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When planning queries to partitioned tables, we clone all EquivalenceMembers belonging to the partitioned table into em_is_child EquivalenceMembers for each non-pruned partition. For partitioned tables with large numbers of partitions, this meant the ec_members list could become large and code searching that list would become slow. Effectively, the more partitions which were present, the more searches needed to be performed for operations such as find_ec_member_matching_expr() during create_plan() and the more partitions present, the longer these searches would take, i.e., a quadratic slowdown. To fix this, here we adjust how we store EquivalenceMembers for em_is_child members. Instead of storing these directly in ec_members, these are now stored in a new array of Lists in the EquivalenceClass, which is indexed by the relid. When we want to find EquivalenceMembers belonging to a certain child relation, we can narrow the search to the array element for that relation. To make EquivalenceMember lookup easier and to reduce the amount of code change, this commit provides a pair of functions to allow iteration over the EquivalenceMembers of an EC which also handles finding the child members, if required. Callers that never need to look at child members can remain using the foreach loop over ec_members, which will now often be faster due to only parent-level members being stored there. The actual performance increases here are highly dependent on the number of partitions and the query being planned. Performance increases can be visible with as few as 8 partitions, but the speedup is marginal for such low numbers of partitions. The speedups become much more visible with a few dozen to hundreds of partitions. With some tested queries using 56 partitions, the planner was around 3x faster than before. For use cases with thousands of partitions, these are likely to become significantly faster. Some testing has shown planner speedups of 60x or more with 8192 partitions. Author: Yuya Watari <watari.yuya@gmail.com> Co-authored-by: David Rowley <dgrowleyml@gmail.com> Reviewed-by: David Rowley <dgrowleyml@gmail.com> Reviewed-by: Tom Lane <tgl@sss.pgh.pa.us> Reviewed-by: Andrey Lepikhov <a.lepikhov@postgrespro.ru> Reviewed-by: Alena Rybakina <lena.ribackina@yandex.ru> Reviewed-by: Dmitry Dolgov <9erthalion6@gmail.com> Reviewed-by: Amit Langote <amitlangote09@gmail.com> Reviewed-by: Ashutosh Bapat <ashutosh.bapat.oss@gmail.com> Tested-by: Thom Brown <thom@linux.com> Tested-by: newtglobal postgresql_contributors <postgresql_contributors@newtglobalcorp.com> Discussion: https://postgr.es/m/CAJ2pMkZNCgoUKSE%2B_5LthD%2BKbXKvq6h2hQN8Esxpxd%2Bcxmgomg%40mail.gmail.com
The PostgreSQL contrib tree
---------------------------
This subtree contains porting tools, analysis utilities, and plug-in
features that are not part of the core PostgreSQL system, mainly
because they address a limited audience or are too experimental to be
part of the main source tree. This does not preclude their
usefulness.
User documentation for each module appears in the main SGML
documentation.
When building from the source distribution, these modules are not
built automatically, unless you build the "world" target. You can
also build and install them all by running "make all" and "make
install" in this directory; or to build and install just one selected
module, do the same in that module's subdirectory.
Some directories supply new user-defined functions, operators, or
types. To make use of one of these modules, after you have installed
the code you need to register the new SQL objects in the database
system by executing a CREATE EXTENSION command. In a fresh database,
you can simply do
CREATE EXTENSION module_name;
See the PostgreSQL documentation for more information about this
procedure.