When we introduced separate ProjectSetPath nodes for application of
set-returning functions in v10, we inadvertently broke some cases where
we're supposed to recognize that the result of a subquery is known to be
empty (contain zero rows). That's because IS_DUMMY_REL was just looking
for a childless AppendPath without allowing for a ProjectSetPath being
possibly stuck on top. In itself, this didn't do anything much worse
than produce slightly worse plans for some corner cases.
Then in v11, commit 11cf92f6e rearranged things to allow the scan/join
targetlist to be applied directly to partial paths before they get
gathered. But it inserted a short-circuit path for dummy relations
that was a little too short: it failed to insert a ProjectSetPath node
at all for a targetlist containing set-returning functions, resulting in
bogus "set-valued function called in context that cannot accept a set"
errors, as reported in bug #15669 from Madelaine Thibaut.
The best way to fix this mess seems to be to reimplement IS_DUMMY_REL
so that it drills down through any ProjectSetPath nodes that might be
there (and it seems like we'd better allow for ProjectionPath as well).
While we're at it, make it look at rel->pathlist not cheapest_total_path,
so that it gives the right answer independently of whether set_cheapest
has been done lately. That dependency looks pretty shaky in the context
of code like apply_scanjoin_target_to_paths, and even if it's not broken
today it'd certainly bite us at some point. (Nastily, unsafe use of the
old coding would almost always work; the hazard comes down to possibly
looking through a dangling pointer, and only once in a blue moon would
you find something there that resulted in the wrong answer.)
It now looks like it was a mistake for IS_DUMMY_REL to be a macro: if
there are any extensions using it, they'll continue to use the old
inadequate logic until they're recompiled, after which they'll fail
to load into server versions predating this fix. Hopefully there are
few such extensions.
Having fixed IS_DUMMY_REL, the special path for dummy rels in
apply_scanjoin_target_to_paths is unnecessary as well as being wrong,
so we can just drop it.
Also change a few places that were testing for partitioned-ness of a
planner relation but not using IS_PARTITIONED_REL for the purpose; that
seems unsafe as well as inconsistent, plus it required an ugly hack in
apply_scanjoin_target_to_paths.
In passing, save a few cycles in apply_scanjoin_target_to_paths by
skipping processing of pre-existing paths for partitioned rels,
and do some cosmetic cleanup and comment adjustment in that function.
I renamed IS_DUMMY_PATH to IS_DUMMY_APPEND with the intention of breaking
any code that might be using it, since in almost every case that would
be wrong; IS_DUMMY_REL is what to be using instead.
In HEAD, also make set_dummy_rel_pathlist static (since it's no longer
used from outside allpaths.c), and delete is_dummy_plan, since it's no
longer used anywhere.
Back-patch as appropriate into v11 and v10.
Tom Lane and Julien Rouhaud
Discussion: https://postgr.es/m/15669-02fb3296cca26203@postgresql.org
Commit f49842d, which added support for partitionwise joins, built the
child's tlist by applying adjust_appendrel_attrs() to the parent's. So in
the case where the parent's included a whole-row Var for the parent, the
child's contained a ConvertRowtypeExpr. To cope with that, that commit
added code to the planner, such as setrefs.c, but some code paths still
assumed that the tlist for a scan (or join) rel would only include Vars
and PlaceHolderVars, which was true before that commit, causing errors:
* When creating an explicit sort node for an input path for a mergejoin
path for a child join, prepare_sort_from_pathkeys() threw the 'could not
find pathkey item to sort' error.
* When deparsing a relation participating in a pushed down child join as a
subquery in contrib/postgres_fdw, get_relation_column_alias_ids() threw
the 'unexpected expression in subquery output' error.
* When performing set_plan_references() on a local join plan generated by
contrib/postgres_fdw for EvalPlanQual support for a pushed down child
join, fix_join_expr() threw the 'variable not found in subplan target
lists' error.
To fix these, two approaches have been proposed: one by Ashutosh Bapat and
one by me. While the former keeps building the child's tlist with a
ConvertRowtypeExpr, the latter builds it with a whole-row Var for the
child not to violate the planner assumption, and tries to fix it up later,
But both approaches need more work, so refuse to generate partitionwise
join paths when whole-row Vars are involved, instead. We don't need to
handle ConvertRowtypeExprs in the child's tlists for now, so this commit
also removes the changes to the planner.
Previously, partitionwise join computed attr_needed data for each child
separately, and built the child join's tlist using that data, which also
required an extra step for adding PlaceHolderVars to that tlist, but it
would be more efficient to build it from the parent join's tlist through
the adjust_appendrel_attrs() transformation. So this commit builds that
list that way, and simplifies build_joinrel_tlist() and placeholder.c as
well as part of set_append_rel_size() to basically what they were before
partitionwise join went in.
Back-patch to PG11 where partitionwise join was introduced.
Report by Rajkumar Raghuwanshi. Analysis by Ashutosh Bapat, who also
provided some of regression tests. Patch by me, reviewed by Robert Haas.
Discussion: https://postgr.es/m/CAKcux6ktu-8tefLWtQuuZBYFaZA83vUzuRd7c1YHC-yEWyYFpg@mail.gmail.com
nodeWindowAgg.c failed to cope with the possibility that no ordering
columns are defined in the window frame for GROUPS mode or RANGE OFFSET
mode, leading to assertion failures or odd errors, as reported by Masahiko
Sawada and Lukas Eder. In RANGE OFFSET mode, an ordering column is really
required, so add an Assert about that. In GROUPS mode, the code would
work, except that the node initialization code wasn't in sync with the
execution code about when to set up tuplestore read pointers and spare
slots. Fix the latter for consistency's sake (even though I think the
changes described below make the out-of-sync cases unreachable for now).
Per SQL spec, a single ordering column is required for RANGE OFFSET mode,
and at least one ordering column is required for GROUPS mode. The parser
enforced the former but not the latter; add a check for that.
We were able to reach the no-ordering-column cases even with fully spec
compliant queries, though, because the planner would drop partitioning
and ordering columns from the generated plan if they were redundant with
earlier columns according to the redundant-pathkey logic, for instance
"PARTITION BY x ORDER BY y" in the presence of a "WHERE x=y" qual.
While in principle that's an optimization that could save some pointless
comparisons at runtime, it seems unlikely to be meaningful in the real
world. I think this behavior was not so much an intentional optimization
as a side-effect of an ancient decision to construct the plan node's
ordering-column info by reverse-engineering the PathKeys of the input
path. If we give up redundant-column removal then it takes very little
code to generate the plan node info directly from the WindowClause,
ensuring that we have the expected number of ordering columns in all
cases. (If anyone does complain about this, the planner could perhaps
be taught to remove redundant columns only when it's safe to do so,
ie *not* in RANGE OFFSET mode. But I doubt anyone ever will.)
With these changes, the WindowAggPath.winpathkeys field is not used for
anything anymore, so remove it.
The test cases added here are not actually very interesting given the
removal of the redundant-column-removal logic, but they would represent
important corner cases if anyone ever tries to put that back.
Tom Lane and Masahiko Sawada. Back-patch to v11 where RANGE OFFSET
and GROUPS modes were added.
Discussion: https://postgr.es/m/CAD21AoDrWqycq-w_+Bx1cjc+YUhZ11XTj9rfxNiNDojjBx8Fjw@mail.gmail.com
Discussion: https://postgr.es/m/153086788677.17476.8002640580496698831@wrigleys.postgresql.org
find_appinfos_by_relids had quite a large overhead when the number of
items in the append_rel_list was high, as it had to trawl through the
append_rel_list looking for AppendRelInfos belonging to the given
childrelids. Since there can only be a single AppendRelInfo for each
child rel, it seems much better to store an array in PlannerInfo which
indexes these by child relid, making the function O(1) rather than O(N).
This function was only called once inside the planner, so just replace
that call with a lookup to the new array. find_childrel_appendrelinfo
is now unused and thus removed.
This fixes a planner performance regression new to v11 reported by
Thomas Reiss.
Author: David Rowley
Reported-by: Thomas Reiss
Reviewed-by: Ashutosh Bapat
Reviewed-by: Álvaro Herrera
Discussion: https://postgr.es/m/94dd7a4b-5e50-0712-911d-2278e055c622@dalibo.com
This controls both plan-time and execution-time new-style partition
pruning. While finer-grain control is possible (maybe using an enum GUC
instead of boolean), there doesn't seem to be much need for that.
This new parameter controls partition pruning for all queries:
trivially, SELECT queries that affect partitioned tables are naturally
under its control since they are using the new technology. However,
while UPDATE/DELETE queries do not use the new code, we make the new GUC
control their behavior also (stealing control from
constraint_exclusion), because it is more natural, and it leads to a
more natural transition to the future in which those queries will also
use the new pruning code.
Constraint exclusion still controls pruning for regular inheritance
situations (those not involving partitioned tables).
Author: David Rowley
Review: Amit Langote, Ashutosh Bapat, Justin Pryzby, David G. Johnston
Discussion: https://postgr.es/m/CAKJS1f_0HwsxJG9m+nzU+CizxSdGtfe6iF_ykPYBiYft302DCw@mail.gmail.com
On further reflection, commit e5d83995e didn't go far enough: pretty much
everywhere in the planner that examines a clause's is_pushed_down flag
ought to be changed to use the more complicated behavior where we also
check the clause's required_relids. Otherwise we could make incorrect
decisions about whether, say, a clause is safe to use as a hash clause.
Some (many?) of these places are safe as-is, either because they are
never reached while considering a parameterized path, or because there
are additional checks that would reject a pushed-down clause anyway.
However, it seems smarter to just code them all the same way rather
than rely on easily-broken reasoning of that sort.
In support of that, invent a new macro RINFO_IS_PUSHED_DOWN that should
be used in place of direct tests on the is_pushed_down flag.
Like the previous patch, back-patch to all supported branches.
Discussion: https://postgr.es/m/f8128b11-c5bf-3539-48cd-234178b2314d@proxel.se
This reverts commits d204ef63776b8a00ca220adec23979091564e465,
83454e3c2b28141c0db01c7d2027e01040df5249 and a few more commits thereafter
(complete list at the end) related to MERGE feature.
While the feature was fully functional, with sufficient test coverage and
necessary documentation, it was felt that some parts of the executor and
parse-analyzer can use a different design and it wasn't possible to do that in
the available time. So it was decided to revert the patch for PG11 and retry
again in the future.
Thanks again to all reviewers and bug reporters.
List of commits reverted, in reverse chronological order:
f1464c5380 Improve parse representation for MERGE
ddb4158579 MERGE syntax diagram correction
530e69e59b Allow cpluspluscheck to pass by renaming variable
01b88b4df5 MERGE minor errata
3af7b2b0d4 MERGE fix variable warning in non-assert builds
a5d86181ec MERGE INSERT allows only one VALUES clause
4b2d44031f MERGE post-commit review
4923550c20 Tab completion for MERGE
aa3faa3c7a WITH support in MERGE
83454e3c2b New files for MERGE
d204ef6377 MERGE SQL Command following SQL:2016
Author: Pavan Deolasee
Reviewed-by: Michael Paquier
This patch introduces INCLUDE clause to index definition. This clause
specifies a list of columns which will be included as a non-key part in
the index. The INCLUDE columns exist solely to allow more queries to
benefit from index-only scans. Also, such columns don't need to have
appropriate operator classes. Expressions are not supported as INCLUDE
columns since they cannot be used in index-only scans.
Index access methods supporting INCLUDE are indicated by amcaninclude flag
in IndexAmRoutine. For now, only B-tree indexes support INCLUDE clause.
In B-tree indexes INCLUDE columns are truncated from pivot index tuples
(tuples located in non-leaf pages and high keys). Therefore, B-tree indexes
now might have variable number of attributes. This patch also provides
generic facility to support that: pivot tuples contain number of their
attributes in t_tid.ip_posid. Free 13th bit of t_info is used for indicating
that. This facility will simplify further support of index suffix truncation.
The changes of above are backward-compatible, pg_upgrade doesn't need special
handling of B-tree indexes for that.
Bump catalog version
Author: Anastasia Lubennikova with contribition by Alexander Korotkov and me
Reviewed by: Peter Geoghegan, Tomas Vondra, Antonin Houska, Jeff Janes,
David Rowley, Alexander Korotkov
Discussion: https://www.postgresql.org/message-id/flat/56168952.4010101@postgrespro.ru
Add a new module backend/partitioning/partprune.c, implementing a more
sophisticated algorithm for partition pruning. The new module uses each
partition's "boundinfo" for pruning instead of constraint exclusion,
based on an idea proposed by Robert Haas of a "pruning program": a list
of steps generated from the query quals which are run iteratively to
obtain a list of partitions that must be scanned in order to satisfy
those quals.
At present, this targets planner-time partition pruning, but there exist
further patches to apply partition pruning at execution time as well.
This commit also moves some definitions from include/catalog/partition.h
to a new file include/partitioning/partbounds.h, in an attempt to
rationalize partitioning related code.
Authors: Amit Langote, David Rowley, Dilip Kumar
Reviewers: Robert Haas, Kyotaro Horiguchi, Ashutosh Bapat, Jesper Pedersen.
Discussion: https://postgr.es/m/098b9c71-1915-1a2a-8d52-1a7a50ce79e8@lab.ntt.co.jp
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 other require multiple PL statements.
e.g.
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 and partitioned tables, including
column and row security enforcement, as well as support for
row, statement and transition triggers.
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 statically from PL/pgSQL.
MERGE does not yet support inheritance, write rules,
RETURNING clauses, updatable views or foreign tables.
MERGE follows SQL Standard per the most recent SQL:2016.
Includes full tests and documentation, including full
isolation tests to demonstrate the concurrent behavior.
This version written from scratch in 2017 by Simon Riggs,
using docs and tests originally written in 2009. Later work
from Pavan Deolasee has been both complex and deep, leaving
the lead author credit now in his hands.
Extensive discussion of concurrency from Peter Geoghegan,
with thanks for the time and effort contributed.
Various issues reported via sqlsmith by Andreas Seltenreich
Authors: Pavan Deolasee, Simon Riggs
Reviewer: Peter Geoghegan, Amit Langote, Tomas Vondra, Simon Riggs
Discussion:
https://postgr.es/m/CANP8+jKitBSrB7oTgT9CY2i1ObfOt36z0XMraQc+Xrz8QB0nXA@mail.gmail.comhttps://postgr.es/m/CAH2-WzkJdBuxj9PO=2QaO9-3h3xGbQPZ34kJH=HukRekwM-GZg@mail.gmail.com
If the partition keys of input relation are part of the GROUP BY
clause, all the rows belonging to a given group come from a single
partition. This allows aggregation/grouping over a partitioned
relation to be broken down * into aggregation/grouping on each
partition. This should be no worse, and often better, than the normal
approach.
If the GROUP BY clause does not contain all the partition keys, we can
still perform partial aggregation for each partition and then finalize
aggregation after appending the partial results. This is less certain
to be a win, but it's still useful.
Jeevan Chalke, Ashutosh Bapat, Robert Haas. The larger patch series
of which this patch is a part was also reviewed and tested by Antonin
Houska, Rajkumar Raghuwanshi, David Rowley, Dilip Kumar, Konstantin
Knizhnik, Pascal Legrand, and Rafia Sabih.
Discussion: http://postgr.es/m/CAM2+6=V64_xhstVHie0Rz=KPEQnLJMZt_e314P0jaT_oJ9MR8A@mail.gmail.com
The previous code considered two tables to have the partition scheme
if the underlying columns had the same collation, but what we
actually need to compare is not the collations associated with the
column but the collation used for partitioning. Fix that.
Robert Haas and Amit Langote
Discussion: http://postgr.es/m/0f95f924-0efa-4cf5-eb5f-9a3d1bc3c33d@lab.ntt.co.jp
Up until now, we've abused grouped_rel->partial_pathlist as a place to
store partial paths that have been partially aggregate, but that's
really not correct, because a partial path for a relation is supposed
to be one which produces the correct results with the addition of only
a Gather or Gather Merge node, and these paths also require a Finalize
Aggregate step. Instead, add a new partially_group_rel which can hold
either partial paths (which need to be gathered and then have
aggregation finalized) or non-partial paths (which only need to have
aggregation finalized). This allows us to reuse generate_gather_paths
for partially_grouped_rel instead of writing new code, so that this
patch actually basically no net new code while making things cleaner,
simplifying things for pending patches for partition-wise aggregate.
Robert Haas and Jeevan Chalke. The larger patch series of which this
patch is a part was also reviewed and tested by Antonin Houska,
Rajkumar Raghuwanshi, David Rowley, Dilip Kumar, Konstantin Knizhnik,
Pascal Legrand, Rafia Sabih, and me.
Discussion: http://postgr.es/m/CA+TgmobrzFYS3+U8a_BCy3-hOvh5UyJbC18rEcYehxhpw5=ETA@mail.gmail.com
Discussion: http://postgr.es/m/CA+TgmoZyQEjdBNuoG9-wC5GQ5GrO4544Myo13dVptvx+uLg9uQ@mail.gmail.com
The previous code assumed that we'd always succeed in creating
child-joins for a joinrel for which partition-wise join was considered,
but that's not guaranteed, at least in the case where dummy rels
are involved.
Ashutosh Bapat, with some wordsmithing by me.
Discussion: http://postgr.es/m/CAFjFpRf8=uyMYYfeTBjWDMs1tR5t--FgOe2vKZPULxxdYQ4RNw@mail.gmail.com
When an UPDATE causes a row to no longer match the partition
constraint, try to move it to a different partition where it does
match the partition constraint. In essence, the UPDATE is split into
a DELETE from the old partition and an INSERT into the new one. This
can lead to surprising behavior in concurrency scenarios because
EvalPlanQual rechecks won't work as they normally did; the known
problems are documented. (There is a pending patch to improve the
situation further, but it needs more review.)
Amit Khandekar, reviewed and tested by Amit Langote, David Rowley,
Rajkumar Raghuwanshi, Dilip Kumar, Amul Sul, Thomas Munro, Álvaro
Herrera, Amit Kapila, and me. A few final revisions by me.
Discussion: http://postgr.es/m/CAJ3gD9do9o2ccQ7j7+tSgiE1REY65XRiMb=yJO3u3QhyP8EEPQ@mail.gmail.com
Introduce parallel-aware hash joins that appear in EXPLAIN plans as Parallel
Hash Join with Parallel Hash. While hash joins could already appear in
parallel queries, they were previously always parallel-oblivious and had a
partial subplan only on the outer side, meaning that the work of the inner
subplan was duplicated in every worker.
After this commit, the planner will consider using a partial subplan on the
inner side too, using the Parallel Hash node to divide the work over the
available CPU cores and combine its results in shared memory. If the join
needs to be split into multiple batches in order to respect work_mem, then
workers process different batches as much as possible and then work together
on the remaining batches.
The advantages of a parallel-aware hash join over a parallel-oblivious hash
join used in a parallel query are that it:
* avoids wasting memory on duplicated hash tables
* avoids wasting disk space on duplicated batch files
* divides the work of building the hash table over the CPUs
One disadvantage is that there is some communication between the participating
CPUs which might outweigh the benefits of parallelism in the case of small
hash tables. This is avoided by the planner's existing reluctance to supply
partial plans for small scans, but it may be necessary to estimate
synchronization costs in future if that situation changes. Another is that
outer batch 0 must be written to disk if multiple batches are required.
A potential future advantage of parallel-aware hash joins is that right and
full outer joins could be supported, since there is a single set of matched
bits for each hashtable, but that is not yet implemented.
A new GUC enable_parallel_hash is defined to control the feature, defaulting
to on.
Author: Thomas Munro
Reviewed-By: Andres Freund, Robert Haas
Tested-By: Rafia Sabih, Prabhat Sahu
Discussion:
https://postgr.es/m/CAEepm=2W=cOkiZxcg6qiFQP-dHUe09aqTrEMM7yJDrHMhDv_RA@mail.gmail.comhttps://postgr.es/m/CAEepm=37HKyJ4U6XOLi=JgfSHM3o6B-GaeO-6hkOmneTDkH+Uw@mail.gmail.com
When we create an Append node, we can spread out the workers over the
subplans instead of piling on to each subplan one at a time, which
should typically be a bit more efficient, both because the startup
cost of any plan executed entirely by one worker is paid only once and
also because of reduced contention. We can also construct Append
plans using a mix of partial and non-partial subplans, which may allow
for parallelism in places that otherwise couldn't support it.
Unfortunately, this patch doesn't handle the important case of
parallelizing UNION ALL by running each branch in a separate worker;
the executor infrastructure is added here, but more planner work is
needed.
Amit Khandekar, Robert Haas, Amul Sul, reviewed and tested by
Ashutosh Bapat, Amit Langote, Rafia Sabih, Amit Kapila, and
Rajkumar Raghuwanshi.
Discussion: http://postgr.es/m/CAJ3gD9dy0K_E8r727heqXoBmWZ83HwLFwdcaSSmBQ1+S+vRuUQ@mail.gmail.com
Up until now, we only tracked the number of parameters, which was
sufficient to allocate an array of Datums of the appropriate size,
but not sufficient to, for example, know how to serialize a Datum
stored in one of those slots. An upcoming patch wants to do that,
so add this tracking to make it possible.
Patch by me, reviewed by Tom Lane and Amit Kapila.
Discussion: http://postgr.es/m/CA+TgmoYqpxDKn8koHdW8BEKk8FMUL0=e8m2Qe=M+r0UBjr3tuQ@mail.gmail.com
The lower case spellings are C and C++ standard and are used in most
parts of the PostgreSQL sources. The upper case spellings are only used
in some files/modules. So standardize on the standard spellings.
The APIs for ICU, Perl, and Windows define their own TRUE and FALSE, so
those are left as is when using those APIs.
In code comments, we use the lower-case spelling for the C concepts and
keep the upper-case spelling for the SQL concepts.
Reviewed-by: Michael Paquier <michael.paquier@gmail.com>
Instead of joining two partitioned tables in their entirety we can, if
it is an equi-join on the partition keys, join the matching partitions
individually. This involves teaching the planner about "other join"
rels, which are related to regular join rels in the same way that
other member rels are related to baserels. This can use significantly
more CPU time and memory than regular join planning, because there may
now be a set of "other" rels not only for every base relation but also
for every join relation. In most practical cases, this probably
shouldn't be a problem, because (1) it's probably unusual to join many
tables each with many partitions using the partition keys for all
joins and (2) if you do that scenario then you probably have a big
enough machine to handle the increased memory cost of planning and (3)
the resulting plan is highly likely to be better, so what you spend in
planning you'll make up on the execution side. All the same, for now,
turn this feature off by default.
Currently, we can only perform joins between two tables whose
partitioning schemes are absolutely identical. It would be nice to
cope with other scenarios, such as extra partitions on one side or the
other with no match on the other side, but that will have to wait for
a future patch.
Ashutosh Bapat, reviewed and tested by Rajkumar Raghuwanshi, Amit
Langote, Rafia Sabih, Thomas Munro, Dilip Kumar, Antonin Houska, Amit
Khandekar, and by me. A few final adjustments by me.
Discussion: http://postgr.es/m/CAFjFpRfQ8GrQvzp3jA2wnLqrHmaXna-urjm_UY9BqXj=EaDTSA@mail.gmail.com
Discussion: http://postgr.es/m/CAFjFpRcitjfrULr5jfuKWRPsGUX0LQ0k8-yG0Qw2+1LBGNpMdw@mail.gmail.com
This is not used for anything yet, but it is necessary infrastructure
for partition-wise join and for partition pruning without constraint
exclusion.
Ashutosh Bapat, reviewed by Amit Langote and with quite a few changes,
mostly cosmetic, by me. Additional review and testing of this patch
series by Antonin Houska, Amit Khandekar, Rafia Sabih, Rajkumar
Raghuwanshi, Thomas Munro, and Dilip Kumar.
Discussion: http://postgr.es/m/CAFjFpRfneFG3H+F6BaiXemMrKF+FY-POpx3Ocy+RiH3yBmXSNw@mail.gmail.com
Flattening the partitioning hierarchy at this stage makes various
desirable optimizations difficult. The original use case for this
patch was partition-wise join, which wants to match up the partitions
in one partitioning hierarchy with those in another such hierarchy.
However, it now seems that it will also be useful in making partition
pruning work using the PartitionDesc rather than constraint exclusion,
because with a flattened expansion, we have no easy way to figure out
which PartitionDescs apply to which leaf tables in a multi-level
partition hierarchy.
As it turns out, we end up creating both rte->inh and !rte->inh RTEs
for each intermediate partitioned table, just as we previously did for
the root table. This seems unnecessary since the partitioned tables
have no storage and are not scanned. We might want to go back and
rejigger things so that no partitioned tables (including the parent)
need !rte->inh RTEs, but that seems to require some adjustments not
related to the core purpose of this patch.
Ashutosh Bapat, reviewed by me and by Amit Langote. Some final
adjustments by me.
Discussion: http://postgr.es/m/CAFjFpRd=1venqLL7oGU=C1dEkuvk2DJgvF+7uKbnPHaum1mvHQ@mail.gmail.com
The ExecReScan machinery contains various optimizations for postponing
or skipping rescans of plan subtrees; for example a HashAgg node may
conclude that it can re-use the table it built before, instead of
re-reading its input subtree. But that is wrong if the input contains
a parallel-aware table scan node, since the portion of the table scanned
by the leader process is likely to vary from one rescan to the next.
This explains the timing-dependent buildfarm failures we saw after
commit a2b70c89c.
The established mechanism for showing that a plan node's output is
potentially variable is to mark it as depending on some runtime Param.
Hence, to fix this, invent a dummy Param (one that has a PARAM_EXEC
parameter number, but carries no actual value) associated with each Gather
or GatherMerge node, mark parallel-aware nodes below that node as dependent
on that Param, and arrange for ExecReScanGather[Merge] to flag that Param
as changed whenever the Gather[Merge] node is rescanned.
This solution breaks an undocumented assumption made by the parallel
executor logic, namely that all rescans of nodes below a Gather[Merge]
will happen synchronously during the ReScan of the top node itself.
But that's fundamentally contrary to the design of the ExecReScan code,
and so was doomed to fail someday anyway (even if you want to argue
that the bug being fixed here wasn't a failure of that assumption).
A follow-on patch will address that issue. In the meantime, the worst
that's expected to happen is that given very bad timing luck, the leader
might have to do all the work during a rescan, because workers think
they have nothing to do, if they are able to start up before the eventual
ReScan of the leader's parallel-aware table scan node has reset the
shared scan state.
Although this problem exists in 9.6, there does not seem to be any way
for it to manifest there. Without GatherMerge, it seems that a plan tree
that has a rescan-short-circuiting node below Gather will always also
have one above it that will short-circuit in the same cases, preventing
the Gather from being rescanned. Hence we won't take the risk of
back-patching this change into 9.6. But v10 needs it.
Discussion: https://postgr.es/m/CAA4eK1JkByysFJNh9M349u_nNjqETuEnY_y1VUc_kJiU0bxtaQ@mail.gmail.com
Previously, if we had to estimate the number of distinct values in a
VALUES column, we fell back on the default behavior used whenever we lack
statistics, which effectively is that there are Min(# of entries, 200)
distinct values. This can be very badly off with a large VALUES list,
as noted by Jeff Janes.
We could consider actually running an ANALYZE-like scan on the VALUES,
but that seems unduly expensive, and anyway it could not deliver reliable
info if the entries are not all constants. What seems like a better choice
is to assume that the values are all distinct. This will sometimes be just
as wrong as the old code, but it seems more likely to be more nearly right
in many common cases. Also, it is more consistent with what happens in
some related cases, for example WHERE x = ANY(ARRAY[1,2,3,...,n]) and
WHERE x = ANY(VALUES (1),(2),(3),...,(n)) now are estimated similarly.
This was discussed some time ago, but consensus was it'd be better
to slip it in at the start of a development cycle not near the end.
(It should've gone into v10, really, but I forgot about it.)
Discussion: https://postgr.es/m/CAMkU=1xHkyPa8VQgGcCNg3RMFFvVxUdOpus1gKcFuvVi0w6Acg@mail.gmail.com
The executor is capable of splitting buckets during a hash join if
too much memory is being used by a small number of buckets. However,
this only helps if a bucket's population is actually divisible; if
all the hash keys are alike, the tuples still end up in the same
new bucket. This can result in an OOM failure if there are enough
inner keys with identical hash values. The planner's cost estimates
will bias it against choosing a hash join in such situations, but not
by so much that it will never do so. To mitigate the OOM hazard,
explicitly estimate the hash bucket space needed by just the inner
side's most common value, and if that would exceed work_mem then
add disable_cost to the hash cost estimate.
This approach doesn't account for the possibility that two or more
common values would share the same hash value. On the other hand,
work_mem is normally a fairly conservative bound, so that eating
two or more times that much space is probably not going to kill us.
If we have no stats about the inner side, ignore this consideration.
There was some discussion of making a conservative assumption, but that
would effectively result in disabling hash join whenever we lack stats,
which seems like an overreaction given how seldom the problem manifests
in the field.
Per a complaint from David Hinkle. Although this could be viewed
as a bug fix, the lack of similar complaints weighs against back-
patching; indeed we waited for v11 because it seemed already rather
late in the v10 cycle to be making plan choice changes like this one.
Discussion: https://postgr.es/m/32013.1487271761@sss.pgh.pa.us
Change pg_bsd_indent to follow upstream rules for placement of comments
to the right of code, and remove pgindent hack that caused comments
following #endif to not obey the general rule.
Commit e3860ffa4dd0dad0dd9eea4be9cc1412373a8c89 wasn't actually using
the published version of pg_bsd_indent, but a hacked-up version that
tried to minimize the amount of movement of comments to the right of
code. The situation of interest is where such a comment has to be
moved to the right of its default placement at column 33 because there's
code there. BSD indent has always moved right in units of tab stops
in such cases --- but in the previous incarnation, indent was working
in 8-space tab stops, while now it knows we use 4-space tabs. So the
net result is that in about half the cases, such comments are placed
one tab stop left of before. This is better all around: it leaves
more room on the line for comment text, and it means that in such
cases the comment uniformly starts at the next 4-space tab stop after
the code, rather than sometimes one and sometimes two tabs after.
Also, ensure that comments following #endif are indented the same
as comments following other preprocessor commands such as #else.
That inconsistency turns out to have been self-inflicted damage
from a poorly-thought-through post-indent "fixup" in pgindent.
This patch is much less interesting than the first round of indent
changes, but also bulkier, so I thought it best to separate the effects.
Discussion: https://postgr.es/m/E1dAmxK-0006EE-1r@gemulon.postgresql.org
Discussion: https://postgr.es/m/30527.1495162840@sss.pgh.pa.us
The new indent version includes numerous fixes thanks to Piotr Stefaniak.
The main changes visible in this commit are:
* Nicer formatting of function-pointer declarations.
* No longer unexpectedly removes spaces in expressions using casts,
sizeof, or offsetof.
* No longer wants to add a space in "struct structname *varname", as
well as some similar cases for const- or volatile-qualified pointers.
* Declarations using PG_USED_FOR_ASSERTS_ONLY are formatted more nicely.
* Fixes bug where comments following declarations were sometimes placed
with no space separating them from the code.
* Fixes some odd decisions for comments following case labels.
* Fixes some cases where comments following code were indented to less
than the expected column 33.
On the less good side, it now tends to put more whitespace around typedef
names that are not listed in typedefs.list. This might encourage us to
put more effort into typedef name collection; it's not really a bug in
indent itself.
There are more changes coming after this round, having to do with comment
indentation and alignment of lines appearing within parentheses. I wanted
to limit the size of the diffs to something that could be reviewed without
one's eyes completely glazing over, so it seemed better to split up the
changes as much as practical.
Discussion: https://postgr.es/m/E1dAmxK-0006EE-1r@gemulon.postgresql.org
Discussion: https://postgr.es/m/30527.1495162840@sss.pgh.pa.us
Even though no actual tuples are ever inserted into a partitioned
table (the actual tuples are in the partitions, not the partitioned
table itself), we still need to have a ResultRelInfo for the
partitioned table, or per-statement triggers won't get fired.
Amit Langote, per a report from Rajkumar Raghuwanshi. Reviewed by me.
Discussion: http://postgr.es/m/CAKcux6%3DwYospCRY2J4XEFuVy0L41S%3Dfic7rmkbsU-GXhhSbmBg%40mail.gmail.com
If there can certainly be no more than one matching inner row for a given
outer row, then the executor can move on to the next outer row as soon as
it's found one match; there's no need to continue scanning the inner
relation for this outer row. This saves useless scanning in nestloop
and hash joins. In merge joins, it offers the opportunity to skip
mark/restore processing, because we know we have not advanced past the
first possible match for the next outer row.
Of course, the devil is in the details: the proof of uniqueness must
depend only on joinquals (not otherquals), and if we want to skip
mergejoin mark/restore then it must depend only on merge clauses.
To avoid adding more planning overhead than absolutely necessary,
the present patch errs in the conservative direction: there are cases
where inner_unique or skip_mark_restore processing could be used, but
it will not do so because it's not sure that the uniqueness proof
depended only on "safe" clauses. This could be improved later.
David Rowley, reviewed and rather heavily editorialized on by me
Discussion: https://postgr.es/m/CAApHDvqF6Sw-TK98bW48TdtFJ+3a7D2mFyZ7++=D-RyPsL76gw@mail.gmail.com
Currently, the only type of child relation is an "other member rel",
which is the child of a baserel, but in the future joins and even
upper relations may have child rels. To facilitate that, introduce
macros that test to test for particular RelOptKind values, and use
them in various places where they help to clarify the sense of a test.
(For example, a test may allow RELOPT_OTHER_MEMBER_REL either because
it intends to allow child rels, or because it intends to allow simple
rels.)
Also, remove find_childrel_top_parent, which will not work for a
child rel that is not a baserel. Instead, add a new RelOptInfo
member top_parent_relids to track the same kind of information in a
more generic manner.
Ashutosh Bapat, slightly tweaked by me. Review and testing of the
patch set from which this was taken by Rajkumar Raghuwanshi and Rafia
Sabih.
Discussion: http://postgr.es/m/CA+TgmoagTnF2yqR3PT2rv=om=wJiZ4-A+ATwdnriTGku1CLYxA@mail.gmail.com
Commit 45be99f8cd5d606086e0a458c9c72910ba8a613d removed GatherPath's
num_workers field, but this is entirely bogus. Normally, a path's
parallel_workers flag is supposed to indicate the number of workers
that it wants, and should be 0 for a non-partial path. In that
commit, I mistakenly thought that GatherPath could also use that field
to indicate the number of workers that it would try to start, but
that's disastrous, because then it can propagate up to higher nodes in
the plan tree, which will then get incorrect rowcounts because the
parallel_workers flag is involved in computing those values. Repair
by putting the separate field back.
Report by Tomas Vondra. Patch by me, reviewed by Amit Kapila.
Discussion: http://postgr.es/m/f91b4a44-f739-04bd-c4b6-f135bd643669@2ndquadrant.com
This extends the Aggregate node with two new features: HashAggregate
can now run multiple hashtables concurrently, and a new strategy
MixedAggregate populates hashtables while doing sorted grouping.
The planner will now attempt to save as many sorts as possible when
planning grouping sets queries, while not exceeding work_mem for the
estimated combined sizes of all hashtables used. No SQL-level changes
are required. There should be no user-visible impact other than the
new EXPLAIN output and possible changes to result ordering when ORDER
BY was not used (which affected a few regression tests). The
enable_hashagg option is respected.
Author: Andrew Gierth
Reviewers: Mark Dilger, Andres Freund
Discussion: https://postgr.es/m/87vatszyhj.fsf@news-spur.riddles.org.uk
Add support for explicitly declared statistic objects (CREATE
STATISTICS), allowing collection of statistics on more complex
combinations that individual table columns. Companion commands DROP
STATISTICS and ALTER STATISTICS ... OWNER TO / SET SCHEMA / RENAME are
added too. All this DDL has been designed so that more statistic types
can be added later on, such as multivariate most-common-values and
multivariate histograms between columns of a single table, leaving room
for permitting columns on multiple tables, too, as well as expressions.
This commit only adds support for collection of n-distinct coefficient
on user-specified sets of columns in a single table. This is useful to
estimate number of distinct groups in GROUP BY and DISTINCT clauses;
estimation errors there can cause over-allocation of memory in hashed
aggregates, for instance, so it's a worthwhile problem to solve. A new
special pseudo-type pg_ndistinct is used.
(num-distinct estimation was deemed sufficiently useful by itself that
this is worthwhile even if no further statistic types are added
immediately; so much so that another version of essentially the same
functionality was submitted by Kyotaro Horiguchi:
https://postgr.es/m/20150828.173334.114731693.horiguchi.kyotaro@lab.ntt.co.jp
though this commit does not use that code.)
Author: Tomas Vondra. Some code rework by Álvaro.
Reviewed-by: Dean Rasheed, David Rowley, Kyotaro Horiguchi, Jeff Janes,
Ideriha Takeshi
Discussion: https://postgr.es/m/543AFA15.4080608@fuzzy.czhttps://postgr.es/m/20170320190220.ixlaueanxegqd5gr@alvherre.pgsql
Partitioned tables do not contain any data; only their unpartitioned
descendents need to be scanned. However, the partitioned tables still
need to be locked, even though they're not scanned. To make that
work, Append and MergeAppend relations now need to carry a list of
(unscanned) partitioned relations that must be locked, and InitPlan
must lock all partitioned result relations.
Aside from the obvious advantage of avoiding some work at execution
time, this has two other advantages. First, it may improve the
planner's decision-making in some cases since the empty relation
might throw things off. Second, it paves the way to getting rid of
the storage for partitioned tables altogether.
Amit Langote, reviewed by me.
Discussion: http://postgr.es/m/6837c359-45c4-8044-34d1-736756335a15@lab.ntt.co.jp
Like Gather, we spawn multiple workers and run the same plan in each
one; however, Gather Merge is used when each worker produces the same
output ordering and we want to preserve that output ordering while
merging together the streams of tuples from various workers. (In a
way, Gather Merge is like a hybrid of Gather and MergeAppend.)
This works out to a win if it saves us from having to perform an
expensive Sort. In cases where only a small amount of data would need
to be sorted, it may actually be faster to use a regular Gather node
and then sort the results afterward, because Gather Merge sometimes
needs to wait synchronously for tuples whereas a pure Gather generally
doesn't. But if this avoids an expensive sort then it's a win.
Rushabh Lathia, reviewed and tested by Amit Kapila, Thomas Munro,
and Neha Sharma, and reviewed and revised by me.
Discussion: http://postgr.es/m/CAGPqQf09oPX-cQRpBKS0Gq49Z+m6KBxgxd_p9gX8CKk_d75HoQ@mail.gmail.com
In combination with 569174f1be92be93f5366212cc46960d28a5c5cd, which
taught the btree AM how to perform parallel index scans, this allows
parallel index scan plans on btree indexes. This infrastructure
should be general enough to support parallel index scans for other
index AMs as well, if someone updates them to support parallel
scans.
Amit Kapila, reviewed and tested by Anastasia Lubennikova, Tushar
Ahuja, and Haribabu Kommi, and me.
Evaluation of set returning functions (SRFs_ in the targetlist (like SELECT
generate_series(1,5)) so far was done in the expression evaluation (i.e.
ExecEvalExpr()) and projection (i.e. ExecProject/ExecTargetList) code.
This meant that most executor nodes performing projection, and most
expression evaluation functions, had to deal with the possibility that an
evaluated expression could return a set of return values.
That's bad because it leads to repeated code in a lot of places. It also,
and that's my (Andres's) motivation, made it a lot harder to implement a
more efficient way of doing expression evaluation.
To fix this, introduce a new executor node (ProjectSet) that can evaluate
targetlists containing one or more SRFs. To avoid the complexity of the old
way of handling nested expressions returning sets (e.g. having to pass up
ExprDoneCond, and dealing with arguments to functions returning sets etc.),
those SRFs can only be at the top level of the node's targetlist. The
planner makes sure (via split_pathtarget_at_srfs()) that SRF evaluation is
only necessary in ProjectSet nodes and that SRFs are only present at the
top level of the node's targetlist. If there are nested SRFs the planner
creates multiple stacked ProjectSet nodes. The ProjectSet nodes always get
input from an underlying node.
We also discussed and prototyped evaluating targetlist SRFs using ROWS
FROM(), but that turned out to be more complicated than we'd hoped.
While moving SRF evaluation to ProjectSet would allow to retain the old
"least common multiple" behavior when multiple SRFs are present in one
targetlist (i.e. continue returning rows until all SRFs are at the end of
their input at the same time), we decided to instead only return rows till
all SRFs are exhausted, returning NULL for already exhausted ones. We
deemed the previous behavior to be too confusing, unexpected and actually
not particularly useful.
As a side effect, the previously prohibited case of multiple set returning
arguments to a function, is now allowed. Not because it's particularly
desirable, but because it ends up working and there seems to be no argument
for adding code to prohibit it.
Currently the behavior for COALESCE and CASE containing SRFs has changed,
returning multiple rows from the expression, even when the SRF containing
"arm" of the expression is not evaluated. That's because the SRFs are
evaluated in a separate ProjectSet node. As that's quite confusing, we're
likely to instead prohibit SRFs in those places. But that's still being
discussed, and the code would reside in places not touched here, so that's
a task for later.
There's a lot of, now superfluous, code dealing with set return expressions
around. But as the changes to get rid of those are verbose largely boring,
it seems better for readability to keep the cleanup as a separate commit.
Author: Tom Lane and Andres Freund
Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de
In an RLS query, we must ensure that security filter quals are evaluated
before ordinary query quals, in case the latter contain "leaky" functions
that could expose the contents of sensitive rows. The original
implementation of RLS planning ensured this by pushing the scan of a
secured table into a sub-query that it marked as a security-barrier view.
Unfortunately this results in very inefficient plans in many cases, because
the sub-query cannot be flattened and gets planned independently of the
rest of the query.
To fix, drop the use of sub-queries to enforce RLS qual order, and instead
mark each qual (RestrictInfo) with a security_level field establishing its
priority for evaluation. Quals must be evaluated in security_level order,
except that "leakproof" quals can be allowed to go ahead of quals of lower
security_level, if it's helpful to do so. This has to be enforced within
the ordering of any one list of quals to be evaluated at a table scan node,
and we also have to ensure that quals are not chosen for early evaluation
(i.e., use as an index qual or TID scan qual) if they're not allowed to go
ahead of other quals at the scan node.
This is sufficient to fix the problem for RLS quals, since we only support
RLS policies on simple tables and thus RLS quals will always exist at the
table scan level only. Eventually these qual ordering rules should be
enforced for join quals as well, which would permit improving planning for
explicit security-barrier views; but that's a task for another patch.
Note that FDWs would need to be aware of these rules --- and not, for
example, send an insecure qual for remote execution --- but since we do
not yet allow RLS policies on foreign tables, the case doesn't arise.
This will need to be addressed before we can allow such policies.
Patch by me, reviewed by Stephen Frost and Dean Rasheed.
Discussion: https://postgr.es/m/8185.1477432701@sss.pgh.pa.us
