We've had multiple issues with the clause_is_computable_at logic that
I introduced in 2489d76c4: it's been known to accept more than one
clone of the same qual at the same plan node, and also to accept no
clones at all. It's looking impractical to get it 100% right on the
basis of the currently-stored information, so fix it by introducing a
new RestrictInfo field "incompatible_relids" that explicitly shows
which outer joins a given clone mustn't be pushed above.
In principle we could populate this field in every RestrictInfo, but
that would cost space and there doesn't presently seem to be a need
for it in general. Also, while deconstruct_distribute_oj_quals can
easily fill the field with the remaining members of the commutative
join set that it's considering, computing it in the general case
seems again pretty complicated. So for now, just fill it for
clone quals.
Along the way, fix a bug that may or may not be only latent:
equivclass.c was generating replacement clauses with is_pushed_down
and has_clone/is_clone markings that didn't match their
required_relids. This led me to conclude that leaving the clone flags
out of make_restrictinfo's purview wasn't such a great idea after all,
so add them.
Per report from Richard Guo.
Discussion: https://postgr.es/m/CAMbWs48EYi_9-pSd0ORes1kTmTeAjT4Q3gu49hJtYCbSn2JyeA@mail.gmail.com
After applying outer-join identity 3 in the forward direction,
it was possible for the planner to mistakenly apply a qual clause
from above the two outer joins at the now-lower join level.
This can give the wrong answer, since a value that would get nulled
by the now-upper join might not yet be null.
To fix, when we perform such a transformation, consider that the
now-lower join hasn't really completed the outer join it's nominally
responsible for and thus its relid set should not include that OJ's
relid (nor should its output Vars have that nullingrel bit set).
Instead we add those bits when the now-upper join is performed.
The existing rules for qual placement then suffice to prevent
higher qual clauses from dropping below the now-upper join.
There are a few complications from needing to consider transitive
closures in case multiple pushdowns have happened, but all in all
it's not a very complex patch.
This is all new logic (from 2489d76c4) so no need to back-patch.
The added test cases all have the same results as in v15.
Tom Lane and Richard Guo
Discussion: https://postgr.es/m/0b819232-4b50-f245-1c7d-c8c61bf41827@postgrespro.ru
Merge and hash joins can support antijoin with the non-nullable input
on the right, using very simple combinations of their existing logic
for right join and anti join. This gives the planner more freedom
about how to order the join. It's particularly useful for hash join,
since we may now have the option to hash the smaller table instead
of the larger.
Richard Guo, reviewed by Ronan Dunklau and myself
Discussion: https://postgr.es/m/CAMbWs48xh9hMzXzSy3VaPzGAz+fkxXXTUbCLohX1_L8THFRm2Q@mail.gmail.com
In commit b78f6264e I opined that it was "too risky" to delete a
relation's RelOptInfo from the planner's data structures when we have
realized that we don't need to join to it; so instead we just marked
it as a dead relation. In hindsight that judgment seems flawed: any
subsequent access to such a dead relation is arguably a bug in
itself, so leaving the RelOptInfo present just helps to mask bugs.
Let's delete it instead, allowing removal of the whole notion of a
"dead relation". So far as the regression tests can find, this
requires no other code changes, except for one Assert in equivclass.c
that was very dubiously not complaining about access to a dead rel.
Discussion: https://postgr.es/m/229905.1676062220@sss.pgh.pa.us
This commit removes most of the Plan and Path nodes, which should never
be included in the query jumbling because we ignore these in Query
nodes. This is facilitated by making no_query_jumble an inherited
attribute, like no_copy, no_equal and no_read when the supertype of a
node is found as marked with that.
RawStmt is not used in parsed queries, so it can be removed from the
query jumbling. A couple of nodes defined in pathnodes.h, plannodes.h
and primnodes.h with NodeTag as supertype need to be marked
individually.
Forcing the execution of the query jumbling code with compute_query_id =
auto while pg_stat_statements is loaded brings the code coverage of
queryjumblefuncs.funcs.c to 95.6%.
The core code does not yet include a way to enforce the execution in
query jumbling except in pg_stat_statements, so the numbers I am
mentioning above will not reflect on the default coverage report with
just what is done in this commit.
Reported-by: Tom Lane
Reviewed-by: Tom Lane
Discussion: https://postgr.es/m/3344827.1675809127@sss.pgh.pa.us
Here remove some dead code from heapgettup() and heapgettup_pagemode()
which was trying to support NoMovementScanDirection scans. This code can
never be reached as standard_ExecutorRun() never calls ExecutePlan with
NoMovementScanDirection.
Additionally, plans which were scanning an unordered index would use
NoMovementScanDirection rather than ForwardScanDirection. There was no
real need for this, so here we adjust this so we use ForwardScanDirection
for unordered index scans. A comment in pathnodes.h claimed that
NoMovementScanDirection was used for PathKey reasons, but if that was
true, it no longer is, per code in build_index_paths().
This does change the non-text format of the EXPLAIN output so that
unordered index scans now have a "Forward" scan direction rather than
"NoMovement". The text format of EXPLAIN has not changed.
Author: Melanie Plageman
Reviewed-by: Tom Lane, David Rowley
Discussion: https://postgr.es/m/CAAKRu_bvkhka0CZQun28KTqhuUh5ZqY=_T8QEqZqOL02rpi2bw@mail.gmail.com
EquivalenceClasses are now understood as applying within a "join
domain", which is a set of inner-joined relations (possibly underneath
an outer join). We no longer need to treat an EC from below an outer
join as a second-class citizen.
I have hopes of eventually being able to treat outer-join clauses via
EquivalenceClasses, by means of only applying deductions within the
EC's join domain. There are still problems in the way of that, though,
so for now the reconsider_outer_join_clause logic is still here.
I haven't been able to get rid of RestrictInfo.is_pushed_down either,
but I wonder if that could be recast using JoinDomains.
I had to hack one test case in postgres_fdw.sql to make it still test
what it was meant to, because postgres_fdw is inconsistent about
how it deals with quals containing non-shippable expressions; see
https://postgr.es/m/1691374.1671659838@sss.pgh.pa.us. That should
be improved, but I don't think it's within the scope of this patch
series.
Patch by me; thanks to Richard Guo for review.
Discussion: https://postgr.es/m/830269.1656693747@sss.pgh.pa.us
Remove RestrictInfo.nullable_relids, along with a good deal of
infrastructure that calculated it. One use-case for it was in
join_clause_is_movable_to, but we can now replace that usage with
a check to see if the clause's relids include any outer join
that can null the target relation. The other use-case was in
join_clause_is_movable_into, but that test can just be dropped
entirely now that the clause's relids include outer joins.
Furthermore, join_clause_is_movable_into should now be
accurate enough that it will accept anything returned by
generate_join_implied_equalities, so we can restore the Assert
that was diked out in commit 95f4e59c3.
Remove the outerjoin_delayed mechanism. We needed this before to
prevent quals from getting evaluated below outer joins that should
null some of their vars. Now that we consider varnullingrels while
placing quals, that's taken care of automatically, so throw the
whole thing away.
Teach remove_useless_result_rtes to also remove useless FromExprs.
Having done that, the delay_upper_joins flag serves no purpose any
more and we can remove it, largely reverting 11086f2f2.
Use constant TRUE for "dummy" clauses when throwing back outer joins.
This improves on a hack I introduced in commit 6a6522529. If we
have a left-join clause l.x = r.y, and a WHERE clause l.x = constant,
we generate r.y = constant and then don't really have a need for the
join clause. But we must throw the join clause back anyway after
marking it redundant, so that the join search heuristics won't think
this is a clauseless join and avoid it. That was a kluge introduced
under time pressure, and after looking at it I thought of a better
way: let's just introduce constant-TRUE "join clauses" instead,
and get rid of them at the end. This improves the generated plans for
such cases by not having to test a redundant join clause. We can also
get rid of the ugly hack used to mark such clauses as redundant for
selectivity estimation.
Patch by me; thanks to Richard Guo for review.
Discussion: https://postgr.es/m/830269.1656693747@sss.pgh.pa.us
Traditionally we used the same Var struct to represent the value
of a table column everywhere in parse and plan trees. This choice
predates our support for SQL outer joins, and it's really a pretty
bad idea with outer joins, because the Var's value can depend on
where it is in the tree: it might go to NULL above an outer join.
So expression nodes that are equal() per equalfuncs.c might not
represent the same value, which is a huge correctness hazard for
the planner.
To improve this, decorate Var nodes with a bitmapset showing
which outer joins (identified by RTE indexes) may have nulled
them at the point in the parse tree where the Var appears.
This allows us to trust that equal() Vars represent the same value.
A certain amount of klugery is still needed to cope with cases
where we re-order two outer joins, but it's possible to make it
work without sacrificing that core principle. PlaceHolderVars
receive similar decoration for the same reason.
In the planner, we include these outer join bitmapsets into the relids
that an expression is considered to depend on, and in consequence also
add outer-join relids to the relids of join RelOptInfos. This allows
us to correctly perceive whether an expression can be calculated above
or below a particular outer join.
This change affects FDWs that want to plan foreign joins. They *must*
follow suit when labeling foreign joins in order to match with the
core planner, but for many purposes (if postgres_fdw is any guide)
they'd prefer to consider only base relations within the join.
To support both requirements, redefine ForeignScan.fs_relids as
base+OJ relids, and add a new field fs_base_relids that's set up by
the core planner.
Large though it is, this commit just does the minimum necessary to
install the new mechanisms and get check-world passing again.
Follow-up patches will perform some cleanup. (The README additions
and comments mention some stuff that will appear in the follow-up.)
Patch by me; thanks to Richard Guo for review.
Discussion: https://postgr.es/m/830269.1656693747@sss.pgh.pa.us
Avoid explicitly grouping by columns that we know are redundant
for sorting, for example we need group by only one of x and y in
SELECT ... WHERE x = y GROUP BY x, y
This comes up more often than you might think, as shown by the
changes in the regression tests. It's nearly free to detect too,
since we are just piggybacking on the existing logic that detects
redundant pathkeys. (In some of the existing plans that change,
it's visible that a sort step preceding the grouping step already
didn't bother to sort by the redundant column, making the old plan
a bit silly-looking.)
To do this, build processed_groupClause and processed_distinctClause
lists that omit any provably-redundant sort items, and consult those
not the originals where relevant. This means that within the
planner, one should usually consult root->processed_groupClause or
root->processed_distinctClause if one wants to know which columns
are to be grouped on; but to check whether grouping or distinct-ing
is happening at all, check non-NIL-ness of parse->groupClause or
parse->distinctClause. This is comparable to longstanding rules
about handling the HAVING clause, so I don't think it'll be a huge
maintenance problem.
nodeAgg.c also needs minor mods, because it's now possible to generate
AGG_PLAIN and AGG_SORTED Agg nodes with zero grouping columns.
Patch by me; thanks to Richard Guo and David Rowley for review.
Discussion: https://postgr.es/m/185315.1672179489@sss.pgh.pa.us
This allows left join removals and unique joins to work with partitioned
tables. The planner just lacked sufficient proofs that a given join
would not cause any row duplication. Unique indexes currently serve as
that proof, so have get_relation_info() populate the indexlist for
partitioned tables too.
Author: Arne Roland
Reviewed-by: Alvaro Herrera, Zhihong Yu, Amit Langote, David Rowley
Discussion: https://postgr.es/m/c3b2408b7a39433b8230bbcd02e9f302@index.de
Currently, information about the permissions to be checked on relations
mentioned in a query is stored in their range table entries. So the
executor must scan the entire range table looking for relations that
need to have permissions checked. This can make the permission checking
part of the executor initialization needlessly expensive when many
inheritance children are present in the range range. While the
permissions need not be checked on the individual child relations, the
executor still must visit every range table entry to filter them out.
This commit moves the permission checking information out of the range
table entries into a new plan node called RTEPermissionInfo. Every
top-level (inheritance "root") RTE_RELATION entry in the range table
gets one and a list of those is maintained alongside the range table.
This new list is initialized by the parser when initializing the range
table. The rewriter can add more entries to it as rules/views are
expanded. Finally, the planner combines the lists of the individual
subqueries into one flat list that is passed to the executor for
checking.
To make it quick to find the RTEPermissionInfo entry belonging to a
given relation, RangeTblEntry gets a new Index field 'perminfoindex'
that stores the corresponding RTEPermissionInfo's index in the query's
list of the latter.
ExecutorCheckPerms_hook has gained another List * argument; the
signature is now:
typedef bool (*ExecutorCheckPerms_hook_type) (List *rangeTable,
List *rtePermInfos,
bool ereport_on_violation);
The first argument is no longer used by any in-core uses of the hook,
but we leave it in place because there may be other implementations that
do. Implementations should likely scan the rtePermInfos list to
determine which operations to allow or deny.
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqGjJDmUhDSfv-U2qhKJjt9ST7Xh9JXC_irsAQ1TAUsJYg@mail.gmail.com
A couple of places weren't up to speed for this. By sheer good
luck, we didn't fail but just selected a non-memoized join plan,
at least in the test case we have. Nonetheless, it's a bug,
and I'm not quite sure that it couldn't have worse consequences
in other examples. So back-patch to v14 where Memoize came in.
Richard Guo
Discussion: https://postgr.es/m/CAMbWs48GkNom272sfp0-WeD6_0HSR19BJ4H1c9ZKSfbVnJsvRg@mail.gmail.com
It seems better to deal with this by explicit annotations on the
fields in question, instead of magic knowledge embedded in the
script. While that creates a risk-of-omission from failing to
annotate fields, the preceding commit should catch any such
oversights.
Discussion: https://postgr.es/m/263413.1669513145@sss.pgh.pa.us
The planner will now add a given PartitioPruneInfo to
PlannedStmt.partPruneInfos instead of directly to the
Append/MergeAppend plan node. What gets set instead in the
latter is an index field which points to the list element
of PlannedStmt.partPruneInfos containing the PartitioPruneInfo
belonging to the plan node.
A later commit will make AcquireExecutorLocks() do the initial
partition pruning to determine a minimal set of partitions to be
locked when validating a plan tree and it will need to consult the
PartitioPruneInfos referenced therein to do so. It would be better
for the PartitioPruneInfos to be accessible directly than requiring
a walk of the plan tree to find them, which is easier when it can be
done by simply iterating over PlannedStmt.partPruneInfos.
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqFGkMSge6TgC9KQzde0ohpAycLQuV7ooitEEpbKB0O_mg@mail.gmail.com
A future commit will move the checkAsUser field from RangeTblEntry
to a new node that, unlike RTEs, will only be created for tables
mentioned in the query but not for the inheritance child relations
added to the query by the planner. So, checkAsUser value for a
given child relation will have to be obtained by referring to that
for its ancestor mentioned in the query.
In preparation, it seems better to expand the use of RelOptInfo.userid
during planning in place of rte->checkAsUser so that there will be
fewer places to adjust for the above change.
Given that the child-to-ancestor mapping is not available during the
execution of a given "child" ForeignScan node, add a checkAsUser
field to ForeignScan to carry the child relation's RelOptInfo.userid.
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqGFCs2uq7VRKi7g+FFKbP6Ea_2_HkgZb2HPhUfaAKT3ng@mail.gmail.com
Add a NodeTag field to struct Bitmapset. This is free because of
alignment considerations on 64-bit hardware. While it adds some
space on 32-bit machines, we aren't optimizing for that case anymore.
The advantage is that data structures such as Lists of Bitmapsets
are now first-class objects to the Node infrastructure, and don't
require special-case code to handle.
This patch includes removal of one such special case, in indxpath.c:
bms_equal_any() can now be replaced by list_member(). There may be
more existing code that could be simplified, but I didn't look very
hard. We also get to drop the read_write_ignore annotations on a
couple of RelOptInfo fields.
The outfuncs/readfuncs support is arranged so that nothing changes
in the string representation of a Bitmapset field; therefore, this
doesn't need a catversion bump.
Amit Langote and Tom Lane
Discussion: https://postgr.es/m/109089.1668197158@sss.pgh.pa.us
This reverts commit db0d67db24 and
several follow-on fixes. The idea of making a cost-based choice
of the order of the sorting columns is not fundamentally unsound,
but it requires cost information and data statistics that we don't
really have. For example, relying on procost to distinguish the
relative costs of different sort comparators is pretty pointless
so long as most such comparator functions are labeled with cost 1.0.
Moreover, estimating the number of comparisons done by Quicksort
requires more than just an estimate of the number of distinct values
in the input: you also need some idea of the sizes of the larger
groups, if you want an estimate that's good to better than a factor of
three or so. That's data that's often unknown or not very reliable.
Worse, to arrive at estimates of the number of calls made to the
lower-order-column comparison functions, the code needs to make
estimates of the numbers of distinct values of multiple columns,
which are necessarily even less trustworthy than per-column stats.
Even if all the inputs are perfectly reliable, the cost algorithm
as-implemented cannot offer useful information about how to order
sorting columns beyond the point at which the average group size
is estimated to drop to 1.
Close inspection of the code added by db0d67db2 shows that there
are also multiple small bugs. These could have been fixed, but
there's not much point if we don't trust the estimates to be
accurate in-principle.
Finally, the changes in cost_sort's behavior made for very large
changes (often a factor of 2 or so) in the cost estimates for all
sorting operations, not only those for multi-column GROUP BY.
That naturally changes plan choices in many situations, and there's
precious little evidence to show that the changes are for the better.
Given the above doubts about whether the new estimates are really
trustworthy, it's hard to summon much confidence that these changes
are better on the average.
Since we're hard up against the release deadline for v15, let's
revert these changes for now. We can always try again later.
Note: in v15, I left T_PathKeyInfo in place in nodes.h even though
it's unreferenced. Removing it would be an ABI break, and it seems
a bit late in the release cycle for that.
Discussion: https://postgr.es/m/TYAPR01MB586665EB5FB2C3807E893941F5579@TYAPR01MB5866.jpnprd01.prod.outlook.com
The present implementations of adjust_appendrel_attrs_multilevel and
its sibling adjust_child_relids_multilevel are very messy, because
they work by reconstructing the relids of the child's immediate
parent and then seeing if that's bms_equal to the relids of the
target parent. Aside from being quite inefficient, this will not
work with planned future changes to make joinrels' relid sets
contain outer-join relids in addition to baserels.
The whole thing can be solved at a stroke by adding explicit parent
and top_parent links to child RelOptInfos, and making these functions
work with RelOptInfo pointers instead of relids. Doing that is
simpler for most callers, too.
In my original version of this patch, I got rid of
RelOptInfo.top_parent_relids on the grounds that it was now redundant.
However, that adds a lot of code churn in places that otherwise would
not need changing, and arguably the extra indirection needed to fetch
top_parent->relids in those places costs something. So this version
leaves that field in place.
Discussion: https://postgr.es/m/553080.1657481916@sss.pgh.pa.us
Up to now, callers of find_placeholder_info() were required to pass
a flag indicating if it's OK to make a new PlaceHolderInfo. That'd
be fine if the callers had free choice, but they do not. Once we
begin deconstruct_jointree() it's no longer OK to make more PHIs;
while callers before that always want to create a PHI if it's not
there already. So there's no freedom of action, only the opportunity
to cause bugs by creating PHIs too late. Let's get rid of that in
favor of adding a state flag PlannerInfo.placeholdersFrozen, which
we can set at the point where it's no longer OK to make more PHIs.
This patch also simplifies a couple of call sites that were using
complicated logic to avoid calling find_placeholder_info() as much
as possible. Now that that lookup is O(1) thanks to the previous
commit, the extra bitmap manipulations are probably a net negative.
Discussion: https://postgr.es/m/1405792.1660677844@sss.pgh.pa.us
Up to now we've just searched the placeholder_list when we want to
find the PlaceHolderInfo with a given ID. While there's no evidence
of that being a problem in the field, an upcoming patch will add
find_placeholder_info() calls in build_joinrel_tlist(), which seems
likely to make it more of an issue: a joinrel emitting lots of
PlaceHolderVars would incur O(N^2) cost, and we might be building
a lot of joinrels in complex queries. Hence, add an array that
can be indexed directly by phid to make the lookups constant-time.
Discussion: https://postgr.es/m/1405792.1660677844@sss.pgh.pa.us
ORDER BY / DISTINCT aggreagtes have, since implemented in Postgres, been
executed by always performing a sort in nodeAgg.c to sort the tuples in
the current group into the correct order before calling the transition
function on the sorted tuples. This was not great as often there might be
an index that could have provided pre-sorted input and allowed the
transition functions to be called as the rows come in, rather than having
to store them in a tuplestore in order to sort them once all the tuples
for the group have arrived.
Here we change the planner so it requests a path with a sort order which
supports the most amount of ORDER BY / DISTINCT aggregate functions and
add new code to the executor to allow it to support the processing of
ORDER BY / DISTINCT aggregates where the tuples are already sorted in the
correct order.
Since there can be many ORDER BY / DISTINCT aggregates in any given query
level, it's very possible that we can't find an order that suits all of
these aggregates. The sort order that the planner chooses is simply the
one that suits the most aggregate functions. We take the most strictly
sorted variation of each order and see how many aggregate functions can
use that, then we try again with the order of the remaining aggregates to
see if another order would suit more aggregate functions. For example:
SELECT agg(a ORDER BY a),agg2(a ORDER BY a,b) ...
would request the sort order to be {a, b} because {a} is a subset of the
sort order of {a,b}, but;
SELECT agg(a ORDER BY a),agg2(a ORDER BY c) ...
would just pick a plan ordered by {a} (we give precedence to aggregates
which are earlier in the targetlist).
SELECT agg(a ORDER BY a),agg2(a ORDER BY b),agg3(a ORDER BY b) ...
would choose to order by {b} since two aggregates suit that vs just one
that requires input ordered by {a}.
Author: David Rowley
Reviewed-by: Ronan Dunklau, James Coleman, Ranier Vilela, Richard Guo, Tom Lane
Discussion: https://postgr.es/m/CAApHDvpHzfo92%3DR4W0%2BxVua3BUYCKMckWAmo-2t_KiXN-wYH%3Dw%40mail.gmail.com
Commit 964d01ae9 marked a lot of fields as read_write_ignore
to stay consistent with what was dumped by the manually-maintained
outfuncs.c code. However, it seems that a pretty fair number
of those omissions were either flat-out oversights, or a shortcut
taken because hand-written code seemed like it'd be too much trouble.
Let's upgrade things where it seems to make sense to dump.
To do this, we need to add support to gen_node_support.pl and
outfuncs.c for variable-length arrays of Node pointers. That's
pretty straightforward given the model of the existing code
for arrays of scalars, but I found I needed to tighten the
type-recognizing regexes in gen_node_support.pl. (As they
stood, they mistook "foo **" for "foo *". Make sure they're
all fully anchored to prevent additional problems.)
The main thing left un-done here is that a lot of partitioning-related
structs are still not dumped, because they are bare structs not Nodes.
I'm not sure about the wisdom of that choice ... but changing it would
be fairly invasive, so it probably requires more justification than
just making planner node dumps more complete.
Discussion: https://postgr.es/m/1295668.1658258637@sss.pgh.pa.us
This is mostly just to get outfuncs.c support for them, so that
the agginfos and aggtransinfos lists can be dumped when dumping
the contents of PlannerInfo.
While here, improve some related comments; notably, clean up
obsolete comments left over from when preprocess_minmax_aggregates
had to make its own scan of the query tree.
Discussion: https://postgr.es/m/742479.1658160504@sss.pgh.pa.us
Teach this script to handle function pointer fields honestly.
Previously they were just silently ignored, but that's not likely to
be a behavior we can accept indefinitely. This mostly entails fixing
it so that a field declaration spanning multiple lines can be parsed,
because we have a bunch of such fields that're laid out that way.
But that's a good improvement in its own right.
With that change and a minor regex adjustment, the only struct it
fails to parse in the node-defining headers is A_Const, because
of the embedded union. The path of least resistance is to move
that union declaration outside the struct.
Having done those things, we can make it error out if it finds
any within-struct syntax it doesn't understand, which seems like
a pretty important property for robustness.
This commit doesn't change the output files at all; it's just in
the way of future-proofing.
Discussion: https://postgr.es/m/2593369.1657759779@sss.pgh.pa.us
Add a script to automatically generate the node support functions
(copy, equal, out, and read, as well as the node tags enum) from the
struct definitions.
For each of the four node support files, it creates two include files,
e.g., copyfuncs.funcs.c and copyfuncs.switch.c, to include in the main
file. All the scaffolding of the main file stays in place.
I have tried to mostly make the coverage of the output match what is
currently there. For example, one could now do out/read coverage of
utility statement nodes, but I have manually excluded those for now.
The reason is mainly that it's easier to diff the before and after,
and adding a bunch of stuff like this might require a separate
analysis and review.
Subtyping (TidScan -> Scan) is supported.
For the hard cases, you can just write a manual function and exclude
generating one. For the not so hard cases, there is a way of
annotating struct fields to get special behaviors. For example,
pg_node_attr(equal_ignore) has the field ignored in equal functions.
(In this patch, I have only ifdef'ed out the code to could be removed,
mainly so that it won't constantly have merge conflicts. It will be
deleted in a separate patch. All the code comments that are worth
keeping from those sections have already been moved to the header
files where the structs are defined.)
Reviewed-by: Tom Lane <tgl@sss.pgh.pa.us>
Discussion: https://www.postgresql.org/message-id/flat/c1097590-a6a4-486a-64b1-e1f9cc0533ce%40enterprisedb.com
inline_cte() expected to find exactly as many references to the
target CTE as its cterefcount indicates. While that should be
accurate for the tree as emitted by the parser, there are some
optimizations that occur upstream of here that could falsify it,
notably removal of unused subquery output expressions.
Trying to make the accounting 100% accurate seems expensive and
doomed to future breakage. It's not really worth it, because
all this code is protecting is downstream assumptions that every
referenced CTE has a plan. Let's convert those assertions to
regular test-and-elog just in case there's some actual problem,
and then drop the failing assertion.
Per report from Tomas Vondra (thanks also to Richard Guo for
analysis). Back-patch to v12 where the faulty code came in.
Discussion: https://postgr.es/m/29196a1e-ed47-c7ca-9be2-b1c636816183@enterprisedb.com
Window functions such as row_number() always return a value higher than
the previously returned value for tuples in any given window partition.
Traditionally queries such as;
SELECT * FROM (
SELECT *, row_number() over (order by c) rn
FROM t
) t WHERE rn <= 10;
were executed fairly inefficiently. Neither the query planner nor the
executor knew that once rn made it to 11 that nothing further would match
the outer query's WHERE clause. It would blindly continue until all
tuples were exhausted from the subquery.
Here we implement means to make the above execute more efficiently.
This is done by way of adding a pg_proc.prosupport function to various of
the built-in window functions and adding supporting code to allow the
support function to inform the planner if the window function is
monotonically increasing, monotonically decreasing, both or neither. The
planner is then able to make use of that information and possibly allow
the executor to short-circuit execution by way of adding a "run condition"
to the WindowAgg to allow it to determine if some of its execution work
can be skipped.
This "run condition" is not like a normal filter. These run conditions
are only built using quals comparing values to monotonic window functions.
For monotonic increasing functions, quals making use of the btree
operators for <, <= and = can be used (assuming the window function column
is on the left). You can see here that once such a condition becomes false
that a monotonic increasing function could never make it subsequently true
again. For monotonically decreasing functions the >, >= and = btree
operators for the given type can be used for run conditions.
The best-case situation for this is when there is a single WindowAgg node
without a PARTITION BY clause. Here when the run condition becomes false
the WindowAgg node can simply return NULL. No more tuples will ever match
the run condition. It's a little more complex when there is a PARTITION
BY clause. In this case, we cannot return NULL as we must still process
other partitions. To speed this case up we pull tuples from the outer
plan to check if they're from the same partition and simply discard them
if they are. When we find a tuple belonging to another partition we start
processing as normal again until the run condition becomes false or we run
out of tuples to process.
When there are multiple WindowAgg nodes to evaluate then this complicates
the situation. For intermediate WindowAggs we must ensure we always
return all tuples to the calling node. Any filtering done could lead to
incorrect results in WindowAgg nodes above. For all intermediate nodes,
we can still save some work when the run condition becomes false. We've
no need to evaluate the WindowFuncs anymore. Other WindowAgg nodes cannot
reference the value of these and these tuples will not appear in the final
result anyway. The savings here are small in comparison to what can be
saved in the top-level WingowAgg, but still worthwhile.
Intermediate WindowAgg nodes never filter out tuples, but here we change
WindowAgg so that the top-level WindowAgg filters out tuples that don't
match the intermediate WindowAgg node's run condition. Such filters
appear in the "Filter" clause in EXPLAIN for the top-level WindowAgg node.
Here we add prosupport functions to allow the above to work for;
row_number(), rank(), dense_rank(), count(*) and count(expr). It appears
technically possible to do the same for min() and max(), however, it seems
unlikely to be useful enough, so that's not done here.
Bump catversion
Author: David Rowley
Reviewed-by: Andy Fan, Zhihong Yu
Discussion: https://postgr.es/m/CAApHDvqvp3At8++yF8ij06sdcoo1S_b2YoaT9D4Nf+MObzsrLQ@mail.gmail.com
When evaluating a query with a multi-column GROUP BY clause using sort,
the cost may be heavily dependent on the order in which the keys are
compared when building the groups. Grouping does not imply any ordering,
so we're allowed to compare the keys in arbitrary order, and a Hash Agg
leverages this. But for Group Agg, we simply compared keys in the order
as specified in the query. This commit explores alternative ordering of
the keys, trying to find a cheaper one.
In principle, we might generate grouping paths for all permutations of
the keys, and leave the rest to the optimizer. But that might get very
expensive, so we try to pick only a couple interesting orderings based
on both local and global information.
When planning the grouping path, we explore statistics (number of
distinct values, cost of the comparison function) for the keys and
reorder them to minimize comparison costs. Intuitively, it may be better
to perform more expensive comparisons (for complex data types etc.)
last, because maybe the cheaper comparisons will be enough. Similarly,
the higher the cardinality of a key, the lower the probability we’ll
need to compare more keys. The patch generates and costs various
orderings, picking the cheapest ones.
The ordering of group keys may interact with other parts of the query,
some of which may not be known while planning the grouping. E.g. there
may be an explicit ORDER BY clause, or some other ordering-dependent
operation, higher up in the query, and using the same ordering may allow
using either incremental sort or even eliminate the sort entirely.
The patch generates orderings and picks those minimizing the comparison
cost (for various pathkeys), and then adds orderings that might be
useful for operations higher up in the plan (ORDER BY, etc.). Finally,
it always keeps the ordering specified in the query, on the assumption
the user might have additional insights.
This introduces a new GUC enable_group_by_reordering, so that the
optimization may be disabled if needed.
The original patch was proposed by Teodor Sigaev, and later improved and
reworked by Dmitry Dolgov. Reviews by a number of people, including me,
Andrey Lepikhov, Claudio Freire, Ibrar Ahmed and Zhihong Yu.
Author: Dmitry Dolgov, Teodor Sigaev, Tomas Vondra
Reviewed-by: Tomas Vondra, Andrey Lepikhov, Claudio Freire, Ibrar Ahmed, Zhihong Yu
Discussion: https://postgr.es/m/7c79e6a5-8597-74e8-0671-1c39d124c9d6%40sigaev.ru
Discussion: https://postgr.es/m/CA%2Bq6zcW_4o2NC0zutLkOJPsFt80megSpX_dVRo6GK9PC-Jx_Ag%40mail.gmail.com
MERGE performs actions that modify rows in the target table using a
source table or query. MERGE provides a single SQL statement that can
conditionally INSERT/UPDATE/DELETE rows -- a task that would otherwise
require multiple PL statements. For example,
MERGE INTO target AS t
USING source AS s
ON t.tid = s.sid
WHEN MATCHED AND t.balance > s.delta THEN
UPDATE SET balance = t.balance - s.delta
WHEN MATCHED THEN
DELETE
WHEN NOT MATCHED AND s.delta > 0 THEN
INSERT VALUES (s.sid, s.delta)
WHEN NOT MATCHED THEN
DO NOTHING;
MERGE works with regular tables, partitioned tables and inheritance
hierarchies, including column and row security enforcement, as well as
support for row and statement triggers and transition tables therein.
MERGE is optimized for OLTP and is parameterizable, though also useful
for large scale ETL/ELT. MERGE is not intended to be used in preference
to existing single SQL commands for INSERT, UPDATE or DELETE since there
is some overhead. MERGE can be used from PL/pgSQL.
MERGE does not support targetting updatable views or foreign tables, and
RETURNING clauses are not allowed either. These limitations are likely
fixable with sufficient effort. Rewrite rules are also not supported,
but it's not clear that we'd want to support them.
Author: Pavan Deolasee <pavan.deolasee@gmail.com>
Author: Álvaro Herrera <alvherre@alvh.no-ip.org>
Author: Amit Langote <amitlangote09@gmail.com>
Author: Simon Riggs <simon.riggs@enterprisedb.com>
Reviewed-by: Peter Eisentraut <peter.eisentraut@enterprisedb.com>
Reviewed-by: Andres Freund <andres@anarazel.de> (earlier versions)
Reviewed-by: Peter Geoghegan <pg@bowt.ie> (earlier versions)
Reviewed-by: Robert Haas <robertmhaas@gmail.com> (earlier versions)
Reviewed-by: Japin Li <japinli@hotmail.com>
Reviewed-by: Justin Pryzby <pryzby@telsasoft.com>
Reviewed-by: Tomas Vondra <tomas.vondra@enterprisedb.com>
Reviewed-by: Zhihong Yu <zyu@yugabyte.com>
Discussion: https://postgr.es/m/CANP8+jKitBSrB7oTgT9CY2i1ObfOt36z0XMraQc+Xrz8QB0nXA@mail.gmail.com
Discussion: https://postgr.es/m/CAH2-WzkJdBuxj9PO=2QaO9-3h3xGbQPZ34kJH=HukRekwM-GZg@mail.gmail.com
Discussion: https://postgr.es/m/20201231134736.GA25392@alvherre.pgsql
Add pg_statistic_ext_data.stxdinherit flag, so that for each extended
statistics definition we can store two versions of data - one for the
relation alone, one for the whole inheritance tree. This is analogous to
pg_statistic.stainherit, but we failed to include such flag in catalogs
for extended statistics, and we had to work around it (see commits
859b3003de, 36c4bc6e72 and 20b9fa308e).
This changes the relationship between the two catalogs storing extended
statistics objects (pg_statistic_ext and pg_statistic_ext_data). Until
now, there was a simple 1:1 mapping - for each definition there was one
pg_statistic_ext_data row, and this row was inserted while creating the
statistics (and then updated during ANALYZE). With the stxdinherit flag,
we don't know how many rows there will be (child relations may be added
after the statistics object is defined), so there may be up to two rows.
We could make CREATE STATISTICS to always create both rows, but that
seems wasteful - without partitioning we only need stxdinherit=false
rows, and declaratively partitioned tables need only stxdinherit=true.
So we no longer initialize pg_statistic_ext_data in CREATE STATISTICS,
and instead make that a responsibility of ANALYZE. Which is what we do
for regular statistics too.
Patch by me, with extensive improvements and fixes by Justin Pryzby.
Author: Tomas Vondra, Justin Pryzby
Reviewed-by: Tomas Vondra, Justin Pryzby
Discussion: https://postgr.es/m/20210923212624.GI831%40telsasoft.com
Memoize would always use the hash equality operator for the cache key
types to determine if the current set of parameters were the same as some
previously cached set. Certain types such as floating points where -0.0
and +0.0 differ in their binary representation but are classed as equal by
the hash equality operator may cause problems as unless the join uses the
same operator it's possible that whichever join operator is being used
would be able to distinguish the two values. In which case we may
accidentally return in the incorrect rows out of the cache.
To fix this here we add a binary mode to Memoize to allow it to the
current set of parameters to previously cached values by comparing
bit-by-bit rather than logically using the hash equality operator. This
binary mode is always used for LATERAL joins and it's used for normal
joins when any of the join operators are not hashable.
Reported-by: Tom Lane
Author: David Rowley
Discussion: https://postgr.es/m/3004308.1632952496@sss.pgh.pa.us
Backpatch-through: 14, where Memoize was added
In v14, because we don't have a field in RestrictInfo to cache both the
left and right type's hash equality operator, we just restrict the scope
of Memoize to only when the left and right types of a RestrictInfo are the
same.
In master we add another field to RestrictInfo and cache both hash
equality operators.
Reported-by: Jaime Casanova
Author: David Rowley
Discussion: https://postgr.es/m/20210929185544.GB24346%40ahch-to
Backpatch-through: 14
It's possible for us to copy an AlternativeSubPlan expression node
into multiple places, for example the scan quals of several
partition children. Then it's possible that we choose a different
one of the alternatives as optimal in each place. Commit 41efb8340
failed to consider this scenario, so its attempt to remove "unused"
subplans could remove subplans that were still used elsewhere.
Fix by delaying the removal logic until we've examined all the
AlternativeSubPlans in a given query level. (This does assume that
AlternativeSubPlans couldn't get copied to other query levels, but
for the foreseeable future that's fine; cf qual_is_pushdown_safe.)
Per report from Rajkumar Raghuwanshi. Back-patch to v14
where the faulty logic came in.
Discussion: https://postgr.es/m/CAKcux6==O3NNZC3bZ2prRYv3cjm3_Zw1GfzmOjEVqYN4jub2+Q@mail.gmail.com
We've supported parallel aggregation since e06a38965. At the time, we
didn't quite get around to also adding parallel DISTINCT. So, let's do
that now.
This is implemented by introducing a two-phase DISTINCT. Phase 1 is
performed on parallel workers, rows are made distinct there either by
hashing or by sort/unique. The results from the parallel workers are
combined and the final distinct phase is performed serially to get rid of
any duplicate rows that appear due to combining rows for each of the
parallel workers.
Author: David Rowley
Reviewed-by: Zhihong Yu
Discussion: https://postgr.es/m/CAApHDvrjRxVKwQN0he79xS+9wyotFXL=RmoWqGGO2N45Farpgw@mail.gmail.com
For partitioned tables with large numbers of partitions where queries are
able to prune all but a very small number of partitions, the time spent in
the planner looping over RelOptInfo.part_rels checking for non-NULL
RelOptInfos could become a large portion of the overall planning time.
Here we add a Bitmapset that records the non-pruned partitions. This
allows us to more efficiently skip the pruned partitions by looping over
the Bitmapset.
This will cause a very slight slow down in cases where no or not many
partitions could be pruned, however, those cases are already slow to plan
anyway and the overhead of looping over the Bitmapset would be
unmeasurable when compared with the other tasks such as path creation for
a large number of partitions.
Reviewed-by: Amit Langote, Zhihong Yu
Discussion: https://postgr.es/m/CAApHDvqnPx6JnUuPwaf5ao38zczrAb9mxt9gj4U1EKFfd4AqLA@mail.gmail.com
"Result Cache" was never a great name for this node, but nobody managed
to come up with another name that anyone liked enough. That was until
David Johnston mentioned "Node Memoization", which Tom Lane revised to
just "Memoize". People seem to like "Memoize", so let's do the rename.
Reviewed-by: Justin Pryzby
Discussion: https://postgr.es/m/20210708165145.GG1176@momjian.us
Backpatch-through: 14, where Result Cache was introduced
Also "make reformat-dat-files".
The only change worthy of note is that pgindent messed up the formatting
of launcher.c's struct LogicalRepWorkerId, which led me to notice that
that struct wasn't used at all anymore, so I just took it out.
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu, Hou Zhijie
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
Tom Lane
Alvaro Herrera
Michael Paquier
David Rowley
Bruce Momjian
Peter Eisentraut
Tomas Vondra
Daniel Gustafsson