fields: now they are valid whenever the clause is a binary opclause,
not only when it is a potential join clause (there is a new boolean
field canjoin to signal the latter condition). This lets us avoid
recomputing the relid sets over and over while examining indexes.
Still more work to do to make this as useful as it could be, because
there are places that could use the info but don't have access to the
RestrictInfo node.
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.
There are two implementation techniques: the executor understands a new
JOIN_IN jointype, which emits at most one matching row per left-hand row,
or the result of the IN's sub-select can be fed through a DISTINCT filter
and then joined as an ordinary relation.
Along the way, some minor code cleanup in the optimizer; notably, break
out most of the jointree-rearrangement preprocessing in planner.c and
put it in a new file prep/prepjointree.c.
containing a volatile function), rather than only on 'Var = Var' clauses
as before. This makes it practical to do flatten_join_alias_vars at the
start of planning, which in turn eliminates a bunch of klugery inside the
planner to deal with alias vars. As a free side effect, we now detect
implied equality of non-Var expressions; for example in
SELECT ... WHERE a.x = b.y and b.y = 42
we will deduce a.x = 42 and use that as a restriction qual on a. Also,
we can remove the restriction introduced 12/5/02 to prevent pullup of
subqueries whose targetlists contain sublinks.
Still TODO: make statistical estimation routines in selfuncs.c and costsize.c
smarter about expressions that are more complex than plain Vars. The need
for this is considerably greater now that we have to be able to estimate
the suitability of merge and hash join techniques on such expressions.
instead of only one. This should speed up planning (only one hash path
to consider for a given pair of relations) as well as allow more effective
hashing, when there are multiple hashable joinclauses.
joinclauses is determined accurately for each join. Formerly, the code only
considered joinclauses that used all of the rels from the outer side of the
join; thus for example
FROM (a CROSS JOIN b) JOIN c ON (c.f1 = a.x AND c.f2 = b.y)
could not exploit a two-column index on c(f1,f2), since neither of the
qual clauses would be in the joininfo list it looked in. The new code does
this correctly, and also is able to eliminate redundant clauses, thus fixing
the problem noted 24-Oct-02 by Hans-Jürgen Schönig.
now has an RTE of its own, and references to its outputs now are Vars
referencing the JOIN RTE, rather than CASE-expressions. This allows
reverse-listing in ruleutils.c to use the correct alias easily, rather
than painfully reverse-engineering the alias namespace as it used to do.
Also, nested FULL JOINs work correctly, because the result of the inner
joins are simple Vars that the planner can cope with. This fixes a bug
reported a couple times now, notably by Tatsuo on 18-Nov-01. The alias
Vars are expanded into COALESCE expressions where needed at the very end
of planning, rather than during parsing.
Also, beginnings of support for showing plan qualifier expressions in
EXPLAIN. There are probably still cases that need work.
initdb forced due to change of stored-rule representation.
mergeclauses in RIGHT/FULL join cases, just like the other routines have.
I'm not quite sure why I thought it didn't need one --- but Nick
Fankhauser's recent bug report proves that it does.
of costsize.c routines to pass Query root, so that costsize can figure
more things out by itself and not be so dependent on its callers to tell
it everything it needs to know. Use selectivity of hash or merge clause
to estimate number of tuples processed internally in these joins
(this is more useful than it would've been before, since eqjoinsel is
somewhat more accurate than before).
a separate statement (though it can still be invoked as part of VACUUM, too).
pg_statistic redesigned to be more flexible about what statistics are
stored. ANALYZE now collects a list of several of the most common values,
not just one, plus a histogram (not just the min and max values). Random
sampling is used to make the process reasonably fast even on very large
tables. The number of values and histogram bins collected is now
user-settable via an ALTER TABLE command.
There is more still to do; the new stats are not being used everywhere
they could be in the planner. But the remaining changes for this project
should be localized, and the behavior is already better than before.
A not-very-related change is that sorting now makes use of btree comparison
routines if it can find one, rather than invoking '<' twice.
join clauses. The mergejoin executor wants all the join clauses to appear
as merge quals, not as extra joinquals, for these kinds of joins. But the
planner would consider plans in which partially-sorted input paths were
used, leading to only some of the join clauses becoming merge quals.
This is fine for inner/left joins, not fine for right/full joins.
comparison does not consider paths different when they differ only in
uninteresting aspects of sort order. (We had a special case of this
consideration for indexscans already, but generalize it to apply to
ordered join paths too.) Be stricter about what is a canonical pathkey
to allow faster pathkey comparison. Cache canonical pathkeys and
dispersion stats for left and right sides of a RestrictInfo's clause,
to avoid repeated computation. Total speedup will depend on number of
tables in a query, but I see about 4x speedup of planning phase for
a sample seven-table query.
(Don't forget that an alias is required.) Views reimplemented as expanding
to subselect-in-FROM. Grouping, aggregates, DISTINCT in views actually
work now (he says optimistically). No UNION support in subselects/views
yet, but I have some ideas about that. Rule-related permissions checking
moved out of rewriter and into executor.
INITDB REQUIRED!
accesses versus sequential accesses, a (very crude) estimate of the
effects of caching on random page accesses, and cost to evaluate WHERE-
clause expressions. Export critical parameters for this model as SET
variables. Also, create SET variables for the planner's enable flags
(enable_seqscan, enable_indexscan, etc) so that these can be controlled
more conveniently than via PGOPTIONS.
Planner now estimates both startup cost (cost before retrieving
first tuple) and total cost of each path, so it can optimize queries
with LIMIT on a reasonable basis by interpolating between these costs.
Same facility is a win for EXISTS(...) subqueries and some other cases.
Redesign pathkey representation to achieve a major speedup in planning
(I saw as much as 5X on a 10-way join); also minor changes in planner
to reduce memory consumption by recycling discarded Path nodes and
not constructing unnecessary lists.
Minor cleanups to display more-plausible costs in some cases in
EXPLAIN output.
Initdb forced by change in interface to index cost estimation
functions.
fields in JoinPaths --- turns out that we do need that after all :-(.
Also, rearrange planner so that only one RelOptInfo is created for a
particular set of joined base relations, no matter how many different
subsets of relations it can be created from. This saves memory and
processing time compared to the old method of making a bunch of RelOptInfos
and then removing the duplicates. Clean up the jointree iteration logic;
not sure if it's better, but I sure find it more readable and plausible
now, particularly for the case of 'bushy plans'.
nonoverlap_sets() and is_subset() to list.c, where they should have lived
to begin with, and rename to nonoverlap_setsi and is_subseti since they
only work on integer lists.
pghackers discussion of 5-Jan-2000. The amopselect and amopnpages
estimators are gone, and in their place is a per-AM amcostestimate
procedure (linked to from pg_am, not pg_amop).
sort order down into planner, instead of handling it only at the very top
level of the planner. This fixes many things. An explicit sort is now
avoided if there is a cheaper alternative (typically an indexscan) not
only for ORDER BY, but also for the internal sort of GROUP BY. It works
even when there is no other reason (such as a WHERE condition) to consider
the indexscan. It works for indexes on functions. It works for indexes
on functions, backwards. It's just so cool...
CAUTION: I have changed the representation of SortClause nodes, therefore
THIS UPDATE BREAKS STORED RULES. You will need to initdb.
store all ordering information in pathkeys lists (which are now lists of
lists of PathKeyItem nodes, not just lists of lists of vars). This was
a big win --- the code is smaller and IMHO more understandable than it
was, even though it handles more cases. I believe the node changes will
not force an initdb for anyone; planner nodes don't show up in stored
rules.
hashjoinable clause, not one path for a randomly-chosen element of each
set of clauses with the same join operator. That is, if you wrote
SELECT ... WHERE t1.f1 = t2.f2 and t1.f3 = t2.f4,
and both '=' ops were the same opcode (say, all four fields are int4),
then the system would either consider hashing on f1=f2 or on f3=f4,
but it would *not* consider both possibilities. Boo hiss.
Also, revise estimation of hashjoin costs to include a penalty when the
inner join var has a high disbursion --- ie, the most common value is
pretty common. This tends to lead to badly skewed hash bucket occupancy
and way more comparisons than you'd expect on average.
I imagine that the cost calculation still needs tweaking, but at least
it generates a more reasonable plan than before on George Young's example.
rels that the inner path needs to join to, but it was only checking for
the first one. Failure could only have been observed with an OR-clause
that mentions 3 or more tables, and then only if the bogus path was
actually selected as cheapest ...
remove optimizer's arbitrary limit on how large a join it will use hashing
for. (The limit was too large to prevent the problems we'd been seeing,
anyway...)
Tom Lane
PostgreSQL Daemon
Peter Eisentraut
Bruce Momjian
Marc G. Fournier