Since C99, there can be a trailing comma after the last value in an
enum definition. A lot of new code has been introducing this style on
the fly. Some new patches are now taking an inconsistent approach to
this. Some add the last comma on the fly if they add a new last
value, some are trying to preserve the existing style in each place,
some are even dropping the last comma if there was one. We could
nudge this all in a consistent direction if we just add the trailing
commas everywhere once.
I omitted a few places where there was a fixed "last" value that will
always stay last. I also skipped the header files of libpq and ecpg,
in case people want to use those with older compilers. There were
also a small number of cases where the enum type wasn't used anywhere
(but the enum values were), which ended up confusing pgindent a bit,
so I left those alone.
Discussion: https://www.postgresql.org/message-id/flat/386f8c45-c8ac-4681-8add-e3b0852c1620%40eisentraut.org
It is unnecessary to include this field in IndexInfo. It is only used
by DDL code, not during execution. It is really only used to pass
local information around between functions in index.c and indexcmds.c,
for which it is clearer to use local variables, like in similar cases.
Discussion: https://www.postgresql.org/message-id/flat/f84640e3-00d3-5abd-3f41-e6a19d33c40b@eisentraut.org
This adjusts the expression evaluation code for CoerceViaIO and
CoerceToDomain to handle errors softly if needed.
For CoerceViaIo, this means using InputFunctionCallSafe(), which
provides the option to handle errors softly, instead of calling the
type input function directly.
For CoerceToDomain, this simply entails replacing the ereport() in
ExecEvalConstraintCheck() by errsave().
In both cases, the ErrorSaveContext to be used when evaluating the
expression is stored by ExecInitExprRec() in the expression's struct
in the expression's ExprEvalStep. The ErrorSaveContext is passed by
setting ExprState.escontext to point to it when calling
ExecInitExprRec() on the expression whose errors are to be handled
softly.
Note that no call site of ExecInitExprRec() has been changed in this
commit, so there's no functional change. This is intended for
implementing new SQL/JSON expression nodes in future commits that
will use to it suppress errors that may occur during type coercions.
Reviewed-by: Álvaro Herrera
Discussion: https://postgr.es/m/CA+HiwqE4XTdfb1nW=Ojoy_tQSRhYt-q_kb6i5d4xcKyrLC1Nbg@mail.gmail.com
Under some circumstances, concurrent MERGE operations could lead to
inconsistent results, that varied according the plan chosen. This was
caused by a lack of rowmarks on the source relation, which meant that
EvalPlanQual rechecking was not guaranteed to return the same source
tuples when re-running the join query.
Fix by ensuring that preprocess_rowmarks() sets up PlanRowMarks for
all non-target relations used in MERGE, in the same way that it does
for UPDATE and DELETE.
Per bug #18103. Back-patch to v15, where MERGE was introduced.
Dean Rasheed, reviewed by Richard Guo.
Discussion: https://postgr.es/m/18103-c4386baab8e355e3%40postgresql.org
The idea of EvalPlanQual is that we replace the query's scan of the
result relation with a single injected tuple, and see if we get a
tuple out, thereby implying that the injected tuple still passes the
query quals. (In join cases, other relations in the query are still
scanned normally.) This logic was not updated when commit 86dc90056
made it possible for a single DML query plan to have multiple result
relations, when the query target relation has inheritance or partition
children. We replaced the output for the current result relation
successfully, but other result relations were still scanned normally;
thus, if any other result relation contained a tuple satisfying the
quals, we'd think the EPQ check passed, even if it did not pass for
the injected tuple itself. This would lead to update or delete
actions getting performed when they should have been skipped due to
a conflicting concurrent update in READ COMMITTED isolation mode.
Fix by blocking all sibling result relations from emitting tuples
during an EvalPlanQual recheck. In the back branches, the fix is
complicated a bit by the need to not change the size of struct
EPQState (else we'd have ABI-breaking changes in offsets in
struct ModifyTableState). Like the back-patches of 3f7836ff6
and 4b3e37993, add a separately palloc'd struct to avoid that.
The logic is the same as in HEAD otherwise.
This is only a live bug back to v14 where 86dc90056 came in.
However, I chose to back-patch the test cases further, on the
grounds that this whole area is none too well tested. I skipped
doing so in v11 though because none of the test applied cleanly,
and it didn't quite seem worth extra work for a branch with only
six months to live.
Per report from Ante Krešić (via Aleksander Alekseev)
Discussion: https://postgr.es/m/CAJ7c6TMBTN3rcz4=AjYhLPD_w3FFT0Wq_C15jxCDn8U4tZnH1g@mail.gmail.com
pg_stat_statements relies on EState->es_processed to count the number of
rows processed by ExecutorRun(). This proves to be a problem under the
extended query protocol when the result of a query is fetched through
more than one call of ExecutorRun(), as es_processed is reset each time
ExecutorRun() is called. This causes pg_stat_statements to report the
number of rows calculated in the last execute fetch, rather than the
global sum of all the rows processed.
As pquery.c tells, this is a problem when a portal does not use
holdStore. For example, DMLs with RETURNING would report a correct
tuple count as these do one execution cycle when the query is first
executed to fill in the portal's store with one ExecutorRun(), feeding
on the portal's store for each follow-up execute fetch depending on the
fetch size requested by the client.
The fix proposed for this issue is simple with the addition of an extra
counter in EState that's preserved across multiple ExecutorRun() calls,
incremented with the value calculated in es_processed. This approach is
not back-patchable, unfortunately.
Note that libpq does not currently give any way to control the fetch
size when using the extended v3 protocol, meaning that in-core testing
is not possible yet. This issue can be easily verified with the JDBC
driver, though, with *autocommit disabled*. Hence, having in-core tests
requires more features, left for future discussion:
- At least two new libpq routines splitting PQsendQueryGuts(), one for
the bind/describe and a second for a series of execute fetches with a
custom fetch size, likely in a fashion similar to what JDBC does.
- A psql meta-command for the execute phase. This part is not strictly
mandatory, still it could be handy.
Reported-by: Andrew Dunstan (original discovery by Simon Siggs)
Author: Sami Imseih
Reviewed-by: Tom Lane, Michael Paquier
Discussion: https://postgr.es/m/EBE6C507-9EB6-4142-9E4D-38B1673363A7@amazon.com
Discussion: https://postgr.es/m/c90890e7-9c89-c34f-d3c5-d5c763a34bd8@dunslane.net
Merge and hash joins can support antijoin with the non-nullable input
on the right, using very simple combinations of their existing logic
for right join and anti join. This gives the planner more freedom
about how to order the join. It's particularly useful for hash join,
since we may now have the option to hash the smaller table instead
of the larger.
Richard Guo, reviewed by Ronan Dunklau and myself
Discussion: https://postgr.es/m/CAMbWs48xh9hMzXzSy3VaPzGAz+fkxXXTUbCLohX1_L8THFRm2Q@mail.gmail.com
When determining whether an index update may be skipped by using HOT, we
can ignore attributes indexed by block summarizing indexes without
references to individual tuples that need to be cleaned up.
A new type TU_UpdateIndexes provides a signal to the executor to
determine which indexes to update - no indexes, all indexes, or only the
summarizing indexes.
This also removes rd_indexattr list, and replaces it with rd_attrsvalid
flag. The list was not used anywhere, and a simple flag is sufficient.
This was originally committed as 5753d4ee32, but then got reverted by
e3fcca0d0d because of correctness issues.
Original patch by Josef Simanek, various fixes and improvements by Tomas
Vondra and me.
Authors: Matthias van de Meent, Josef Simanek, Tomas Vondra
Reviewed-by: Tomas Vondra, Alvaro Herrera
Discussion: https://postgr.es/m/05ebcb44-f383-86e3-4f31-0a97a55634cf@enterprisedb.com
Discussion: https://postgr.es/m/CAFp7QwpMRGcDAQumN7onN9HjrJ3u4X3ZRXdGFT0K5G2JWvnbWg%40mail.gmail.com
If UPDATE is forced to retry after an EvalPlanQual check, it neglected
to repeat GENERATED-column computations, even though those might well
have changed since we're dealing with a different tuple than before.
Fixing this is mostly a matter of looping back a bit further when
we retry. In v15 and HEAD that's most easily done by altering the API
of ExecUpdateAct so that it includes computing GENERATED expressions.
Also, if an UPDATE in a partitioned table turns into a cross-partition
INSERT operation, we failed to recompute GENERATED columns. That's a
bug since 8bf6ec3ba allowed partitions to have different generation
expressions; although it seems to have no ill effects before that.
Fixing this is messier because we can now have situations where the same
query needs both the UPDATE-aligned set of GENERATED columns and the
INSERT-aligned set, and it's unclear which set will be generated first
(else we could hack things by forcing the INSERT-aligned set to be
generated, which is indeed how fe9e658f4 made it work for MERGE).
The best fix seems to be to build and store separate sets of expressions
for the INSERT and UPDATE cases. That would create ABI issues in the
back branches, but so far it seems we can leave this alone in the back
branches.
Per bug #17823 from Hisahiro Kauchi. The first part of this affects all
branches back to v12 where GENERATED columns were added.
Discussion: https://postgr.es/m/17823-b64909cf7d63de84@postgresql.org
nodeAppend.c used non-nullness of appendstate->as_valid_subplans as
a state flag to indicate whether it'd done ExecFindMatchingSubPlans
(or some sufficient approximation to that). This was pretty
questionable even in the beginning, since it wouldn't really work
right if there are no valid subplans. It got more questionable
after commit 27e1f1456 added logic that could reduce as_valid_subplans
to an empty set: at that point we were depending on unspecified
behavior of bms_del_members, namely that it'd not return an empty
set as NULL. It's about to start doing that, which breaks this
logic entirely. Hence, add a separate boolean flag to signal
whether as_valid_subplans has been computed.
Also fix a previously-cosmetic bug in nodeAgg.c, wherein it ignored
the return value of bms_del_member instead of updating its pointer.
Patch by me; thanks to Nathan Bossart and Richard Guo for review.
Discussion: https://postgr.es/m/1159933.1677621588@sss.pgh.pa.us
We were identifying the updatable generated columns of inheritance
children by transposing the calculation made for their parent.
However, there's nothing that says a traditional-inheritance child
can't have generated columns that aren't there in its parent, or that
have different dependencies than are in the parent's expression.
(At present it seems that we don't enforce that for partitioning
either, which is likely wrong to some degree or other; but the case
clearly needs to be handled with traditional inheritance.)
Hence, drop the very-klugy-anyway "extraUpdatedCols" RTE field
in favor of identifying which generated columns depend on updated
columns during executor startup. In HEAD we can remove
extraUpdatedCols altogether; in back branches, it's still there but
always empty. Another difference between the HEAD and back-branch
versions of this patch is that in HEAD we can add the new bitmap field
to ResultRelInfo, but that would cause an ABI break in back branches.
Like 4b3e37993, add a List field at the end of struct EState instead.
Back-patch to v13. The bogus calculation is also being made in v12,
but it doesn't have the same visible effect because we don't use it
to decide which generated columns to recalculate; as a consequence of
which the patch doesn't apply easily. I think that there might still
be a demonstrable bug associated with trigger firing conditions, but
that's such a weird corner-case usage that I'm content to leave it
unfixed in v12.
Amit Langote and Tom Lane
Discussion: https://postgr.es/m/CA+HiwqFshLKNvQUd1DgwJ-7tsTp=dwv7KZqXC4j2wYBV1aCDUA@mail.gmail.com
Discussion: https://postgr.es/m/2793383.1672944799@sss.pgh.pa.us
In commit ffbb7e65a, I added a ModifyTableState member to ResultRelInfo
to save the owning ModifyTableState for use by nodeModifyTable.c when
performing batch inserts, but as pointed out by Tom Lane, that changed
the array stride of es_result_relations, and that would break any
previously-compiled extension code that accesses that array. Fix by
removing that member from ResultRelInfo and instead adding a List member
at the end of EState to save such ModifyTableStates.
Per report from Tom Lane. Back-patch to v14, like the previous commit;
I chose to apply the patch to HEAD as well, to make back-patching easy.
Discussion: http://postgr.es/m/4065383.1669395453%40sss.pgh.pa.us
Currently, information about the permissions to be checked on relations
mentioned in a query is stored in their range table entries. So the
executor must scan the entire range table looking for relations that
need to have permissions checked. This can make the permission checking
part of the executor initialization needlessly expensive when many
inheritance children are present in the range range. While the
permissions need not be checked on the individual child relations, the
executor still must visit every range table entry to filter them out.
This commit moves the permission checking information out of the range
table entries into a new plan node called RTEPermissionInfo. Every
top-level (inheritance "root") RTE_RELATION entry in the range table
gets one and a list of those is maintained alongside the range table.
This new list is initialized by the parser when initializing the range
table. The rewriter can add more entries to it as rules/views are
expanded. Finally, the planner combines the lists of the individual
subqueries into one flat list that is passed to the executor for
checking.
To make it quick to find the RTEPermissionInfo entry belonging to a
given relation, RangeTblEntry gets a new Index field 'perminfoindex'
that stores the corresponding RTEPermissionInfo's index in the query's
list of the latter.
ExecutorCheckPerms_hook has gained another List * argument; the
signature is now:
typedef bool (*ExecutorCheckPerms_hook_type) (List *rangeTable,
List *rtePermInfos,
bool ereport_on_violation);
The first argument is no longer used by any in-core uses of the hook,
but we leave it in place because there may be other implementations that
do. Implementations should likely scan the rtePermInfos list to
determine which operations to allow or deny.
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqGjJDmUhDSfv-U2qhKJjt9ST7Xh9JXC_irsAQ1TAUsJYg@mail.gmail.com
ri_RootToPartitionMap is currently only initialized for tuple routing
target partitions, though a future commit will need the ability to use
it even for the non-partition child tables, so make adjustments to the
decouple it from the partitioning code.
Also, make it lazily initialized via ExecGetRootToChildMap(), making
that function its preferred access path. Existing third-party code
accessing it directly should no longer do so; consequently, it's been
renamed to ri_RootToChildMap, which also makes it consistent with
ri_ChildToRootMap.
ExecGetRootToChildMap() houses the logic of setting the map appropriately
depending on whether a given child relation is partition or not.
To support this, also add a separate entry point for TupleConversionMap
creation that receives an AttrMap. No new code here, just split an
existing function in two.
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqEYUhDXSK5BTvG_xk=eaAEJCD4GS3C6uH7ybBvv+Z_Tmg@mail.gmail.com
The planner will now add a given PartitioPruneInfo to
PlannedStmt.partPruneInfos instead of directly to the
Append/MergeAppend plan node. What gets set instead in the
latter is an index field which points to the list element
of PlannedStmt.partPruneInfos containing the PartitioPruneInfo
belonging to the plan node.
A later commit will make AcquireExecutorLocks() do the initial
partition pruning to determine a minimal set of partitions to be
locked when validating a plan tree and it will need to consult the
PartitioPruneInfos referenced therein to do so. It would be better
for the PartitioPruneInfos to be accessible directly than requiring
a walk of the plan tree to find them, which is easier when it can be
done by simply iterating over PlannedStmt.partPruneInfos.
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqFGkMSge6TgC9KQzde0ohpAycLQuV7ooitEEpbKB0O_mg@mail.gmail.com
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
Some custom table access method may have their tuple format and use custom
executor nodes for their custom scan types. The ability to set a custom slot
would save them from tuple format conversion. Other users of custom executor
nodes may also benefit.
Discussion: https://postgr.es/m/CAPpHfduJUU6ToecvTyRE_yjxTS80FyPpct4OHaLFk3OEheMTNA@mail.gmail.com
Author: Alexander Korotkov
Reviewed-by: Pavel Borisov
copyfuncs.c and friends no longer seem like great places to put
high-level remarks about what's covered and what isn't. Move that
material to backend/nodes/README and other more-prominent places.
Add back (versions of) some remarks that disappeared in 2be87f092.
Discussion: https://postgr.es/m/3843645.1657385930@sss.pgh.pa.us
In the same vein as commit 251154beb, make it clear that we never
instantiate PlanState.
Also mark MemoryContextData as abstract. This has no effect right now,
since memnodes.h isn't one of the files fed to gen_node_support.pl.
But it seems like good documentation and future-proofing.
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 f56f8f8da6 (which added support for
foreign keys to reference partitioned tables) not paying sufficient
attention to commit 2f17844104 (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 f4566345cf, 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
Bruce Momjian
Peter Eisentraut
Amit Langote
Dean Rasheed
Tom Lane
Alvaro Herrera
Michael Paquier
Tomas Vondra
Etsuro Fujita
Alexander Korotkov
David Rowley
Peter Geoghegan
Heikki Linnakangas
Andrew Dunstan