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
Andrew Gierth reported that it's possible to crash the backend if no
pg_attrdef record is found to match an attribute that has atthasdef set.
AttrDefaultFetch warns about this situation, but then leaves behind
a relation tupdesc that has null "adbin" pointer(s), which most places
don't guard against.
We considered promoting the warning to an error, but throwing errors
during relcache load is pretty drastic: it effectively locks one out
of using the relation at all. What seems better is to leave the
load-time behavior as a warning, but then throw an error in any code
path that wants to use a default and can't find it. This confines
the error to a subset of INSERT/UPDATE operations on the table, and
in particular will at least allow a pg_dump to succeed.
Also, we should fix AttrDefaultFetch to not leave any null pointers
in the tupdesc, because that just creates an untested bug hazard.
While at it, apply the same philosophy of "warn at load, throw error
only upon use of the known-missing info" to CHECK constraints.
CheckConstraintFetch is very nearly the same logic as AttrDefaultFetch,
but for reasons lost in the mists of time, it was throwing ERROR for
the same cases that AttrDefaultFetch treats as WARNING. Make the two
functions more nearly alike.
In passing, get rid of potentially-O(N^2) loops in equalTupleDesc
by making AttrDefaultFetch sort the entries after fetching them,
so that equalTupleDesc can assume that entries in two equal tupdescs
must be in matching order. (CheckConstraintFetch already was sorting
CHECK constraints, but equalTupleDesc hadn't been told about it.)
There's some argument for back-patching this, but with such a small
number of field reports, I'm content to fix it in HEAD.
Discussion: https://postgr.es/m/87pmzaq4gx.fsf@news-spur.riddles.org.uk
At present, if we want to update publications in a subscription, we
can use SET PUBLICATION. However, it requires supplying all
publications that exists and the new publications. If we want to add
new publications, it's inconvenient. The new syntax only supplies the
new publications. When the refresh is true, it only refreshes the new
publications.
Author: Japin Li <japinli@hotmail.com>
Author: Bharath Rupireddy <bharath.rupireddyforpostgres@gmail.com>
Discussion: https://www.postgresql.org/message-id/flat/MEYP282MB166939D0D6C480B7FBE7EFFBB6BC0@MEYP282MB1669.AUSP282.PROD.OUTLOOK.COM
Reorganize the state struct used by VACUUM -- group related items
together to make it easier to understand. Also stop relying on stack
variables inside lazy_scan_heap() -- move those into the state struct
instead. Doing things this way simplifies large groups of related
functions whose function signatures had a lot of unnecessary redundancy.
Switch over to using int64 for the struct fields used to count things
that are reported to the user via log_autovacuum and VACUUM VERBOSE
output. We were using double, but that doesn't seem to have any
advantages. Using int64 makes it possible to add assertions that verify
that the first pass over the heap (pruning) encounters precisely the
same number of LP_DEAD items that get deleted from indexes later on, in
the second pass over the heap. These assertions will be added in later
commits.
Finally, adjust the signatures of functions with IndexBulkDeleteResult
pointer arguments in cases where there was ambiguity about whether or
not the argument relates to a single index or all indexes. Functions
now use the idiom that both ambulkdelete() and amvacuumcleanup() have
always used (where appropriate): accept a mutable IndexBulkDeleteResult
pointer argument, and return a result IndexBulkDeleteResult pointer to
caller.
Author: Peter Geoghegan <pg@bowt.ie>
Reviewed-By: Masahiko Sawada <sawada.mshk@gmail.com>
Reviewed-By: Robert Haas <robertmhaas@gmail.com>
Discussion: https://postgr.es/m/CAH2-WzkeOSYwC6KNckbhk2b1aNnWum6Yyn0NKP9D-Hq1LGTDPw@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 gives a small performance gain, by reducing the number of calls
to the conversion/verification function, and letting it work with
larger inputs. Also, reorganizing the input pipeline makes it easier
to parallelize the input parsing: after the input has been converted
to the database encoding, the next stage of finding the newlines can
be done in parallel, because there cannot be any newline chars
"embedded" in multi-byte characters in the encodings that we support
as server encodings.
This changes behavior in one corner case: if client and server
encodings are the same single-byte encoding (e.g. latin1), previously
the input would not be checked for zero bytes ('\0'). Any fields
containing zero bytes would be truncated at the zero. But if encoding
conversion was needed, the conversion routine would throw an error on
the zero. After this commit, the input is always checked for zeros.
Reviewed-by: John Naylor
Discussion: https://www.postgresql.org/message-id/e7861509-3960-538a-9025-b75a61188e01%40iki.fi
With the 'noError' argument, you can try to convert a buffer without
knowing the character boundaries beforehand. The functions now need to
return the number of input bytes successfully converted.
This is is a backwards-incompatible change, if you have created a custom
encoding conversion with CREATE CONVERSION. This adds a check to
pg_upgrade for that, refusing the upgrade if there are any user-defined
encoding conversions. Custom conversions are very rare, there are no
commonly used extensions that I know of that uses that feature. No other
objects can depend on conversions, so if you do have one, you can fairly
easily drop it before upgrading, and recreate it after the upgrade with
an updated version.
Add regression tests for built-in encoding conversions. This doesn't cover
every conversion, but it covers all the internal functions in conv.c that
are used to implement the conversions.
Reviewed-by: John Naylor
Discussion: https://www.postgresql.org/message-id/e7861509-3960-538a-9025-b75a61188e01%40iki.fi
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
Put the remote end's error message into the primary error string,
instead of relegating it to errdetail(). Although this could end up
being awkward if the remote sends us a really long error message,
it seems more in keeping with our message style guidelines, and more
helpful in situations where the errdetail could get dropped.
Peter Smith
Discussion: https://postgr.es/m/CAHut+Ps-Qv2yQceCwobQDP0aJOkfDzRFrOaR6+2Op2K=WHGeWg@mail.gmail.com
This patch makes two closely related sets of changes:
1. For UPDATE, the subplan of the ModifyTable node now only delivers
the new values of the changed columns (i.e., the expressions computed
in the query's SET clause) plus row identity information such as CTID.
ModifyTable must re-fetch the original tuple to merge in the old
values of any unchanged columns. The core advantage of this is that
the changed columns are uniform across all tables of an inherited or
partitioned target relation, whereas the other columns might not be.
A secondary advantage, when the UPDATE involves joins, is that less
data needs to pass through the plan tree. The disadvantage of course
is an extra fetch of each tuple to be updated. However, that seems to
be very nearly free in context; even worst-case tests don't show it to
add more than a couple percent to the total query cost. At some point
it might be interesting to combine the re-fetch with the tuple access
that ModifyTable must do anyway to mark the old tuple dead; but that
would require a good deal of refactoring and it seems it wouldn't buy
all that much, so this patch doesn't attempt it.
2. For inherited UPDATE/DELETE, instead of generating a separate
subplan for each target relation, we now generate a single subplan
that is just exactly like a SELECT's plan, then stick ModifyTable
on top of that. To let ModifyTable know which target relation a
given incoming row refers to, a tableoid junk column is added to
the row identity information. This gets rid of the horrid hack
that was inheritance_planner(), eliminating O(N^2) planning cost
and memory consumption in cases where there were many unprunable
target relations.
Point 2 of course requires point 1, so that there is a uniform
definition of the non-junk columns to be returned by the subplan.
We can't insist on uniform definition of the row identity junk
columns however, if we want to keep the ability to have both
plain and foreign tables in a partitioning hierarchy. Since
it wouldn't scale very far to have every child table have its
own row identity column, this patch includes provisions to merge
similar row identity columns into one column of the subplan result.
In particular, we can merge the whole-row Vars typically used as
row identity by FDWs into one column by pretending they are type
RECORD. (It's still okay for the actual composite Datums to be
labeled with the table's rowtype OID, though.)
There is more that can be done to file down residual inefficiencies
in this patch, but it seems to be committable now.
FDW authors should note several API changes:
* The argument list for AddForeignUpdateTargets() has changed, and so
has the method it must use for adding junk columns to the query. Call
add_row_identity_var() instead of manipulating the parse tree directly.
You might want to reconsider exactly what you're adding, too.
* PlanDirectModify() must now work a little harder to find the
ForeignScan plan node; if the foreign table is part of a partitioning
hierarchy then the ForeignScan might not be the direct child of
ModifyTable. See postgres_fdw for sample code.
* To check whether a relation is a target relation, it's no
longer sufficient to compare its relid to root->parse->resultRelation.
Instead, check it against all_result_relids or leaf_result_relids,
as appropriate.
Amit Langote and Tom Lane
Discussion: https://postgr.es/m/CA+HiwqHpHdqdDn48yCEhynnniahH78rwcrv1rEX65-fsZGBOLQ@mail.gmail.com
This implements asynchronous execution, which runs multiple parts of a
non-parallel-aware Append concurrently rather than serially to improve
performance when possible. Currently, the only node type that can be
run concurrently is a ForeignScan that is an immediate child of such an
Append. In the case where such ForeignScans access data on different
remote servers, this would run those ForeignScans concurrently, and
overlap the remote operations to be performed simultaneously, so it'll
improve the performance especially when the operations involve
time-consuming ones such as remote join and remote aggregation.
We may extend this to other node types such as joins or aggregates over
ForeignScans in the future.
This also adds the support for postgres_fdw, which is enabled by the
table-level/server-level option "async_capable". The default is false.
Robert Haas, Kyotaro Horiguchi, Thomas Munro, and myself. This commit
is mostly based on the patch proposed by Robert Haas, but also uses
stuff from the patch proposed by Kyotaro Horiguchi and from the patch
proposed by Thomas Munro. Reviewed by Kyotaro Horiguchi, Konstantin
Knizhnik, Andrey Lepikhov, Movead Li, Thomas Munro, Justin Pryzby, and
others.
Discussion: https://postgr.es/m/CA%2BTgmoaXQEt4tZ03FtQhnzeDEMzBck%2BLrni0UWHVVgOTnA6C1w%40mail.gmail.com
Discussion: https://postgr.es/m/CA%2BhUKGLBRyu0rHrDCMC4%3DRn3252gogyp1SjOgG8SEKKZv%3DFwfQ%40mail.gmail.com
Discussion: https://postgr.es/m/20200228.170650.667613673625155850.horikyota.ntt%40gmail.com
Whilst poking at nodeModifyTable.c, I chanced to notice that while
its calls to ExecBR*Triggers and ExecIR*Triggers are protected by
tests to see if there are any relevant triggers to fire, its calls
to ExecAR*Triggers are not; the latter functions do the equivalent
tests themselves. This seems possibly reasonable given the more
complex conditions involved, but what's less reasonable is that
the ExecAR* functions aren't careful to do no work when there is
no work to be done. ExecARInsertTriggers gets this right, but the
other two will both force creation of a slot that the query may
have no use for. ExecARUpdateTriggers additionally performed a
usually-useless ExecClearTuple() on that slot. This is probably
all pretty microscopic in real workloads, but a cycle shaved is a
cycle earned.
Allow defining extended statistics on expressions, not just just on
simple column references. With this commit, expressions are supported
by all existing extended statistics kinds, improving the same types of
estimates. A simple example may look like this:
CREATE TABLE t (a int);
CREATE STATISTICS s ON mod(a,10), mod(a,20) FROM t;
ANALYZE t;
The collected statistics are useful e.g. to estimate queries with those
expressions in WHERE or GROUP BY clauses:
SELECT * FROM t WHERE mod(a,10) = 0 AND mod(a,20) = 0;
SELECT 1 FROM t GROUP BY mod(a,10), mod(a,20);
This introduces new internal statistics kind 'e' (expressions) which is
built automatically when the statistics object definition includes any
expressions. This represents single-expression statistics, as if there
was an expression index (but without the index maintenance overhead).
The statistics is stored in pg_statistics_ext_data as an array of
composite types, which is possible thanks to 79f6a942bd.
CREATE STATISTICS allows building statistics on a single expression, in
which case in which case it's not possible to specify statistics kinds.
A new system view pg_stats_ext_exprs can be used to display expression
statistics, similarly to pg_stats and pg_stats_ext views.
ALTER TABLE ... ALTER COLUMN ... TYPE now treats indexes the same way it
treats indexes, i.e. it drops and recreates the statistics. This means
all statistics are reset, and we no longer try to preserve at least the
functional dependencies. This should not be a major issue in practice,
as the functional dependencies actually rely on per-column statistics,
which were always reset anyway.
Author: Tomas Vondra
Reviewed-by: Justin Pryzby, Dean Rasheed, Zhihong Yu
Discussion: https://postgr.es/m/ad7891d2-e90c-b446-9fe2-7419143847d7%40enterprisedb.com
Membership consists, implicitly, of the current database owner. Expect
use in template databases. Once pg_database_owner has rights within a
template, each owner of a database instantiated from that template will
exercise those rights.
Reviewed by John Naylor.
Discussion: https://postgr.es/m/20201228043148.GA1053024@rfd.leadboat.com
Allow a partition be detached from its partitioned table without
blocking concurrent queries, by running in two transactions and only
requiring ShareUpdateExclusive in the partitioned table.
Because it runs in two transactions, it cannot be used in a transaction
block. This is the main reason to use dedicated syntax: so that users
can choose to use the original mode if they need it. But also, it
doesn't work when a default partition exists (because an exclusive lock
would still need to be obtained on it, in order to change its partition
constraint.)
In case the second transaction is cancelled or a crash occurs, there's
ALTER TABLE .. DETACH PARTITION .. FINALIZE, which executes the final
steps.
The main trick to make this work is the addition of column
pg_inherits.inhdetachpending, initially false; can only be set true in
the first part of this command. Once that is committed, concurrent
transactions that use a PartitionDirectory will include or ignore
partitions so marked: in optimizer they are ignored if the row is marked
committed for the snapshot; in executor they are always included. As a
result, and because of the way PartitionDirectory caches partition
descriptors, queries that were planned before the detach will see the
rows in the detached partition and queries that are planned after the
detach, won't.
A CHECK constraint is created that duplicates the partition constraint.
This is probably not strictly necessary, and some users will prefer to
remove it afterwards, but if the partition is re-attached to a
partitioned table, the constraint needn't be rechecked.
Author: Álvaro Herrera <alvherre@alvh.no-ip.org>
Reviewed-by: Amit Langote <amitlangote09@gmail.com>
Reviewed-by: Justin Pryzby <pryzby@telsasoft.com>
Discussion: https://postgr.es/m/20200803234854.GA24158@alvherre.pgsql
Struct AlteredRelationInfo gains a new Relation member, to be used only
by Phase 2 (ATRewriteCatalogs); this allows ATExecCmd() subroutines open
and close the relation internally.
A future commit will use this facility to implement an ALTER TABLE
subcommand that closes and reopens the relation across transaction
boundaries.
(It is possible to keep the relation open past phase 2 to be used by
phase 3 instead of having to reopen it that point, but there are some
minor complications with that; it's not clear that there is much to be
won from doing that, though.)
Author: Álvaro Herrera <alvherre@alvh.no-ip.org>
Discussion: https://postgr.es/m/20200803234854.GA24158@alvherre.pgsql
I introduced this duplicate code in commit 8b08f7d482 for no good
reason. Remove it, and backpatch to 11 where it was introduced.
Author: Álvaro Herrera <alvherre@alvh.no-ip.org>
Previously, to check relation permanence, the Relation's Form_pg_class
structure member relpersistence was compared to the value
RELPERSISTENCE_PERMANENT ("p"). This commit adds the macro
RelationIsPermanent() and is used in appropirate places to simplify the
code. This matches other RelationIs* macros.
This macro will be used in more places in future cluster file encryption
patches.
Discussion: https://postgr.es/m/20210318153134.GH20766@tamriel.snowman.net
It's not okay to scribble directly on a syscache entry.
Nor to continue accessing said entry after releasing it.
Also get rid of not-used local variables.
Per valgrind testing.
There is now a per-column COMPRESSION option which can be set to pglz
(the default, and the only option in up until now) or lz4. Or, if you
like, you can set the new default_toast_compression GUC to lz4, and
then that will be the default for new table columns for which no value
is specified. We don't have lz4 support in the PostgreSQL code, so
to use lz4 compression, PostgreSQL must be built --with-lz4.
In general, TOAST compression means compression of individual column
values, not the whole tuple, and those values can either be compressed
inline within the tuple or compressed and then stored externally in
the TOAST table, so those properties also apply to this feature.
Prior to this commit, a TOAST pointer has two unused bits as part of
the va_extsize field, and a compessed datum has two unused bits as
part of the va_rawsize field. These bits are unused because the length
of a varlena is limited to 1GB; we now use them to indicate the
compression type that was used. This means we only have bit space for
2 more built-in compresison types, but we could work around that
problem, if necessary, by introducing a new vartag_external value for
any further types we end up wanting to add. Hopefully, it won't be
too important to offer a wide selection of algorithms here, since
each one we add not only takes more coding but also adds a build
dependency for every packager. Nevertheless, it seems worth doing
at least this much, because LZ4 gets better compression than PGLZ
with less CPU usage.
It's possible for LZ4-compressed datums to leak into composite type
values stored on disk, just as it is for PGLZ. It's also possible for
LZ4-compressed attributes to be copied into a different table via SQL
commands such as CREATE TABLE AS or INSERT .. SELECT. It would be
expensive to force such values to be decompressed, so PostgreSQL has
never done so. For the same reasons, we also don't force recompression
of already-compressed values even if the target table prefers a
different compression method than was used for the source data. These
architectural decisions are perhaps arguable but revisiting them is
well beyond the scope of what seemed possible to do as part of this
project. However, it's relatively cheap to recompress as part of
VACUUM FULL or CLUSTER, so this commit adjusts those commands to do
so, if the configured compression method of the table happens not to
match what was used for some column value stored therein.
Dilip Kumar. The original patches on which this work was based were
written by Ildus Kurbangaliev, and those were patches were based on
even earlier work by Nikita Glukhov, but the design has since changed
very substantially, since allow a potentially large number of
compression methods that could be added and dropped on a running
system proved too problematic given some of the architectural issues
mentioned above; the choice of which specific compression method to
add first is now different; and a lot of the code has been heavily
refactored. More recently, Justin Przyby helped quite a bit with
testing and reviewing and this version also includes some code
contributions from him. Other design input and review from Tomas
Vondra, Álvaro Herrera, Andres Freund, Oleg Bartunov, Alexander
Korotkov, and me.
Discussion: http://postgr.es/m/20170907194236.4cefce96%40wp.localdomain
Discussion: http://postgr.es/m/CAFiTN-uUpX3ck%3DK0mLEk-G_kUQY%3DSNOTeqdaNRR9FMdQrHKebw%40mail.gmail.com
Pass CURSOR_OPT_PARALLEL_OK to pg_plan_query() so that parallel plans
are considered when running the underlying SELECT query. This wasn't
done in commit e9baa5e9, which did this for CREATE MATERIALIZED VIEW,
because it wasn't yet known to be safe.
Since REFRESH always inserts into a freshly created table before later
merging or swapping the data into place with separate operations, we can
enable such plans here too.
Author: Bharath Rupireddy <bharath.rupireddyforpostgres@gmail.com>
Reviewed-by: Hou, Zhijie <houzj.fnst@cn.fujitsu.com>
Reviewed-by: Luc Vlaming <luc@swarm64.com>
Reviewed-by: Thomas Munro <thomas.munro@gmail.com>
Discussion: https://postgr.es/m/CALj2ACXg-4hNKJC6nFnepRHYT4t5jJVstYvri%2BtKQHy7ydcr8A%40mail.gmail.com
When we have posix_fadvise() available, we can improve the performance
of an ANALYZE by quite a bit by using it to inform the kernel of the
blocks that we're going to be asking for. Similar to bitmap index
scans, the number of buffers pre-fetched is based off of the
maintenance_io_concurrency setting (for the particular tablespace or,
if not set, globally, via get_tablespace_maintenance_io_concurrency()).
Reviewed-By: Heikki Linnakangas, Tomas Vondra
Discussion: https://www.postgresql.org/message-id/VI1PR0701MB69603A433348EDCF783C6ECBF6EF0%40VI1PR0701MB6960.eurprd07.prod.outlook.com
vacuumlazy.c sometimes fails to update pg_class entries for each index
(to ensure that pg_class.reltuples is current), even though analyze.c
assumed that that must have happened during VACUUM ANALYZE. There are
at least a couple of reasons for this. For example, vacuumlazy.c could
fail to update pg_class when the index AM indicated that its statistics
are merely an estimate, per the contract for amvacuumcleanup() routines
established by commit e57345975c back in 2006.
Stop assuming that pg_class must have been updated with accurate
statistics within VACUUM ANALYZE -- update pg_class for indexes at the
same time as the table relation in all cases. That way VACUUM ANALYZE
will never fail to keep pg_class.reltuples reasonably accurate.
The only downside of this approach (compared to the old approach) is
that it might inaccurately set pg_class.reltuples for indexes whose heap
relation ends up with the same inaccurate value anyway. This doesn't
seem too bad. We already consistently called vac_update_relstats() (to
update pg_class) for the heap/table relation twice during any VACUUM
ANALYZE -- once in vacuumlazy.c, and once in analyze.c. We now make
sure that we call vac_update_relstats() at least once (though often
twice) for each index.
This is follow up work to commit 9f3665fb, which dealt with issues in
btvacuumcleanup(). Technically this fixes an unrelated issue, though.
btvacuumcleanup() no longer provides an accurate num_index_tuples value
following commit 9f3665fb (when there was no btbulkdelete() call during
the VACUUM operation in question), but hashvacuumcleanup() has worked in
the same way for many years now.
Author: Peter Geoghegan <pg@bowt.ie>
Reviewed-By: Masahiko Sawada <sawada.mshk@gmail.com>
Discussion: https://postgr.es/m/CAH2-WzknxdComjhqo4SUxVFk_Q1171GJO2ZgHZ1Y6pion6u8rA@mail.gmail.com
Backpatch: 13-, just like commit 9f3665fb.
The command (TO or FROM), its type (file, pipe, program or callback),
and the number of tuples excluded by a WHERE clause in COPY FROM are
added to the progress reporting already available.
The column "lines_processed" is renamed to "tuples_processed" to
disambiguate the meaning of this column in the cases of CSV and BINARY
COPY and to be more consistent with the other catalog progress views.
Bump catalog version, again.
Author: Matthias van de Meent
Reviewed-by: Michael Paquier, Justin Pryzby, Bharath Rupireddy, Josef
Šimánek, Tomas Vondra
Discussion: https://postgr.es/m/CAEze2WiOcgdH4aQA8NtZq-4dgvnJzp8PohdeKchPkhMY-jWZXA@mail.gmail.com
"SELECT pg_import_system_collations(0)" caused an assertion failure.
With a random nonzero argument --- or indeed with zero, in non-assert
builds --- it would happily make pg_collation entries with garbage
values of collnamespace. These are harmless as far as I can tell
(unless maybe the OID happens to become used for a schema, later on?).
In any case this isn't a security issue, since the function is
superuser-only. But it seems like a gotcha for unwary DBAs, so let's
add a check that the given OID belongs to some schema.
Back-patch to v10 where this function was introduced.
Protocol version 3 was introduced in PostgreSQL 7.4. There shouldn't be
many clients or servers left out there without version 3 support. But as
a courtesy, I kept just enough of the old protocol support that we can
still send the "unsupported protocol version" error in v2 format, so that
old clients can display the message properly. Likewise, libpq still
understands v2 ErrorResponse messages when establishing a connection.
The impetus to do this now is that I'm working on a patch to COPY
FROM, to always prefetch some data. We cannot do that safely with the
old protocol, because it requires parsing the input one byte at a time
to detect the end-of-copy marker.
Reviewed-by: Tom Lane, Alvaro Herrera, John Naylor
Discussion: https://www.postgresql.org/message-id/9ec25819-0a8a-d51a-17dc-4150bb3cca3b%40iki.fi
The same code pattern was repeated twice to enable or disable ROW LEVEL
SECURITY with an ALTER TABLE command. This makes the code slightly
cleaner.
Author: Justin Pryzby
Reviewed-by: Zhihong Yu
Discussion: https://postgr.es/m/20210228211854.GC20769@telsasoft.com
AfterTriggerSaveEvent() wrongly allocates the slot in execution-span
memory context, whereas the correct thing is to allocate it in
a transaction-span context, because that's where the enclosing
AfterTriggersTableData instance belongs into.
Backpatch to 12 (the test back to 11, where it works well with no code
changes, and it's good to have to confirm that the case was previously
well supported); this bug seems introduced by commit ff11e7f4b9.
Reported-by: Bertrand Drouvot <bdrouvot@amazon.com>
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/39a71864-b120-5a5c-8cc5-c632b6f16761@amazon.com
This adds a new executor node named TID Range Scan. The query planner
will generate paths for TID Range scans when quals are discovered on base
relations which search for ranges on the table's ctid column. These
ranges may be open at either end. For example, WHERE ctid >= '(10,0)';
will return all tuples on page 10 and over.
To support this, two new optional callback functions have been added to
table AM. scan_set_tidrange is used to set the scan range to just the
given range of TIDs. scan_getnextslot_tidrange fetches the next tuple
in the given range.
For AMs were scanning ranges of TIDs would not make sense, these functions
can be set to NULL in the TableAmRoutine. The query planner won't
generate TID Range Scan Paths in that case.
Author: Edmund Horner, David Rowley
Reviewed-by: David Rowley, Tomas Vondra, Tom Lane, Andres Freund, Zhihong Yu
Discussion: https://postgr.es/m/CAMyN-kB-nFTkF=VA_JPwFNo08S0d-Yk0F741S2B7LDmYAi8eyA@mail.gmail.com
Instead of an unsightly internal "cache lookup failed" message, just
return NULL for bad OIDs, as is the convention for other similar things.
Reported-by: Justin Pryzby <pryzby@telsasoft.com>
Reviewed-by: Michael Paquier <michael@paquier.xyz>
Discussion: https://postgr.es/m/20210117215940.GE8560%40telsasoft.com
This commit changes one code path in REINDEX INDEX and one code path
in CREATE INDEX CONCURRENTLY to report the progress of each operation
using pgstat_progress_update_multi_param() rather than
multiple calls to pgstat_progress_update_param(). This has the
advantage to make the progress report more consistent to the end-user
without impacting the amount of information provided.
Author: Bharath Rupireddy
Discussion: https://postgr.es/m/CALj2ACV5zW7GxD8D_tyO==bcj6ZktQchEKWKPBOAGKiLhAQo=w@mail.gmail.com
For the initial table data synchronization in logical replication, we use
a single transaction to copy the entire table and then synchronize the
position in the stream with the main apply worker.
There are multiple downsides of this approach: (a) We have to perform the
entire copy operation again if there is any error (network breakdown,
error in the database operation, etc.) while we synchronize the WAL
position between tablesync worker and apply worker; this will be onerous
especially for large copies, (b) Using a single transaction in the
synchronization-phase (where we can receive WAL from multiple
transactions) will have the risk of exceeding the CID limit, (c) The slot
will hold the WAL till the entire sync is complete because we never commit
till the end.
This patch solves all the above downsides by allowing multiple
transactions during the tablesync phase. The initial copy is done in a
single transaction and after that, we commit each transaction as we
receive. To allow recovery after any error or crash, we use a permanent
slot and origin to track the progress. The slot and origin will be removed
once we finish the synchronization of the table. We also remove slot and
origin of tablesync workers if the user performs DROP SUBSCRIPTION .. or
ALTER SUBSCRIPTION .. REFERESH and some of the table syncs are still not
finished.
The commands ALTER SUBSCRIPTION ... REFRESH PUBLICATION and
ALTER SUBSCRIPTION ... SET PUBLICATION ... with refresh option as true
cannot be executed inside a transaction block because they can now drop
the slots for which we have no provision to rollback.
This will also open up the path for logical replication of 2PC
transactions on the subscriber side. Previously, we can't do that because
of the requirement of maintaining a single transaction in tablesync
workers.
Bump catalog version due to change of state in the catalog
(pg_subscription_rel).
Author: Peter Smith, Amit Kapila, and Takamichi Osumi
Reviewed-by: Ajin Cherian, Petr Jelinek, Hou Zhijie and Amit Kapila
Discussion: https://postgr.es/m/CAA4eK1KHJxaZS-fod-0fey=0tq3=Gkn4ho=8N4-5HWiCfu0H1A@mail.gmail.com
Currently, we get the origin id from the name and then drop the origin by
taking ExclusiveLock on ReplicationOriginRelationId. So, two concurrent
sessions can get the id from the name at the same time and then when they
try to drop the origin, one of the sessions will get the either
"tuple concurrently deleted" or "cache lookup failed for replication
origin ..".
To prevent this race condition we do the entire operation under lock. This
obviates the need for replorigin_drop() API and we have removed it so if
any extension authors are using it they need to instead use
replorigin_drop_by_name. See it's usage in pg_replication_origin_drop().
Author: Peter Smith
Reviewed-by: Amit Kapila, Euler Taveira, Petr Jelinek, and Alvaro
Herrera
Discussion: https://www.postgresql.org/message-id/CAHut%2BPuW8DWV5fskkMWWMqzt-x7RPcNQOtJQBp6SdwyRghCk7A%40mail.gmail.com
This option controls if toast tables associated with a relation are
vacuumed or not when running a manual VACUUM. It was already possible
to trigger a manual VACUUM on a toast relation without processing its
main relation, but a manual vacuum on a main relation always forced a
vacuum on its toast table. This is useful in scenarios where the level
of bloat or transaction age of the main and toast relations differs a
lot.
This option is an extension of the existing VACOPT_SKIPTOAST that was
used by autovacuum to control if toast relations should be skipped or
not. This internal flag is renamed to VACOPT_PROCESS_TOAST for
consistency with the new option.
A new option switch, called --no-process-toast, is added to vacuumdb.
Author: Nathan Bossart
Reviewed-by: Kirk Jamison, Michael Paquier, Justin Pryzby
Discussion: https://postgr.es/m/BA8951E9-1524-48C5-94AF-73B1F0D7857F@amazon.com
Tom Lane
Peter Eisentraut
Peter Geoghegan
Michael Paquier
David Rowley
Stephen Frost
Heikki Linnakangas
Etsuro Fujita
Tomas Vondra
Noah Misch
Alvaro Herrera
Bruce Momjian
Robert Haas
Thomas Munro
Amit Kapila