Run pgindent, pgperltidy, and reformat-dat-files.
This set of diffs is a bit larger than typical. We've updated to
pg_bsd_indent 2.1.2, which properly indents variable declarations that
have multi-line initialization expressions (the continuation lines are
now indented one tab stop). We've also updated to perltidy version
20230309 and changed some of its settings, which reduces its desire to
add whitespace to lines to make assignments etc. line up. Going
forward, that should make for fewer random-seeming changes to existing
code.
Discussion: https://postgr.es/m/20230428092545.qfb3y5wcu4cm75ur@alvherre.pgsql
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
In ancient times, these belonged to arguments or fields that were
actually of type long, but now they are not anymore, so this "L"
decoration is just confusing. (Some other 0L and other "L" constants
remain, where they are actually associated with a long type.)
This provides a very simple way to see the generic plan for a
parameterized query. Without this, it's necessary to define
a prepared statement and temporarily change plan_cache_mode,
which is a bit tedious.
One thing that's a bit of a hack perhaps is that we disable
execution-time partition pruning when the GENERIC_PLAN option
is given. That's because the pruning code may attempt to
fetch the value of one of the parameters, which would fail.
Laurenz Albe, reviewed by Julien Rouhaud, Christoph Berg,
Michel Pelletier, Jim Jones, and myself
Discussion: https://postgr.es/m/0a29b954b10b57f0d135fe12aa0909bd41883eb0.camel@cybertec.at
force_parallel_mode is meant to be used to allow us to exercise the
parallel query infrastructure to ensure that it's working as we expect.
It seems some users think this GUC is for forcing the query planner into
picking a parallel plan regardless of the costs. A quick look at the
documentation would have made them realize that they were wrong, but the
GUC is likely too conveniently named which, evidently, seems to often
result in users expecting that it forces the planner into usefully
parallelizing queries.
Here we rename the GUC to something which casual users are less likely to
mistakenly think is what they need to make their query run more quickly.
For now, the old name can still be used. We'll revisit if the old name
mapping can be removed once the buildfarm configs are all updated.
Reviewed-by: John Naylor
Discussion: https://postgr.es/m/CAApHDvrsOi92_uA7PEaHZMH-S4Xv+MGhQWA+GrP8b1kjpS1HjQ@mail.gmail.com
Here remove some dead code from heapgettup() and heapgettup_pagemode()
which was trying to support NoMovementScanDirection scans. This code can
never be reached as standard_ExecutorRun() never calls ExecutePlan with
NoMovementScanDirection.
Additionally, plans which were scanning an unordered index would use
NoMovementScanDirection rather than ForwardScanDirection. There was no
real need for this, so here we adjust this so we use ForwardScanDirection
for unordered index scans. A comment in pathnodes.h claimed that
NoMovementScanDirection was used for PathKey reasons, but if that was
true, it no longer is, per code in build_index_paths().
This does change the non-text format of the EXPLAIN output so that
unordered index scans now have a "Forward" scan direction rather than
"NoMovement". The text format of EXPLAIN has not changed.
Author: Melanie Plageman
Reviewed-by: Tom Lane, David Rowley
Discussion: https://postgr.es/m/CAAKRu_bvkhka0CZQun28KTqhuUh5ZqY=_T8QEqZqOL02rpi2bw@mail.gmail.com
Traditionally we used the same Var struct to represent the value
of a table column everywhere in parse and plan trees. This choice
predates our support for SQL outer joins, and it's really a pretty
bad idea with outer joins, because the Var's value can depend on
where it is in the tree: it might go to NULL above an outer join.
So expression nodes that are equal() per equalfuncs.c might not
represent the same value, which is a huge correctness hazard for
the planner.
To improve this, decorate Var nodes with a bitmapset showing
which outer joins (identified by RTE indexes) may have nulled
them at the point in the parse tree where the Var appears.
This allows us to trust that equal() Vars represent the same value.
A certain amount of klugery is still needed to cope with cases
where we re-order two outer joins, but it's possible to make it
work without sacrificing that core principle. PlaceHolderVars
receive similar decoration for the same reason.
In the planner, we include these outer join bitmapsets into the relids
that an expression is considered to depend on, and in consequence also
add outer-join relids to the relids of join RelOptInfos. This allows
us to correctly perceive whether an expression can be calculated above
or below a particular outer join.
This change affects FDWs that want to plan foreign joins. They *must*
follow suit when labeling foreign joins in order to match with the
core planner, but for many purposes (if postgres_fdw is any guide)
they'd prefer to consider only base relations within the join.
To support both requirements, redefine ForeignScan.fs_relids as
base+OJ relids, and add a new field fs_base_relids that's set up by
the core planner.
Large though it is, this commit just does the minimum necessary to
install the new mechanisms and get check-world passing again.
Follow-up patches will perform some cleanup. (The README additions
and comments mention some stuff that will appear in the follow-up.)
Patch by me; thanks to Richard Guo for review.
Discussion: https://postgr.es/m/830269.1656693747@sss.pgh.pa.us
When auto_explain.log_verbose is on, auto_explain should print in the
logs plans equivalent to the EXPLAIN (VERBOSE). However, when
compute_query_id is on, query identifiers were not showing up, being
only handled by EXPLAIN (VERBOSE). This brings auto_explain on par with
EXPLAIN regarding that. Note that like EXPLAIN, auto_explain does not
show the query identifier when compute_query_id=regress.
The change is done so as the choice of printing the query identifier is
done in ExplainPrintPlan() rather than in ExplainOnePlan(), to avoid a
duplication of the logic dealing with the query ID. auto_explain is the
only in-core caller of ExplainPrintPlan().
While looking at the area, I have noticed that more consolidation
between EXPLAIN and auto_explain would be in order for the logging of
the plan duration and the buffer usage. This refactoring is left as a
future change.
Author: Atsushi Torikoshi
Reviewed-by: Justin Pryzby, Julien Rouhaud
Discussion: https://postgr.es/m/1ea21936981f161bccfce05765c03bee@oss.nttdata.com
This adjusts a few places which were appending a string constant
containing spaces onto a StringInfo. We have appendStringInfoSpaces for
that job, so let's use that instead.
For the change to jsonb.c's add_indent() function, appendStringInfoString
was being called inside a loop to append 4 spaces on each loop. This
meant that enlargeStringInfo would get called once per loop. Here it
should be much more efficient to get rid of the loop and just calculate
the number of spaces with "level * 4" and just append all the spaces in
one go.
Here we additionally adjust the appendStringInfoSpaces function so it
makes use of memset rather than a while loop to apply the required spaces
to the StringInfo. One of the problems with the while loop was that it
was incrementing one variable and decrementing another variable once per
loop. That's more work than what's required to get the job done. We may
as well use memset for this rather than trying to optimize the existing
loop. Some testing has shown memset is faster even for very small sizes.
Discussion: https://postgr.es/m/CAApHDvp_rKkvwudBKgBHniNRg67bzXVjyvVKfX0G2zS967K43A@mail.gmail.com
Make sure that function declarations use names that exactly match the
corresponding names from function definitions in optimizer, parser,
utility, libpq, and "commands" code, as well as in remaining library
code. Do the same for all code related to frontend programs (with the
exception of pg_dump/pg_dumpall related code).
Like other recent commits that cleaned up function parameter names, this
commit was written with help from clang-tidy. Later commits will handle
ecpg and pg_dump/pg_dumpall.
Author: Peter Geoghegan <pg@bowt.ie>
Reviewed-By: David Rowley <dgrowleyml@gmail.com>
Discussion: https://postgr.es/m/CAH2-WznJt9CMM9KJTMjJh_zbL5hD9oX44qdJ4aqZtjFi-zA3Tg@mail.gmail.com
The reverts the following and makes some associated cleanups:
commit f79b803dc: Common SQL/JSON clauses
commit f4fb45d15: SQL/JSON constructors
commit 5f0adec25: Make STRING an unreserved_keyword.
commit 33a377608: IS JSON predicate
commit 1a36bc9db: SQL/JSON query functions
commit 606948b05: SQL JSON functions
commit 49082c2cc: RETURNING clause for JSON() and JSON_SCALAR()
commit 4e34747c8: JSON_TABLE
commit fadb48b00: PLAN clauses for JSON_TABLE
commit 2ef6f11b0: Reduce running time of jsonb_sqljson test
commit 14d3f24fa: Further improve jsonb_sqljson parallel test
commit a6baa4bad: Documentation for SQL/JSON features
commit b46bcf7a4: Improve readability of SQL/JSON documentation.
commit 112fdb352: Fix finalization for json_objectagg and friends
commit fcdb35c32: Fix transformJsonBehavior
commit 4cd8717af: Improve a couple of sql/json error messages
commit f7a605f63: Small cleanups in SQL/JSON code
commit 9c3d25e17: Fix JSON_OBJECTAGG uniquefying bug
commit a79153b7a: Claim SQL standard compliance for SQL/JSON features
commit a1e7616d6: Rework SQL/JSON documentation
commit 8d9f9634e: Fix errors in copyfuncs/equalfuncs support for JSON node types.
commit 3c633f32b: Only allow returning string types or bytea from json_serialize
commit 67b26703b: expression eval: Fix EEOP_JSON_CONSTRUCTOR and EEOP_JSONEXPR size.
The release notes are also adjusted.
Backpatch to release 15.
Discussion: https://postgr.es/m/40d2c882-bcac-19a9-754d-4299e1d87ac7@postgresql.org
auto_explain.log_parameter_max_length is a new GUC part of the
extension, similar to the corresponding core setting, that controls the
inclusion of query parameters in the logged explain output.
More tests are added to check the behavior of this new parameter: when
parameters logged in full (the default of -1), when disabled (value of
0) and when partially truncated (value different than the two others).
Author: Dagfinn Ilmari Mannsåker
Discussion: https://postgr.es/m/87ee09mohb.fsf@wibble.ilmari.org
We can use a single line to print all tuple counts that MERGE processed,
for conciseness, and elide those that are zeroes. Non-text formats
report all numbers, as is typical.
Per comment from Justin Pryzby <pryzby@telsasoft.com>
Discussion: https://postgr.es/m/20220511163350.GL19626@telsasoft.com
Previously, the output of EXPLAIN (BUFFERS) option showed only the I/O
timing spent reading and writing shared and local buffers. This commit
adds on top of that the I/O timing for temporary buffers in the output
of EXPLAIN (for spilled external sorts, hashes, materialization. etc).
This can be helpful for users in cases where the I/O related to
temporary buffers is the bottleneck.
Like its cousin, this information is available only when track_io_timing
is enabled. Playing the patch, this is showing an extra overhead of up
to 1% even when using gettimeofday() as implementation for interval
timings, which is slightly within the usual range noise still that's
measurable.
Author: Masahiko Sawada
Reviewed-by: Georgios Kokolatos, Melanie Plageman, Julien Rouhaud,
Ranier Vilela
Discussion: https://postgr.es/m/CAD21AoAJgotTeP83p6HiAGDhs_9Fw9pZ2J=_tYTsiO5Ob-V5GQ@mail.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
This feature allows jsonb data to be treated as a table and thus used in
a FROM clause like other tabular data. Data can be selected from the
jsonb using jsonpath expressions, and hoisted out of nested structures
in the jsonb to form multiple rows, more or less like an outer join.
Nikita Glukhov
Reviewers have included (in no particular order) Andres Freund, Alexander
Korotkov, Pavel Stehule, Andrew Alsup, Erik Rijkers, Zhihong Yu (whose
name I previously misspelled), Himanshu Upadhyaya, Daniel Gustafsson,
Justin Pryzby.
Discussion: https://postgr.es/m/7e2cb85d-24cf-4abb-30a5-1a33715959bd@postgrespro.ru
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
"regress" is a new mode added to compute_query_id aimed at facilitating
regression testing when a module computing query IDs is loaded into the
backend, like pg_stat_statements. It works the same way as "auto",
meaning that query IDs are computed if a module enables it, except that
query IDs are hidden in EXPLAIN outputs to ensure regression output
stability.
Like any GUCs of the kind (force_parallel_mode, etc.), this new
configuration can be added to an instance's postgresql.conf, or just
passed down with PGOPTIONS at command level. compute_query_id uses an
enum for its set of option values, meaning that this addition ensures
ABI compatibility.
Using this new configuration mode allows installcheck-world to pass when
running the tests on an instance with pg_stat_statements enabled,
stabilizing the test output while checking the paths doing query ID
computations.
Reported-by: Anton Melnikov
Reviewed-by: Julien Rouhaud
Discussion: https://postgr.es/m/1634283396.372373993@f75.i.mail.ru
Discussion: https://postgr.es/m/YgHlxgc/OimuPYhH@paquier.xyz
Backpatch-through: 14
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
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
This saves a few lines of code. Also add a comment to mention why we use
ExplainPropertyInteger instead of ExplainPropertyUInteger given that
queryid is a uint64 type.
Author: David Rowley
Reviewed-by: Julien Rouhaud
Discussion: https://postgr.es/m/CAApHDvqhSLYpSU_EqUdN39w9Uvb8ogmHV7_3YhJ0S3aScGBjsg@mail.gmail.com
Backpatch-through: 14, where this code was originally added
This patch causes EXPLAIN output to refer to objects that are in
the current session's temp schema with the "pg_temp" schema alias
rather than that schema's actual name. This is useful for our own
testing purposes since it will stabilize EXPLAIN VERBOSE output
for such cases, allowing us to use that in regression tests.
It should be less confusing for end users too.
Since ruleutils.c needs to change behavior for this, the change
also leaks into a few other users of ruleutils.c, for example
pg_get_viewdef(). AFAICS that won't cause any problems.
We did find that aggressively trying to change this behavior
across-the-board would cause issues, but as long as "pg_temp"
only appears within generated SQL text, I think it'll be fine.
Along the way, make get_namespace_name_or_temp conform to the
same API as get_namespace_name, ie that it returns a palloc'd
string or NULL. The current behavior hasn't caused any bugs
since no callers attempt to pfree the result, but if it gets
more widespread usage that could become a problem.
Amul Sul, reviewed and extended by me
Discussion: https://postgr.es/m/CAAJ_b97W=QaGmag9AhWNbmx3uEYsNkXWL+OVW1_E1D3BtgWvtw@mail.gmail.com
"Result Cache" was never a great name for this node, but nobody managed
to come up with another name that anyone liked enough. That was until
David Johnston mentioned "Node Memoization", which Tom Lane revised to
just "Memoize". People seem to like "Memoize", so let's do the rename.
Reviewed-by: Justin Pryzby
Discussion: https://postgr.es/m/20210708165145.GG1176@momjian.us
Backpatch-through: 14, where Result Cache was introduced
In the "simple Query" code path, it's fine for parse analysis or
execution of a utility statement to scribble on the statement's node
tree, since that'll just be thrown away afterwards. However it's
not fine if the node tree is in the plan cache, as then it'd be
corrupted for subsequent executions. Up to now we've dealt with
that by having individual utility-statement functions apply
copyObject() if they were going to modify the tree. But that's
prone to errors of omission. Bug #17053 from Charles Samborski
shows that CREATE/ALTER DOMAIN didn't get this memo, and can
crash if executed repeatedly from plan cache.
In the back branches, we'll just apply a narrow band-aid for that,
but in HEAD it seems prudent to have a more principled fix that
will close off the possibility of other similar bugs in future.
Hence, let's hoist the responsibility for doing copyObject up into
ProcessUtility from its children, thus ensuring that it happens for
all utility statement types.
Also, modify ProcessUtility's API so that its callers can tell it
whether a copy step is necessary. It turns out that in all cases,
the immediate caller knows whether the node tree is transient, so
this doesn't involve a huge amount of code thrashing. In this way,
while we lose a little bit in the execute-from-cache code path due
to sometimes copying node trees that wouldn't be mutated anyway,
we gain something in the simple-Query code path by not copying
throwaway node trees. Statements that are complex enough to be
expensive to copy are almost certainly ones that would have to be
copied anyway, so the loss in the cache code path shouldn't be much.
(Note that this whole problem applies only to utility statements.
Optimizable statements don't have the issue because we long ago made
the executor treat Plan trees as read-only. Perhaps someday we will
make utility statement execution act likewise, but I'm not holding
my breath.)
Discussion: https://postgr.es/m/931771.1623893989@sss.pgh.pa.us
Discussion: https://postgr.es/m/17053-3ca3f501bbc212b4@postgresql.org
Allowing only on/off meant that all either all existing configuration
guides would become obsolete if we disabled it by default, or that we
would have to accept a performance loss in the default config if we
enabled it by default. By allowing 'auto' as a middle ground, the
performance cost is only paid by those who enable pg_stat_statements and
similar modules.
I only edited the release notes to comment-out a paragraph that is now
factually wrong; further edits are probably needed to describe the
related change in more detail.
Author: Julien Rouhaud <rjuju123@gmail.com>
Reviewed-by: Justin Pryzby <pryzby@telsasoft.com>
Discussion: https://postgr.es/m/20210513002623.eugftm4nk2lvvks3@nol
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.
Previously long was used as the data type for some counters in BufferUsage
and WalUsage. But long is only four byte, e.g., on Windows, and it's entirely
possible to wrap a four byte counter. For example, emitting more than
four billion WAL records in one transaction isn't actually particularly rare.
To avoid the overflows of those counters, this commit changes the data type
of them from long to int64.
Suggested-by: Andres Freund
Author: Masahiro Ikeda
Reviewed-by: Fujii Masao
Discussion: https://postgr.es/m/20201221211650.k7b53tcnadrciqjo@alap3.anarazel.de
Discussion: https://postgr.es/m/af0964ac-7080-1984-dc23-513754987716@oss.nttdata.com
Here we adjust the EXPLAIN ANALYZE output for Result Cache so that we
don't show any Result Cache stats for parallel workers who don't
contribute anything to Result Cache plan nodes.
I originally had ideas that workers who don't help could still have their
Result Cache stats displayed. The idea with that was so that I could
write some parallel Result Cache regression tests that show the EXPLAIN
ANALYZE output. However, I realized a little too late that such tests
would just not be possible to have run in a stable way on the buildfarm.
With that knowledge, before 9eacee2e6 went in, I had removed all of the
tests that were showing the EXPLAIN ANALYZE output of a parallel Result
Cache plan, however, I forgot to put back the code that adjusts the
EXPLAIN output to hide the Result Cache stats for parallel workers who
were not fast enough to help out before query execution was over. All
other nodes behave this way and so should Result Cache.
Additionally, with this change, it now seems safe enough to remove the SET
force_parallel_mode = off that I had added to the regression tests.
Also, perform some cleanup in the partition_prune tests. I had adjusted
the explain_parallel_append() function to sanitize the Result Cache
EXPLAIN ANALYZE output. However, since I didn't actually include any
parallel Result Cache tests that show their EXPLAIN ANALYZE output, that
code does nothing and can be removed.
In passing, move the setting of memPeakKb into the scope where it's used.
Reported-by: Amit Khandekar
Author: David Rowley, Amit Khandekar
Discussion: https://postgr.es/m/CAJ3gD9d8SkfY95GpM1zmsOtX2-Ogx5q-WLsf8f0ykEb0hCRK3w@mail.gmail.com
This is only a latent bug, since these calls are only reached for
non-text output formats, and currently none of those will print
the units. Still, we should get it right in case that ever changes.
Justin Pryzby
Discussion: https://postgr.es/m/20210415163846.GA3315@telsasoft.com
Use the in-core query id computation for pg_stat_activity,
log_line_prefix, and EXPLAIN VERBOSE.
Similar to other fields in pg_stat_activity, only the queryid from the
top level statements are exposed, and if the backends status isn't
active then the queryid from the last executed statements is displayed.
Add a %Q placeholder to include the queryid in log_line_prefix, which
will also only expose top level statements.
For EXPLAIN VERBOSE, if a query identifier has been computed, either by
enabling compute_query_id or using a third-party module, display it.
Bump catalog version.
Discussion: https://postgr.es/m/20210407125726.tkvjdbw76hxnpwfi@nol
Author: Julien Rouhaud
Reviewed-by: Alvaro Herrera, Nitin Jadhav, Zhihong Yu
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 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
IF NOT EXISTS was ignored when specified in an EXPLAIN query for CREATE
MATERIALIZED VIEW or CREATE TABLE AS. Hence, if this clause was
specified, the caller would get a failure if the relation already
exists instead of a success with a NOTICE message.
This commit makes the behavior of IF NOT EXISTS in EXPLAIN consistent
with the non-EXPLAIN'd DDL queries, preventing a failure with IF NOT
EXISTS if the relation to-be-created already exists. The skip is done
before the SELECT query used for the relation is planned or executed,
and a "dummy" plan is generated instead depending on the format used by
EXPLAIN.
Author: Bharath Rupireddy
Reviewed-by: Zhijie Hou, Michael Paquier
Discussion: https://postgr.es/m/CALj2ACVa3oJ9O_wcGd+FtHWZds04dEKcakxphGz5POVgD4wC7Q@mail.gmail.com
The ExplainCloseGroup arguments for incremental sort usage data
didn't match the corresponding ExplainOpenGroup. This only matters
for XML-format output, which is probably why we'd not noticed.
Daniel Gustafsson, per bug #16683 from Frits Jalvingh
Discussion: https://postgr.es/m/16683-8005033324ad34e9@postgresql.org
A number of places were using appendStringInfo() when they could have been
using appendStringInfoString() instead. While there's no functionality
change there, it's just more efficient to use appendStringInfoString()
when no formatting is required. Likewise for some
appendStringInfoString() calls which were just appending a single char.
We can just use appendStringInfoChar() for that.
Additionally, many places were using appendPQExpBuffer() when they could
have used appendPQExpBufferStr(). Change those too.
Patch by Zhijie Hou, but further searching by me found significantly more
places that deserved the same treatment.
Author: Zhijie Hou, David Rowley
Discussion: https://postgr.es/m/cb172cf4361e4c7ba7167429070979d4@G08CNEXMBPEKD05.g08.fujitsu.local
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
Commit ce77abe63c allowed EXPLAIN (BUFFERS) to report the information
on buffer usage during planning phase. However three issues were
reported regarding this feature.
(1) Previously, EXPLAIN option BUFFERS required ANALYZE. So the query
had to be actually executed by specifying ANALYZE even when we
want to see only the planner's buffer usage. This was inconvenient
especially when the query was write one like DELETE.
(2) EXPLAIN included the planner's buffer usage in summary
information. So SUMMARY option had to be enabled to report that.
Also this format was confusing.
(3) The output structure for planning information was not consistent
between TEXT format and the others. For example, "Planning" tag
was output in JSON format, but not in TEXT format.
For (1), this commit allows us to perform EXPLAIN (BUFFERS) without
ANALYZE to report the planner's buffer usage.
For (2), this commit changed EXPLAIN output so that the planner's
buffer usage is reported before summary information.
For (3), this commit made the output structure for planning
information more consistent between the formats.
Back-patch to v13 where the planner's buffer usage was allowed to
be reported in EXPLAIN.
Reported-by: Pierre Giraud, David Rowley
Author: Fujii Masao
Reviewed-by: David Rowley, Julien Rouhaud, Pierre Giraud
Discussion: https://postgr.es/m/07b226e6-fa49-687f-b110-b7c37572f69e@dalibo.com
9bdb300de modified the EXPLAIN output for Hash Aggregate to show details
from parallel workers. However, it neglected to consider that a given
parallel worker may not have assisted with the given Hash Aggregate. This
can occur when workers fail to start or during Parallel Append with
enable_partitionwise_join enabled when only a single worker is working on
a non-parallel aware sub-plan. It could also happen if a worker simply
wasn't fast enough to get any work done before other processes went and
finished all the work.
The bogus output came from the fact that ExplainOpenWorker() skipped
showing any details for non-initialized workers but show_hashagg_info()
did show details from the worker. This meant that the worker properties
that were shown were not properly attributed to the worker that they
belong to.
In passing, we also now don't show Hash Aggregate properties for the
leader process when it did not contribute any work to the Hash Aggregate.
This can occur either during Parallel Append when only a parallel worker
worked on a given sub plan or with parallel_leader_participation set to
off. This aims to make the behavior of Hash Aggregate's EXPLAIN output
more similar to Sort's.
Reported-by: Justin Pryzby
Discussion: https://postgr.es/m/20200805012105.GZ28072%40telsasoft.com
Backpatch-through: 13, where the original breakage was introduced
Tom Lane
Peter Eisentraut
David Rowley
Michael Paquier
Bruce Momjian
Peter Geoghegan
Andrew Dunstan
John Naylor
Alvaro Herrera
Fujii Masao
Etsuro Fujita
Heikki Linnakangas