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The task of the planner/optimizer 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.
Note: In some situations, examining each possible way in which a query may 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 optimal) query plan in a reasonable amount of time, PostgreSQL uses a Genetic Query Optimizer.
The planner's search procedure actually works with data structures called paths, 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 plan tree 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.
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 relation.attribute OPR constant. If relation.attribute happens to match the key of the B-tree index and OPR is one of the operators listed in the index's operator class, 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.
After all feasible plans have been found for scanning single relations, plans for joining relations are created. The planner/optimizer preferentially considers joins between any two relations for which there exist a corresponding join clause in the WHERE qualification (i.e. for which a restriction like where rel1.attr1=rel2.attr2 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/optimizer. The three possible join strategies are:
nested loop join: 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.)
merge sort join: 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 may be achieved either by an explicit sort step, or by scanning the relation in the proper order using an index on the join key.
hash join: 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.
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.
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 selection (discarding rows that do not meet a specified boolean condition) and projection (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 WHERE clause and computation of required output expressions to the most appropriate nodes of the plan tree.
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