Arrange to do some things on-demand, rather than immediately during
executor startup, because there's a fair chance of never having to do
them at all:
* Don't open result relations' indexes until needed.
* Don't initialize partition tuple routing, nor the child-to-root
tuple conversion map, until needed.
This wins in UPDATEs on partitioned tables when only some of the
partitions will actually receive updates; with larger partition
counts the savings is quite noticeable. Also, we can remove some
sketchy heuristics in ExecInitModifyTable about whether to set up
tuple routing.
Also, remove execPartition.c's private hash table tracking which
partitions were already opened by the ModifyTable node. Instead
use the hash added to ModifyTable itself by commit 86dc90056.
To allow lazy computation of the conversion maps, we now set
ri_RootResultRelInfo in all child ResultRelInfos. We formerly set it
only in some, not terribly well-defined, cases. This has user-visible
side effects in that now more error messages refer to the root
relation instead of some partition (and provide error data in the
root's column order, too). It looks to me like this is a strict
improvement in consistency, so I don't have a problem with the
output changes visible in this commit.
Extracted from a larger patch, which seemed to me to be too messy
to push in one commit.
Amit Langote, reviewed at different times by Heikki Linnakangas and
myself
Discussion: https://postgr.es/m/CA+HiwqG7ZruBmmih3wPsBZ4s0H2EhywrnXEduckY5Hr3fWzPWA@mail.gmail.com
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu, Hou Zhijie
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
This removes "Add Result Cache executor node". It seems that something
weird is going on with the tracking of cache hits and misses as
highlighted by many buildfarm animals. It's not yet clear what the
problem is as other parts of the plan indicate that the cache did work
correctly, it's just the hits and misses that were being reported as 0.
This is especially a bad time to have the buildfarm so broken, so
reverting before too many more animals go red.
Discussion: https://postgr.es/m/CAApHDvq_hydhfovm4=izgWs+C5HqEeRScjMbOgbpC-jRAeK3Yw@mail.gmail.com
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
This patch makes two closely related sets of changes:
1. For UPDATE, the subplan of the ModifyTable node now only delivers
the new values of the changed columns (i.e., the expressions computed
in the query's SET clause) plus row identity information such as CTID.
ModifyTable must re-fetch the original tuple to merge in the old
values of any unchanged columns. The core advantage of this is that
the changed columns are uniform across all tables of an inherited or
partitioned target relation, whereas the other columns might not be.
A secondary advantage, when the UPDATE involves joins, is that less
data needs to pass through the plan tree. The disadvantage of course
is an extra fetch of each tuple to be updated. However, that seems to
be very nearly free in context; even worst-case tests don't show it to
add more than a couple percent to the total query cost. At some point
it might be interesting to combine the re-fetch with the tuple access
that ModifyTable must do anyway to mark the old tuple dead; but that
would require a good deal of refactoring and it seems it wouldn't buy
all that much, so this patch doesn't attempt it.
2. For inherited UPDATE/DELETE, instead of generating a separate
subplan for each target relation, we now generate a single subplan
that is just exactly like a SELECT's plan, then stick ModifyTable
on top of that. To let ModifyTable know which target relation a
given incoming row refers to, a tableoid junk column is added to
the row identity information. This gets rid of the horrid hack
that was inheritance_planner(), eliminating O(N^2) planning cost
and memory consumption in cases where there were many unprunable
target relations.
Point 2 of course requires point 1, so that there is a uniform
definition of the non-junk columns to be returned by the subplan.
We can't insist on uniform definition of the row identity junk
columns however, if we want to keep the ability to have both
plain and foreign tables in a partitioning hierarchy. Since
it wouldn't scale very far to have every child table have its
own row identity column, this patch includes provisions to merge
similar row identity columns into one column of the subplan result.
In particular, we can merge the whole-row Vars typically used as
row identity by FDWs into one column by pretending they are type
RECORD. (It's still okay for the actual composite Datums to be
labeled with the table's rowtype OID, though.)
There is more that can be done to file down residual inefficiencies
in this patch, but it seems to be committable now.
FDW authors should note several API changes:
* The argument list for AddForeignUpdateTargets() has changed, and so
has the method it must use for adding junk columns to the query. Call
add_row_identity_var() instead of manipulating the parse tree directly.
You might want to reconsider exactly what you're adding, too.
* PlanDirectModify() must now work a little harder to find the
ForeignScan plan node; if the foreign table is part of a partitioning
hierarchy then the ForeignScan might not be the direct child of
ModifyTable. See postgres_fdw for sample code.
* To check whether a relation is a target relation, it's no
longer sufficient to compare its relid to root->parse->resultRelation.
Instead, check it against all_result_relids or leaf_result_relids,
as appropriate.
Amit Langote and Tom Lane
Discussion: https://postgr.es/m/CA+HiwqHpHdqdDn48yCEhynnniahH78rwcrv1rEX65-fsZGBOLQ@mail.gmail.com
If a cross-partition UPDATE violates a constraint on the target partition,
and the columns in the new partition are in different physical order than
in the parent, the error message can reveal columns that the user does not
have SELECT permission on. A similar bug was fixed earlier in commit
804b6b6db4.
The cause of the bug is that the callers of the
ExecBuildSlotValueDescription() function got confused when constructing
the list of modified columns. If the tuple was routed from a parent, we
converted the tuple to the parent's format, but the list of modified
columns was grabbed directly from the child's RTE entry.
ExecUpdateLockMode() had a similar issue. That lead to confusion on which
columns are key columns, leading to wrong tuple lock being taken on tables
referenced by foreign keys, when a row is updated with INSERT ON CONFLICT
UPDATE. A new isolation test is added for that corner case.
With this patch, the ri_RangeTableIndex field is no longer set for
partitions that don't have an entry in the range table. Previously, it was
set to the RTE entry of the parent relation, but that was confusing.
NOTE: This modifies the ResultRelInfo struct, replacing the
ri_PartitionRoot field with ri_RootResultRelInfo. That's a bit risky to
backpatch, because it breaks any extensions accessing the field. The
change that ri_RangeTableIndex is not set for partitions could potentially
break extensions, too. The ResultRelInfos are visible to FDWs at least,
and this patch required small changes to postgres_fdw. Nevertheless, this
seem like the least bad option. I don't think these fields widely used in
extensions; I don't think there are FDWs out there that uses the FDW
"direct update" API, other than postgres_fdw. If there is, you will get a
compilation error, so hopefully it is caught quickly.
Backpatch to 11, where support for both cross-partition UPDATEs, and unique
indexes on partitioned tables, were added.
Reviewed-by: Amit Langote
Security: CVE-2021-3393
Add an executor aminsert() hint mechanism that informs index AMs that
the incoming index tuple (the tuple that accompanies the hint) is not
being inserted by execution of an SQL statement that logically modifies
any of the index's key columns.
The hint is received by indexes when an UPDATE takes place that does not
apply an optimization like heapam's HOT (though only for indexes where
all key columns are logically unchanged). Any index tuple that receives
the hint on insert is expected to be a duplicate of at least one
existing older version that is needed for the same logical row. Related
versions will typically be stored on the same index page, at least
within index AMs that apply the hint.
Recognizing the difference between MVCC version churn duplicates and
true logical row duplicates at the index AM level can help with cleanup
of garbage index tuples. Cleanup can intelligently target tuples that
are likely to be garbage, without wasting too many cycles on less
promising tuples/pages (index pages with little or no version churn).
This is infrastructure for an upcoming commit that will teach nbtree to
perform bottom-up index deletion. No index AM actually applies the hint
just yet.
Author: Peter Geoghegan <pg@bowt.ie>
Reviewed-By: Victor Yegorov <vyegorov@gmail.com>
Discussion: https://postgr.es/m/CAH2-Wz=CEKFa74EScx_hFVshCOn6AA5T-ajFASTdzipdkLTNQQ@mail.gmail.com
Previously, ExecInitModifyTable relied on ExecInitJunkFilter,
and thence ExecCleanTypeFromTL, to build the target descriptor from
the query tlist. While we just checked (in ExecCheckPlanOutput)
that the tlist produces compatible output, this is not a great
substitute for the relation's actual tuple descriptor that's
available from the relcache. For one thing, dropped columns will
not be correctly marked attisdropped; it's a bit surprising that
we've gotten away with that this long. But the real reason for
being concerned with this is that using the table's descriptor means
that the slot will have correct attrmissing data, allowing us to
revert the klugy fix of commit ba9f18abd. (This commit undoes
that one's changes in trigger.c, but keeps the new test case.)
Thus we can solve the bogus-trigger-tuple problem with fewer cycles
rather than more.
No back-patch, since this doesn't fix any additional bug, and it
seems somewhat more likely to have unforeseen side effects than
ba9f18abd's narrow fix.
Discussion: https://postgr.es/m/16644-5da7ef98a7ac4545@postgresql.org
Maintaining 'es_result_relation_info' correctly at all times has become
cumbersome, especially with partitioning where each partition gets its
own result relation info. Having to set and reset it across arbitrary
operations has caused bugs in the past.
This changes all the places that used 'es_result_relation_info', to
receive the currently active ResultRelInfo via function parameters
instead.
Author: Amit Langote
Discussion: https://www.postgresql.org/message-id/CA%2BHiwqGEmiib8FLiHMhKB%2BCH5dRgHSLc5N5wnvc4kym%2BZYpQEQ%40mail.gmail.com
Instead of allocating all the ResultRelInfos upfront in one big array,
allocate them in ExecInitModifyTable(). es_result_relations is now an
array of ResultRelInfo pointers, rather than an array of structs, and it
is indexed by the RT index.
This simplifies things: we get rid of the separate concept of a "result
rel index", and don't need to set it in setrefs.c anymore. This also
allows follow-up optimizations (not included in this commit yet) to skip
initializing ResultRelInfos for target relations that were not needed at
runtime, and removal of the es_result_relation_info pointer.
The EState arrays of regular result rels and root result rels are merged
into one array. Similarly, the resultRelations and rootResultRelations
lists in PlannedStmt are merged into one. It's not actually clear to me
why they were kept separate in the first place, but now that the
es_result_relations array is indexed by RT index, it certainly seems
pointless.
The PlannedStmt->resultRelations list is now only needed for
ExecRelationIsTargetRelation(). One visible effect of this change is that
ExecRelationIsTargetRelation() will now return 'true' also for the
partition root, if a partitioned table is updated. That seems like a good
thing, although the function isn't used in core code, and I don't see any
reason for an FDW to call it on a partition root.
Author: Amit Langote
Discussion: https://www.postgresql.org/message-id/CA%2BHiwqGEmiib8FLiHMhKB%2BCH5dRgHSLc5N5wnvc4kym%2BZYpQEQ%40mail.gmail.com
If the memory context's maxBlockSize is too big, a single block
allocation can suddenly exceed work_mem. For Hash Aggregation, this
can mean spilling to disk too early or reporting a confusing memory
usage number for EXPLAN ANALYZE.
Introduce CreateWorkExprContext(), which is like CreateExprContext(),
except that it creates the AllocSet with a maxBlockSize that is
reasonable in proportion to work_mem.
Right now, CreateWorkExprContext() is only used by Hash Aggregation,
but it may be generally useful in the future.
Discussion: https://postgr.es/m/412a3fbf306f84d8d78c4009e11791867e62b87c.camel@j-davis.com
This reverts the parts of commit 17a28b03645e27d73bf69a95d7569b61e58f06eb
that changed ereport's auxiliary functions from returning dummy integer
values to returning void. It turns out that a minority of compilers
complain (not entirely unreasonably) about constructs such as
(condition) ? errdetail(...) : 0
if errdetail() returns void rather than int. We could update those
call sites to say "(void) 0" perhaps, but the expectation for this
patch set was that ereport callers would not have to change anything.
And this aspect of the patch set was already the most invasive and
least compelling part of it, so let's just drop it.
Per buildfarm.
Discussion: https://postgr.es/m/CA+fd4k6N8EjNvZpM8nme+y+05mz-SM8Z_BgkixzkA34R+ej0Kw@mail.gmail.com
Change all the auxiliary error-reporting routines to return void,
now that we no longer need to pretend they are passing something
useful to errfinish(). While this probably doesn't save anything
significant at the machine-code level, it allows detection of some
additional types of mistakes.
Pass the error location details (__FILE__, __LINE__, PG_FUNCNAME_MACRO)
to errfinish not errstart. This shaves a few cycles off the case where
errstart decides we're not going to emit anything.
Re-implement elog() as a trivial wrapper around ereport(), removing
the separate support infrastructure it used to have. Aside from
getting rid of some now-surplus code, this means that elog() now
really does have exactly the same semantics as ereport(), in particular
that it can skip evaluation work if the message is not to be emitted.
Andres Freund and Tom Lane
Discussion: https://postgr.es/m/CA+fd4k6N8EjNvZpM8nme+y+05mz-SM8Z_BgkixzkA34R+ej0Kw@mail.gmail.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
Commit 4eaea3db introduced TupleHashTableHash(), but the signature
didn't match the other exposed functions. Separate it into internal
and external versions. The external version hides the details behind
an API more consistent with the other external functions, and the
internal version is still suitable for simplehash.
Expose two new entry points: one for only calculating the hash value
of a tuple, and another for looking up a hash entry when the hash
value is already known. This will be useful for disk-based Hash
Aggregation to avoid recomputing the hash value for the same tuple
after saving and restoring it from disk.
Discussion: https://postgr.es/m/37091115219dd522fd9ed67333ee8ed1b7e09443.camel%40j-davis.com
In ad0bda5d24ea I changed the EvalPlanQual machinery to store
substitution tuples in slot, instead of using plain HeapTuples. The
main motivation for that was that using HeapTuples will be inefficient
for future tableams. But it turns out that that conversion was buggy
for non-locking rowmarks - the wrong tuple descriptor was used to
create the slot.
As a secondary issue 5db6df0c0 changed ExecLockRows() to begin EPQ
earlier, to allow to fetch the locked rows directly into the EPQ
slots, instead of having to copy tuples around. Unfortunately, as Tom
complained, that forces some expensive initialization to happen
earlier.
As a third issue, the test coverage for EPQ was clearly insufficient.
Fixing the first issue is unfortunately not trivial: Non-locked row
marks were fetched at the start of EPQ, and we don't have the type
information for the rowmarks available at that point. While we could
change that, it's not easy. It might be worthwhile to change that at
some point, but to fix this bug, it seems better to delay fetching
non-locking rowmarks when they're actually needed, rather than
eagerly. They're referenced at most once, and in cases where EPQ
fails, might never be referenced. Fetching them when needed also
increases locality a bit.
To be able to fetch rowmarks during execution, rather than
initialization, we need to be able to access the active EPQState, as
that contains necessary data. To do so move EPQ related data from
EState to EPQState, and, only for EStates creates as part of EPQ,
reference the associated EPQState from EState.
To fix the second issue, change EPQ initialization to allow use of
EvalPlanQualSlot() to be used before EvalPlanQualBegin() (but
obviously still requiring EvalPlanQualInit() to have been done).
As these changes made struct EState harder to understand, e.g. by
adding multiple EStates, significantly reorder the members, and add a
lot more comments.
Also add a few more EPQ tests, including one that fails for the first
issue above. More is needed.
Reported-By: yi huang
Author: Andres Freund
Reviewed-By: Tom Lane
Discussion:
https://postgr.es/m/CAHU7rYZo_C4ULsAx_LAj8az9zqgrD8WDd4hTegDTMM1LMqrBsg@mail.gmail.comhttps://postgr.es/m/24530.1562686693@sss.pgh.pa.us
Backpatch: 12-, where the EPQ changes were introduced
Now that list_nth is O(1), there's no good reason to maintain a
separate array of RTE pointers rather than indexing into
estate->es_range_table. Deleting the array doesn't save all that
much either; but just on cleanliness grounds, it's better not to
have duplicate representations of the identical information.
Discussion: https://postgr.es/m/14960.1565384592@sss.pgh.pa.us
This addresses a couple of issues in the code:
- Typos and inconsistencies in comments and function declarations.
- Removal of unreferenced function declarations.
- Removal of unnecessary compile flags.
- A cleanup error in regressplans.sh.
Author: Alexander Lakhin
Discussion: https://postgr.es/m/0c991fdf-2670-1997-c027-772a420c4604@gmail.com
There were a number of issues in the recent commits which include typos,
code and comments mismatch, leftover function declarations. Fix them.
Reported-by: Alexander Lakhin
Author: Alexander Lakhin, Amit Kapila and Amit Langote
Reviewed-by: Amit Kapila
Discussion: https://postgr.es/m/ef0c0232-0c1d-3a35-63d4-0ebd06e31387@gmail.com
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 new, required, table AM callbacks for insert/delete/update
and lock_tuple. To be able to reasonably use those, the EvalPlanQual
mechanism had to be adapted, moving more logic into the AM.
Previously both delete/update/lock call-sites and the EPQ mechanism had
to have awareness of the specific tuple format to be able to fetch the
latest version of a tuple. Obviously that needs to be abstracted
away. To do so, move the logic that find the latest row version into
the AM. lock_tuple has a new flag argument,
TUPLE_LOCK_FLAG_FIND_LAST_VERSION, that forces it to lock the last
version, rather than the current one. It'd have been possible to do
so via a separate callback as well, but finding the last version
usually also necessitates locking the newest version, making it
sensible to combine the two. This replaces the previous use of
EvalPlanQualFetch(). Additionally HeapTupleUpdated, which previously
signaled either a concurrent update or delete, is now split into two,
to avoid callers needing AM specific knowledge to differentiate.
The move of finding the latest row version into tuple_lock means that
encountering a row concurrently moved into another partition will now
raise an error about "tuple to be locked" rather than "tuple to be
updated/deleted" - which is accurate, as that always happens when
locking rows. While possible slightly less helpful for users, it seems
like an acceptable trade-off.
As part of this commit HTSU_Result has been renamed to TM_Result, and
its members been expanded to differentiated between updating and
deleting. HeapUpdateFailureData has been renamed to TM_FailureData.
The interface to speculative insertion is changed so nodeModifyTable.c
does not have to set the speculative token itself anymore. Instead
there's a version of tuple_insert, tuple_insert_speculative, that
performs the speculative insertion (without requiring a flag to signal
that fact), and the speculative insertion is either made permanent
with table_complete_speculative(succeeded = true) or aborted with
succeeded = false).
Note that multi_insert is not yet routed through tableam, nor is
COPY. Changing multi_insert requires changes to copy.c that are large
enough to better be done separately.
Similarly, although simpler, CREATE TABLE AS and CREATE MATERIALIZED
VIEW are also only going to be adjusted in a later commit.
Author: Andres Freund and Haribabu Kommi
Discussion:
https://postgr.es/m/20180703070645.wchpu5muyto5n647@alap3.anarazel.dehttps://postgr.es/m/20190313003903.nwvrxi7rw3ywhdel@alap3.anarazel.dehttps://postgr.es/m/20160812231527.GA690404@alvherre.pgsql
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
For the upcoming pluggable table access methods it's quite
inconvenient to store tuples as HeapTuples, as that'd require
converting tuples from a their native format into HeapTuples. Instead
use slots to manage epq tuples.
To fit into that scheme, change the foreign data wrapper callback
RefetchForeignRow, to store the tuple in a slot. Insist on using the
caller provided slot, so it conveniently can be stored in the
corresponding EPQ slot. As there is no in core user of
RefetchForeignRow, that change was done blindly, but we plan to test
that soon.
To avoid duplicating that work for row locks, move row locks to just
directly use the EPQ slots - it previously temporarily stored tuples
in LockRowsState.lr_curtuples, but that doesn't seem beneficial, given
we'd possibly end up with a significant number of additional slots.
The behaviour of es_epqTupleSet[rti -1] is now checked by
es_epqTupleSlot[rti -1] != NULL, as that is distinguishable from a
slot containing an empty tuple.
Author: Andres Freund, Haribabu Kommi, Ashutosh Bapat
Discussion: https://postgr.es/m/20180703070645.wchpu5muyto5n647@alap3.anarazel.de
In preparation for abstracting table storage, convert trigger.c to
track tuples in slots. Which also happens to make code calling
triggers simpler.
As the calling interface for triggers themselves is not changed in
this patch, HeapTuples still are extracted from the slot at that
time. But that's handled solely inside trigger.c, not visible to
callers. It's quite likely that we'll want to revise the external
trigger interface, but that's a separate large project.
As part of this work the slots used for old/new/return tuples are
moved from EState into ResultRelInfo, as different updated tables
might need different slots. The slots are now also now created
on-demand, which is good both from an efficiency POV, but also makes
the modifying code simpler.
Author: Andres Freund, Amit Khandekar and Ashutosh Bapat
Discussion: https://postgr.es/m/20180703070645.wchpu5muyto5n647@alap3.anarazel.de
This has the advantage that the comparator expression, the table's
slot, etc do not have to be rebuilt. Additionally the simplehash.h
hashtable within the tuple hashtable now keeps its previous size and
doesn't need to be reallocated. That both reduces allocator overhead,
and improves performance in cases where the input estimation was off
by a significant factor.
To avoid an API/ABI break, the new parameter is exposed via the new
BuildTupleHashTableExt(), and BuildTupleHashTable() now is a wrapper
around the former, that continues to allocate the table itself in the
tablecxt.
Using this fixes performance issues discovered in the two bugs
referenced. This commit however has not converted the callers, that's
done in a separate commit.
Bug: #15592#15486
Reported-By: Jakub Janeček, Dmitry Marakasov
Author: Andres Freund
Discussion:
https://postgr.es/m/15486-05850f065da42931@postgresql.orghttps://postgr.es/m/20190114180423.ywhdg2iagzvh43we@alap3.anarazel.de
Backpatch: 11, this is a prerequisite for other fixes
The old name of this file was never a very good indication of what it
was for. Now that there's also access/relation.h, we have a potential
confusion hazard as well, so let's rename it to something more apropos.
Per discussion, "pathnodes.h" is reasonable, since a good fraction of
the file is Path node definitions.
While at it, tweak a couple of other headers that were gratuitously
importing relation.h into modules that don't need it.
Discussion: https://postgr.es/m/7719.1548688728@sss.pgh.pa.us
heapam.h previously was included in a number of widely used
headers (e.g. execnodes.h, indirectly in executor.h, ...). That's
problematic on its own, as heapam.h contains a lot of low-level
details that don't need to be exposed that widely, but becomes more
problematic with the upcoming introduction of pluggable table storage
- it seems inappropriate for heapam.h to be included that widely
afterwards.
heapam.h was largely only included in other headers to get the
HeapScanDesc typedef (which was defined in heapam.h, even though
HeapScanDescData is defined in relscan.h). The better solution here
seems to be to just use the underlying struct (forward declared where
necessary). Similar for BulkInsertState.
Another problem was that LockTupleMode was used in executor.h - parts
of the file tried to cope without heapam.h, but due to the fact that
it indirectly included it, several subsequent violations of that goal
were not not noticed. We could just reuse the approach of declaring
parameters as int, but it seems nicer to move LockTupleMode to
lockoptions.h - that's not a perfect location, but also doesn't seem
bad.
As a number of files relied on implicitly included heapam.h, a
significant number of files grew an explicit include. It's quite
probably that a few external projects will need to do the same.
Author: Andres Freund
Reviewed-By: Alvaro Herrera
Discussion: https://postgr.es/m/20190114000701.y4ttcb74jpskkcfb@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
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
In a lot of nodes the return slot is not required. That can either be
because the node doesn't do any projection (say an Append node), or
because the node does perform projections but the projection is
optimized away because the projection would yield an identical row.
Slots aren't that small, especially for wide rows, so it's worthwhile
to avoid creating them. It's not possible to just skip creating the
slot - it's currently used to determine the tuple descriptor returned
by ExecGetResultType(). So separate the determination of the result
type from the slot creation. The work previously done internally
ExecInitResultTupleSlotTL() can now also be done separately with
ExecInitResultTypeTL() and ExecInitResultSlot(). That way nodes that
aren't guaranteed to need a result slot, can use
ExecInitResultTypeTL() to determine the result type of the node, and
ExecAssignScanProjectionInfo() (via
ExecConditionalAssignProjectionInfo()) determines that a result slot
is needed, it is created with ExecInitResultSlot().
Besides the advantage of avoiding to create slots that then are
unused, this is necessary preparation for later patches around tuple
table slot abstraction. In particular separating the return descriptor
and slot is a prerequisite to allow JITing of tuple deforming with
knowledge of the underlying tuple format, and to avoid unnecessarily
creating JITed tuple deforming for virtual slots.
This commit removes a redundant argument from
ExecInitResultTupleSlotTL(). While this commit touches a lot of the
relevant lines anyway, it'd normally still not worthwhile to cause
breakage, except that aforementioned later commits will touch *all*
ExecInitResultTupleSlotTL() callers anyway (but fits worse
thematically).
Author: Andres Freund
Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
There's several reasons for this change:
1) It reduces the total size of TupleTableSlot / reduces alignment
padding, making the commonly accessed members fit into a single
cacheline (but we currently do not force proper alignment, so
that's not yet guaranteed to be helpful)
2) Combining the booleans into a flag allows to combine read/writes
from memory.
3) With the upcoming slot abstraction changes, it allows to have core
and extended flags, in a memory efficient way.
Author: Ashutosh Bapat and Andres Freund
Discussion: https://postgr.es/m/20180220224318.gw4oe5jadhpmcdnm@alap3.anarazel.de
We already have appropriate locks on every relation listed in the
query's rangetable before we reach the executor. Take the next step
in exploiting that knowledge by removing code that worries about
taking locks on non-leaf result relations in a partitioned table.
In particular, get rid of ExecLockNonLeafAppendTables and a stanza in
InitPlan that asserts we already have locks on certain such tables.
In passing, clean up some now-obsolete comments in InitPlan.
Amit Langote, reviewed by David Rowley and Jesper Pedersen,
and whacked around a bit more by me
Discussion: https://postgr.es/m/468c85d9-540e-66a2-1dde-fec2b741e688@lab.ntt.co.jp
Instead of doing a lot of list_nth() accesses to es_range_table,
create a flattened pointer array during executor startup and index
into that to get at individual RangeTblEntrys.
This eliminates one source of O(N^2) behavior with lots of partitions.
(I'm not exactly convinced that it's the most important source, but
it's an easy one to fix.)
Amit Langote and David Rowley
Discussion: https://postgr.es/m/468c85d9-540e-66a2-1dde-fec2b741e688@lab.ntt.co.jp
Create an array estate->es_relations[] paralleling the es_range_table,
and store references to Relations (relcache entries) there, so that any
given RT entry is opened and closed just once per executor run. Scan
nodes typically still call ExecOpenScanRelation, but ExecCloseScanRelation
is no more; relation closing is now done centrally in ExecEndPlan.
This is slightly more complex than one would expect because of the
interactions with relcache references held in ResultRelInfo nodes.
The general convention is now that ResultRelInfo->ri_RelationDesc does
not represent a separate relcache reference and so does not need to be
explicitly closed; but there is an exception for ResultRelInfos in the
es_trig_target_relations list, which are manufactured by
ExecGetTriggerResultRel and have to be cleaned up by
ExecCleanUpTriggerState. (That much was true all along, but these
ResultRelInfos are now more different from others than they used to be.)
To allow the partition pruning logic to make use of es_relations[] rather
than having its own relcache references, adjust PartitionedRelPruneInfo
to store an RT index rather than a relation OID.
Amit Langote, reviewed by David Rowley and Jesper Pedersen,
some mods by me
Discussion: https://postgr.es/m/468c85d9-540e-66a2-1dde-fec2b741e688@lab.ntt.co.jp
Starting with commit f0e44751d717, ExecConstraints was in charge of
running the partition constraint; commit 19c47e7c8202 modified that so
that caller could request to skip that checking depending on some
conditions, but that commit and 15ce775faa42 together introduced a small
bug there which caused ExecInsert to request skipping the constraint
check but have this not be honored -- in effect doing the check twice.
This could have been fixed in a very small patch, but on further
analysis of the involved function and its callsites, it turns out to be
simpler to give the responsibility of checking the partition constraint
fully to the caller, and return ExecConstraints to its original
(pre-partitioning) shape where it only checked tuple descriptor-related
constraints. Each caller must do partition constraint checking on its
own schedule, which is more convenient after commit 2f178441044 anyway.
Reported-by: David Rowley
Author: David Rowley, Álvaro Herrera
Reviewed-by: Amit Langote, Amit Khandekar, Simon Riggs
Discussion: https://postgr.es/m/CAKJS1f8w8+awsxgea8wt7_UX8qzOQ=Tm1LD+U1fHqBAkXxkW2w@mail.gmail.com
The reason for doing so is that it will allow expression evaluation to
optimize based on the underlying tupledesc. In particular it will
allow to JIT tuple deforming together with the expression itself.
For that expression initialization needs to be moved after the
relevant slots are initialized - mostly unproblematic, except in the
case of nodeWorktablescan.c.
After doing so there's no need for ExecAssignResultType() and
ExecAssignResultTypeFromTL() anymore, as all former callers have been
converted to create a slot with a fixed descriptor.
When creating a slot with a fixed descriptor, tts_values/isnull can be
allocated together with the main slot, reducing allocation overhead
and increasing cache density a bit.
Author: Andres Freund
Discussion: https://postgr.es/m/20171206093717.vqdxe5icqttpxs3p@alap3.anarazel.de
This has a performance benefit on own, although not hugely so. The
primary benefit is that it will allow for to JIT tuple deforming and
comparator invocations.
Large parts of this were previously committed (773aec7aa), but the
commit contained an omission around cross-type comparisons and was
thus reverted.
Author: Andres Freund
Discussion: https://postgr.es/m/20171129080934.amqqkke2zjtekd4t@alap3.anarazel.de
This reverts commit 773aec7aa98abd38d6d9435913bb8e14e392c274.
There's an unresolved issue in the reverted commit: It only creates
one comparator function, but in for the nodeSubplan.c case we need
more (c.f. FindTupleHashEntry vs LookupTupleHashEntry calls in
nodeSubplan.c).
This isn't too difficult to fix, but it's not entirely trivial
either. The fact that the issue only causes breakage on 32bit systems
shows that the current test coverage isn't that great. To avoid
turning half the buildfarm red till those two issues are addressed,
revert.
It's a common task to evaluate a qual and reset the corresponding
expression context. Currently that requires storing the result of the
qual eval, resetting the context, and then reacting on the result. As
that's awkward several places only reset the context next time through
a node. That's not great, so introduce a helper that evaluates and
resets.
It's a bit ugly that it currently uses MemoryContextReset() instead of
ResetExprContext(), but that seems easier than reordering all of
executor.h.
Author: Andres Freund
Discussion: https://postgr.es/m/20180109222544.f7loxrunqh3xjl5f@alap3.anarazel.de
