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${ noResults }
175 Commits (f4adc41c4f92cc91d507b19e397140c35bb9fd71)
| Author | SHA1 | Message | Date |
|---|---|---|---|
|
|
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
|
5 years ago |
|
|
ca3b37487b |
Update copyright for 2021
Backpatch-through: 9.5 |
5 years ago |
|
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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 |
|
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357889eb17
|
Support FETCH FIRST WITH TIES
WITH TIES is an option to the FETCH FIRST N ROWS clause (the SQL standard's spelling of LIMIT), where you additionally get rows that compare equal to the last of those N rows by the columns in the mandatory ORDER BY clause. There was a proposal by Andrew Gierth to implement this functionality in a more powerful way that would yield more features, but the other patch had not been finished at this time, so we decided to use this one for now in the spirit of incremental development. Author: Surafel Temesgen <surafel3000@gmail.com> Reviewed-by: Álvaro Herrera <alvherre@alvh.no-ip.org> Reviewed-by: Tomas Vondra <tomas.vondra@2ndquadrant.com> Discussion: https://postgr.es/m/CALAY4q9ky7rD_A4vf=FVQvCGngm3LOes-ky0J6euMrg=_Se+ag@mail.gmail.com Discussion: https://postgr.es/m/87o8wvz253.fsf@news-spur.riddles.org.uk |
6 years ago |
|
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d2d8a229bc |
Implement Incremental Sort
Incremental Sort is an optimized variant of multikey sort for cases when the input is already sorted by a prefix of the requested sort keys. For example when the relation is already sorted by (key1, key2) and we need to sort it by (key1, key2, key3) we can simply split the input rows into groups having equal values in (key1, key2), and only sort/compare the remaining column key3. This has a number of benefits: - Reduced memory consumption, because only a single group (determined by values in the sorted prefix) needs to be kept in memory. This may also eliminate the need to spill to disk. - Lower startup cost, because Incremental Sort produce results after each prefix group, which is beneficial for plans where startup cost matters (like for example queries with LIMIT clause). We consider both Sort and Incremental Sort, and decide based on costing. The implemented algorithm operates in two different modes: - Fetching a minimum number of tuples without check of equality on the prefix keys, and sorting on all columns when safe. - Fetching all tuples for a single prefix group and then sorting by comparing only the remaining (non-prefix) keys. We always start in the first mode, and employ a heuristic to switch into the second mode if we believe it's beneficial - the goal is to minimize the number of unnecessary comparions while keeping memory consumption below work_mem. This is a very old patch series. The idea was originally proposed by Alexander Korotkov back in 2013, and then revived in 2017. In 2018 the patch was taken over by James Coleman, who wrote and rewrote most of the current code. There were many reviewers/contributors since 2013 - I've done my best to pick the most active ones, and listed them in this commit message. Author: James Coleman, Alexander Korotkov Reviewed-by: Tomas Vondra, Andreas Karlsson, Marti Raudsepp, Peter Geoghegan, Robert Haas, Thomas Munro, Antonin Houska, Andres Freund, Alexander Kuzmenkov Discussion: https://postgr.es/m/CAPpHfdscOX5an71nHd8WSUH6GNOCf=V7wgDaTXdDd9=goN-gfA@mail.gmail.com Discussion: https://postgr.es/m/CAPpHfds1waRZ=NOmueYq0sx1ZSCnt+5QJvizT8ndT2=etZEeAQ@mail.gmail.com |
6 years ago |
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7559d8ebfa |
Update copyrights for 2020
Backpatch-through: update all files in master, backpatch legal files through 9.4 |
6 years ago |
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1661a40505 |
Cosmetic improvements in setup of planner's per-RTE arrays.
Merge setup_append_rel_array into setup_simple_rel_arrays. There's no particularly good reason to keep them separate, and it's inconsistent with the lack of separation in expand_planner_arrays. The only apparent benefit was that the fast path for trivial queries in query_planner() doesn't need to set up the append_rel_array; but all we're saving there is an if-test and NULL assignment, which surely ought to be negligible. Also improve some obsolete comments. Discussion: https://postgr.es/m/17220.1565301350@sss.pgh.pa.us |
6 years ago |
|
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c74d49d41c |
Fix many typos and inconsistencies
Author: Alexander Lakhin Discussion: https://postgr.es/m/af27d1b3-a128-9d62-46e0-88f424397f44@gmail.com |
7 years ago |
|
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8255c7a5ee |
Phase 2 pgindent run for v12.
Switch to 2.1 version of pg_bsd_indent. This formats multiline function declarations "correctly", that is with additional lines of parameter declarations indented to match where the first line's left parenthesis is. Discussion: https://postgr.es/m/CAEepm=0P3FeTXRcU5B2W3jv3PgRVZ-kGUXLGfd42FFhUROO3ug@mail.gmail.com |
7 years ago |
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959d00e9db |
Use Append rather than MergeAppend for scanning ordered partitions.
If we need ordered output from a scan of a partitioned table, but the ordering matches the partition ordering, then we don't need to use a MergeAppend to combine the pre-ordered per-partition scan results: a plain Append will produce the same results. This both saves useless comparison work inside the MergeAppend proper, and allows us to start returning tuples after istarting up just the first child node not all of them. However, all is not peaches and cream, because if some of the child nodes have high startup costs then there will be big discontinuities in the tuples-returned-versus-elapsed-time curve. The planner's cost model cannot handle that (yet, anyway). If we model the Append's startup cost as being just the first child's startup cost, we may drastically underestimate the cost of fetching slightly more tuples than are available from the first child. Since we've had bad experiences with over-optimistic choices of "fast start" plans for ORDER BY LIMIT queries, that seems scary. As a klugy workaround, set the startup cost estimate for an ordered Append to be the sum of its children's startup costs (as MergeAppend would). This doesn't really describe reality, but it's less likely to cause a bad plan choice than an underestimated startup cost would. In practice, the cases where we really care about this optimization will have child plans that are IndexScans with zero startup cost, so that the overly conservative estimate is still just zero. David Rowley, reviewed by Julien Rouhaud and Antonin Houska Discussion: https://postgr.es/m/CAKJS1f-hAqhPLRk_RaSFTgYxd=Tz5hA7kQ2h4-DhJufQk8TGuw@mail.gmail.com |
7 years ago |
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aef65db676 |
Refactor create_limit_path() to share cost adjustment code with FDWs.
This is in preparation for an upcoming commit. Author: Etsuro Fujita Reviewed-By: Antonin Houska and Jeff Janes Discussion: https://postgr.es/m/87pnz1aby9.fsf@news-spur.riddles.org.uk |
7 years ago |
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428b260f87 |
Speed up planning when partitions can be pruned at plan time.
Previously, the planner created RangeTblEntry and RelOptInfo structs for every partition of a partitioned table, even though many of them might later be deemed uninteresting thanks to partition pruning logic. This incurred significant overhead when there are many partitions. Arrange to postpone creation of these data structures until after we've processed the query enough to identify restriction quals for the partitioned table, and then apply partition pruning before not after creation of each partition's data structures. In this way we need not open the partition relations at all for partitions that the planner has no real interest in. For queries that can be proven at plan time to access only a small number of partitions, this patch improves the practical maximum number of partitions from under 100 to perhaps a few thousand. Amit Langote, reviewed at various times by Dilip Kumar, Jesper Pedersen, Yoshikazu Imai, and David Rowley Discussion: https://postgr.es/m/9d7c5112-cb99-6a47-d3be-cf1ee6862a1d@lab.ntt.co.jp |
7 years ago |
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53bcf5e3db |
Build "other rels" of appendrel baserels in a separate step.
Up to now, otherrel RelOptInfos were built at the same time as baserel RelOptInfos, thanks to recursion in build_simple_rel(). However, nothing in query_planner's preprocessing cares at all about otherrels, only baserels, so we don't really need to build them until just before we enter make_one_rel. This has two benefits: * create_lateral_join_info did a lot of extra work to propagate lateral-reference information from parents to the correct children. But if we delay creation of the children till after that, it's trivial (and much harder to break, too). * Since we have all the restriction quals correctly assigned to parent appendrels by this point, it'll be possible to do plan-time pruning and never make child RelOptInfos at all for partitions that can be pruned away. That's not done here, but will be later on. Amit Langote, reviewed at various times by Dilip Kumar, Jesper Pedersen, Yoshikazu Imai, and David Rowley Discussion: https://postgr.es/m/9d7c5112-cb99-6a47-d3be-cf1ee6862a1d@lab.ntt.co.jp |
7 years ago |
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1a8d5afb0d |
Refactor the representation of indexable clauses in IndexPaths.
In place of three separate but interrelated lists (indexclauses, indexquals, and indexqualcols), an IndexPath now has one list "indexclauses" of IndexClause nodes. This holds basically the same information as before, but in a more useful format: in particular, there is now a clear connection between an indexclause (an original restriction clause from WHERE or JOIN/ON) and the indexquals (directly usable index conditions) derived from it. We also change the ground rules a bit by mandating that clause commutation, if needed, be done up-front so that what is stored in the indexquals list is always directly usable as an index condition. This gets rid of repeated re-determination of which side of the clause is the indexkey during costing and plan generation, as well as repeated lookups of the commutator operator. To minimize the added up-front cost, the typical case of commuting a plain OpExpr is handled by a new special-purpose function commute_restrictinfo(). For RowCompareExprs, generating the new clause properly commuted to begin with is not really any more complex than before, it's just different --- and we can save doing that work twice, as the pretty-klugy original implementation did. Tracking the connection between original and derived clauses lets us also track explicitly whether the derived clauses are an exact or lossy translation of the original. This provides a cheap solution to getting rid of unnecessary rechecks of boolean index clauses, which previously seemed like it'd be more expensive than it was worth. Another pleasant (IMO) side-effect is that EXPLAIN now always shows index clauses with the indexkey on the left; this seems less confusing. This commit leaves expand_indexqual_conditions() and some related functions in a slightly messy state. I didn't bother to change them any more than minimally necessary to work with the new data structure, because all that code is going to be refactored out of existence in a follow-on patch. Discussion: https://postgr.es/m/22182.1549124950@sss.pgh.pa.us |
7 years ago |
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34ea1ab7fd |
Split create_foreignscan_path() into three functions.
Up to now postgres_fdw has been using create_foreignscan_path() to
generate not only base-relation paths, but also paths for foreign joins
and foreign upperrels. This is wrong, because create_foreignscan_path()
calls get_baserel_parampathinfo() which will only do the right thing for
baserels. It accidentally fails to fail for unparameterized paths, which
are the only ones postgres_fdw (thought it) was handling, but we really
need different APIs for the baserel and join cases.
In HEAD, the best thing to do seems to be to split up the baserel,
joinrel, and upperrel cases into three functions so that they can
have different APIs. I haven't actually given create_foreign_join_path
a different API in this commit: we should spend a bit of time thinking
about just what we want to do there, since perhaps FDWs would want to
do something different from the build-up-a-join-pairwise approach that
get_joinrel_parampathinfo expects. In the meantime, since postgres_fdw
isn't prepared to generate parameterized joins anyway, just give it a
defense against trying to plan joins with lateral refs.
In addition (and this is what triggered this whole mess) fix bug #15613
from Srinivasan S A, by teaching file_fdw and postgres_fdw that plain
baserel foreign paths still have outer refs if the relation has
lateral_relids. Add some assertions in relnode.c to catch future
occurrences of the same error --- in particular, to catch other FDWs
doing that, but also as backstop against core-code mistakes like the
one fixed by commit
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7 years ago |
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fa2cf164aa |
Rename nodes/relation.h to nodes/pathnodes.h.
The old name of this file was never a very good indication of what it was for. Now that there's also access/relation.h, we have a potential confusion hazard as well, so let's rename it to something more apropos. Per discussion, "pathnodes.h" is reasonable, since a good fraction of the file is Path node definitions. While at it, tweak a couple of other headers that were gratuitously importing relation.h into modules that don't need it. Discussion: https://postgr.es/m/7719.1548688728@sss.pgh.pa.us |
7 years ago |
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4be058fe9e |
In the planner, replace an empty FROM clause with a dummy RTE.
The fact that "SELECT expression" has no base relations has long been a thorn in the side of the planner. It makes it hard to flatten a sub-query that looks like that, or is a trivial VALUES() item, because the planner generally uses relid sets to identify sub-relations, and such a sub-query would have an empty relid set if we flattened it. prepjointree.c contains some baroque logic that works around this in certain special cases --- but there is a much better answer. We can replace an empty FROM clause with a dummy RTE that acts like a table of one row and no columns, and then there are no such corner cases to worry about. Instead we need some logic to get rid of useless dummy RTEs, but that's simpler and covers more cases than what was there before. For really trivial cases, where the query is just "SELECT expression" and nothing else, there's a hazard that adding the extra RTE makes for a noticeable slowdown; even though it's not much processing, there's not that much for the planner to do overall. However testing says that the penalty is very small, close to the noise level. In more complex queries, this is able to find optimizations that we could not find before. The new RTE type is called RTE_RESULT, since the "scan" plan type it gives rise to is a Result node (the same plan we produced for a "SELECT expression" query before). To avoid confusion, rename the old ResultPath path type to GroupResultPath, reflecting that it's only used in degenerate grouping cases where we know the query produces just one grouped row. (It wouldn't work to unify the two cases, because there are different rules about where the associated quals live during query_planner.) Note: although this touches readfuncs.c, I don't think a catversion bump is required, because the added case can't occur in stored rules, only plans. Patch by me, reviewed by David Rowley and Mark Dilger Discussion: https://postgr.es/m/15944.1521127664@sss.pgh.pa.us |
7 years ago |
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97c39498e5 |
Update copyright for 2019
Backpatch-through: certain files through 9.4 |
7 years ago |
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52ed730d51 |
Remove some unnecessary fields from Plan trees.
In the wake of commit
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7 years ago |
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ff4f889164 |
Fix bugs with degenerate window ORDER BY clauses in GROUPS/RANGE mode.
nodeWindowAgg.c failed to cope with the possibility that no ordering columns are defined in the window frame for GROUPS mode or RANGE OFFSET mode, leading to assertion failures or odd errors, as reported by Masahiko Sawada and Lukas Eder. In RANGE OFFSET mode, an ordering column is really required, so add an Assert about that. In GROUPS mode, the code would work, except that the node initialization code wasn't in sync with the execution code about when to set up tuplestore read pointers and spare slots. Fix the latter for consistency's sake (even though I think the changes described below make the out-of-sync cases unreachable for now). Per SQL spec, a single ordering column is required for RANGE OFFSET mode, and at least one ordering column is required for GROUPS mode. The parser enforced the former but not the latter; add a check for that. We were able to reach the no-ordering-column cases even with fully spec compliant queries, though, because the planner would drop partitioning and ordering columns from the generated plan if they were redundant with earlier columns according to the redundant-pathkey logic, for instance "PARTITION BY x ORDER BY y" in the presence of a "WHERE x=y" qual. While in principle that's an optimization that could save some pointless comparisons at runtime, it seems unlikely to be meaningful in the real world. I think this behavior was not so much an intentional optimization as a side-effect of an ancient decision to construct the plan node's ordering-column info by reverse-engineering the PathKeys of the input path. If we give up redundant-column removal then it takes very little code to generate the plan node info directly from the WindowClause, ensuring that we have the expected number of ordering columns in all cases. (If anyone does complain about this, the planner could perhaps be taught to remove redundant columns only when it's safe to do so, ie *not* in RANGE OFFSET mode. But I doubt anyone ever will.) With these changes, the WindowAggPath.winpathkeys field is not used for anything anymore, so remove it. The test cases added here are not actually very interesting given the removal of the redundant-column-removal logic, but they would represent important corner cases if anyone ever tries to put that back. Tom Lane and Masahiko Sawada. Back-patch to v11 where RANGE OFFSET and GROUPS modes were added. Discussion: https://postgr.es/m/CAD21AoDrWqycq-w_+Bx1cjc+YUhZ11XTj9rfxNiNDojjBx8Fjw@mail.gmail.com Discussion: https://postgr.es/m/153086788677.17476.8002640580496698831@wrigleys.postgresql.org |
8 years ago |
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7d872c91a3 |
Allow direct lookups of AppendRelInfo by child relid
find_appinfos_by_relids had quite a large overhead when the number of items in the append_rel_list was high, as it had to trawl through the append_rel_list looking for AppendRelInfos belonging to the given childrelids. Since there can only be a single AppendRelInfo for each child rel, it seems much better to store an array in PlannerInfo which indexes these by child relid, making the function O(1) rather than O(N). This function was only called once inside the planner, so just replace that call with a lookup to the new array. find_childrel_appendrelinfo is now unused and thus removed. This fixes a planner performance regression new to v11 reported by Thomas Reiss. Author: David Rowley Reported-by: Thomas Reiss Reviewed-by: Ashutosh Bapat Reviewed-by: Álvaro Herrera Discussion: https://postgr.es/m/94dd7a4b-5e50-0712-911d-2278e055c622@dalibo.com |
8 years ago |
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08ea7a2291 |
Revert MERGE patch
This reverts commits |
8 years ago |
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499be013de |
Support partition pruning at execution time
Existing partition pruning is only able to work at plan time, for query quals that appear in the parsed query. This is good but limiting, as there can be parameters that appear later that can be usefully used to further prune partitions. This commit adds support for pruning subnodes of Append which cannot possibly contain any matching tuples, during execution, by evaluating Params to determine the minimum set of subnodes that can possibly match. We support more than just simple Params in WHERE clauses. Support additionally includes: 1. Parameterized Nested Loop Joins: The parameter from the outer side of the join can be used to determine the minimum set of inner side partitions to scan. 2. Initplans: Once an initplan has been executed we can then determine which partitions match the value from the initplan. Partition pruning is performed in two ways. When Params external to the plan are found to match the partition key we attempt to prune away unneeded Append subplans during the initialization of the executor. This allows us to bypass the initialization of non-matching subplans meaning they won't appear in the EXPLAIN or EXPLAIN ANALYZE output. For parameters whose value is only known during the actual execution then the pruning of these subplans must wait. Subplans which are eliminated during this stage of pruning are still visible in the EXPLAIN output. In order to determine if pruning has actually taken place, the EXPLAIN ANALYZE must be viewed. If a certain Append subplan was never executed due to the elimination of the partition then the execution timing area will state "(never executed)". Whereas, if, for example in the case of parameterized nested loops, the number of loops stated in the EXPLAIN ANALYZE output for certain subplans may appear lower than others due to the subplan having been scanned fewer times. This is due to the list of matching subnodes having to be evaluated whenever a parameter which was found to match the partition key changes. This commit required some additional infrastructure that permits the building of a data structure which is able to perform the translation of the matching partition IDs, as returned by get_matching_partitions, into the list index of a subpaths list, as exist in node types such as Append, MergeAppend and ModifyTable. This allows us to translate a list of clauses into a Bitmapset of all the subpath indexes which must be included to satisfy the clause list. Author: David Rowley, based on an earlier effort by Beena Emerson Reviewers: Amit Langote, Robert Haas, Amul Sul, Rajkumar Raghuwanshi, Jesper Pedersen Discussion: https://postgr.es/m/CAOG9ApE16ac-_VVZVvv0gePSgkg_BwYEV1NBqZFqDR2bBE0X0A@mail.gmail.com |
8 years ago |
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d204ef6377 |
MERGE SQL Command following SQL:2016
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 other require multiple PL statements. e.g. 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 and partitioned tables, including column and row security enforcement, as well as support for row, statement and transition triggers. 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 statically from PL/pgSQL. MERGE does not yet support inheritance, write rules, RETURNING clauses, updatable views or foreign tables. MERGE follows SQL Standard per the most recent SQL:2016. Includes full tests and documentation, including full isolation tests to demonstrate the concurrent behavior. This version written from scratch in 2017 by Simon Riggs, using docs and tests originally written in 2009. Later work from Pavan Deolasee has been both complex and deep, leaving the lead author credit now in his hands. Extensive discussion of concurrency from Peter Geoghegan, with thanks for the time and effort contributed. Various issues reported via sqlsmith by Andreas Seltenreich Authors: Pavan Deolasee, Simon Riggs Reviewer: Peter Geoghegan, Amit Langote, Tomas Vondra, Simon Riggs Discussion: https://postgr.es/m/CANP8+jKitBSrB7oTgT9CY2i1ObfOt36z0XMraQc+Xrz8QB0nXA@mail.gmail.com https://postgr.es/m/CAH2-WzkJdBuxj9PO=2QaO9-3h3xGbQPZ34kJH=HukRekwM-GZg@mail.gmail.com |
8 years ago |
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7cf8a5c302 |
Revert "Modified files for MERGE"
This reverts commit
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8 years ago |
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354f13855e |
Modified files for MERGE
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8 years ago |
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94150513ec |
Don't pass the grouping target around unnecessarily.
Since commit
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8 years ago |
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2f17844104 |
Allow UPDATE to move rows between partitions.
When an UPDATE causes a row to no longer match the partition constraint, try to move it to a different partition where it does match the partition constraint. In essence, the UPDATE is split into a DELETE from the old partition and an INSERT into the new one. This can lead to surprising behavior in concurrency scenarios because EvalPlanQual rechecks won't work as they normally did; the known problems are documented. (There is a pending patch to improve the situation further, but it needs more review.) Amit Khandekar, reviewed and tested by Amit Langote, David Rowley, Rajkumar Raghuwanshi, Dilip Kumar, Amul Sul, Thomas Munro, Álvaro Herrera, Amit Kapila, and me. A few final revisions by me. Discussion: http://postgr.es/m/CAJ3gD9do9o2ccQ7j7+tSgiE1REY65XRiMb=yJO3u3QhyP8EEPQ@mail.gmail.com |
8 years ago |
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9d4649ca49 |
Update copyright for 2018
Backpatch-through: certain files through 9.3 |
8 years ago |
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1804284042 |
Add parallel-aware hash joins.
Introduce parallel-aware hash joins that appear in EXPLAIN plans as Parallel
Hash Join with Parallel Hash. While hash joins could already appear in
parallel queries, they were previously always parallel-oblivious and had a
partial subplan only on the outer side, meaning that the work of the inner
subplan was duplicated in every worker.
After this commit, the planner will consider using a partial subplan on the
inner side too, using the Parallel Hash node to divide the work over the
available CPU cores and combine its results in shared memory. If the join
needs to be split into multiple batches in order to respect work_mem, then
workers process different batches as much as possible and then work together
on the remaining batches.
The advantages of a parallel-aware hash join over a parallel-oblivious hash
join used in a parallel query are that it:
* avoids wasting memory on duplicated hash tables
* avoids wasting disk space on duplicated batch files
* divides the work of building the hash table over the CPUs
One disadvantage is that there is some communication between the participating
CPUs which might outweigh the benefits of parallelism in the case of small
hash tables. This is avoided by the planner's existing reluctance to supply
partial plans for small scans, but it may be necessary to estimate
synchronization costs in future if that situation changes. Another is that
outer batch 0 must be written to disk if multiple batches are required.
A potential future advantage of parallel-aware hash joins is that right and
full outer joins could be supported, since there is a single set of matched
bits for each hashtable, but that is not yet implemented.
A new GUC enable_parallel_hash is defined to control the feature, defaulting
to on.
Author: Thomas Munro
Reviewed-By: Andres Freund, Robert Haas
Tested-By: Rafia Sabih, Prabhat Sahu
Discussion:
https://postgr.es/m/CAEepm=2W=cOkiZxcg6qiFQP-dHUe09aqTrEMM7yJDrHMhDv_RA@mail.gmail.com
https://postgr.es/m/CAEepm=37HKyJ4U6XOLi=JgfSHM3o6B-GaeO-6hkOmneTDkH+Uw@mail.gmail.com
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8 years ago |
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ab72716778 |
Support Parallel Append plan nodes.
When we create an Append node, we can spread out the workers over the subplans instead of piling on to each subplan one at a time, which should typically be a bit more efficient, both because the startup cost of any plan executed entirely by one worker is paid only once and also because of reduced contention. We can also construct Append plans using a mix of partial and non-partial subplans, which may allow for parallelism in places that otherwise couldn't support it. Unfortunately, this patch doesn't handle the important case of parallelizing UNION ALL by running each branch in a separate worker; the executor infrastructure is added here, but more planner work is needed. Amit Khandekar, Robert Haas, Amul Sul, reviewed and tested by Ashutosh Bapat, Amit Langote, Rafia Sabih, Amit Kapila, and Rajkumar Raghuwanshi. Discussion: http://postgr.es/m/CAJ3gD9dy0K_E8r727heqXoBmWZ83HwLFwdcaSSmBQ1+S+vRuUQ@mail.gmail.com |
8 years ago |
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f49842d1ee |
Basic partition-wise join functionality.
Instead of joining two partitioned tables in their entirety we can, if it is an equi-join on the partition keys, join the matching partitions individually. This involves teaching the planner about "other join" rels, which are related to regular join rels in the same way that other member rels are related to baserels. This can use significantly more CPU time and memory than regular join planning, because there may now be a set of "other" rels not only for every base relation but also for every join relation. In most practical cases, this probably shouldn't be a problem, because (1) it's probably unusual to join many tables each with many partitions using the partition keys for all joins and (2) if you do that scenario then you probably have a big enough machine to handle the increased memory cost of planning and (3) the resulting plan is highly likely to be better, so what you spend in planning you'll make up on the execution side. All the same, for now, turn this feature off by default. Currently, we can only perform joins between two tables whose partitioning schemes are absolutely identical. It would be nice to cope with other scenarios, such as extra partitions on one side or the other with no match on the other side, but that will have to wait for a future patch. Ashutosh Bapat, reviewed and tested by Rajkumar Raghuwanshi, Amit Langote, Rafia Sabih, Thomas Munro, Dilip Kumar, Antonin Houska, Amit Khandekar, and by me. A few final adjustments by me. Discussion: http://postgr.es/m/CAFjFpRfQ8GrQvzp3jA2wnLqrHmaXna-urjm_UY9BqXj=EaDTSA@mail.gmail.com Discussion: http://postgr.es/m/CAFjFpRcitjfrULr5jfuKWRPsGUX0LQ0k8-yG0Qw2+1LBGNpMdw@mail.gmail.com |
8 years ago |
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e139f1953f |
Assorted preparatory refactoring for partition-wise join.
Instead of duplicating the logic to search for a matching ParamPathInfo in multiple places, factor it out into a separate function. Pass only the relevant bits of the PartitionKey to partition_bounds_equal instead of the whole thing, because partition-wise join will want to call this without having a PartitionKey available. Adjust allow_star_schema_join and calc_nestloop_required_outer to take relevant Relids rather than the entire Path, because partition-wise join will want to call it with the top-parent relids to determine whether a child join is allowable. Ashutosh Bapat. Review and testing of the larger patch set of which this is a part by Amit Langote, Rajkumar Raghuwanshi, Rafia Sabih, Thomas Munro, Dilip Kumar, and me. Discussion: http://postgr.es/m/CA+TgmobQK80vtXjAsPZWWXd7c8u13G86gmuLupN+uUJjA+i4nA@mail.gmail.com |
9 years ago |
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c7b8998ebb |
Phase 2 of pgindent updates.
Change pg_bsd_indent to follow upstream rules for placement of comments
to the right of code, and remove pgindent hack that caused comments
following #endif to not obey the general rule.
Commit
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9 years ago |
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a6fd7b7a5f |
Post-PG 10 beta1 pgindent run
perltidy run not included. |
9 years ago |
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9c7f5229ad |
Optimize joins when the inner relation can be proven unique.
If there can certainly be no more than one matching inner row for a given outer row, then the executor can move on to the next outer row as soon as it's found one match; there's no need to continue scanning the inner relation for this outer row. This saves useless scanning in nestloop and hash joins. In merge joins, it offers the opportunity to skip mark/restore processing, because we know we have not advanced past the first possible match for the next outer row. Of course, the devil is in the details: the proof of uniqueness must depend only on joinquals (not otherquals), and if we want to skip mergejoin mark/restore then it must depend only on merge clauses. To avoid adding more planning overhead than absolutely necessary, the present patch errs in the conservative direction: there are cases where inner_unique or skip_mark_restore processing could be used, but it will not do so because it's not sure that the uniqueness proof depended only on "safe" clauses. This could be improved later. David Rowley, reviewed and rather heavily editorialized on by me Discussion: https://postgr.es/m/CAApHDvqF6Sw-TK98bW48TdtFJ+3a7D2mFyZ7++=D-RyPsL76gw@mail.gmail.com |
9 years ago |
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7a39b5e4d1 |
Abstract logic to allow for multiple kinds of child rels.
Currently, the only type of child relation is an "other member rel", which is the child of a baserel, but in the future joins and even upper relations may have child rels. To facilitate that, introduce macros that test to test for particular RelOptKind values, and use them in various places where they help to clarify the sense of a test. (For example, a test may allow RELOPT_OTHER_MEMBER_REL either because it intends to allow child rels, or because it intends to allow simple rels.) Also, remove find_childrel_top_parent, which will not work for a child rel that is not a baserel. Instead, add a new RelOptInfo member top_parent_relids to track the same kind of information in a more generic manner. Ashutosh Bapat, slightly tweaked by me. Review and testing of the patch set from which this was taken by Rajkumar Raghuwanshi and Rafia Sabih. Discussion: http://postgr.es/m/CA+TgmoagTnF2yqR3PT2rv=om=wJiZ4-A+ATwdnriTGku1CLYxA@mail.gmail.com |
9 years ago |
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18ce3a4ab2 |
Add infrastructure to support EphemeralNamedRelation references.
A QueryEnvironment concept is added, which allows new types of objects to be passed into queries from parsing on through execution. At this point, the only thing implemented is a collection of EphemeralNamedRelation objects -- relations which can be referenced by name in queries, but do not exist in the catalogs. The only type of ENR implemented is NamedTuplestore, but provision is made to add more types fairly easily. An ENR can carry its own TupleDesc or reference a relation in the catalogs by relid. Although these features can be used without SPI, convenience functions are added to SPI so that ENRs can easily be used by code run through SPI. The initial use of all this is going to be transition tables in AFTER triggers, but that will be added to each PL as a separate commit. An incidental effect of this patch is to produce a more informative error message if an attempt is made to modify the contents of a CTE from a referencing DML statement. No tests previously covered that possibility, so one is added. Kevin Grittner and Thomas Munro Reviewed by Heikki Linnakangas, David Fetter, and Thomas Munro with valuable comments and suggestions from many others |
9 years ago |
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b5635948ab |
Support hashed aggregation with grouping sets.
This extends the Aggregate node with two new features: HashAggregate can now run multiple hashtables concurrently, and a new strategy MixedAggregate populates hashtables while doing sorted grouping. The planner will now attempt to save as many sorts as possible when planning grouping sets queries, while not exceeding work_mem for the estimated combined sizes of all hashtables used. No SQL-level changes are required. There should be no user-visible impact other than the new EXPLAIN output and possible changes to result ordering when ORDER BY was not used (which affected a few regression tests). The enable_hashagg option is respected. Author: Andrew Gierth Reviewers: Mark Dilger, Andres Freund Discussion: https://postgr.es/m/87vatszyhj.fsf@news-spur.riddles.org.uk |
9 years ago |
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d3cc37f1d8 |
Don't scan partitioned tables.
Partitioned tables do not contain any data; only their unpartitioned descendents need to be scanned. However, the partitioned tables still need to be locked, even though they're not scanned. To make that work, Append and MergeAppend relations now need to carry a list of (unscanned) partitioned relations that must be locked, and InitPlan must lock all partitioned result relations. Aside from the obvious advantage of avoiding some work at execution time, this has two other advantages. First, it may improve the planner's decision-making in some cases since the empty relation might throw things off. Second, it paves the way to getting rid of the storage for partitioned tables altogether. Amit Langote, reviewed by me. Discussion: http://postgr.es/m/6837c359-45c4-8044-34d1-736756335a15@lab.ntt.co.jp |
9 years ago |
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355d3993c5 |
Add a Gather Merge executor node.
Like Gather, we spawn multiple workers and run the same plan in each one; however, Gather Merge is used when each worker produces the same output ordering and we want to preserve that output ordering while merging together the streams of tuples from various workers. (In a way, Gather Merge is like a hybrid of Gather and MergeAppend.) This works out to a win if it saves us from having to perform an expensive Sort. In cases where only a small amount of data would need to be sorted, it may actually be faster to use a regular Gather node and then sort the results afterward, because Gather Merge sometimes needs to wait synchronously for tuples whereas a pure Gather generally doesn't. But if this avoids an expensive sort then it's a win. Rushabh Lathia, reviewed and tested by Amit Kapila, Thomas Munro, and Neha Sharma, and reviewed and revised by me. Discussion: http://postgr.es/m/CAGPqQf09oPX-cQRpBKS0Gq49Z+m6KBxgxd_p9gX8CKk_d75HoQ@mail.gmail.com |
9 years ago |
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f35742ccb7 |
Support parallel bitmap heap scans.
The index is scanned by a single process, but then all cooperating processes can iterate jointly over the resulting set of heap blocks. In the future, we might also want to support using a parallel bitmap index scan to set up for a parallel bitmap heap scan, but that's a job for another day. Dilip Kumar, with some corrections and cosmetic changes by me. The larger patch set of which this is a part has been reviewed and tested by (at least) Andres Freund, Amit Khandekar, Tushar Ahuja, Rafia Sabih, Haribabu Kommi, Thomas Munro, and me. Discussion: http://postgr.es/m/CAFiTN-uc4=0WxRGfCzs-xfkMYcSEWUC-Fon6thkJGjkh9i=13A@mail.gmail.com |
9 years ago |
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fcec6caafa |
Support XMLTABLE query expression
XMLTABLE is defined by the SQL/XML standard as a feature that allows turning XML-formatted data into relational form, so that it can be used as a <table primary> in the FROM clause of a query. This new construct provides significant simplicity and performance benefit for XML data processing; what in a client-side custom implementation was reported to take 20 minutes can be executed in 400ms using XMLTABLE. (The same functionality was said to take 10 seconds using nested PostgreSQL XPath function calls, and 5 seconds using XMLReader under PL/Python). The implemented syntax deviates slightly from what the standard requires. First, the standard indicates that the PASSING clause is optional and that multiple XML input documents may be given to it; we make it mandatory and accept a single document only. Second, we don't currently support a default namespace to be specified. This implementation relies on a new executor node based on a hardcoded method table. (Because the grammar is fixed, there is no extensibility in the current approach; further constructs can be implemented on top of this such as JSON_TABLE, but they require changes to core code.) Author: Pavel Stehule, Álvaro Herrera Extensively reviewed by: Craig Ringer Discussion: https://postgr.es/m/CAFj8pRAgfzMD-LoSmnMGybD0WsEznLHWap8DO79+-GTRAPR4qA@mail.gmail.com |
9 years ago |
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5262f7a4fc |
Add optimizer and executor support for parallel index scans.
In combination with
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9 years ago |
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69f4b9c85f |
Move targetlist SRF handling from expression evaluation to new executor node.
Evaluation of set returning functions (SRFs_ in the targetlist (like SELECT generate_series(1,5)) so far was done in the expression evaluation (i.e. ExecEvalExpr()) and projection (i.e. ExecProject/ExecTargetList) code. This meant that most executor nodes performing projection, and most expression evaluation functions, had to deal with the possibility that an evaluated expression could return a set of return values. That's bad because it leads to repeated code in a lot of places. It also, and that's my (Andres's) motivation, made it a lot harder to implement a more efficient way of doing expression evaluation. To fix this, introduce a new executor node (ProjectSet) that can evaluate targetlists containing one or more SRFs. To avoid the complexity of the old way of handling nested expressions returning sets (e.g. having to pass up ExprDoneCond, and dealing with arguments to functions returning sets etc.), those SRFs can only be at the top level of the node's targetlist. The planner makes sure (via split_pathtarget_at_srfs()) that SRF evaluation is only necessary in ProjectSet nodes and that SRFs are only present at the top level of the node's targetlist. If there are nested SRFs the planner creates multiple stacked ProjectSet nodes. The ProjectSet nodes always get input from an underlying node. We also discussed and prototyped evaluating targetlist SRFs using ROWS FROM(), but that turned out to be more complicated than we'd hoped. While moving SRF evaluation to ProjectSet would allow to retain the old "least common multiple" behavior when multiple SRFs are present in one targetlist (i.e. continue returning rows until all SRFs are at the end of their input at the same time), we decided to instead only return rows till all SRFs are exhausted, returning NULL for already exhausted ones. We deemed the previous behavior to be too confusing, unexpected and actually not particularly useful. As a side effect, the previously prohibited case of multiple set returning arguments to a function, is now allowed. Not because it's particularly desirable, but because it ends up working and there seems to be no argument for adding code to prohibit it. Currently the behavior for COALESCE and CASE containing SRFs has changed, returning multiple rows from the expression, even when the SRF containing "arm" of the expression is not evaluated. That's because the SRFs are evaluated in a separate ProjectSet node. As that's quite confusing, we're likely to instead prohibit SRFs in those places. But that's still being discussed, and the code would reside in places not touched here, so that's a task for later. There's a lot of, now superfluous, code dealing with set return expressions around. But as the changes to get rid of those are verbose largely boring, it seems better for readability to keep the cleanup as a separate commit. Author: Tom Lane and Andres Freund Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de |
9 years ago |
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1d25779284 |
Update copyright via script for 2017
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9 years ago |
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19e972d558 |
Rethink node-level representation of partial-aggregation modes.
The original coding had three separate booleans representing partial
aggregation behavior, which was confusing, unreadable, and error-prone,
not least because the booleans weren't always listed in the same order.
It was also inadequate for the allegedly-desirable future extension to
support intermediate partial aggregation, because we'd need separate
markers for serialization and deserialization in such a case.
Merge these bools into an enum "AggSplit" to provide symbolic names for
the supported operating modes (and document what those are). By assigning
the values of the enum constants carefully, we can treat AggSplit values
as options bitmasks so that tests of what to do aren't noticeably more
expensive than before.
While at it, get rid of Aggref.aggoutputtype. That's not needed since
commit
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10 years ago |
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8b9d323cb9 |
Refactor planning of projection steps that don't need a Result plan node.
The original upper-planner-pathification design (commit |
10 years ago |
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54f5c5150f |
Try again to fix the way the scanjoin_target is used with partial paths.
Commit
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10 years ago |
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c9ce4a1c61 |
Eliminate "parallel degree" terminology.
This terminology provoked widespread complaints. So, instead, rename the GUC max_parallel_degree to max_parallel_workers_per_gather (leaving room for a possible future GUC max_parallel_workers that acts as a system-wide limit), and rename the parallel_degree reloption to parallel_workers. Rename structure members to match. These changes create a dump/restore hazard for users of PostgreSQL 9.6beta1 who have set the reloption (or applied the GUC using ALTER USER or ALTER DATABASE). |
10 years ago |