The document discusses how the PostgreSQL optimizer works. It generates optimal query plans by exhaustively searching through different ways of executing joins and selecting relations. It builds "Path" trees representing different implementation methods and selects the cheapest. The optimizer considers outer, inner, and bushy join plans by building join relations between base relations in a dynamic programming approach until it constructs the full join. It ensures valid join orders by checking for restrictions from outer joins, semijoins, and antijoins.

1. Optimizer
These directories take the Query structure returned by the parser, and gen-
erate 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 pos-
sible 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
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2. 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 exhaus-
tive 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 sequen-
tial 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 generat-
ing 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 flat-
tened, 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
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3. 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 --- obvi-
ously 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 sin-
gle 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. Al-
though the jointree scanning code produces these potential join combinations
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4. 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 im-
mediately 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 re-
lations 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 iden-
tities:
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 can-
not 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.
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5. 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 gen-
erating 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 re-
quired 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 as-
sociated 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
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6. 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 con-
strain 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()
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7. 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_pathlists()
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)
A plain Path node can represent several simple plans, per its pathtype:
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8. T_SeqScan - sequential scan
T_SampleScan - tablesample scan
T_FunctionScan - function-in-FROM scan
T_TableFuncScan - table function scan
T_ValuesScan - VALUES scan
T_CteScan - CTE (WITH) scan
T_NamedTuplestoreScan - ENR scan
T_WorkTableScan - scan worktable of a recursive CTE
T_Result - childless Result plan node (used for FROM-less SELECT)
IndexPath - index scan
BitmapHeapPath - top of a bitmapped index scan
TidPath - scan by CTID
TidRangePath - scan a contiguous range of CTIDs
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
GroupResultPath - childless Result plan node (used for degenerate grouping)
MaterialPath - a Material plan node
MemoizePath - a Memoize plan node for caching tuples from sub-paths
UniquePath - remove duplicate rows (either by hashing or sorting)
GatherPath - collect the results of parallel workers
GatherMergePath - collect parallel results, preserving their common sort order
ProjectionPath - a Result plan node with child (used for projection)
ProjectSetPath - a ProjectSet plan node applied to some sub-path
SortPath - a Sort plan node applied to some sub-path
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9. IncrementalSortPath - an IncrementalSort 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
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10. 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 test-
ing 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 Equiva-
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11. lenceClasses, hence a join need not recheck equality of values that were com-
putable 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 discus-
sion 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 Equiv-
alenceClass. 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
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12. from the above cases because such an EquivalenceClass might contain an ag-
gregate function or volatile expression. (A clause containing a volatile function
will never be considered mergejoinable, even if its top operator is mergejoin-
able, 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 uni-
form 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 deter-
mines 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 over-
all 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 scan-
ning the relation and the resulting ordering of the tuples. Sequential scan
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13. 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 key index column. Non-key columns specified in
the INCLUDE clause of covering indexes don’t have corresponding PathKeys
in the list, because the have no influence on index ordering. (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 merge-
join 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 rep-
resented 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 guar-
antees 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
13

14. 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, Sort-
GroupClause 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 col-
umn: 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 equiva-
lence 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 con-
tains 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 con-
stant ECs from pathkeys makes it happen cleanly and automatically.
You might object that a below-outer-join EquivalenceClass doesn’t always rep-
resent 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.
14

15. Order of processing for EquivalenceClasses and
PathKeys
As alluded to above, there is a specific sequence of phases in the process-
ing of EquivalenceClasses and PathKeys during planning. During the
initial scanning of the query’s quals (deconstruct_jointree followed by re-
consider_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 genera-
tion, 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 dur-
ing 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
15

16. -> 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 intermedi-
ate 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 pa-
rameters 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
-> 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
16

17. 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 unparame-
terized 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 num-
ber 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 row-
count 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 fil-
ter 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 evalua-
tion 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 row-
count estimate, which would greatly reduce the value of the pre-filter mecha-
nism.
17

18. 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 parameter-
ized 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 gener-
ating 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 rela-
tions used in its lateral references. (And in turn, join relations including such
a subquery might not have any unparameterized paths.) All the other com-
ments 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 row-
count.
We also allow LATERAL subqueries to be flattened (pulled up into the par-
ent query) by the optimizer, but only when this does not introduce lateral ref-
erences 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
18

19. 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?)
Security-level constraints on qual clauses
To support row-level security and security-barrier views efficiently, we mark
qual clauses (RestrictInfo nodes) with a "security_level" field. The basic con-
cept is that a qual with a lower security_level must be evaluated before one
with a higher security_level. This ensures that "leaky" quals that might ex-
pose sensitive data are not evaluated until after the security barrier quals that
are supposed to filter out security-sensitive rows. However, many qual con-
ditions are "leakproof", that is we trust the functions they use to not expose
data. To avoid unnecessarily inefficient plans, a leakproof qual is not delayed
by security-level considerations, even if it has a higher syntactic security_level
than another qual.
In a query that contains no use of RLS or security-barrier views, all quals will
have security_level zero, so that none of these restrictions kick in; we don’t
even need to check leakproofness of qual conditions.
If there are security-barrier quals, they get security_level zero (and possibly
higher, if there are multiple layers of barriers). Regular quals coming from the
query text get a security_level one more than the highest level used for barrier
quals.
When new qual clauses are generated by EquivalenceClass processing, they
must be assigned a security_level. This is trickier than it seems. One’s first
instinct is that it would be safe to use the largest level found among the source
quals for the EquivalenceClass, but that isn’t safe at all, because it allows un-
wanted delays of security-barrier quals. Consider a barrier qual "t.x = t.y"
plus a query qual "t.x = constant", and suppose there is another query qual
"leaky_function(t.z)" that we mustn’t evaluate before the barrier qual has been
checked. We will have an EC {t.x, t.y, constant} which will lead us to replace the
EC quals with "t.x = constant AND t.y = constant". (We do not want to give up
that behavior, either, since the latter condition could allow use of an index on
t.y, which we would never discover from the original quals.) If these generated
quals are assigned the same security_level as the query quals, then it’s possible
for the leaky_function qual to be evaluated first, allowing leaky_function to see
data from rows that possibly don’t pass the barrier condition.
Instead, our handling of security levels with ECs works like this: * Quals
are not accepted as source clauses for ECs in the first place unless they are
leakproof or have security_level zero. * EC-derived quals are assigned the min-
imum (not maximum) security_level found among the EC’s source clauses. *
19

20. If the maximum security_level found among the EC’s source clauses is above
zero, then the equality operators selected for derived quals must be leakproof.
When no such operator can be found, the EC is treated as "broken" and we fall
back to emitting its source clauses without any additional derived quals.
These rules together ensure that an untrusted qual clause (one with secu-
rity_level above zero) cannot cause an EC to generate a leaky derived clause.
This makes it safe to use the minimum not maximum security_level for
derived clauses. The rules could result in poor plans due to not being able to
generate derived clauses at all, but the risk of that is small in practice because
most btree equality operators are leakproof. Also, by making exceptions for
level-zero quals, we ensure that there is no plan degradation when no barrier
quals are present.
Once we have security levels assigned to all clauses, enforcement of barrier-
qual ordering restrictions boils down to two rules:
* Table scan plan nodes must not select quals for early execution (for example,
use them as index qualifiers in an indexscan) unless they are leakproof or have
security_level no higher than any other qual that is due to be executed at the
same plan node. (Use the utility function restriction_is_securely_promotable()
to check whether it’s okay to select a qual for early execution.)
* Normal execution of a list of quals must execute them in an order that sat-
isfies the same security rule, ie higher security_levels must be evaluated later
unless leakproof. (This is handled in a single place by order_qual_clauses() in
createplan.c.)
order_qual_clauses() uses a heuristic to decide exactly what to do with
leakproof clauses. Normally it sorts clauses by security_level then cost, being
careful that the sort is stable so that we don’t reorder clauses without a clear
reason. But this could result in a very expensive qual being done before a
cheaper one that is of higher security_level. If the cheaper qual is leaky we
have no choice, but if it is leakproof we could put it first. We choose to sort
leakproof quals as if they have security_level zero, but only when their cost
is less than 10X cpu_operator_cost; that restriction alleviates the opposite
problem of doing expensive quals first just because they’re leakproof.
Additional rules will be needed to support safe handling of join quals when
there is a mix of security levels among join quals; for example, it will be
necessary to prevent leaky higher-security-level quals from being evaluated at
a lower join level than other quals of lower security level. Currently there is no
need to consider that since security-prioritized quals can only be single-table
restriction quals coming from RLS policies or security-barrier views, and
security-barrier view subqueries are never flattened into the parent query.
Hence enforcement of security-prioritized quals only happens at the table scan
level. With extra rules for safe handling of security levels among join quals,
it should be possible to let security-barrier views be flattened into the parent
20

21. query, allowing more flexibility of planning while still preserving required
ordering of qual evaluation. But that will come later.
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 repre-
senting 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 UPPER-
REL_PARTIAL_GROUP_AGG result of partial grouping/aggregation,
if any UPPERREL_GROUP_AGG result of grouping/aggregation, if
any UPPERREL_WINDOW result of window functions, if any UPPER-
REL_PARTIAL_DISTINCT result of partial "SELECT DISTINCT", if any
UPPERREL_DISTINCT result of "SELECT DISTINCT", if any UPPER-
REL_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.
21

22. 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. The
academic literature on parallel query suggests 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) are
satisfied by the executor node Gather (or GatherMerge), 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 table scan 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 scan node is executed.
Just as we do for non-parallel access methods, we build Paths to represent ac-
cess 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 im-
portant 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.
22

23. Partitionwise joins
A join between two similarly partitioned tables can be broken down into joins
between their matching partitions if there exists an equi-join condition between
the partition keys of the joining tables. The equi-join between partition keys im-
plies that all join partners for a given row in one partitioned table must be in
the corresponding partition of the other partitioned table. Because of this the
join between partitioned tables to be broken into joins between the matching
partitions. The resultant join is partitioned in the same way as the joining rela-
tions, thus allowing an N-way join between similarly partitioned tables having
equi-join condition between their partition keys to be broken down into N-way
joins between their matching partitions. This technique of breaking down a
join between partitioned tables into joins between their partitions is called par-
titionwise join. We will use term "partitioned relation" for either a partitioned
table or a join between compatibly partitioned tables.
Even if the joining relations don’t have exactly the same partition bounds, par-
titionwise join can still be applied by using an advanced partition-matching
algorithm. For both the joining relations, the algorithm checks whether every
partition of one joining relation only matches one partition of the other join-
ing relation at most. In such a case the join between the joining relations can
be broken down into joins between the matching partitions. The join relation
can then be considered partitioned. The algorithm produces the pairs of the
matching partitions, plus the partition bounds for the join relation, to allow
partitionwise join for computing the join. The algorithm is implemented in
partition_bounds_merge(). For an N-way join relation considered partitioned
this way, not every pair of joining relations can use partitionwise join. For ex-
ample:
(A leftjoin B on (Pab)) innerjoin C on (Pac)
where A, B, and C are partitioned tables, and A has an extra partition compared
to B and C. When considering partitionwise join for the join {A B}, the extra par-
tition of A doesn’t have a matching partition on the nullable side, which is the
case that the current implementation of partitionwise join can’t handle. So {A
B} is not considered partitioned, and the pair of {A B} and C considered for the
3-way join can’t use partitionwise join. On the other hand, the pair of {A C} and
B can use partitionwise join because {A C} is considered partitioned by elimi-
nating the extra partition (see identity 1 on outer join reordering). Whether an
N-way join can use partitionwise join is determined based on the first pair of
joining relations that are both partitioned and can use partitionwise join.
The partitioning properties of a partitioned relation are stored in its RelOpt-
Info. The information about data types of partition keys are stored in Parti-
tionSchemeData structure. The planner maintains a list of canonical partition
schemes (distinct PartitionSchemeData objects) so that RelOptInfo of any two
partitioned relations with same partitioning scheme point to the same Partition-
SchemeData object. This reduces memory consumed by PartitionSchemeData
23

24. objects and makes it easy to compare the partition schemes of joining relations.
Partitionwise aggregates/grouping
If the GROUP BY clause contains all of the partition keys, all the rows that be-
long to a given group must come from a single partition; therefore, aggregation
can be done completely separately for each partition. Otherwise, partial aggre-
gates can be computed for each partition, and then finalized after appending
the results from the individual partitions. This technique of breaking down
aggregation or grouping over a partitioned relation into aggregation or group-
ing over its partitions is called partitionwise aggregation. Especially when the
partition keys match the GROUP BY clause, this can be significantly faster than
the regular method.
24