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${ noResults }
152 Commits (5c0675215e153ba1297fd494b34af2fdebd645d1)
Author | SHA1 | Message | Date |
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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
|
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 |
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 |
8 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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8 years ago |
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a6fd7b7a5f |
Post-PG 10 beta1 pgindent run
perltidy run not included. |
8 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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9 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 |
9 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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9 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). |
9 years ago |
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5fe5a2cee9 |
Allow aggregate transition states to be serialized and deserialized.
This is necessary infrastructure for supporting parallel aggregation for aggregates whose transition type is "internal". Such values can't be passed between cooperating processes, because they are just pointers. David Rowley, reviewed by Tomas Vondra and by me. |
10 years ago |
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e06a38965b |
Support parallel aggregation.
Parallel workers can now partially aggregate the data and pass the transition values back to the leader, which can combine the partial results to produce the final answer. David Rowley, based on earlier work by Haribabu Kommi. Reviewed by Álvaro Herrera, Tomas Vondra, Amit Kapila, James Sewell, and me. |
10 years ago |
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28048cbaa2 |
Allow callers of create_foreignscan_path to specify nondefault PathTarget.
Although the default choice of rel->reltarget should typically be sufficient for scan or join paths, it's not at all sufficient for the purposes PathTargets were invented for; in particular not for upper-relation Paths. So break API compatibility by adding a PathTarget argument to create_foreignscan_path(). To ease updating of existing code, accept a NULL value of the argument as selecting rel->reltarget. |
10 years ago |
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9e8b99420f |
Improve handling of group-column indexes in GroupingSetsPath.
Instead of having planner.c compute a groupColIdx array and store it in GroupingSetsPaths, make create_groupingsets_plan() find the grouping columns by searching in the child plan node's tlist. Although that's probably a bit slower for create_groupingsets_plan(), it's more like the way every other plan node type does this, and it provides positive confirmation that we know which child output columns we're supposed to be grouping on. (Indeed, looking at this now, I'm not at all sure that it wasn't broken before, because create_groupingsets_plan() isn't demanding an exact tlist match from its child node.) Also, this allows substantial simplification in planner.c, because it no longer needs to compute the groupColIdx array at all; no other cases were using it. I'd intended to put off this refactoring until later (like 9.7), but in view of the likely bug fix and the need to rationalize planner.c's tlist handling so we can do something sane with Konstantin Knizhnik's function-evaluation-postponement patch, I think it can't wait. |
10 years ago |
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8c314b9853 |
Finish refactoring make_foo() functions in createplan.c.
This patch removes some redundant cost calculations that I left for later
cleanup in commit
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10 years ago |
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3fc6e2d7f5 |
Make the upper part of the planner work by generating and comparing Paths.
I've been saying we needed to do this for more than five years, and here it finally is. This patch removes the ever-growing tangle of spaghetti logic that grouping_planner() used to use to try to identify the best plan for post-scan/join query steps. Now, there is (nearly) independent consideration of each execution step, and entirely separate construction of Paths to represent each of the possible ways to do that step. We choose the best Path or set of Paths using the same add_path() logic that's been used inside query_planner() for years. In addition, this patch removes the old restriction that subquery_planner() could return only a single Plan. It now returns a RelOptInfo containing a set of Paths, just as query_planner() does, and the parent query level can use each of those Paths as the basis of a SubqueryScanPath at its level. This allows finding some optimizations that we missed before, wherein a subquery was capable of returning presorted data and thereby avoiding a sort in the parent level, making the overall cost cheaper even though delivering sorted output was not the cheapest plan for the subquery in isolation. (A couple of regression test outputs change in consequence of that. However, there is very little change in visible planner behavior overall, because the point of this patch is not to get immediate planning benefits but to create the infrastructure for future improvements.) There is a great deal left to do here. This patch unblocks a lot of planner work that was basically impractical in the old code structure, such as allowing FDWs to implement remote aggregation, or rewriting plan_set_operations() to allow consideration of multiple implementation orders for set operations. (The latter will likely require a full rewrite of plan_set_operations(); what I've done here is only to fix it to return Paths not Plans.) I have also left unfinished some localized refactoring in createplan.c and planner.c, because it was not necessary to get this patch to a working state. Thanks to Robert Haas, David Rowley, and Amit Kapila for review. |
10 years ago |
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45be99f8cd |
Support parallel joins, and make related improvements.
The core innovation of this patch is the introduction of the concept of a partial path; that is, a path which if executed in parallel will generate a subset of the output rows in each process. Gathering a partial path produces an ordinary (complete) path. This allows us to generate paths for parallel joins by joining a partial path for one side (which at the baserel level is currently always a Partial Seq Scan) to an ordinary path on the other side. This is subject to various restrictions at present, especially that this strategy seems unlikely to be sensible for merge joins, so only nested loops and hash joins paths are generated. This also allows an Append node to be pushed below a Gather node in the case of a partitioned table. Testing revealed that early versions of this patch made poor decisions in some cases, which turned out to be caused by the fact that the original cost model for Parallel Seq Scan wasn't very good. So this patch tries to make some modest improvements in that area. There is much more to be done in the area of generating good parallel plans in all cases, but this seems like a useful step forward. Patch by me, reviewed by Dilip Kumar and Amit Kapila. |
10 years ago |
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ee94300446 |
Update copyright for 2016
Backpatch certain files through 9.1 |
10 years ago |
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acfcd45cac |
Still more fixes for planner's handling of LATERAL references.
More fuzz testing by Andreas Seltenreich exposed that the planner did not
cope well with chains of lateral references. If relation X references Y
laterally, and Y references Z laterally, then we will have to scan X on the
inside of a nestloop with Z, so for all intents and purposes X is laterally
dependent on Z too. The planner did not understand this and would generate
intermediate joins that could not be used. While that was usually harmless
except for wasting some planning cycles, under the right circumstances it
would lead to "failed to build any N-way joins" or "could not devise a
query plan" planner failures.
To fix that, convert the existing per-relation lateral_relids and
lateral_referencers relid sets into their transitive closures; that is,
they now show all relations on which a rel is directly or indirectly
laterally dependent. This not only fixes the chained-reference problem
but allows some of the relevant tests to be made substantially simpler
and faster, since they can be reduced to simple bitmap manipulations
instead of searches of the LateralJoinInfo list.
Also, when a PlaceHolderVar that is due to be evaluated at a join contains
lateral references, we should treat those references as indirect lateral
dependencies of each of the join's base relations. This prevents us from
trying to join any individual base relations to the lateral reference
source before the join is formed, which again cannot work.
Andreas' testing also exposed another oversight in the "dangerous
PlaceHolderVar" test added in commit
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10 years ago |
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385f337c9f |
Allow foreign and custom joins to handle EvalPlanQual rechecks.
Commit
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10 years ago |
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7e19db0c09 |
Fix another oversight in checking if a join with LATERAL refs is legal.
It was possible for the planner to decide to join a LATERAL subquery to the outer side of an outer join before the outer join itself is completed. Normally that's fine because of the associativity rules, but it doesn't work if the subquery contains a lateral reference to the inner side of the outer join. In such a situation the outer join *must* be done first. join_is_legal() missed this consideration and would allow the join to be attempted, but the actual path-building code correctly decided that no valid join path could be made, sometimes leading to planner errors such as "failed to build any N-way joins". Per report from Andreas Seltenreich. Back-patch to 9.3 where LATERAL support was added. |
10 years ago |
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f0661c4e8c |
Make sequential scans parallel-aware.
In addition, this path fills in a number of missing bits and pieces in the parallel infrastructure. Paths and plans now have a parallel_aware flag indicating whether whatever parallel-aware logic they have should be engaged. It is believed that we will need this flag for a number of path/plan types, not just sequential scans, which is why the flag is generic rather than part of the SeqScan structures specifically. Also, execParallel.c now gives parallel nodes a chance to initialize their PlanState nodes from the DSM during parallel worker startup. Amit Kapila, with a fair amount of adjustment by me. Review of previous patch versions by Haribabu Kommi and others. |
10 years ago |
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3bd909b220 |
Add a Gather executor node.
A Gather executor node runs any number of copies of a plan in an equal number of workers and merges all of the results into a single tuple stream. It can also run the plan itself, if the workers are unavailable or haven't started up yet. It is intended to work with the Partial Seq Scan node which will be added in future commits. It could also be used to implement parallel query of a different sort by itself, without help from Partial Seq Scan, if the single_copy mode is used. In that mode, a worker executes the plan, and the parallel leader does not, merely collecting the worker's results. So, a Gather node could be inserted into a plan to split the execution of that plan across two processes. Nested Gather nodes aren't currently supported, but we might want to add support for that in the future. There's nothing in the planner to actually generate Gather nodes yet, so it's not quite time to break out the champagne. But we're getting close. Amit Kapila. Some designs suggestions were provided by me, and I also reviewed the patch. Single-copy mode, documentation, and other minor changes also by me. |
10 years ago |
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807b9e0dff |
pgindent run for 9.5
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10 years ago |
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f6d208d6e5 |
TABLESAMPLE, SQL Standard and extensible
Add a TABLESAMPLE clause to SELECT statements that allows user to specify random BERNOULLI sampling or block level SYSTEM sampling. Implementation allows for extensible sampling functions to be written, using a standard API. Basic version follows SQLStandard exactly. Usable concrete use cases for the sampling API follow in later commits. Petr Jelinek Reviewed by Michael Paquier and Simon Riggs |
10 years ago |
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4baaf863ec |
Update copyright for 2015
Backpatch certain files through 9.0 |
11 years ago |
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c2ea2285e9 |
Simplify API for initially hooking custom-path providers into the planner.
Instead of register_custom_path_provider and a CreateCustomScanPath callback, let's just provide a standard function hook in set_rel_pathlist. This is more flexible than what was previously committed, is more like the usual conventions for planner hooks, and requires less support code in the core. We had discussed this design (including centralizing the set_cheapest() calls) back in March or so, so I'm not sure why it wasn't done like this already. |
11 years ago |
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a34fa8ee7c |
Initial code review for CustomScan patch.
Get rid of the pernicious entanglement between planner and executor headers
introduced by commit
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11 years ago |
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0b03e5951b |
Introduce custom path and scan providers.
This allows extension modules to define their own methods for scanning a relation, and get the core code to use them. It's unclear as yet how much use this capability will find, but we won't find out if we never commit it. KaiGai Kohei, reviewed at various times and in various levels of detail by Shigeru Hanada, Tom Lane, Andres Freund, Álvaro Herrera, and myself. |
11 years ago |
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5a6c168c78 |
Fix some more problems with nested append relations.
As of commit |
11 years ago |
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7e04792a1c |
Update copyright for 2014
Update all files in head, and files COPYRIGHT and legal.sgml in all back branches. |
12 years ago |
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784e762e88 |
Support multi-argument UNNEST(), and TABLE() syntax for multiple functions.
This patch adds the ability to write TABLE( function1(), function2(), ...) as a single FROM-clause entry. The result is the concatenation of the first row from each function, followed by the second row from each function, etc; with NULLs inserted if any function produces fewer rows than others. This is believed to be a much more useful behavior than what Postgres currently does with multiple SRFs in a SELECT list. This syntax also provides a reasonable way to combine use of column definition lists with WITH ORDINALITY: put the column definition list inside TABLE(), where it's clear that it doesn't control the ordinality column as well. Also implement SQL-compliant multiple-argument UNNEST(), by turning UNNEST(a,b,c) into TABLE(unnest(a), unnest(b), unnest(c)). The SQL standard specifies TABLE() with only a single function, not multiple functions, and it seems to require an implicit UNNEST() which is not what this patch does. There may be something wrong with that reading of the spec, though, because if it's right then the spec's TABLE() is just a pointless alternative spelling of UNNEST(). After further review of that, we might choose to adopt a different syntax for what this patch does, but in any case this functionality seems clearly worthwhile. Andrew Gierth, reviewed by Zoltán Böszörményi and Heikki Linnakangas, and significantly revised by me |
12 years ago |
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3ced8837db |
Simplify query_planner's API by having it return the top-level RelOptInfo.
Formerly, query_planner returned one or possibly two Paths for the topmost join relation, so that grouping_planner didn't see the join RelOptInfo (at least not directly; it didn't have any hesitation about examining cheapest_path->parent, though). However, correct selection of the Paths involved a significant amount of coupling between query_planner and grouping_planner, a problem which has gotten worse over time. It seems best to give up on this API choice and instead return the topmost RelOptInfo explicitly. Then grouping_planner can pull out the Paths it wants from the rel's path list. In this way we can remove all knowledge of grouping behaviors from query_planner. The only real benefit of the old way is that in the case of an empty FROM clause, we never made any RelOptInfos at all, just a Path. Now we have to gin up a dummy RelOptInfo to represent the empty FROM clause. That's not a very big deal though. While at it, simplify query_planner's API a bit more by having the caller set up root->tuple_fraction and root->limit_tuples, rather than passing those values as separate parameters. Since query_planner no longer does anything with either value, requiring it to fill the PlannerInfo fields seemed pretty arbitrary. This patch just rearranges code; it doesn't (intentionally) change any behaviors. Followup patches will do more interesting things. |
12 years ago |