Truncating off the end of a freshly copied List is not a very efficient
way of copying the first N elements of a List.
In many of the cases that are updated here, the pattern was only being
used to remove the final element of a List. That's about the best case
for it, but there were many instances where the truncate trimming the List
down much further.
4cc832f94 added list_copy_head(), so let's use it in cases where it's
useful.
Author: David Rowley
Discussion: https://postgr.es/m/1986787.1657666922%40sss.pgh.pa.us
9d9c02ccd added code to allow the executor to take shortcuts when quals
on monotonic window functions guaranteed that once the qual became false
it could never become true again. When possible, baserestrictinfo quals
are converted to become these quals, which we call run conditions.
Unfortunately, in 9d9c02ccd, I forgot to update
remove_unused_subquery_outputs to teach it about these run conditions.
This could cause a WindowFunc column which was unused in the target list
but referenced by an upper-level WHERE clause to be removed from the
subquery when the qual in the WHERE clause was converted into a window run
condition. Because of this, the entire WindowClause would be removed from
the query resulting in additional rows making it into the resultset when
they should have been filtered out by the WHERE clause.
Here we fix this by recording which target list items in the subquery have
run conditions. That gets passed along to remove_unused_subquery_outputs
to tell it not to remove these items from the target list.
Bug: #17495
Reported-by: Jeremy Evans
Reviewed-by: Richard Guo
Discussion: https://postgr.es/m/17495-7ffe2fa0b261b9fa@postgresql.org
inline_cte() expected to find exactly as many references to the
target CTE as its cterefcount indicates. While that should be
accurate for the tree as emitted by the parser, there are some
optimizations that occur upstream of here that could falsify it,
notably removal of unused subquery output expressions.
Trying to make the accounting 100% accurate seems expensive and
doomed to future breakage. It's not really worth it, because
all this code is protecting is downstream assumptions that every
referenced CTE has a plan. Let's convert those assertions to
regular test-and-elog just in case there's some actual problem,
and then drop the failing assertion.
Per report from Tomas Vondra (thanks also to Richard Guo for
analysis). Back-patch to v12 where the faulty code came in.
Discussion: https://postgr.es/m/29196a1e-ed47-c7ca-9be2-b1c636816183@enterprisedb.com
Window functions such as row_number() always return a value higher than
the previously returned value for tuples in any given window partition.
Traditionally queries such as;
SELECT * FROM (
SELECT *, row_number() over (order by c) rn
FROM t
) t WHERE rn <= 10;
were executed fairly inefficiently. Neither the query planner nor the
executor knew that once rn made it to 11 that nothing further would match
the outer query's WHERE clause. It would blindly continue until all
tuples were exhausted from the subquery.
Here we implement means to make the above execute more efficiently.
This is done by way of adding a pg_proc.prosupport function to various of
the built-in window functions and adding supporting code to allow the
support function to inform the planner if the window function is
monotonically increasing, monotonically decreasing, both or neither. The
planner is then able to make use of that information and possibly allow
the executor to short-circuit execution by way of adding a "run condition"
to the WindowAgg to allow it to determine if some of its execution work
can be skipped.
This "run condition" is not like a normal filter. These run conditions
are only built using quals comparing values to monotonic window functions.
For monotonic increasing functions, quals making use of the btree
operators for <, <= and = can be used (assuming the window function column
is on the left). You can see here that once such a condition becomes false
that a monotonic increasing function could never make it subsequently true
again. For monotonically decreasing functions the >, >= and = btree
operators for the given type can be used for run conditions.
The best-case situation for this is when there is a single WindowAgg node
without a PARTITION BY clause. Here when the run condition becomes false
the WindowAgg node can simply return NULL. No more tuples will ever match
the run condition. It's a little more complex when there is a PARTITION
BY clause. In this case, we cannot return NULL as we must still process
other partitions. To speed this case up we pull tuples from the outer
plan to check if they're from the same partition and simply discard them
if they are. When we find a tuple belonging to another partition we start
processing as normal again until the run condition becomes false or we run
out of tuples to process.
When there are multiple WindowAgg nodes to evaluate then this complicates
the situation. For intermediate WindowAggs we must ensure we always
return all tuples to the calling node. Any filtering done could lead to
incorrect results in WindowAgg nodes above. For all intermediate nodes,
we can still save some work when the run condition becomes false. We've
no need to evaluate the WindowFuncs anymore. Other WindowAgg nodes cannot
reference the value of these and these tuples will not appear in the final
result anyway. The savings here are small in comparison to what can be
saved in the top-level WingowAgg, but still worthwhile.
Intermediate WindowAgg nodes never filter out tuples, but here we change
WindowAgg so that the top-level WindowAgg filters out tuples that don't
match the intermediate WindowAgg node's run condition. Such filters
appear in the "Filter" clause in EXPLAIN for the top-level WindowAgg node.
Here we add prosupport functions to allow the above to work for;
row_number(), rank(), dense_rank(), count(*) and count(expr). It appears
technically possible to do the same for min() and max(), however, it seems
unlikely to be useful enough, so that's not done here.
Bump catversion
Author: David Rowley
Reviewed-by: Andy Fan, Zhihong Yu
Discussion: https://postgr.es/m/CAApHDvqvp3At8++yF8ij06sdcoo1S_b2YoaT9D4Nf+MObzsrLQ@mail.gmail.com
When building append paths, we've been looking only at startup and total
costs for the paths. When building fractional paths that may eliminate
the cheapest one, because it may be dominated by two separate paths (one
for startup, one for total cost).
This extends generate_orderedappend_paths() to also consider which paths
have lowest fractional cost. Currently we only consider paths matching
pathkeys - in the future this may be improved by also considering paths
that are only partially sorted, with an incremental sort on top.
Original report of an issue by Arne Roland, patch by me (based on a
suggestion by Tom Lane).
Reviewed-by: Arne Roland, Zhihong Yu
Discussion: https://postgr.es/m/e8f9ec90-546d-e948-acce-0525f3e92773%40enterprisedb.com
Discussion: https://postgr.es/m/1581042da8044e71ada2d6e3a51bf7bb%40index.de
959d00e9d added the ability to make use of an Append node instead of a
MergeAppend when we wanted to perform a scan of a partitioned table and
the required sort order was the same as the partitioned keys and the
partitioned table was defined in such a way that earlier partitions were
guaranteed to only contain lower-order values than later partitions.
However, previously we didn't allow these ordered partition scans for
LIST partitioned table when there were any partitions that allowed
multiple Datums. This was a very cheap check to make and we could likely
have done a little better by checking if there were interleaved
partitions, but at the time we didn't have visibility about which
partitions were pruned, so we still may have disallowed cases where all
interleaved partitions were pruned.
Since 475dbd0b7, we now have knowledge of pruned partitions, we can do a
much better job inside partitions_are_ordered().
Here we pass which partitions survived partition pruning into
partitions_are_ordered() and, for LIST partitioning, have it check to see
if any live partitions exist that are also in the new "interleaved_parts"
field defined in PartitionBoundInfo.
For RANGE partitioning we can relax the code which caused the partitions
to be unordered if a DEFAULT partition existed. Since we now know which
partitions were pruned, partitions_are_ordered() now returns true when the
DEFAULT partition was pruned.
Reviewed-by: Amit Langote, Zhihong Yu
Discussion: https://postgr.es/m/CAApHDvrdoN_sXU52i=QDXe2k3WAo=EVry29r2+Tq2WYcn2xhEA@mail.gmail.com
"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
I didn't particularly like this function name, as it fails to
express what's going on. Also, returning the sort expression
alone isn't too helpful --- typically, a caller would also
need some other fields of the EquivalenceMember. But the
sole caller really only needs a bool result, so let's make
it "bool relation_can_be_sorted_early()".
Discussion: https://postgr.es/m/91f3ec99-85a4-fa55-ea74-33f85a5c651f@swarm64.com
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
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
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
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
Here we aim to reduce duplicate work done by contain_volatile_functions()
by caching whether PathTargets and RestrictInfos contain any volatile
functions the first time contain_volatile_functions() is called for them.
Any future calls for these nodes just use the cached value rather than
going to the trouble of recursively checking the sub-node all over again.
Thanks to Tom Lane for the idea.
Any locations in the code which make changes to a PathTarget or
RestrictInfo which could change the outcome of the volatility check must
change the cached value back to VOLATILITY_UNKNOWN again.
contain_volatile_functions() is the only code in charge of setting the
cache value to either VOLATILITY_VOLATILE or VOLATILITY_NOVOLATILE.
Some existing code does benefit from this additional caching, however,
this change is mainly aimed at an upcoming patch that must check for
volatility during the join search. Repeated volatility checks in that
case can become very expensive when the join search contains more than a
few relations.
Author: David Rowley
Discussion: https://postgr.es/m/3795226.1614059027@sss.pgh.pa.us
d2d8a229bc introduced a new function generate_useful_gather_paths to
be used as a replacement for generate_gather_paths, but forgot to update
a couple of places that referenced the older function.
This is possibly not 100% complete (ref. create_ordered_paths), but it's
better than not changing anything.
Author: "Hou, Zhijie" <houzj.fnst@cn.fujitsu.com>
Reviewed-by: Tomas Vondra <tomas.vondra@enterprisedb.com>
Discussion: https://postgr.es/m/4ce1d5116fe746a699a6d29858c6a39a@G08CNEXMBPEKD05.g08.fujitsu.local
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
Commit ebb7ae839d ensured we ignore pathkeys with volatile expressions
when considering adding a sort below a Gather Merge. Turns out we need
to care about parallel safety of the pathkeys too, otherwise we might
try sorting e.g. on results of a correlated subquery (as demonstrated
by a report from Luis Roberto).
Initial investigation by Tom Lane, patch by James Coleman. Backpatch
to 13, where the code was instroduced (as part of Incremental Sort).
Reported-by: Luis Roberto
Author: James Coleman
Reviewed-by: Tomas Vondra
Backpatch-through: 13
Discussion: https://postgr.es/m/622580997.37108180.1604080457319.JavaMail.zimbra%40siscobra.com.br
Discussion: https://postgr.es/m/CAAaqYe8cK3g5CfLC4w7bs=hC0mSksZC=H5M8LSchj5e5OxpTAg@mail.gmail.com
generate_useful_gather_paths used to skip unsorted paths (without any
pathkeys), but that is unnecessary - the later code actually can handle
such paths just fine by adding a Sort node. This is clearly a thinko,
preventing construction of useful plans.
Backpatch to 13, where Incremental Sort was introduced.
Author: James Coleman
Reviewed-by: Tomas Vondra
Backpatch-through: 13
Discussion: https://postgr.es/m/CAAaqYe8cK3g5CfLC4w7bs=hC0mSksZC=H5M8LSchj5e5OxpTAg@mail.gmail.com
When considering Incremental Sort below a Gather Merge, we need to be
a bit more careful when matching pathkeys to EC members. It's not enough
to find a member whose Vars are all in the current relation's target;
volatile expressions in particular need to be contained in the target,
otherwise it's too early to use the pathkey.
Reported-by: Jaime Casanova
Author: James Coleman
Reviewed-by: Tomas Vondra
Backpatch-through: 13, where the incremental sort code was added
Discussion: https://postgr.es/m/CAJGNTeNaxpXgBVcRhJX%2B2vSbq%2BF2kJqGBcvompmpvXb7pq%2BoFA%40mail.gmail.com
Previously we only tagged on the required information to allow the
executor to perform run-time partition pruning for Append/MergeAppend
nodes belonging to base relations. It was thought that nested
Append/MergeAppend nodes were just about always pulled up into the
top-level Append/MergeAppend and that making the run-time pruning info for
any sub Append/MergeAppend nodes was a waste of time. However, that was
likely badly thought through.
Some examples of cases we're unable to pullup nested Append/MergeAppends
are: 1) Parallel Append nodes with a mix of parallel and non-parallel
paths into a Parallel Append. 2) When planning an ordered Append scan a
sub-partition which is unordered may require a nested MergeAppend path to
ensure sub-partitions don't mix up the order of tuples being fed into the
top-level Append.
Unfortunately, it was not just as simple as removing the lines in
createplan.c which were purposefully not building the run-time pruning
info for anything but RELOPT_BASEREL relations. The code in
add_paths_to_append_rel() was far too sloppy about which partitioned_rels
it included for the Append/MergeAppend paths. The original code there
would always assume accumulate_append_subpath() would pull each sub-Append
and sub-MergeAppend path into the top-level path. While it does not
appear that there were any actual bugs caused by having the additional
partitioned table RT indexes recorded, what it did mean is that later in
planning, when we built the run-time pruning info that we wasted effort
and built PartitionedRelPruneInfos for partitioned tables that we had no
subpaths for the executor to run-time prune.
Here we tighten that up so that partitioned_rels only ever contains the RT
index for partitioned tables which actually have subpaths in the given
Append/MergeAppend. We can now Assert that every PartitionedRelPruneInfo
has a non-empty present_parts. That should allow us to catch any weird
corner cases that have been missed.
In passing, it seems there is no longer a good reason to have the
AppendPath and MergeAppendPath's partitioned_rel fields a List of IntList.
We can simply have a List of Relids instead. This is more compact in
memory and faster to add new members to. We still know which is the root
level partition as these always have a lower relid than their children.
Previously this field was used for more things, but run-time partition
pruning now remains the only user of it and it has no need for a List of
IntLists.
Here we also get rid of the RelOptInfo partitioned_child_rels field. This
is what was previously used to (sometimes incorrectly) set the
Append/MergeAppend path's partitioned_rels field. That was the only usage
of that field, so we can happily just remove it.
I also couldn't resist changing some nearby code to make use of the newly
added for_each_from macro so we can skip the first element in the list
without checking if the current item was the first one on each
iteration.
A bug report from Andreas Kretschmer prompted all this work, however,
after some consideration, I'm not personally classing this as a bug fix.
So no backpatch. In Andreas' test case, it just wasn't that clear that
there was a nested Append since the top-level Append just had a single
sub-path which was pulled up a level, per 8edd0e794.
Author: David Rowley
Reviewed-by: Amit Langote
Discussion: https://postgr.es/m/flat/CAApHDvqSchs%2BubdybcfFaSPB%2B%2BEA7kqMaoqajtP0GtZvzOOR3g%40mail.gmail.com
Historically, we've considered the state with relpages and reltuples
both zero as indicating that we do not know the table's tuple density.
This is problematic because it's impossible to distinguish "never yet
vacuumed" from "vacuumed and seen to be empty". In particular, a user
cannot use VACUUM or ANALYZE to override the planner's normal heuristic
that an empty table should not be believed to be empty because it is
probably about to get populated. That heuristic is a good safety
measure, so I don't care to abandon it, but there should be a way to
override it if the table is indeed intended to stay empty.
Hence, represent the initial state of ignorance by setting reltuples
to -1 (relpages is still set to zero), and apply the minimum-ten-pages
heuristic only when reltuples is still -1. If the table is empty,
VACUUM or ANALYZE (but not CREATE INDEX) will override that to
reltuples = relpages = 0, and then we'll plan on that basis.
This requires a bunch of fiddly little changes, but we can get rid of
some ugly kluges that were formerly needed to maintain the old definition.
One notable point is that FDWs' GetForeignRelSize methods will see
baserel->tuples = -1 when no ANALYZE has been done on the foreign table.
That seems like a net improvement, since those methods were formerly
also in the dark about what baserel->tuples = 0 really meant. Still,
it is an API change.
I bumped catversion because code predating this change would get confused
by seeing reltuples = -1.
Discussion: https://postgr.es/m/F02298E0-6EF4-49A1-BCB6-C484794D9ACC@thebuild.com
The trouble with doing this is that an apparently-constant subquery
output column isn't really constant if it is a grouping column that
appears in only some of the grouping sets. A qual using such a
column would be subject to incorrect const-folding after push-down,
as seen in bug #16585 from Paul Sivash.
To fix, just disable qual pushdown altogether if the sub-query has
nonempty groupingSets. While we could imagine far less restrictive
solutions, there is not much point in working harder right now,
because subquery_planner() won't move HAVING clauses to WHERE within
such a subquery. If the qual stays in HAVING it's not going to be
a lot more useful than if we'd kept it at the outer level.
Having said that, this restriction could be removed if we used a
parsetree representation that distinguished such outputs from actual
constants, which is something I hope to do in future. Hence, make
the patch a minimal addition rather than integrating it more tightly
(e.g. by renumbering the existing items in subquery_is_pushdown_safe's
comment).
Back-patch to 9.5 where grouping sets were introduced.
Discussion: https://postgr.es/m/16585-9d8c340d23ade8c1@postgresql.org
The qual pushdown logic assumed that all Vars in a restriction clause
must be Vars referencing subquery outputs; but since we introduced
LATERAL, it's possible for such a Var to be a lateral reference instead.
This led to an assertion failure in debug builds. In a non-debug
build, there might be no ill effects (if qual_is_pushdown_safe decided
the qual was unsafe anyway), or we could get failures later due to
construction of an invalid plan. I've not gone to much length to
characterize the possible failures, but at least segfaults in the
executor have been observed.
Given that this has been busted since 9.3 and it took this long for
anybody to notice, I judge that the case isn't worth going to great
lengths to optimize. Hence, fix by just teaching qual_is_pushdown_safe
that such quals are unsafe to push down, matching the previous behavior
when it accidentally didn't fail.
Per report from Tom Ellis. Back-patch to all supported branches.
Discussion: https://postgr.es/m/20200713175124.GQ8220@cloudinit-builder
After running GetForeignRelSize for a foreign table, adjust rel->tuples
to be at least as large as rel->rows. This prevents bizarre behavior
in estimate_num_groups() and perhaps other places, especially in the
scenario where rel->tuples is zero because pg_class.reltuples is
(suggesting that ANALYZE has never been run for the table). As things
stood, we'd end up estimating one group out of any GROUP BY on such a
table, whereas the default group-count estimate is more likely to result
in a sane plan.
Also, clarify in the documentation that GetForeignRelSize has the option
to override the rel->tuples value if it has a better idea of what to use
than what is in pg_class.reltuples.
Per report from Jeff Janes. Back-patch to all supported branches.
Patch by me; thanks to Etsuro Fujita for review
Discussion: https://postgr.es/m/CAMkU=1xNo9cnan+Npxgz0eK7394xmjmKg-QEm8wYG9P5-CcaqQ@mail.gmail.com
Includes some manual cleanup of places that pgindent messed up,
most of which weren't per project style anyway.
Notably, it seems some people didn't absorb the style rules of
commit c9d297751, because there were a bunch of new occurrences
of function calls with a newline just after the left paren, all
with faulty expectations about how the rest of the call would get
indented.
Commit d2d8a229bc introduced Incremental Sort, but it was considered
only in create_ordered_paths() as an alternative to regular Sort. There
are many other places that require sorted input and might benefit from
considering Incremental Sort too.
This patch modifies a number of those places, but not all. The concern
is that just adding Incremental Sort to any place that already adds
Sort may increase the number of paths considered, negatively affecting
planning time, without any benefit. So we've taken a more conservative
approach, based on analysis of which places do affect a set of queries
that did seem practical. This means some less common queries may not
benefit from Incremental Sort yet.
Author: Tomas Vondra
Reviewed-by: James Coleman
Discussion: https://postgr.es/m/CAPpHfds1waRZ=NOmueYq0sx1ZSCnt+5QJvizT8ndT2=etZEeAQ@mail.gmail.com
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
In the wake of commit 1cff1b95a, the result of list_concat no longer
shares the ListCells of the second input. Therefore, we can replace
"list_concat(x, list_copy(y))" with just "list_concat(x, y)".
To improve call sites that were list_copy'ing the first argument,
or both arguments, invent "list_concat_copy()" which produces a new
list sharing no ListCells with either input. (This is a bit faster
than "list_concat(list_copy(x), y)" because it makes the result list
the right size to start with.)
In call sites that were not list_copy'ing the second argument, the new
semantics mean that we are usually leaking the second List's storage,
since typically there is no remaining pointer to it. We considered
inventing another list_copy variant that would list_free the second
input, but concluded that for most call sites it isn't worth worrying
about, given the relative compactness of the new List representation.
(Note that in cases where such leakage would happen, the old code
already leaked the second List's header; so we're only discussing
the size of the leak not whether there is one. I did adjust two or
three places that had been troubling to free that header so that
they manually free the whole second List.)
Patch by me; thanks to David Rowley for review.
Discussion: https://postgr.es/m/11587.1550975080@sss.pgh.pa.us
Originally, Postgres Lists were a more or less exact reimplementation of
Lisp lists, which consist of chains of separately-allocated cons cells,
each having a value and a next-cell link. We'd hacked that once before
(commit d0b4399d8) to add a separate List header, but the data was still
in cons cells. That makes some operations -- notably list_nth() -- O(N),
and it's bulky because of the next-cell pointers and per-cell palloc
overhead, and it's very cache-unfriendly if the cons cells end up
scattered around rather than being adjacent.
In this rewrite, we still have List headers, but the data is in a
resizable array of values, with no next-cell links. Now we need at
most two palloc's per List, and often only one, since we can allocate
some values in the same palloc call as the List header. (Of course,
extending an existing List may require repalloc's to enlarge the array.
But this involves just O(log N) allocations not O(N).)
Of course this is not without downsides. The key difficulty is that
addition or deletion of a list entry may now cause other entries to
move, which it did not before.
For example, that breaks foreach() and sister macros, which historically
used a pointer to the current cons-cell as loop state. We can repair
those macros transparently by making their actual loop state be an
integer list index; the exposed "ListCell *" pointer is no longer state
carried across loop iterations, but is just a derived value. (In
practice, modern compilers can optimize things back to having just one
loop state value, at least for simple cases with inline loop bodies.)
In principle, this is a semantics change for cases where the loop body
inserts or deletes list entries ahead of the current loop index; but
I found no such cases in the Postgres code.
The change is not at all transparent for code that doesn't use foreach()
but chases lists "by hand" using lnext(). The largest share of such
code in the backend is in loops that were maintaining "prev" and "next"
variables in addition to the current-cell pointer, in order to delete
list cells efficiently using list_delete_cell(). However, we no longer
need a previous-cell pointer to delete a list cell efficiently. Keeping
a next-cell pointer doesn't work, as explained above, but we can improve
matters by changing such code to use a regular foreach() loop and then
using the new macro foreach_delete_current() to delete the current cell.
(This macro knows how to update the associated foreach loop's state so
that no cells will be missed in the traversal.)
There remains a nontrivial risk of code assuming that a ListCell *
pointer will remain good over an operation that could now move the list
contents. To help catch such errors, list.c can be compiled with a new
define symbol DEBUG_LIST_MEMORY_USAGE that forcibly moves list contents
whenever that could possibly happen. This makes list operations
significantly more expensive so it's not normally turned on (though it
is on by default if USE_VALGRIND is on).
There are two notable API differences from the previous code:
* lnext() now requires the List's header pointer in addition to the
current cell's address.
* list_delete_cell() no longer requires a previous-cell argument.
These changes are somewhat unfortunate, but on the other hand code using
either function needs inspection to see if it is assuming anything
it shouldn't, so it's not all bad.
Programmers should be aware of these significant performance changes:
* list_nth() and related functions are now O(1); so there's no
major access-speed difference between a list and an array.
* Inserting or deleting a list element now takes time proportional to
the distance to the end of the list, due to moving the array elements.
(However, it typically *doesn't* require palloc or pfree, so except in
long lists it's probably still faster than before.) Notably, lcons()
used to be about the same cost as lappend(), but that's no longer true
if the list is long. Code that uses lcons() and list_delete_first()
to maintain a stack might usefully be rewritten to push and pop at the
end of the list rather than the beginning.
* There are now list_insert_nth...() and list_delete_nth...() functions
that add or remove a list cell identified by index. These have the
data-movement penalty explained above, but there's no search penalty.
* list_concat() and variants now copy the second list's data into
storage belonging to the first list, so there is no longer any
sharing of cells between the input lists. The second argument is
now declared "const List *" to reflect that it isn't changed.
This patch just does the minimum needed to get the new implementation
in place and fix bugs exposed by the regression tests. As suggested
by the foregoing, there's a fair amount of followup work remaining to
do.
Also, the ENABLE_LIST_COMPAT macros are finally removed in this
commit. Code using those should have been gone a dozen years ago.
Patch by me; thanks to David Rowley, Jesper Pedersen, and others
for review.
Discussion: https://postgr.es/m/11587.1550975080@sss.pgh.pa.us
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
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
While trying to plan a partitionwise join, we may be faced with cases
where one or both input partitions for a particular segment of the join
have been pruned away. In HEAD and v11, this is problematic because
earlier processing didn't bother to make a pruned RelOptInfo fully
valid. With an upcoming patch to make partition pruning more efficient,
this'll be even more problematic because said RelOptInfo won't exist at
all.
The existing code attempts to deal with this by retroactively making the
RelOptInfo fully valid, but that causes crashes under GEQO because join
planning is done in a short-lived memory context. In v11 we could
probably have fixed this by switching to the planner's main context
while fixing up the RelOptInfo, but that idea doesn't scale well to the
upcoming patch. It would be better not to mess with the base-relation
data structures during join planning, anyway --- that's just a recipe
for order-of-operations bugs.
In many cases, though, we don't actually need the child RelOptInfo,
because if the input is certainly empty then the join segment's result
is certainly empty, so we can skip making a join plan altogether. (The
existing code ultimately arrives at the same conclusion, but only after
doing a lot more work.) This approach works except when the pruned-away
partition is on the nullable side of a LEFT, ANTI, or FULL join, and the
other side isn't pruned. But in those cases the existing code leaves a
lot to be desired anyway --- the correct output is just the result of
the unpruned side of the join, but we were emitting a useless outer join
against a dummy Result. Pending somebody writing code to handle that
more nicely, let's just abandon the partitionwise-join optimization in
such cases.
When the modified code skips making a join plan, it doesn't make a
join RelOptInfo either; this requires some upper-level code to
cope with nulls in part_rels[] arrays. We would have had to have
that anyway after the upcoming patch.
Back-patch to v11 since the crash is demonstrable there.
Discussion: https://postgr.es/m/8305.1553884377@sss.pgh.pa.us
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
If there's only one child relation, the Append or MergeAppend isn't
doing anything useful, and can be elided. It does have a purpose
during planning though, which is to serve as a buffer between parent
and child Var numbering. Therefore we keep it all the way through
to setrefs.c, and get rid of it only after fixing references in the
plan level(s) above it. This works largely the same as setrefs.c's
ancient hack to get rid of no-op SubqueryScan nodes, and can even
share some code with that.
Note the change to make setrefs.c use apply_tlist_labeling rather than
ad-hoc code. This has the effect of propagating the child's resjunk
and ressortgroupref labels, which formerly weren't propagated when
removing a SubqueryScan. Doing that is demonstrably necessary for
the [Merge]Append cases, and seems harmless for SubqueryScan, if only
because trivial_subqueryscan is afraid to collapse cases where the
resjunk marking differs. (I suspect that restriction could now be
removed, though it's unclear that it'd make any new matches possible,
since the outer query can't have references to a child resjunk column.)
David Rowley, reviewed by Alvaro Herrera and Tomas Vondra
Discussion: https://postgr.es/m/CAKJS1f_7u8ATyJ1JGTMHFoKDvZdeF-iEBhs+sM_SXowOr9cArg@mail.gmail.com
The assertions added by commits 34ea1ab7f et al found another problem:
set_dummy_rel_pathlist and mark_dummy_rel were failing to label
the dummy paths they create with the correct outer_relids, in case
the relation is necessarily parameterized due to having lateral
references in its tlist. It's likely that this has no user-visible
consequences in production builds, at the moment; but still an assertion
failure is a bad thing, so back-patch the fix.
Per bug #15694 from Roman Zharkov (via Alexander Lakhin)
and an independent report by Tushar Ahuja.
Discussion: https://postgr.es/m/15694-74f2ca97e7044f7f@postgresql.org
Discussion: https://postgr.es/m/7d72ab20-c725-3ce2-f99d-4e64dd8a0de6@enterprisedb.com
When we introduced separate ProjectSetPath nodes for application of
set-returning functions in v10, we inadvertently broke some cases where
we're supposed to recognize that the result of a subquery is known to be
empty (contain zero rows). That's because IS_DUMMY_REL was just looking
for a childless AppendPath without allowing for a ProjectSetPath being
possibly stuck on top. In itself, this didn't do anything much worse
than produce slightly worse plans for some corner cases.
Then in v11, commit 11cf92f6e rearranged things to allow the scan/join
targetlist to be applied directly to partial paths before they get
gathered. But it inserted a short-circuit path for dummy relations
that was a little too short: it failed to insert a ProjectSetPath node
at all for a targetlist containing set-returning functions, resulting in
bogus "set-valued function called in context that cannot accept a set"
errors, as reported in bug #15669 from Madelaine Thibaut.
The best way to fix this mess seems to be to reimplement IS_DUMMY_REL
so that it drills down through any ProjectSetPath nodes that might be
there (and it seems like we'd better allow for ProjectionPath as well).
While we're at it, make it look at rel->pathlist not cheapest_total_path,
so that it gives the right answer independently of whether set_cheapest
has been done lately. That dependency looks pretty shaky in the context
of code like apply_scanjoin_target_to_paths, and even if it's not broken
today it'd certainly bite us at some point. (Nastily, unsafe use of the
old coding would almost always work; the hazard comes down to possibly
looking through a dangling pointer, and only once in a blue moon would
you find something there that resulted in the wrong answer.)
It now looks like it was a mistake for IS_DUMMY_REL to be a macro: if
there are any extensions using it, they'll continue to use the old
inadequate logic until they're recompiled, after which they'll fail
to load into server versions predating this fix. Hopefully there are
few such extensions.
Having fixed IS_DUMMY_REL, the special path for dummy rels in
apply_scanjoin_target_to_paths is unnecessary as well as being wrong,
so we can just drop it.
Also change a few places that were testing for partitioned-ness of a
planner relation but not using IS_PARTITIONED_REL for the purpose; that
seems unsafe as well as inconsistent, plus it required an ugly hack in
apply_scanjoin_target_to_paths.
In passing, save a few cycles in apply_scanjoin_target_to_paths by
skipping processing of pre-existing paths for partitioned rels,
and do some cosmetic cleanup and comment adjustment in that function.
I renamed IS_DUMMY_PATH to IS_DUMMY_APPEND with the intention of breaking
any code that might be using it, since in almost every case that would
be wrong; IS_DUMMY_REL is what to be using instead.
In HEAD, also make set_dummy_rel_pathlist static (since it's no longer
used from outside allpaths.c), and delete is_dummy_plan, since it's no
longer used anywhere.
Back-patch as appropriate into v11 and v10.
Tom Lane and Julien Rouhaud
Discussion: https://postgr.es/m/15669-02fb3296cca26203@postgresql.org
The previous ordering of these steps satisfied the nominal requirement
that set_rel_pathlist_hook could editorialize on the whole set of Paths
constructed for a base relation. In practice, though, trying to change
the set of partial paths was impossible. Adding one didn't work because
(a) it was too late to be included in Gather paths made by the core code,
and (b) calling add_partial_path after generate_gather_paths is unsafe,
because it might try to delete a path it thinks is dominated, but that
is already embedded in some Gather path(s). Nor could the hook safely
remove partial paths, for the same reason that they might already be
embedded in Gathers.
Better to call extensions first, let them add partial paths as desired,
and then gather. In v11 and up, we already doubled down on that ordering
by postponing gathering even further for single-relation queries; so even
if the hook wished to editorialize on Gather path construction, it could
not.
Report and patch by KaiGai Kohei. Back-patch to 9.6 where Gather paths
were added.
Discussion: https://postgr.es/m/CAOP8fzahwpKJRTVVTqo2AE=mDTz_efVzV6Get_0=U3SO+-ha1A@mail.gmail.com
An upcoming patch which changes how inheritance planning works requires
adding a new function that does a similar job to set_append_rel_size() but
for child target relations. To save it from having to duplicate the qual
building code, move that to a separate function first.
Here we also change things so that we never attempt to build security quals
after detecting some const false child quals. We needlessly used to do this
just before we marked the child relation as a dummy rel.
In passing, this also moves the partition pruned check to before the qual
building code. We don't need to build the child quals before we check if
the partition has been pruned.
Author: David Rowley
Discussion: https://postgr.es/m/CAKJS1f_i+jrrD+if8qC7KPuTAAWsd=dtepgY_7u=P86GDEwm7A@mail.gmail.com
Create a new header optimizer/optimizer.h, which exposes just the
planner functions that can be used "at arm's length", without need
to access Paths or the other planner-internal data structures defined
in nodes/relation.h. This is intended to provide the whole planner
API seen by most of the rest of the system; although FDWs still need
to use additional stuff, and more thought is also needed about just
what selfuncs.c should rely on.
The main point of doing this now is to limit the amount of new
#include baggage that will be needed by "planner support functions",
which I expect to introduce later, and which will be in relevant
datatype modules rather than anywhere near the planner.
This commit just moves relevant declarations into optimizer.h from
other header files (a couple of which go away because everything
got moved), and adjusts #include lists to match. There's further
cleanup that could be done if we want to decide that some stuff
being exposed by optimizer.h doesn't belong in the planner at all,
but I'll leave that for another day.
Discussion: https://postgr.es/m/11460.1548706639@sss.pgh.pa.us
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
Previously, in set_append_rel_size(), we generated tlists and EC members
for dummy children for possible use by partition-wise join, even if
partition-wise join was disabled or the top parent was not a partitioned
table, but adding such EC members causes noticeable planning speed
degradation for queries with certain kinds of join quals like
"(foo.x + bar.y) = constant" where foo and bar are partitioned tables in
cases where there are lots of dummy children, as the EC members lists
grow huge, especially for the ECs derived from such join quals, which
makes the search for the parent EC members in add_child_rel_equivalences()
very time-consuming. Postpone the work until such children are actually
involved in a partition-wise join.
Reported-by: Sanyo Capobiango
Analyzed-by: Justin Pryzby and Alvaro Herrera
Author: Amit Langote, with a few additional changes by me
Reviewed-by: Ashutosh Bapat
Backpatch-through: v11 where partition-wise join was added
Discussion: https://postgr.es/m/CAO698qZnrxoZu7MEtfiJmpmUtz3AVYFVnwzR%2BpqjF%3DrmKBTgpw%40mail.gmail.com
David Rowley
Tom Lane
Tomas Vondra
Bruce Momjian
Peter Eisentraut
Alvaro Herrera
Michael Paquier
Amit Kapila
Etsuro Fujita