In the initial revision of the upper-planner pathification work, the only
available way for an FDW or custom-scan provider to inject Paths
representing post-scan-join processing was to insert them during scan-level
GetForeignPaths or similar processing.  While that's not impossible, it'd
require quite a lot of duplicative processing to look forward and see if
the extension would be capable of implementing the whole query.  To improve
matters for custom-scan providers, provide a hook function at the point
where the core code is about to start filling in upperrel Paths.  At this
point Paths are available for the whole scan/join tree, which should reduce
the amount of redundant effort considerably.

(An alternative design that was suggested was to provide a separate hook
for each post-scan-join processing step, but that seems messy and not
clearly more useful.)

Following our time-honored tradition, there's no documentation for this
hook outside the source code.

As-is, this hook is only meant for custom scan providers, which we can't
assume very much about.  A followon patch will implement an FDW callback
to let FDWs do the same thing in a somewhat more structured fashion.

In commit 19a54114 I did not make PathTarget a subtype of Node,
and embedded a RelOptInfo's reltarget directly into it rather than having
a separately-allocated Node.  In hindsight that was misguided
micro-optimization, enabled by the fact that at that point we didn't have
any Paths with custom PathTargets.  Now that PathTarget processing has
been fleshed out some more, it's easier to see that it's better to have
PathTarget as an indepedent Node type, even if it does cost us one more
palloc to create a RelOptInfo.  So change it while we still can.

This commit just changes the representation, without doing anything more
interesting than that.

It is frequently useful for volatile, set-returning, or expensive functions
in a SELECT's targetlist to be postponed till after ORDER BY and LIMIT are
done.  Otherwise, the functions might be executed for every row of the
table despite the presence of LIMIT, and/or be executed in an unexpected
order.  For example, in
	SELECT x, nextval('seq') FROM tab ORDER BY x LIMIT 10;
it's probably desirable that the nextval() values are ordered the same
as x, and that nextval() is not run more than 10 times.

In the past, Postgres was inconsistent in this area: you would get the
desirable behavior if the ordering were performed via an indexscan, but
not if it had to be done by an explicit sort step.  Getting the desired
behavior reliably required contortions like
	SELECT x, nextval('seq')
	  FROM (SELECT x FROM tab ORDER BY x) ss LIMIT 10;

This patch conditionally postpones evaluation of pure-output target
expressions (that is, those that are not used as DISTINCT, ORDER BY, or
GROUP BY columns) so that they effectively occur after sorting, even if an
explicit sort step is necessary.  Volatile expressions and set-returning
expressions are always postponed, so as to provide consistent semantics.
Expensive expressions (costing more than 10 times typical operator cost,
which by default would include any user-defined function) are postponed
if there is a LIMIT or if there are expressions that must be postponed.

We could be more aggressive and postpone any nontrivial expression, but
there are costs associated with doing so: it requires an extra Result plan
node which adds some overhead, and postponement changes the volume of data
going through the sort step, perhaps for the worse.  Since we tend not to
have very good estimates of the output width of nontrivial expressions,
it's hard to have much confidence in our ability to predict whether
postponement would increase or decrease the cost of the sort; therefore
this patch doesn't attempt to make decisions conditionally on that.
Between these factors and a general desire not to change query behavior
when there's not a demonstrable benefit, it seems best to be conservative
about applying postponement.  We might tweak the decision rules in the
future, though.

Konstantin Knizhnik, heavily rewritten by me

Teach make_group_input_target() and make_window_input_target() to work
entirely with the PathTarget representation of tlists, rather than
constructing a tlist and immediately deconstructing it into PathTarget
format.  In itself this only saves a few palloc's; the bigger picture is
that it opens the door for sharing cost_qual_eval work across all of
planner.c's constructions of PathTargets.  I'll come back to that later.

In support of this, flesh out tlist.c's infrastructure for PathTargets
a bit more.

All along, this function should have treated WindowFuncs in a manner
similar to Aggrefs, ie with an option whether or not to recurse into them.
By not considering the case, it was always recursing, which is OK for most
callers (although I suspect that the case in prepare_sort_from_pathkeys
might represent a bug).  But now we need return-without-recursing behavior
as well.  There are also more than a few callers that should never see a
WindowFunc, and now we'll get some error checking on that.

In commit 1d97c19a and later c1d9579d, we extended
pull_var_clause's API by adding enum-type arguments.  That's sort of a pain
to maintain, though, because it means every time we add a new behavior we
must touch every last one of the call sites, even if there's a reasonable
default behavior that most of them could use.  Let's switch over to using a
bitmask of flags, instead; that seems more maintainable and might save a
nanosecond or two as well.  This commit changes no behavior in itself,
though I'm going to follow it up with one that does add a new behavior.

In passing, remove flatten_tlist(), which has not been used since 9.1
and would otherwise need the same API changes.

Removing these enums means that optimizer/tlist.h no longer needs to
depend on optimizer/var.h.  Changing that caused a number of C files to
need addition of #include "optimizer/var.h" (probably we can thank old
runs of pgrminclude for that); but on balance it seems like a good change
anyway.

Refactor so that the internal APIs in planner.c deal in PathTargets not
targetlists, and establish a more regular structure for deriving the
targets needed for successive steps.

There is more that could be done here; calculating the eval costs of each
successive target independently is both inefficient and wrong in detail,
since we won't actually recompute values available from the input node's
tlist.  But it's no worse than what happened before the pathification
rewrite.  In any case this seems like a good starting point for considering
how to handle Konstantin Knizhnik's function-evaluation-postponement patch.

Instead of having planner.c compute a groupColIdx array and store it in
GroupingSetsPaths, make create_groupingsets_plan() find the grouping
columns by searching in the child plan node's tlist.  Although that's
probably a bit slower for create_groupingsets_plan(), it's more like
the way every other plan node type does this, and it provides positive
confirmation that we know which child output columns we're supposed to be
grouping on.  (Indeed, looking at this now, I'm not at all sure that it
wasn't broken before, because create_groupingsets_plan() isn't demanding
an exact tlist match from its child node.)  Also, this allows substantial
simplification in planner.c, because it no longer needs to compute the
groupColIdx array at all; no other cases were using it.

I'd intended to put off this refactoring until later (like 9.7), but
in view of the likely bug fix and the need to rationalize planner.c's
tlist handling so we can do something sane with Konstantin Knizhnik's
function-evaluation-postponement patch, I think it can't wait.

This patch removes some redundant cost calculations that I left for later
cleanup in commit 3fc6e2d7.  There's now a uniform policy that the
make_foo() convenience functions don't do any cost calculations.  Most of
their callers copy costs from the source Path node, and for those that
don't, the calculation in the make_foo() function wasn't necessarily right
anyhow.  (make_result() was particularly a mess, as it was serving multiple
callers using cost calcs designed for only the first one or two that had
ever existed.)  Aside from saving a few cycles, this ensures that what
EXPLAIN prints matches the costs we used for planning purposes.  It does
not change any planner decisions, since the decisions are already made.

I've been saying we needed to do this for more than five years, and here it
finally is.  This patch removes the ever-growing tangle of spaghetti logic
that grouping_planner() used to use to try to identify the best plan for
post-scan/join query steps.  Now, there is (nearly) independent
consideration of each execution step, and entirely separate construction of
Paths to represent each of the possible ways to do that step.  We choose
the best Path or set of Paths using the same add_path() logic that's been
used inside query_planner() for years.

In addition, this patch removes the old restriction that subquery_planner()
could return only a single Plan.  It now returns a RelOptInfo containing a
set of Paths, just as query_planner() does, and the parent query level can
use each of those Paths as the basis of a SubqueryScanPath at its level.
This allows finding some optimizations that we missed before, wherein a
subquery was capable of returning presorted data and thereby avoiding a
sort in the parent level, making the overall cost cheaper even though
delivering sorted output was not the cheapest plan for the subquery in
isolation.  (A couple of regression test outputs change in consequence of
that.  However, there is very little change in visible planner behavior
overall, because the point of this patch is not to get immediate planning
benefits but to create the infrastructure for future improvements.)

There is a great deal left to do here.  This patch unblocks a lot of
planner work that was basically impractical in the old code structure,
such as allowing FDWs to implement remote aggregation, or rewriting
plan_set_operations() to allow consideration of multiple implementation
orders for set operations.  (The latter will likely require a full
rewrite of plan_set_operations(); what I've done here is only to fix it
to return Paths not Plans.)  I have also left unfinished some localized
refactoring in createplan.c and planner.c, because it was not necessary
to get this patch to a working state.

Thanks to Robert Haas, David Rowley, and Amit Kapila for review.

Up to now, there's been an assumption that all Paths for a given relation
compute the same output column set (targetlist).  However, there are good
reasons to remove that assumption.  For example, an indexscan on an
expression index might be able to return the value of an expensive function
"for free".  While we have the ability to generate such a plan today in
simple cases, we don't have a way to model that it's cheaper than a plan
that computes the function from scratch, nor a way to create such a plan
in join cases (where the function computation would normally happen at
the topmost join node).  Also, we need this so that we can have Paths
representing post-scan/join steps, where the targetlist may well change
from one step to the next.  Therefore, invent a "struct PathTarget"
representing the columns we expect a plan step to emit.  It's convenient
to include the output tuple width and tlist evaluation cost in this struct,
and there will likely be additional fields in future.

While Path nodes that actually do have custom outputs will need their own
PathTargets, it will still be true that most Paths for a given relation
will compute the same tlist.  To reduce the overhead added by this patch,
keep a "default PathTarget" in RelOptInfo, and allow Paths that compute
that column set to just point to their parent RelOptInfo's reltarget.
(In the patch as committed, actually every Path is like that, since we
do not yet have any cases of custom PathTargets.)

I took this opportunity to provide some more-honest costing of
PlaceHolderVar evaluation.  Up to now, the assumption that "scan/join
reltargetlists have cost zero" was applied not only to Vars, where it's
reasonable, but also PlaceHolderVars where it isn't.  Now, we add the eval
cost of a PlaceHolderVar's expression to the first plan level where it can
be computed, by including it in the PathTarget cost field and adding that
to the cost estimates for Paths.  This isn't perfect yet but it's much
better than before, and there is a way forward to improve it more.  This
costing change affects the join order chosen for a couple of the regression
tests, changing expected row ordering.

If a GROUP BY clause includes all columns of a non-deferred primary key,
as well as other columns of the same relation, those other columns are
redundant and can be dropped from the grouping; the pkey is enough to
ensure that each row of the table corresponds to a separate group.
Getting rid of the excess columns will reduce the cost of the sorting or
hashing needed to implement GROUP BY, and can indeed remove the need for
a sort step altogether.

This seems worth testing for since many query authors are not aware of
the GROUP-BY-primary-key exception to the rule about queries not being
allowed to reference non-grouped-by columns in their targetlists or
HAVING clauses.  Thus, redundant GROUP BY items are not uncommon.  Also,
we can make the test pretty cheap in most queries where it won't help
by not looking up a rel's primary key until we've found that at least
two of its columns are in GROUP BY.

David Rowley, reviewed by Julien Rouhaud

In 61444bfb we started to allow HAVING clauses to be fully pushed down
into WHERE, even when grouping sets are in use. That turns out not to
work correctly, because grouping sets can "produce" NULLs, meaning that
filtering in WHERE and HAVING can have different results, even when no
aggregates or volatile functions are involved.

Instead only allow pushdown of empty grouping sets.

It'd be nice to do better, but the exact mechanics of deciding which
cases are safe are still being debated. It's important to give correct
results till we find a good solution, and such a solution might not be
appropriate for backpatching anyway.

Bug: #13863
Reported-By: 'wrb'
Diagnosed-By: Dean Rasheed
Author: Andrew Gierth
Reviewed-By: Dean Rasheed and Andres Freund
Discussion: 20160113183558.12989.56904@wrigleys.postgresql.org
Backpatch: 9.5, where grouping sets were introduced

When force_parallel_mode = true, we enable the parallel mode restrictions
for all queries for which this is believed to be safe.  For the subset of
those queries believed to be safe to run entirely within a worker, we spin
up a worker and run the query there instead of running it in the
original process.  When force_parallel_mode = regress, make additional
changes to allow the regression tests to run cleanly even though parallel
workers have been injected under the hood.

Taken together, this facilitates both better user testing and better
regression testing of the parallelism code.

Robert Haas, with help from Amit Kapila and Rushabh Lathia.

Previously, the foreign join pushdown infrastructure left the question
of security entirely up to individual FDWs, but it would be easy for
a foreign data wrapper to inadvertently open up subtle security holes
that way.  So, make it the core code's job to determine which user
mapping OID is relevant, and don't attempt join pushdown unless it's
the same for all relevant relations.

Per a suggestion from Tom Lane.  Shigeru Hanada and Ashutosh Bapat,
reviewed by Etsuro Fujita and KaiGai Kohei, with some further
changes by me.

Aggregate nodes now have two new modes: a "partial" mode where they
output the unfinalized transition state, and a "finalize" mode where
they accept unfinalized transition states rather than individual
values as input.

These new modes are not used anywhere yet, but they will be necessary
for parallel aggregation.  The infrastructure also figures to be
useful for cases where we want to aggregate local data and remote
data via the FDW interface, and want to bring back partial aggregates
from the remote side that can then be combined with locally generated
partial aggregates to produce the final value.  It may also be useful
even when neither FDWs nor parallelism are in play, as explained in
the comments in nodeAgg.c.

David Rowley and Simon Riggs, reviewed by KaiGai Kohei, Heikki
Linnakangas, Haribabu Kommi, and me.

There's no good reason for stomping on the input data; it makes the logic
in this function no simpler, in fact probably the reverse.  And it makes
it impossible to separate path generation from plan generation, as I'm
working towards doing; that will require more than one traversal of these
lists.

Noted while reviewing a question from Dickson S. Guedes.

Improve comments and make it a shade less messy.  I think we might want
to move all of this somewhere else later, but it needs to be more
readable first.

In passing, re-pgindent the file, affecting some recently-added comments
concerning parallel query planning.

Once upon a time it was necessary for grouping_planner() to determine
the tlist it wanted from the scan/join plan subtree before it called
query_planner(), because query_planner() would actually make a Plan using
that.  But we refactored things a long time ago to delay construction of
the Plan tree till later, so there's no need to build that tlist until
(and indeed unless) we're ready to plaster it onto the Plan.  The only
thing query_planner() cares about is what Vars are going to be needed for
the tlist, and it can perfectly well get that by looking at the real tlist
rather than some masticated version.

Well, actually, there is one minor glitch in that argument, which is that
make_subplanTargetList also adds Vars appearing only in HAVING to the
tlist it produces.  So now we have to account for HAVING explicitly in
build_base_rel_tlists.  But that just adds a few lines of code, and
I doubt it moves the needle much on processing time; we might be doing
pull_var_clause() twice on the havingQual, but before we had it scanning
dummy tlist entries instead.

This is a very small down payment on rationalizing grouping_planner
enough so it can be refactored.

Backpatch certain files through 9.1

We carry around information about if a given query has row security or
not to allow the plancache to use that information to invalidate a
planned query in the event that the environment changes.

Previously, the flag of one of the subqueries was simply being copied
into place to indicate if the query overall included RLS components.
That's wrong as we need the global OR of all subqueries.  Fix by
changing the code to match how fireRIRules works, which is results
in OR'ing all of the flags.

Noted by Tom.

Back-patch to 9.5 where RLS was introduced.

I originally modeled this data structure on SpecialJoinInfo, but after
commit acfcd45c that looks like a pretty poor decision.
All we really need is relid sets identifying laterally-referenced rels;
and most of the time, what we want to know about includes indirect lateral
references, a case the LateralJoinInfo data was unsuited to compute with
any efficiency.  The previous commit redefined RelOptInfo.lateral_relids
as the transitive closure of lateral references, so that it easily supports
checking indirect references.  For the places where we really do want just
direct references, add a new RelOptInfo field direct_lateral_relids, which
is easily set up as a copy of lateral_relids before we perform the
transitive closure calculation.  Then we can just drop lateral_info_list
and LateralJoinInfo and the supporting code.  This makes the planner's
handling of lateral references noticeably more efficient, and shorter too.

Such a change can't be back-patched into stable branches for fear of
breaking extensions that might be looking at the planner's data structures;
but it seems not too late to push it into 9.5, so I've done so.

Add a new flag, consider_parallel, to each RelOptInfo, indicating
whether a plan for that relation could conceivably be run inside of
a parallel worker.  Right now, we're pretty conservative: for example,
it might be possible to defer applying a parallel-restricted qual
in a worker, and later do it in the leader, but right now we just
don't try to parallelize access to that relation.  That's probably
the right decision in most cases, anyway.

Using the new flag, generate parallel sequential scan plans for plain
baserels, meaning that we now have parallel sequential scan in
PostgreSQL.  The logic here is pretty unsophisticated right now: the
costing model probably isn't right in detail, and we can't push joins
beneath Gather nodes, so the number of plans that can actually benefit
from this is pretty limited right now.  Lots more work is needed.
Nevertheless, it seems time to enable this functionality so that all
this code can actually be tested easily by users and developers.

Note that, if you wish to test this functionality, it will be
necessary to set max_parallel_degree to a value greater than the
default of 0.  Once a few more loose ends have been tidied up here, we
might want to consider changing the default value of this GUC, but
I'm leaving it alone for now.

Along the way, fix a bug in cost_gather: the previous coding thought
that a Gather node's transfer overhead should be costed on the basis of
the relation size rather than the number of tuples that actually need
to be passed off to the leader.

Patch by me, reviewed in earlier versions by Amit Kapila.

In addition, this path fills in a number of missing bits and pieces in
the parallel infrastructure.  Paths and plans now have a parallel_aware
flag indicating whether whatever parallel-aware logic they have should
be engaged.  It is believed that we will need this flag for a number of
path/plan types, not just sequential scans, which is why the flag is
generic rather than part of the SeqScan structures specifically.
Also, execParallel.c now gives parallel nodes a chance to initialize
their PlanState nodes from the DSM during parallel worker startup.

Amit Kapila, with a fair amount of adjustment by me.  Review of previous
patch versions by Haribabu Kommi and others.

In order for this to be safe, the code which hands true serializability
will need to taught that the SIRead locks taken by a parallel worker
pertain to the same transaction as those taken by the parallel leader.
Some further changes may be needed as well.  Until the necessary
adaptations are made, don't generate parallel plans in serializable
mode, and if a previously-generated parallel plan is used after
serializable mode has been activated, run it serially.

This fixes a bug in commit 7aea8e4f.

This code provides infrastructure for a parallel leader to start up
parallel workers to execute subtrees of the plan tree being executed
in the master.  User-supplied parameters from ParamListInfo are passed
down, but PARAM_EXEC parameters are not.  Various other constructs,
such as initplans, subplans, and CTEs, are also not currently shared.
Nevertheless, there's enough here to support a basic implementation of
parallel query, and we can lift some of the current restrictions as
needed.

Amit Kapila and Robert Haas

Commit 924bcf4f introduced a framework
for parallel computation in PostgreSQL that makes most but not all
built-in functions safe to execute in parallel mode.  In order to have
parallel query, we'll need to be able to determine whether that query
contains functions (either built-in or user-defined) that cannot be
safely executed in parallel mode.  This requires those functions to be
labeled, so this patch introduces an infrastructure for that.  Some
functions currently labeled as safe may need to be revised depending on
how pending issues related to heavyweight locking under paralllelism
are resolved.

Parallel plans can't be used except for the case where the query will
run to completion.  If portal execution were suspended, the parallel
mode restrictions would need to remain in effect during that time, but
that might make other queries fail.  Therefore, this patch introduces
a framework that enables consideration of parallel plans only when it
is known that the plan will be run to completion.  This probably needs
some refinement; for example, at bind time, we do not know whether a
query run via the extended protocol will be execution to completion or
run with a limited fetch count.  Having the client indicate its
intentions at bind time would constitute a wire protocol break.  Some
contexts in which parallel mode would be safe are not adjusted by this
patch; the default is not to try parallel plans except from call sites
that have been updated to say that such plans are OK.

This commit doesn't introduce any parallel paths or plans; it just
provides a way to determine whether they could potentially be used.
I'm committing it on the theory that the remaining parallel sequential
scan patches will also get committed to this release, hopefully in the
not-too-distant future.

Robert Haas and Amit Kapila.  Reviewed (in earlier versions) by Noah
Misch.

Until now we computed these Param ID sets at the end of subquery_planner,
but that approach depends on subquery_planner returning a concrete Plan
tree.  We would like to switch over to returning one or more Paths for a
subquery, and in that representation the necessary details aren't fully
fleshed out (not to mention that we don't really want to do this work for
Paths that end up getting discarded).  Hence, refactor so that we can
compute the param ID sets at the end of planning, just before
set_plan_references is run.

The main change necessary to make this work is that we need to capture
the set of outer-level Param IDs available to the current query level
before exiting subquery_planner, since the outer levels' plan_params lists
are transient.  (That's not going to pose a problem for returning Paths,
since all the work involved in producing that data is part of expression
preprocessing, which will continue to happen before Paths are produced.)
On the plus side, this change gets rid of several existing kluges.

Eventually I'd like to get rid of SS_finalize_plan altogether in favor of
doing this work during set_plan_references, but that will require some
complex rejiggering because SS_finalize_plan needs to visit subplans and
initplans before the main plan.  So leave that idea for another day.

Although I think on all modern machines floating division by zero
results in Infinity not SIGFPE, we still don't want infinities
running around in the planner's costing estimates; too much risk
of that leading to insane behavior.

grouping_planner() failed to consider the possibility that final_rel
might be known dummy and hence have zero rowcount.  (I wonder if it
would be better to set a rows estimate of 1 for dummy relations?
But at least in the back branches, changing this convention seems
like a bad idea, so I'll leave that for another day.)

Make certain that get_variable_numdistinct() produces a nonzero result.
The case that can be shown to be broken is with stadistinct < 0.0 and
small ntuples; we did not prevent the result from rounding to zero.
For good luck I applied clamp_row_est() to all the nonconstant return
values.

In ExecChooseHashTableSize(), Assert that we compute positive nbuckets
and nbatch.  I know of no reason to think this isn't the case, but it
seems like a good safety check.

Per reports from Piotr Stefaniak.  Back-patch to all active branches.

Previously we disallowed pushing down quals to WHERE in the presence of
grouping sets. That's overly restrictive.

We now instead copy quals to WHERE if applicable, leaving the
one in HAVING in place. That's because, at that stage of the planning
process, it's nontrivial to determine if it's safe to remove the one in
HAVING.

Author: Andrew Gierth
Discussion: 874mkt3l59.fsf@news-spur.riddles.org.uk
Backpatch: 9.5, where grouping sets were introduced. This isn't exactly
    a bugfix, but it seems better to keep the branches in sync at this point.

The previous coding frequently failed to fail because for one it's
unusual to have rollup clauses with one column, and for another
sometimes the wrong mapping didn't cause obvious problems.

Author: Jeevan Chalke
Reviewed-By: Andrew Gierth
Discussion: CAM2+6=W=9=hQOipH0HAPbkun3Z3TFWij_EiHue0_6UX=oR=1kw@mail.gmail.com
Backpatch: 9.5, where grouping sets were introduced

The original implementation of TABLESAMPLE modeled the tablesample method
API on index access methods, which wasn't a good choice because, without
specialized DDL commands, there's no way to build an extension that can
implement a TSM.  (Raw inserts into system catalogs are not an acceptable
thing to do, because we can't undo them during DROP EXTENSION, nor will
pg_upgrade behave sanely.)  Instead adopt an API more like procedural
language handlers or foreign data wrappers, wherein the only SQL-level
support object needed is a single handler function identified by having
a special return type.  This lets us get rid of the supporting catalog
altogether, so that no custom DDL support is needed for the feature.

Adjust the API so that it can support non-constant tablesample arguments
(the original coding assumed we could evaluate the argument expressions at
ExecInitSampleScan time, which is undesirable even if it weren't outright
unsafe), and discourage sampling methods from looking at invisible tuples.
Make sure that the BERNOULLI and SYSTEM methods are genuinely repeatable
within and across queries, as required by the SQL standard, and deal more
honestly with methods that can't support that requirement.

Make a full code-review pass over the tablesample additions, and fix
assorted bugs, omissions, infelicities, and cosmetic issues (such as
failure to put the added code stanzas in a consistent ordering).
Improve EXPLAIN's output of tablesample plans, too.

Back-patch to 9.5 so that we don't have to support the original API
in production.

Commit c03ad560 introduced a planner
performance regression for UPDATE/DELETE on large inheritance sets.
It required copying the append_rel_list (which is of size proportional to
the number of inherited tables) once for each inherited table, thus
resulting in O(N^2) time and memory consumption.  While it's difficult to
avoid that in general, the extra work only has to be done for
append_rel_list entries that actually reference subquery RTEs, which
inheritance-set entries will not.  So we can buy back essentially all of
the loss in cases without subqueries in FROM; and even for those, the added
work is mainly proportional to the number of UNION ALL subqueries.

Back-patch to 9.2, like the previous commit.

Tom Lane and Dean Rasheed, per a complaint from Thomas Munro.

Fix some places where pgindent did silly stuff, often because project
style wasn't followed to begin with.  (I've not touched the atomics
headers, though.)

Previously, INSERT with ON CONFLICT DO UPDATE specified used a new
command tag -- UPSERT.  It was introduced out of concern that INSERT as
a command tag would be a misrepresentation for ON CONFLICT DO UPDATE, as
some affected rows may actually have been updated.

Alvaro Herrera noticed that the implementation of that new command tag
was incomplete; in subsequent discussion we concluded that having it
doesn't provide benefits that are in line with the compatibility breaks
it requires.

Catversion bump due to the removal of PlannedStmt->isUpsert.

Author: Peter Geoghegan
Discussion: 20150520215816.GI5885@postgresql.org

This SQL standard functionality allows to aggregate data by different
GROUP BY clauses at once. Each grouping set returns rows with columns
grouped by in other sets set to NULL.

This could previously be achieved by doing each grouping as a separate
query, conjoined by UNION ALLs. Besides being considerably more concise,
grouping sets will in many cases be faster, requiring only one scan over
the underlying data.

The current implementation of grouping sets only supports using sorting
for input. Individual sets that share a sort order are computed in one
pass. If there are sets that don't share a sort order, additional sort &
aggregation steps are performed. These additional passes are sourced by
the previous sort step; thus avoiding repeated scans of the source data.

The code is structured in a way that adding support for purely using
hash aggregation or a mix of hashing and sorting is possible. Sorting
was chosen to be supported first, as it is the most generic method of
implementation.

Instead of, as in an earlier versions of the patch, representing the
chain of sort and aggregation steps as full blown planner and executor
nodes, all but the first sort are performed inside the aggregation node
itself. This avoids the need to do some unusual gymnastics to handle
having to return aggregated and non-aggregated tuples from underlying
nodes, as well as having to shut down underlying nodes early to limit
memory usage.  The optimizer still builds Sort/Agg node to describe each
phase, but they're not part of the plan tree, but instead additional
data for the aggregation node. They're a convenient and preexisting way
to describe aggregation and sorting.  The first (and possibly only) sort
step is still performed as a separate execution step. That retains
similarity with existing group by plans, makes rescans fairly simple,
avoids very deep plans (leading to slow explains) and easily allows to
avoid the sorting step if the underlying data is sorted by other means.

A somewhat ugly side of this patch is having to deal with a grammar
ambiguity between the new CUBE keyword and the cube extension/functions
named cube (and rollup). To avoid breaking existing deployments of the
cube extension it has not been renamed, neither has cube been made a
reserved keyword. Instead precedence hacking is used to make GROUP BY
cube(..) refer to the CUBE grouping sets feature, and not the function
cube(). To actually group by a function cube(), unlikely as that might
be, the function name has to be quoted.

Needs a catversion bump because stored rules may change.

Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund
Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas
    Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule
Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com

Add a TABLESAMPLE clause to SELECT statements that allows
user to specify random BERNOULLI sampling or block level
SYSTEM sampling. Implementation allows for extensible
sampling functions to be written, using a standard API.
Basic version follows SQLStandard exactly. Usable
concrete use cases for the sampling API follow in later
commits.

Petr Jelinek

Reviewed by Michael Paquier and Simon Riggs