310 Commits (18fea737b5e47cc677eaf97365c765a47f346763)

Author	SHA1	Message	Date
Peter Eisentraut	18fea737b5	Change NestPath node to contain JoinPath node ... This makes the structure of all JoinPath-derived nodes the same, independent of whether they have additional fields. Discussion: https://www.postgresql.org/message-id/flat/c1097590-a6a4-486a-64b1-e1f9cc0533ce@enterprisedb.com	4 years ago
Tom Lane	28d936031a	Get rid of artificial restriction on hash table sizes on Windows. ... The point of introducing the hash_mem_multiplier GUC was to let users reproduce the old behavior of hash aggregation, i.e. that it could use more than work_mem at need. However, the implementation failed to get the job done on Win64, where work_mem is clamped to 2GB to protect various places that calculate memory sizes using "long int". As written, the same clamp was applied to hash_mem. This resulted in severe performance regressions for queries requiring a bit more than 2GB for hash aggregation, as they now spill to disk and there's no way to stop that. Getting rid of the work_mem restriction seems like a good idea, but it's a big job and could not conceivably be back-patched. However, there's only a fairly small number of places that are concerned with the hash_mem value, and it turns out to be possible to remove the restriction there without too much code churn or any ABI breaks. So, let's do that for now to fix the regression, and leave the larger task for another day. This patch does introduce a bit more infrastructure that should help with the larger task, namely pg_bitutils.h support for working with size_t values. Per gripe from Laurent Hasson. Back-patch to v13 where the behavior change came in. Discussion: https://postgr.es/m/997817.1627074924@sss.pgh.pa.us Discussion: https://postgr.es/m/MN2PR15MB25601E80A9B6D1BA6F592B1985E39@MN2PR15MB2560.namprd15.prod.outlook.com	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	4 years ago
Tom Lane	6ee41a301e	Fix mis-planning of repeated application of a projection. ... create_projection_plan contains a hidden assumption (here made explicit by an Assert) that a projection-capable Path will yield a projection-capable Plan. Unfortunately, that assumption is violated only a few lines away, by create_projection_plan itself. This means that two stacked ProjectionPaths can yield an outcome where we try to jam the upper path's tlist into a non-projection-capable child node, resulting in an invalid plan. There isn't any good reason to have stacked ProjectionPaths; indeed the whole concept is faulty, since the set of Vars/Aggs/etc needed by the upper one wouldn't necessarily be available in the output of the lower one, nor could the lower one create such values if they weren't available from its input. Hence, we can fix this by adjusting create_projection_path to strip any top-level ProjectionPath from the subpath it's given. (This amounts to saying "oh, we changed our minds about what we need to project here".) The test case added here only fails in v13 and HEAD; before that, we don't attempt to shove the Sort into the parallel part of the plan, for reasons that aren't entirely clear to me. However, all the directly-related code looks generally the same as far back as v11, where the hazard was introduced (by d7c19e62a). So I've got no faith that the same type of bug doesn't exist in v11 and v12, given the right test case. Hence, back-patch the code changes, but not the irrelevant test case, into those branches. Per report from Bas Poot. Discussion: https://postgr.es/m/534fca83789c4a378c7de379e9067d4f@politie.nl	4 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
David Rowley	ed934d4fa3	Allow estimate_num_groups() to pass back further details about the estimation ... Here we add a new output parameter to estimate_num_groups() to allow it to inform the caller of additional, possibly useful information about the estimation. The new output parameter is a struct that currently contains just a single field with a set of flags. This was done rather than having the flags as an output parameter to allow future fields to be added without having to change the signature of the function at a later date when we want to pass back further information that might not be suitable to store in the flags field. It seems reasonable that one day in the future that the planner would want to know more about the estimation. For example, how many individual sets of statistics was the estimation generated from? The planner may want to take that into account if we ever want to consider risks as well as costs when generating plans. For now, there's only 1 flag we set in the flags field. This is to indicate if the estimation fell back on using the hard-coded constants in any part of the estimation. Callers may like to change their behavior if this is set, and this gives them the ability to do so. Callers may pass the flag pointer as NULL if they have no interest in obtaining any additional information about the estimate. We're not adding any actual usages of these flags here. Some follow-up commits will make use of this feature. Additionally, we're also not making any changes to add support for clauselist_selectivity() and clauselist_selectivity_ext(). However, if this is required in the future then the same struct being added here should be fine to use as a new output argument for those functions too. Author: David Rowley Discussion: https://postgr.es/m/CAApHDvqQqpk=1W-G_ds7A9CsXX3BggWj_7okinzkLVhDubQzjA@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	5076f88bc9	Remove incidental dependencies on partitioned_rels lists. ... 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. This patch undoes a couple of very minor uses of the partitioned_rels values. createplan.c was testing for nil-ness to optimize away the preparatory work for make_partition_pruneinfo(). That is worth doing if the check is nigh free, but it's not worth going to any great lengths to avoid. create_append_path() was testing for nil-ness as part of deciding how to set up ParamPathInfo for an AppendPath. I replaced that with a check for the appendrel's parent rel being partitioned. That's not quite the same thing but should cover most cases. If we note any interesting loss of optimizations, we can dumb this down to just always use the more expensive method when the parent is a baserel. 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
Bruce Momjian	ca3b37487b	Update copyright for 2021 ... Backpatch-through: 9.5	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
Thomas Munro	f0f13a3a08	Fix estimates for ModifyTable paths without RETURNING. ... In the past, we always estimated that a ModifyTable node would emit the same number of rows as its subpaths. Without a RETURNING clause, the correct estimate is zero. Fix, in preparation for a proposed parallel write patch that is sensitive to that number. A remaining problem is that for RETURNING queries, the estimated width is based on subpath output rather than the RETURNING tlist. Reviewed-by: Greg Nancarrow <gregn4422@gmail.com> Discussion: https://postgr.es/m/CAJcOf-cXnB5cnMKqWEp2E2z7Mvcd04iLVmV%3DqpFJrR3AcrTS3g%40mail.gmail.com	5 years ago
Peter Geoghegan	d6c08e29e7	Add hash_mem_multiplier GUC. ... Add a GUC that acts as a multiplier on work_mem. It gets applied when sizing executor node hash tables that were previously size constrained using work_mem alone. The new GUC can be used to preferentially give hash-based nodes more memory than the generic work_mem limit. It is intended to enable admin tuning of the executor's memory usage. Overall system throughput and system responsiveness can be improved by giving hash-based executor nodes more memory (especially over sort-based alternatives, which are often much less sensitive to being memory constrained). The default value for hash_mem_multiplier is 1.0, which is also the minimum valid value. This means that hash-based nodes continue to apply work_mem in the traditional way by default. hash_mem_multiplier is generally useful. However, it is being added now due to concerns about hash aggregate performance stability for users that upgrade to Postgres 13 (which added disk-based hash aggregation in commit 1f39bce0). While the old hash aggregate behavior risked out-of-memory errors, it is nevertheless likely that many users actually benefited. Hash agg's previous indifference to work_mem during query execution was not just faster; it also accidentally made aggregation resilient to grouping estimate problems (at least in cases where this didn't create destabilizing memory pressure). hash_mem_multiplier can provide a certain kind of continuity with the behavior of Postgres 12 hash aggregates in cases where the planner incorrectly estimates that all groups (plus related allocations) will fit in work_mem/hash_mem. This seems necessary because hash-based aggregation is usually much slower when only a small fraction of all groups can fit. Even when it isn't possible to totally avoid hash aggregates that spill, giving hash aggregation more memory will reliably improve performance (the same cannot be said for external sort operations, which appear to be almost unaffected by memory availability provided it's at least possible to get a single merge pass). The PostgreSQL 13 release notes should advise users that increasing hash_mem_multiplier can help with performance regressions associated with hash aggregation. That can be taken care of by a later commit. Author: Peter Geoghegan Reviewed-By: Álvaro Herrera, Jeff Davis Discussion: https://postgr.es/m/20200625203629.7m6yvut7eqblgmfo@alap3.anarazel.de Discussion: https://postgr.es/m/CAH2-WzmD%2Bi1pG6rc1%2BCjc4V6EaFJ_qSuKCCHVnH%3DoruqD-zqow%40mail.gmail.com Backpatch: 13-, where disk-based hash aggregation was introduced.	5 years ago
Tom Lane	689696c711	Fix bitmap AND/OR scans on the inside of a nestloop partition-wise join. ... reparameterize_path_by_child() failed to reparameterize BitmapAnd and BitmapOr paths. This matters only if such a path is chosen as the inside of a nestloop partition-wise join, where we have to pass in parameters from the outside of the nestloop. If that did happen, we generated a bad plan that would likely lead to crashes at execution. This is not entirely reparameterize_path_by_child()'s fault though; it's the victim of an ancient decision (my ancient decision, I think) to not bother filling in param_info in BitmapAnd/Or path nodes. That caused the function to believe that such nodes and their children contain no parameter references and so need not be processed. In hindsight that decision looks pretty penny-wise and pound-foolish: while it saves a few cycles during path node setup, we do commonly need the information later. In particular, by reversing the decision and requiring valid param_info data in all nodes of a bitmap path tree, we can get rid of indxpath.c's get_bitmap_tree_required_outer() function, which computed the data on-demand. It's not unlikely that that nets out as a savings of cycles in many scenarios. A couple of other things in indxpath.c can be simplified as well. While here, get rid of some cases in reparameterize_path_by_child() that are visibly dead or useless, given that we only care about reparameterizing paths that can be on the inside of a parameterized nestloop. This case reminds one of the maxim that untested code probably does not work, so I'm unwilling to leave unreachable code in this function. (I did leave the T_Gather case in place even though it's not reached in the regression tests. It's not very clear to me when the planner might prefer to put Gather below rather than above a nestloop, but at least in principle the case might be interesting.) Per bug #16536, originally from Arne Roland but with a test case by Andrew Gierth. Back-patch to v11 where this code came in. Discussion: https://postgr.es/m/16536-2213ee0b3aad41fd@postgresql.org	5 years ago
Tom Lane	fa27dd40d5	Run pgindent with new pg_bsd_indent version 2.1.1. ... Thomas Munro fixed a longstanding annoyance in pg_bsd_indent, that it would misformat lines containing IsA() macros on the assumption that the IsA() call should be treated like a cast. This improves some other cases involving field/variable names that match typedefs, too. The only places that get worse are a couple of uses of the OpenSSL macro STACK_OF(); we'll gladly take that trade-off. Discussion: https://postgr.es/m/20200114221814.GA19630@alvherre.pgsql	5 years ago
Tom Lane	0da06d9faf	Get rid of trailing semicolons in C macro definitions. ... Writing a trailing semicolon in a macro is almost never the right thing, because you almost always want to write a semicolon after each macro call instead. (Even if there was some reason to prefer not to, pgindent would probably make a hash of code formatted that way; so within PG the rule should basically be "don't do it".) Thus, if we have a semi inside the macro, the compiler sees "something;;". Much of the time the extra empty statement is harmless, but it could lead to mysterious syntax errors at call sites. In perhaps an overabundance of neatnik-ism, let's run around and get rid of the excess semicolons whereever possible. The only thing worse than a mysterious syntax error is a mysterious syntax error that only happens in the back branches; therefore, backpatch these changes where relevant, which is most of them because most of these mistakes are old. (The lack of reported problems shows that this is largely a hypothetical issue, but still, it could bite us in some future patch.) John Naylor and Tom Lane Discussion: https://postgr.es/m/CACPNZCs0qWTqJ2QUSGJ07B7uvAvzMb-KbG2q+oo+J3tsWN5cqw@mail.gmail.com	5 years ago
Alvaro Herrera	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
Tomas Vondra	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
Jeff Davis	1f39bce021	Disk-based Hash Aggregation. ... While performing hash aggregation, track memory usage when adding new groups to a hash table. If the memory usage exceeds work_mem, enter "spill mode". In spill mode, new groups are not created in the hash table(s), but existing groups continue to be advanced if input tuples match. Tuples that would cause a new group to be created are instead spilled to a logical tape to be processed later. The tuples are spilled in a partitioned fashion. When all tuples from the outer plan are processed (either by advancing the group or spilling the tuple), finalize and emit the groups from the hash table. Then, create new batches of work from the spilled partitions, and select one of the saved batches and process it (possibly spilling recursively). Author: Jeff Davis Reviewed-by: Tomas Vondra, Adam Lee, Justin Pryzby, Taylor Vesely, Melanie Plageman Discussion: https://postgr.es/m/507ac540ec7c20136364b5272acbcd4574aa76ef.camel@j-davis.com	6 years ago
Jeff Davis	c11cb17dc5	Save calculated transitionSpace in Agg node. ... This will be useful in the upcoming Hash Aggregation work to improve estimates for hash table sizing. Discussion: https://postgr.es/m/37091115219dd522fd9ed67333ee8ed1b7e09443.camel%40j-davis.com	6 years ago
Bruce Momjian	7559d8ebfa	Update copyrights for 2020 ... Backpatch-through: update all files in master, backpatch legal files through 9.4	6 years ago
Amit Kapila	14aec03502	Make the order of the header file includes consistent in backend modules. ... Similar to commits 7e735035f2 and dddf4cdc33, this commit makes the order of header file inclusion consistent for backend modules. In the passing, removed a couple of duplicate inclusions. Author: Vignesh C Reviewed-by: Kuntal Ghosh and Amit Kapila Discussion: https://postgr.es/m/CALDaNm2Sznv8RR6Ex-iJO6xAdsxgWhCoETkaYX=+9DW3q0QCfA@mail.gmail.com	6 years ago
Michael Paquier	940c8b01b0	Fix typo in pathnode.c ... Author: Amit Langote Discussion: https://postgr.es/m/CA+HiwqFhZ6ABoz-i=JZ5wMMyz-orx4asjR0og9qBtgEwOww6Yg@mail.gmail.com	6 years ago
Tom Lane	569ed7f483	Redesign the API for list sorting (list_qsort becomes list_sort). ... In the wake of commit 1cff1b95a, the obvious way to sort a List is to apply qsort() directly to the array of ListCells. list_qsort was building an intermediate array of pointers-to-ListCells, which we no longer need, but getting rid of it forces an API change: the comparator functions need to do one less level of indirection. Since we're having to touch the callers anyway, let's do two additional changes: sort the given list in-place rather than making a copy (as none of the existing callers have any use for the copying behavior), and rename list_qsort to list_sort. It was argued that the old name exposes more about the implementation than it should, which I find pretty questionable, but a better reason to rename it is to be sure we get the attention of any external callers about the need to fix their comparator functions. While we're at it, change four existing callers of qsort() to use list_sort instead; previously, they all had local reinventions of list_qsort, ie build-an-array-from-a-List-and-qsort-it. (There are some other places where changing to list_sort perhaps would be worthwhile, but they're less obviously wins.) Discussion: https://postgr.es/m/29361.1563220190@sss.pgh.pa.us	6 years ago
Tom Lane	1cff1b95ab	Represent Lists as expansible arrays, not chains of cons-cells. ... Originally, Postgres Lists were a more or less exact reimplementation of Lisp lists, which consist of chains of separately-allocated cons cells, each having a value and a next-cell link. We'd hacked that once before (commit d0b4399d8) to add a separate List header, but the data was still in cons cells. That makes some operations -- notably list_nth() -- O(N), and it's bulky because of the next-cell pointers and per-cell palloc overhead, and it's very cache-unfriendly if the cons cells end up scattered around rather than being adjacent. In this rewrite, we still have List headers, but the data is in a resizable array of values, with no next-cell links. Now we need at most two palloc's per List, and often only one, since we can allocate some values in the same palloc call as the List header. (Of course, extending an existing List may require repalloc's to enlarge the array. But this involves just O(log N) allocations not O(N).) Of course this is not without downsides. The key difficulty is that addition or deletion of a list entry may now cause other entries to move, which it did not before. For example, that breaks foreach() and sister macros, which historically used a pointer to the current cons-cell as loop state. We can repair those macros transparently by making their actual loop state be an integer list index; the exposed "ListCell *" pointer is no longer state carried across loop iterations, but is just a derived value. (In practice, modern compilers can optimize things back to having just one loop state value, at least for simple cases with inline loop bodies.) In principle, this is a semantics change for cases where the loop body inserts or deletes list entries ahead of the current loop index; but I found no such cases in the Postgres code. The change is not at all transparent for code that doesn't use foreach() but chases lists "by hand" using lnext(). The largest share of such code in the backend is in loops that were maintaining "prev" and "next" variables in addition to the current-cell pointer, in order to delete list cells efficiently using list_delete_cell(). However, we no longer need a previous-cell pointer to delete a list cell efficiently. Keeping a next-cell pointer doesn't work, as explained above, but we can improve matters by changing such code to use a regular foreach() loop and then using the new macro foreach_delete_current() to delete the current cell. (This macro knows how to update the associated foreach loop's state so that no cells will be missed in the traversal.) There remains a nontrivial risk of code assuming that a ListCell * pointer will remain good over an operation that could now move the list contents. To help catch such errors, list.c can be compiled with a new define symbol DEBUG_LIST_MEMORY_USAGE that forcibly moves list contents whenever that could possibly happen. This makes list operations significantly more expensive so it's not normally turned on (though it is on by default if USE_VALGRIND is on). There are two notable API differences from the previous code: * lnext() now requires the List's header pointer in addition to the current cell's address. * list_delete_cell() no longer requires a previous-cell argument. These changes are somewhat unfortunate, but on the other hand code using either function needs inspection to see if it is assuming anything it shouldn't, so it's not all bad. Programmers should be aware of these significant performance changes: * list_nth() and related functions are now O(1); so there's no major access-speed difference between a list and an array. * Inserting or deleting a list element now takes time proportional to the distance to the end of the list, due to moving the array elements. (However, it typically *doesn't* require palloc or pfree, so except in long lists it's probably still faster than before.) Notably, lcons() used to be about the same cost as lappend(), but that's no longer true if the list is long. Code that uses lcons() and list_delete_first() to maintain a stack might usefully be rewritten to push and pop at the end of the list rather than the beginning. * There are now list_insert_nth...() and list_delete_nth...() functions that add or remove a list cell identified by index. These have the data-movement penalty explained above, but there's no search penalty. * list_concat() and variants now copy the second list's data into storage belonging to the first list, so there is no longer any sharing of cells between the input lists. The second argument is now declared "const List *" to reflect that it isn't changed. This patch just does the minimum needed to get the new implementation in place and fix bugs exposed by the regression tests. As suggested by the foregoing, there's a fair amount of followup work remaining to do. Also, the ENABLE_LIST_COMPAT macros are finally removed in this commit. Code using those should have been gone a dozen years ago. Patch by me; thanks to David Rowley, Jesper Pedersen, and others for review. Discussion: https://postgr.es/m/11587.1550975080@sss.pgh.pa.us	6 years ago
Tom Lane	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	6 years ago
Tom Lane	be76af171c	Initial pgindent run for v12. ... This is still using the 2.0 version of pg_bsd_indent. I thought it would be good to commit this separately, so as to document the differences between 2.0 and 2.1 behavior. Discussion: https://postgr.es/m/16296.1558103386@sss.pgh.pa.us	6 years ago
Tom Lane	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
Etsuro Fujita	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
Tom Lane	8edd0e7946	Suppress Append and MergeAppend plan nodes that have a single child. ... If there's only one child relation, the Append or MergeAppend isn't doing anything useful, and can be elided. It does have a purpose during planning though, which is to serve as a buffer between parent and child Var numbering. Therefore we keep it all the way through to setrefs.c, and get rid of it only after fixing references in the plan level(s) above it. This works largely the same as setrefs.c's ancient hack to get rid of no-op SubqueryScan nodes, and can even share some code with that. Note the change to make setrefs.c use apply_tlist_labeling rather than ad-hoc code. This has the effect of propagating the child's resjunk and ressortgroupref labels, which formerly weren't propagated when removing a SubqueryScan. Doing that is demonstrably necessary for the [Merge]Append cases, and seems harmless for SubqueryScan, if only because trivial_subqueryscan is afraid to collapse cases where the resjunk marking differs. (I suspect that restriction could now be removed, though it's unclear that it'd make any new matches possible, since the outer query can't have references to a child resjunk column.) David Rowley, reviewed by Alvaro Herrera and Tomas Vondra Discussion: https://postgr.es/m/CAKJS1f_7u8ATyJ1JGTMHFoKDvZdeF-iEBhs+sM_SXowOr9cArg@mail.gmail.com	7 years ago
Tom Lane	a391ff3c3d	Build out the planner support function infrastructure. ... Add support function requests for estimating the selectivity, cost, and number of result rows (if a SRF) of the target function. The lack of a way to estimate selectivity of a boolean-returning function in WHERE has been a recognized deficiency of the planner since Berkeley days. This commit finally fixes it. In addition, non-constant estimates of cost and number of output rows are now possible. We still fall back to looking at procost and prorows if the support function doesn't service the request, of course. To make concrete use of the possibility of estimating output rowcount for SRFs, this commit adds support functions for array_unnest(anyarray) and the integer variants of generate_series; the lack of plausible rowcount estimates for those, even when it's obvious to a human, has been a repeated subject of complaints. Obviously, much more could now be done in this line, but I'm mostly just trying to get the infrastructure in place. Discussion: https://postgr.es/m/15193.1548028093@sss.pgh.pa.us	7 years ago
Tom Lane	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
Tom Lane	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 bdd9a99aa. Bug #15613 also needs to be fixed in the back branches, but the appropriate fix will look quite a bit different there, since we don't want to assume that existing FDWs get the word right away. Discussion: https://postgr.es/m/15613-092be1be9576c728@postgresql.org	7 years ago
Tom Lane	f09346a9c6	Refactor planner's header files. ... Create a new header optimizer/optimizer.h, which exposes just the planner functions that can be used "at arm's length", without need to access Paths or the other planner-internal data structures defined in nodes/relation.h. This is intended to provide the whole planner API seen by most of the rest of the system; although FDWs still need to use additional stuff, and more thought is also needed about just what selfuncs.c should rely on. The main point of doing this now is to limit the amount of new #include baggage that will be needed by "planner support functions", which I expect to introduce later, and which will be in relevant datatype modules rather than anywhere near the planner. This commit just moves relevant declarations into optimizer.h from other header files (a couple of which go away because everything got moved), and adjusts #include lists to match. There's further cleanup that could be done if we want to decide that some stuff being exposed by optimizer.h doesn't belong in the planner at all, but I'll leave that for another day. Discussion: https://postgr.es/m/11460.1548706639@sss.pgh.pa.us	7 years ago
Tom Lane	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
Alvaro Herrera	b60c397599	Move inheritance expansion code into its own file ... This commit moves expand_inherited_tables and underlings from optimizer/prep/prepunionc.c to optimizer/utils/inherit.c. Also, all of the AppendRelInfo-based expression manipulation routines are moved to optimizer/utils/appendinfo.c. No functional code changes. One exception is the introduction of make_append_rel_info, but that's still just moving around code. Also, stop including <limits.h> in prepunion.c, which no longer needs it since 3fc6e2d7f5. I (Álvaro) noticed this because Amit was copying that to inherit.c, which likewise doesn't need it. Author: Amit Langote Discussion: https://postgr.es/m/3be67028-a00a-502c-199a-da00eec8fb6e@lab.ntt.co.jp	7 years ago
Bruce Momjian	97c39498e5	Update copyright for 2019 ... Backpatch-through: certain files through 9.4	7 years ago
Tom Lane	b5415e3c21	Support parameterized TidPaths. ... Up to now we've not worried much about joins where the join key is a relation's CTID column, reasoning that storing a table's CTIDs in some other table would be pretty useless. However, there are use-cases for this sort of query involving self-joins, so that argument doesn't really hold water. This patch allows generating plans for joins on CTID that use a nestloop with inner TidScan, similar to what we might do with an index on the join column. This is the most efficient way to join when the outer side of the nestloop is expected to yield relatively few rows. This change requires upgrading tidpath.c and the generated TidPaths to work with RestrictInfos instead of bare qual clauses, but that's long-postponed technical debt anyway. Discussion: https://postgr.es/m/17443.1545435266@sss.pgh.pa.us	7 years ago
Tom Lane	52ed730d51	Remove some unnecessary fields from Plan trees. ... In the wake of commit f2343653f, we no longer need some fields that were used before to control executor lock acquisitions: * PlannedStmt.nonleafResultRelations can go away entirely. * partitioned_rels can go away from Append, MergeAppend, and ModifyTable. However, ModifyTable still needs to know the RT index of the partition root table if any, which was formerly kept in the first entry of that list. Add a new field "rootRelation" to remember that. rootRelation is partly redundant with nominalRelation, in that if it's set it will have the same value as nominalRelation. However, the latter field has a different purpose so it seems best to keep them distinct. Amit Langote, reviewed by David Rowley and Jesper Pedersen, and whacked around a bit more by me Discussion: https://postgr.es/m/468c85d9-540e-66a2-1dde-fec2b741e688@lab.ntt.co.jp	7 years ago
Michael Paquier	c6598b8b05	Fix re-parameterize of MergeAppendPath ... Instead of MergeAppendPath, MergeAppend nodes were considered. This code is not covered by any tests now, which should be addressed at some point. This is an oversight from f49842d, which introduced partition-wise joins in v11, so back-patch down to that. Author: Michael Paquier Reviewed-by: Ashutosh Bapat Discussion: https://postgr.es/m/20180718062202.GC8565@paquier.xyz	7 years ago
Tom Lane	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	7 years ago
Amit Kapila	98d476a965	Improve coding pattern in Parallel Append code. ... The create_append_path code didn't consider that list_concat will modify it's first argument leading to inconsistent traversal of resulting list. In practice, it won't lead to any user-visible bug but changing it for making the code behave consistently. Reported-by: Tom Lane Author: Tom Lane Reviewed-by: Amit Khandekar and Amit Kapila Discussion: https://postgr.es/m/32365.1528994120@sss.pgh.pa.us	7 years ago
Robert Haas	dc1057fcd8	Prevent generation of bogus subquery scan paths. ... Commit 0927d2f46d didn't check that consider_parallel was set for the target relation or account for the possibility that required_outer might be non-empty. To prevent future bugs of this ilk, add some assertions to add_partial_path and do a bit of future-proofing of the code recently added to recurse_set_operations. Report by Andreas Seltenreich. Patch by Jeevan Chalke. Review by Amit Kapila and by me. Discussion: http://postgr.es/m/CAM2+6=U+9otsyF2fYB8x_2TBeHTR90itarqW=qAEjN-kHaC7kw@mail.gmail.com	8 years ago
Simon Riggs	08ea7a2291	Revert MERGE patch ... This reverts commits d204ef6377, 83454e3c2b and a few more commits thereafter (complete list at the end) related to MERGE feature. While the feature was fully functional, with sufficient test coverage and necessary documentation, it was felt that some parts of the executor and parse-analyzer can use a different design and it wasn't possible to do that in the available time. So it was decided to revert the patch for PG11 and retry again in the future. Thanks again to all reviewers and bug reporters. List of commits reverted, in reverse chronological order: f1464c5380 Improve parse representation for MERGE ddb4158579 MERGE syntax diagram correction 530e69e59b Allow cpluspluscheck to pass by renaming variable 01b88b4df5 MERGE minor errata 3af7b2b0d4 MERGE fix variable warning in non-assert builds a5d86181ec MERGE INSERT allows only one VALUES clause 4b2d44031f MERGE post-commit review 4923550c20 Tab completion for MERGE aa3faa3c7a WITH support in MERGE 83454e3c2b New files for MERGE d204ef6377 MERGE SQL Command following SQL:2016 Author: Pavan Deolasee Reviewed-by: Michael Paquier	8 years ago
Alvaro Herrera	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
Simon Riggs	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
Simon Riggs	7cf8a5c302	Revert "Modified files for MERGE" ... This reverts commit 354f13855e.	8 years ago