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Backpatch-through: 13
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Reported-by: Michael Paquier
Discussion: https://postgr.es/m/ZZKTDPxBBMt3C0J9@paquier.xyz
Backpatch-through: 12
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Most of these are recent, and the documentation portions are new as of
v16 so there is no need for a backpatch.
Author: Justin Pryzby
Discussion: https://postgr.es/m/20230208155644.GM1653@telsasoft.com
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These allow to test whether an element is in a list by checking whether
prev/next are NULL. Needed to replace SHMQueueIsDetached() when converting
from SHM_QUEUE to ilist.h style lists.
Reviewed-by: Thomas Munro <thomas.munro@gmail.com>
Discussion: https://postgr.es/m/20221120055930.t6kl3tyivzhlrzu2@awork3.anarazel.de
Discussion: https://postgr.es/m/20200211042229.msv23badgqljrdg2@alap3.anarazel.de
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Const qualifiers ensure that we don't do something stupid in the
function implementation. Additionally they clarify the interface. As
an example:
void
slist_delete(slist_head *head, const slist_node *node)
Here one can instantly tell that node->next is not going to be set to
NULL. Finally, const qualifiers potentially allow the compiler to do
more optimizations. This being said, no benchmarking was done for
this patch.
The functions that return non-const pointers like slist_next_node(),
dclist_next_node() etc. are not affected by the patch intentionally.
Author: Aleksander Alekseev
Reviewed-by: Andres Freund
Discussion: https://postgr.es/m/CAJ7c6TM2%3D08mNKD9aJg8vEY9hd%2BG4L7%2BNvh30UiNT3kShgRgNg%40mail.gmail.com
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While on it, newlines are removed from the end of two elog() strings.
The others are simple grammar mistakes. One comment in pg_upgrade
referred incorrectly to sequences since a7e5457.
Author: Justin Pryzby
Discussion: https://postgr.es/m/20221230231257.GI1153@telsasoft.com
Backpatch-through: 11
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Backpatch-through: 11
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We have various requirements when using a dlist_head to keep track of the
number of items in the list. This, traditionally, has been done by
maintaining a counter variable in the calling code. Here we tidy this up
by adding "dclist", which is very similar to dlist but also keeps track of
the number of items stored in the list.
Callers may use the new dclist_count() function when they need to know how
many items are stored. Obtaining the count is an O(1) operation.
For simplicity reasons, dclist and dlist both use dlist_node as their node
type and dlist_iter/dlist_mutable_iter as their iterator type. dclists
have all of the same functionality as dlists except there is no function
named dclist_delete(). To remove an item from a list dclist_delete_from()
must be used. This requires knowing which dclist the given item is stored
in.
Additionally, here we also convert some dlists where additional code
exists to keep track of the number of items stored and to make these use
dclists instead.
Author: David Rowley
Reviewed-by: Bharath Rupireddy, Aleksander Alekseev
Discussion: https://postgr.es/m/CAApHDvrtVxr+FXEX0VbViCFKDGxA3tWDgw9oFewNXCJMmwLjLg@mail.gmail.com
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Backpatch-through: 10
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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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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
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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
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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Backpatch-through: 9.5
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Backpatch-through: update all files in master, backpatch legal files through 9.4
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Backpatch-through: certain files through 9.4
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Backpatch-through: certain files through 9.3
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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 e3860ffa4dd0dad0dd9eea4be9cc1412373a8c89 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
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Backpatch certain files through 9.1
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So far we have worked around the fact that some very old compilers do
not support 'inline' functions by only using inline functions
conditionally (or not at all). Since such compilers are very rare by
now, we have decided to rely on inline functions from 9.6 onwards.
To avoid breaking these old compilers inline is defined away when not
supported. That'll cause "function x defined but not used" type of
warnings, but since nobody develops on such compilers anymore that's
ok.
This change in policy will allow us to more easily employ inline
functions.
I chose to remove code previously conditional on PG_USE_INLINE as it
seemed confusing to have code dependent on a define that's always
defined.
Blacklisting of compilers, like in c53f73879f, now has to be done
differently. A platform template can define PG_FORCE_DISABLE_INLINE to
force inline to be defined empty.
Discussion: 20150701161447.GB30708@awork2.anarazel.de
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Backpatch certain files through 9.0
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This includes removing tabs after periods in C comments, which was
applied to back branches, so this change should not effect backpatching.
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Update all files in head, and files COPYRIGHT and legal.sgml in all back
branches.
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Previously one had to use slist_delete(), implying an additional scan of
the list, making this infrastructure considerably less efficient than
traditional Lists when deletion of element(s) in a long list is needed.
Modify the slist_foreach_modify() macro to support deleting the current
element in O(1) time, by keeping a "prev" pointer in addition to "cur"
and "next". Although this makes iteration with this macro a bit slower,
no real harm is done, since in any scenario where you're not going to
delete the current list element you might as well just use slist_foreach
instead. Improve the comments about when to use each macro.
Back-patch to 9.3 so that we'll have consistent semantics in all branches
that provide ilist.h. Note this is an ABI break for callers of
slist_foreach_modify().
Andres Freund and Tom Lane
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Fully update git head, and update back branches in ./COPYRIGHT and
legal.sgml files.
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Needed to silence C++ errors, per report from Peter Eisentraut.
Andres Freund
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dlist_delete, dlist_insert_after, dlist_insert_before, slist_insert_after
do not need access to the list header, and indeed insisting on that negates
one of the main advantages of a doubly-linked list.
In consequence, revert addition of "cache_bucket" field to CatCTup.
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Make foreach macros less syntactically dangerous, and fix some typos in
evidently-never-tested ones. Add missing slist_next_node and
slist_head_node functions. Fix broken dlist_check code. Assorted comment
improvements.
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Provide a common implementation of embedded singly-linked and
doubly-linked lists. "Embedded" in the sense that the nodes'
next/previous pointers exist within some larger struct; this design
choice reduces memory allocation overhead.
Most of the implementation uses inlineable functions (where supported),
for performance.
Some existing uses of both types of lists have been converted to the new
code, for demonstration purposes. Other uses can (and probably will) be
converted in the future. Since dllist.c is unused after this conversion,
it has been removed.
Author: Andres Freund
Some tweaks by me
Reviewed by Tom Lane, Peter Geoghegan
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