Until now we've only used a single multivariate MCV list per relation,
covering the largest number of clauses. So for example given a query
SELECT * FROM t WHERE a = 1 AND b =1 AND c = 1 AND d = 1
and extended statistics on (a,b) and (c,d), we'd only pick and use one
of them. This commit improves this by repeatedly picking and applying
the best statistics (matching the largest number of remaining clauses)
until no additional statistics is applicable.
This greedy algorithm is simple, but may not be optimal. A different
choice of statistics may leave fewer clauses unestimated and/or give
better estimates for some other reason.
This can however happen only when there are overlapping statistics, and
selecting one makes it impossible to use the other. E.g. with statistics
on (a,b), (c,d), (b,c,d), we may pick either (a,b) and (c,d) or (b,c,d).
But it's not clear which option is the best one.
We however assume cases like this are rare, and the easiest solution is
to define statistics covering the whole group of correlated columns. In
the future we might support overlapping stats, using some of the clauses
as conditions (in conditional probability sense).
Author: Tomas Vondra
Reviewed-by: Mark Dilger, Kyotaro Horiguchi
Discussion: https://postgr.es/m/20191028152048.jc6pqv5hb7j77ocp@development
When considering functional dependencies during selectivity estimation,
it's not necessary to bother with selecting the best extended statistic
object and then use just dependencies from it. We can simply consider
all applicable functional dependencies at once.
This means we need to deserialie all (applicable) dependencies before
applying them to the clauses. This is a bit more expensive than picking
the best statistics and deserializing dependencies for it. To minimize
the additional cost, we ignore statistics that are not applicable.
Author: Tomas Vondra
Reviewed-by: Mark Dilger
Discussion: https://postgr.es/m/20191028152048.jc6pqv5hb7j77ocp@development
When picking the best extended statistics object for a list of clauses,
it's not enough to look at attnums extracted from the clause list as a
whole. Consider for example this query with OR clauses:
SELECT * FROM t WHERE (t.a = 1) OR (t.b = 1) OR (t.c = 1)
with a statistics defined on columns (a,b). Relying on attnums extracted
from the whole OR clause, we'd consider the statistics usable. That does
not work, as we see the conditions as a single OR-clause, referencing an
attribute not covered by the statistic, leading to empty list of clauses
to be estimated using the statistics and an assert failure.
This changes choose_best_statistics to check which clauses are actually
covered, and only using attributes from the fully covered ones. For the
previous example this means the statistics object will not be considered
as compatible with the OR-clause.
Backpatch to 12, where MCVs were introduced. The issue does not affect
older versions because functional dependencies don't handle OR clauses.
Author: Tomas Vondra
Reviewed-by: Dean Rasheed
Reported-By: Manuel Rigger
Discussion: https://postgr.es/m/CA+u7OA7H5rcE2=8f263w4NZD6ipO_XOrYB816nuLXbmSTH9pQQ@mail.gmail.com
Backpatch-through: 12
When estimating number of distinct groups, we failed to ignore system
attributes when matching the group expressions to mvdistinct stats,
causing failures like
ERROR: negative bitmapset member not allowed
Fix that by simply skipping anything that is not a regular attribute.
Backpatch to PostgreSQL 10, where the extended stats were introduced.
Bug: #16111
Reported-by: Tuomas Leikola
Author: Tomas Vondra
Backpatch-through: 10
Discussion: https://postgr.es/m/16111-687799584c3a7e73@postgresql.org
Commit d7f8d26d9 added new tests to the stats_ext regression test that
included creating a view in the public schema, without realising that
the stats_ext test runs in the same parallel group as the rules test,
which makes doing that unsafe.
This led to intermittent failures of the rules test on the buildfarm,
although I wasn't able to reproduce that locally. Fix by creating the
view in a different schema.
Tomas Vondra and Dean Rasheed, report and diagnosis by Thomas Munro.
Discussion: https://postgr.es/m/CA+hUKGKX9hFZrYA7rQzAMRE07L4hziCc-nO_b3taJpiuKyLLxg@mail.gmail.com
When building statistics, we need to decide how many rows to sample and
how accurate the resulting statistics should be. Until now, it was not
possible to explicitly define statistics target for extended statistics
objects, the value was always computed from the per-attribute targets
with a fallback to the system-wide default statistics target.
That's a bit inconvenient, as it ties together the statistics target set
for per-column and extended statistics. In some cases it may be useful
to require larger sample / higher accuracy for extended statics (or the
other way around), but with this approach that's not possible.
So this commit introduces a new command, allowing to specify statistics
target for individual extended statistics objects, overriding the value
derived from per-attribute targets (and the system default).
ALTER STATISTICS stat_name SET STATISTICS target_value;
When determining statistics target for an extended statistics object we
first look at this explicitly set value. When this value is -1, we fall
back to the old formula, looking at the per-attribute targets first and
then the system default. This means the behavior is backwards compatible
with older PostgreSQL releases.
Author: Tomas Vondra
Discussion: https://postgr.es/m/20190618213357.vli3i23vpkset2xd@development
Reviewed-by: Kirk Jamison, Dean Rasheed
When performing ANALYZE on inheritance trees, we collect two samples for
each relation - one for the relation alone, and one for the inheritance
subtree (relation and its child relations). And then we build statistics
on each sample, so for each relation we get two sets of statistics.
For regular (per-column) statistics this works fine, because the catalog
includes a flag differentiating statistics built from those two samples.
But we don't have such flag in the extended statistics catalogs, and we
ended up updating the same row twice, triggering this error:
ERROR: tuple already updated by self
The simplest solution is to disable extended statistics on inheritance
trees, which is what this commit is doing. In the future we may need to
do something similar to per-column statistics, but that requires adding a
flag to the catalog - and that's not backpatchable. Moreover, the current
selectivity estimation code only works with individual relations, so
building statistics on inheritance trees would be pointless anyway.
Author: Tomas Vondra
Backpatch-to: 10-
Discussion: https://postgr.es/m/20190618231233.GA27470@telsasoft.com
Reported-by: Justin Pryzby
There were two issues in how the extended statistics handled NULL values
in opclauses. Firstly, the code was oblivious to the possibility that
Const may be NULL (constisnull=true) in which case the constvalue is
undefined. We need to treat this as a mismatch, and not call the proc.
Secondly, the MCV item itself may contain NULL values too - the code
already did check that, and updated the match bitmap accordingly, but
failed to ensure we won't call the operator procedure anyway. It did
work for AND-clauses, because in that case false in the bitmap stops
evaluation of further clauses. But for OR-clauses ir was not easy to
get incorrect estimates or even trigger a crash.
This fixes both issues by extending the existing check so that it looks
at constisnull too, and making sure it skips calling the procedure.
Discussion: https://postgr.es/m/8736jdhbhc.fsf%40ansel.ydns.eu
Commit d7f8d26d9 added a test case that created a user, but forgot
to drop it again. This is no good; for one thing, it causes repeated
"make installcheck" runs to fail.
The multivariate MCV estimation code may run user-defined operators on
the values in the MCV list, which means that those operators may
potentially leak the values from the MCV list. Guard against leaking
data to unprivileged users by checking that the user has SELECT
privileges on the table or all of the columns referred to by the
statistics.
Additionally, if there are any securityQuals on the RTE (either due to
RLS policies on the table, or accessing the table via a security
barrier view), not all rows may be visible to the current user, even
if they have table or column privileges. Thus we further insist that
the operator be leakproof in this case.
Dean Rasheed, reviewed by Tomas Vondra.
Discussion: https://postgr.es/m/CAEZATCUhT9rt7Ui=Vdx4N==VV5XOK5dsXfnGgVOz_JhAicB=ZA@mail.gmail.com
Since extended statistic got introduced in PostgreSQL 10, there was a
single catalog pg_statistic_ext storing both the definitions and built
statistic. That's however problematic when a user is supposed to have
access only to the definitions, but not to user data.
Consider for example pg_dump on a database with RLS enabled - if the
pg_statistic_ext catalog respects RLS (which it should, if it contains
user data), pg_dump would not see any records and the result would not
define any extended statistics. That would be a surprising behavior.
Until now this was not a pressing issue, because the existing types of
extended statistic (functional dependencies and ndistinct coefficients)
do not include any user data directly. This changed with introduction
of MCV lists, which do include most common combinations of values.
The easiest way to fix this is to split the pg_statistic_ext catalog
into two - one for definitions, one for the built statistic values.
The new catalog is called pg_statistic_ext_data, and we're maintaining
a 1:1 relationship with the old catalog - either there are matching
records in both catalogs, or neither of them.
Bumped CATVERSION due to changing system catalog definitions.
Author: Dean Rasheed, with improvements by me
Reviewed-by: Dean Rasheed, John Naylor
Discussion: https://postgr.es/m/CAEZATCUhT9rt7Ui%3DVdx4N%3D%3DVV5XOK5dsXfnGgVOz_JhAicB%3DZA%40mail.gmail.com
The regression tests added in commit 7300a69950 test cardinality
estimates using a function that extracts the interesting pieces
from the EXPLAIN output, instead of testing the whole plan. That
seems both easier to understand and less fragile, so this applies
the same approach to pre-existing tests of ndistinct coefficients
and functional dependencies.
Discussion: https://postgr.es/m/dfdac334-9cf2-2597-fb27-f0fb3753f435@2ndquadrant.com
The memcpy() was copying type OIDs in the wrong direction, so the
deserialized MCV list always had them as 0. This is mostly harmless
except when printing the data in pg_mcv_list_items(), in which case
it reported
ERROR: cache lookup failed for type 0
Also added a simple regression test for pg_mcv_list_items() function,
printing a single-item MCV list.
Reported-By: Dean Rasheed
Discussion: https://postgr.es/m/CAEZATCX6T0iDTTZrqyec4Cd6b4yuL7euu4=rQRXaVBAVrUi1Cg@mail.gmail.com
Introduce a third extended statistic type, supported by the CREATE
STATISTICS command - MCV lists, a generalization of the statistic
already built and used for individual columns.
Compared to the already supported types (n-distinct coefficients and
functional dependencies), MCV lists are more complex, include column
values and allow estimation of much wider range of common clauses
(equality and inequality conditions, IS NULL, IS NOT NULL etc.).
Similarly to the other types, a new pseudo-type (pg_mcv_list) is used.
Author: Tomas Vondra
Reviewed-by: Dean Rasheed, David Rowley, Mark Dilger, Alvaro Herrera
Discussion: https://postgr.es/m/dfdac334-9cf2-2597-fb27-f0fb3753f435@2ndquadrant.com
Now that the ordering of DROP messages ought to be stable everywhere,
we should not need these kluges of hiding DETAIL output just to avoid
unstable ordering. Hiding it's not great for test coverage, so
let's undo that where possible.
In a small number of places, it's necessary to leave it in, for
example because the output might include a variable pg_temp_nnn
schema name. I also left things alone in places where the details
would depend on other regression test scripts, e.g. plpython_drop.sql.
Perhaps buildfarm experience will show this to be a bad idea,
but if so I'd like to know why.
Discussion: https://postgr.es/m/E1h6eep-0001Mw-Vd@gemulon.postgresql.org
DROP CASCADE doesn't currently promise to visit dependent objects in
a fixed order, so when the regression tests use it, we typically need
to suppress the details of which objects get dropped in order to have
predictable test output. Traditionally we've done that by setting
client_min_messages higher than NOTICE, but there's a better way:
we can "\set VERBOSITY terse" in psql. That suppresses the DETAIL
message with the object list, but we still get the basic notice telling
how many objects were dropped. So at least the test case can verify
that the expected number of objects were dropped.
The VERBOSITY method was already in use in a few places, but run
around and use it wherever it makes sense.
Discussion: https://postgr.es/m/10766.1501608885@sss.pgh.pa.us
I misplaced the IF NOT EXISTS clause in commit 7b504eb282, before the
word STATISTICS. Put it where it belongs.
Patch written independently by Amit Langote and myself. I adopted his
submitted test case with a slight edit also.
Reported-by: Bruno Wolff III
Discussion: https://postgr.es/m/20170621004237.GB8337@wolff.to
ALTER COLUMN TYPE on a column used by a statistics object fails since
commit 928c4de30, because the relevant switch in ATExecAlterColumnType
is unprepared for columns to have dependencies from OCLASS_STATISTIC_EXT
objects.
Although the existing types of extended statistics don't actually need us
to do any work for a column type change, it seems completely indefensible
that that assumption is hidden behind the failure of an unrelated module
to contain any code for the case. Hence, create and call an API function
in statscmds.c where the assumption can be explained, and where we could
add code to deal with the problem when it inevitably becomes real.
Also, the reason this wasn't handled before, neither for extended stats
nor for the last half-dozen new OCLASS kinds :-(, is that the default:
in that switch suppresses compiler warnings, allowing people to miss the
need to consider it when adding an OCLASS. We don't really need a default
because surely getObjectClass should only return valid values of the enum;
so remove it, and add the missed OCLASS entries where they should be.
Discussion: https://postgr.es/m/20170512221010.nglatgt5azzdxjlj@alvherre.pgsql
Consistently refer to such an entry as a "statistics object", not just
"statistics" or "extended statistics". Previously we had a mismash of
terms, accompanied by utter confusion as to whether the term was
singular or plural. That's not only grating (at least to the ear of
a native English speaker) but could be outright misleading, eg in error
messages that seemed to be referring to multiple objects where only one
could be meant.
This commit fixes the code and a lot of comments (though I may have
missed a few). I also renamed two new SQL functions,
pg_get_statisticsextdef -> pg_get_statisticsobjdef
pg_statistic_ext_is_visible -> pg_statistics_obj_is_visible
to conform better with this terminology.
I have not touched the SGML docs other than fixing those function
names; the docs certainly need work but it seems like a separable task.
Discussion: https://postgr.es/m/22676.1494557205@sss.pgh.pa.us
A stats object ought to have a dependency on each individual column
it reads, not the entire table. Doing this honestly lets us get rid
of the hard-wired logic in RemoveStatisticsExt, which seems to have
been misguidedly modeled on RemoveStatistics; and it will be far easier
to extend to multiple tables later.
Also, add overlooked dependency on owner, and make the dependency on
schema be NORMAL like every other such dependency.
There remains some unfinished work here, which is to allow statistics
objects to be extension members. That takes more effort than just
adding the dependency call, though, so I left it out for now.
initdb forced because this changes the set of pg_depend records that
should exist for a statistics object.
Discussion: https://postgr.es/m/22676.1494557205@sss.pgh.pa.us
Previously, we had the WITH clause in the middle of the command, where
you'd specify both generic options as well as statistic types. Few
people liked this, so this commit changes it to remove the WITH keyword
from that clause and makes it accept statistic types only. (We
currently don't have any generic options, but if we invent in the
future, we will gain a new WITH clause, probably at the end of the
command).
Also, the column list is now specified without parens, which makes the
whole command look more similar to a SELECT command. This change will
let us expand the command to supporting expressions (not just columns
names) as well as multiple tables and their join conditions.
Tom added lots of code comments and fixed some parts of the CREATE
STATISTICS reference page, too; more changes in this area are
forthcoming. He also fixed a potential problem in the alter_generic
regression test, reducing verbosity on a cascaded drop to avoid
dependency on message ordering, as we do in other tests.
Tom also closed a security bug: we documented that table ownership was
required in order to create a statistics object on it, but didn't
actually implement it.
Implement tab-completion for statistics objects. This can stand some
more improvement.
Authors: Alvaro Herrera, with lots of cleanup by Tom Lane
Discussion: https://postgr.es/m/20170420212426.ltvgyhnefvhixm6i@alvherre.pgsql
We were accepting creation of extended statistics only for regular
tables, but they can usefully be created for foreign tables, partitioned
tables, and materialized views, too. Allow those cases.
While at it, make sure all the rejected cases throw a consistent error
message, and add regression tests for the whole thing.
Author: David Rowley, Álvaro Herrera
Discussion: https://postgr.es/m/CAKJS1f-BmGo410bh5RSPZUvOO0LhmHL2NYmdrC_Jm8pk_FfyCA@mail.gmail.com
Either because of a previous ALTER TABLE .. SET STATISTICS 0 or because
of being invoked with a partial column list, ANALYZE could fail to
acquire sufficient data to build extended statistics. Previously, this
would draw an ERROR and fail to collect any statistics at all (extended
and regular). Change things so that we raise a WARNING instead, and
remove a hint that was wrong in half the cases.
Reported by: David Rowley
Discussion: https://postgr.es/m/CAKJS1f9Kk0NF6Fg7TA=JUXsjpS9kX6NVu27pb5QDCpOYAvb-Og@mail.gmail.com
Follow on patch in the multi-variate statistics patch series.
CREATE STATISTICS s1 WITH (dependencies) ON (a, b) FROM t;
ANALYZE;
will collect dependency stats on (a, b) and then use the measured
dependency in subsequent query planning.
Commit 7b504eb282 added
CREATE STATISTICS with n-distinct coefficients. These are now
specified using the mutually exclusive option WITH (ndistinct).
Author: Tomas Vondra, David Rowley
Reviewed-by: Kyotaro HORIGUCHI, Álvaro Herrera, Dean Rasheed, Robert Haas
and many other comments and contributions
Discussion: https://postgr.es/m/56f40b20-c464-fad2-ff39-06b668fac47c@2ndquadrant.com
As suggested by Tom Lane, avoid printing specific estimated cost values,
because they vary across architectures; instead, verify plan shapes (in
this case, HashAggregate vs. GroupAggregate), as we do in other planner
tests.
We can now remove expected/stats_ext_1.out.
Author: Tomas Vondra
There was a thinko whereby we tested the wrong tuple after fetching it
from cache; avoid that by using generate_relation_name instead, which is
simpler. Also, the statistics name was not qualified, so add that. (It
could be argued that qualification should be conditional on the schema
not being on search path. We can add that later, but at least this form
is correct.)
Author: David Rowley, Álvaro Herrera
Discussion: https://postgr.es/m/CAKJS1f8RjLeVZJ2+93pdQGuZJeBF-ifsHaFMR-q-6-Z0qxA8cA@mail.gmail.com
Add support for explicitly declared statistic objects (CREATE
STATISTICS), allowing collection of statistics on more complex
combinations that individual table columns. Companion commands DROP
STATISTICS and ALTER STATISTICS ... OWNER TO / SET SCHEMA / RENAME are
added too. All this DDL has been designed so that more statistic types
can be added later on, such as multivariate most-common-values and
multivariate histograms between columns of a single table, leaving room
for permitting columns on multiple tables, too, as well as expressions.
This commit only adds support for collection of n-distinct coefficient
on user-specified sets of columns in a single table. This is useful to
estimate number of distinct groups in GROUP BY and DISTINCT clauses;
estimation errors there can cause over-allocation of memory in hashed
aggregates, for instance, so it's a worthwhile problem to solve. A new
special pseudo-type pg_ndistinct is used.
(num-distinct estimation was deemed sufficiently useful by itself that
this is worthwhile even if no further statistic types are added
immediately; so much so that another version of essentially the same
functionality was submitted by Kyotaro Horiguchi:
https://postgr.es/m/20150828.173334.114731693.horiguchi.kyotaro@lab.ntt.co.jp
though this commit does not use that code.)
Author: Tomas Vondra. Some code rework by Álvaro.
Reviewed-by: Dean Rasheed, David Rowley, Kyotaro Horiguchi, Jeff Janes,
Ideriha Takeshi
Discussion: https://postgr.es/m/543AFA15.4080608@fuzzy.czhttps://postgr.es/m/20170320190220.ixlaueanxegqd5gr@alvherre.pgsql
