181 Commits (83f4fcc65503c5d4e5d5eefc8e7a70d3c9a6496f)

Author	SHA1	Message	Date
David Rowley	83f4fcc655	Change the name of the Result Cache node to Memoize ... "Result Cache" was never a great name for this node, but nobody managed to come up with another name that anyone liked enough. That was until David Johnston mentioned "Node Memoization", which Tom Lane revised to just "Memoize". People seem to like "Memoize", so let's do the rename. Reviewed-by: Justin Pryzby Discussion: https://postgr.es/m/20210708165145.GG1176@momjian.us Backpatch-through: 14, where Result Cache was introduced	5 years ago
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	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
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
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
Bruce Momjian	7559d8ebfa	Update copyrights for 2020 ... Backpatch-through: update all files in master, backpatch legal files through 9.4	6 years ago
Tom Lane	1661a40505	Cosmetic improvements in setup of planner's per-RTE arrays. ... Merge setup_append_rel_array into setup_simple_rel_arrays. There's no particularly good reason to keep them separate, and it's inconsistent with the lack of separation in expand_planner_arrays. The only apparent benefit was that the fast path for trivial queries in query_planner() doesn't need to set up the append_rel_array; but all we're saving there is an if-test and NULL assignment, which surely ought to be negligible. Also improve some obsolete comments. Discussion: https://postgr.es/m/17220.1565301350@sss.pgh.pa.us	6 years ago
Michael Paquier	c74d49d41c	Fix many typos and inconsistencies ... Author: Alexander Lakhin Discussion: https://postgr.es/m/af27d1b3-a128-9d62-46e0-88f424397f44@gmail.com	7 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	7 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	428b260f87	Speed up planning when partitions can be pruned at plan time. ... Previously, the planner created RangeTblEntry and RelOptInfo structs for every partition of a partitioned table, even though many of them might later be deemed uninteresting thanks to partition pruning logic. This incurred significant overhead when there are many partitions. Arrange to postpone creation of these data structures until after we've processed the query enough to identify restriction quals for the partitioned table, and then apply partition pruning before not after creation of each partition's data structures. In this way we need not open the partition relations at all for partitions that the planner has no real interest in. For queries that can be proven at plan time to access only a small number of partitions, this patch improves the practical maximum number of partitions from under 100 to perhaps a few thousand. Amit Langote, reviewed at various times by Dilip Kumar, Jesper Pedersen, Yoshikazu Imai, and David Rowley Discussion: https://postgr.es/m/9d7c5112-cb99-6a47-d3be-cf1ee6862a1d@lab.ntt.co.jp	7 years ago
Tom Lane	53bcf5e3db	Build "other rels" of appendrel baserels in a separate step. ... Up to now, otherrel RelOptInfos were built at the same time as baserel RelOptInfos, thanks to recursion in build_simple_rel(). However, nothing in query_planner's preprocessing cares at all about otherrels, only baserels, so we don't really need to build them until just before we enter make_one_rel. This has two benefits: * create_lateral_join_info did a lot of extra work to propagate lateral-reference information from parents to the correct children. But if we delay creation of the children till after that, it's trivial (and much harder to break, too). * Since we have all the restriction quals correctly assigned to parent appendrels by this point, it'll be possible to do plan-time pruning and never make child RelOptInfos at all for partitions that can be pruned away. That's not done here, but will be later on. Amit Langote, reviewed at various times by Dilip Kumar, Jesper Pedersen, Yoshikazu Imai, and David Rowley Discussion: https://postgr.es/m/9d7c5112-cb99-6a47-d3be-cf1ee6862a1d@lab.ntt.co.jp	7 years ago
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	fa2cf164aa	Rename nodes/relation.h to nodes/pathnodes.h. ... The old name of this file was never a very good indication of what it was for. Now that there's also access/relation.h, we have a potential confusion hazard as well, so let's rename it to something more apropos. Per discussion, "pathnodes.h" is reasonable, since a good fraction of the file is Path node definitions. While at it, tweak a couple of other headers that were gratuitously importing relation.h into modules that don't need it. Discussion: https://postgr.es/m/7719.1548688728@sss.pgh.pa.us	7 years ago
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
Bruce Momjian	97c39498e5	Update copyright for 2019 ... Backpatch-through: certain files through 9.4	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
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	8 years ago
Alvaro Herrera	7d872c91a3	Allow direct lookups of AppendRelInfo by child relid ... find_appinfos_by_relids had quite a large overhead when the number of items in the append_rel_list was high, as it had to trawl through the append_rel_list looking for AppendRelInfos belonging to the given childrelids. Since there can only be a single AppendRelInfo for each child rel, it seems much better to store an array in PlannerInfo which indexes these by child relid, making the function O(1) rather than O(N). This function was only called once inside the planner, so just replace that call with a lookup to the new array. find_childrel_appendrelinfo is now unused and thus removed. This fixes a planner performance regression new to v11 reported by Thomas Reiss. Author: David Rowley Reported-by: Thomas Reiss Reviewed-by: Ashutosh Bapat Reviewed-by: Álvaro Herrera Discussion: https://postgr.es/m/94dd7a4b-5e50-0712-911d-2278e055c622@dalibo.com	8 years ago
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
Simon Riggs	354f13855e	Modified files for MERGE	8 years ago
Robert Haas	94150513ec	Don't pass the grouping target around unnecessarily. ... Since commit 4f15e5d09d made grouped_rel set reltarget, a variety of other functions can just get it from grouped_rel instead of having to pass it around explicitly. Simplify accordingly. Patch by me, reviewed by Ashutosh Bapat. Discussion: http://postgr.es/m/CA+TgmoZ+ZJTVad-=vEq393N99KTooxv9k7M+z73qnTAqkb49BQ@mail.gmail.com	8 years ago
Robert Haas	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
Bruce Momjian	9d4649ca49	Update copyright for 2018 ... Backpatch-through: certain files through 9.3	8 years ago
Andres Freund	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
Robert Haas	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
Robert Haas	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
Robert Haas	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
Tom Lane	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 e3860ffa4d wasn't actually using the published version of pg_bsd_indent, but a hacked-up version that tried to minimize the amount of movement of comments to the right of code. The situation of interest is where such a comment has to be moved to the right of its default placement at column 33 because there's code there. BSD indent has always moved right in units of tab stops in such cases --- but in the previous incarnation, indent was working in 8-space tab stops, while now it knows we use 4-space tabs. So the net result is that in about half the cases, such comments are placed one tab stop left of before. This is better all around: it leaves more room on the line for comment text, and it means that in such cases the comment uniformly starts at the next 4-space tab stop after the code, rather than sometimes one and sometimes two tabs after. Also, ensure that comments following #endif are indented the same as comments following other preprocessor commands such as #else. That inconsistency turns out to have been self-inflicted damage from a poorly-thought-through post-indent "fixup" in pgindent. This patch is much less interesting than the first round of indent changes, but also bulkier, so I thought it best to separate the effects. Discussion: https://postgr.es/m/E1dAmxK-0006EE-1r@gemulon.postgresql.org Discussion: https://postgr.es/m/30527.1495162840@sss.pgh.pa.us	9 years ago
Bruce Momjian	a6fd7b7a5f	Post-PG 10 beta1 pgindent run ... perltidy run not included.	9 years ago
Tom Lane	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
Robert Haas	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
Kevin Grittner	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
Andrew Gierth	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
Robert Haas	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
Robert Haas	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
Robert Haas	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
Alvaro Herrera	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
Robert Haas	5262f7a4fc	Add optimizer and executor support for parallel index scans. ... In combination with 569174f1be, which taught the btree AM how to perform parallel index scans, this allows parallel index scan plans on btree indexes. This infrastructure should be general enough to support parallel index scans for other index AMs as well, if someone updates them to support parallel scans. Amit Kapila, reviewed and tested by Anastasia Lubennikova, Tushar Ahuja, and Haribabu Kommi, and me.	9 years ago