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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 |
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| .. | ||
| adminpack | ||
| amcheck | ||
| auth_delay | ||
| auto_explain | ||
| bloom | ||
| bool_plperl | ||
| btree_gin | ||
| btree_gist | ||
| citext | ||
| cube | ||
| dblink | ||
| dict_int | ||
| dict_xsyn | ||
| earthdistance | ||
| file_fdw | ||
| fuzzystrmatch | ||
| hstore | ||
| hstore_plperl | ||
| hstore_plpython | ||
| intagg | ||
| intarray | ||
| isn | ||
| jsonb_plperl | ||
| jsonb_plpython | ||
| lo | ||
| ltree | ||
| ltree_plpython | ||
| oid2name | ||
| old_snapshot | ||
| pageinspect | ||
| passwordcheck | ||
| pg_buffercache | ||
| pg_freespacemap | ||
| pg_prewarm | ||
| pg_stat_statements | ||
| pg_surgery | ||
| pg_trgm | ||
| pg_visibility | ||
| pgcrypto | ||
| pgrowlocks | ||
| pgstattuple | ||
| postgres_fdw | ||
| seg | ||
| sepgsql | ||
| spi | ||
| sslinfo | ||
| start-scripts | ||
| tablefunc | ||
| tcn | ||
| test_decoding | ||
| tsm_system_rows | ||
| tsm_system_time | ||
| unaccent | ||
| uuid-ossp | ||
| vacuumlo | ||
| xml2 | ||
| contrib-global.mk | ||
| Makefile | ||
| README | ||
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.