707 Commits (d1ce745db2d7db193c0be461db3cd34edd776dac)

Author	SHA1	Message	Date
Tom Lane	2f17b57017	Improve performance of adjust_appendrel_attrs_multilevel. ... The present implementations of adjust_appendrel_attrs_multilevel and its sibling adjust_child_relids_multilevel are very messy, because they work by reconstructing the relids of the child's immediate parent and then seeing if that's bms_equal to the relids of the target parent. Aside from being quite inefficient, this will not work with planned future changes to make joinrels' relid sets contain outer-join relids in addition to baserels. The whole thing can be solved at a stroke by adding explicit parent and top_parent links to child RelOptInfos, and making these functions work with RelOptInfo pointers instead of relids. Doing that is simpler for most callers, too. In my original version of this patch, I got rid of RelOptInfo.top_parent_relids on the grounds that it was now redundant. However, that adds a lot of code churn in places that otherwise would not need changing, and arguably the extra indirection needed to fetch top_parent->relids in those places costs something. So this version leaves that field in place. Discussion: https://postgr.es/m/553080.1657481916@sss.pgh.pa.us	3 years ago
Tom Lane	b3ff6c742f	Use an explicit state flag to control PlaceHolderInfo creation. ... Up to now, callers of find_placeholder_info() were required to pass a flag indicating if it's OK to make a new PlaceHolderInfo. That'd be fine if the callers had free choice, but they do not. Once we begin deconstruct_jointree() it's no longer OK to make more PHIs; while callers before that always want to create a PHI if it's not there already. So there's no freedom of action, only the opportunity to cause bugs by creating PHIs too late. Let's get rid of that in favor of adding a state flag PlannerInfo.placeholdersFrozen, which we can set at the point where it's no longer OK to make more PHIs. This patch also simplifies a couple of call sites that were using complicated logic to avoid calling find_placeholder_info() as much as possible. Now that that lookup is O(1) thanks to the previous commit, the extra bitmap manipulations are probably a net negative. Discussion: https://postgr.es/m/1405792.1660677844@sss.pgh.pa.us	3 years ago
Tom Lane	1aa8dad41f	Fix incorrect tests for SRFs in relation_can_be_sorted_early(). ... Commit fac1b470a thought we could check for set-returning functions by testing only the top-level node in an expression tree. This is wrong in itself, and to make matters worse it encouraged others to make the same mistake, by exporting tlist.c's special-purpose IS_SRF_CALL() as a widely-visible macro. I can't find any evidence that anyone's taken the bait, but it was only a matter of time. Use expression_returns_set() instead, and stuff the IS_SRF_CALL() genie back in its bottle, this time with a warning label. I also added a couple of cross-reference comments. After a fair amount of fooling around, I've despaired of making a robust test case that exposes the bug reliably, so no test case here. (Note that the test case added by fac1b470a is itself broken, in that it doesn't notice if you remove the code change. The repro given by the bug submitter currently doesn't fail either in v15 or HEAD, though I suspect that may indicate an unrelated bug.) Per bug #17564 from Martijn van Oosterhout. Back-patch to v13, as the faulty patch was. Discussion: https://postgr.es/m/17564-c7472c2f90ef2da3@postgresql.org	4 years ago
David Rowley	1349d2790b	Improve performance of ORDER BY / DISTINCT aggregates ... ORDER BY / DISTINCT aggreagtes have, since implemented in Postgres, been executed by always performing a sort in nodeAgg.c to sort the tuples in the current group into the correct order before calling the transition function on the sorted tuples. This was not great as often there might be an index that could have provided pre-sorted input and allowed the transition functions to be called as the rows come in, rather than having to store them in a tuplestore in order to sort them once all the tuples for the group have arrived. Here we change the planner so it requests a path with a sort order which supports the most amount of ORDER BY / DISTINCT aggregate functions and add new code to the executor to allow it to support the processing of ORDER BY / DISTINCT aggregates where the tuples are already sorted in the correct order. Since there can be many ORDER BY / DISTINCT aggregates in any given query level, it's very possible that we can't find an order that suits all of these aggregates. The sort order that the planner chooses is simply the one that suits the most aggregate functions. We take the most strictly sorted variation of each order and see how many aggregate functions can use that, then we try again with the order of the remaining aggregates to see if another order would suit more aggregate functions. For example: SELECT agg(a ORDER BY a),agg2(a ORDER BY a,b) ... would request the sort order to be {a, b} because {a} is a subset of the sort order of {a,b}, but; SELECT agg(a ORDER BY a),agg2(a ORDER BY c) ... would just pick a plan ordered by {a} (we give precedence to aggregates which are earlier in the targetlist). SELECT agg(a ORDER BY a),agg2(a ORDER BY b),agg3(a ORDER BY b) ... would choose to order by {b} since two aggregates suit that vs just one that requires input ordered by {a}. Author: David Rowley Reviewed-by: Ronan Dunklau, James Coleman, Ranier Vilela, Richard Guo, Tom Lane Discussion: https://postgr.es/m/CAApHDvpHzfo92%3DR4W0%2BxVua3BUYCKMckWAmo-2t_KiXN-wYH%3Dw%40mail.gmail.com	4 years ago
Tom Lane	e2f6c307c0	Estimate cost of elided SubqueryScan, Append, MergeAppend nodes better. ... setrefs.c contains logic to discard no-op SubqueryScan nodes, that is, ones that have no qual to check and copy the input targetlist unchanged. (Formally it's not very nice to be applying such optimizations so late in the planner, but there are practical reasons for it; mostly that we can't unify relids between the subquery and the parent query until we flatten the rangetable during setrefs.c.) This behavior falsifies our previous cost estimates, since we would've charged cpu_tuple_cost per row just to pass data through the node. Most of the time that's little enough to not matter, but there are cases where this effect visibly changes the plan compared to what you would've gotten with no sub-select. To improve the situation, make the callers of cost_subqueryscan tell it whether they think the targetlist is trivial. cost_subqueryscan already has the qual list, so it can check the other half of the condition easily. It could make its own determination of tlist triviality too, but doing so would be repetitive (for callers that may call it several times) or unnecessarily expensive (for callers that can determine this more cheaply than a general test would do). This isn't a 100% solution, because createplan.c also does things that can falsify any earlier estimate of whether the tlist is trivial. However, it fixes nearly all cases in practice, if results for the regression tests are anything to go by. setrefs.c also contains logic to discard no-op Append and MergeAppend nodes. We did have knowledge of that behavior at costing time, but somebody failed to update it when a check on parallel-awareness was added to the setrefs.c logic. Fix that while we're here. These changes result in two minor changes in query plans shown in our regression tests. Neither is relevant to the purposes of its test case AFAICT. Patch by me; thanks to Richard Guo for review. Discussion: https://postgr.es/m/2581077.1651703520@sss.pgh.pa.us	4 years ago
David Rowley	80ad91ea8c	Fix inconsistent parameter names between prototype and declaration ... Noticed while working in this area. This code was introduced in PG15, which is still in beta, so backpatch to there for consistency. Backpatch-through: 15	4 years ago
Tom Lane	f172b11d61	Remove no-longer-used parameter for create_groupingsets_path(). ... numGroups is unused since commit b5635948a; let's get rid of it. XueJing Zhao, reviewed by Richard Guo Discussion: https://postgr.es/m/DM6PR05MB64923CC8B63A2CAF3B2E5D47B7AD9@DM6PR05MB6492.namprd05.prod.outlook.com	4 years ago
Tom Lane	a916cb9d5a	Avoid overflow hazard when clamping group counts to "long int". ... Several places in the planner tried to clamp a double value to fit in a "long" by doing (long) Min(x, (double) LONG_MAX); This is subtly incorrect, because it casts LONG_MAX to double and potentially back again. If long is 64 bits then the double value is inexact, and the platform might round it up to LONG_MAX+1 resulting in an overflow and an undesirably negative output. While it's not hard to rewrite the expression into a safe form, let's put it into a common function to reduce the risk of someone doing it wrong in future. In principle this is a bug fix, but since the problem could only manifest with group count estimates exceeding 2^63, it seems unlikely that anyone has actually hit this or will do so anytime soon. We're fixing it mainly to satisfy fuzzer-type tools. That being the case, a HEAD-only fix seems sufficient. Andrey Lepikhov Discussion: https://postgr.es/m/ebbc2efb-7ef9-bf2f-1ada-d6ec48f70e58@postgrespro.ru	4 years ago
Tom Lane	23e7b38bfe	Pre-beta mechanical code beautification. ... Run pgindent, pgperltidy, and reformat-dat-files. I manually fixed a couple of comments that pgindent uglified.	4 years ago
Robert Haas	8ec569479f	Apply PGDLLIMPORT markings broadly. ... Up until now, we've had a policy of only marking certain variables in the PostgreSQL header files with PGDLLIMPORT, but now we've decided to mark them all. This means that extensions running on Windows should no longer operate at a disadvantage as compared to extensions running on Linux: if the variable is present in a header file, it should be accessible. Discussion: http://postgr.es/m/CA+TgmoYanc1_FSfimhgiWSqVyP5KKmh5NP2BWNwDhO8Pg2vGYQ@mail.gmail.com	4 years ago
David Rowley	9d9c02ccd1	Teach planner and executor about monotonic window funcs ... Window functions such as row_number() always return a value higher than the previously returned value for tuples in any given window partition. Traditionally queries such as; SELECT * FROM ( SELECT *, row_number() over (order by c) rn FROM t ) t WHERE rn <= 10; were executed fairly inefficiently. Neither the query planner nor the executor knew that once rn made it to 11 that nothing further would match the outer query's WHERE clause. It would blindly continue until all tuples were exhausted from the subquery. Here we implement means to make the above execute more efficiently. This is done by way of adding a pg_proc.prosupport function to various of the built-in window functions and adding supporting code to allow the support function to inform the planner if the window function is monotonically increasing, monotonically decreasing, both or neither. The planner is then able to make use of that information and possibly allow the executor to short-circuit execution by way of adding a "run condition" to the WindowAgg to allow it to determine if some of its execution work can be skipped. This "run condition" is not like a normal filter. These run conditions are only built using quals comparing values to monotonic window functions. For monotonic increasing functions, quals making use of the btree operators for <, <= and = can be used (assuming the window function column is on the left). You can see here that once such a condition becomes false that a monotonic increasing function could never make it subsequently true again. For monotonically decreasing functions the >, >= and = btree operators for the given type can be used for run conditions. The best-case situation for this is when there is a single WindowAgg node without a PARTITION BY clause. Here when the run condition becomes false the WindowAgg node can simply return NULL. No more tuples will ever match the run condition. It's a little more complex when there is a PARTITION BY clause. In this case, we cannot return NULL as we must still process other partitions. To speed this case up we pull tuples from the outer plan to check if they're from the same partition and simply discard them if they are. When we find a tuple belonging to another partition we start processing as normal again until the run condition becomes false or we run out of tuples to process. When there are multiple WindowAgg nodes to evaluate then this complicates the situation. For intermediate WindowAggs we must ensure we always return all tuples to the calling node. Any filtering done could lead to incorrect results in WindowAgg nodes above. For all intermediate nodes, we can still save some work when the run condition becomes false. We've no need to evaluate the WindowFuncs anymore. Other WindowAgg nodes cannot reference the value of these and these tuples will not appear in the final result anyway. The savings here are small in comparison to what can be saved in the top-level WingowAgg, but still worthwhile. Intermediate WindowAgg nodes never filter out tuples, but here we change WindowAgg so that the top-level WindowAgg filters out tuples that don't match the intermediate WindowAgg node's run condition. Such filters appear in the "Filter" clause in EXPLAIN for the top-level WindowAgg node. Here we add prosupport functions to allow the above to work for; row_number(), rank(), dense_rank(), count(*) and count(expr). It appears technically possible to do the same for min() and max(), however, it seems unlikely to be useful enough, so that's not done here. Bump catversion Author: David Rowley Reviewed-by: Andy Fan, Zhihong Yu Discussion: https://postgr.es/m/CAApHDvqvp3At8++yF8ij06sdcoo1S_b2YoaT9D4Nf+MObzsrLQ@mail.gmail.com	4 years ago
Etsuro Fujita	c2bb02bc2e	Allow asynchronous execution in more cases. ... In commit 27e1f1456, create_append_plan() only allowed the subplan created from a given subpath to be executed asynchronously when it was an async-capable ForeignPath. To extend coverage, this patch handles cases when the given subpath includes some other Path types as well that can be omitted in the plan processing, such as a ProjectionPath directly atop an async-capable ForeignPath, allowing asynchronous execution in partitioned-scan/partitioned-join queries with non-Var tlist expressions and more UNION queries. Andrey Lepikhov and Etsuro Fujita, reviewed by Alexander Pyhalov and Zhihong Yu. Discussion: https://postgr.es/m/659c37a8-3e71-0ff2-394c-f04428c76f08%40postgrespro.ru	4 years ago
Tom Lane	f3dd9fe1dd	Fix postgres_fdw to check shippability of sort clauses properly. ... postgres_fdw would push ORDER BY clauses to the remote side without verifying that the sort operator is safe to ship. Moreover, it failed to print a suitable USING clause if the sort operator isn't default for the sort expression's type. The net result of this is that the remote sort might not have anywhere near the semantics we expect, which'd be disastrous for locally-performed merge joins in particular. We addressed similar issues in the context of ORDER BY within an aggregate function call in commit 7012b132d, but failed to notice that query-level ORDER BY was broken. Thus, much of the necessary logic already existed, but it requires refactoring to be usable in both cases. Back-patch to all supported branches. In HEAD only, remove the core code's copy of find_em_expr_for_rel, which is no longer used and really should never have been pushed into equivclass.c in the first place. Ronan Dunklau, per report from David Rowley; reviews by David Rowley, Ranier Vilela, and myself Discussion: https://postgr.es/m/CAApHDvr4OeC2DBVY--zVP83-K=bYrTD7F8SZDhN4g+pj2f2S-A@mail.gmail.com	4 years ago
Tomas Vondra	db0d67db24	Optimize order of GROUP BY keys ... 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	4 years ago
Alvaro Herrera	7103ebb7aa	Add support for MERGE SQL command ... MERGE performs actions that modify rows in the target table using a source table or query. MERGE provides a single SQL statement that can conditionally INSERT/UPDATE/DELETE rows -- a task that would otherwise require multiple PL statements. For example, MERGE INTO target AS t USING source AS s ON t.tid = s.sid WHEN MATCHED AND t.balance > s.delta THEN UPDATE SET balance = t.balance - s.delta WHEN MATCHED THEN DELETE WHEN NOT MATCHED AND s.delta > 0 THEN INSERT VALUES (s.sid, s.delta) WHEN NOT MATCHED THEN DO NOTHING; MERGE works with regular tables, partitioned tables and inheritance hierarchies, including column and row security enforcement, as well as support for row and statement triggers and transition tables therein. MERGE is optimized for OLTP and is parameterizable, though also useful for large scale ETL/ELT. MERGE is not intended to be used in preference to existing single SQL commands for INSERT, UPDATE or DELETE since there is some overhead. MERGE can be used from PL/pgSQL. MERGE does not support targetting updatable views or foreign tables, and RETURNING clauses are not allowed either. These limitations are likely fixable with sufficient effort. Rewrite rules are also not supported, but it's not clear that we'd want to support them. Author: Pavan Deolasee <pavan.deolasee@gmail.com> Author: Álvaro Herrera <alvherre@alvh.no-ip.org> Author: Amit Langote <amitlangote09@gmail.com> Author: Simon Riggs <simon.riggs@enterprisedb.com> Reviewed-by: Peter Eisentraut <peter.eisentraut@enterprisedb.com> Reviewed-by: Andres Freund <andres@anarazel.de> (earlier versions) Reviewed-by: Peter Geoghegan <pg@bowt.ie> (earlier versions) Reviewed-by: Robert Haas <robertmhaas@gmail.com> (earlier versions) Reviewed-by: Japin Li <japinli@hotmail.com> Reviewed-by: Justin Pryzby <pryzby@telsasoft.com> Reviewed-by: Tomas Vondra <tomas.vondra@enterprisedb.com> Reviewed-by: Zhihong Yu <zyu@yugabyte.com> Discussion: https://postgr.es/m/CANP8+jKitBSrB7oTgT9CY2i1ObfOt36z0XMraQc+Xrz8QB0nXA@mail.gmail.com Discussion: https://postgr.es/m/CAH2-WzkJdBuxj9PO=2QaO9-3h3xGbQPZ34kJH=HukRekwM-GZg@mail.gmail.com Discussion: https://postgr.es/m/20201231134736.GA25392@alvherre.pgsql	4 years ago
Tom Lane	0bd7af082a	Invent recursive_worktable_factor GUC to replace hard-wired constant. ... Up to now, the planner estimated the size of a recursive query's worktable as 10 times the size of the non-recursive term. It's hard to see how to do significantly better than that automatically, but we can give users control over the multiplier to allow tuning for specific use-cases. The default behavior remains the same. Simon Riggs Discussion: https://postgr.es/m/CANbhV-EuaLm4H3g0+BSTYHEGxJj3Kht0R+rJ8vT57Dejnh=_nA@mail.gmail.com	4 years ago
Bruce Momjian	27b77ecf9f	Update copyright for 2022 ... Backpatch-through: 10	4 years ago
Tom Lane	3804539e48	Replace random(), pg_erand48(), etc with a better PRNG API and algorithm. ... Standardize on xoroshiro128** as our basic PRNG algorithm, eliminating a bunch of platform dependencies as well as fundamentally-obsolete PRNG code. In addition, this API replacement will ease replacing the algorithm again in future, should that become necessary. xoroshiro128** is a few percent slower than the drand48 family, but it can produce full-width 64-bit random values not only 48-bit, and it should be much more trustworthy. It's likely to be noticeably faster than the platform's random(), depending on which platform you are thinking about; and we can have non-global state vectors easily, unlike with random(). It is not cryptographically strong, but neither are the functions it replaces. Fabien Coelho, reviewed by Dean Rasheed, Aleksander Alekseev, and myself Discussion: https://postgr.es/m/alpine.DEB.2.22.394.2105241211230.165418@pseudo	4 years ago
David Rowley	411137a429	Flush Memoize cache when non-key parameters change, take 2 ... It's possible that a subplan below a Memoize node contains a parameter from above the Memoize node. If this parameter changes then cache entries may become out-dated due to the new parameter value. Previously Memoize was mistakenly not aware of this. We fix this here by flushing the cache whenever a parameter that's not part of the cache key changes. Bug: #17213 Reported by: Elvis Pranskevichus Author: David Rowley Discussion: https://postgr.es/m/17213-988ed34b225a2862@postgresql.org Backpatch-through: 14, where Memoize was added	4 years ago
David Rowley	dad20ad470	Revert "Flush Memoize cache when non-key parameters change" ... This reverts commit 1050048a31.	4 years ago
David Rowley	1050048a31	Flush Memoize cache when non-key parameters change ... It's possible that a subplan below a Memoize node contains a parameter from above the Memoize node. If this parameter changes then cache entries may become out-dated due to the new parameter value. Previously Memoize was mistakenly not aware of this. We fix this here by flushing the cache whenever a parameter that's not part of the cache key changes. Bug: #17213 Reported by: Elvis Pranskevichus Author: David Rowley Discussion: https://postgr.es/m/17213-988ed34b225a2862@postgresql.org Backpatch-through: 14, where Memoize was added	4 years ago
David Rowley	e502150f7d	Allow Memoize to operate in binary comparison mode ... Memoize would always use the hash equality operator for the cache key types to determine if the current set of parameters were the same as some previously cached set. Certain types such as floating points where -0.0 and +0.0 differ in their binary representation but are classed as equal by the hash equality operator may cause problems as unless the join uses the same operator it's possible that whichever join operator is being used would be able to distinguish the two values. In which case we may accidentally return in the incorrect rows out of the cache. To fix this here we add a binary mode to Memoize to allow it to the current set of parameters to previously cached values by comparing bit-by-bit rather than logically using the hash equality operator. This binary mode is always used for LATERAL joins and it's used for normal joins when any of the join operators are not hashable. Reported-by: Tom Lane Author: David Rowley Discussion: https://postgr.es/m/3004308.1632952496@sss.pgh.pa.us Backpatch-through: 14, where Memoize was added	4 years ago
David Rowley	83f4fcc655	Change the name of the Result Cache node to Memoize ... "Result Cache" was never a great name for this node, but nobody managed to come up with another name that anyone liked enough. That was until David Johnston mentioned "Node Memoization", which Tom Lane revised to just "Memoize". People seem to like "Memoize", so let's do the rename. Reviewed-by: Justin Pryzby Discussion: https://postgr.es/m/20210708165145.GG1176@momjian.us Backpatch-through: 14, where Result Cache was introduced	5 years ago
Tom Lane	e56bce5d43	Reconsider the handling of procedure OUT parameters. ... Commit 2453ea142 redefined pg_proc.proargtypes to include the types of OUT parameters, for procedures only. While that had some advantages for implementing the SQL-spec behavior of DROP PROCEDURE, it was pretty disastrous from a number of other perspectives. Notably, since the primary key of pg_proc is name + proargtypes, this made it possible to have multiple procedures with identical names + input arguments and differing output argument types. That would make it impossible to call any one of the procedures by writing just NULL (or "?", or any other data-type-free notation) for the output argument(s). The change also seems likely to cause grave confusion for client applications that examine pg_proc and expect the traditional definition of proargtypes. Hence, revert the definition of proargtypes to what it was, and undo a number of complications that had been added to support that. To support the SQL-spec behavior of DROP PROCEDURE, when there are no argmode markers in the command's parameter list, we perform the lookup both ways (that is, matching against both proargtypes and proallargtypes), succeeding if we get just one unique match. In principle this could result in ambiguous-function failures that would not happen when using only one of the two rules. However, overloading of procedure names is thought to be a pretty rare usage, so this shouldn't cause many problems in practice. Postgres-specific code such as pg_dump can defend against any possibility of such failures by being careful to specify argmodes for all procedure arguments. This also fixes a few other bugs in the area of CALL statements with named parameters, and improves the documentation a little. catversion bump forced because the representation of procedures with OUT arguments changes. Discussion: https://postgr.es/m/3742981.1621533210@sss.pgh.pa.us	5 years ago
Tom Lane	049e1e2edb	Fix mishandling of resjunk columns in ON CONFLICT ... UPDATE tlists. ... It's unusual to have any resjunk columns in an ON CONFLICT ... UPDATE list, but it can happen when MULTIEXPR_SUBLINK SubPlans are present. If it happens, the ON CONFLICT UPDATE code path would end up storing tuples that include the values of the extra resjunk columns. That's fairly harmless in the short run, but if new columns are added to the table then the values would become accessible, possibly leading to malfunctions if they don't match the datatypes of the new columns. This had escaped notice through a confluence of missing sanity checks, including * There's no cross-check that a tuple presented to heap_insert or heap_update matches the table rowtype. While it's difficult to check that fully at reasonable cost, we can easily add assertions that there aren't too many columns. * The output-column-assignment cases in execExprInterp.c lacked any sanity checks on the output column numbers, which seems like an oversight considering there are plenty of assertion checks on input column numbers. Add assertions there too. * We failed to apply nodeModifyTable's ExecCheckPlanOutput() to the ON CONFLICT UPDATE tlist. That wouldn't have caught this specific error, since that function is chartered to ignore resjunk columns; but it sure seems like a bad omission now that we've seen this bug. In HEAD, the right way to fix this is to make the processing of ON CONFLICT UPDATE tlists work the same as regular UPDATE tlists now do, that is don't add "SET x = x" entries, and use ExecBuildUpdateProjection to evaluate the tlist and combine it with old values of the not-set columns. This adds a little complication to ExecBuildUpdateProjection, but allows removal of a comparable amount of now-dead code from the planner. In the back branches, the most expedient solution seems to be to (a) use an output slot for the ON CONFLICT UPDATE projection that actually matches the target table, and then (b) invent a variant of ExecBuildProjectionInfo that can be told to not store values resulting from resjunk columns, so it doesn't try to store into nonexistent columns of the output slot. (We can't simply ignore the resjunk columns altogether; they have to be evaluated for MULTIEXPR_SUBLINK to work.) This works back to v10. In 9.6, projections work much differently and we can't cheaply give them such an option. The 9.6 version of this patch works by inserting a JunkFilter when it's necessary to get rid of resjunk columns. In addition, v11 and up have the reverse problem when trying to perform ON CONFLICT UPDATE on a partitioned table. Through a further oversight, adjust_partition_tlist() discarded resjunk columns when re-ordering the ON CONFLICT UPDATE tlist to match a partition. This accidentally prevented the storing-bogus-tuples problem, but at the cost that MULTIEXPR_SUBLINK cases didn't work, typically crashing if more than one row has to be updated. Fix by preserving resjunk columns in that routine. (I failed to resist the temptation to add more assertions there too, and to do some minor code beautification.) Per report from Andres Freund. Back-patch to all supported branches. Security: CVE-2021-32028	5 years ago
Tom Lane	7645376774	Rename find_em_expr_usable_for_sorting_rel. ... I didn't particularly like this function name, as it fails to express what's going on. Also, returning the sort expression alone isn't too helpful --- typically, a caller would also need some other fields of the EquivalenceMember. But the sole caller really only needs a bool result, so let's make it "bool relation_can_be_sorted_early()". Discussion: https://postgr.es/m/91f3ec99-85a4-fa55-ea74-33f85a5c651f@swarm64.com	5 years ago
Tom Lane	3753982441	Fix planner failure in some cases of sorting by an aggregate. ... An oversight introduced by the incremental-sort patches caused "could not find pathkey item to sort" errors in some situations where a sort key involves an aggregate or window function. The basic problem here is that find_em_expr_usable_for_sorting_rel isn't properly modeling what prepare_sort_from_pathkeys will do later. Rather than hoping we can keep those functions in sync, let's refactor so that they actually share the code for identifying a suitable sort expression. With this refactoring, tlist.c's tlist_member_ignore_relabel is unused. I removed it in HEAD but left it in place in v13, in case any extensions are using it. Per report from Luc Vlaming. Back-patch to v13 where the problem arose. James Coleman and Tom Lane Discussion: https://postgr.es/m/91f3ec99-85a4-fa55-ea74-33f85a5c651f@swarm64.com	5 years ago
David Rowley	50e17ad281	Speedup ScalarArrayOpExpr evaluation ... ScalarArrayOpExprs with "useOr=true" and a set of Consts on the righthand side have traditionally been evaluated by using a linear search over the array. When these arrays contain large numbers of elements then this linear search could become a significant part of execution time. Here we add a new method of evaluating ScalarArrayOpExpr expressions to allow them to be evaluated by first building a hash table containing each element, then on subsequent evaluations, we just probe that hash table to determine if there is a match. The planner is in charge of determining when this optimization is possible and it enables it by setting hashfuncid in the ScalarArrayOpExpr. The executor will only perform the hash table evaluation when the hashfuncid is set. This means that not all cases are optimized. For example CHECK constraints containing an IN clause won't go through the planner, so won't get the hashfuncid set. We could maybe do something about that at some later date. The reason we're not doing it now is from fear that we may slow down cases where the expression is evaluated only once. Those cases can be common, for example, a single row INSERT to a table with a CHECK constraint containing an IN clause. In the planner, we enable this when there are suitable hash functions for the ScalarArrayOpExpr's operator and only when there is at least MIN_ARRAY_SIZE_FOR_HASHED_SAOP elements in the array. The threshold is currently set to 9. Author: James Coleman, David Rowley Reviewed-by: David Rowley, Tomas Vondra, Heikki Linnakangas Discussion: https://postgr.es/m/CAAaqYe8x62+=wn0zvNKCj55tPpg-JBHzhZFFc6ANovdqFw7-dA@mail.gmail.com	5 years ago
David Rowley	9eacee2e62	Add Result Cache executor node (take 2) ... Here we add a new executor node type named "Result Cache". The planner can include this node type in the plan to have the executor cache the results from the inner side of parameterized nested loop joins. This allows caching of tuples for sets of parameters so that in the event that the node sees the same parameter values again, it can just return the cached tuples instead of rescanning the inner side of the join all over again. Internally, result cache uses a hash table in order to quickly find tuples that have been previously cached. For certain data sets, this can significantly improve the performance of joins. The best cases for using this new node type are for join problems where a large portion of the tuples from the inner side of the join have no join partner on the outer side of the join. In such cases, hash join would have to hash values that are never looked up, thus bloating the hash table and possibly causing it to multi-batch. Merge joins would have to skip over all of the unmatched rows. If we use a nested loop join with a result cache, then we only cache tuples that have at least one join partner on the outer side of the join. The benefits of using a parameterized nested loop with a result cache increase when there are fewer distinct values being looked up and the number of lookups of each value is large. Also, hash probes to lookup the cache can be much faster than the hash probe in a hash join as it's common that the result cache's hash table is much smaller than the hash join's due to result cache only caching useful tuples rather than all tuples from the inner side of the join. This variation in hash probe performance is more significant when the hash join's hash table no longer fits into the CPU's L3 cache, but the result cache's hash table does. The apparent "random" access of hash buckets with each hash probe can cause a poor L3 cache hit ratio for large hash tables. Smaller hash tables generally perform better. The hash table used for the cache limits itself to not exceeding work_mem * hash_mem_multiplier in size. We maintain a dlist of keys for this cache and when we're adding new tuples and realize we've exceeded the memory budget, we evict cache entries starting with the least recently used ones until we have enough memory to add the new tuples to the cache. For parameterized nested loop joins, we now consider using one of these result cache nodes in between the nested loop node and its inner node. We determine when this might be useful based on cost, which is primarily driven off of what the expected cache hit ratio will be. Estimating the cache hit ratio relies on having good distinct estimates on the nested loop's parameters. For now, the planner will only consider using a result cache for parameterized nested loop joins. This works for both normal joins and also for LATERAL type joins to subqueries. It is possible to use this new node for other uses in the future. For example, to cache results from correlated subqueries. However, that's not done here due to some difficulties obtaining a distinct estimation on the outer plan to calculate the estimated cache hit ratio. Currently we plan the inner plan before planning the outer plan so there is no good way to know if a result cache would be useful or not since we can't estimate the number of times the subplan will be called until the outer plan is generated. The functionality being added here is newly introducing a dependency on the return value of estimate_num_groups() during the join search. Previously, during the join search, we only ever needed to perform selectivity estimations. With this commit, we need to use estimate_num_groups() in order to estimate what the hit ratio on the result cache will be. In simple terms, if we expect 10 distinct values and we expect 1000 outer rows, then we'll estimate the hit ratio to be 99%. Since cache hits are very cheap compared to scanning the underlying nodes on the inner side of the nested loop join, then this will significantly reduce the planner's cost for the join. However, it's fairly easy to see here that things will go bad when estimate_num_groups() incorrectly returns a value that's significantly lower than the actual number of distinct values. If this happens then that may cause us to make use of a nested loop join with a result cache instead of some other join type, such as a merge or hash join. Our distinct estimations have been known to be a source of trouble in the past, so the extra reliance on them here could cause the planner to choose slower plans than it did previous to having this feature. Distinct estimations are also fairly hard to estimate accurately when several tables have been joined already or when a WHERE clause filters out a set of values that are correlated to the expressions we're estimating the number of distinct value for. For now, the costing we perform during query planning for result caches does put quite a bit of faith in the distinct estimations being accurate. When these are accurate then we should generally see faster execution times for plans containing a result cache. However, in the real world, we may find that we need to either change the costings to put less trust in the distinct estimations being accurate or perhaps even disable this feature by default. There's always an element of risk when we teach the query planner to do new tricks that it decides to use that new trick at the wrong time and causes a regression. Users may opt to get the old behavior by turning the feature off using the enable_resultcache GUC. Currently, this is enabled by default. It remains to be seen if we'll maintain that setting for the release. Additionally, the name "Result Cache" is the best name I could think of for this new node at the time I started writing the patch. Nobody seems to strongly dislike the name. A few people did suggest other names but no other name seemed to dominate in the brief discussion that there was about names. Let's allow the beta period to see if the current name pleases enough people. If there's some consensus on a better name, then we can change it before the release. Please see the 2nd discussion link below for the discussion on the "Result Cache" name. Author: David Rowley Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu, Hou Zhijie Tested-By: Konstantin Knizhnik Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com	5 years ago
David Rowley	28b3e3905c	Revert b6002a796 ... This removes "Add Result Cache executor node". It seems that something weird is going on with the tracking of cache hits and misses as highlighted by many buildfarm animals. It's not yet clear what the problem is as other parts of the plan indicate that the cache did work correctly, it's just the hits and misses that were being reported as 0. This is especially a bad time to have the buildfarm so broken, so reverting before too many more animals go red. Discussion: https://postgr.es/m/CAApHDvq_hydhfovm4=izgWs+C5HqEeRScjMbOgbpC-jRAeK3Yw@mail.gmail.com	5 years ago
David Rowley	b6002a796d	Add Result Cache executor node ... Here we add a new executor node type named "Result Cache". The planner can include this node type in the plan to have the executor cache the results from the inner side of parameterized nested loop joins. This allows caching of tuples for sets of parameters so that in the event that the node sees the same parameter values again, it can just return the cached tuples instead of rescanning the inner side of the join all over again. Internally, result cache uses a hash table in order to quickly find tuples that have been previously cached. For certain data sets, this can significantly improve the performance of joins. The best cases for using this new node type are for join problems where a large portion of the tuples from the inner side of the join have no join partner on the outer side of the join. In such cases, hash join would have to hash values that are never looked up, thus bloating the hash table and possibly causing it to multi-batch. Merge joins would have to skip over all of the unmatched rows. If we use a nested loop join with a result cache, then we only cache tuples that have at least one join partner on the outer side of the join. The benefits of using a parameterized nested loop with a result cache increase when there are fewer distinct values being looked up and the number of lookups of each value is large. Also, hash probes to lookup the cache can be much faster than the hash probe in a hash join as it's common that the result cache's hash table is much smaller than the hash join's due to result cache only caching useful tuples rather than all tuples from the inner side of the join. This variation in hash probe performance is more significant when the hash join's hash table no longer fits into the CPU's L3 cache, but the result cache's hash table does. The apparent "random" access of hash buckets with each hash probe can cause a poor L3 cache hit ratio for large hash tables. Smaller hash tables generally perform better. The hash table used for the cache limits itself to not exceeding work_mem * hash_mem_multiplier in size. We maintain a dlist of keys for this cache and when we're adding new tuples and realize we've exceeded the memory budget, we evict cache entries starting with the least recently used ones until we have enough memory to add the new tuples to the cache. For parameterized nested loop joins, we now consider using one of these result cache nodes in between the nested loop node and its inner node. We determine when this might be useful based on cost, which is primarily driven off of what the expected cache hit ratio will be. Estimating the cache hit ratio relies on having good distinct estimates on the nested loop's parameters. For now, the planner will only consider using a result cache for parameterized nested loop joins. This works for both normal joins and also for LATERAL type joins to subqueries. It is possible to use this new node for other uses in the future. For example, to cache results from correlated subqueries. However, that's not done here due to some difficulties obtaining a distinct estimation on the outer plan to calculate the estimated cache hit ratio. Currently we plan the inner plan before planning the outer plan so there is no good way to know if a result cache would be useful or not since we can't estimate the number of times the subplan will be called until the outer plan is generated. The functionality being added here is newly introducing a dependency on the return value of estimate_num_groups() during the join search. Previously, during the join search, we only ever needed to perform selectivity estimations. With this commit, we need to use estimate_num_groups() in order to estimate what the hit ratio on the result cache will be. In simple terms, if we expect 10 distinct values and we expect 1000 outer rows, then we'll estimate the hit ratio to be 99%. Since cache hits are very cheap compared to scanning the underlying nodes on the inner side of the nested loop join, then this will significantly reduce the planner's cost for the join. However, it's fairly easy to see here that things will go bad when estimate_num_groups() incorrectly returns a value that's significantly lower than the actual number of distinct values. If this happens then that may cause us to make use of a nested loop join with a result cache instead of some other join type, such as a merge or hash join. Our distinct estimations have been known to be a source of trouble in the past, so the extra reliance on them here could cause the planner to choose slower plans than it did previous to having this feature. Distinct estimations are also fairly hard to estimate accurately when several tables have been joined already or when a WHERE clause filters out a set of values that are correlated to the expressions we're estimating the number of distinct value for. For now, the costing we perform during query planning for result caches does put quite a bit of faith in the distinct estimations being accurate. When these are accurate then we should generally see faster execution times for plans containing a result cache. However, in the real world, we may find that we need to either change the costings to put less trust in the distinct estimations being accurate or perhaps even disable this feature by default. There's always an element of risk when we teach the query planner to do new tricks that it decides to use that new trick at the wrong time and causes a regression. Users may opt to get the old behavior by turning the feature off using the enable_resultcache GUC. Currently, this is enabled by default. It remains to be seen if we'll maintain that setting for the release. Additionally, the name "Result Cache" is the best name I could think of for this new node at the time I started writing the patch. Nobody seems to strongly dislike the name. A few people did suggest other names but no other name seemed to dominate in the brief discussion that there was about names. Let's allow the beta period to see if the current name pleases enough people. If there's some consensus on a better name, then we can change it before the release. Please see the 2nd discussion link below for the discussion on the "Result Cache" name. Author: David Rowley Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu Tested-By: Konstantin Knizhnik Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com	5 years ago
Tom Lane	86dc90056d	Rework planning and execution of UPDATE and DELETE. ... This patch makes two closely related sets of changes: 1. For UPDATE, the subplan of the ModifyTable node now only delivers the new values of the changed columns (i.e., the expressions computed in the query's SET clause) plus row identity information such as CTID. ModifyTable must re-fetch the original tuple to merge in the old values of any unchanged columns. The core advantage of this is that the changed columns are uniform across all tables of an inherited or partitioned target relation, whereas the other columns might not be. A secondary advantage, when the UPDATE involves joins, is that less data needs to pass through the plan tree. The disadvantage of course is an extra fetch of each tuple to be updated. However, that seems to be very nearly free in context; even worst-case tests don't show it to add more than a couple percent to the total query cost. At some point it might be interesting to combine the re-fetch with the tuple access that ModifyTable must do anyway to mark the old tuple dead; but that would require a good deal of refactoring and it seems it wouldn't buy all that much, so this patch doesn't attempt it. 2. For inherited UPDATE/DELETE, instead of generating a separate subplan for each target relation, we now generate a single subplan that is just exactly like a SELECT's plan, then stick ModifyTable on top of that. To let ModifyTable know which target relation a given incoming row refers to, a tableoid junk column is added to the row identity information. This gets rid of the horrid hack that was inheritance_planner(), eliminating O(N^2) planning cost and memory consumption in cases where there were many unprunable target relations. Point 2 of course requires point 1, so that there is a uniform definition of the non-junk columns to be returned by the subplan. We can't insist on uniform definition of the row identity junk columns however, if we want to keep the ability to have both plain and foreign tables in a partitioning hierarchy. Since it wouldn't scale very far to have every child table have its own row identity column, this patch includes provisions to merge similar row identity columns into one column of the subplan result. In particular, we can merge the whole-row Vars typically used as row identity by FDWs into one column by pretending they are type RECORD. (It's still okay for the actual composite Datums to be labeled with the table's rowtype OID, though.) There is more that can be done to file down residual inefficiencies in this patch, but it seems to be committable now. FDW authors should note several API changes: * The argument list for AddForeignUpdateTargets() has changed, and so has the method it must use for adding junk columns to the query. Call add_row_identity_var() instead of manipulating the parse tree directly. You might want to reconsider exactly what you're adding, too. * PlanDirectModify() must now work a little harder to find the ForeignScan plan node; if the foreign table is part of a partitioning hierarchy then the ForeignScan might not be the direct child of ModifyTable. See postgres_fdw for sample code. * To check whether a relation is a target relation, it's no longer sufficient to compare its relid to root->parse->resultRelation. Instead, check it against all_result_relids or leaf_result_relids, as appropriate. Amit Langote and Tom Lane Discussion: https://postgr.es/m/CA+HiwqHpHdqdDn48yCEhynnniahH78rwcrv1rEX65-fsZGBOLQ@mail.gmail.com	5 years ago
Etsuro Fujita	27e1f14563	Add support for asynchronous execution. ... This implements asynchronous execution, which runs multiple parts of a non-parallel-aware Append concurrently rather than serially to improve performance when possible. Currently, the only node type that can be run concurrently is a ForeignScan that is an immediate child of such an Append. In the case where such ForeignScans access data on different remote servers, this would run those ForeignScans concurrently, and overlap the remote operations to be performed simultaneously, so it'll improve the performance especially when the operations involve time-consuming ones such as remote join and remote aggregation. We may extend this to other node types such as joins or aggregates over ForeignScans in the future. This also adds the support for postgres_fdw, which is enabled by the table-level/server-level option "async_capable". The default is false. Robert Haas, Kyotaro Horiguchi, Thomas Munro, and myself. This commit is mostly based on the patch proposed by Robert Haas, but also uses stuff from the patch proposed by Kyotaro Horiguchi and from the patch proposed by Thomas Munro. Reviewed by Kyotaro Horiguchi, Konstantin Knizhnik, Andrey Lepikhov, Movead Li, Thomas Munro, Justin Pryzby, and others. Discussion: https://postgr.es/m/CA%2BTgmoaXQEt4tZ03FtQhnzeDEMzBck%2BLrni0UWHVVgOTnA6C1w%40mail.gmail.com Discussion: https://postgr.es/m/CA%2BhUKGLBRyu0rHrDCMC4%3DRn3252gogyp1SjOgG8SEKKZv%3DFwfQ%40mail.gmail.com Discussion: https://postgr.es/m/20200228.170650.667613673625155850.horikyota.ntt%40gmail.com	5 years ago
Amit Kapila	26acb54a13	Revert "Enable parallel SELECT for "INSERT INTO ... SELECT ..."." ... To allow inserts in parallel-mode this feature has to ensure that all the constraints, triggers, etc. are parallel-safe for the partition hierarchy which is costly and we need to find a better way to do that. Additionally, we could have used existing cached information in some cases like indexes, domains, etc. to determine the parallel-safety. List of commits reverted, in reverse chronological order: ed62d3737c Doc: Update description for parallel insert reloption. c8f78b6161 Add a new GUC and a reloption to enable inserts in parallel-mode. c5be48f092 Improve FK trigger parallel-safety check added by 05c8482f7f. e2cda3c20a Fix use of relcache TriggerDesc field introduced by commit 05c8482f7f. e4e87a32cc Fix valgrind issue in commit 05c8482f7f. 05c8482f7f Enable parallel SELECT for "INSERT INTO ... SELECT ...". Discussion: https://postgr.es/m/E1lMiB9-0001c3-SY@gemulon.postgresql.org	5 years ago
Amit Kapila	c8f78b6161	Add a new GUC and a reloption to enable inserts in parallel-mode. ... Commit 05c8482f7f added the implementation of parallel SELECT for "INSERT INTO ... SELECT ..." which may incur non-negligible overhead in the additional parallel-safety checks that it performs, even when, in the end, those checks determine that parallelism can't be used. This is normally only ever a problem in the case of when the target table has a large number of partitions. A new GUC option "enable_parallel_insert" is added, to allow insert in parallel-mode. The default is on. In addition to the GUC option, the user may want a mechanism to allow inserts in parallel-mode with finer granularity at table level. The new table option "parallel_insert_enabled" allows this. The default is true. Author: "Hou, Zhijie" Reviewed-by: Greg Nancarrow, Amit Langote, Takayuki Tsunakawa, Amit Kapila Discussion: https://postgr.es/m/CAA4eK1K-cW7svLC2D7DHoGHxdAdg3P37BLgebqBOC2ZLc9a6QQ%40mail.gmail.com Discussion: https://postgr.es/m/CAJcOf-cXnB5cnMKqWEp2E2z7Mvcd04iLVmV=qpFJrR3AcrTS3g@mail.gmail.com	5 years ago
Amit Kapila	05c8482f7f	Enable parallel SELECT for "INSERT INTO ... SELECT ...". ... Parallel SELECT can't be utilized for INSERT in the following cases: - INSERT statement uses the ON CONFLICT DO UPDATE clause - Target table has a parallel-unsafe: trigger, index expression or predicate, column default expression or check constraint - Target table has a parallel-unsafe domain constraint on any column - Target table is a partitioned table with a parallel-unsafe partition key expression or support function The planner is updated to perform additional parallel-safety checks for the cases listed above, for determining whether it is safe to run INSERT in parallel-mode with an underlying parallel SELECT. The planner will consider using parallel SELECT for "INSERT INTO ... SELECT ...", provided nothing unsafe is found from the additional parallel-safety checks, or from the existing parallel-safety checks for SELECT. While checking parallel-safety, we need to check it for all the partitions on the table which can be costly especially when we decide not to use a parallel plan. So, in a separate patch, we will introduce a GUC and or a reloption to enable/disable parallelism for Insert statements. Prior to entering parallel-mode for the execution of INSERT with parallel SELECT, a TransactionId is acquired and assigned to the current transaction state. This is necessary to prevent the INSERT from attempting to assign the TransactionId whilst in parallel-mode, which is not allowed. This approach has a disadvantage in that if the underlying SELECT does not return any rows, then the TransactionId is not used, however that shouldn't happen in practice in many cases. Author: Greg Nancarrow, Amit Langote, Amit Kapila Reviewed-by: Amit Langote, Hou Zhijie, Takayuki Tsunakawa, Antonin Houska, Bharath Rupireddy, Dilip Kumar, Vignesh C, Zhihong Yu, Amit Kapila Tested-by: Tang, Haiying Discussion: https://postgr.es/m/CAJcOf-cXnB5cnMKqWEp2E2z7Mvcd04iLVmV=qpFJrR3AcrTS3g@mail.gmail.com Discussion: https://postgr.es/m/CAJcOf-fAdj=nDKMsRhQzndm-O13NY4dL6xGcEvdX5Xvbbi0V7g@mail.gmail.com	5 years ago
David Rowley	bb437f995d	Add TID Range Scans to support efficient scanning ranges of TIDs ... This adds a new executor node named TID Range Scan. The query planner will generate paths for TID Range scans when quals are discovered on base relations which search for ranges on the table's ctid column. These ranges may be open at either end. For example, WHERE ctid >= '(10,0)'; will return all tuples on page 10 and over. To support this, two new optional callback functions have been added to table AM. scan_set_tidrange is used to set the scan range to just the given range of TIDs. scan_getnextslot_tidrange fetches the next tuple in the given range. For AMs were scanning ranges of TIDs would not make sense, these functions can be set to NULL in the TableAmRoutine. The query planner won't generate TID Range Scan Paths in that case. Author: Edmund Horner, David Rowley Reviewed-by: David Rowley, Tomas Vondra, Tom Lane, Andres Freund, Zhihong Yu Discussion: https://postgr.es/m/CAMyN-kB-nFTkF=VA_JPwFNo08S0d-Yk0F741S2B7LDmYAi8eyA@mail.gmail.com	5 years ago
Tom Lane	f003a7522b	Remove [Merge]AppendPath.partitioned_rels. ... It turns out that the calculation of [Merge]AppendPath.partitioned_rels in allpaths.c is faulty and sometimes omits relevant non-leaf partitions, allowing an assertion added by commit a929e17e5a to trigger. Rather than fix that, it seems better to get rid of those fields altogether. We don't really need the info until create_plan time, and calculating it once for the selected plan should be cheaper than calculating it for each append path we consider. The preceding two commits did away with all use of the partitioned_rels values; this commit just mechanically removes the fields and the code that calculated them. Discussion: https://postgr.es/m/87sg8tqhsl.fsf@aurora.ydns.eu Discussion: https://postgr.es/m/CAJKUy5gCXDSmFs2c=R+VGgn7FiYcLCsEFEuDNNLGfoha=pBE_g@mail.gmail.com	5 years ago
Tom Lane	55dc86eca7	Fix pull_varnos' miscomputation of relids set for a PlaceHolderVar. ... Previously, pull_varnos() took the relids of a PlaceHolderVar as being equal to the relids in its contents, but that fails to account for the possibility that we have to postpone evaluation of the PHV due to outer joins. This could result in a malformed plan. The known cases end up triggering the "failed to assign all NestLoopParams to plan nodes" sanity check in createplan.c, but other symptoms may be possible. The right value to use is the join level we actually intend to evaluate the PHV at. We can get that from the ph_eval_at field of the associated PlaceHolderInfo. However, there are some places that call pull_varnos() before the PlaceHolderInfos have been created; in that case, fall back to the conservative assumption that the PHV will be evaluated at its syntactic level. (In principle this might result in missing some legal optimization, but I'm not aware of any cases where it's an issue in practice.) Things are also a bit ticklish for calls occurring during deconstruct_jointree(), but AFAICS the ph_eval_at fields should have reached their final values by the time we need them. The main problem in making this work is that pull_varnos() has no way to get at the PlaceHolderInfos. We can fix that easily, if a bit tediously, in HEAD by passing it the planner "root" pointer. In the back branches that'd cause an unacceptable API/ABI break for extensions, so leave the existing entry points alone and add new ones with the additional parameter. (If an old entry point is called and encounters a PHV, it'll fall back to using the syntactic level, again possibly missing some valid optimization.) Back-patch to v12. The computation is surely also wrong before that, but it appears that we cannot reach a bad plan thanks to join order restrictions imposed on the subquery that the PlaceHolderVar came from. The error only became reachable when commit 4be058fe9 allowed trivial subqueries to be collapsed out completely, eliminating their join order restrictions. Per report from Stephan Springl. Discussion: https://postgr.es/m/171041.1610849523@sss.pgh.pa.us	5 years ago
Bruce Momjian	ca3b37487b	Update copyright for 2021 ... Backpatch-through: 9.5	5 years ago
Tomas Vondra	fac1b470a9	Disallow SRFs when considering sorts below Gather Merge ... While we do allow SRFs in ORDER BY, scan/join processing should not consider such cases - such sorts should only happen via final Sort atop a ProjectSet. So make sure we don't try adding such sorts below Gather Merge, just like we do for expressions that are volatile and/or not parallel safe. Backpatch to PostgreSQL 13, where this code was introduced as part of the Incremental Sort patch. Author: James Coleman Reviewed-by: Tomas Vondra Backpatch-through: 13 Discussion: https://postgr.es/m/CAAaqYe8cK3g5CfLC4w7bs=hC0mSksZC=H5M8LSchj5e5OxpTAg@mail.gmail.com Discussion: https://postgr.es/m/295524.1606246314%40sss.pgh.pa.us	5 years ago
Tomas Vondra	86b7cca72d	Check parallel safety in generate_useful_gather_paths ... Commit ebb7ae839d ensured we ignore pathkeys with volatile expressions when considering adding a sort below a Gather Merge. Turns out we need to care about parallel safety of the pathkeys too, otherwise we might try sorting e.g. on results of a correlated subquery (as demonstrated by a report from Luis Roberto). Initial investigation by Tom Lane, patch by James Coleman. Backpatch to 13, where the code was instroduced (as part of Incremental Sort). Reported-by: Luis Roberto Author: James Coleman Reviewed-by: Tomas Vondra Backpatch-through: 13 Discussion: https://postgr.es/m/622580997.37108180.1604080457319.JavaMail.zimbra%40siscobra.com.br Discussion: https://postgr.es/m/CAAaqYe8cK3g5CfLC4w7bs=hC0mSksZC=H5M8LSchj5e5OxpTAg@mail.gmail.com	5 years ago
Dean Rasheed	25a9e54d2d	Improve estimation of OR clauses using extended statistics. ... Formerly we only applied extended statistics to an OR clause as part of the clauselist_selectivity() code path for an OR clause appearing in an implicitly-ANDed list of clauses. This meant that it could only use extended statistics if all sub-clauses of the OR clause were covered by a single extended statistics object. Instead, teach clause_selectivity() how to apply extended statistics to an OR clause by handling its ORed list of sub-clauses in a similar manner to an implicitly-ANDed list of sub-clauses, but with different combination rules. This allows one or more extended statistics objects to be used to estimate all or part of the list of sub-clauses. Any remaining sub-clauses are then treated as if they are independent. Additionally, to avoid double-application of extended statistics, this introduces "extended" versions of clause_selectivity() and clauselist_selectivity(), which include an option to ignore extended statistics. This replaces the old clauselist_selectivity_simple() function which failed to completely ignore extended statistics when called from the extended statistics code. A known limitation of the current infrastructure is that an AND clause under an OR clause is not treated as compatible with extended statistics (because we don't build RestrictInfos for such sub-AND clauses). Thus, for example, "(a=1 AND b=1) OR (a=2 AND b=2)" will currently be treated as two independent AND clauses (each of which may be estimated using extended statistics), but extended statistics will not currently be used to account for any possible overlap between those clauses. Improving that is left as a task for the future. Original patch by Tomas Vondra, with additional improvements by me. Discussion: https://postgr.es/m/20200113230008.g67iyk4cs3xbnjju@development	5 years ago
Tom Lane	8286223f3d	Fix missing outfuncs.c support for IncrementalSortPath. ... For debugging purposes, Path nodes are supposed to have outfuncs support, but this was overlooked in the original incremental sort patch. While at it, clean up a couple other minor oversights, as well as bizarre choice of return type for create_incremental_sort_path(). (All the existing callers just cast it to "Path *" immediately, so they don't care, but some future caller might care.) outfuncs.c fix by Zhijie Hou, the rest by me Discussion: https://postgr.es/m/324c4d81d8134117972a5b1f6cdf9560@G08CNEXMBPEKD05.g08.fujitsu.local	5 years ago
Fujii Masao	6742e14959	Fix typo in comment. ... Author: Haiying Tang <tanghy.fnst@cn.fujitsu.com> Discussion: https://postgr.es/m/48a0928ac94b497d9c40acf1de394c15@G08CNEXMBPEKD05.g08.fujitsu.local	5 years ago
Heikki Linnakangas	0a2bc5d61e	Move per-agg and per-trans duplicate finding to the planner. ... This has the advantage that the cost estimates for aggregates can count the number of calls to transition and final functions correctly. Bump catalog version, because views can contain Aggrefs. Reviewed-by: Andres Freund Discussion: https://www.postgresql.org/message-id/b2e3536b-1dbc-8303-c97e-89cb0b4a9a48%40iki.fi	5 years ago
Tom Lane	3b9b01f75d	Remove unnecessary #include. ... Justin Pryzby Discussion: https://postgr.es/m/20201123205505.GJ24052@telsasoft.com	5 years ago
Tomas Vondra	ebb7ae839d	Fix get_useful_pathkeys_for_relation for volatile expressions ... When considering Incremental Sort below a Gather Merge, we need to be a bit more careful when matching pathkeys to EC members. It's not enough to find a member whose Vars are all in the current relation's target; volatile expressions in particular need to be contained in the target, otherwise it's too early to use the pathkey. Reported-by: Jaime Casanova Author: James Coleman Reviewed-by: Tomas Vondra Backpatch-through: 13, where the incremental sort code was added Discussion: https://postgr.es/m/CAJGNTeNaxpXgBVcRhJX%2B2vSbq%2BF2kJqGBcvompmpvXb7pq%2BoFA%40mail.gmail.com	5 years ago
Tom Lane	ad1c36b070	Fix foreign-key selectivity estimation in the presence of constants. ... get_foreign_key_join_selectivity() looks for join clauses that equate the two sides of the FK constraint. However, if we have a query like "WHERE fktab.a = pktab.a and fktab.a = 1", it won't find any such join clause, because equivclass.c replaces the given clauses with "fktab.a = 1 and pktab.a = 1", which can be enforced at the scan level, leaving nothing to be done for column "a" at the join level. We can fix that expectation without much trouble, but then a new problem arises: applying the foreign-key-based selectivity rule produces a rowcount underestimate, because we're effectively double-counting the selectivity of the "fktab.a = 1" clause. So we have to cancel that selectivity out of the estimate. To fix, refactor process_implied_equality() so that it can pass back the new RestrictInfo to its callers in equivclass.c, allowing the generated "fktab.a = 1" clause to be saved in the EquivalenceClass's ec_derives list. Then it's not much trouble to dig out the relevant RestrictInfo when we need to adjust an FK selectivity estimate. (While at it, we can also remove the expensive use of initialize_mergeclause_eclasses() to set up the new RestrictInfo's left_ec and right_ec pointers. The equivclass.c code can set those basically for free.) This seems like clearly a bug fix, but I'm hesitant to back-patch it, first because there's some API/ABI risk for extensions and second because we're usually loath to destabilize plan choices in stable branches. Per report from Sigrid Ehrenreich. Discussion: https://postgr.es/m/1019549.1603770457@sss.pgh.pa.us Discussion: https://postgr.es/m/AM6PR02MB5287A0ADD936C1FA80973E72AB190@AM6PR02MB5287.eurprd02.prod.outlook.com	5 years ago
Tom Lane	1e7629d2c9	Be more careful about the shape of hashable subplan clauses. ... nodeSubplan.c expects that the testexpr for a hashable ANY SubPlan has the form of one or more OpExprs whose LHS is an expression of the outer query's, while the RHS is an expression over Params representing output columns of the subquery. However, the planner only went as far as verifying that the clauses were all binary OpExprs. This works 99.99% of the time, because the clauses have the right shape when emitted by the parser --- but it's possible for function inlining to break that, as reported by PegoraroF10. To fix, teach the planner to check that the LHS and RHS contain the right things, or more accurately don't contain the wrong things. Given that this has been broken for years without anyone noticing, it seems sufficient to just give up hashing when it happens, rather than go to the trouble of commuting the clauses back again (which wouldn't necessarily work anyway). While poking at that, I also noticed that nodeSubplan.c had a baked-in assumption that the number of hash clauses is identical to the number of subquery output columns. Again, that's fine as far as parser output goes, but it's not hard to break it via function inlining. There seems little reason for that assumption though --- AFAICS, the only thing it's buying us is not having to store the number of hash clauses explicitly. Adding code to the planner to reject such cases would take more code than getting nodeSubplan.c to cope, so I fixed it that way. This has been broken for as long as we've had hashable SubPlans, so back-patch to all supported branches. Discussion: https://postgr.es/m/1549209182255-0.post@n3.nabble.com	6 years ago