Commit b663a4136, which allowed FDWs to INSERT rows in bulk, added to
nodeModifyTable.c code to flush pending inserts to the foreign-table
result relation(s) before completing processing of the ModifyTable node,
but the code failed to take into account the case where the INSERT query
has modifying CTEs, leading to incorrect results.
Also, that commit failed to flush pending inserts before firing BEFORE
ROW triggers so that rows are visible to such triggers.
In that commit we scanned through EState's
es_tuple_routing_result_relations or es_opened_result_relations list to
find the foreign-table result relations to which pending inserts are
flushed, but that would be inefficient in some cases. So to fix, 1) add
a List member to EState to record the insert-pending result relations,
and 2) modify nodeModifyTable.c so that it adds the foreign-table result
relation to the list in ExecInsert() if appropriate, and flushes pending
inserts properly using the list where needed.
While here, fix a copy-and-pasteo in a comment in ExecBatchInsert(),
which was added by that commit.
Back-patch to v14 where that commit appeared.
Discussion: https://postgr.es/m/CAPmGK16qutyCmyJJzgQOhfBq%3DNoGDqTB6O0QBZTihrbqre%2BoxA%40mail.gmail.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
MERGE performs actions that modify rows in the target table using a
source table or query. MERGE provides a single SQL statement that can
conditionally INSERT/UPDATE/DELETE rows -- a task that would otherwise
require multiple PL statements. For example,
MERGE INTO target AS t
USING source AS s
ON t.tid = s.sid
WHEN MATCHED AND t.balance > s.delta THEN
UPDATE SET balance = t.balance - s.delta
WHEN MATCHED THEN
DELETE
WHEN NOT MATCHED AND s.delta > 0 THEN
INSERT VALUES (s.sid, s.delta)
WHEN NOT MATCHED THEN
DO NOTHING;
MERGE works with regular tables, partitioned tables and inheritance
hierarchies, including column and row security enforcement, as well as
support for row and statement triggers and transition tables therein.
MERGE is optimized for OLTP and is parameterizable, though also useful
for large scale ETL/ELT. MERGE is not intended to be used in preference
to existing single SQL commands for INSERT, UPDATE or DELETE since there
is some overhead. MERGE can be used from PL/pgSQL.
MERGE does not support targetting updatable views or foreign tables, and
RETURNING clauses are not allowed either. These limitations are likely
fixable with sufficient effort. Rewrite rules are also not supported,
but it's not clear that we'd want to support them.
Author: Pavan Deolasee <pavan.deolasee@gmail.com>
Author: Álvaro Herrera <alvherre@alvh.no-ip.org>
Author: Amit Langote <amitlangote09@gmail.com>
Author: Simon Riggs <simon.riggs@enterprisedb.com>
Reviewed-by: Peter Eisentraut <peter.eisentraut@enterprisedb.com>
Reviewed-by: Andres Freund <andres@anarazel.de> (earlier versions)
Reviewed-by: Peter Geoghegan <pg@bowt.ie> (earlier versions)
Reviewed-by: Robert Haas <robertmhaas@gmail.com> (earlier versions)
Reviewed-by: Japin Li <japinli@hotmail.com>
Reviewed-by: Justin Pryzby <pryzby@telsasoft.com>
Reviewed-by: Tomas Vondra <tomas.vondra@enterprisedb.com>
Reviewed-by: Zhihong Yu <zyu@yugabyte.com>
Discussion: https://postgr.es/m/CANP8+jKitBSrB7oTgT9CY2i1ObfOt36z0XMraQc+Xrz8QB0nXA@mail.gmail.com
Discussion: https://postgr.es/m/CAH2-WzkJdBuxj9PO=2QaO9-3h3xGbQPZ34kJH=HukRekwM-GZg@mail.gmail.com
Discussion: https://postgr.es/m/20201231134736.GA25392@alvherre.pgsql
When an update on a partitioned table referenced in foreign key
constraints causes a row to move from one partition to another,
the fact that the move is implemented as a delete followed by an insert
on the target partition causes the foreign key triggers to have
surprising behavior. For example, a given foreign key's delete trigger
which implements the ON DELETE CASCADE clause of that key will delete
any referencing rows when triggered for that internal DELETE, although
it should not, because the referenced row is simply being moved from one
partition of the referenced root partitioned table into another, not
being deleted from it.
This commit teaches trigger.c to skip queuing such delete trigger events
on the leaf partitions in favor of an UPDATE event fired on the root
target relation. Doing so is sensible because both the old and the new
tuple "logically" belong to the root relation.
The after trigger event queuing interface now allows passing the source
and the target partitions of a particular cross-partition update when
registering the update event for the root partitioned table. Along with
the two ctids of the old and the new tuple, the after trigger event now
also stores the OIDs of those partitions. The tuples fetched from the
source and the target partitions are converted into the root table
format, if necessary, before they are passed to the trigger function.
The implementation currently has a limitation that only the foreign keys
pointing into the query's target relation are considered, not those of
its sub-partitioned partitions. That seems like a reasonable
limitation, because it sounds rare to have distinct foreign keys
pointing to sub-partitioned partitions instead of to the root table.
This misbehavior stems from commit f56f8f8da6af (which added support for
foreign keys to reference partitioned tables) not paying sufficient
attention to commit 2f178441044b (which had introduced cross-partition
updates a year earlier). Even though the former commit goes back to
Postgres 12, we're not backpatching this fix at this time for fear of
destabilizing things too much, and because there are a few ABI breaks in
it that we'd have to work around in older branches. It also depends on
commit f4566345cf40, which had its own share of backpatchability issues
as well.
Author: Amit Langote <amitlangote09@gmail.com>
Reviewed-by: Masahiko Sawada <sawada.mshk@gmail.com>
Reviewed-by: Álvaro Herrera <alvherre@alvh.no-ip.org>
Reported-by: Eduard Català <eduard.catala@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqFvkBCmfwkQX_yBqv2Wz8ugUGiBDxum8=WvVbfU1TXaNg@mail.gmail.com
Discussion: https://postgr.es/m/CAL54xNZsLwEM1XCk5yW9EqaRzsZYHuWsHQkA2L5MOSKXAwviCQ@mail.gmail.com
The SQL standard has been ambiguous about whether null values in
unique constraints should be considered equal or not. Different
implementations have different behaviors. In the SQL:202x draft, this
has been formalized by making this implementation-defined and adding
an option on unique constraint definitions UNIQUE [ NULLS [NOT]
DISTINCT ] to choose a behavior explicitly.
This patch adds this option to PostgreSQL. The default behavior
remains UNIQUE NULLS DISTINCT. Making this happen in the btree code
is pretty easy; most of the patch is just to carry the flag around to
all the places that need it.
The CREATE UNIQUE INDEX syntax extension is not from the standard,
it's my own invention.
I named all the internal flags, catalog columns, etc. in the negative
("nulls not distinct") so that the default PostgreSQL behavior is the
default if the flag is false.
Reviewed-by: Maxim Orlov <orlovmg@gmail.com>
Reviewed-by: Pavel Borisov <pashkin.elfe@gmail.com>
Discussion: https://www.postgresql.org/message-id/flat/84e5ee1b-387e-9a54-c326-9082674bde78@enterprisedb.com
Commit 9dc718bd added a "logically unchanged by UPDATE" hinting
mechanism, which is currently used within nbtree indexes only (see
commit d168b666). This mechanism determined whether or not the incoming
item is a logically unchanged duplicate (a duplicate needed only for
MVCC versioning purposes) once per row updated per non-HOT update. This
approach led to memory leaks which were noticeable with an UPDATE
statement that updated sufficiently many rows, at least on tables that
happen to have an expression index.
On HEAD, fix the issue by adding a cache to the executor's per-index
IndexInfo struct.
Take a different approach on Postgres 14 to avoid an ABI break: simply
pass down the hint to all indexes unconditionally with non-HOT UPDATEs.
This is deemed acceptable because the hint is currently interpreted
within btinsert() as "perform a bottom-up index deletion pass if and
when the only alternative is splitting the leaf page -- prefer to delete
any LP_DEAD-set items first". nbtree must always treat the hint as a
noisy signal about what might work, as a strategy of last resort, with
costs imposed on non-HOT updaters. (The same thing might not be true
within another index AM that applies the hint, which is why the original
behavior is preserved on HEAD.)
Author: Peter Geoghegan <pg@bowt.ie>
Reported-By: Klaudie Willis <Klaudie.Willis@protonmail.com>
Diagnosed-By: Tom Lane <tgl@sss.pgh.pa.us>
Discussion: https://postgr.es/m/261065.1639497535@sss.pgh.pa.us
Backpatch: 14-, where the hinting mechanism was added.
Commit 4ace45677 failed to fix the problem fully, because the
same issue of attempting to fetch a non-returnable index column
can occur when rechecking the indexqual after using a lossy index
operator. Moreover, it broke EXPLAIN for such indexquals (which
indicates a gap in our test cases :-().
Revert the code changes of 4ace45677 in favor of adding a new field
to struct IndexOnlyScan, containing a version of the indexqual that
can be executed against the index-returned tuple without using any
non-returnable columns. (The restrictions imposed by check_index_only
guarantee this is possible, although we may have to recompute indexed
expressions.) Support construction of that during setrefs.c
processing by marking IndexOnlyScan.indextlist entries as resjunk
if they can't be returned, rather than removing them entirely.
(We could alternatively require setrefs.c to look up the IndexOptInfo
again, but abusing resjunk this way seems like a reasonably safe way
to avoid needing to do that.)
This solution isn't great from an API-stability standpoint: if there
are any extensions out there that build IndexOnlyScan structs directly,
they'll be broken in the next minor releases. However, only a very
invasive extension would be likely to do such a thing. There's no
change in the Path representation, so typical planner extensions
shouldn't have a problem.
As before, back-patch to all supported branches.
Discussion: https://postgr.es/m/3179992.1641150853@sss.pgh.pa.us
Discussion: https://postgr.es/m/17350-b5bdcf476e5badbb@postgresql.org
It's possible that a subplan below a Memoize node contains a parameter
from above the Memoize node. If this parameter changes then cache entries
may become out-dated due to the new parameter value.
Previously Memoize was mistakenly not aware of this. We fix this here by
flushing the cache whenever a parameter that's not part of the cache
key changes.
Bug: #17213
Reported by: Elvis Pranskevichus
Author: David Rowley
Discussion: https://postgr.es/m/17213-988ed34b225a2862@postgresql.org
Backpatch-through: 14, where Memoize was added
It's possible that a subplan below a Memoize node contains a parameter
from above the Memoize node. If this parameter changes then cache entries
may become out-dated due to the new parameter value.
Previously Memoize was mistakenly not aware of this. We fix this here by
flushing the cache whenever a parameter that's not part of the cache
key changes.
Bug: #17213
Reported by: Elvis Pranskevichus
Author: David Rowley
Discussion: https://postgr.es/m/17213-988ed34b225a2862@postgresql.org
Backpatch-through: 14, where Memoize was added
Memoize would always use the hash equality operator for the cache key
types to determine if the current set of parameters were the same as some
previously cached set. Certain types such as floating points where -0.0
and +0.0 differ in their binary representation but are classed as equal by
the hash equality operator may cause problems as unless the join uses the
same operator it's possible that whichever join operator is being used
would be able to distinguish the two values. In which case we may
accidentally return in the incorrect rows out of the cache.
To fix this here we add a binary mode to Memoize to allow it to the
current set of parameters to previously cached values by comparing
bit-by-bit rather than logically using the hash equality operator. This
binary mode is always used for LATERAL joins and it's used for normal
joins when any of the join operators are not hashable.
Reported-by: Tom Lane
Author: David Rowley
Discussion: https://postgr.es/m/3004308.1632952496@sss.pgh.pa.us
Backpatch-through: 14, where Memoize was added
All the tape functions, like LogicalTapeRead and LogicalTapeWrite, now
take a LogicalTape as argument, instead of LogicalTapeSet+tape number.
You can create any number of LogicalTapes in a single LogicalTapeSet, and
you don't need to decide the number upfront, when you create the tape set.
This makes the tape management in hash agg spilling in nodeAgg.c simpler.
Discussion: https://www.postgresql.org/message-id/420a0ec7-602c-d406-1e75-1ef7ddc58d83%40iki.fi
Reviewed-by: Peter Geoghegan, Zhihong Yu, John Naylor
Datum sorts can be significantly faster than tuple sorts, especially when
the data type being sorted is a pass-by-value type. Something in the
region of 50-70% performance improvements appear to be possible.
Just in case there's any confusion; the Datum sort is only used when the
targetlist of the Sort node contains a single column, not when there's a
single column in the sort key and multiple items in the target list.
Author: Ronan Dunklau
Reviewed-by: James Coleman, David Rowley, Ranier Vilela, Hou Zhijie
Tested-by: John Naylor
Discussion: https://postgr.es/m/3177670.itZtoPt7T5@aivenronan
"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
Commit b663a41363 introduced bulk inserts for FDW, but the handling of
tuple slots turned out to be problematic for two reasons. Firstly, the
slots were re-created for each individual batch. Secondly, all slots
referenced the same tuple descriptor - with reasonably small batches
this is not an issue, but with large batches this triggers O(N^2)
behavior in the resource owner code.
These two issues work against each other - to reduce the number of times
a slot has to be created/dropped, larger batches are needed. However,
the larger the batch, the more expensive the resource owner gets. For
practical batch sizes (100 - 1000) this would not be a big problem, as
the benefits (latency savings) greatly exceed the resource owner costs.
But for extremely large batches it might be much worse, possibly even
losing with non-batching mode.
Fixed by initializing tuple slots only once (and reusing them across
batches) and by using a new tuple descriptor copy for each slot.
Discussion: https://postgr.es/m/ebbbcc7d-4286-8c28-0272-61b4753af761%40enterprisedb.com
Also "make reformat-dat-files".
The only change worthy of note is that pgindent messed up the formatting
of launcher.c's struct LogicalRepWorkerId, which led me to notice that
that struct wasn't used at all anymore, so I just took it out.
EXPLAIN ANALYZE for an async-capable ForeignScan node associated with
postgres_fdw is done just by using instrumentation for ExecProcNode()
called from the node's callbacks, causing the following problems:
1) If the remote table to scan is empty, the node is incorrectly
considered as "never executed" by the command even if the node is
executed, as ExecProcNode() isn't called from the node's callbacks at
all in that case.
2) The command fails to collect timings for things other than
ExecProcNode() done in the node, such as creating a cursor for the
node's remote query.
To fix these problems, add instrumentation for async-capable nodes, and
modify postgres_fdw accordingly.
My oversight in commit 27e1f1456.
While at it, update a comment for the AsyncRequest struct in execnodes.h
and the documentation for the ForeignAsyncRequest API in fdwhandler.sgml
to match the code in ExecAsyncAppendResponse() in nodeAppend.c, and fix
typos in comments in nodeAppend.c.
Per report from Andrey Lepikhov, though I didn't use his patch.
Reviewed-by: Andrey Lepikhov
Discussion: https://postgr.es/m/2eb662bb-105d-fc20-7412-2f027cc3ca72%40postgrespro.ru
Delay creation of the projections for INSERT and UPDATE tuples
until they're needed. This saves a pretty fair amount of work
when only some of the partitions are actually touched.
The logic associated with identifying junk columns in UPDATE/DELETE
is moved to another loop, allowing removal of one loop over the
target relations; but it didn't actually change at all.
Extracted from a larger patch, which seemed to me to be too messy
to push in one commit.
Amit Langote, reviewed at different times by Heikki Linnakangas and
myself
Discussion: https://postgr.es/m/CA+HiwqG7ZruBmmih3wPsBZ4s0H2EhywrnXEduckY5Hr3fWzPWA@mail.gmail.com
Arrange to do some things on-demand, rather than immediately during
executor startup, because there's a fair chance of never having to do
them at all:
* Don't open result relations' indexes until needed.
* Don't initialize partition tuple routing, nor the child-to-root
tuple conversion map, until needed.
This wins in UPDATEs on partitioned tables when only some of the
partitions will actually receive updates; with larger partition
counts the savings is quite noticeable. Also, we can remove some
sketchy heuristics in ExecInitModifyTable about whether to set up
tuple routing.
Also, remove execPartition.c's private hash table tracking which
partitions were already opened by the ModifyTable node. Instead
use the hash added to ModifyTable itself by commit 86dc90056.
To allow lazy computation of the conversion maps, we now set
ri_RootResultRelInfo in all child ResultRelInfos. We formerly set it
only in some, not terribly well-defined, cases. This has user-visible
side effects in that now more error messages refer to the root
relation instead of some partition (and provide error data in the
root's column order, too). It looks to me like this is a strict
improvement in consistency, so I don't have a problem with the
output changes visible in this commit.
Extracted from a larger patch, which seemed to me to be too messy
to push in one commit.
Amit Langote, reviewed at different times by Heikki Linnakangas and
myself
Discussion: https://postgr.es/m/CA+HiwqG7ZruBmmih3wPsBZ4s0H2EhywrnXEduckY5Hr3fWzPWA@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, 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
This implements asynchronous execution, which runs multiple parts of a
non-parallel-aware Append concurrently rather than serially to improve
performance when possible. Currently, the only node type that can be
run concurrently is a ForeignScan that is an immediate child of such an
Append. In the case where such ForeignScans access data on different
remote servers, this would run those ForeignScans concurrently, and
overlap the remote operations to be performed simultaneously, so it'll
improve the performance especially when the operations involve
time-consuming ones such as remote join and remote aggregation.
We may extend this to other node types such as joins or aggregates over
ForeignScans in the future.
This also adds the support for postgres_fdw, which is enabled by the
table-level/server-level option "async_capable". The default is false.
Robert Haas, Kyotaro Horiguchi, Thomas Munro, and myself. This commit
is mostly based on the patch proposed by Robert Haas, but also uses
stuff from the patch proposed by Kyotaro Horiguchi and from the patch
proposed by Thomas Munro. Reviewed by Kyotaro Horiguchi, Konstantin
Knizhnik, Andrey Lepikhov, Movead Li, Thomas Munro, Justin Pryzby, and
others.
Discussion: https://postgr.es/m/CA%2BTgmoaXQEt4tZ03FtQhnzeDEMzBck%2BLrni0UWHVVgOTnA6C1w%40mail.gmail.com
Discussion: https://postgr.es/m/CA%2BhUKGLBRyu0rHrDCMC4%3DRn3252gogyp1SjOgG8SEKKZv%3DFwfQ%40mail.gmail.com
Discussion: https://postgr.es/m/20200228.170650.667613673625155850.horikyota.ntt%40gmail.com
This adds a new executor node named TID Range Scan. The query planner
will generate paths for TID Range scans when quals are discovered on base
relations which search for ranges on the table's ctid column. These
ranges may be open at either end. For example, WHERE ctid >= '(10,0)';
will return all tuples on page 10 and over.
To support this, two new optional callback functions have been added to
table AM. scan_set_tidrange is used to set the scan range to just the
given range of TIDs. scan_getnextslot_tidrange fetches the next tuple
in the given range.
For AMs were scanning ranges of TIDs would not make sense, these functions
can be set to NULL in the TableAmRoutine. The query planner won't
generate TID Range Scan Paths in that case.
Author: Edmund Horner, David Rowley
Reviewed-by: David Rowley, Tomas Vondra, Tom Lane, Andres Freund, Zhihong Yu
Discussion: https://postgr.es/m/CAMyN-kB-nFTkF=VA_JPwFNo08S0d-Yk0F741S2B7LDmYAi8eyA@mail.gmail.com
If ExecGetInsertedCols(), ExecGetUpdatedCols() or ExecGetExtraUpdatedCols()
were called with a ResultRelInfo that's not in the range table and isn't a
partition routing target, the functions would dereference a NULL pointer,
relinfo->ri_RootResultRelInfo. Such ResultRelInfos are created when firing
RI triggers in tables that are not modified directly. None of the current
callers of these functions pass such relations, so this isn't a live bug,
but let's make them more robust.
Also update comment in ResultRelInfo; after commit 6214e2b228,
ri_RangeTableIndex is zero for ResultRelInfos created for partition tuple
routing.
Noted by Coverity. Backpatch down to v11, like commit 6214e2b228.
Reviewed-by: Tom Lane, Amit Langote
If a cross-partition UPDATE violates a constraint on the target partition,
and the columns in the new partition are in different physical order than
in the parent, the error message can reveal columns that the user does not
have SELECT permission on. A similar bug was fixed earlier in commit
804b6b6db4.
The cause of the bug is that the callers of the
ExecBuildSlotValueDescription() function got confused when constructing
the list of modified columns. If the tuple was routed from a parent, we
converted the tuple to the parent's format, but the list of modified
columns was grabbed directly from the child's RTE entry.
ExecUpdateLockMode() had a similar issue. That lead to confusion on which
columns are key columns, leading to wrong tuple lock being taken on tables
referenced by foreign keys, when a row is updated with INSERT ON CONFLICT
UPDATE. A new isolation test is added for that corner case.
With this patch, the ri_RangeTableIndex field is no longer set for
partitions that don't have an entry in the range table. Previously, it was
set to the RTE entry of the parent relation, but that was confusing.
NOTE: This modifies the ResultRelInfo struct, replacing the
ri_PartitionRoot field with ri_RootResultRelInfo. That's a bit risky to
backpatch, because it breaks any extensions accessing the field. The
change that ri_RangeTableIndex is not set for partitions could potentially
break extensions, too. The ResultRelInfos are visible to FDWs at least,
and this patch required small changes to postgres_fdw. Nevertheless, this
seem like the least bad option. I don't think these fields widely used in
extensions; I don't think there are FDWs out there that uses the FDW
"direct update" API, other than postgres_fdw. If there is, you will get a
compilation error, so hopefully it is caught quickly.
Backpatch to 11, where support for both cross-partition UPDATEs, and unique
indexes on partitioned tables, were added.
Reviewed-by: Amit Langote
Security: CVE-2021-3393
Extends the FDW API to allow batching inserts into foreign tables. That
is usually much more efficient than inserting individual rows, due to
high latency for each round-trip to the foreign server.
It was possible to implement something similar in the regular FDW API,
but it was inconvenient and there were issues with reporting the number
of actually inserted rows etc. This extends the FDW API with two new
functions:
* GetForeignModifyBatchSize - allows the FDW picking optimal batch size
* ExecForeignBatchInsert - inserts a batch of rows at once
Currently, only INSERT queries support batching. Support for DELETE and
UPDATE may be added in the future.
This also implements batching for postgres_fdw. The batch size may be
specified using "batch_size" option both at the server and table level.
The initial patch version was written by me, but it was rewritten and
improved in many ways by Takayuki Tsunakawa.
Author: Takayuki Tsunakawa
Reviewed-by: Tomas Vondra, Amit Langote
Discussion: https://postgr.es/m/20200628151002.7x5laxwpgvkyiu3q@development
Store the tuple conversion map to convert a tuple from a child table's
format to the root format in a new ri_ChildToRootMap field in
ResultRelInfo. It is initialized if transition tuple capture for FOR
STATEMENT triggers or INSERT tuple routing on a partitioned table is
needed. Previously, ModifyTable kept the maps in the per-subplan
ModifyTableState->mt_per_subplan_tupconv_maps array, or when tuple
routing was used, in
ResultRelInfo->ri_Partitioninfo->pi_PartitionToRootMap. The new field
replaces both of those.
Now that the child-to-root tuple conversion map is always available in
ResultRelInfo (when needed), remove the TransitionCaptureState.tcs_map
field. The callers of Exec*Trigger() functions no longer need to set or
save it, which is much less confusing and bug-prone. Also, as a future
optimization, this will allow us to delay creating the map for a given
result relation until the relation is actually processed during
execution.
Author: Amit Langote
Discussion: https://www.postgresql.org/message-id/CA%2BHiwqHtCWLdK-LO%3DNEsvOdHx%2B7yv4mE_zYK0i3BH7dXb-wxog%40mail.gmail.com
When executing DDL on a partitioned table or on a table with inheritance
children, statement-level triggers must be fired against the table given
in the original statement. The code to look that up was a bit messy and
duplicative. Commit 501ed02cf6 added a helper function,
getASTriggerResultRelInfo() (later renamed to getTargetResultRelInfo())
for it, but for some reason it was only used when firing AFTER STATEMENT
triggers and the code to fire BEFORE STATEMENT triggers duplicated the
logic.
Determine the target relation in ExecInitModifyTable(), and set it always
in ModifyTableState. Code that used to call getTargetResultRelInfo() can
now use ModifyTableState->rootResultRelInfo directly.
Discussion: https://www.postgresql.org/message-id/CA%2BHiwqFViT47Zbr_ASBejiK7iDG8%3DQ1swQ-tjM6caRPQ67pT%3Dw%40mail.gmail.com
Maintaining 'es_result_relation_info' correctly at all times has become
cumbersome, especially with partitioning where each partition gets its
own result relation info. Having to set and reset it across arbitrary
operations has caused bugs in the past.
This changes all the places that used 'es_result_relation_info', to
receive the currently active ResultRelInfo via function parameters
instead.
Author: Amit Langote
Discussion: https://www.postgresql.org/message-id/CA%2BHiwqGEmiib8FLiHMhKB%2BCH5dRgHSLc5N5wnvc4kym%2BZYpQEQ%40mail.gmail.com
FDWs that can perform an UPDATE/DELETE remotely using the "direct modify"
set of APIs need to access the ResultRelInfo of the target table. That's
currently available in EState.es_result_relation_info, but the next
commit will remove that field.
This commit adds a new resultRelation field in ForeignScan, to store the
target relation's RT index, and the corresponding ResultRelInfo in
ForeignScanState. The FDW's PlanDirectModify callback is expected to set
'resultRelation' along with 'operation'. The core code doesn't need them
for anything, they are for the convenience of FDW's Begin- and
IterateDirectModify callbacks.
Authors: Amit Langote, Etsuro Fujita
Discussion: https://www.postgresql.org/message-id/CA%2BHiwqGEmiib8FLiHMhKB%2BCH5dRgHSLc5N5wnvc4kym%2BZYpQEQ%40mail.gmail.com
Instead of allocating all the ResultRelInfos upfront in one big array,
allocate them in ExecInitModifyTable(). es_result_relations is now an
array of ResultRelInfo pointers, rather than an array of structs, and it
is indexed by the RT index.
This simplifies things: we get rid of the separate concept of a "result
rel index", and don't need to set it in setrefs.c anymore. This also
allows follow-up optimizations (not included in this commit yet) to skip
initializing ResultRelInfos for target relations that were not needed at
runtime, and removal of the es_result_relation_info pointer.
The EState arrays of regular result rels and root result rels are merged
into one array. Similarly, the resultRelations and rootResultRelations
lists in PlannedStmt are merged into one. It's not actually clear to me
why they were kept separate in the first place, but now that the
es_result_relations array is indexed by RT index, it certainly seems
pointless.
The PlannedStmt->resultRelations list is now only needed for
ExecRelationIsTargetRelation(). One visible effect of this change is that
ExecRelationIsTargetRelation() will now return 'true' also for the
partition root, if a partitioned table is updated. That seems like a good
thing, although the function isn't used in core code, and I don't see any
reason for an FDW to call it on a partition root.
Author: Amit Langote
Discussion: https://www.postgresql.org/message-id/CA%2BHiwqGEmiib8FLiHMhKB%2BCH5dRgHSLc5N5wnvc4kym%2BZYpQEQ%40mail.gmail.com
When commit bd3daddaf introduced AlternativeSubPlans, I had some
ambitions towards allowing the choice of subplan to change during
execution. That has not happened, or even been thought about, in the
ensuing twelve years; so it seems like a failed experiment. So let's
rip that out and resolve the choice of subplan at the end of planning
(in setrefs.c) rather than during executor startup. This has a number
of positive benefits:
* Removal of a few hundred lines of executor code, since
AlternativeSubPlans need no longer be supported there.
* Removal of executor-startup overhead (particularly, initialization
of subplans that won't be used).
* Removal of incidental costs of having a larger plan tree, such as
tree-scanning and copying costs in the plancache; not to mention
setrefs.c's own costs of processing the discarded subplans.
* EXPLAIN no longer has to print a weird (and undocumented)
representation of an AlternativeSubPlan choice; it sees only the
subplan actually used. This should mean less confusion for users.
* Since setrefs.c knows which subexpression of a plan node it's
working on at any instant, it's possible to adjust the estimated
number of executions of the subplan based on that. For example,
we should usually estimate more executions of a qual expression
than a targetlist expression. The implementation used here is
pretty simplistic, because we don't want to expend a lot of cycles
on the issue; but it's better than ignoring the point entirely,
as the executor had to.
That last point might possibly result in shifting the choice
between hashed and non-hashed EXISTS subplans in a few cases,
but in general this patch isn't meant to change planner choices.
Since we're doing the resolution so late, it's really impossible
to change any plan choices outside the AlternativeSubPlan itself.
Patch by me; thanks to David Rowley for review.
Discussion: https://postgr.es/m/1992952.1592785225@sss.pgh.pa.us
Since there is only one place that actually needs the partition check
expression, namely ExecPartitionCheck, it's better to fetch it from
the relcache there. In this way we will never fetch it at all if
the query never has use for it, and we still fetch it just once when
we do need it.
The reason for taking an interest in this is that if the relcache
doesn't already have the check expression cached, fetching it
requires obtaining AccessShareLock on the partition root. That
means that operations that look like they should only touch the
partition itself will also take a lock on the root. In particular
we observed that TRUNCATE on a partition may take a lock on the
partition's root, contributing to a deadlock situation in parallel
pg_restore.
As written, this patch does have a small cost, which is that we
are microscopically reducing efficiency for the case where a partition
has an empty check expression. ExecPartitionCheck will be called,
and will go through the motions of setting up and checking an empty
qual, where before it would not have been called at all. We could
avoid that by adding a separate boolean flag to track whether there
is a partition expression to test. However, this case only arises
for a default partition with no siblings, which surely is not an
interesting case in practice. Hence adding complexity for it
does not seem like a good trade-off.
Amit Langote, per a suggestion by me
Discussion: https://postgr.es/m/VI1PR03MB31670CA1BD9625C3A8C5DD05EB230@VI1PR03MB3167.eurprd03.prod.outlook.com
nodeSubplan.c expects that the testexpr for a hashable ANY SubPlan
has the form of one or more OpExprs whose LHS is an expression of the
outer query's, while the RHS is an expression over Params representing
output columns of the subquery. However, the planner only went as far
as verifying that the clauses were all binary OpExprs. This works
99.99% of the time, because the clauses have the right shape when
emitted by the parser --- but it's possible for function inlining to
break that, as reported by PegoraroF10. To fix, teach the planner
to check that the LHS and RHS contain the right things, or more
accurately don't contain the wrong things. Given that this has been
broken for years without anyone noticing, it seems sufficient to just
give up hashing when it happens, rather than go to the trouble of
commuting the clauses back again (which wouldn't necessarily work
anyway).
While poking at that, I also noticed that nodeSubplan.c had a baked-in
assumption that the number of hash clauses is identical to the number
of subquery output columns. Again, that's fine as far as parser output
goes, but it's not hard to break it via function inlining. There seems
little reason for that assumption though --- AFAICS, the only thing
it's buying us is not having to store the number of hash clauses
explicitly. Adding code to the planner to reject such cases would take
more code than getting nodeSubplan.c to cope, so I fixed it that way.
This has been broken for as long as we've had hashable SubPlans,
so back-patch to all supported branches.
Discussion: https://postgr.es/m/1549209182255-0.post@n3.nabble.com
Since 1f39bce02, HashAgg nodes have had the ability to spill to disk when
memory consumption exceeds work_mem. That commit added new properties to
EXPLAIN ANALYZE to show the maximum memory usage and disk usage, however,
it didn't quite go as far as showing that information for parallel
workers. Since workers may have experienced something very different from
the main process, we should show this information per worker, as is done
in Sort.
Reviewed-by: Justin Pryzby
Reviewed-by: Jeff Davis
Discussion: https://postgr.es/m/CAApHDvpEKbfZa18mM1TD7qV6PG+w97pwCWq5tVD0dX7e11gRJw@mail.gmail.com
Backpatch-through: 13, where the hashagg spilling code was added.
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.
Before discarding the old hash table in ExecReScanHashJoin, capture
its statistics, ensuring that we report the maximum hashtable size
across repeated rescans of the hash input relation. We can repurpose
the existing code for reporting hashtable size in parallel workers
to help with this, making the patch pretty small. This also ensures
that if rescans happen within parallel workers, we get the correct
maximums across all instances.
Konstantin Knizhnik and Tom Lane, per diagnosis by Thomas Munro
of a trouble report from Alvaro Herrera.
Discussion: https://postgr.es/m/20200323165059.GA24950@alvherre.pgsql
