reparameterize_path_by_child() failed to reparameterize BitmapAnd
and BitmapOr paths. This matters only if such a path is chosen as
the inside of a nestloop partition-wise join, where we have to pass
in parameters from the outside of the nestloop. If that did happen,
we generated a bad plan that would likely lead to crashes at execution.
This is not entirely reparameterize_path_by_child()'s fault though;
it's the victim of an ancient decision (my ancient decision, I think)
to not bother filling in param_info in BitmapAnd/Or path nodes. That
caused the function to believe that such nodes and their children
contain no parameter references and so need not be processed.
In hindsight that decision looks pretty penny-wise and pound-foolish:
while it saves a few cycles during path node setup, we do commonly
need the information later. In particular, by reversing the decision
and requiring valid param_info data in all nodes of a bitmap path
tree, we can get rid of indxpath.c's get_bitmap_tree_required_outer()
function, which computed the data on-demand. It's not unlikely that
that nets out as a savings of cycles in many scenarios. A couple
of other things in indxpath.c can be simplified as well.
While here, get rid of some cases in reparameterize_path_by_child()
that are visibly dead or useless, given that we only care about
reparameterizing paths that can be on the inside of a parameterized
nestloop. This case reminds one of the maxim that untested code
probably does not work, so I'm unwilling to leave unreachable code
in this function. (I did leave the T_Gather case in place even
though it's not reached in the regression tests. It's not very
clear to me when the planner might prefer to put Gather below
rather than above a nestloop, but at least in principle the case
might be interesting.)
Per bug #16536, originally from Arne Roland but with a test case
by Andrew Gierth. Back-patch to v11 where this code came in.
Discussion: https://postgr.es/m/16536-2213ee0b3aad41fd@postgresql.org
This case didn't work because columns merged by FULL JOIN USING are
represented in the parse tree by COALESCE expressions, and the logic
for recognizing a partitionable join failed to match upper-level join
clauses to such expressions. To fix, synthesize suitable COALESCE
expressions and add them to the nullable_partexprs lists. This is
pretty ugly and brute-force, but it gets the job done. (I have
ambitions of rethinking the way outer-join output Vars are
represented, so maybe that will provide a cleaner solution someday.
For now, do this.)
Amit Langote, reviewed by Justin Pryzby, Richard Guo, and myself
Discussion: https://postgr.es/m/CA+HiwqG2WVUGmLJqtR0tPFhniO=H=9qQ+Z3L_ZC+Y3-EVQHFGg@mail.gmail.com
Previously, the partitionwise join technique only allowed partitionwise
join when input partitioned tables had exactly the same partition
bounds. This commit extends the technique to some cases when the tables
have different partition bounds, by using an advanced partition-matching
algorithm introduced by this commit. For both the input partitioned
tables, the algorithm checks whether every partition of one input
partitioned table only matches one partition of the other input
partitioned table at most, and vice versa. In such a case the join
between the tables can be broken down into joins between the matching
partitions, so the algorithm produces the pairs of the matching
partitions, plus the partition bounds for the join relation, to allow
partitionwise join for computing the join. Currently, the algorithm
works for list-partitioned and range-partitioned tables, but not
hash-partitioned tables. See comments in partition_bounds_merge().
Ashutosh Bapat and Etsuro Fujita, most of regression tests by Rajkumar
Raghuwanshi, some of the tests by Mark Dilger and Amul Sul, reviewed by
Dmitry Dolgov and Amul Sul, with additional review at various points by
Ashutosh Bapat, Mark Dilger, Robert Haas, Antonin Houska, Amit Langote,
Justin Pryzby, and Tomas Vondra
Discussion: https://postgr.es/m/CAFjFpRdjQvaUEV5DJX3TW6pU5eq54NCkadtxHX2JiJG_GvbrCA@mail.gmail.com
Commit d25ea0127 got rid of what I thought were entirely unnecessary
derived child expressions in EquivalenceClasses for EC members that
mention multiple baserels. But it turns out that some of the child
expressions that code created are necessary for partitionwise joins,
else we fail to find matching pathkeys for Sort nodes. (This happens
only for certain shapes of the resulting plan; it may be that
partitionwise aggregation is also necessary to show the failure,
though I'm not sure of that.)
Reverting that commit entirely would be quite painful performance-wise
for large partition sets. So instead, add code that explicitly
generates child expressions that match only partitionwise child join
rels we have actually generated.
Per report from Justin Pryzby. (Amit Langote noticed the problem
earlier, though it's not clear if he recognized then that it could
result in a planner error, not merely failure to exploit partitionwise
join, in the code as-committed.) Back-patch to v12 where commit
d25ea0127 came in.
Amit Langote, with lots of kibitzing from me
Discussion: https://postgr.es/m/CA+HiwqG2WVUGmLJqtR0tPFhniO=H=9qQ+Z3L_ZC+Y3-EVQHFGg@mail.gmail.com
Discussion: https://postgr.es/m/20191011143703.GN10470@telsasoft.com
While trying to plan a partitionwise join, we may be faced with cases
where one or both input partitions for a particular segment of the join
have been pruned away. In HEAD and v11, this is problematic because
earlier processing didn't bother to make a pruned RelOptInfo fully
valid. With an upcoming patch to make partition pruning more efficient,
this'll be even more problematic because said RelOptInfo won't exist at
all.
The existing code attempts to deal with this by retroactively making the
RelOptInfo fully valid, but that causes crashes under GEQO because join
planning is done in a short-lived memory context. In v11 we could
probably have fixed this by switching to the planner's main context
while fixing up the RelOptInfo, but that idea doesn't scale well to the
upcoming patch. It would be better not to mess with the base-relation
data structures during join planning, anyway --- that's just a recipe
for order-of-operations bugs.
In many cases, though, we don't actually need the child RelOptInfo,
because if the input is certainly empty then the join segment's result
is certainly empty, so we can skip making a join plan altogether. (The
existing code ultimately arrives at the same conclusion, but only after
doing a lot more work.) This approach works except when the pruned-away
partition is on the nullable side of a LEFT, ANTI, or FULL join, and the
other side isn't pruned. But in those cases the existing code leaves a
lot to be desired anyway --- the correct output is just the result of
the unpruned side of the join, but we were emitting a useless outer join
against a dummy Result. Pending somebody writing code to handle that
more nicely, let's just abandon the partitionwise-join optimization in
such cases.
When the modified code skips making a join plan, it doesn't make a
join RelOptInfo either; this requires some upper-level code to
cope with nulls in part_rels[] arrays. We would have had to have
that anyway after the upcoming patch.
Back-patch to v11 since the crash is demonstrable there.
Discussion: https://postgr.es/m/8305.1553884377@sss.pgh.pa.us
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
When considering a partitioning parent rel, we should stop processing that
subroot as soon as we've done adjust_appendrel_attrs and any securityQuals
updates. The rest of this is unnecessary, and indeed adding duplicate
subquery RTEs to the subroot is *wrong*. As the code stood, the children
of that partition ended up with two sets of copied subquery RTEs, confusing
matters greatly. Even more hilarity ensued if all of the children got
excluded by constraint exclusion, so that the extra RTEs didn't make it
back into the parent rtable.
Per fuzz testing by Andreas Seltenreich. Back-patch to v11 where this
got broken (by commit 0a480502b, it looks like).
Discussion: https://postgr.es/m/87va8g7vq0.fsf@ansel.ydns.eu
Commit 7d8ac9814b adjusted these
tests in the hope of preserving the plan shape, but I failed to
notice that the three partitions were, on my local machine, choosing
two different plan shapes. This is probably related to the fact
that all three tables have exactly the same row count. Try to
improve the situation by making pht1_e about half as large as
the other two.
Per Tom Lane and the buildfarm.
Discussion: http://postgr.es/m/25380.1519277713@sss.pgh.pa.us
Previously, Append didn't charge anything at all, and MergeAppend
charged only cpu_operator_cost, about half the value used here. This
change might make MergeAppend plans slightly more likely to be chosen
than before, since this commit increases the assumed cost for Append
-- with default values -- by 0.005 per tuple but MergeAppend by only
0.0025 per tuple. Since the comparisons required by MergeAppend are
costed separately, it's not clear why MergeAppend needs to be
otherwise more expensive than Append, so hopefully this is OK.
Prior to partition-wise join, it didn't really matter whether or not
an Append node had any cost of its own, because every plan had to use
the same number of Append or MergeAppend nodes and in the same places.
Only the relative cost of Append vs. MergeAppend made a difference.
Now, however, it is possible to avoid some of the Append nodes using a
partition-wise join, so it's worth making an effort. Pending patches
for partition-wise aggregate care too, because an Append of Aggregate
nodes will incur the Append overhead fewer times than an Aggregate
over an Append. Although in most cases this change will favor the use
of partition-wise techniques, it does the opposite when the join
cardinality is greater than the sum of the input cardinalities. Since
this situation arises in an existing regression test, I [rhaas]
adjusted it to keep the overall plan shape approximately the same.
Jeevan Chalke, per a suggestion from David Rowley. Reviewed by
Ashutosh Bapat. Some changes by me. 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/CAKJS1f9UXdk6ZYyqbJnjFO9a9hyHKGW7B=ZRh-rxy9qxfPA5Gw@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
Hash partitioning is useful when you want to partition a growing data
set evenly. This can be useful to keep table sizes reasonable, which
makes maintenance operations such as VACUUM faster, or to enable
partition-wise join.
At present, we still depend on constraint exclusion for partitioning
pruning, and the shape of the partition constraints for hash
partitioning is such that that doesn't work. Work is underway to fix
that, which should both improve performance and make partitioning
pruning work with hash partitioning.
Amul Sul, reviewed and tested by Dilip Kumar, Ashutosh Bapat, Yugo
Nagata, Rajkumar Raghuwanshi, Jesper Pedersen, and by me. A few
final tweaks also by me.
Discussion: http://postgr.es/m/CAAJ_b96fhpJAP=ALbETmeLk1Uni_GFZD938zgenhF49qgDTjaQ@mail.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
Tom Lane
Etsuro Fujita
Jeff Davis
Robert Haas
Peter Eisentraut