Similar to 50e17ad28, which allowed hash tables to be used for IN clauses
with a set of constants, here we add the same feature for NOT IN clauses.
NOT IN evaluates the same as: WHERE a <> v1 AND a <> v2 AND a <> v3.
Obviously, if we're using a hash table we must be exactly equivalent to
that and return the same result taking into account that either side of
the condition could contain a NULL. This requires a little bit of
special handling to make work with the hash table version.
When processing NOT IN, the ScalarArrayOpExpr's operator will be the <>
operator. To be able to build and lookup a hash table we must use the
<>'s negator operator. The planner checks if that exists and is hashable
and sets the relevant fields in ScalarArrayOpExpr to instruct the executor
to use hashing.
Author: David Rowley, James Coleman
Reviewed-by: James Coleman, Zhihong Yu
Discussion: https://postgr.es/m/CAApHDvoF1mum_FRk6D621edcB6KSHBi2+GAgWmioj5AhOu2vwQ@mail.gmail.com
It's unusual to have any resjunk columns in an ON CONFLICT ... UPDATE
list, but it can happen when MULTIEXPR_SUBLINK SubPlans are present.
If it happens, the ON CONFLICT UPDATE code path would end up storing
tuples that include the values of the extra resjunk columns. That's
fairly harmless in the short run, but if new columns are added to
the table then the values would become accessible, possibly leading
to malfunctions if they don't match the datatypes of the new columns.
This had escaped notice through a confluence of missing sanity checks,
including
* There's no cross-check that a tuple presented to heap_insert or
heap_update matches the table rowtype. While it's difficult to
check that fully at reasonable cost, we can easily add assertions
that there aren't too many columns.
* The output-column-assignment cases in execExprInterp.c lacked
any sanity checks on the output column numbers, which seems like
an oversight considering there are plenty of assertion checks on
input column numbers. Add assertions there too.
* We failed to apply nodeModifyTable's ExecCheckPlanOutput() to
the ON CONFLICT UPDATE tlist. That wouldn't have caught this
specific error, since that function is chartered to ignore resjunk
columns; but it sure seems like a bad omission now that we've seen
this bug.
In HEAD, the right way to fix this is to make the processing of
ON CONFLICT UPDATE tlists work the same as regular UPDATE tlists
now do, that is don't add "SET x = x" entries, and use
ExecBuildUpdateProjection to evaluate the tlist and combine it with
old values of the not-set columns. This adds a little complication
to ExecBuildUpdateProjection, but allows removal of a comparable
amount of now-dead code from the planner.
In the back branches, the most expedient solution seems to be to
(a) use an output slot for the ON CONFLICT UPDATE projection that
actually matches the target table, and then (b) invent a variant of
ExecBuildProjectionInfo that can be told to not store values resulting
from resjunk columns, so it doesn't try to store into nonexistent
columns of the output slot. (We can't simply ignore the resjunk columns
altogether; they have to be evaluated for MULTIEXPR_SUBLINK to work.)
This works back to v10. In 9.6, projections work much differently and
we can't cheaply give them such an option. The 9.6 version of this
patch works by inserting a JunkFilter when it's necessary to get rid
of resjunk columns.
In addition, v11 and up have the reverse problem when trying to
perform ON CONFLICT UPDATE on a partitioned table. Through a
further oversight, adjust_partition_tlist() discarded resjunk columns
when re-ordering the ON CONFLICT UPDATE tlist to match a partition.
This accidentally prevented the storing-bogus-tuples problem, but
at the cost that MULTIEXPR_SUBLINK cases didn't work, typically
crashing if more than one row has to be updated. Fix by preserving
resjunk columns in that routine. (I failed to resist the temptation
to add more assertions there too, and to do some minor code
beautification.)
Per report from Andres Freund. Back-patch to all supported branches.
Security: CVE-2021-32028
Previously, get_cached_rowtype() cached a pointer to a reference-counted
tuple descriptor from the typcache, relying on the ExprContextCallback
mechanism to release the tupdesc refcount when the expression tree
using the tupdesc was destroyed. This worked fine when it was designed,
but the introduction of within-DO-block COMMITs broke it. The refcount
is logged in a transaction-lifespan resource owner, but plpgsql won't
destroy simple expressions made within the DO block (before its first
commit) until the DO block is exited. That results in a warning about
a leaked tupdesc refcount when the COMMIT destroys the original resource
owner, and then an error about the active resource owner not holding a
matching refcount when the expression is destroyed.
To fix, get rid of the need to have a shutdown callback at all, by
instead caching a pointer to the relevant typcache entry. Those
survive for the life of the backend, so we needn't worry about the
pointer becoming stale. (For registered RECORD types, we can still
cache a pointer to the tupdesc, knowing that it won't change for the
life of the backend.) This mechanism has been in use in plpgsql
and expandedrecord.c since commit 4b93f5799, and seems to work well.
This change requires modifying the ExprEvalStep structs used by the
relevant expression step types, which is slightly worrisome for
back-patching. However, there seems no good reason for extensions
to be familiar with the details of these particular sub-structs.
Per report from Rohit Bhogate. Back-patch to v11 where within-DO-block
COMMITs became a thing.
Discussion: https://postgr.es/m/CAAV6ZkQRCVBh8qAY+SZiHnz+U+FqAGBBDaDTjF2yiKa2nJSLKg@mail.gmail.com
ScalarArrayOpExprs with "useOr=true" and a set of Consts on the righthand
side have traditionally been evaluated by using a linear search over the
array. When these arrays contain large numbers of elements then this
linear search could become a significant part of execution time.
Here we add a new method of evaluating ScalarArrayOpExpr expressions to
allow them to be evaluated by first building a hash table containing each
element, then on subsequent evaluations, we just probe that hash table to
determine if there is a match.
The planner is in charge of determining when this optimization is possible
and it enables it by setting hashfuncid in the ScalarArrayOpExpr. The
executor will only perform the hash table evaluation when the hashfuncid
is set.
This means that not all cases are optimized. For example CHECK constraints
containing an IN clause won't go through the planner, so won't get the
hashfuncid set. We could maybe do something about that at some later
date. The reason we're not doing it now is from fear that we may slow
down cases where the expression is evaluated only once. Those cases can
be common, for example, a single row INSERT to a table with a CHECK
constraint containing an IN clause.
In the planner, we enable this when there are suitable hash functions for
the ScalarArrayOpExpr's operator and only when there is at least
MIN_ARRAY_SIZE_FOR_HASHED_SAOP elements in the array. The threshold is
currently set to 9.
Author: James Coleman, David Rowley
Reviewed-by: David Rowley, Tomas Vondra, Heikki Linnakangas
Discussion: https://postgr.es/m/CAAaqYe8x62+=wn0zvNKCj55tPpg-JBHzhZFFc6ANovdqFw7-dA@mail.gmail.com
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu, Hou Zhijie
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
This removes "Add Result Cache executor node". It seems that something
weird is going on with the tracking of cache hits and misses as
highlighted by many buildfarm animals. It's not yet clear what the
problem is as other parts of the plan indicate that the cache did work
correctly, it's just the hits and misses that were being reported as 0.
This is especially a bad time to have the buildfarm so broken, so
reverting before too many more animals go red.
Discussion: https://postgr.es/m/CAApHDvq_hydhfovm4=izgWs+C5HqEeRScjMbOgbpC-jRAeK3Yw@mail.gmail.com
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
This patch makes two closely related sets of changes:
1. For UPDATE, the subplan of the ModifyTable node now only delivers
the new values of the changed columns (i.e., the expressions computed
in the query's SET clause) plus row identity information such as CTID.
ModifyTable must re-fetch the original tuple to merge in the old
values of any unchanged columns. The core advantage of this is that
the changed columns are uniform across all tables of an inherited or
partitioned target relation, whereas the other columns might not be.
A secondary advantage, when the UPDATE involves joins, is that less
data needs to pass through the plan tree. The disadvantage of course
is an extra fetch of each tuple to be updated. However, that seems to
be very nearly free in context; even worst-case tests don't show it to
add more than a couple percent to the total query cost. At some point
it might be interesting to combine the re-fetch with the tuple access
that ModifyTable must do anyway to mark the old tuple dead; but that
would require a good deal of refactoring and it seems it wouldn't buy
all that much, so this patch doesn't attempt it.
2. For inherited UPDATE/DELETE, instead of generating a separate
subplan for each target relation, we now generate a single subplan
that is just exactly like a SELECT's plan, then stick ModifyTable
on top of that. To let ModifyTable know which target relation a
given incoming row refers to, a tableoid junk column is added to
the row identity information. This gets rid of the horrid hack
that was inheritance_planner(), eliminating O(N^2) planning cost
and memory consumption in cases where there were many unprunable
target relations.
Point 2 of course requires point 1, so that there is a uniform
definition of the non-junk columns to be returned by the subplan.
We can't insist on uniform definition of the row identity junk
columns however, if we want to keep the ability to have both
plain and foreign tables in a partitioning hierarchy. Since
it wouldn't scale very far to have every child table have its
own row identity column, this patch includes provisions to merge
similar row identity columns into one column of the subplan result.
In particular, we can merge the whole-row Vars typically used as
row identity by FDWs into one column by pretending they are type
RECORD. (It's still okay for the actual composite Datums to be
labeled with the table's rowtype OID, though.)
There is more that can be done to file down residual inefficiencies
in this patch, but it seems to be committable now.
FDW authors should note several API changes:
* The argument list for AddForeignUpdateTargets() has changed, and so
has the method it must use for adding junk columns to the query. Call
add_row_identity_var() instead of manipulating the parse tree directly.
You might want to reconsider exactly what you're adding, too.
* PlanDirectModify() must now work a little harder to find the
ForeignScan plan node; if the foreign table is part of a partitioning
hierarchy then the ForeignScan might not be the direct child of
ModifyTable. See postgres_fdw for sample code.
* To check whether a relation is a target relation, it's no
longer sufficient to compare its relid to root->parse->resultRelation.
Instead, check it against all_result_relids or leaf_result_relids,
as appropriate.
Amit Langote and Tom Lane
Discussion: https://postgr.es/m/CA+HiwqHpHdqdDn48yCEhynnniahH78rwcrv1rEX65-fsZGBOLQ@mail.gmail.com
When building aggregate expression steps, strict checks need a bailout
jump for when a null value is encountered, so there is a list of steps
that require later adjustment. Adding entries to that list for steps
that aren't actually strict would be harmless, except that there is an
Assert which catches them. This leads to spurious errors on asserts
builds, for data sets that trigger parallel aggregation of an
aggregate with a non-strict deserialization function (no such
aggregates exist in the core system).
Repair by not adding the adjustment entry when it's not needed.
Backpatch back to 11 where the code was introduced.
Per a report from Darafei (Komzpa) of the PostGIS project; analysis
and patch by me.
Discussion: https://postgr.es/m/87mty7peb3.fsf@news-spur.riddles.org.uk
Commit c7aba7c14 didn't add this, but after more fooling with the
feature I feel that it'd be useful. To make this possible, refactor
getSubscriptingRoutines() so that the caller is responsible for
throwing any error. (In clauses.c, I just chose to make the
most conservative assumption rather than throwing an error. We don't
expect failures there anyway really, so the code space for an error
message would be a poor investment.)
This patch generalizes the subscripting infrastructure so that any
data type can be subscripted, if it provides a handler function to
define what that means. Traditional variable-length (varlena) arrays
all use array_subscript_handler(), while the existing fixed-length
types that support subscripting use raw_array_subscript_handler().
It's expected that other types that want to use subscripting notation
will define their own handlers. (This patch provides no such new
features, though; it only lays the foundation for them.)
To do this, move the parser's semantic processing of subscripts
(including coercion to whatever data type is required) into a
method callback supplied by the handler. On the execution side,
replace the ExecEvalSubscriptingRef* layer of functions with direct
calls to callback-supplied execution routines. (Thus, essentially
no new run-time overhead should be caused by this patch. Indeed,
there is room to remove some overhead by supplying specialized
execution routines. This patch does a little bit in that line,
but more could be done.)
Additional work is required here and there to remove formerly
hard-wired assumptions about the result type, collation, etc
of a SubscriptingRef expression node; and to remove assumptions
that the subscript values must be integers.
One useful side-effect of this is that we now have a less squishy
mechanism for identifying whether a data type is a "true" array:
instead of wiring in weird rules about typlen, we can look to see
if pg_type.typsubscript == F_ARRAY_SUBSCRIPT_HANDLER. For this
to be bulletproof, we have to forbid user-defined types from using
that handler directly; but there seems no good reason for them to
do so.
This patch also removes assumptions that the number of subscripts
is limited to MAXDIM (6), or indeed has any hard-wired limit.
That limit still applies to types handled by array_subscript_handler
or raw_array_subscript_handler, but to discourage other dependencies
on this constant, I've moved it from c.h to utils/array.h.
Dmitry Dolgov, reviewed at various times by Tom Lane, Arthur Zakirov,
Peter Eisentraut, Pavel Stehule
Discussion: https://postgr.es/m/CA+q6zcVDuGBv=M0FqBYX8DPebS3F_0KQ6OVFobGJPM507_SZ_w@mail.gmail.com
Discussion: https://postgr.es/m/CA+q6zcVovR+XY4mfk-7oNk-rF91gH0PebnNfuUjuuDsyHjOcVA@mail.gmail.com
When commit bd3daddaf introduced AlternativeSubPlans, I had some
ambitions towards allowing the choice of subplan to change during
execution. That has not happened, or even been thought about, in the
ensuing twelve years; so it seems like a failed experiment. So let's
rip that out and resolve the choice of subplan at the end of planning
(in setrefs.c) rather than during executor startup. This has a number
of positive benefits:
* Removal of a few hundred lines of executor code, since
AlternativeSubPlans need no longer be supported there.
* Removal of executor-startup overhead (particularly, initialization
of subplans that won't be used).
* Removal of incidental costs of having a larger plan tree, such as
tree-scanning and copying costs in the plancache; not to mention
setrefs.c's own costs of processing the discarded subplans.
* EXPLAIN no longer has to print a weird (and undocumented)
representation of an AlternativeSubPlan choice; it sees only the
subplan actually used. This should mean less confusion for users.
* Since setrefs.c knows which subexpression of a plan node it's
working on at any instant, it's possible to adjust the estimated
number of executions of the subplan based on that. For example,
we should usually estimate more executions of a qual expression
than a targetlist expression. The implementation used here is
pretty simplistic, because we don't want to expend a lot of cycles
on the issue; but it's better than ignoring the point entirely,
as the executor had to.
That last point might possibly result in shifting the choice
between hashed and non-hashed EXISTS subplans in a few cases,
but in general this patch isn't meant to change planner choices.
Since we're doing the resolution so late, it's really impossible
to change any plan choices outside the AlternativeSubPlan itself.
Patch by me; thanks to David Rowley for review.
Discussion: https://postgr.es/m/1992952.1592785225@sss.pgh.pa.us
Includes some manual cleanup of places that pgindent messed up,
most of which weren't per project style anyway.
Notably, it seems some people didn't absorb the style rules of
commit c9d297751, because there were a bunch of new occurrences
of function calls with a newline just after the left paren, all
with faulty expectations about how the rest of the call would get
indented.
The need for this was removed by
8b9e9644dc.
A number of files now need to include utils/acl.h or
parser/parse_node.h explicitly where they previously got it indirectly
somehow.
Since parser/parse_node.h already includes nodes/parsenodes.h, the
latter is then removed where the former was added. Also, remove
nodes/pg_list.h from objectaddress.h, since that's included via
nodes/parsenodes.h.
Reviewed-by: Tom Lane <tgl@sss.pgh.pa.us>
Reviewed-by: Alvaro Herrera <alvherre@2ndquadrant.com>
Discussion: https://www.postgresql.org/message-id/flat/7601e258-26b2-8481-36d0-dc9dca6f28f1%402ndquadrant.com
Optionally push a step to check for a NULL pointer to the pergroup
state.
This will be important for disk-based hash aggregation in combination
with grouping sets. When memory limits are reached, a given tuple may
find its per-group state for some grouping sets but not others. For
the former, it advances the per-group state as normal; for the latter,
it skips evaluation and the calling code will have to spill the tuple
and reprocess it in a later batch.
Add the NULL check as a separate expression step because in some
common cases it's not needed.
Discussion: https://postgr.es/m/20200221202212.ssb2qpmdgrnx52sj%40alap3.anarazel.de
Do so by combining the various steps that are part of aggregate
transition function invocation into one larger step. As some of the
current steps are only necessary for some aggregates, have one variant
of the aggregate transition step for each possible combination.
To avoid further manual copies of code in the different transition
step implementations, move most of the code into helper functions
marked as "always inline".
The benefit of this change is an increase in performance when
aggregating lots of rows. This comes in part due to the reduced number
of indirect jumps due to the reduced number of steps, and in part by
reducing redundant setup code across steps. This mainly benefits
interpreted execution, but the code generated by JIT is also improved
a bit.
As a nice side-effect it also ends up making the code a bit simpler.
A small additional optimization is removing the need to set
aggstate->curaggcontext before calling ExecAggInitGroup, choosing to
instead passign curaggcontext as an argument. It was, in contrast to
other aggregate related functions, only needed to fetch a memory
context to copy the transition value into.
Author: Andres Freund
Discussion:
https://postgr.es/m/20191023163849.sosqbfs5yenocez3@alap3.anarazel.dehttps://postgr.es/m/5c371df7cee903e8cd4c685f90c6c72086d3a2dc.camel@j-davis.com
It's already tracked via ExprState->parent, so we don't need to also
include it in ExprEvalStep. When that code originally was written
ExprState->parent didn't exist, but it since has been introduced in
6719b238e8.
Author: Andres Freund
Discussion: https://postgr.es/m/20191023163849.sosqbfs5yenocez3@alap3.anarazel.de
This mostly consists of using C99 style for loops, moving variables
into narrower scopes, and a smattering of other minor improvements.
Done separately to make it easier to review patches with actual
functional changes.
Author: Andres Freund
Discussion: https://postgr.es/m/20191023163849.sosqbfs5yenocez3@alap3.anarazel.de
That avoids unnecessary work during both interpreted execution, and
JIT compiled expression evaluation. Both benefit from fewer expression
steps needing be processed, and for interpreted execution there now is
a fastpath dedicated to just fetching a value from a virtual
slot. That's e.g. beneficial for hashjoins over nodes that perform
projections, as the hashed columns are currently fetched individually.
Author: Soumyadeep Chakraborty, Andres Freund
Discussion: https://postgr.es/m/CAE-ML+9OKSN71+mHtfMD-L24oDp8dGTfaVjDU6U+j+FNAW5kRQ@mail.gmail.com
For many queries the fact that the tuple descriptor from the lower
node was not taken into account when determining whether the type of a
slot is fixed, lead to tuple deforming for such upper nodes not to be
JIT accelerated.
I broke this in 675af5c01e.
There is ongoing work to enable writing regression tests for related
behavior (including a patch that would have detected this
regression), by optionally showing such details in EXPLAIN. But as it
seems unlikely that that will be suitable for stable branches, just
merge the fix for now.
While it's fairly close to the 12 release window, the fact that 11
continues to perform JITed tuple deforming in these cases, that
there's still cases where we do so in 12, and the fact that the
performance regression can be sizable, weigh in favor of fixing it
now.
Author: Andres Freund
Discussion: https://postgr.es/m/20190927072053.njf6prdl3vb7y7qb@alap3.anarazel.de
Backpatch: 12-, where 675af5c01e was merged.
For most uses of acl.h the details of how "Acl" internally looks like
are irrelevant. It might make sense to move a lot of the
implementation details into a separate header at a later point.
The main motivation of this change is to avoid including fmgr.h (via
array.h, which needs it for exposed structs) in a lot of files that
otherwise don't need it. A subsequent commit will remove the fmgr.h
include from a lot of files.
Directly include utils/array.h and utils/expandeddatum.h from the
files that need them, but previously included them indirectly, via
acl.h.
Author: Andres Freund
Discussion: https://postgr.es/m/20190803193733.g3l3x3o42uv4qj7l@alap3.anarazel.de
Formerly, lcons was about the same speed as lappend, but with the new
List implementation, that's not so; with a long List, data movement
imposes an O(N) cost on lcons and list_delete_first, but not lappend.
Hence, invent list_delete_last with semantics parallel to
list_delete_first (but O(1) cost), and change various places to use
lappend and list_delete_last where this can be done without much
violence to the code logic.
There are quite a few places that construct result lists using lcons not
lappend. Some have semantic rationales for that; I added comments about
it to a couple that didn't have them already. In many such places though,
I think the coding is that way only because back in the dark ages lcons
was faster than lappend. Hence, switch to lappend where this can be done
without causing semantic changes.
In ExecInitExprRec(), this results in aggregates and window functions that
are in the same plan node being executed in a different order than before.
Generally, the executions of such functions ought to be independent of
each other, so this shouldn't result in visibly different query results.
But if you push it, as one regression test case does, you can show that
the order is different. The new order seems saner; it's closer to
the order of the functions in the query text. And we never documented
or promised anything about this, anyway.
Also, in gistfinishsplit(), don't bother building a reverse-order list;
it's easy now to iterate backwards through the original list.
It'd be possible to go further towards removing uses of lcons and
list_delete_first, but it'd require more extensive logic changes,
and I'm not convinced it's worth it. Most of the remaining uses
deal with queues that probably never get long enough to be worth
sweating over. (Actually, I doubt that any of the changes in this
patch will have measurable performance effects either. But better
to have good examples than bad ones in the code base.)
Patch by me, thanks to David Rowley and Daniel Gustafsson for review.
Discussion: https://postgr.es/m/21272.1563318411@sss.pgh.pa.us
This is still using the 2.0 version of pg_bsd_indent.
I thought it would be good to commit this separately,
so as to document the differences between 2.0 and 2.1 behavior.
Discussion: https://postgr.es/m/16296.1558103386@sss.pgh.pa.us
This adds a flag "deterministic" to collations. If that is false,
such a collation disables various optimizations that assume that
strings are equal only if they are byte-wise equal. That then allows
use cases such as case-insensitive or accent-insensitive comparisons
or handling of strings with different Unicode normal forms.
This functionality is only supported with the ICU provider. At least
glibc doesn't appear to have any locales that work in a
nondeterministic way, so it's not worth supporting this for the libc
provider.
The term "deterministic comparison" in this context is from Unicode
Technical Standard #10
(https://unicode.org/reports/tr10/#Deterministic_Comparison).
This patch makes changes in three areas:
- CREATE COLLATION DDL changes and system catalog changes to support
this new flag.
- Many executor nodes and auxiliary code are extended to track
collations. Previously, this code would just throw away collation
information, because the eventually-called user-defined functions
didn't use it since they only cared about equality, which didn't
need collation information.
- String data type functions that do equality comparisons and hashing
are changed to take the (non-)deterministic flag into account. For
comparison, this just means skipping various shortcuts and tie
breakers that use byte-wise comparison. For hashing, we first need
to convert the input string to a canonical "sort key" using the ICU
analogue of strxfrm().
Reviewed-by: Daniel Verite <daniel@manitou-mail.org>
Reviewed-by: Peter Geoghegan <pg@bowt.ie>
Discussion: https://www.postgresql.org/message-id/flat/1ccc668f-4cbc-0bef-af67-450b47cdfee7@2ndquadrant.com
We have forboth() and forthree() macros that simplify iterating
through several parallel lists, but not everyplace that could
reasonably use those was doing so. Also invent forfour() and
forfive() macros to do the same for four or five parallel lists,
and use those where applicable.
The immediate motivation for doing this is to reduce the number
of ad-hoc lnext() calls, to reduce the footprint of a WIP patch.
However, it seems like good cleanup and error-proofing anyway;
the places that were combining forthree() with a manually iterated
loop seem particularly illegible and bug-prone.
There was some speculation about restructuring related parsetree
representations to reduce the need for parallel list chasing of
this sort. Perhaps that's a win, or perhaps not, but in any case
it would be considerably more invasive than this patch; and it's
not particularly related to my immediate goal of improving the
List infrastructure. So I'll leave that question for another day.
Patch by me; thanks to David Rowley for review.
Discussion: https://postgr.es/m/11587.1550975080@sss.pgh.pa.us
Create a new header optimizer/optimizer.h, which exposes just the
planner functions that can be used "at arm's length", without need
to access Paths or the other planner-internal data structures defined
in nodes/relation.h. This is intended to provide the whole planner
API seen by most of the rest of the system; although FDWs still need
to use additional stuff, and more thought is also needed about just
what selfuncs.c should rely on.
The main point of doing this now is to limit the amount of new
#include baggage that will be needed by "planner support functions",
which I expect to introduce later, and which will be in relevant
datatype modules rather than anywhere near the planner.
This commit just moves relevant declarations into optimizer.h from
other header files (a couple of which go away because everything
got moved), and adjusts #include lists to match. There's further
cleanup that could be done if we want to decide that some stuff
being exposed by optimizer.h doesn't belong in the planner at all,
but I'll leave that for another day.
Discussion: https://postgr.es/m/11460.1548706639@sss.pgh.pa.us
Before this change FunctionCallInfoData, the struct arguments etc for
V1 function calls are stored in, always had space for
FUNC_MAX_ARGS/100 arguments, storing datums and their nullness in two
arrays. For nearly every function call 100 arguments is far more than
needed, therefore wasting memory. Arg and argnull being two separate
arrays also guarantees that to access a single argument, two
cachelines have to be touched.
Change the layout so there's a single variable-length array with pairs
of value / isnull. That drastically reduces memory consumption for
most function calls (on x86-64 a two argument function now uses
64bytes, previously 936 bytes), and makes it very likely that argument
value and its nullness are on the same cacheline.
Arguments are stored in a new NullableDatum struct, which, due to
padding, needs more memory per argument than before. But as usually
far fewer arguments are stored, and individual arguments are cheaper
to access, that's still a clear win. It's likely that there's other
places where conversion to NullableDatum arrays would make sense,
e.g. TupleTableSlots, but that's for another commit.
Because the function call information is now variable-length
allocations have to take the number of arguments into account. For
heap allocations that can be done with SizeForFunctionCallInfoData(),
for on-stack allocations there's a new LOCAL_FCINFO(name, nargs) macro
that helps to allocate an appropriately sized and aligned variable.
Some places with stack allocation function call information don't know
the number of arguments at compile time, and currently variably sized
stack allocations aren't allowed in postgres. Therefore allow for
FUNC_MAX_ARGS space in these cases. They're not that common, so for
now that seems acceptable.
Because of the need to allocate FunctionCallInfo of the appropriate
size, older extensions may need to update their code. To avoid subtle
breakages, the FunctionCallInfoData struct has been renamed to
FunctionCallInfoBaseData. Most code only references FunctionCallInfo,
so that shouldn't cause much collateral damage.
This change is also a prerequisite for more efficient expression JIT
compilation (by allocating the function call information on the stack,
allowing LLVM to optimize it away); previously the size of the call
information caused problems inside LLVM's optimizer.
Author: Andres Freund
Reviewed-By: Tom Lane
Discussion: https://postgr.es/m/20180605172952.x34m5uz6ju6enaem@alap3.anarazel.de
Previously tables declared WITH OIDS, including a significant fraction
of the catalog tables, stored the oid column not as a normal column,
but as part of the tuple header.
This special column was not shown by default, which was somewhat odd,
as it's often (consider e.g. pg_class.oid) one of the more important
parts of a row. Neither pg_dump nor COPY included the contents of the
oid column by default.
The fact that the oid column was not an ordinary column necessitated a
significant amount of special case code to support oid columns. That
already was painful for the existing, but upcoming work aiming to make
table storage pluggable, would have required expanding and duplicating
that "specialness" significantly.
WITH OIDS has been deprecated since 2005 (commit ff02d0a05280e0).
Remove it.
Removing includes:
- CREATE TABLE and ALTER TABLE syntax for declaring the table to be
WITH OIDS has been removed (WITH (oids[ = true]) will error out)
- pg_dump does not support dumping tables declared WITH OIDS and will
issue a warning when dumping one (and ignore the oid column).
- restoring an pg_dump archive with pg_restore will warn when
restoring a table with oid contents (and ignore the oid column)
- COPY will refuse to load binary dump that includes oids.
- pg_upgrade will error out when encountering tables declared WITH
OIDS, they have to be altered to remove the oid column first.
- Functionality to access the oid of the last inserted row (like
plpgsql's RESULT_OID, spi's SPI_lastoid, ...) has been removed.
The syntax for declaring a table WITHOUT OIDS (or WITH (oids = false)
for CREATE TABLE) is still supported. While that requires a bit of
support code, it seems unnecessary to break applications / dumps that
do not use oids, and are explicit about not using them.
The biggest user of WITH OID columns was postgres' catalog. This
commit changes all 'magic' oid columns to be columns that are normally
declared and stored. To reduce unnecessary query breakage all the
newly added columns are still named 'oid', even if a table's column
naming scheme would indicate 'reloid' or such. This obviously
requires adapting a lot code, mostly replacing oid access via
HeapTupleGetOid() with access to the underlying Form_pg_*->oid column.
The bootstrap process now assigns oids for all oid columns in
genbki.pl that do not have an explicit value (starting at the largest
oid previously used), only oids assigned later by oids will be above
FirstBootstrapObjectId. As the oid column now is a normal column the
special bootstrap syntax for oids has been removed.
Oids are not automatically assigned during insertion anymore, all
backend code explicitly assigns oids with GetNewOidWithIndex(). For
the rare case that insertions into the catalog via SQL are called for
the new pg_nextoid() function can be used (which only works on catalog
tables).
The fact that oid columns on system tables are now normal columns
means that they will be included in the set of columns expanded
by * (i.e. SELECT * FROM pg_class will now include the table's oid,
previously it did not). It'd not technically be hard to hide oid
column by default, but that'd mean confusing behavior would either
have to be carried forward forever, or it'd cause breakage down the
line.
While it's not unlikely that further adjustments are needed, the
scope/invasiveness of the patch makes it worthwhile to get merge this
now. It's painful to maintain externally, too complicated to commit
after the code code freeze, and a dependency of a number of other
patches.
Catversion bump, for obvious reasons.
Author: Andres Freund, with contributions by John Naylor
Discussion: https://postgr.es/m/20180930034810.ywp2c7awz7opzcfr@alap3.anarazel.de
Previously this information was computed when JIT compiling an
expression. But the information is useful for assertions in the
non-JIT case too (for assertions), therefore it makes sense to move
it.
This will, in a followup commit, allow to treat different slot types
differently. E.g. for virtual slots there's no need to generate a JIT
function to deform the slot.
Author: Andres Freund
Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
Upcoming work intends to allow pluggable ways to introduce new ways of
storing table data. Accessing those table access methods from the
executor requires TupleTableSlots to be carry tuples in the native
format of such storage methods; otherwise there'll be a significant
conversion overhead.
Different access methods will require different data to store tuples
efficiently (just like virtual, minimal, heap already require fields
in TupleTableSlot). To allow that without requiring additional pointer
indirections, we want to have different structs (embedding
TupleTableSlot) for different types of slots. Thus different types of
slots are needed, which requires adapting creators of slots.
The slot that most efficiently can represent a type of tuple in an
executor node will often depend on the type of slot a child node
uses. Therefore we need to track the type of slot is returned by
nodes, so parent slots can create slots based on that.
Relatedly, JIT compilation of tuple deforming needs to know which type
of slot a certain expression refers to, so it can create an
appropriate deforming function for the type of tuple in the slot.
But not all nodes will only return one type of slot, e.g. an append
node will potentially return different types of slots for each of its
subplans.
Therefore add function that allows to query the type of a node's
result slot, and whether it'll always be the same type (whether it's
fixed). This can be queried using ExecGetResultSlotOps().
The scan, result, inner, outer type of slots are automatically
inferred from ExecInitScanTupleSlot(), ExecInitResultSlot(),
left/right subtrees respectively. If that's not correct for a node,
that can be overwritten using new fields in PlanState.
This commit does not introduce the actually abstracted implementation
of different kind of TupleTableSlots, that will be left for a followup
commit. The different types of slots introduced will, for now, still
use the same backing implementation.
While this already partially invalidates the big comment in
tuptable.h, it seems to make more sense to update it later, when the
different TupleTableSlot implementations actually exist.
Author: Ashutosh Bapat and Andres Freund, with changes by Amit Khandekar
Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
This only became a problem with 4c640f4f38, which didn't synchronize
the value agg_strict_input_check.nargs is set to, with the guard
condition for emitting the operation.
Besides such instructions being unnecessary overhead, currently the
LLVM JIT provider doesn't support them. It seems more sensible to
avoid generating such instruction than supporting them. Add assertions
to make it easier to debug a potential further occurance.
Discussion: https://postgr.es/m/2a505161-2727-2473-7c46-591ed108ac52@email.cz
Backpatch: 11-, like 4c640f4f38.
I (Andres) broke this unintentionally in 69c3936a14, by checking
strictness for all input expressions computed for an aggregate, rather
than just the input for the aggregate transition function.
Reported-By: Ondřej Bouda
Bisected-By: Tom Lane
Diagnosed-By: Andrew Gierth
Discussion: https://postgr.es/m/2a505161-2727-2473-7c46-591ed108ac52@email.cz
Backpatch: 11-, like 69c3936a14
An array-type coercion appearing within a CASE that has a constant
(after const-folding) test expression was mangled by the planner, causing
all the elements of the resulting array to be equal to the coerced value
of the CASE's test expression. This is my oversight in commit c12d570fa:
that changed ArrayCoerceExpr to use a subexpression involving a
CaseTestExpr, and I didn't notice that eval_const_expressions needed an
adjustment to keep from folding such a CaseTestExpr to a constant when
it's inside a suitable CASE.
This is another in what's getting to be a depressingly long line of bugs
associated with misidentification of the referent of a CaseTestExpr.
We're overdue to redesign that mechanism; but any such fix is unlikely
to be back-patchable into v11. As a stopgap, fix eval_const_expressions
to do what it must here. Also add a bunch of comments pointing out the
restrictions and assumptions that are needed to make this work at all.
Also fix a related oversight: contain_context_dependent_node() was not
aware of the relationship of ArrayCoerceExpr to CaseTestExpr. That was
somewhat fail-soft, in that the outcome of a wrong answer would be to
prevent optimizations that could have been made, but let's fix it while
we're at it.
Per bug #15471 from Matt Williams. Back-patch to v11 where the faulty
logic came in.
Discussion: https://postgr.es/m/15471-1117f49271989bad@postgresql.org
Performing JIT compilation for deforming gains performance benefits
over unJITed deforming from compile-time knowledge of the tuple
descriptor. Fixed column widths, NOT NULLness, etc can be taken
advantage of.
Right now the JITed deforming is only used when deforming tuples as
part of expression evaluation (and obviously only if the descriptor is
known). It's likely to be beneficial in other cases, too.
By default tuple deforming is JITed whenever an expression is JIT
compiled. There's a separate boolean GUC controlling it, but that's
expected to be primarily useful for development and benchmarking.
Docs will follow in a later commit containing docs for the whole JIT
feature.
Author: Andres Freund
Discussion: https://postgr.es/m/20170901064131.tazjxwus3k2w3ybh@alap3.anarazel.de
Peter Eisentraut
David Rowley
Tom Lane
Andrew Gierth
Bruce Momjian
Heikki Linnakangas
Michael Paquier
Jeff Davis
Andres Freund
Alvaro Herrera
Peter Eisentraut