Until now PostgreSQL has not been very smart about optimizing away IS
NOT NULL base quals on columns defined as NOT NULL. The evaluation of
these needless quals adds overhead. Ordinarily, anyone who came
complaining about that would likely just have been told to not include
the qual in their query if it's not required. However, a recent bug
report indicates this might not always be possible.
Bug 17540 highlighted that when we optimize Min/Max aggregates the IS NOT
NULL qual that the planner adds to make the rewritten plan ignore NULLs
can cause issues with poor index choice. That particular case
demonstrated that other quals, especially ones where no statistics are
available to allow the planner a chance at estimating an approximate
selectivity for can result in poor index choice due to cheap startup paths
being prefered with LIMIT 1.
Here we take generic approach to fixing this by having the planner check
for NOT NULL columns and just have the planner remove these quals (when
they're not needed) for all queries, not just when optimizing Min/Max
aggregates.
Additionally, here we also detect IS NULL quals on a NOT NULL column and
transform that into a gating qual so that we don't have to perform the
scan at all. This also works for join relations when the Var is not
nullable by any outer join.
This also helps with the self-join removal work as it must replace
strict join quals with IS NOT NULL quals to ensure equivalence with the
original query.
Author: David Rowley, Richard Guo, Andy Fan
Reviewed-by: Richard Guo, David Rowley
Discussion: https://postgr.es/m/CAApHDvqg6XZDhYRPz0zgOcevSMo0d3vxA9DvHrZtKfqO30WTnw@mail.gmail.com
Discussion: https://postgr.es/m/17540-7aa1855ad5ec18b4%40postgresql.org
CREATE SCHEMA AUTHORIZATION with appended schema elements can lead to
crashes when comparing the schema name of the query with the schemas
used in the qualification of some clauses in the elements' queries.
The origin of the problem is that the transformation routine for the
elements listed in a CREATE SCHEMA query uses as new, expected, schema
name the one listed in CreateSchemaStmt itself. However, depending on
the query, CreateSchemaStmt.schemaname may be NULL, being computed
instead from the role specification of the query given by the
AUTHORIZATION clause, that could be either:
- A user name string, with the new schema name being set to the same
value as the role given.
- Guessed from CURRENT_ROLE, SESSION_ROLE or CURRENT_ROLE, with a new
schema name computed from the security context where CREATE SCHEMA is
running.
Regression tests are added for CREATE SCHEMA with some appended elements
(some of them with schema qualifications), covering also some role
specification patterns.
While on it, this simplifies the context structure used during the
transformation of the elements listed in a CREATE SCHEMA query by
removing the fields for the role specification and the role type. They
were not used, and for the role specification this could be confusing as
the schema name may by extracted from that at the beginning of
CreateSchemaCommand().
This issue exists for a long time, so backpatch down to all the versions
supported.
Reported-by: Song Hongyu
Author: Michael Paquier
Reviewed-by: Richard Guo
Discussion: https://postgr.es/m/17909-f65c12dfc5f0451d@postgresql.org
Backpatch-through: 11
This reverts commit e056c557ae and minor later fixes thereof.
There's a few problems in this new feature -- most notably regarding
pg_upgrade behavior, but others as well. This new feature is not in any
way critical on its own, so instead of scrambling to fix it we revert it
and try again in early 17 with these issues in mind.
Discussion: https://postgr.es/m/3801207.1681057430@sss.pgh.pa.us
We now create pg_constaint rows for NOT NULL constraints with
contype='n'.
We propagate these constraints during operations such as adding
inheritance relationships, creating and attaching partitions, creating
tables LIKE other tables. We mostly follow the well-known rules of
conislocal and coninhcount that we have for CHECK constraints, with some
adaptations; for example, as opposed to CHECK constraints, we don't
match NOT NULL ones by name when descending a hierarchy to alter it;
instead we match by column number. This means we don't require the
constraint names to be identical across a hierarchy.
For now, we omit them from system catalogs. Maybe this is worth
reconsidering. We don't support NOT VALID nor DEFERRABLE clauses
either; these can be added as separate features later (this patch is
already large and complicated enough.)
This has been very long in the making. The first patch was written by
Bernd Helmle in 2010 to add a new pg_constraint.contype value ('n'),
which I (Álvaro) then hijacked in 2011 and 2012, until that one was
killed by the realization that we ought to use contype='c' instead:
manufactured CHECK constraints. However, later SQL standard
development, as well as nonobvious emergent properties of that design
(mostly, failure to distinguish them from "normal" CHECK constraints as
well as the performance implication of having to test the CHECK
expression) led us to reconsider this choice, so now the current
implementation uses contype='n' again.
In 2016 Vitaly Burovoy also worked on this feature[1] but found no
consensus for his proposed approach, which was claimed to be closer to
the letter of the standard, requiring additional pg_attribute columns to
track the OID of the NOT NULL constraint for that column.
[1] https://postgr.es/m/CAKOSWNkN6HSyatuys8xZxzRCR-KL1OkHS5-b9qd9bf1Rad3PLA@mail.gmail.com
Author: Álvaro Herrera <alvherre@alvh.no-ip.org>
Author: Bernd Helmle <mailings@oopsware.de>
Reviewed-by: Justin Pryzby <pryzby@telsasoft.com>
Reviewed-by: Peter Eisentraut <peter.eisentraut@enterprisedb.com>
Discussion: https://postgr.es/m/CACA0E642A0267EDA387AF2B%40%5B172.26.14.62%5D
Discussion: https://postgr.es/m/AANLkTinLXMOEMz+0J29tf1POokKi4XDkWJ6-DDR9BKgU@mail.gmail.com
Discussion: https://postgr.es/m/20110707213401.GA27098@alvh.no-ip.org
Discussion: https://postgr.es/m/1343682669-sup-2532@alvh.no-ip.org
Discussion: https://postgr.es/m/CAKOSWNkN6HSyatuys8xZxzRCR-KL1OkHS5-b9qd9bf1Rad3PLA@mail.gmail.com
Discussion: https://postgr.es/m/20220817181249.q7qvj3okywctra3c@alvherre.pgsql
This commit introduces the SQL/JSON standard-conforming constructors for
JSON types:
JSON_ARRAY()
JSON_ARRAYAGG()
JSON_OBJECT()
JSON_OBJECTAGG()
Most of the functionality was already present in PostgreSQL-specific
functions, but these include some new functionality such as the ability
to skip or include NULL values, and to allow duplicate keys or throw
error when they are found, as well as the standard specified syntax to
specify output type and format.
Author: Nikita Glukhov <n.gluhov@postgrespro.ru>
Author: Teodor Sigaev <teodor@sigaev.ru>
Author: Oleg Bartunov <obartunov@gmail.com>
Author: Alexander Korotkov <aekorotkov@gmail.com>
Author: Amit Langote <amitlangote09@gmail.com>
Reviewers have included (in no particular order) Andres Freund, Alexander
Korotkov, Pavel Stehule, Andrew Alsup, Erik Rijkers, Zihong Yu,
Himanshu Upadhyaya, Daniel Gustafsson, Justin Pryzby.
Discussion: https://postgr.es/m/CAF4Au4w2x-5LTnN_bxky-mq4=WOqsGsxSpENCzHRAzSnEd8+WQ@mail.gmail.com
Discussion: https://postgr.es/m/cd0bb935-0158-78a7-08b5-904886deac4b@postgrespro.ru
Discussion: https://postgr.es/m/20220616233130.rparivafipt6doj3@alap3.anarazel.de
Discussion: https://postgr.es/m/abd9b83b-aa66-f230-3d6d-734817f0995d%40postgresql.org
An upcoming test needs to use a tablespace as part of its test. Historically,
we wanted tablespace creation be done in a dedicated file, so it's easy to
disable when testing replication. But that is not necessary anymore, due to
allow_in_place_tablespaces.
Create regress_tblspace tablespace in test_setup. Move the tablespace test to
the end of the parallel schedule, so other tests can use it.
Author: Melanie Plageman <melanieplageman@gmail.com>
Reviewed-by: Andres Freund <andres@anarazel.de>
Discussion: https://postgr.es/m/20200124195226.lth52iydq2n2uilq@alap3.anarazel.de
We had some pretty ad-hoc and inefficient code here. To make
matters worse, it didn't test the properties of the random()
function very thoroughly, and it had a test failure rate of
one in every few tens of thousands of runs. Replace the
script altogether with new test cases that prove much more
about random()'s output, run faster, and can be calculated
to have test failure rates on the order of 1e-9.
Having done that, the failure rate of this script should be
negligible in comparison to other causes of test failures,
so remove the "ignore" marker for it in parallel_schedule.
(If it does fail, we'd like to know about that, so "ignore"
was always pretty counterproductive.)
Tom Lane and Dean Rasheed
Discussion: https://postgr.es/m/4173840.1673290336@sss.pgh.pa.us
Windows can enumerate the locales that are either installed or
supported by calling EnumSystemLocalesEx(), similar to what is already
done in the READ_LOCALE_A_OUTPUT switch. We can refactor some of the
logic already used in that switch into a new function
create_collation_from_locale().
The enumerated locales have BCP 47 shape, that is with a hyphen
between language and territory, instead of POSIX's underscore. The
created collations will retain the BCP 47 shape, but we will also
create a POSIX alias, so xx-YY will have an xx_YY alias.
A new test collate.windows.win1252 is added that is like
collate.linux.utf8.
Author: Juan Jose Santamaria Flecha <juanjo.santamaria@gmail.com>
Reviewed-by: Dmitry Koval <d.koval@postgrespro.ru>
Reviewed-by: Peter Eisentraut <peter.eisentraut@enterprisedb.com>
Discussion: https://www.postgresql.org/message-id/flat/0050ec23-34d9-2765-9015-98c04f0e18ac@postgrespro.ru
In FIPS mode, these calls will fail. By having them in a separate
file, it would make it easier to have an alternative output file or
selectively disable these tests. This isn't done here; this is just
some preparation.
Reviewed-by: Michael Paquier <michael@paquier.xyz>
Discussion: https://www.postgresql.org/message-id/647f6cc1-473d-f788-ade0-c09201e5ab6a@enterprisedb.com
The reverts the following and makes some associated cleanups:
commit f79b803dc: Common SQL/JSON clauses
commit f4fb45d15: SQL/JSON constructors
commit 5f0adec25: Make STRING an unreserved_keyword.
commit 33a377608: IS JSON predicate
commit 1a36bc9db: SQL/JSON query functions
commit 606948b05: SQL JSON functions
commit 49082c2cc: RETURNING clause for JSON() and JSON_SCALAR()
commit 4e34747c8: JSON_TABLE
commit fadb48b00: PLAN clauses for JSON_TABLE
commit 2ef6f11b0: Reduce running time of jsonb_sqljson test
commit 14d3f24fa: Further improve jsonb_sqljson parallel test
commit a6baa4bad: Documentation for SQL/JSON features
commit b46bcf7a4: Improve readability of SQL/JSON documentation.
commit 112fdb352: Fix finalization for json_objectagg and friends
commit fcdb35c32: Fix transformJsonBehavior
commit 4cd8717af: Improve a couple of sql/json error messages
commit f7a605f63: Small cleanups in SQL/JSON code
commit 9c3d25e17: Fix JSON_OBJECTAGG uniquefying bug
commit a79153b7a: Claim SQL standard compliance for SQL/JSON features
commit a1e7616d6: Rework SQL/JSON documentation
commit 8d9f9634e: Fix errors in copyfuncs/equalfuncs support for JSON node types.
commit 3c633f32b: Only allow returning string types or bytea from json_serialize
commit 67b26703b: expression eval: Fix EEOP_JSON_CONSTRUCTOR and EEOP_JSONEXPR size.
The release notes are also adjusted.
Backpatch to release 15.
Discussion: https://postgr.es/m/40d2c882-bcac-19a9-754d-4299e1d87ac7@postgresql.org
- subscriber stats reset path was untested
- slot stat sreset path for all slots was untested
- pg_stat_database.sessions etc was untested
- pg_stat_reset_shared() was untested, for any kind of shared stats
- pg_stat_reset() was untested
Author: Melanie Plageman <melanieplageman@gmail.com>
Author: Andres Freund <andres@anarazel.de>
Discussion: https://postgr.es/m/20220303021600.hs34ghqcw6zcokdh@alap3.anarazel.de
In the stats collector days it was hard to write tests for the stats system,
because fundamentally delivery of stats messages over UDP was not
synchronous (nor guaranteed). Now we easily can force pending stats updates to
be flushed synchronously.
This moves stats.sql into a parallel group, there isn't a reason for it to run
in isolation anymore. And it may shake out some bugs.
Bumps catversion.
Author: Andres Freund <andres@anarazel.de>
Discussion: https://postgr.es/m/20220303021600.hs34ghqcw6zcokdh@alap3.anarazel.de
This introduces the SQL/JSON functions for querying JSON data using
jsonpath expressions. The functions are:
JSON_EXISTS()
JSON_QUERY()
JSON_VALUE()
All of these functions only operate on jsonb. The workaround for now is
to cast the argument to jsonb.
JSON_EXISTS() tests if the jsonpath expression applied to the jsonb
value yields any values. JSON_VALUE() must return a single value, and an
error occurs if it tries to return multiple values. JSON_QUERY() must
return a json object or array, and there are various WRAPPER options for
handling scalar or multi-value results. Both these functions have
options for handling EMPTY and ERROR conditions.
Nikita Glukhov
Reviewers have included (in no particular order) Andres Freund, Alexander
Korotkov, Pavel Stehule, Andrew Alsup, Erik Rijkers, Zihong Yu,
Himanshu Upadhyaya, Daniel Gustafsson, Justin Pryzby.
Discussion: https://postgr.es/m/cd0bb935-0158-78a7-08b5-904886deac4b@postgrespro.ru
MERGE performs actions that modify rows in the target table using a
source table or query. MERGE provides a single SQL statement that can
conditionally INSERT/UPDATE/DELETE rows -- a task that would otherwise
require multiple PL statements. For example,
MERGE INTO target AS t
USING source AS s
ON t.tid = s.sid
WHEN MATCHED AND t.balance > s.delta THEN
UPDATE SET balance = t.balance - s.delta
WHEN MATCHED THEN
DELETE
WHEN NOT MATCHED AND s.delta > 0 THEN
INSERT VALUES (s.sid, s.delta)
WHEN NOT MATCHED THEN
DO NOTHING;
MERGE works with regular tables, partitioned tables and inheritance
hierarchies, including column and row security enforcement, as well as
support for row and statement triggers and transition tables therein.
MERGE is optimized for OLTP and is parameterizable, though also useful
for large scale ETL/ELT. MERGE is not intended to be used in preference
to existing single SQL commands for INSERT, UPDATE or DELETE since there
is some overhead. MERGE can be used from PL/pgSQL.
MERGE does not support targetting updatable views or foreign tables, and
RETURNING clauses are not allowed either. These limitations are likely
fixable with sufficient effort. Rewrite rules are also not supported,
but it's not clear that we'd want to support them.
Author: Pavan Deolasee <pavan.deolasee@gmail.com>
Author: Álvaro Herrera <alvherre@alvh.no-ip.org>
Author: Amit Langote <amitlangote09@gmail.com>
Author: Simon Riggs <simon.riggs@enterprisedb.com>
Reviewed-by: Peter Eisentraut <peter.eisentraut@enterprisedb.com>
Reviewed-by: Andres Freund <andres@anarazel.de> (earlier versions)
Reviewed-by: Peter Geoghegan <pg@bowt.ie> (earlier versions)
Reviewed-by: Robert Haas <robertmhaas@gmail.com> (earlier versions)
Reviewed-by: Japin Li <japinli@hotmail.com>
Reviewed-by: Justin Pryzby <pryzby@telsasoft.com>
Reviewed-by: Tomas Vondra <tomas.vondra@enterprisedb.com>
Reviewed-by: Zhihong Yu <zyu@yugabyte.com>
Discussion: https://postgr.es/m/CANP8+jKitBSrB7oTgT9CY2i1ObfOt36z0XMraQc+Xrz8QB0nXA@mail.gmail.com
Discussion: https://postgr.es/m/CAH2-WzkJdBuxj9PO=2QaO9-3h3xGbQPZ34kJH=HukRekwM-GZg@mail.gmail.com
Discussion: https://postgr.es/m/20201231134736.GA25392@alvherre.pgsql
This patch introduces the SQL/JSON standard constructors for JSON:
JSON()
JSON_ARRAY()
JSON_ARRAYAGG()
JSON_OBJECT()
JSON_OBJECTAGG()
For the most part these functions provide facilities that mimic
existing json/jsonb functions. However, they also offer some useful
additional functionality. In addition to text input, the JSON() function
accepts bytea input, which it will decode and constuct a json value from.
The other functions provide useful options for handling duplicate keys
and null values.
This series of patches will be followed by a consolidated documentation
patch.
Nikita Glukhov
Reviewers have included (in no particular order) Andres Freund, Alexander
Korotkov, Pavel Stehule, Andrew Alsup, Erik Rijkers, Zihong Yu,
Himanshu Upadhyaya, Daniel Gustafsson, Justin Pryzby.
Discussion: https://postgr.es/m/cd0bb935-0158-78a7-08b5-904886deac4b@postgrespro.ru
Rename create_function_0 to create_function_c, and create_function_3
to create_function_sql, to establish their charters more clearly.
This should also reduce confusion versus our underscore-digit
convention for naming variant expected-files.
I separated this from the previous commit on the premise that keeping
the renaming distinct might make "git blame" tracking easier.
Discussion: https://postgr.es/m/1114748.1640383217@sss.pgh.pa.us
The idea behind this patch is to make it possible to run individual
test scripts without running the entire core test suite. Making all
the scripts completely independent would involve a massive rewrite,
and would probably be worse for coverage of things like concurrent DDL.
So this patch just does what seems practical with limited changes.
The net effect is that any test script can be run after running
limited earlier dependencies:
* all scripts depend on test_setup
* many scripts depend on create_index
* other dependencies are few in number, and are documented in
the parallel_schedule file.
To accomplish this, I chose a small number of commonly-used tables
and moved their creation and filling into test_setup. Later scripts
are expected not to modify these tables' data contents, for fear of
affecting other scripts' results. Also, our former habit of declaring
all C functions in one place is now gone in favor of declaring them
where they're used, if that's just one script, or in test_setup if
necessary.
There's more that could be done to remove some of the remaining
inter-script dependencies, but significantly more-invasive changes
would be needed, and at least for now it doesn't seem worth it.
Discussion: https://postgr.es/m/1114748.1640383217@sss.pgh.pa.us
The code-coverage report says that this test doesn't increase
coverage by one single line, which I now realize is because
I made src/test/modules/test_regex/sql/test_regex_utf8.sql
to cover all the code that this would. So really it's pointless
and we should just drop it.
Up to now this has just sat there as a test you could invoke via
EXTRA_TESTS, which of course nobody does. I'm feeling encouraged
because c2e8bd275 hasn't yet broke anything, so let's try making this
run with a suitable guard condition (similar to collate.linux.utf8).
In the wake of commit b073c3ccd, it's necessary to grant create
permissions on the public schema to PUBLIC to get many of the
core regression test scripts to pass. That commit did so via the
quick-n-dirty expedient of adding the GRANT to the tablespace test,
which runs first. This is problematic for single-machine
replication testing, though. The least painful way to run the
regression tests on such a setup is to skip the tablespace test,
and that no longer works.
To fix, let's invent a separate "test_setup" script to run first,
and put the GRANT there. Revert b073c3ccd's changes to
the tablespace.source files.
In the future it might be good to try to reduce coupling between
the various test scripts by having test_setup create widely-used
objects, with the goal that most of the scripts could run after
having run only test_setup. That's going to take some effort,
so this commit just addresses my immediate pain point.
Discussion: https://postgr.es/m/1363170.1639763559@sss.pgh.pa.us
Commit b4af70cb, which simplified state managed by VACUUM, performed
refactoring of parallel VACUUM in passing. Confusion about the exact
details of the tasks that the leader process is responsible for led to
code that made it possible for parallel VACUUM to miss a subset of the
table's indexes entirely. Specifically, indexes that fell under the
min_parallel_index_scan_size size cutoff were missed. These indexes are
supposed to be vacuumed by the leader (alongside any parallel unsafe
indexes), but weren't vacuumed at all. Affected indexes could easily
end up with duplicate heap TIDs, once heap TIDs were recycled for new
heap tuples. This had generic symptoms that might be seen with almost
any index corruption involving structural inconsistencies between an
index and its table.
To fix, make sure that the parallel VACUUM leader process performs any
required index vacuuming for indexes that happen to be below the size
cutoff. Also document the design of parallel VACUUM with these
below-size-cutoff indexes.
It's unclear how many users might be affected by this bug. There had to
be at least three indexes on the table to hit the bug: a smaller index,
plus at least two additional indexes that themselves exceed the size
cutoff. Cases with just one additional index would not run into
trouble, since the parallel VACUUM cost model requires two
larger-than-cutoff indexes on the table to apply any parallel
processing. Note also that autovacuum was not affected, since it never
uses parallel processing.
Test case based on tests from a larger patch to test parallel VACUUM by
Masahiko Sawada.
Many thanks to Kamigishi Rei for her invaluable help with tracking this
problem down.
Author: Peter Geoghegan <pg@bowt.ie>
Author: Masahiko Sawada <sawada.mshk@gmail.com>
Reported-By: Kamigishi Rei <iijima.yun@koumakan.jp>
Reported-By: Andrew Gierth <andrew@tao11.riddles.org.uk>
Diagnosed-By: Andres Freund <andres@anarazel.de>
Bug: #17245
Discussion: https://postgr.es/m/17245-ddf06aaf85735f36@postgresql.org
Discussion: https://postgr.es/m/20211030023740.qbnsl2xaoh2grq3d@alap3.anarazel.de
Backpatch: 14-, where the refactoring commit appears.
"Result Cache" was never a great name for this node, but nobody managed
to come up with another name that anyone liked enough. That was until
David Johnston mentioned "Node Memoization", which Tom Lane revised to
just "Memoize". People seem to like "Memoize", so let's do the rename.
Reviewed-by: Justin Pryzby
Discussion: https://postgr.es/m/20210708165145.GG1176@momjian.us
Backpatch-through: 14, where Result Cache was introduced
opr_sanity's binary_coercible() function has always been meant
to match the parser's notion of binary coercibility, but it also
has always been a rather poor approximation of the parser's
real rules (as embodied in IsBinaryCoercible()). That hasn't
bit us so far, but it's predictable that it will eventually.
It also now emerges that implementing this check in plpgsql
performs absolutely horribly in clobber-cache-always testing.
(Perhaps we could do something about that, but I suspect it just
means that plpgsql is exploiting catalog caching to the hilt.)
Hence, let's replace binary_coercible() with a C shim that directly
invokes IsBinaryCoercible(), eliminating both the semantic hazard
and the performance issue.
Most of regress.c's C functions are declared in create_function_1,
but we can't simply move that to before opr_sanity/type_sanity
since those tests would complain about the resulting shell types.
I chose to split it into create_function_0 and create_function_1.
Since create_function_0 now runs as part of a parallel group while
create_function_1 doesn't, reduce the latter to create just those
functions that opr_sanity and type_sanity would whine about.
To make room for create_function_0 in the second parallel group
of tests, move tstypes to the third parallel group.
In passing, clean up some ordering deviations between
parallel_schedule and serial_schedule.
Discussion: https://postgr.es/m/292305.1620503097@sss.pgh.pa.us
Snapshot caching, introduced in 623a9ba79b, did not increment
xactCompletionCount during subtransaction abort. That could lead to an older
snapshot being reused. That is, at least as far as I can see, not a
correctness issue (for MVCC snapshots there's no difference between "in
progress" and "aborted"). The only difference between the old and new
snapshots would be a newer ->xmax.
While HeapTupleSatisfiesMVCC makes the same visibility determination, reusing
the old snapshot leads HeapTupleSatisfiesMVCC to not set
HEAP_XMIN_INVALID. Which subsequently causes the kill_prior_tuple optimization
to not kick in (via HeapTupleIsSurelyDead() returning false). The performance
effects of doing the same index-lookups over and over again is how the issue
was discovered...
Fix the issue by incrementing xactCompletionCount in
XidCacheRemoveRunningXids. It already acquires ProcArrayLock exclusively,
making that an easy proposition.
Add a test to ensure that kill_prior_tuple prevents index growth when it
involves aborted subtransaction of the current transaction.
Author: Andres Freund
Discussion: https://postgr.es/m/20210406043521.lopeo7bbigad3n6t@alap3.anarazel.de
Discussion: https://postgr.es/m/20210317055718.v6qs3ltzrformqoa%40alap3.anarazel.de
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
Adds BRIN opclasses similar to the existing minmax, except that instead
of summarizing the page range into a single [min,max] range, the summary
consists of multiple ranges and/or points, allowing gaps. This allows
more efficient handling of data with poor correlation to physical
location within the table and/or outlier values, for which the regular
minmax opclassed tend to work poorly.
It's possible to specify the number of values kept for each page range,
either as a single point or an interval boundary.
CREATE TABLE t (a int);
CREATE INDEX ON t
USING brin (a int4_minmax_multi_ops(values_per_range=16));
When building the summary, the values are combined into intervals with
the goal to minimize the "covering" (sum of interval lengths), using a
support procedure computing distance between two values.
Bump catversion, due to various catalog changes.
Author: Tomas Vondra <tomas.vondra@postgresql.org>
Reviewed-by: Alvaro Herrera <alvherre@alvh.no-ip.org>
Reviewed-by: Alexander Korotkov <aekorotkov@gmail.com>
Reviewed-by: Sokolov Yura <y.sokolov@postgrespro.ru>
Reviewed-by: John Naylor <john.naylor@enterprisedb.com>
Discussion: https://postgr.es/m/c1138ead-7668-f0e1-0638-c3be3237e812@2ndquadrant.com
Discussion: https://postgr.es/m/5d78b774-7e9c-c94e-12cf-fef51cc89b1a%402ndquadrant.com
Adds a BRIN opclass using a Bloom filter to summarize the range. Indexes
using the new opclasses allow only equality queries (similar to hash
indexes), but that works fine for data like UUID, MAC addresses etc. for
which range queries are not very common. This also means the indexes
work for data that is not well correlated to physical location within
the table, or perhaps even entirely random (which is a common issue with
existing BRIN minmax opclasses).
It's possible to specify opclass parameters with the usual Bloom filter
parameters, i.e. the desired false-positive rate and the expected number
of distinct values per page range.
CREATE TABLE t (a int);
CREATE INDEX ON t
USING brin (a int4_bloom_ops(false_positive_rate = 0.05,
n_distinct_per_range = 100));
The opclasses do not operate on the indexed values directly, but compute
a 32-bit hash first, and the Bloom filter is built on the hash value.
Collisions should not be a huge issue though, as the number of distinct
values in a page ranges is usually fairly small.
Bump catversion, due to various catalog changes.
Author: Tomas Vondra <tomas.vondra@postgresql.org>
Reviewed-by: Alvaro Herrera <alvherre@alvh.no-ip.org>
Reviewed-by: Alexander Korotkov <aekorotkov@gmail.com>
Reviewed-by: Sokolov Yura <y.sokolov@postgrespro.ru>
Reviewed-by: Nico Williams <nico@cryptonector.com>
Reviewed-by: John Naylor <john.naylor@enterprisedb.com>
Discussion: https://postgr.es/m/c1138ead-7668-f0e1-0638-c3be3237e812@2ndquadrant.com
Discussion: https://postgr.es/m/5d78b774-7e9c-c94e-12cf-fef51cc89b1a%402ndquadrant.com
To allow inserts in parallel-mode this feature has to ensure that all the
constraints, triggers, etc. are parallel-safe for the partition hierarchy
which is costly and we need to find a better way to do that. Additionally,
we could have used existing cached information in some cases like indexes,
domains, etc. to determine the parallel-safety.
List of commits reverted, in reverse chronological order:
ed62d3737c Doc: Update description for parallel insert reloption.
c8f78b6161 Add a new GUC and a reloption to enable inserts in parallel-mode.
c5be48f092 Improve FK trigger parallel-safety check added by 05c8482f7f.
e2cda3c20a Fix use of relcache TriggerDesc field introduced by commit 05c8482f7f.
e4e87a32cc Fix valgrind issue in commit 05c8482f7f.
05c8482f7f Enable parallel SELECT for "INSERT INTO ... SELECT ...".
Discussion: https://postgr.es/m/E1lMiB9-0001c3-SY@gemulon.postgresql.org
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
Parallel SELECT can't be utilized for INSERT in the following cases:
- INSERT statement uses the ON CONFLICT DO UPDATE clause
- Target table has a parallel-unsafe: trigger, index expression or
predicate, column default expression or check constraint
- Target table has a parallel-unsafe domain constraint on any column
- Target table is a partitioned table with a parallel-unsafe partition key
expression or support function
The planner is updated to perform additional parallel-safety checks for
the cases listed above, for determining whether it is safe to run INSERT
in parallel-mode with an underlying parallel SELECT. The planner will
consider using parallel SELECT for "INSERT INTO ... SELECT ...", provided
nothing unsafe is found from the additional parallel-safety checks, or
from the existing parallel-safety checks for SELECT.
While checking parallel-safety, we need to check it for all the partitions
on the table which can be costly especially when we decide not to use a
parallel plan. So, in a separate patch, we will introduce a GUC and or a
reloption to enable/disable parallelism for Insert statements.
Prior to entering parallel-mode for the execution of INSERT with parallel
SELECT, a TransactionId is acquired and assigned to the current
transaction state. This is necessary to prevent the INSERT from attempting
to assign the TransactionId whilst in parallel-mode, which is not allowed.
This approach has a disadvantage in that if the underlying SELECT does not
return any rows, then the TransactionId is not used, however that
shouldn't happen in practice in many cases.
Author: Greg Nancarrow, Amit Langote, Amit Kapila
Reviewed-by: Amit Langote, Hou Zhijie, Takayuki Tsunakawa, Antonin Houska, Bharath Rupireddy, Dilip Kumar, Vignesh C, Zhihong Yu, Amit Kapila
Tested-by: Tang, Haiying
Discussion: https://postgr.es/m/CAJcOf-cXnB5cnMKqWEp2E2z7Mvcd04iLVmV=qpFJrR3AcrTS3g@mail.gmail.com
Discussion: https://postgr.es/m/CAJcOf-fAdj=nDKMsRhQzndm-O13NY4dL6xGcEvdX5Xvbbi0V7g@mail.gmail.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
This follows in the spirit of commit dfb75e478, which created primary
key and uniqueness constraints to improve the visibility of constraints
imposed on the system catalogs. While our catalogs contain many
foreign-key-like relationships, they don't quite follow SQL semantics,
in that the convention for an omitted reference is to write zero not
NULL. Plus, we have some cases in which there are arrays each of whose
elements is supposed to be an FK reference; SQL has no way to model that.
So we can't create actual foreign key constraints to describe the
situation. Nonetheless, we can collect and use knowledge about these
relationships.
This patch therefore adds annotations to the catalog header files to
declare foreign-key relationships. (The BKI_LOOKUP annotations cover
simple cases, but we weren't previously distinguishing which such
columns are allowed to contain zeroes; we also need new markings for
multi-column FK references.) Then, Catalog.pm and genbki.pl are
taught to collect this information into a table in a new generated
header "system_fk_info.h". The only user of that at the moment is
a new SQL function pg_get_catalog_foreign_keys(), which exposes the
table to SQL. The oidjoins regression test is rewritten to use
pg_get_catalog_foreign_keys() to find out which columns to check.
Aside from removing the need for manual maintenance of that test
script, this allows it to cover numerous relationships that were not
checked by the old implementation based on findoidjoins. (As of this
commit, 217 relationships are checked by the test, versus 181 before.)
Discussion: https://postgr.es/m/3240355.1612129197@sss.pgh.pa.us
Multiranges are basically sorted arrays of non-overlapping ranges with
set-theoretic operations defined over them.
Since v14, each range type automatically gets a corresponding multirange
datatype. There are both manual and automatic mechanisms for naming multirange
types. Once can specify multirange type name using multirange_type_name
attribute in CREATE TYPE. Otherwise, a multirange type name is generated
automatically. If the range type name contains "range" then we change that to
"multirange". Otherwise, we add "_multirange" to the end.
Implementation of multiranges comes with a space-efficient internal
representation format, which evades extra paddings and duplicated storage of
oids. Altogether this format allows fetching a particular range by its index
in O(n).
Statistic gathering and selectivity estimation are implemented for multiranges.
For this purpose, stored multirange is approximated as union range without gaps.
This field will likely need improvements in the future.
Catversion is bumped.
Discussion: https://postgr.es/m/CALNJ-vSUpQ_Y%3DjXvTxt1VYFztaBSsWVXeF1y6gTYQ4bOiWDLgQ%40mail.gmail.com
Discussion: https://postgr.es/m/a0b8026459d1e6167933be2104a6174e7d40d0ab.camel%40j-davis.com#fe7218c83b08068bfffb0c5293eceda0
Author: Paul Jungwirth, revised by me
Reviewed-by: David Fetter, Corey Huinker, Jeff Davis, Pavel Stehule
Reviewed-by: Alvaro Herrera, Tom Lane, Isaac Morland, David G. Johnston
Reviewed-by: Zhihong Yu, Alexander Korotkov
This makes use of CheckBuffer() introduced in c780a7a, adding a SQL
wrapper able to do checks for all the pages of a relation. By default,
all the fork types of a relation are checked, and it is possible to
check only a given relation fork. Note that if the relation given in
input has no physical storage or is temporary, then no errors are
generated, allowing full-database checks when coupled with a simple scan
of pg_class for example. This is not limited to clusters with data
checksums enabled, as clusters without data checksums can still apply
checks on pages using the page headers or for the case of a page full of
zeros.
This function returns a set of tuples consisting of:
- The physical file where a broken page has been detected (without the
segment number as that can be AM-dependent, which can be guessed from
the block number for heap). A relative path from PGPATH is used.
- The block number of the broken page.
By default, only superusers have an access to this function but
execution rights can be granted to other users.
The feature introduced here is still minimal, and more improvements
could be done, like:
- Addition of a start and end block number to run checks on a range
of blocks, which would apply only if one fork type is checked.
- Addition of some progress reporting.
- Throttling, with configuration parameters in function input or
potentially some cost-based GUCs.
Regression tests are added for positive cases in the main regression
test suite, and TAP tests are added for cases involving the emulation of
page corruptions.
Bump catalog version.
Author: Julien Rouhaud, Michael Paquier
Reviewed-by: Masahiko Sawada, Justin Pryzby
Discussion: https://postgr.es/m/CAOBaU_aVvMjQn=ge5qPiJOPMmOj5=ii3st5Q0Y+WuLML5sR17w@mail.gmail.com
Our infinite_recurse() test to verify sane stack-overrun behavior
is affected by a bug of the Linux kernel on PPC64: it will get SIGSEGV
if it receives a signal when the stack depth is (a) over 1MB and
(b) within a few kB of filling the current physical stack allocation.
See https://bugzilla.kernel.org/show_bug.cgi?id=205183.
Since this test is a bit time-consuming and we run it in parallel with
test scripts that do a lot of DDL, it can be expected to get an sinval
catchup interrupt at some point, leading to failure if the timing is
wrong. This has caused more than 100 buildfarm failures over the
past year or so.
While a fix exists for the kernel bug, it might be years before that
propagates into all production kernels, particularly in some of the
older distros we have in the buildfarm. For now, let's just back off
and not run this test on Linux PPC64; that loses nothing in test
coverage so far as our own code is concerned.
To do that, split this test into a new script infinite_recurse.sql
and skip the test when the platform name is powerpc64...-linux-gnu.
Back-patch to v12. Branches before that have not been seen to get
this failure. No doubt that's because the "errors" test was not
run in parallel with other tests before commit 798070ec0, greatly
reducing the odds of an sinval catchup being necessary.
I also back-patched 3c8553547 into v12, just so the new regression
script would look the same in all branches having it.
Discussion: https://postgr.es/m/3479046.1602607848@sss.pgh.pa.us
Discussion: https://postgr.es/m/20190723162703.GM22387%40telsasoft.com
A relation that has no storage initializes rd_tableam to NULL, which
caused those two functions to crash because of a pointer dereference.
Note that in 11 and older versions, this has always failed with a
confusing error "could not open file".
These two functions are used by the Postgres ODBC driver, which requires
them only when connecting to a backend strictly older than 8.1. When
connected to 8.2 or a newer version, the driver uses a RETURNING clause
instead whose support has been added in 8.2, so it should be possible to
just remove both functions in the future. This is left as an issue to
address later.
While on it, add more regression tests for those functions as we never
really had coverage for them, and for aggregates of TIDs.
Reported-by: Jaime Casanova, via sqlsmith
Author: Michael Paquier
Reviewed-by: Álvaro Herrera
Discussion: https://postgr.es/m/CAJGNTeO93u-5APMga6WH41eTZ3Uee9f3s8dCpA-GSSqNs1b=Ug@mail.gmail.com
Backpatch-through: 12
Similar to xid, but 64 bits wide. This new type is suitable for use in
various system views and administration functions.
Reviewed-by: Fujii Masao <masao.fujii@oss.nttdata.com>
Reviewed-by: Takao Fujii <btfujiitkp@oss.nttdata.com>
Reviewed-by: Yoshikazu Imai <imai.yoshikazu@fujitsu.com>
Reviewed-by: Mark Dilger <mark.dilger@enterprisedb.com>
Discussion: https://postgr.es/m/20190725000636.666m5mad25wfbrri%40alap3.anarazel.de
Incremental Sort is an optimized variant of multikey sort for cases when
the input is already sorted by a prefix of the requested sort keys. For
example when the relation is already sorted by (key1, key2) and we need
to sort it by (key1, key2, key3) we can simply split the input rows into
groups having equal values in (key1, key2), and only sort/compare the
remaining column key3.
This has a number of benefits:
- Reduced memory consumption, because only a single group (determined by
values in the sorted prefix) needs to be kept in memory. This may also
eliminate the need to spill to disk.
- Lower startup cost, because Incremental Sort produce results after each
prefix group, which is beneficial for plans where startup cost matters
(like for example queries with LIMIT clause).
We consider both Sort and Incremental Sort, and decide based on costing.
The implemented algorithm operates in two different modes:
- Fetching a minimum number of tuples without check of equality on the
prefix keys, and sorting on all columns when safe.
- Fetching all tuples for a single prefix group and then sorting by
comparing only the remaining (non-prefix) keys.
We always start in the first mode, and employ a heuristic to switch into
the second mode if we believe it's beneficial - the goal is to minimize
the number of unnecessary comparions while keeping memory consumption
below work_mem.
This is a very old patch series. The idea was originally proposed by
Alexander Korotkov back in 2013, and then revived in 2017. In 2018 the
patch was taken over by James Coleman, who wrote and rewrote most of the
current code.
There were many reviewers/contributors since 2013 - I've done my best to
pick the most active ones, and listed them in this commit message.
Author: James Coleman, Alexander Korotkov
Reviewed-by: Tomas Vondra, Andreas Karlsson, Marti Raudsepp, Peter Geoghegan, Robert Haas, Thomas Munro, Antonin Houska, Andres Freund, Alexander Kuzmenkov
Discussion: https://postgr.es/m/CAPpHfdscOX5an71nHd8WSUH6GNOCf=V7wgDaTXdDd9=goN-gfA@mail.gmail.com
Discussion: https://postgr.es/m/CAPpHfds1waRZ=NOmueYq0sx1ZSCnt+5QJvizT8ndT2=etZEeAQ@mail.gmail.com
This adds SQL expressions NORMALIZE() and IS NORMALIZED to convert and
check Unicode normal forms, per SQL standard.
To support fast IS NORMALIZED tests, we pull in a new data file
DerivedNormalizationProps.txt from Unicode and build a lookup table
from that, using techniques similar to ones already used for other
Unicode data. make update-unicode will keep it up to date. We only
build and use these tables for the NFC and NFKC forms, because they
are too big for NFD and NFKD and the improvement is not significant
enough there.
Reviewed-by: Daniel Verite <daniel@manitou-mail.org>
Reviewed-by: Andreas Karlsson <andreas@proxel.se>
Discussion: https://www.postgresql.org/message-id/flat/c1909f27-c269-2ed9-12f8-3ab72c8caf7a@2ndquadrant.com
Previously, it was possible for EXPLAIN ANALYZE of a parallel query
to produce several different "Workers" fields for a single plan node,
because different portions of explain.c independently generated
per-worker data and wrapped that output in separate fields. This
is pretty bogus, especially for the structured output formats: even
if it's not technically illegal, most programs would have a hard time
dealing with such data.
To improve matters, add infrastructure that allows redirecting
per-worker values into a side data structure, and then collect that
data into a single "Workers" field after we've finished running all
the relevant code for a given plan node.
There are a few visible side-effects:
* In text format, instead of something like
Sort Method: external merge Disk: 4920kB
Worker 0: Sort Method: external merge Disk: 5880kB
Worker 1: Sort Method: external merge Disk: 5920kB
Buffers: shared hit=682 read=10188, temp read=1415 written=2101
Worker 0: actual time=130.058..130.324 rows=1324 loops=1
Buffers: shared hit=337 read=3489, temp read=505 written=739
Worker 1: actual time=130.273..130.512 rows=1297 loops=1
Buffers: shared hit=345 read=3507, temp read=505 written=744
you get
Sort Method: external merge Disk: 4920kB
Buffers: shared hit=682 read=10188, temp read=1415 written=2101
Worker 0: actual time=130.058..130.324 rows=1324 loops=1
Sort Method: external merge Disk: 5880kB
Buffers: shared hit=337 read=3489, temp read=505 written=739
Worker 1: actual time=130.273..130.512 rows=1297 loops=1
Sort Method: external merge Disk: 5920kB
Buffers: shared hit=345 read=3507, temp read=505 written=744
* When JIT is enabled, any relevant per-worker JIT stats are attached
to the child node of the Gather or Gather Merge node, which is where
the other per-worker output has always been. Previously, that info
was attached directly to a Gather node, or missed entirely for Gather
Merge.
* A query's summary JIT data no longer includes a bogus
"Worker Number: -1" field.
A notable code-level change is that indenting for lines of text-format
output should now be handled by calling "ExplainIndentText(es)",
instead of hard-wiring how much space to emit. This seems a good deal
cleaner anyway.
This patch also adds a new "explain.sql" regression test script that's
dedicated to testing EXPLAIN. There is more that can be done in that
line, certainly, but for now it just adds some coverage of the XML and
YAML output formats, which had been completely untested.
Although this is surely a bug fix, it's not clear that people would
be happy with rearranging EXPLAIN output in a minor release, so apply
to HEAD only.
Maciek Sakrejda and Tom Lane, based on an idea of Andres Freund's;
reviewed by Georgios Kokolatos
Discussion: https://postgr.es/m/CAOtHd0AvAA8CLB9Xz0wnxu1U=zJCKrr1r4QwwXi_kcQsHDVU=Q@mail.gmail.com
Previously significant parts of tuplesort.c were untested. This
commit, while not testing every path, significantly increases
coverage. In particular, this adds tests for abbreviated key logic,
forward/backward scans & scrolling and mark/restore.
I tried to keep the table sizes reasonable, and stress the on-disk
paths by setting work_mem to low values for specific tests. The
buildfarm will tell whether more attention to test time is needed.
Author: Andres Freund
Discussion: https://postgr.es/m/20191013144153.ooxrfglvnaocsrx2@alap3.anarazel.de
The tests collate.icu.utf8 and collate.linux.utf8 were previously only
run when explicitly selected via EXTRA_TESTS. They require a UTF8
database, because the error messages in the expected files refer to
that, and they use some non-ASCII characters in the tests. Since
users can select any locale and encoding for the regression test run,
it was not possible to include these tests automatically.
To fix, use psql's \if facility to check various prerequisites such as
platform and the server encoding and quit the tests at the very
beginning if the configuration is not adequate. We then need to
maintain alternative expected files for these tests, but they are very
tiny and never need to change after this.
These two tests are now run automatically as part of the regression
tests.
Reviewed-by: Tom Lane <tgl@sss.pgh.pa.us>
Discussion: https://www.postgresql.org/message-id/flat/052295c2-a2e1-9a21-bd36-8fbff8686cf3%402ndquadrant.com
This test script is unsafe to run in "make installcheck" mode for
(at least) two reasons: it creates and destroys some role names
that don't follow the "regress_xxx" naming convention, and it
sets and then resets the application_name GUC attached to every
existing role. While we've not had complaints, these surely are
not good things to do within a production installation, and
regress.sgml pretty clearly implies that we won't do them.
Rather than lose test coverage altogether, let's just move this
script somewhere where it will get run by "make check" but not
"make installcheck". src/test/modules/ already has that property.
Since it seems likely that we'll want other regression tests in
future that also exceed the constraints of "make installcheck",
create a generically-named src/test/modules/unsafe_tests/
directory to hold them.
Discussion: https://postgr.es/m/16638.1468620817@sss.pgh.pa.us
David Rowley
Alexander Korotkov
Alvaro Herrera
Michael Paquier
Andres Freund
Tom Lane
Peter Eisentraut
Andrew Dunstan
Peter Geoghegan
Tomas Vondra
Amit Kapila
Robert Haas
Thomas Munro