subqueries into the same thing you'd have gotten from IN (except always with
unknownEqFalse = true, so as to get the proper semantics for an EXISTS).
I believe this fixes the last case within CVS HEAD in which an EXISTS could
give worse performance than an equivalent IN subquery.
The tricky part of this is that if the upper query probes the EXISTS for only
a few rows, the hashing implementation can actually be worse than the default,
and therefore we need to make a cost-based decision about which way to use.
But at the time when the planner generates plans for subqueries, it doesn't
really know how many times the subquery will be executed. The least invasive
solution seems to be to generate both plans and postpone the choice until
execution. Therefore, in a query that has been optimized this way, EXPLAIN
will show two subplans for the EXISTS, of which only one will actually get
executed.
There is a lot more that could be done based on this infrastructure: in
particular it's interesting to consider switching to the hash plan if we start
out using the non-hashed plan but find a lot more upper rows going by than we
expected. I have therefore left some minor inefficiencies in place, such as
initializing both subplans even though we will currently only use one.
the old JOIN_IN code, but antijoins are new functionality.) Teach the planner
to convert appropriate EXISTS and NOT EXISTS subqueries into semi and anti
joins respectively. Also, LEFT JOINs with suitable upper-level IS NULL
filters are recognized as being anti joins. Unify the InClauseInfo and
OuterJoinInfo infrastructure into "SpecialJoinInfo". With that change,
it becomes possible to associate a SpecialJoinInfo with every join attempt,
which permits some cleanup of join selectivity estimation. That needs to be
taken much further than this patch does, but the next step is to change the
API for oprjoin selectivity functions, which seems like material for a
separate patch. So for the moment the output size estimates for semi and
especially anti joins are quite bogus.
(because they are uncorrelated with the immediate parent query). We were
charging the full run cost to the parent node, disregarding the fact that
only one row need be fetched for EXISTS. While this would only be a
cosmetic issue in most cases, it might possibly affect planning outcomes
if the parent query were itself a subquery to some upper query.
Per recent discussion with Steve Crawford.
is using mark/restore but not rewind or backward-scan capability. Insert a
materialize plan node between a mergejoin and its inner child if the inner
child is a sort that is expected to spill to disk. The materialize shields
the sort from the need to do mark/restore and thereby allows it to perform
its final merge pass on-the-fly; while the materialize itself is normally
cheap since it won't spill to disk unless the number of tuples with equal
key values exceeds work_mem.
Greg Stark, with some kibitzing from Tom Lane.
need be returned. We keep a heap of the current best N tuples and sift-up
new tuples into it as we scan the input. For M input tuples this means
only about M*log(N) comparisons instead of M*log(M), not to mention a lot
less workspace when N is small --- avoiding spill-to-disk for large M
is actually the most attractive thing about it. Patch includes planner
and executor support for invoking this facility in ORDER BY ... LIMIT
queries. Greg Stark, with some editorialization by moi.
useless substructure for its RangeTblEntry nodes. (I chose to keep using the
same struct node type and just zero out the link fields for unneeded info,
rather than making a separate ExecRangeTblEntry type --- it seemed too
fragile to have two different rangetable representations.)
Along the way, put subplans into a list in the toplevel PlannedStmt node,
and have SubPlan nodes refer to them by list index instead of direct pointers.
Vadim wanted to do that years ago, but I never understood what he was on about
until now. It makes things a *whole* lot more robust, because we can stop
worrying about duplicate processing of subplans during expression tree
traversals. That's been a constant source of bugs, and it's finally gone.
There are some consequent simplifications yet to be made, like not using
a separate EState for subplans in the executor, but I'll tackle that later.
columns procost and prorows, to allow simple user adjustment of the estimated
cost of a function call, as well as control of the estimated number of rows
returned by a set-returning function. We might eventually wish to extend this
to allow function-specific estimation routines, but there seems to be
consensus that we should try a simple constant estimate first. In particular
this provides a relatively simple way to control the order in which different
WHERE clauses are applied in a plan node, which is a Good Thing in view of the
fact that the recent EquivalenceClass planner rewrite made that much less
predictable than before.
tables in the query compete for cache space, not just the one we are
currently costing an indexscan for. This seems more realistic, and it
definitely will help in examples recently exhibited by Stefan
Kaltenbrunner. To get the total size of all the tables involved, we must
tweak the handling of 'append relations' a bit --- formerly we looked up
information about the child tables on-the-fly during set_append_rel_pathlist,
but it needs to be done before we start doing any cost estimation, so
push it into the add_base_rels_to_query scan.
(e.g. "INSERT ... VALUES (...), (...), ...") and elsewhere as allowed
by the spec. (e.g. similar to a FROM clause subselect). initdb required.
Joe Conway and Tom Lane.
effects in a nestloop inner indexscan, I had only dealt with plain index
scans and the index portion of bitmap scans. But there will be cache
benefits for the heap accesses of bitmap scans too, so fix
cost_bitmap_heap_scan() to account for that.
that the Mackert-Lohmann formula applies across all the repetitions of the
nestloop, not just each scan independently. We use the M-L formula to
estimate the number of pages fetched from the index as well as from the table;
that isn't what it was designed for, but it seems reasonably applicable
anyway. This makes large numbers of repetitions look much cheaper than
before, which accords with many reports we've received of overestimation
of the cost of a nestloop. Also, change the index access cost model to
charge random_page_cost per index leaf page touched, while explicitly
not counting anything for access to metapage or upper tree pages. This
may all need tweaking after we get some field experience, but in simple
tests it seems to be giving saner results than before. The main thing
is to get the infrastructure in place to let cost_index() and amcostestimate
functions take repeated scans into account at all. Per my recent proposal.
Note: this patch changes pg_proc.h, but I did not force initdb because
the changes are basically cosmetic --- the system does not look into
pg_proc to decide how to call an index amcostestimate function, and
there's no way to call such a function from SQL at all.
This shouldn't affect simple indexscans much, while for bitmap scans that
are touching a lot of index rows, this seems to bring the estimates more
in line with reality. Per recent discussion.
assumed that a sequential page fetch has cost 1.0. This patch doesn't
in itself change the system's behavior at all, but it opens the door to
people adopting other units of measurement for EXPLAIN costs. Also, if
we ever decide it's worth inventing per-tablespace access cost settings,
this change provides a workable intellectual framework for that.
"ctid IN (list)" will still work after we convert IN to ScalarArrayOpExpr.
Make some minor efficiency improvements while at it, such as ensuring that
multiple TIDs are fetched in physical heap order. And fix EXPLAIN so that
it shows what's really going on for a TID scan.
a new PlannerInfo struct, which is passed around instead of the bare
Query in all the planning code. This commit is essentially just a
code-beautification exercise, but it does open the door to making
larger changes to the planner data structures without having to muck
with the widely-known Query struct.
but the code is basically working. Along the way, rewrite the entire
approach to processing OR index conditions, and make it work in join
cases for the first time ever. orindxpath.c is now basically obsolete,
but I left it in for the time being to allow easy comparison testing
against the old implementation.
logic operations during planning. Seems cleaner to create two new Path
node types, instead --- this avoids duplication of cost-estimation code.
Also, create an enable_bitmapscan GUC parameter to control use of bitmap
plans.
scans, using in-memory tuple ID bitmaps as the intermediary. The planner
frontend (path creation and cost estimation) is not there yet, so none
of this code can be executed. I have tested it using some hacked planner
code that is far too ugly to see the light of day, however. Committing
now so that the bulk of the infrastructure changes go in before the tree
drifts under me.
structs. There are many places in the planner where we were passing
both a rel and an index to subroutines, and now need only pass the
index struct. Notationally simpler, and perhaps a tad faster.
join conditions in which each OR subclause includes a constraint on
the same relation. This implements the other useful side-effect of
conversion to CNF format, without its unpleasant side-effects. As
per pghackers discussion of a few weeks ago.
teaching the latter to accept either RestrictInfo nodes or bare
clause expressions; and cache the selectivity result in the RestrictInfo
node when possible. This extends the caching behavior of approx_selectivity
to many more contexts, and should reduce duplicate selectivity
calculations.
passed to join selectivity estimators. Make use of this in eqjoinsel
to derive non-bogus selectivity for IN clauses. Further tweaking of
cost estimation for IN.
initdb forced because of pg_proc.h changes.
Try to model the effect of rescanning input tuples in mergejoins;
account for JOIN_IN short-circuiting where appropriate. Also, recognize
that mergejoin and hashjoin clauses may now be more than single operator
calls, so we have to charge appropriate execution costs.
costs for expression evaluation, not only per-tuple cost as before.
This extension is needed in order to deal realistically with hashed or
materialized sub-selects.
parameter to allow it to be forced off for comparison purposes.
Add ORDER BY clauses to a bunch of regression test queries that will
otherwise produce randomly-ordered output in the new regime.
pg_language.lancompiler
pg_operator.oprprec
pg_operator.oprisleft
pg_proc.proimplicit
pg_proc.probyte_pct
pg_proc.properbyte_cpu
pg_proc.propercall_cpu
pg_proc.prooutin_ratio
pg_shadow.usetrace
pg_type.typprtlen
pg_type.typreceive
pg_type.typsend
Attempts to use the obsoleted attributes of pg_operator or pg_proc
in the CREATE commands will be greeted by a warning. For pg_type,
there is no warning (yet) because pg_dump scripts still contain these
attributes.
Also remove new but already obsolete spellings
isVolatile, isStable, isImmutable in WITH clause. (Use new syntax
instead.)
some kibitzing from Tom Lane. Not everything works yet, and there's
no documentation or regression test, but let's commit this so Joe
doesn't need to cope with tracking changes in so many files ...
Tom Lane