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
BRIN is a new index access method intended to accelerate scans of very
large tables, without the maintenance overhead of btrees or other
traditional indexes. They work by maintaining "summary" data about
block ranges. Bitmap index scans work by reading each summary tuple and
comparing them with the query quals; all pages in the range are returned
in a lossy TID bitmap if the quals are consistent with the values in the
summary tuple, otherwise not. Normal index scans are not supported
because these indexes do not store TIDs.
As new tuples are added into the index, the summary information is
updated (if the block range in which the tuple is added is already
summarized) or not; in the latter case, a subsequent pass of VACUUM or
the brin_summarize_new_values() function will create the summary
information.
For data types with natural 1-D sort orders, the summary info consists
of the maximum and the minimum values of each indexed column within each
page range. This type of operator class we call "Minmax", and we
supply a bunch of them for most data types with B-tree opclasses.
Since the BRIN code is generalized, other approaches are possible for
things such as arrays, geometric types, ranges, etc; even for things
such as enum types we could do something different than minmax with
better results. In this commit I only include minmax.
Catalog version bumped due to new builtin catalog entries.
There's more that could be done here, but this is a good step forwards.
Loosely based on ideas from Simon Riggs; code mostly by Álvaro Herrera,
with contribution by Heikki Linnakangas.
Patch reviewed by: Amit Kapila, Heikki Linnakangas, Robert Haas.
Testing help from Jeff Janes, Erik Rijkers, Emanuel Calvo.
PS:
The research leading to these results has received funding from the
European Union's Seventh Framework Programme (FP7/2007-2013) under
grant agreement n° 318633.
Previously, pattern_fixed_prefix() was defined to return whatever fixed
prefix it could extract from the pattern, plus the "rest" of the pattern.
That definition was sensible for LIKE patterns, but not so much for
regexes, where reconstituting a valid pattern minus the prefix could be
quite tricky (certainly the existing code wasn't doing that correctly).
Since the only thing that callers ever did with the "rest" of the pattern
was to pass it to like_selectivity() or regex_selectivity(), let's cut out
the middle-man and just have pattern_fixed_prefix's subroutines do this
directly. Then pattern_fixed_prefix can return a simple selectivity
number, and the question of how to cope with partial patterns is removed
from its API specification.
While at it, adjust the API spec so that callers who don't actually care
about the pattern's selectivity (which is a lot of them) can pass NULL for
the selectivity pointer to skip doing the work of computing a selectivity
estimate.
This patch is only an API refactoring that doesn't actually change any
processing, other than allowing a little bit of useless work to be skipped.
However, it's necessary infrastructure for my upcoming fix to regex prefix
extraction, because after that change there won't be any simple way to
identify the "rest" of the regex, not even to the low level of fidelity
needed by regex_selectivity. We can cope with that if regex_fixed_prefix
and regex_selectivity communicate directly, but not if we have to work
within the old API. Hence, back-patch to all active branches.
This patch improves selectivity estimation for the array <@, &&, and @>
(containment and overlaps) operators. It enables collection of statistics
about individual array element values by ANALYZE, and introduces
operator-specific estimators that use these stats. In addition,
ScalarArrayOpExpr constructs of the forms "const = ANY/ALL (array_column)"
and "const <> ANY/ALL (array_column)" are estimated by treating them as
variants of the containment operators.
Since we still collect scalar-style stats about the array values as a
whole, the pg_stats view is expanded to show both these stats and the
array-style stats in separate columns. This creates an incompatible change
in how stats for tsvector columns are displayed in pg_stats: the stats
about lexemes are now displayed in the array-related columns instead of the
original scalar-related columns.
There are a few loose ends here, notably that it'd be nice to be able to
suppress either the scalar-style stats or the array-element stats for
columns for which they're not useful. But the patch is in good enough
shape to commit for wider testing.
Alexander Korotkov, reviewed by Noah Misch and Nathan Boley
Formerly, we just punted when trying to estimate stats for variables coming
out of sub-queries using DISTINCT, on the grounds that whatever stats we
might have for underlying table columns would be inapplicable. But if the
sub-query has only one DISTINCT column, we can consider its output variable
as being unique, which is useful information all by itself. The scope of
this improvement is pretty narrow, but it costs nearly nothing, so we might
as well do it. Per discussion with Andres Freund.
This patch differs from the draft I submitted yesterday in updating various
comments about vardata.isunique (to reflect its extended meaning) and in
tweaking the interaction with security_barrier views. There does not seem
to be a reason why we can't use this sort of knowledge even when the
sub-query is such a view.
SP-GiST is comparable to GiST in flexibility, but supports non-balanced
partitioned search structures rather than balanced trees. As described at
PGCon 2011, this new indexing structure can beat GiST in both index build
time and query speed for search problems that it is well matched to.
There are a number of areas that could still use improvement, but at this
point the code seems committable.
Teodor Sigaev and Oleg Bartunov, with considerable revisions by Tom Lane
Since collation is effectively an argument, not a property of the function,
FmgrInfo is really the wrong place for it; and this becomes critical in
cases where a cached FmgrInfo is used for varying purposes that might need
different collation settings. Fix by passing it in FunctionCallInfoData
instead. In particular this allows a clean fix for bug #5970 (record_cmp
not working). This requires touching a bit more code than the original
method, but nobody ever thought that collations would not be an invasive
patch...
This is necessary, not optional, now that ILIKE and regexes are collation
aware --- else we might derive a wrong comparison constant for index
optimized pattern matches.
While this will give wrong answers when estimating selectivity for a
comparison operator that's using a non-default collation, the estimation
error probably won't be large; and anyway the former approach created
estimation errors of its own by trying to use a histogram that might have
been computed with some other collation. So we'll adopt this simplified
approach for now and perhaps improve it sometime in the future.
This patch incorporates changes from Andres Freund to make sure that
selfuncs.c passes a valid collation OID to any datatype-specific function
it calls, in case that function wants collation information. Said OID will
now always be DEFAULT_COLLATION_OID, but at least we won't get errors.
This adds collation support for columns and domains, a COLLATE clause
to override it per expression, and B-tree index support.
Peter Eisentraut
reviewed by Pavel Stehule, Itagaki Takahiro, Robert Haas, Noah Misch
and anti joins. To do this, pass the SpecialJoinInfo struct for the current
join as an additional optional argument to operator join selectivity
estimation functions. This allows the estimator to tell not only what kind
of join is being formed, but which variable is on which side of the join;
a requirement long recognized but not dealt with till now. This also leaves
the door open for future improvements in the estimators, such as accounting
for the null-insertion effects of lower outer joins. I didn't do anything
about that in the current patch but the information is in principle deducible
from what's passed.
The patch also clarifies the definition of join selectivity for semi/anti
joins: it's the fraction of the left input that has (at least one) match
in the right input. This allows getting rid of some very fuzzy thinking
that I had committed in the original 7.4-era IN-optimization patch.
There's probably room to estimate this better than the present patch does,
but at least we know what to estimate.
Since I had to touch CREATE OPERATOR anyway to allow a variant signature
for join estimator functions, I took the opportunity to add a couple of
additional checks that were missing, per my recent message to -hackers:
* Check that estimator functions return float8;
* Require execute permission at the time of CREATE OPERATOR on the
operator's function as well as the estimator functions;
* Require ownership of any pre-existing operator that's modified by
the command.
I also moved the lookup of the functions out of OperatorCreate() and
into operatorcmds.c, since that seemed more consistent with most of
the other catalog object creation processes, eg CREATE TYPE.
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.
pattern-examination heuristic method to purely histogram-driven selectivity at
histogram size 100, we compute both estimates and use a weighted average.
The weight put on the heuristic estimate decreases linearly with histogram
size, dropping to zero for 100 or more histogram entries.
Likewise in ltreeparentsel(). After a patch by Greg Stark, though I
reorganized the logic a bit to give the caller of histogram_selectivity()
more control.
the two join variables at both ends: not only trailing rows that need not be
scanned because there cannot be a match on the other side, but initial rows
that will be scanned without possibly having a match. This allows a more
realistic estimate of startup cost to be made, per recent pgsql-performance
discussion. In passing, fix a couple of bugs that had crept into
mergejoinscansel: it was not quite up to speed for the task of estimating
descending-order scans, which is a new requirement in 8.3.
make_greater_string() try harder to generate a string that's actually greater
than its input string. Before we just assumed that making a string that was
memcmp-greater was enough, but it is easy to generate examples where this is
not so when the locale is not C. Instead, loop until the relevant comparison
function agrees that the generated string is greater than the input.
Unfortunately this is probably not enough to guarantee that the generated
string is greater than all extensions of the input, so we cannot relax the
restriction to C locale for the LIKE/regex index optimization. But it should
at least improve the odds of getting a useful selectivity estimate in
prefix_selectivity(). Per example from Guillaume Smet.
Backpatch to 8.1, mainly because that's what the complainant is using...
the number of rows likely to be produced by a query such as
SELECT * FROM t1 LEFT JOIN t2 USING (key) WHERE t2.key IS NULL;
What this is doing is selecting for t1 rows with no match in t2, and thus
it may produce a significant number of rows even if the t2.key table column
contains no nulls at all. 8.2 thinks the table column's null fraction is
relevant and thus may estimate no rows out, which results in terrible plans
if there are more joins above this one. A proper fix for this will involve
passing much more information about the context of a clause to the selectivity
estimator functions than we ever have. There's no time left to write such a
patch for 8.3, and it wouldn't be back-patchable into 8.2 anyway. Instead,
put in an ad-hoc test to defeat the normal table-stats-based estimation when
an IS NULL test is evaluated at an outer join, and just use a constant
estimate instead --- I went with 0.5 for lack of a better idea. This won't
catch every case but it will catch the typical ways of writing such queries,
and it seems unlikely to make things worse for other queries.
which I had removed in the first cut of the EquivalenceClass rewrite to
simplify that patch a little. But it's still important --- in a four-way
join problem mergejoinscansel() was eating about 40% of the planning time
according to gprof. Also, improve the EquivalenceClass code to re-use
join RestrictInfos rather than generating fresh ones for each join
considered. This saves some memory space but more importantly improves
the effectiveness of caching planning info in RestrictInfos.
cases. Operator classes now exist within "operator families". While most
families are equivalent to a single class, related classes can be grouped
into one family to represent the fact that they are semantically compatible.
Cross-type operators are now naturally adjunct parts of a family, without
having to wedge them into a particular opclass as we had done originally.
This commit restructures the catalogs and cleans up enough of the fallout so
that everything still works at least as well as before, but most of the work
needed to actually improve the planner's behavior will come later. Also,
there are not yet CREATE/DROP/ALTER OPERATOR FAMILY commands; the only way
to create a new family right now is to allow CREATE OPERATOR CLASS to make
one by default. I owe some more documentation work, too. But that can all
be done in smaller pieces once this infrastructure is in place.
is a large enough histogram, it will use the number of matches in the
histogram to derive a selectivity estimate, rather than the admittedly
pretty bogus heuristics involving examining the pattern contents. I set
'large enough' at 100, but perhaps we should change that later. Also
apply the same technique in contrib/ltree's <@ and @> estimator. Per
discussion with Stefan Kaltenbrunner and Matteo Beccati.
ScalarArrayOpExpr index quals: we were estimating the right total
number of rows returned, but treating the index-access part of the
cost as if a single scan were fetching that many consecutive index
tuples. Actually we should treat it as a multiple indexscan, and
if there are enough of 'em the Mackert-Lohman discount should kick in.
cases. This was not needed in the existing uses within selfuncs.c, but if
we're gonna export it for general use, the extra generality seems helpful.
Motivated by looking at ltree example.
qualification when the underlying operator is indexable and useOr is true.
That is, indexkey op ANY (ARRAY[...]) is effectively translated into an
OR combination of one indexscan for each array element. This only works
for bitmap index scans, of course, since regular indexscans no longer
support OR'ing of scans. There are still some loose ends to clean up
before changing 'x IN (list)' to translate as a ScalarArrayOpExpr;
for instance predtest.c ought to be taught about it. But this gets the
basic functionality in place.
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.
on-the-fly, and thereby avoid blowing out memory when the planner has
underestimated the hash table size. Hash join will now obey the
work_mem limit with some faithfulness. Per my recent proposal
(hash aggregate part isn't done yet though).