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As part of upper planner pathification (commit
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src/backend/optimizer/README Optimizer ========= 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 various preprocessing steps for special cases. /util is utility stuff. /geqo is the separate "genetic optimization" planner --- it does a semi-random search through the join tree space, rather than exhaustively considering all possible join trees. (But each join considered by /geqo is given to /path to create paths for, so we consider all possible implementation paths for each specific join pair even in GEQO mode.) Paths and Join Pairs -------------------- 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 nearly 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 optimizer builds a RelOptInfo structure for each base relation used in the query. Base rels are either primitive tables, or subquery subselects that are planned via a separate recursive invocation of the planner. A RelOptInfo is also built for each join relation that is considered during planning. A join rel is simply a combination of base rels. There is only one join RelOptInfo for any given set of baserels --- for example, the join {A B C} is represented by the same RelOptInfo no matter whether we build it by joining A and B first and then adding C, or joining B and C first and then adding A, etc. These different means of building the joinrel are represented as Paths. For each RelOptInfo we build a list of Paths that represent plausible ways to implement the scan or join of that relation. Once we've considered all the plausible Paths for a rel, we select the one that is cheapest according to the planner's cost estimates. The final plan is derived from the cheapest Path for the RelOptInfo that includes all the base rels of the query. Possible Paths for a primitive table relation include plain old sequential scan, plus index scans for any indexes that exist on the table, plus bitmap index scans using one or more indexes. Specialized RTE types, such as function RTEs, may have only one possible Path. 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 topmost Path node representing the last-applied join method. It has left and right subpaths that represent the scan or join methods used for the two input relations. Join Tree Construction ---------------------- The optimizer generates optimal query plans by doing a more-or-less exhaustive search through the ways of executing the query. 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 Paths representing those ways. 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 and parameterization of the relation.) Also create a RelOptInfo.joininfo list including all the join clauses that involve this relation. For example, the WHERE clause "tab1.col1 = tab2.col1" generates entries in both tab1 and tab2's joininfo lists. If we have only a single base relation in the query, we are done. Otherwise we have to figure out how to join the base relations into a single join relation. 2) Normally, any explicit JOIN clauses are "flattened" so that we just have a list of relations to join. However, FULL OUTER JOIN clauses are never flattened, and other kinds of JOIN might not be either, if the flattening process is stopped by join_collapse_limit or from_collapse_limit restrictions. Therefore, we end up with a planning problem that contains lists of relations to be joined in any order, where any individual item might be a sub-list that has to be joined together before we can consider joining it to its siblings. We process these sub-problems recursively, bottom up. Note that the join list structure constrains the possible join orders, but it doesn't constrain the join implementation method at each join (nestloop, merge, hash), nor does it say which rel is considered outer or inner at each join. We consider all these possibilities in building Paths. We generate a Path for each feasible join method, and select the cheapest Path. For each planning problem, therefore, we will have a list of relations that are either base rels or joinrels constructed per sub-join-lists. We can join these rels together in any order the planner sees fit. The standard (non-GEQO) planner does this as follows: Consider joining each RelOptInfo to each other RelOptInfo for which there is a usable joinclause, and generate a Path for each possible join method for each such pair. (If we have a RelOptInfo with no join clauses, we have no choice but to generate a clauseless Cartesian-product join; so we consider joining that rel to each other available rel. But in the presence of join clauses we will only consider joins that use available join clauses. Note that join-order restrictions induced by outer joins and IN/EXISTS clauses are also checked, to ensure that we find a workable join order in cases where those restrictions force a clauseless join to be done.) If we only had two relations in the list, 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 list items. The join tree is constructed using a "dynamic programming" algorithm: in the first pass (already described) we consider ways to create join rels representing exactly two list items. The second pass considers ways to make join rels that represent exactly three list items; the next pass, four items, etc. The last pass considers how to make the final join relation that includes all list items --- obviously there can be only one join rel at this top level, whereas there can be more than one join rel at lower levels. At each level we use joins that follow available join clauses, if possible, just as described for the first level. 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} (other possibilities will be excluded for lack of join clauses) 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} We consider left-handed plans (the outer rel of an upper join is a joinrel, but the inner is always a single list item); right-handed plans (outer rel is always a single item); and bushy plans (both inner and outer can be joins themselves). For example, when building {1 2 3 4} we consider joining {1 2 3} to {4} (left-handed), {4} to {1 2 3} (right-handed), and {1 2} to {3 4} (bushy), among other choices. Although the jointree scanning code produces these potential join combinations one at a time, all the ways to produce the same set of joined base rels will share the same RelOptInfo, so the paths produced from different join combinations that produce equivalent joinrels will compete in add_path(). The dynamic-programming approach has an important property that's not immediately obvious: we will finish constructing all paths for a given relation before we construct any paths for relations containing that rel. This means that we can reliably identify the "cheapest path" for each rel before higher-level relations need to know that. Also, we can safely discard a path when we find that another path for the same rel is better, without worrying that maybe there is already a reference to that path in some higher-level join path. Without this, memory management for paths would be much more complicated. Once we have built the final join rel, we use either the cheapest path for it or the cheapest path with the desired ordering (if that's cheaper than applying a sort to the cheapest other path). If the query contains one-sided outer joins (LEFT or RIGHT joins), or IN or EXISTS WHERE clauses that were converted to semijoins or antijoins, then some of the possible join orders may be illegal. These are excluded by having join_is_legal consult a side list of such "special" joins to see whether a proposed join is illegal. (The same consultation allows it to see which join style should be applied for a valid join, ie, JOIN_INNER, JOIN_LEFT, etc.) Valid OUTER JOIN Optimizations ------------------------------ The planner's treatment of outer join reordering is based on the following identities: 1. (A leftjoin B on (Pab)) innerjoin C on (Pac) = (A innerjoin C on (Pac)) leftjoin B on (Pab) where Pac is a predicate referencing A and C, etc (in this case, clearly Pac cannot reference B, or the transformation is nonsensical). 2. (A leftjoin B on (Pab)) leftjoin C on (Pac) = (A leftjoin C on (Pac)) leftjoin B on (Pab) 3. (A leftjoin B on (Pab)) leftjoin C on (Pbc) = A leftjoin (B leftjoin C on (Pbc)) on (Pab) Identity 3 only holds if predicate Pbc must fail for all-null B rows (that is, Pbc is strict for at least one column of B). If Pbc is not strict, the first form might produce some rows with nonnull C columns where the second form would make those entries null. RIGHT JOIN is equivalent to LEFT JOIN after switching the two input tables, so the same identities work for right joins. An example of a case that does *not* work is moving an innerjoin into or out of the nullable side of an outer join: A leftjoin (B join C on (Pbc)) on (Pab) != (A leftjoin B on (Pab)) join C on (Pbc) SEMI joins work a little bit differently. A semijoin can be reassociated into or out of the lefthand side of another semijoin, left join, or antijoin, but not into or out of the righthand side. Likewise, an inner join, left join, or antijoin can be reassociated into or out of the lefthand side of a semijoin, but not into or out of the righthand side. ANTI joins work approximately like LEFT joins, except that identity 3 fails if the join to C is an antijoin (even if Pbc is strict, and in both the cases where the other join is a leftjoin and where it is an antijoin). So we can't reorder antijoins into or out of the RHS of a leftjoin or antijoin, even if the relevant clause is strict. The current code does not attempt to re-order FULL JOINs at all. FULL JOIN ordering is enforced by not collapsing FULL JOIN nodes when translating the jointree to "joinlist" representation. Other types of JOIN nodes are normally collapsed so that they participate fully in the join order search. To avoid generating illegal join orders, the planner creates a SpecialJoinInfo node for each non-inner join, and join_is_legal checks this list to decide if a proposed join is legal. What we store in SpecialJoinInfo nodes are the minimum sets of Relids required on each side of the join to form the outer join. Note that these are minimums; there's no explicit maximum, since joining other rels to the OJ's syntactic rels may be legal. Per identities 1 and 2, non-FULL joins can be freely associated into the lefthand side of an OJ, but in some cases they can't be associated into the righthand side. So the restriction enforced by join_is_legal is that a proposed join can't join a rel within or partly within an RHS boundary to one outside the boundary, unless the proposed join is a LEFT join that can associate into the SpecialJoinInfo's RHS using identity 3. The use of minimum Relid sets has some pitfalls; consider a query like A leftjoin (B leftjoin (C innerjoin D) on (Pbcd)) on Pa where Pa doesn't mention B/C/D at all. In this case a naive computation would give the upper leftjoin's min LHS as {A} and min RHS as {C,D} (since we know that the innerjoin can't associate out of the leftjoin's RHS, and enforce that by including its relids in the leftjoin's min RHS). And the lower leftjoin has min LHS of {B} and min RHS of {C,D}. Given such information, join_is_legal would think it's okay to associate the upper join into the lower join's RHS, transforming the query to B leftjoin (A leftjoin (C innerjoin D) on Pa) on (Pbcd) which yields totally wrong answers. We prevent that by forcing the min RHS for the upper join to include B. This is perhaps overly restrictive, but such cases don't arise often so it's not clear that it's worth developing a more complicated system. Pulling Up Subqueries --------------------- As we described above, a subquery appearing in the range table is planned independently and treated as a "black box" during planning of the outer query. This is necessary when the subquery uses features such as aggregates, GROUP, or DISTINCT. But if the subquery is just a simple scan or join, treating the subquery as a black box may produce a poor plan compared to considering it as part of the entire plan search space. Therefore, at the start of the planning process the planner looks for simple subqueries and pulls them up into the main query's jointree. Pulling up a subquery may result in FROM-list joins appearing below the top of the join tree. Each FROM-list is planned using the dynamic-programming search method described above. If pulling up a subquery produces a FROM-list as a direct child of another FROM-list, then we can merge the two FROM-lists together. Once that's done, the subquery is an absolutely integral part of the outer query and will not constrain the join tree search space at all. However, that could result in unpleasant growth of planning time, since the dynamic-programming search has runtime exponential in the number of FROM-items considered. Therefore, we don't merge FROM-lists if the result would have too many FROM-items in one list. Optimizer Functions ------------------- The primary entry point is planner(). planner() set up for recursive handling of subqueries -subquery_planner() pull up sublinks and subqueries from rangetable, if possible canonicalize qual Attempt to simplify WHERE clause to the most useful form; this includes flattening nested AND/ORs and detecting clauses that are duplicated in different branches of an OR. simplify constant expressions process sublinks convert Vars of outer query levels into Params --grouping_planner() preprocess target list for non-SELECT queries handle UNION/INTERSECT/EXCEPT, GROUP BY, HAVING, aggregates, ORDER BY, DISTINCT, LIMIT --query_planner() make list of base relations used in query split up the qual into restrictions (a=1) and joins (b=c) find qual clauses that enable merge and hash joins ----make_one_rel() set_base_rel_pathlist() find seqscan and all index paths for each base relation find selectivity of columns used in joins make_rel_from_joinlist() hand off join subproblems to a plugin, GEQO, or standard_join_search() -----standard_join_search() call join_search_one_level() for each level of join tree needed join_search_one_level(): For each joinrel of the prior level, do make_rels_by_clause_joins() if it has join clauses, or make_rels_by_clauseless_joins() if not. Also generate "bushy plan" joins between joinrels of lower levels. Back at standard_join_search(), generate gather paths if needed for each newly constructed joinrel, then apply set_cheapest() to extract the cheapest path for it. Loop back if this wasn't the top join level. Back at grouping_planner: do grouping (GROUP BY) and aggregation do window functions make unique (DISTINCT) do sorting (ORDER BY) do limit (LIMIT/OFFSET) Back at planner(): convert finished Path tree into a Plan tree do final cleanup after planning Optimizer Data Structures ------------------------- PlannerGlobal - global information for a single planner invocation PlannerInfo - information for planning a particular Query (we make a separate PlannerInfo node for each sub-Query) RelOptInfo - a relation or joined relations RestrictInfo - WHERE clauses, like "x = 3" or "y = z" (note the same structure is used for restriction and join clauses) Path - every way to generate a RelOptInfo(sequential,index,joins) SeqScan - represents a sequential scan plan IndexPath - index scan BitmapHeapPath - top of a bitmapped index scan TidPath - scan by CTID SubqueryScanPath - scan a subquery-in-FROM ForeignPath - scan a foreign table, foreign join or foreign upper-relation CustomPath - for custom scan providers AppendPath - append multiple subpaths together MergeAppendPath - merge multiple subpaths, preserving their common sort order ResultPath - a childless Result plan node (used for FROM-less SELECT) MaterialPath - a Material plan node UniquePath - remove duplicate rows (either by hashing or sorting) GatherPath - collect the results of parallel workers ProjectionPath - a Result plan node with child (used for projection) SortPath - a Sort plan node applied to some sub-path GroupPath - a Group plan node applied to some sub-path UpperUniquePath - a Unique plan node applied to some sub-path AggPath - an Agg plan node applied to some sub-path GroupingSetsPath - an Agg plan node used to implement GROUPING SETS MinMaxAggPath - a Result plan node with subplans performing MIN/MAX WindowAggPath - a WindowAgg plan node applied to some sub-path SetOpPath - a SetOp plan node applied to some sub-path RecursiveUnionPath - a RecursiveUnion plan node applied to two sub-paths LockRowsPath - a LockRows plan node applied to some sub-path ModifyTablePath - a ModifyTable plan node applied to some sub-path(s) LimitPath - a Limit plan node applied to some sub-path NestPath - nested-loop joins MergePath - merge joins HashPath - hash joins EquivalenceClass - a data structure representing a set of values known equal PathKey - a data structure representing the sort 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, so the sort ordering is of interest even at the top level. grouping_planner() will look for the cheapest path with a sort order matching the desired order, then compare its cost to the cost of using the cheapest-overall path and doing an explicit sort on that. 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 (without an explicit sort, that is). 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. EquivalenceClasses ------------------ During the deconstruct_jointree() scan of the query's qual clauses, we look for mergejoinable equality clauses A = B whose applicability is not delayed by an outer join; these are called "equivalence clauses". When we find one, we create an EquivalenceClass containing the expressions A and B to record this knowledge. If we later find another equivalence clause B = C, we add C to the existing EquivalenceClass for {A B}; this may require merging two existing EquivalenceClasses. At the end of the scan, we have sets of values that are known all transitively equal to each other. We can therefore use a comparison of any pair of the values as a restriction or join clause (when these values are available at the scan or join, of course); furthermore, we need test only one such comparison, not all of them. Therefore, equivalence clauses are removed from the standard qual distribution process. Instead, when preparing a restriction or join clause list, we examine each EquivalenceClass to see if it can contribute a clause, and if so we select an appropriate pair of values to compare. For example, if we are trying to join A's relation to C's, we can generate the clause A = C, even though this appeared nowhere explicitly in the original query. This may allow us to explore join paths that otherwise would have been rejected as requiring Cartesian-product joins. Sometimes an EquivalenceClass may contain a pseudo-constant expression (i.e., one not containing Vars or Aggs of the current query level, nor volatile functions). In this case we do not follow the policy of dynamically generating join clauses: instead, we dynamically generate restriction clauses "var = const" wherever one of the variable members of the class can first be computed. For example, if we have A = B and B = 42, we effectively generate the restriction clauses A = 42 and B = 42, and then we need not bother with explicitly testing the join clause A = B when the relations are joined. In effect, all the class members can be tested at relation-scan level and there's never a need for join tests. The precise technical interpretation of an EquivalenceClass is that it asserts that at any plan node where more than one of its member values can be computed, output rows in which the values are not all equal may be discarded without affecting the query result. (We require all levels of the plan to enforce EquivalenceClasses, hence a join need not recheck equality of values that were computable by one of its children.) For an ordinary EquivalenceClass that is "valid everywhere", we can further infer that the values are all non-null, because all mergejoinable operators are strict. However, we also allow equivalence clauses that appear below the nullable side of an outer join to form EquivalenceClasses; for these classes, the interpretation is that either all the values are equal, or all (except pseudo-constants) have gone to null. (This requires a limitation that non-constant members be strict, else they might not go to null when the other members do.) Consider for example SELECT * FROM a LEFT JOIN (SELECT * FROM b JOIN c ON b.y = c.z WHERE b.y = 10) ss ON a.x = ss.y WHERE a.x = 42; We can form the below-outer-join EquivalenceClass {b.y c.z 10} and thereby apply c.z = 10 while scanning c. (The reason we disallow outerjoin-delayed clauses from forming EquivalenceClasses is exactly that we want to be able to push any derived clauses as far down as possible.) But once above the outer join it's no longer necessarily the case that b.y = 10, and thus we cannot use such EquivalenceClasses to conclude that sorting is unnecessary (see discussion of PathKeys below). In this example, notice also that a.x = ss.y (really a.x = b.y) is not an equivalence clause because its applicability to b is delayed by the outer join; thus we do not try to insert b.y into the equivalence class {a.x 42}. But since we see that a.x has been equated to 42 above the outer join, we are able to form a below-outer-join class {b.y 42}; this restriction can be added because no b/c row not having b.y = 42 can contribute to the result of the outer join, and so we need not compute such rows. Now this class will get merged with {b.y c.z 10}, leading to the contradiction 10 = 42, which lets the planner deduce that the b/c join need not be computed at all because none of its rows can contribute to the outer join. (This gets implemented as a gating Result filter, since more usually the potential contradiction involves Param values rather than just Consts, and thus has to be checked at runtime.) To aid in determining the sort ordering(s) that can work with a mergejoin, we mark each mergejoinable clause with the EquivalenceClasses of its left and right inputs. For an equivalence clause, these are of course the same EquivalenceClass. For a non-equivalence mergejoinable clause (such as an outer-join qualification), we generate two separate EquivalenceClasses for the left and right inputs. This may result in creating single-item equivalence "classes", though of course these are still subject to merging if other equivalence clauses are later found to bear on the same expressions. Another way that we may form a single-item EquivalenceClass is in creation of a PathKey to represent a desired sort order (see below). This is a bit different from the above cases because such an EquivalenceClass might contain an aggregate function or volatile expression. (A clause containing a volatile function will never be considered mergejoinable, even if its top operator is mergejoinable, so there is no way for a volatile expression to get into EquivalenceClasses otherwise. Aggregates are disallowed in WHERE altogether, so will never be found in a mergejoinable clause.) This is just a convenience to maintain a uniform PathKey representation: such an EquivalenceClass will never be merged with any other. Note in particular that a single-item EquivalenceClass {a.x} is *not* meant to imply an assertion that a.x = a.x; the practical effect of this is that a.x could be NULL. An EquivalenceClass also contains a list of btree opfamily OIDs, which determines what the equalities it represents actually "mean". All the equivalence clauses that contribute to an EquivalenceClass must have equality operators that belong to the same set of opfamilies. (Note: most of the time, a particular equality operator belongs to only one family, but it's possible that it belongs to more than one. We keep track of all the families to ensure that we can make use of an index belonging to any one of the families for mergejoin purposes.) An EquivalenceClass can contain "em_is_child" members, which are copies of members that contain appendrel parent relation Vars, transposed to contain the equivalent child-relation variables or expressions. These members are *not* full-fledged members of the EquivalenceClass and do not affect the class's overall properties at all. They are kept only to simplify matching of child-relation expressions to EquivalenceClasses. Most operations on EquivalenceClasses should ignore child members. PathKeys -------- The PathKeys data structure represents what is known about the sort order of the tuples generated by a particular Path. A path's pathkeys field is a list of PathKey nodes, where the n'th item represents the n'th sort key of the result. Each PathKey contains these fields: * a reference to an EquivalenceClass * a btree opfamily OID (must match one of those in the EC) * a sort direction (ascending or descending) * a nulls-first-or-last flag The EquivalenceClass represents the value being sorted on. Since the various members of an EquivalenceClass are known equal according to the opfamily, we can consider a path sorted by any one of them to be sorted by any other too; this is what justifies referencing the whole EquivalenceClass rather than just one member of it. In single/base relation RelOptInfo's, the Paths represent various ways of scanning the relation and the resulting ordering of the tuples. Sequential scan Paths have NIL pathkeys, indicating no known ordering. Index scans have Path.pathkeys that represent the chosen index's ordering, if any. A single-key index would create a single-PathKey list, while a multi-column index generates a list with one element per index column. (Actually, since an index can be scanned either forward or backward, there are two possible sort orders and two possible PathKey lists it can generate.) Note that a bitmap scan has NIL pathkeys since we can say nothing about the overall order of its result. Also, an indexscan on an unordered type of index generates NIL pathkeys. However, we can always create a pathkey by doing an explicit sort. The pathkeys for a Sort plan's output just represent the sort key fields and the ordering operators used. Things get more interesting when we consider joins. Suppose we do a mergejoin between A and B using the mergeclause A.X = B.Y. The output of the mergejoin is sorted by X --- but it is also sorted by Y. Again, this can be represented by a PathKey referencing an EquivalenceClass containing both X and Y. With a little further thought, it becomes apparent that nestloop joins can also produce sorted output. For example, if we do a nestloop join between outer relation A and inner relation B, then any pathkeys relevant to A are still valid for the join result: we have not altered the order of the tuples from A. Even more interesting, if there was an equivalence clause A.X=B.Y, and A.X was a pathkey for the outer relation A, then we can assert that B.Y is a pathkey for the join result; X was ordered before and still is, and the joined values of Y are equal to the joined values of X, so Y must now be ordered too. This is true even though we used neither an explicit sort nor a mergejoin on Y. (Note: hash joins cannot be counted on to preserve the order of their outer relation, because the executor might decide to "batch" the join, so we always set pathkeys to NIL for a hashjoin path.) Exception: a RIGHT or FULL join doesn't preserve the ordering of its outer relation, because it might insert nulls at random points in the ordering. In general, we can justify using EquivalenceClasses as the basis for pathkeys because, whenever we scan a relation containing multiple EquivalenceClass members or join two relations each containing EquivalenceClass members, we apply restriction or join clauses derived from the EquivalenceClass. This guarantees that any two values listed in the EquivalenceClass are in fact equal in all tuples emitted by the scan or join, and therefore that if the tuples are sorted by one of the values, they can be considered sorted by any other as well. It does not matter whether the test clause is used as a mergeclause, or merely enforced after-the-fact as a qpqual filter. Note that there is no particular difficulty in labeling a path's sort order with a PathKey referencing an EquivalenceClass that contains variables not yet joined into the path's output. We can simply ignore such entries as not being relevant (yet). This makes it possible to use the same EquivalenceClasses throughout the join planning process. In fact, by being careful not to generate multiple identical PathKey objects, we can reduce comparison of EquivalenceClasses and PathKeys to simple pointer comparison, which is a huge savings because add_path has to make a large number of PathKey comparisons in deciding whether competing Paths are equivalently sorted. Pathkeys are also useful to represent an ordering that we wish to achieve, since they are easily compared to the pathkeys of a potential candidate path. So, SortGroupClause lists are turned into pathkeys lists for use inside the optimizer. An additional refinement we can make is to insist that canonical pathkey lists (sort orderings) do not mention the same EquivalenceClass more than once. For example, in all these cases the second sort column is redundant, because it cannot distinguish values that are the same according to the first sort column: SELECT ... ORDER BY x, x SELECT ... ORDER BY x, x DESC SELECT ... WHERE x = y ORDER BY x, y Although a user probably wouldn't write "ORDER BY x,x" directly, such redundancies are more probable once equivalence classes have been considered. Also, the system may generate redundant pathkey lists when computing the sort ordering needed for a mergejoin. By eliminating the redundancy, we save time and improve planning, since the planner will more easily recognize equivalent orderings as being equivalent. Another interesting property is that if the underlying EquivalenceClass contains a constant and is not below an outer join, then the pathkey is completely redundant and need not be sorted by at all! Every row must contain the same constant value, so there's no need to sort. (If the EC is below an outer join, we still have to sort, since some of the rows might have gone to null and others not. In this case we must be careful to pick a non-const member to sort by. The assumption that all the non-const members go to null at the same plan level is critical here, else they might not produce the same sort order.) This might seem pointless because users are unlikely to write "... WHERE x = 42 ORDER BY x", but it allows us to recognize when particular index columns are irrelevant to the sort order: if we have "... WHERE x = 42 ORDER BY y", scanning an index on (x,y) produces correctly ordered data without a sort step. We used to have very ugly ad-hoc code to recognize that in limited contexts, but discarding constant ECs from pathkeys makes it happen cleanly and automatically. You might object that a below-outer-join EquivalenceClass doesn't always represent the same values at every level of the join tree, and so using it to uniquely identify a sort order is dubious. This is true, but we can avoid dealing with the fact explicitly because we always consider that an outer join destroys any ordering of its nullable inputs. Thus, even if a path was sorted by {a.x} below an outer join, we'll re-sort if that sort ordering was important; and so using the same PathKey for both sort orderings doesn't create any real problem. Order of processing for EquivalenceClasses and PathKeys ------------------------------------------------------- As alluded to above, there is a specific sequence of phases in the processing of EquivalenceClasses and PathKeys during planning. During the initial scanning of the query's quals (deconstruct_jointree followed by reconsider_outer_join_clauses), we construct EquivalenceClasses based on mergejoinable clauses found in the quals. At the end of this process, we know all we can know about equivalence of different variables, so subsequently there will be no further merging of EquivalenceClasses. At that point it is possible to consider the EquivalenceClasses as "canonical" and build canonical PathKeys that reference them. At this time we construct PathKeys for the query's ORDER BY and related clauses. (Any ordering expressions that do not appear elsewhere will result in the creation of new EquivalenceClasses, but this cannot result in merging existing classes, so canonical-ness is not lost.) Because all the EquivalenceClasses are known before we begin path generation, we can use them as a guide to which indexes are of interest: if an index's column is not mentioned in any EquivalenceClass then that index's sort order cannot possibly be helpful for the query. This allows short-circuiting of much of the processing of create_index_paths() for irrelevant indexes. There are some cases where planner.c constructs additional EquivalenceClasses and PathKeys after query_planner has completed. In these cases, the extra ECs/PKs are needed to represent sort orders that were not considered during query_planner. Such situations should be minimized since it is impossible for query_planner to return a plan producing such a sort order, meaning an explicit sort will always be needed. Currently this happens only for queries involving multiple window functions with different orderings, for which extra sorts are needed anyway. Parameterized Paths ------------------- The naive way to join two relations using a clause like WHERE A.X = B.Y is to generate a nestloop plan like this: NestLoop Filter: A.X = B.Y -> Seq Scan on A -> Seq Scan on B We can make this better by using a merge or hash join, but it still requires scanning all of both input relations. If A is very small and B is very large, but there is an index on B.Y, it can be enormously better to do something like this: NestLoop -> Seq Scan on A -> Index Scan using B_Y_IDX on B Index Condition: B.Y = A.X Here, we are expecting that for each row scanned from A, the nestloop plan node will pass down the current value of A.X into the scan of B. That allows the indexscan to treat A.X as a constant for any one invocation, and thereby use it as an index key. This is the only plan type that can avoid fetching all of B, and for small numbers of rows coming from A, that will dominate every other consideration. (As A gets larger, this gets less attractive, and eventually a merge or hash join will win instead. So we have to cost out all the alternatives to decide what to do.) It can be useful for the parameter value to be passed down through intermediate layers of joins, for example: NestLoop -> Seq Scan on A Hash Join Join Condition: B.Y = C.W -> Seq Scan on B -> Index Scan using C_Z_IDX on C Index Condition: C.Z = A.X If all joins are plain inner joins then this is usually unnecessary, because it's possible to reorder the joins so that a parameter is used immediately below the nestloop node that provides it. But in the presence of outer joins, such join reordering may not be possible. Also, the bottom-level scan might require parameters from more than one other relation. In principle we could join the other relations first so that all the parameters are supplied from a single nestloop level. But if those other relations have no join clause in common (which is common in star-schema queries for instance), the planner won't consider joining them directly to each other. In such a case we need to be able to create a plan like NestLoop -> Seq Scan on SmallTable1 A NestLoop -> Seq Scan on SmallTable2 B NestLoop -> Index Scan using XYIndex on LargeTable C Index Condition: C.X = A.AID and C.Y = B.BID so we should be willing to pass down A.AID through a join even though there is no join order constraint forcing the plan to look like this. Before version 9.2, Postgres used ad-hoc methods for planning and executing nestloop queries of this kind, and those methods could not handle passing parameters down through multiple join levels. To plan such queries, we now use a notion of a "parameterized path", which is a path that makes use of a join clause to a relation that's not scanned by the path. In the example two above, we would construct a path representing the possibility of doing this: -> Index Scan using C_Z_IDX on C Index Condition: C.Z = A.X This path will be marked as being parameterized by relation A. (Note that this is only one of the possible access paths for C; we'd still have a plain unparameterized seqscan, and perhaps other possibilities.) The parameterization marker does not prevent joining the path to B, so one of the paths generated for the joinrel {B C} will represent Hash Join Join Condition: B.Y = C.W -> Seq Scan on B -> Index Scan using C_Z_IDX on C Index Condition: C.Z = A.X This path is still marked as being parameterized by A. When we attempt to join {B C} to A to form the complete join tree, such a path can only be used as the inner side of a nestloop join: it will be ignored for other possible join types. So we will form a join path representing the query plan shown above, and it will compete in the usual way with paths built from non-parameterized scans. While all ordinary paths for a particular relation generate the same set of rows (since they must all apply the same set of restriction clauses), parameterized paths typically generate fewer rows than less-parameterized paths, since they have additional clauses to work with. This means we must consider the number of rows generated as an additional figure of merit. A path that costs more than another, but generates fewer rows, must be kept since the smaller number of rows might save work at some intermediate join level. (It would not save anything if joined immediately to the source of the parameters.) To keep cost estimation rules relatively simple, we make an implementation restriction that all paths for a given relation of the same parameterization (i.e., the same set of outer relations supplying parameters) must have the same rowcount estimate. This is justified by insisting that each such path apply *all* join clauses that are available with the named outer relations. Different paths might, for instance, choose different join clauses to use as index clauses; but they must then apply any other join clauses available from the same outer relations as filter conditions, so that the set of rows returned is held constant. This restriction doesn't degrade the quality of the finished plan: it amounts to saying that we should always push down movable join clauses to the lowest possible evaluation level, which is a good thing anyway. The restriction is useful in particular to support pre-filtering of join paths in add_path_precheck. Without this rule we could never reject a parameterized path in advance of computing its rowcount estimate, which would greatly reduce the value of the pre-filter mechanism. To limit planning time, we have to avoid generating an unreasonably large number of parameterized paths. We do this by only generating parameterized relation scan paths for index scans, and then only for indexes for which suitable join clauses are available. There are also heuristics in join planning that try to limit the number of parameterized paths considered. In particular, there's been a deliberate policy decision to favor hash joins over merge joins for parameterized join steps (those occurring below a nestloop that provides parameters to the lower join's inputs). While we do not ignore merge joins entirely, joinpath.c does not fully explore the space of potential merge joins with parameterized inputs. Also, add_path treats parameterized paths as having no pathkeys, so that they compete only on cost and rowcount; they don't get preference for producing a special sort order. This creates additional bias against merge joins, since we might discard a path that could have been useful for performing a merge without an explicit sort step. Since a parameterized path must ultimately be used on the inside of a nestloop, where its sort order is uninteresting, these choices do not affect any requirement for the final output order of a query --- they only make it harder to use a merge join at a lower level. The savings in planning work justifies that. Similarly, parameterized paths do not normally get preference in add_path for having cheap startup cost; that's seldom of much value when on the inside of a nestloop, so it seems not worth keeping extra paths solely for that. An exception occurs for parameterized paths for the RHS relation of a SEMI or ANTI join: in those cases, we can stop the inner scan after the first match, so it's primarily startup not total cost that we care about. LATERAL subqueries ------------------ As of 9.3 we support SQL-standard LATERAL references from subqueries in FROM (and also functions in FROM). The planner implements these by generating parameterized paths for any RTE that contains lateral references. In such cases, *all* paths for that relation will be parameterized by at least the set of relations used in its lateral references. (And in turn, join relations including such a subquery might not have any unparameterized paths.) All the other comments made above for parameterized paths still apply, though; in particular, each such path is still expected to enforce any join clauses that can be pushed down to it, so that all paths of the same parameterization have the same rowcount. We also allow LATERAL subqueries to be flattened (pulled up into the parent query) by the optimizer, but only when this does not introduce lateral references into JOIN/ON quals that would refer to relations outside the lowest outer join at/above that qual. The semantics of such a qual would be unclear. Note that even with this restriction, pullup of a LATERAL subquery can result in creating PlaceHolderVars that contain lateral references to relations outside their syntactic scope. We still evaluate such PHVs at their syntactic location or lower, but the presence of such a PHV in the quals or targetlist of a plan node requires that node to appear on the inside of a nestloop join relative to the rel(s) supplying the lateral reference. (Perhaps now that that stuff works, we could relax the pullup restriction?) Post scan/join planning ----------------------- So far we have discussed only scan/join planning, that is, implementation of the FROM and WHERE clauses of a SQL query. But the planner must also determine how to deal with GROUP BY, aggregation, and other higher-level features of queries; and in many cases there are multiple ways to do these steps and thus opportunities for optimization choices. These steps, like scan/join planning, are handled by constructing Paths representing the different ways to do a step, then choosing the cheapest Path. Since all Paths require a RelOptInfo as "parent", we create RelOptInfos representing the outputs of these upper-level processing steps. These RelOptInfos are mostly dummy, but their pathlist lists hold all the Paths considered useful for each step. Currently, we may create these types of additional RelOptInfos during upper-level planning: UPPERREL_SETOP result of UNION/INTERSECT/EXCEPT, if any UPPERREL_GROUP_AGG result of grouping/aggregation, if any UPPERREL_WINDOW result of window functions, if any UPPERREL_DISTINCT result of "SELECT DISTINCT", if any UPPERREL_ORDERED result of ORDER BY, if any UPPERREL_FINAL result of any remaining top-level actions UPPERREL_FINAL is used to represent any final processing steps, currently LockRows (SELECT FOR UPDATE), LIMIT/OFFSET, and ModifyTable. There is no flexibility about the order in which these steps are done, and thus no need to subdivide this stage more finely. These "upper relations" are identified by the UPPERREL enum values shown above, plus a relids set, which allows there to be more than one upperrel of the same kind. We use NULL for the relids if there's no need for more than one upperrel of the same kind. Currently, in fact, the relids set is vestigial because it's always NULL, but that's expected to change in the future. For example, in planning set operations, we might need the relids to denote which subset of the leaf SELECTs has been combined in a particular group of Paths that are competing with each other. The result of subquery_planner() is always returned as a set of Paths stored in the UPPERREL_FINAL rel with NULL relids. The other types of upperrels are created only if needed for the particular query. The upper-relation infrastructure is designed so that things will work properly if a particular upper relation is created and Paths are added to it sooner than would normally happen. This allows, for example, for an FDW's GetForeignPaths function to insert a Path representing remote aggregation into the UPPERREL_GROUP_AGG upperrel, if it notices that the query represents an aggregation that could be done entirely on the foreign server. That Path will then compete with Paths representing local aggregation on a regular scan of the foreign table, once the core planner reaches the point of considering aggregation. (In practice, it will usually be more convenient for FDWs to detect such cases in a GetForeignUpperPaths callback; but that still represents injecting a Path before the core code has touched the corresponding upperrel.) Parallel Query and Partial Paths -------------------------------- Parallel query involves dividing up the work that needs to be performed either by an entire query or some portion of the query in such a way that some of that work can be done by one or more worker processes, which are called parallel workers. Parallel workers are a subtype of dynamic background workers; see src/backend/access/transam/README.parallel for a fuller description. Academic literature on parallel query suggests that that parallel execution strategies can be divided into essentially two categories: pipelined parallelism, where the execution of the query is divided into multiple stages and each stage is handled by a separate process; and partitioning parallelism, where the data is split between multiple processes and each process handles a subset of it. The literature, however, suggests that gains from pipeline parallelism are often very limited due to the difficulty of avoiding pipeline stalls. Consequently, we do not currently attempt to generate query plans that use this technique. Instead, we focus on partitioning parallelism, which does not require that the underlying table be partitioned. It only requires that (1) there is some method of dividing the data from at least one of the base tables involved in the relation across multiple processes, (2) allowing each process to handle its own portion of the data, and then (3) collecting the results. Requirements (2) and (3) is satisfied by the executor node Gather, which launches any number of worker processes and executes its single child plan in all of them (and perhaps in the leader also, if the children aren't generating enough data to keep the leader busy). Requirement (1) is handled by the SeqScan node: when invoked with parallel_aware = true, this node will, in effect, partition the table on a block by block basis, returning a subset of the tuples from the relation in each worker where that SeqScan is executed. A similar scheme could be (and probably should be) implemented for bitmap heap scans. Just as we do for non-parallel access methods, we build Paths to represent access strategies that can be used in a parallel plan. These are, in essence, the same strategies that are available in the non-parallel plan, but there is an important difference: a path that will run beneath a Gather node returns only a subset of the query results in each worker, not all of them. To form a path that can actually be executed, the (rather large) cost of the Gather node must be accounted for. For this reason among others, paths intended to run beneath a Gather node - which we call "partial" paths since they return only a subset of the results in each worker - must be kept separate from ordinary paths (see RelOptInfo's partial_pathlist and the function add_partial_path). One of the keys to making parallel query effective is to run as much of the query in parallel as possible. Therefore, we expect it to generally be desirable to postpone the Gather stage until as near to the top of the plan as possible. Expanding the range of cases in which more work can be pushed below the Gather (and costing them accurately) is likely to keep us busy for a long time to come.