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To estimate with extended statistics, we need to clear the varnullingrels
field in the expression, and duplicates are not allowed in the GroupVarInfo
list. We might re-use add_unique_group_var(), but we don't do so for two
reasons.
1) We must keep the origin_rinfos list ordered exactly the same way as
varinfos.
2) add_unique_group_var() is designed for estimate_num_groups(), where a
larger number of groups is worse. While estimating the number of hash
buckets, we have the opposite: a lesser number of groups is worse.
Therefore, we don't have to remove "known equal" vars: the removed var
may valuably contribute to the multivariate statistics to grow the number
of groups.
This commit adds custom code to estimate_multivariate_bucketsize() to
initialize varinfos properly.
Reported-by: Robins Tharakan <tharakan@gmail.com>
Discussion: https://postgr.es/m/18885-da51324078588253%40postgresql.org
Author: Andrei Lepikhov <lepihov@gmail.com>
Reviewed-by: David Rowley <dgrowleyml@gmail.com>
Reviewed-by: Tomas Vondra <tomas@vondra.me>
Reviewed-by: Alexander Korotkov <aekorotkov@gmail.com>
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estimate_multivariate_ndistinct() is coded to assume the caller handles
passing it a list of GroupVarInfos with unique 'var' fields over the
entire list. 6bb6a62f3 added code which didn't ensure this and that
could result in estimate_multivariate_ndistinct() erroring out with:
ERROR: corrupt MVNDistinct entry
This occurred because estimate_multivariate_ndistinct() first searches
for a set of stats that match to at least two of the given GroupVarInfos
and then later assumes that the MVNDistinctItem.items array of the
best matching stats will have an entry for those two columns. If the
GroupVarInfos List contained a duplicate entry then the same column could
be matched to twice and that could trick the code into thinking we have
>= 2 columns matched in cases where only a single distinct column has been
matched. This could result in a failure to find the correct
MVNDistinctItem in the stats as the array containing those never
contains an item for single columns.
Here we make it more clear that the function needs a distinct set of
GroupVarInfos and also tidy up a few other comments to make things a bit
easier to follow.
Author: David Rowley <drowleyml@gmail.com>
Discussion: https://postgr.es/m/CAApHDvocZCUhM9W9mJ39d6oQz7ePKoqFnao_347mvC-A7QatcQ@mail.gmail.com
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There were several places in ordering-related planning where a
requirement for btree was hardcoded but an amcanorder index could
suffice. This fixes that. We just need to do the necessary mapping
between strategy numbers and compare types and adjust some related
APIs so that this works independent of btree strategy numbers. For
instance, non-btree amcanorder indexes can now be used to support
sorting and merge joins. Also, predtest.c works independent of btree
strategy numbers now.
To avoid performance regressions, some details on btree and other
built-in index types are still hardcoded as shortcuts, but other index
types now have access to the same features by providing the required
flags and callbacks.
Author: Mark Dilger <mark.dilger@enterprisedb.com>
Co-authored-by: Peter Eisentraut <peter@eisentraut.org>
Discussion: https://www.postgresql.org/message-id/flat/E72EAA49-354D-4C2E-8EB9-255197F55330@enterprisedb.com
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Teach nbtree multi-column index scans to opportunistically skip over
irrelevant sections of the index given a query with no "=" conditions on
one or more prefix index columns. When nbtree is passed input scan keys
derived from a predicate "WHERE b = 5", new nbtree preprocessing steps
output "WHERE a = ANY(<every possible 'a' value>) AND b = 5" scan keys.
That is, preprocessing generates a "skip array" (and an output scan key)
for the omitted prefix column "a", which makes it safe to mark the scan
key on "b" as required to continue the scan. The scan is therefore able
to repeatedly reposition itself by applying both the "a" and "b" keys.
A skip array has "elements" that are generated procedurally and on
demand, but otherwise works just like a regular ScalarArrayOp array.
Preprocessing can freely add a skip array before or after any input
ScalarArrayOp arrays. Index scans with a skip array decide when and
where to reposition the scan using the same approach as any other scan
with array keys. This design builds on the design for array advancement
and primitive scan scheduling added to Postgres 17 by commit 5bf748b8.
Testing has shown that skip scans of an index with a low cardinality
skipped prefix column can be multiple orders of magnitude faster than an
equivalent full index scan (or sequential scan). In general, the
cardinality of the scan's skipped column(s) limits the number of leaf
pages that can be skipped over.
The core B-Tree operator classes on most discrete types generate their
array elements with the help of their own custom skip support routine.
This infrastructure gives nbtree a way to generate the next required
array element by incrementing (or decrementing) the current array value.
It can reduce the number of index descents in cases where the next
possible indexable value frequently turns out to be the next value
stored in the index. Opclasses that lack a skip support routine fall
back on having nbtree "increment" (or "decrement") a skip array's
current element by setting the NEXT (or PRIOR) scan key flag, without
directly changing the scan key's sk_argument. These sentinel values
behave just like any other value from an array -- though they can never
locate equal index tuples (they can only locate the next group of index
tuples containing the next set of non-sentinel values that the scan's
arrays need to advance to).
A skip array's range is constrained by "contradictory" inequality keys.
For example, a skip array on "x" will only generate the values 1 and 2
given a qual such as "WHERE x BETWEEN 1 AND 2 AND y = 66". Such a skip
array qual usually has near-identical performance characteristics to a
comparable SAOP qual "WHERE x = ANY('{1, 2}') AND y = 66". However,
improved performance isn't guaranteed. Much depends on physical index
characteristics.
B-Tree preprocessing is optimistic about skipping working out: it
applies static, generic rules when determining where to generate skip
arrays, which assumes that the runtime overhead of maintaining skip
arrays will pay for itself -- or lead to only a modest performance loss.
As things stand, these assumptions are much too optimistic: skip array
maintenance will lead to unacceptable regressions with unsympathetic
queries (queries whose scan can't skip over many irrelevant leaf pages).
An upcoming commit will address the problems in this area by enhancing
_bt_readpage's approach to saving cycles on scan key evaluation, making
it work in a way that directly considers the needs of = array keys
(particularly = skip array keys).
Author: Peter Geoghegan <pg@bowt.ie>
Reviewed-By: Masahiro Ikeda <masahiro.ikeda@nttdata.com>
Reviewed-By: Heikki Linnakangas <heikki.linnakangas@iki.fi>
Reviewed-By: Matthias van de Meent <boekewurm+postgres@gmail.com>
Reviewed-By: Tomas Vondra <tomas@vondra.me>
Reviewed-By: Aleksander Alekseev <aleksander@timescale.com>
Reviewed-By: Alena Rybakina <a.rybakina@postgrespro.ru>
Discussion: https://postgr.es/m/CAH2-Wzmn1YsLzOGgjAQZdn1STSG_y8qP__vggTaPAYXJP+G4bw@mail.gmail.com
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Fix for commit 9ef1851685b: We have to skip indexes where sortopfamily
is NULL. This takes the place of the previous btree check. Detected
by valgrind on the buildfarm.
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This was previously not supported because the btree strategy numbers
were hardcoded. Now we can support this for any index that has the
required strategy mapping support and the required operators.
If an index scan used for get_actual_variable_range() requires
recheck, we now just ignore it instead of erroring out. With btree we
knew this couldn't happen, but now it might.
Author: Mark Dilger <mark.dilger@enterprisedb.com>
Co-authored-by: Peter Eisentraut <peter@eisentraut.org>
Discussion: https://www.postgresql.org/message-id/flat/E72EAA49-354D-4C2E-8EB9-255197F55330@enterprisedb.com
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Expose the count of index searches/index descents in EXPLAIN ANALYZE's
output for index scan/index-only scan/bitmap index scan nodes. This
information is particularly useful with scans that use ScalarArrayOp
quals, where the number of index searches can be unpredictable due to
implementation details that interact with physical index characteristics
(at least with nbtree SAOP scans, since Postgres 17 commit 5bf748b8).
The information shown also provides useful context when EXPLAIN ANALYZE
runs a plan with an index scan node that successfully applied the skip
scan optimization (set to be added to nbtree by an upcoming patch).
The instrumentation works by teaching all index AMs to increment a new
nsearches counter whenever a new index search begins. The counter is
incremented at exactly the same point that index AMs already increment
the pg_stat_*_indexes.idx_scan counter (we're counting the same event,
but at the scan level rather than the relation level). Parallel queries
have workers copy their local counter struct into shared memory when an
index scan node ends -- even when it isn't a parallel aware scan node.
An earlier version of this patch that only worked with parallel aware
scans became commit 5ead85fb (though that was quickly reverted by commit
d00107cd following "debug_parallel_query=regress" buildfarm failures).
Our approach doesn't match the approach used when tracking other index
scan related costs (e.g., "Rows Removed by Filter:"). It is comparable
to the approach used in similar cases involving costs that are only
readily accessible inside an access method, not from the executor proper
(e.g., "Heap Blocks:" output for a Bitmap Heap Scan, which was recently
enhanced to show per-worker costs by commit 5a1e6df3, using essentially
the same scheme as the one used here). It is necessary for index AMs to
have direct responsibility for maintaining the new counter, since the
counter might need to be incremented multiple times per amgettuple call
(or per amgetbitmap call). But it is also necessary for the executor
proper to manage the shared memory now used to transfer each worker's
counter struct to the leader.
Author: Peter Geoghegan <pg@bowt.ie>
Reviewed-By: Robert Haas <robertmhaas@gmail.com>
Reviewed-By: Tomas Vondra <tomas@vondra.me>
Reviewed-By: Masahiro Ikeda <ikedamsh@oss.nttdata.com>
Reviewed-By: Matthias van de Meent <boekewurm+postgres@gmail.com>
Discussion: https://postgr.es/m/CAH2-WzkRqvaqR2CTNqTZP0z6FuL4-3ED6eQB0yx38XBNj1v-4Q@mail.gmail.com
Discussion: https://postgr.es/m/CAH2-Wz=PKR6rB7qbx+Vnd7eqeB5VTcrW=iJvAsTsKbdG+kW_UA@mail.gmail.com
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Recognizing the real-life complexity where columns in the table often have
functional dependencies, PostgreSQL's estimation of the number of distinct
values over a set of columns can be underestimated (or much rarely,
overestimated) when dealing with multi-clause JOIN. In the case of hash
join, it can end up with a small number of predicted hash buckets and, as
a result, picking non-optimal merge join.
To improve the situation, we introduce one additional stage of bucket size
estimation - having two or more join clauses estimator lookup for extended
statistics and use it for multicolumn estimation. Clauses are grouped into
lists, each containing expressions referencing the same relation. The result
of the multicolumn estimation made over such a list is combined with others
according to the caller's logic. Clauses that are not estimated are returned
to the caller for further estimation.
Discussion: https://postgr.es/m/52257607-57f6-850d-399a-ec33a654457b%40postgrespro.ru
Author: Andrei Lepikhov <lepihov@gmail.com>
Reviewed-by: Andy Fan <zhihui.fan1213@gmail.com>
Reviewed-by: Tomas Vondra <tomas.vondra@enterprisedb.com>
Reviewed-by: Alena Rybakina <lena.ribackina@yandex.ru>
Reviewed-by: Alexander Korotkov <aekorotkov@gmail.com>
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This commit follows the idea of the 4767bc8ff2. If sub-query has only one
GROUP BY column, we can consider its output variable as being unique. We can
employ this fact in the statistics to make more precise estimations in the
upper query block.
Author: Andrei Lepikhov <lepihov@gmail.com>
Reviewed-by: Heikki Linnakangas <hlinnaka@iki.fi>
Reviewed-by: Alexander Korotkov <aekorotkov@gmail.com>
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The overwhelming majority of places already did this, but a small
handful of places had a hyphen.
Yugo Nagata.
Discussion: https://postgr.es/m/CAEZATCXnnuORE2BoGwHw2zbtVvsPOLhbfVmEk9GxRzK%2Bx3OW-Q%40mail.gmail.com
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When looking up statistical data about an expression, we do not need
to concern ourselves with the outer joins that could null the
Vars/PHVs contained in the expression. Accounting for nullingrels in
the expression could cause estimate_num_groups to count the same Var
multiple times if it's marked with different nullingrels. This is
incorrect, and could lead to "ERROR: corrupt MVNDistinct entry" when
searching for multivariate n-distinct.
Furthermore, the nullingrels could prevent us from matching an
expression to expressional index columns or to the expressions in
extended statistics, leading to inaccurate estimates.
To fix, strip out all the nullingrels from the expression before we
look up statistical data about it. There is one ensuing plan change
in the regression tests, but it looks reasonable and does not
compromise its original purpose.
This patch could result in plan changes, but it fixes an actual bug,
so back-patch to v16 where the outer-join-aware-Var infrastructure was
introduced.
Author: Richard Guo
Discussion: https://postgr.es/m/CAMbWs4-2Z4k+nFTiZe0Qbu5n8juUWenDAtMzi98bAZQtwHx0-w@mail.gmail.com
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Backpatch-through: 13
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Commit 9391f7152 added a "PlannerInfo *root" parameter to
estimate_array_length, but failed to consider the possibility that
NULL would be passed for that, leading to a null pointer dereference.
We could rectify the particular case shown in the bug report by fixing
simplify_function/inline_function to pass through the root pointer.
However, as long as eval_const_expressions is documented to accept
NULL for root, similar hazards would remain. For now, let's just do
the narrow fix of hardening estimate_array_length to not crash.
Its behavior with NULL root will be the same as it was before
9391f7152, so this is not too awful.
Per report from Fredrik Widlert (via Paul Ramsey). Back-patch to v17
where 9391f7152 came in.
Discussion: https://postgr.es/m/518339E7-173E-45EC-A0FF-9A4A62AA4F40@cleverelephant.ca
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Instead just look up the collation and check collate_is_c field.
Author: Andreas Karlsson
Discussion: https://postgr.es/m/60929555-4709-40a7-b136-bcb44cff5a3c@proxel.se
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pg_strxfrm() takes a pg_locale_t, so it works properly with all
providers. This improves estimates for ICU when performing linear
interpolation within a histogram bin.
Previously, convert_string_datum() always used strxfrm() and relied on
setlocale(). That did not produce good estimates for non-default or
non-libc collations.
Discussion: https://postgr.es/m/89475ee5487d795124f4e25118ea8f1853edb8cb.camel@j-davis.com
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To determine if the two relations being joined can use partitionwise
join, we need to verify the existence of equi-join conditions
involving pairs of matching partition keys for all partition keys.
Currently we do that by looking through the join's restriction
clauses. However, it has been discovered that this approach is
insufficient, because there might be partition keys known equal by a
specific EC, but they do not form a join clause because it happens
that other members of the EC than the partition keys are constrained
to become a join clause.
To address this issue, in addition to examining the join's restriction
clauses, we also check if any partition keys are known equal by ECs,
by leveraging function exprs_known_equal(). To accomplish this, we
enhance exprs_known_equal() to check equality per the semantics of the
opfamily, if provided.
It could be argued that exprs_known_equal() could be called O(N^2)
times, where N is the number of partition key expressions, resulting
in noticeable performance costs if there are a lot of partition key
expressions. But I think this is not a problem. The number of a
joinrel's partition key expressions would only be equal to the join
degree, since each base relation within the join contributes only one
partition key expression. That is to say, it does not scale with the
number of partitions. A benchmark with a query involving 5-way joins
of partitioned tables, each with 3 partition keys and 1000 partitions,
shows that the planning time is not significantly affected by this
patch (within the margin of error), particularly when compared to the
impact caused by partitionwise join.
Thanks to Tom Lane for the idea of leveraging exprs_known_equal() to
check if partition keys are known equal by ECs.
Author: Richard Guo, Tom Lane
Reviewed-by: Tom Lane, Ashutosh Bapat, Robert Haas
Discussion: https://postgr.es/m/CAN_9JTzo_2F5dKLqXVtDX5V6dwqB0Xk+ihstpKEt3a1LT6X78A@mail.gmail.com
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This fixes various typos, duplicated words, and tiny bits of whitespace
mainly in code comments but also in docs.
Author: Daniel Gustafsson <daniel@yesql.se>
Author: Heikki Linnakangas <hlinnaka@iki.fi>
Author: Alexander Lakhin <exclusion@gmail.com>
Author: David Rowley <dgrowleyml@gmail.com>
Author: Nazir Bilal Yavuz <byavuz81@gmail.com>
Discussion: https://postgr.es/m/3F577953-A29E-4722-98AD-2DA9EFF2CBB8@yesql.se
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Commit 9e8da0f7 taught nbtree to handle ScalarArrayOpExpr quals
natively. This works by pushing down the full context (the array keys)
to the nbtree index AM, enabling it to execute multiple primitive index
scans that the planner treats as one continuous index scan/index path.
This earlier enhancement enabled nbtree ScalarArrayOp index-only scans.
It also allowed scans with ScalarArrayOp quals to return ordered results
(with some notable restrictions, described further down).
Take this general approach a lot further: teach nbtree SAOP index scans
to decide how to execute ScalarArrayOp scans (when and where to start
the next primitive index scan) based on physical index characteristics.
This can be far more efficient. All SAOP scans will now reliably avoid
duplicative leaf page accesses (just like any other nbtree index scan).
SAOP scans whose array keys are naturally clustered together now require
far fewer index descents, since we'll reliably avoid starting a new
primitive scan just to get to a later offset from the same leaf page.
The scan's arrays now advance using binary searches for the array
element that best matches the next tuple's attribute value. Required
scan key arrays (i.e. arrays from scan keys that can terminate the scan)
ratchet forward in lockstep with the index scan. Non-required arrays
(i.e. arrays from scan keys that can only exclude non-matching tuples)
"advance" without the process ever rolling over to a higher-order array.
Naturally, only required SAOP scan keys trigger skipping over leaf pages
(non-required arrays cannot safely end or start primitive index scans).
Consequently, even index scans of a composite index with a high-order
inequality scan key (which we'll mark required) and a low-order SAOP
scan key (which we won't mark required) now avoid repeating leaf page
accesses -- that benefit isn't limited to simpler equality-only cases.
In general, all nbtree index scans now output tuples as if they were one
continuous index scan -- even scans that mix a high-order inequality
with lower-order SAOP equalities reliably output tuples in index order.
This allows us to remove a couple of special cases that were applied
when building index paths with SAOP clauses during planning.
Bugfix commit 807a40c5 taught the planner to avoid generating unsafe
path keys: path keys on a multicolumn index path, with a SAOP clause on
any attribute beyond the first/most significant attribute. These cases
are now all safe, so we go back to generating path keys without regard
for the presence of SAOP clauses (just like with any other clause type).
Affected queries can now exploit scan output order in all the usual ways
(e.g., certain "ORDER BY ... LIMIT n" queries can now terminate early).
Also undo changes from follow-up bugfix commit a4523c5a, which taught
the planner to produce alternative index paths, with path keys, but
without low-order SAOP index quals (filter quals were used instead).
We'll no longer generate these alternative paths, since they can no
longer offer any meaningful advantages over standard index qual paths.
Affected queries thereby avoid all of the disadvantages that come from
using filter quals within index scan nodes. They can avoid extra heap
page accesses from using filter quals to exclude non-matching tuples
(index quals will never have that problem). They can also skip over
irrelevant sections of the index in more cases (though only when nbtree
determines that starting another primitive scan actually makes sense).
There is a theoretical risk that removing restrictions on SAOP index
paths from the planner will break compatibility with amcanorder-based
index AMs maintained as extensions. Such an index AM could have the
same limitations around ordered SAOP scans as nbtree had up until now.
Adding a pro forma incompatibility item about the issue to the Postgres
17 release notes seems like a good idea.
Author: Peter Geoghegan <pg@bowt.ie>
Author: Matthias van de Meent <boekewurm+postgres@gmail.com>
Reviewed-By: Heikki Linnakangas <hlinnaka@iki.fi>
Reviewed-By: Matthias van de Meent <boekewurm+postgres@gmail.com>
Reviewed-By: Tomas Vondra <tomas.vondra@enterprisedb.com>
Discussion: https://postgr.es/m/CAH2-Wz=ksvN_sjcnD1+Bt-WtifRA5ok48aDYnq3pkKhxgMQpcw@mail.gmail.com
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Since commit 599b33b94, this function assumed that every RTE_RELATION
RangeTblEntry would have an associated RelOptInfo. But that's not so:
we only build RelOptInfos for relations that are scanned by the query.
In particular the target of an INSERT won't have one, so that Vars
appearing in an INSERT ... RETURNING list will not have an associated
RelOptInfo. This apparently wasn't a problem before commit f7816aec2
taught examine_simple_variable() to drill down into CTEs containing
INSERT RETURNING, but it is now.
To fix, add a fallback code path that gets the userid to use directly
from the RTEPermissionInfo associated with the RTE. (Sadly, we must
have two code paths, because not every RTE has a RTEPermissionInfo
either.)
Per report from Alexander Lakhin. No back-patch, since the case is
apparently unreachable before f7816aec2.
Discussion: https://postgr.es/m/608a4886-6c60-0f9e-97d5-591256bd4150@gmail.com
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If we have DECHIST statistics about the argument expression, use
the average number of distinct elements as the array length estimate.
(It'd be better to use the average total number of elements, but
that is not currently calculated by compute_array_stats(), and
it's unclear that it'd be worth extra effort to get.)
To do this, we have to change the signature of estimate_array_length
to pass the "root" pointer. While at it, also change its result
type to "double". That's probably not really necessary, but it
avoids any risk of overflow of the value extracted from DECHIST.
All existing callers are going to use the result in a "double"
calculation anyway.
Paul Jungwirth, reviewed by Jian He and myself
Discussion: https://postgr.es/m/CA+renyUnM2d+SmrxKpDuAdpiq6FOM=FByvi6aS6yi__qyf6j9A@mail.gmail.com
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Reported-by: Michael Paquier
Discussion: https://postgr.es/m/ZZKTDPxBBMt3C0J9@paquier.xyz
Backpatch-through: 12
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00b41463c adjusted Bitmapset so that an empty set is always represented
as NULL. This makes checking for empty sets far cheaper than it used
to be.
There were various places in the code where we'd call bms_membership()
to handle the 3 possible BMS_Membership values. For the BMS_SINGLETON
case, we'd also call bms_singleton_member() to find the single set member.
This can now be done in a more optimal way by first checking if the set is
NULL and then not bothering with bms_membership() and simply call
bms_get_singleton_member() instead to find the single member. This
function will return false if there are multiple members in the set.
Here we also tidy up some logic in examine_variable() for the single
member case. There's now no need to call bms_is_member() as we've
already established that we're working with a singleton Bitmapset, so we
can just check if varRelid matches the singleton member.
Reviewed-by: Richard Guo
Discussion: https://postgr.es/m/CAApHDvqW+CxNPcY245GaWiuqkkqgTudtG2ncGvvSjGn2wdTZLA@mail.gmail.com
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examine_simple_variable() left this as an unimplemented case years
ago, with the result that plans for queries involving un-flattened
CTEs might be much stupider than necessary. It's not hard to extend
the existing logic for RTE_SUBQUERY cases to also be able to drill
down into CTEs, so let's do that.
There was some discussion of whether this patch breaks the idea
of a MATERIALIZED CTE being an optimization fence. We concluded
it's okay, because we already allow the outer planner level to
see the estimated width and rowcount of the CTE result, and
letting it see column statistics too seems fairly equivalent.
Basically, what we expect of the optimization fence is that the
outer query should not affect the plan chosen for the CTE query.
Once that plan is chosen, it's okay for the outer planner level
to make use of whatever information we have about it.
Jian Guo and Tom Lane, per complaint from Hans Buschmann
Discussion: https://postgr.es/m/4504e67078d648cdac3651b2960da6e7@nidsa.net
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This adds support for infinity to the interval data type, using the
same input/output representation as the other date/time data types
that support infinity. This allows various arithmetic operations on
infinite dates, timestamps and intervals.
The new values are represented by setting all fields of the interval
to INT32/64_MIN for -infinity, and INT32/64_MAX for +infinity. This
ensures that they compare as less/greater than all other interval
values, without the need for any special-case comparison code.
Note that, since those 2 values were formerly accepted as legal finite
intervals, pg_upgrade and dump/restore from an old database will turn
them from finite to infinite intervals. That seems OK, since those
exact values should be extremely rare in practice, and they are
outside the documented range supported by the interval type, which
gives us a certain amount of leeway.
Bump catalog version.
Joseph Koshakow, Jian He, and Ashutosh Bapat, reviewed by me.
Discussion: https://postgr.es/m/CAAvxfHea4%2BsPybKK7agDYOMo9N-Z3J6ZXf3BOM79pFsFNcRjwA%40mail.gmail.com
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The changes done in this commit impact comments with no direct
user-visible changes, with fixes for incorrect function, variable or
structure names.
Author: Alexander Lakhin
Discussion: https://postgr.es/m/e8c38840-596a-83d6-bd8d-cebc51111572@gmail.com
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Traditionally we used the same Var struct to represent the value
of a table column everywhere in parse and plan trees. This choice
predates our support for SQL outer joins, and it's really a pretty
bad idea with outer joins, because the Var's value can depend on
where it is in the tree: it might go to NULL above an outer join.
So expression nodes that are equal() per equalfuncs.c might not
represent the same value, which is a huge correctness hazard for
the planner.
To improve this, decorate Var nodes with a bitmapset showing
which outer joins (identified by RTE indexes) may have nulled
them at the point in the parse tree where the Var appears.
This allows us to trust that equal() Vars represent the same value.
A certain amount of klugery is still needed to cope with cases
where we re-order two outer joins, but it's possible to make it
work without sacrificing that core principle. PlaceHolderVars
receive similar decoration for the same reason.
In the planner, we include these outer join bitmapsets into the relids
that an expression is considered to depend on, and in consequence also
add outer-join relids to the relids of join RelOptInfos. This allows
us to correctly perceive whether an expression can be calculated above
or below a particular outer join.
This change affects FDWs that want to plan foreign joins. They *must*
follow suit when labeling foreign joins in order to match with the
core planner, but for many purposes (if postgres_fdw is any guide)
they'd prefer to consider only base relations within the join.
To support both requirements, redefine ForeignScan.fs_relids as
base+OJ relids, and add a new field fs_base_relids that's set up by
the core planner.
Large though it is, this commit just does the minimum necessary to
install the new mechanisms and get check-world passing again.
Follow-up patches will perform some cleanup. (The README additions
and comments mention some stuff that will appear in the follow-up.)
Patch by me; thanks to Richard Guo for review.
Discussion: https://postgr.es/m/830269.1656693747@sss.pgh.pa.us
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RelOptInfo.userid is the same for all relations in a given inheritance
tree, so the code in examine_variable() and example_simple_variable()
that repeats the ACL checks on the root parent rel instead of a given
leaf child relations need not recompute userid too.
Author: Amit Langote <amitlangote09@gmail.com>
Reported-by: Justin Pryzby <pryzby@telsasoft.com>
Discussion: https://postgr.es/m/20221210201753.GA27893@telsasoft.com
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This allows left join removals and unique joins to work with partitioned
tables. The planner just lacked sufficient proofs that a given join
would not cause any row duplication. Unique indexes currently serve as
that proof, so have get_relation_info() populate the indexlist for
partitioned tables too.
Author: Arne Roland
Reviewed-by: Alvaro Herrera, Zhihong Yu, Amit Langote, David Rowley
Discussion: https://postgr.es/m/c3b2408b7a39433b8230bbcd02e9f302@index.de
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GIN index scans were not taking any descent CPU-based cost into account. That
made them look cheaper than other types of indexes when they shouldn't be.
We use the same heuristic as for btree indexes, but multiply it by the number
of searched entries.
Additionally, the CPU cost for the tree was based largely on a
genericcostestimate. For a GIN index, we should not charge index quals per
tuple, but per entry. On top of this, charge cpu_index_tuple_cost per actual
tuple.
This should fix the cases where a GIN index is preferred over a btree and
the ones where a memoize node is not added on top of the GIN index scan
because it seemed too cheap.
We don't packpatch this to evade unexpected plan changes in stable versions.
Discussion: https://postgr.es/m/CABs3KGQnOkyQ42-zKQqiE7M0Ks9oWDSee%3D%2BJx3-TGq%3D68xqWYw%40mail.gmail.com
Discussion: https://postgr.es/m/3188617.44csPzL39Z%40aivenronan
Author: Ronan Dunklau
Reported-By: Hung Nguyen
Reviewed-by: Tom Lane, Alexander Korotkov
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B-tree, GiST and SP-GiST all charge 50.0 * cpu_operator_cost for processing
an index page. Extract this to a macro to avoid repeated magic numbers.
Discussion: https://mail.google.com/mail/u/0/?ik=a20b091faa&view=om&permmsgid=msg-f%3A1751459697261369543
Author: Ronan Dunklau
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Backpatch-through: 11
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Just because I'm a neatnik, and I'm currently working on
code in this area. It annoys me to not be able to pgindent
my patches without working around unrelated changes.
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A future commit will move the checkAsUser field from RangeTblEntry
to a new node that, unlike RTEs, will only be created for tables
mentioned in the query but not for the inheritance child relations
added to the query by the planner. So, checkAsUser value for a
given child relation will have to be obtained by referring to that
for its ancestor mentioned in the query.
In preparation, it seems better to expand the use of RelOptInfo.userid
during planning in place of rte->checkAsUser so that there will be
fewer places to adjust for the above change.
Given that the child-to-ancestor mapping is not available during the
execution of a given "child" ForeignScan node, add a checkAsUser
field to ForeignScan to carry the child relation's RelOptInfo.userid.
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqGFCs2uq7VRKi7g+FFKbP6Ea_2_HkgZb2HPhUfaAKT3ng@mail.gmail.com
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We've made multiple attempts at preventing get_actual_variable_range
from taking an unreasonable amount of time (3ca930fc3, fccebe421).
But there's still an issue for the very first planning attempt after
deletion of a large number of extremal-valued tuples. While that
planning attempt will set "killed" bits on the tuples it visits and
thereby reduce effort for next time, there's still a lot of work it
has to do to visit the heap and then set those bits. It's (usually?)
not worth it to do that much work at plan time to have a slightly
better estimate, especially in a context like this where the table
contents are known to be mutating rapidly.
Therefore, let's bound the amount of work to be done by giving up
after we've visited 100 heap pages. Giving up just means we'll
fall back on the extremal value recorded in pg_statistic, so it
shouldn't mean that planner estimates suddenly become worthless.
Note that this means we'll still gradually whittle down the problem
by setting a few more index "killed" bits in each planning attempt;
so eventually we'll reach a good state (barring further deletions),
even in the absence of VACUUM.
Simon Riggs, per a complaint from Jakub Wartak (with cosmetic
adjustments by me). Back-patch to all supported branches.
Discussion: https://postgr.es/m/CAKZiRmznOwi0oaV=4PHOCM4ygcH4MgSvt8=5cu_vNCfc8FSUug@mail.gmail.com
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eqjoinsel() currently makes use of MCV stats only when we have such
stats for both sides of the clause. As coded, though, it would
fetch those stats even when they're present for just one side.
This can be a bit expensive with high statistics targets, leading
to wasted effort in common cases such as joining a unique column
to a non-unique column. So it seems worth the trouble to do a quick
pre-check to confirm that both sides have MCVs before fetching either.
Also, tweak the API spec for get_attstatsslot() to document the
method we're using here.
David Geier, Tomas Vondra, Tom Lane
Discussion: https://postgr.es/m/b9846ca0-5f1c-9b26-5881-aad3f42b07f0@gmail.com
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Some cases would result in "cache lookup failed for statistics object",
due to trying to fetch inherited statistics when only non-inherited
ones are available or vice versa.
Richard Guo and Justin Pryzby
Discussion: https://postgr.es/m/20221030170520.GM16921@telsasoft.com
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Use C standard abs() or fabs() instead.
Reviewed-by: Zhang Mingli <zmlpostgres@gmail.com>
Reviewed-by: Tom Lane <tgl@sss.pgh.pa.us>
Discussion: https://www.postgresql.org/message-id/flat/4beb42b5-216b-bce8-d452-d924d5794c63%40enterprisedb.com
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This reverts commit db0d67db2401eb6238ccc04c6407a4fd4f985832 and
several follow-on fixes. The idea of making a cost-based choice
of the order of the sorting columns is not fundamentally unsound,
but it requires cost information and data statistics that we don't
really have. For example, relying on procost to distinguish the
relative costs of different sort comparators is pretty pointless
so long as most such comparator functions are labeled with cost 1.0.
Moreover, estimating the number of comparisons done by Quicksort
requires more than just an estimate of the number of distinct values
in the input: you also need some idea of the sizes of the larger
groups, if you want an estimate that's good to better than a factor of
three or so. That's data that's often unknown or not very reliable.
Worse, to arrive at estimates of the number of calls made to the
lower-order-column comparison functions, the code needs to make
estimates of the numbers of distinct values of multiple columns,
which are necessarily even less trustworthy than per-column stats.
Even if all the inputs are perfectly reliable, the cost algorithm
as-implemented cannot offer useful information about how to order
sorting columns beyond the point at which the average group size
is estimated to drop to 1.
Close inspection of the code added by db0d67db2 shows that there
are also multiple small bugs. These could have been fixed, but
there's not much point if we don't trust the estimates to be
accurate in-principle.
Finally, the changes in cost_sort's behavior made for very large
changes (often a factor of 2 or so) in the cost estimates for all
sorting operations, not only those for multi-column GROUP BY.
That naturally changes plan choices in many situations, and there's
precious little evidence to show that the changes are for the better.
Given the above doubts about whether the new estimates are really
trustworthy, it's hard to summon much confidence that these changes
are better on the average.
Since we're hard up against the release deadline for v15, let's
revert these changes for now. We can always try again later.
Note: in v15, I left T_PathKeyInfo in place in nodes.h even though
it's unreferenced. Removing it would be an ABI break, and it seems
a bit late in the release cycle for that.
Discussion: https://postgr.es/m/TYAPR01MB586665EB5FB2C3807E893941F5579@TYAPR01MB5866.jpnprd01.prod.outlook.com
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Fix selfuncs.h cpluspluscheck complaint, without reintroducing a
parameter name inconsistency (restore the original declaration names,
and then make corresponding function definitions consistent with that).
Oversight in commit a601366a.
Author: Peter Geoghegan <pg@bowt.ie>
Reported-By: Andres Freund <andres@anarazel.de>
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guc.c has grown to be one of our largest .c files, making it
a bottleneck for compilation. It's also acquired a bunch of
knowledge that'd be better kept elsewhere, because of our not
very good habit of putting variable-specific check hooks here.
Hence, split it up along these lines:
* guc.c itself retains just the core GUC housekeeping mechanisms.
* New file guc_funcs.c contains the SET/SHOW interfaces and some
SQL-accessible functions for GUC manipulation.
* New file guc_tables.c contains the data arrays that define the
built-in GUC variables, along with some already-exported constant
tables.
* GUC check/assign/show hook functions are moved to the variable's
home module, whenever that's clearly identifiable. A few hard-
to-classify hooks ended up in commands/variable.c, which was
already a home for miscellaneous GUC hook functions.
To avoid cluttering a lot more header files with #include "guc.h",
I also invented a new header file utils/guc_hooks.h and put all
the GUC hook functions' declarations there, regardless of their
originating module. That allowed removal of #include "guc.h"
from some existing headers. The fallout from that (hopefully
all caught here) demonstrates clearly why such inclusions are
best minimized: there are a lot of files that, for example,
were getting array.h at two or more levels of remove, despite
not having any connection at all to GUCs in themselves.
There is some very minor code beautification here, such as
renaming a couple of inconsistently-named hook functions
and improving some comments. But mostly this just moves
code from point A to point B and deals with the ensuing
needs for #include adjustments and exporting a few functions
that previously weren't exported.
Patch by me, per a suggestion from Andres Freund; thanks also
to Michael Paquier for the idea to invent guc_funcs.c.
Discussion: https://postgr.es/m/587607.1662836699@sss.pgh.pa.us
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The standard way to check for list emptiness is to compare the
List pointer to NIL; our list code goes out of its way to ensure
that that is the only representation of an empty list. (An
acceptable alternative is a plain boolean test for non-null
pointer, but explicit mention of NIL is usually preferable.)
Various places didn't get that memo and expressed the condition
with list_length(), which might not be so bad except that there
were such a variety of ways to check it exactly: equal to zero,
less than or equal to zero, less than one, yadda yadda. In the
name of code readability, let's standardize all those spellings
as "list == NIL" or "list != NIL". (There's probably some
microscopic efficiency gain too, though few of these look to be
at all performance-critical.)
A very small number of cases were left as-is because they seemed
more consistent with other adjacent list_length tests that way.
Peter Smith, with bikeshedding from a number of us
Discussion: https://postgr.es/m/CAHut+PtQYe+ENX5KrONMfugf0q6NHg4hR5dAhqEXEc2eefFeig@mail.gmail.com
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The patch that added regcollation doesn't seem to have been too
thorough about supporting it everywhere that other reg* types
are supported. Fix that. (The find_expr_references omission
is moderately serious, since it could result in missing expression
dependencies. The others are less exciting.)
Noted while fixing bug #17483. Back-patch to v13 where
regcollation was added.
Discussion: https://postgr.es/m/1423433.1652722406@sss.pgh.pa.us
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This replaces all MemSet() calls with struct initialization where that
is easily and obviously possible. (For example, some cases have to
worry about padding bits, so I left those.)
(The same could be done with appropriate memset() calls, but this
patch is part of an effort to phase out MemSet(), so it doesn't touch
memset() calls.)
Reviewed-by: Ranier Vilela <ranier.vf@gmail.com>
Reviewed-by: Alvaro Herrera <alvherre@alvh.no-ip.org>
Discussion: https://www.postgresql.org/message-id/9847b13c-b785-f4e2-75c3-12ec77a3b05c@enterprisedb.com
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Run pgindent, pgperltidy, and reformat-dat-files.
I manually fixed a couple of comments that pgindent uglified.
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These are useless and distracting. We wouldn't have written the code
with them to begin with, so there's no reason to keep them.
Author: Justin Pryzby <pryzby@telsasoft.com>
Discussion: https://postgr.es/m/20220411020336.GB26620@telsasoft.com
Discussion: https://postgr.es/m/attachment/133167/0016-Extraneous-blank-lines.patch
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When evaluating a query with a multi-column GROUP BY clause using sort,
the cost may be heavily dependent on the order in which the keys are
compared when building the groups. Grouping does not imply any ordering,
so we're allowed to compare the keys in arbitrary order, and a Hash Agg
leverages this. But for Group Agg, we simply compared keys in the order
as specified in the query. This commit explores alternative ordering of
the keys, trying to find a cheaper one.
In principle, we might generate grouping paths for all permutations of
the keys, and leave the rest to the optimizer. But that might get very
expensive, so we try to pick only a couple interesting orderings based
on both local and global information.
When planning the grouping path, we explore statistics (number of
distinct values, cost of the comparison function) for the keys and
reorder them to minimize comparison costs. Intuitively, it may be better
to perform more expensive comparisons (for complex data types etc.)
last, because maybe the cheaper comparisons will be enough. Similarly,
the higher the cardinality of a key, the lower the probability we’ll
need to compare more keys. The patch generates and costs various
orderings, picking the cheapest ones.
The ordering of group keys may interact with other parts of the query,
some of which may not be known while planning the grouping. E.g. there
may be an explicit ORDER BY clause, or some other ordering-dependent
operation, higher up in the query, and using the same ordering may allow
using either incremental sort or even eliminate the sort entirely.
The patch generates orderings and picks those minimizing the comparison
cost (for various pathkeys), and then adds orderings that might be
useful for operations higher up in the plan (ORDER BY, etc.). Finally,
it always keeps the ordering specified in the query, on the assumption
the user might have additional insights.
This introduces a new GUC enable_group_by_reordering, so that the
optimization may be disabled if needed.
The original patch was proposed by Teodor Sigaev, and later improved and
reworked by Dmitry Dolgov. Reviews by a number of people, including me,
Andrey Lepikhov, Claudio Freire, Ibrar Ahmed and Zhihong Yu.
Author: Dmitry Dolgov, Teodor Sigaev, Tomas Vondra
Reviewed-by: Tomas Vondra, Andrey Lepikhov, Claudio Freire, Ibrar Ahmed, Zhihong Yu
Discussion: https://postgr.es/m/7c79e6a5-8597-74e8-0671-1c39d124c9d6%40sigaev.ru
Discussion: https://postgr.es/m/CA%2Bq6zcW_4o2NC0zutLkOJPsFt80megSpX_dVRo6GK9PC-Jx_Ag%40mail.gmail.com
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Add pg_statistic_ext_data.stxdinherit flag, so that for each extended
statistics definition we can store two versions of data - one for the
relation alone, one for the whole inheritance tree. This is analogous to
pg_statistic.stainherit, but we failed to include such flag in catalogs
for extended statistics, and we had to work around it (see commits
859b3003de, 36c4bc6e72 and 20b9fa308e).
This changes the relationship between the two catalogs storing extended
statistics objects (pg_statistic_ext and pg_statistic_ext_data). Until
now, there was a simple 1:1 mapping - for each definition there was one
pg_statistic_ext_data row, and this row was inserted while creating the
statistics (and then updated during ANALYZE). With the stxdinherit flag,
we don't know how many rows there will be (child relations may be added
after the statistics object is defined), so there may be up to two rows.
We could make CREATE STATISTICS to always create both rows, but that
seems wasteful - without partitioning we only need stxdinherit=false
rows, and declaratively partitioned tables need only stxdinherit=true.
So we no longer initialize pg_statistic_ext_data in CREATE STATISTICS,
and instead make that a responsibility of ANALYZE. Which is what we do
for regular statistics too.
Patch by me, with extensive improvements and fixes by Justin Pryzby.
Author: Tomas Vondra, Justin Pryzby
Reviewed-by: Tomas Vondra, Justin Pryzby
Discussion: https://postgr.es/m/20210923212624.GI831%40telsasoft.com
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Commit 859b3003de disabled building of extended stats for inheritance
trees, to prevent updating the same catalog row twice. While that
resolved the issue, it also means there are no extended stats for
declaratively partitioned tables, because there are no data in the
non-leaf relations.
That also means declaratively partitioned tables were not affected by
the issue 859b3003de addressed, which means this is a regression
affecting queries that calculate estimates for the whole inheritance
tree as a whole (which includes e.g. GROUP BY queries).
But because partitioned tables are empty, we can invert the condition
and build statistics only for the case with inheritance, without losing
anything. And we can consider them when calculating estimates.
It may be necessary to run ANALYZE on partitioned tables, to collect
proper statistics. For declarative partitioning there should no prior
statistics, and it might take time before autoanalyze is triggered. For
tables partitioned by inheritance the statistics may include data from
child relations (if built 859b3003de), contradicting the current code.
Report and patch by Justin Pryzby, minor fixes and cleanup by me.
Backpatch all the way back to PostgreSQL 10, where extended statistics
were introduced (same as 859b3003de).
Author: Justin Pryzby
Reported-by: Justin Pryzby
Backpatch-through: 10
Discussion: https://postgr.es/m/20210923212624.GI831%40telsasoft.com
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Since commit 859b3003de we only build extended statistics for individual
relations, ignoring the child relations. This resolved the issue with
updating catalog tuple twice, but we still tried to use the statistics
when calculating estimates for the whole inheritance tree. When the
relations contain very distinct data, it may produce bogus estimates.
This is roughly the same issue 427c6b5b9 addressed ~15 years ago, and we
fix it the same way - by ignoring extended statistics when calculating
estimates for the inheritance tree as a whole. We still consider
extended statistics when calculating estimates for individual child
relations, of course.
This may result in plan changes due to different estimates, but if the
old statistics were not describing the inheritance tree particularly
well it's quite likely the new plans is actually better.
Report and patch by Justin Pryzby, minor fixes and cleanup by me.
Backpatch all the way back to PostgreSQL 10, where extended statistics
were introduced (same as 859b3003de).
Author: Justin Pryzby
Reported-by: Justin Pryzby
Backpatch-through: 10
Discussion: https://postgr.es/m/20210923212624.GI831%40telsasoft.com
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Backpatch-through: 10
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