs to make
its decisions. After the cheapest path is determined, a full-fledged
<I
CLASS="FIRSTTERM"
>plan tree</I
> is built to pass to the executor. This represents
the desired execution plan in sufficient detail for the executor to run it.
In the rest of this section we'll ignore the distinction between paths
and plans.
</P
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="AEN88201"
>44.5.1. Generating Possible Plans</A
></H2
><P
> The planner/optimizer starts by generating plans for scanning each
individual relation (table) used in the query. The possible plans
are determined by the available indexes on each relation.
There is always the possibility of performing a
sequential scan on a relation, so a sequential scan plan is always
created. Assume an index is defined on a
relation (for example a B-tree index) and a query contains the
restriction
<TT
CLASS="LITERAL"
>relation.attribute OPR constant</TT
>. If
<TT
CLASS="LITERAL"
>relation.attribute</TT
> happens to match the key of the B-tree
index and <TT
CLASS="LITERAL"
>OPR</TT
> is one of the operators listed in
the index's <I
CLASS="FIRSTTERM"
>operator class</I
>, another plan is created using
the B-tree index to scan the relation. If there are further indexes
present and the restrictions in the query happen to match a key of an
index, further plans will be considered. Index scan plans are also
generated for indexes that have a sort ordering that can match the
query's <TT
CLASS="LITERAL"
>ORDER BY</TT
> clause (if any), or a sort ordering that
might be useful for merge joining (see below).
</P
><P
> If the query requires joining two or more relations,
plans for joining relations are considered
after all feasible plans have been found for scanning single relations.
The three available join strategies are:
<P
></P
></P><UL
><LI
><P
> <I
CLASS="FIRSTTERM"
>nested loop join</I
>: The right relation is scanned
once for every row found in the left relation. This strategy
is easy to implement but can be very time consuming. (However,
if the right relation can be scanned with an index scan, this can
be a good strategy. It is possible to use values from the current
row of the left relation as keys for the index scan of the right.)
</P
></LI
><LI
><P
> <I
CLASS="FIRSTTERM"
>merge join</I
>: Each relation is sorted on the join
attributes before the join starts. Then the two relations are
scanned in parallel, and matching rows are combined to form
join rows. This kind of join is more
attractive because each relation has to be scanned only once.
The required sorting might be achieved either by an explicit sort
step, or by scanning the relation in the proper order using an
index on the join key.
</P
></LI
><LI
><P
> <I
CLASS="FIRSTTERM"
>hash join</I
>: the right relation is first scanned
and loaded into a hash table, using its join attributes as hash keys.
Next the left relation is scanned and the
appropriate values of every row found are used as hash keys to
locate the matching rows in the table.
</P
></LI
></UL
><P>
</P
><P
> When the query involves more than two relations, the final result
must be built up by a tree of join steps, each with two inputs.
The planner examines different possible join sequences to find the
cheapest one.
</P
><P
> If the query uses fewer than <A
HREF="runtime-config-query.html#GUC-GEQO-THRESHOLD"
>geqo_threshold</A
>
relations, a near-exhaustive search is conducted to find the best
join sequence. The planner preferentially considers joins between any
two relations for which there exist a corresponding join clause in the
<TT
CLASS="LITERAL"
>WHERE</TT
> qualification (i.e., for
which a restriction like <TT
CLASS="LITERAL"
>where rel1.attr1=rel2.attr2</TT
>
exists). Join pairs with no join clause are considered only when there
is no other choice, that is, a particular relation has no available
join clauses to any other relation. All possible plans are generated for
every join pair considered by the planner, and the one that is
(estimated to be) the cheapest is chosen.
</P
><P
> When <TT
CLASS="VARNAME"
>geqo_threshold</TT
> is exceeded, the join
sequences considered are determined by heuristics, as described
in <A
HREF="geqo.html"
>Chapter 51</A
>. Otherwise the process is the same.
</P
><P
> The finished plan tree consists of sequential or index scans of
the base relations, plus nested-loop, merge, or hash join nodes as
needed, plus any auxiliary steps needed, such as sort nodes or
aggregate-function calculation nodes. Most of these plan node
types have the additional ability to do <I
CLASS="FIRSTTERM"
>selection</I
>
(discarding rows that do not meet a specified Boolean condition)
and <I
CLASS="FIRSTTERM"
>projection</I
> (computation of a derived column set
based on given column values, that is, evaluation of scalar
expressions where needed). One of the responsibilities of the
planner is to attach selection conditions from the
<TT
CLASS="LITERAL"
>WHERE</TT
> clause and computation of required
output expressions to the most appropriate nodes of the plan
tree.
</P
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