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SELECT DISTINCT ON (expr [, expr ...]) targetlist ... and there is a check to make sure that the user didn't specify an ORDER BY that's incompatible with the DISTINCT operation. Reimplement nodeUnique and nodeGroup to use the proper datatype-specific equality function for each column being compared --- they used to do bitwise comparisons or convert the data to text strings and strcmp(). (To add insult to injury, they'd look up the conversion functions once for each tuple...) Parse/plan representation of DISTINCT is now a list of SortClause nodes. initdb forced by querytree change... |
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geqo | ||
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plan | ||
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README |
Summary ------- The optimizer generates optimal query plans by doing a more-or-less exhaustive search through the ways of executing the query. During the planning/optimizing process, we build "Path" trees representing the different ways of doing a query. We select the cheapest Path that generates the desired relation and turn it into a Plan to pass to the executor. (There is pretty much a one-to-one correspondence between the Path and Plan trees, but Path nodes omit info that won't be needed during planning, and include info needed for planning that won't be needed by the executor.) The best Path tree is found by a recursive process: 1) Take each base relation in the query, and make a RelOptInfo structure for it. Find each potentially useful way of accessing the relation, including sequential and index scans, and make a Path representing that way. All the Paths made for a given relation are placed in its RelOptInfo.pathlist. (Actually, we discard Paths that are obviously inferior alternatives before they ever get into the pathlist --- what ends up in the pathlist is the cheapest way of generating each potentially useful sort ordering of the relation.) Also create RelOptInfo.joininfo nodes that list all the joins that involve this relation. For example, the WHERE clause "tab1.col1 = tab2.col1" generates a JoinInfo for tab1 listing tab2 as an unjoined relation, and also one for tab2 showing tab1 as an unjoined relation. 2) Consider joining each RelOptInfo to each other RelOptInfo specified in its RelOptInfo.joininfo, and generate a Path for each possible join method. At this stage each input RelOptInfo is a single relation, so we are joining every relation to the other relations as joined in the WHERE clause. We generate a new "join" RelOptInfo for each possible combination of two "base" RelOptInfos, and put all the plausible paths for that combination into the join RelOptInfo's pathlist. (As before, we keep only the cheapest alternative that generates any one sort ordering of the result.) Joins always occur using two RelOptInfos. One is outer, the other inner. Outers drive lookups of values in the inner. In a nested loop, lookups of values in the inner occur by scanning the inner path once per outer tuple to find each matching inner row. In a mergejoin, inner and outer rows are ordered, and are accessed in order, so only one scan is required to perform the entire join: both inner and outer paths are scanned in-sync. (There's not a lot of difference between inner and outer in a mergejoin...) In a hashjoin, the inner is scanned first and all its rows are entered in a hashtable, then the outer is scanned and for each row we lookup the join key in the hashtable. A Path for a join relation is actually a tree structure, with the top Path node representing the join method. It has left and right subpaths that represent the scan methods used for the two input relations. 3) If we only had two base relations, we are done: we just pick the cheapest path for the join RelOptInfo. If we had more than two, we now need to consider ways of joining join RelOptInfos to each other to make join RelOptInfos that represent more than two base relations. This process is repeated until we have finally built a RelOptInfo that represents all the base relations in the query. Then we pick its cheapest Path. For example: SELECT * FROM tab1, tab2, tab3, tab4 WHERE tab1.col = tab2.col AND tab2.col = tab3.col AND tab3.col = tab4.col Tables 1, 2, 3, and 4 are joined as: {1 2},{2 3},{3 4} {1 2 3},{2 3 4} {1 2 3 4} SELECT * FROM tab1, tab2, tab3, tab4 WHERE tab1.col = tab2.col AND tab1.col = tab3.col AND tab1.col = tab4.col Tables 1, 2, 3, and 4 are joined as: {1 2},{1 3},{1 4} {1 2 3},{1 3 4},{1,2,4} {1 2 3 4} In the default left-handed joins, each RelOptInfo adds one single-relation RelOptInfo in each join pass, and the added RelOptInfo is always the inner relation in the join. In right-handed joins, the added RelOptInfo is the outer relation in the join. In bushy plans, multi-relation RelOptInfo's can be joined to other multi-relation RelOptInfo's. Optimizer Functions ------------------- These directories take the Query structure returned by the parser, and generate a plan used by the executor. The /plan directory generates the actual output plan, the /path code generates all possible ways to join the tables, and /prep handles special cases like inheritance. /util is utility stuff. /geqo is the separate "genetic optimization" planner --- it does a semi-random search rather than exhaustively considering all possible join trees. planner() handle inheritance by processing separately -init_query_planner() preprocess target list preprocess qualifications(WHERE) --query_planner() cnfify() Summary: Simple cases with all AND's are handled by removing the AND's: convert: a = 1 AND b = 2 AND c = 3 to: a = 1, b = 2, c = 3 Qualifications with OR's are handled differently. OR's inside AND clauses are not modified drastically: convert: a = 1 AND b = 2 AND (c = 3 OR d = 4) to: a = 1, b = 2, c = 3 OR d = 4 OR's in the upper level are more complex to handle: convert: (a = 1 AND b = 2) OR c = 3 to: (a = 1 OR c = 3) AND (b = 2 OR c = 3) finally: (a = 1 OR c = 3), (b = 2 OR c = 3) These clauses all have to be true for a result to be returned, so the optimizer can choose the most restrictive clauses. pull out constants from target list get a target list that only contains column names, no expressions if none, then return ---subplanner() make list of relations in target make list of relations in where clause split up the qual into restrictions (a=1) and joins (b=c) find relation clauses can do merge sort and hash joins ----make_one_rel() set_base_rel_pathlist() find scan and all index paths for each relation find selectivity of columns used in joins -----make_one_rel_by_joins() jump to geqo if needed again: make_rels_by_joins(): for each joinrel: make_rels_by_clause_joins() for each rel's joininfo list: if a join from the join clause adds only one relation, do the join or make_rels_by_clauseless_joins() update_rels_pathlist_for_joins() generate nested,merge,hash join paths for new rel's created above merge_rels_with_same_relids() merge RelOptInfo paths that have the same relids because of joins rels_set_cheapest() set cheapest path if all relations in one RelOptInfo, return do group(GROUP) do aggregate put back constants re-flatten target list make unique(DISTINCT) make sort(ORDER BY) Optimizer Data Structures ------------------------- RelOptInfo - a relation or joined relations RestrictInfo - restriction clauses, like "x = 3" JoinInfo - join clauses, including the relids needed for the join Path - every way to generate a RelOptInfo(sequential,index,joins) SeqScan - a plain Path node with nodeTag = T_SeqScan IndexPath - index scans NestPath - nested-loop joins MergePath - merge joins HashPath - hash joins PathKeys - a data structure representing the ordering of a path The optimizer spends a good deal of its time worrying about the ordering of the tuples returned by a path. The reason this is useful is that by knowing the sort ordering of a path, we may be able to use that path as the left or right input of a mergejoin and avoid an explicit sort step. Nestloops and hash joins don't really care what the order of their inputs is, but mergejoin needs suitably ordered inputs. Therefore, all paths generated during the optimization process are marked with their sort order (to the extent that it is known) for possible use by a higher-level merge. It is also possible to avoid an explicit sort step to implement a user's ORDER BY clause if the final path has the right ordering already. Currently, this is not very well implemented: we avoid generating a redundant sort if the chosen path has the desired order, but we do not do anything to encourage the selection of such a path --- so we only avoid the sort if the path that would be chosen anyway (because it is cheapest without regard to its ordering) is properly sorted. The path winnowing process needs to be aware of the desired output order and account for the cost of doing an explicit sort while it is choosing the best path. When we are generating paths for a particular RelOptInfo, we discard a path if it is more expensive than another known path that has the same or better sort order. We will never discard a path that is the only known way to achieve a given sort order. In this way, the next level up will have the maximum freedom to build mergejoins without sorting, since it can pick from any of the paths retained for its inputs. See path/pathkeys.c for an explanation of the PathKeys data structure that represents what is known about the sort order of a particular Path.