ORDER BY / DISTINCT aggreagtes have, since implemented in Postgres, been
executed by always performing a sort in nodeAgg.c to sort the tuples in
the current group into the correct order before calling the transition
function on the sorted tuples. This was not great as often there might be
an index that could have provided pre-sorted input and allowed the
transition functions to be called as the rows come in, rather than having
to store them in a tuplestore in order to sort them once all the tuples
for the group have arrived.
Here we change the planner so it requests a path with a sort order which
supports the most amount of ORDER BY / DISTINCT aggregate functions and
add new code to the executor to allow it to support the processing of
ORDER BY / DISTINCT aggregates where the tuples are already sorted in the
correct order.
Since there can be many ORDER BY / DISTINCT aggregates in any given query
level, it's very possible that we can't find an order that suits all of
these aggregates. The sort order that the planner chooses is simply the
one that suits the most aggregate functions. We take the most strictly
sorted variation of each order and see how many aggregate functions can
use that, then we try again with the order of the remaining aggregates to
see if another order would suit more aggregate functions. For example:
SELECT agg(a ORDER BY a),agg2(a ORDER BY a,b) ...
would request the sort order to be {a, b} because {a} is a subset of the
sort order of {a,b}, but;
SELECT agg(a ORDER BY a),agg2(a ORDER BY c) ...
would just pick a plan ordered by {a} (we give precedence to aggregates
which are earlier in the targetlist).
SELECT agg(a ORDER BY a),agg2(a ORDER BY b),agg3(a ORDER BY b) ...
would choose to order by {b} since two aggregates suit that vs just one
that requires input ordered by {a}.
Author: David Rowley
Reviewed-by: Ronan Dunklau, James Coleman, Ranier Vilela, Richard Guo, Tom Lane
Discussion: https://postgr.es/m/CAApHDvpHzfo92%3DR4W0%2BxVua3BUYCKMckWAmo-2t_KiXN-wYH%3Dw%40mail.gmail.com
Here we add code which detects when ExecFindPartition() continually finds
the same partition and add a caching layer to improve partition lookup
performance for such cases.
Both RANGE and LIST partitioned tables traditionally require a binary
search for the set of Datums that a partition needs to be found for. This
binary search is commonly visible in profiles when bulk loading into a
partitioned table. Here we aim to reduce the overhead of bulk-loading
into partitioned tables for cases where many consecutive tuples belong to
the same partition and make the performance of this operation closer to
what it is with a traditional non-partitioned table.
When we find the same partition 16 times in a row, the next search will
result in us simply just checking if the current set of values belongs to
the last found partition. For LIST partitioning we record the index into
the PartitionBoundInfo's datum array. This allows us to check if the
current Datum is the same as the Datum that was last looked up. This
means if any given LIST partition supports storing multiple different
Datum values, then the caching only works when we find the same value as
we did the last time. For RANGE partitioning we simply check if the given
Datums are in the same range as the previously found partition.
We store the details of the cached partition in PartitionDesc (i.e.
relcache) so that the cached values are maintained over multiple
statements.
No caching is done for HASH partitions. The majority of the cost in HASH
partition lookups are in the hashing function(s), which would also have to
be executed if we were to try to do caching for HASH partitioned tables.
Since most of the cost is already incurred, we just don't bother. We also
don't do any caching for LIST partitions when we continually find the
values being looked up belong to the DEFAULT partition. We've no
corresponding index in the PartitionBoundInfo's datum array for this case.
We also don't cache when we find the given values match to a LIST
partitioned table's NULL partition. This is so cheap that there's no
point in doing any caching for this. We also don't cache for a RANGE
partitioned table's DEFAULT partition.
There have been a number of different patches submitted to improve
partition lookups. Hou, Zhijie submitted a patch to detect when the value
belonging to the partition key column(s) were constant and added code to
cache the partition in that case. Amit Langote then implemented an idea
suggested by me to remember the last found partition and start to check if
the current values work for that partition. The final patch here was
written by me and was done by taking many of the ideas I liked from the
patches in the thread and redesigning other aspects.
Discussion: https://postgr.es/m/OS0PR01MB571649B27E912EA6CC4EEF03942D9%40OS0PR01MB5716.jpnprd01.prod.outlook.com
Author: Amit Langote, Hou Zhijie, David Rowley
Reviewed-by: Amit Langote, Hou Zhijie
A code comment said that the standard does not define a number for
ERRCODE_SQL_JSON_ITEM_CANNOT_BE_CAST_TO_TARGET_TYPE, but this was
fixed in a later draft version of the standard, so use that number
now.
The general convention in the executor is to refer to child plans
and planstates via the outerPlan[State] and innerPlan[State]
macros, but a few places didn't do it like that. For consistency
and readability, convert all the stragglers to use the macros.
(See also commit 40f42d2a3, which did some similar cleanup a few
years ago, but missed these cases.)
Richard Guo
Discussion: https://postgr.es/m/CAMbWs4-vYhh1xsa_veah4PUed2Xq=Ed_YH3=Mqt5A3Y=EgfCEg@mail.gmail.com
50e17ad28 increased the size of ExprEvalStep from 64 bytes up to 88 bytes.
Lots of effort was spent during the development of the current expression
evaluation code to make an instance of this struct as small as possible.
Making this struct larger than needed reduces CPU cache efficiency during
expression evaluation which causes noticeable slowdowns during query
execution.
In order to reduce the size of the struct, here we remove the fn_addr
field. The values from this field can be obtained via fcinfo, just with
some extra pointer dereferencing. The extra indirection does not seem to
cause any noticeable slowdowns.
Various other fields have been moved into the ScalarArrayOpExprHashTable
struct. These fields are only used when the ScalarArrayOpExprHashTable
pointer has already been dereferenced, so no additional pointer
dereferences occur for these. Here we also make hash_fcinfo_data the last
field in ScalarArrayOpExprHashTable so that we can avoid a further pointer
dereference to get the FunctionCallInfoBaseData. This also saves a call to
palloc().
50e17ad28 was added in 14, but it's too late to adjust the size of the
ExprEvalStep in that version, so here we just backpatch to 15, which is
currently in beta.
Author: Andres Freund, David Rowley
Discussion: https://postgr.es/m/20220616233130.rparivafipt6doj3@alap3.anarazel.de
Backpatch-through: 15
The new expression step types increased the size of ExprEvalStep by ~4 for all
types of expression steps, slowing down expression evaluation noticeably. Move
them out of line.
There's other issues with these expression steps, but addressing them is
largely independent of this aspect.
Author: Andres Freund <andres@anarazel.de>
Reviewed-By: Andrew Dunstan <andrew@dunslane.net>
Discussion: https://postgr.es/m/20220616233130.rparivafipt6doj3@alap3.anarazel.de
Backpatch: 15-
There were many calls to construct_array() and deconstruct_array() for
built-in types, for example, when dealing with system catalog columns.
These all hardcoded the type attributes necessary to pass to these
functions.
To simplify this a bit, add construct_array_builtin(),
deconstruct_array_builtin() as wrappers that centralize this hardcoded
knowledge. This simplifies many call sites and reduces the amount of
hardcoded stuff that is spread around.
Reviewed-by: Tom Lane <tgl@sss.pgh.pa.us>
Discussion: https://www.postgresql.org/message-id/flat/2914356f-9e5f-8c59-2995-5997fc48bcba%40enterprisedb.com
In commit ec62cb0aa, I foolishly replaced ExecEvalWholeRowVar's
lookup_rowtype_tupdesc_domain call with just lookup_rowtype_tupdesc,
because I didn't see how a domain could be involved there, and
there were no regression test cases to jog my memory. But the
existing code was correct, so revert that change and add a test
case showing why it's necessary. (Note: per comment in struct
DatumTupleFields, it is correct to produce an output tuple that's
labeled with the base composite type, not the domain; hence just
blindly looking through the domain is correct here.)
Per bug #17515 from Dan Kubb. Back-patch to v11 where domains over
composites became a thing.
Discussion: https://postgr.es/m/17515-a24737438363aca0@postgresql.org
Bug #17512 highlighted that a suitably broken data type could cause the
backend to crash if either the hash function or equality function were in
someway non-deterministic based on their input values. Such a data type
could cause a crash of the backend due to some code which assumes that
we'll always find a hash table entry corresponding to an item in the
Memoize LRU list.
Here we remove the assumption that we'll always find the entry
corresponding to the given LRU list item and add run-time checks to verify
we have found the given item in the cache.
This is not a fix for bug #17512, but it will turn the crash reported by
that bug report into an internal ERROR.
Reported-by: Ales Zeleny
Reviewed-by: Tom Lane
Discussion: https://postgr.es/m/CAApHDvpxFSTwvoYWT7kmFVSZ9zLAeHb=S9vrz=RExMgSkQNWqw@mail.gmail.com
Backpatch-through: 14, where Memoize was added.
Several places in the planner tried to clamp a double value to fit
in a "long" by doing
(long) Min(x, (double) LONG_MAX);
This is subtly incorrect, because it casts LONG_MAX to double and
potentially back again. If long is 64 bits then the double value
is inexact, and the platform might round it up to LONG_MAX+1
resulting in an overflow and an undesirably negative output.
While it's not hard to rewrite the expression into a safe form,
let's put it into a common function to reduce the risk of someone
doing it wrong in future.
In principle this is a bug fix, but since the problem could only
manifest with group count estimates exceeding 2^63, it seems unlikely
that anyone has actually hit this or will do so anytime soon. We're
fixing it mainly to satisfy fuzzer-type tools. That being the case,
a HEAD-only fix seems sufficient.
Andrey Lepikhov
Discussion: https://postgr.es/m/ebbc2efb-7ef9-bf2f-1ada-d6ec48f70e58@postgrespro.ru
I started out with the intention to rename value_type to item_type to
avoid a collision with a typedef name that appears on some platforms.
Along the way, I noticed that the adjacent field "format" was not being
correctly handled by the backend/nodes/ infrastructure functions:
copyfuncs.c erroneously treated it as a scalar, while equalfuncs,
outfuncs, and readfuncs omitted handling it at all. This looks like
it might be cosmetic at the moment because the field is always NULL
after parse analysis; but that's likely a bug in itself, and the code's
certainly not very future-proof. Let's fix it while we can still do so
without forcing an initdb on beta testers.
Further study found a few other inconsistencies in the backend/nodes/
infrastructure for the recently-added JSON node types, so fix those too.
catversion bumped because of potential change in stored rules.
Discussion: https://postgr.es/m/526703.1652385613@sss.pgh.pa.us
Should have been done this way to start with, but I failed to notice
This way we avoid some pointless initialization, and better contains the
variable to exist in the scope where it is really used.
Reviewed-by: Michaël Paquier <michael@paquier.xyz>
Discussion: https://postgr.es/m/202204191345.qerjy3kxi3eb@alvherre.pgsql
There's no reason to keep a separate local variable when we have a place
for it elsewhere. This allows to simplify some code.
Reviewed-by: Michaël Paquier <michael@paquier.xyz>
Discussion: https://postgr.es/m/202204191345.qerjy3kxi3eb@alvherre.pgsql
Previously, the output of EXPLAIN (BUFFERS) option showed only the I/O
timing spent reading and writing shared and local buffers. This commit
adds on top of that the I/O timing for temporary buffers in the output
of EXPLAIN (for spilled external sorts, hashes, materialization. etc).
This can be helpful for users in cases where the I/O related to
temporary buffers is the bottleneck.
Like its cousin, this information is available only when track_io_timing
is enabled. Playing the patch, this is showing an extra overhead of up
to 1% even when using gettimeofday() as implementation for interval
timings, which is slightly within the usual range noise still that's
measurable.
Author: Masahiko Sawada
Reviewed-by: Georgios Kokolatos, Melanie Plageman, Julien Rouhaud,
Ranier Vilela
Discussion: https://postgr.es/m/CAD21AoAJgotTeP83p6HiAGDhs_9Fw9pZ2J=_tYTsiO5Ob-V5GQ@mail.gmail.com
Window functions such as row_number() always return a value higher than
the previously returned value for tuples in any given window partition.
Traditionally queries such as;
SELECT * FROM (
SELECT *, row_number() over (order by c) rn
FROM t
) t WHERE rn <= 10;
were executed fairly inefficiently. Neither the query planner nor the
executor knew that once rn made it to 11 that nothing further would match
the outer query's WHERE clause. It would blindly continue until all
tuples were exhausted from the subquery.
Here we implement means to make the above execute more efficiently.
This is done by way of adding a pg_proc.prosupport function to various of
the built-in window functions and adding supporting code to allow the
support function to inform the planner if the window function is
monotonically increasing, monotonically decreasing, both or neither. The
planner is then able to make use of that information and possibly allow
the executor to short-circuit execution by way of adding a "run condition"
to the WindowAgg to allow it to determine if some of its execution work
can be skipped.
This "run condition" is not like a normal filter. These run conditions
are only built using quals comparing values to monotonic window functions.
For monotonic increasing functions, quals making use of the btree
operators for <, <= and = can be used (assuming the window function column
is on the left). You can see here that once such a condition becomes false
that a monotonic increasing function could never make it subsequently true
again. For monotonically decreasing functions the >, >= and = btree
operators for the given type can be used for run conditions.
The best-case situation for this is when there is a single WindowAgg node
without a PARTITION BY clause. Here when the run condition becomes false
the WindowAgg node can simply return NULL. No more tuples will ever match
the run condition. It's a little more complex when there is a PARTITION
BY clause. In this case, we cannot return NULL as we must still process
other partitions. To speed this case up we pull tuples from the outer
plan to check if they're from the same partition and simply discard them
if they are. When we find a tuple belonging to another partition we start
processing as normal again until the run condition becomes false or we run
out of tuples to process.
When there are multiple WindowAgg nodes to evaluate then this complicates
the situation. For intermediate WindowAggs we must ensure we always
return all tuples to the calling node. Any filtering done could lead to
incorrect results in WindowAgg nodes above. For all intermediate nodes,
we can still save some work when the run condition becomes false. We've
no need to evaluate the WindowFuncs anymore. Other WindowAgg nodes cannot
reference the value of these and these tuples will not appear in the final
result anyway. The savings here are small in comparison to what can be
saved in the top-level WingowAgg, but still worthwhile.
Intermediate WindowAgg nodes never filter out tuples, but here we change
WindowAgg so that the top-level WindowAgg filters out tuples that don't
match the intermediate WindowAgg node's run condition. Such filters
appear in the "Filter" clause in EXPLAIN for the top-level WindowAgg node.
Here we add prosupport functions to allow the above to work for;
row_number(), rank(), dense_rank(), count(*) and count(expr). It appears
technically possible to do the same for min() and max(), however, it seems
unlikely to be useful enough, so that's not done here.
Bump catversion
Author: David Rowley
Reviewed-by: Andy Fan, Zhihong Yu
Discussion: https://postgr.es/m/CAApHDvqvp3At8++yF8ij06sdcoo1S_b2YoaT9D4Nf+MObzsrLQ@mail.gmail.com
Modify the subroutines called by RI trigger functions that want to check
if a given referenced value exists in the referenced relation to simply
scan the foreign key constraint's unique index, instead of using SPI to
execute
SELECT 1 FROM referenced_relation WHERE ref_key = $1
This saves a lot of work, especially when inserting into or updating a
referencing relation.
This rewrite allows to fix a PK row visibility bug caused by a partition
descriptor hack which requires ActiveSnapshot to be set to come up with
the correct set of partitions for the RI query running under REPEATABLE
READ isolation. We now set that snapshot indepedently of the snapshot
to be used by the PK index scan, so the two no longer interfere. The
buggy output in src/test/isolation/expected/fk-snapshot.out of the
relevant test case added by commit 00cb86e75d has been corrected.
(The bug still exists in branch 14, however, but this fix is too
invasive to backpatch.)
Author: Amit Langote <amitlangote09@gmail.com>
Reviewed-by: Kyotaro Horiguchi <horikyota.ntt@gmail.com>
Reviewed-by: Corey Huinker <corey.huinker@gmail.com>
Reviewed-by: Li Japin <japinli@hotmail.com>
Reviewed-by: Tom Lane <tgl@sss.pgh.pa.us>
Reviewed-by: Zhihong Yu <zyu@yugabyte.com>
Discussion: https://postgr.es/m/CA+HiwqGkfJfYdeq5vHPh6eqPKjSbfpDDY+j-kXYFePQedtSLeg@mail.gmail.com
* Move the execution pruning initialization steps that are common
between both ExecInitAppend() and ExecInitMergeAppend() into a new
function ExecInitPartitionPruning() defined in execPartition.c.
Those steps include creation of a PartitionPruneState to be used for
all instances of pruning and determining the minimal set of child
subplans that need to be initialized by performing initial pruning if
needed, and finally adjusting the subplan_map arrays in the
PartitionPruneState to reflect the new set of subplans remaining
after initial pruning if it was indeed performed.
ExecCreatePartitionPruneState() is no longer exported out of
execPartition.c and has been renamed to CreatePartitionPruneState()
as a local sub-routine of ExecInitPartitionPruning().
* Likewise, ExecFindInitialMatchingSubPlans() that was in charge of
performing initial pruning no longer needs to be exported. In fact,
since it would now have the same body as the more generally named
ExecFindMatchingSubPlans(), except differing in the value of
initial_prune passed to the common subroutine
find_matching_subplans_recurse(), it seems better to remove it and add
an initial_prune argument to ExecFindMatchingSubPlans().
* Add an ExprContext field to PartitionPruneContext to remove the
implicit assumption in the runtime pruning code that the ExprContext to
use to compute pruning expressions that need one can always rely on the
PlanState providing it. A future patch will allow runtime pruning (at
least the initial pruning steps) to be performed without the
corresponding PlanState yet having been created, so this will help.
Author: Amit Langote <amitlangote09@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqEYCpEqh2LMDOp9mT+4-QoVe8HgFMKBjntEMCTZLpcCCA@mail.gmail.com
This feature allows jsonb data to be treated as a table and thus used in
a FROM clause like other tabular data. Data can be selected from the
jsonb using jsonpath expressions, and hoisted out of nested structures
in the jsonb to form multiple rows, more or less like an outer join.
Nikita Glukhov
Reviewers have included (in no particular order) Andres Freund, Alexander
Korotkov, Pavel Stehule, Andrew Alsup, Erik Rijkers, Zhihong Yu (whose
name I previously misspelled), Himanshu Upadhyaya, Daniel Gustafsson,
Justin Pryzby.
Discussion: https://postgr.es/m/7e2cb85d-24cf-4abb-30a5-1a33715959bd@postgrespro.ru
The general usage pattern when we store tuples in tuplesort.c is that
we store a series of tuples one by one then either perform a sort or spill
them to disk. In the common case, there is no pfreeing of already stored
tuples. For the common case since we do not individually pfree tuples, we
have very little need for aset.c memory allocation behavior which
maintains freelists and always rounds allocation sizes up to the next
power of 2 size.
Here we conditionally use generation.c contexts for storing tuples in
tuplesort.c when the sort will never be bounded. Unfortunately, the
memory context to store tuples is already created by the time any calls
would be made to tuplesort_set_bound(), so here we add a new sort option
that allows callers to specify if they're going to need a bounded sort or
not. We'll use a standard aset.c allocator when this sort option is not
set.
Extension authors must ensure that the TUPLESORT_ALLOWBOUNDED flag is
used when calling tuplesort_begin_* for any sorts that make a call to
tuplesort_set_bound().
Author: David Rowley
Reviewed-by: Andy Fan
Discussion: https://postgr.es/m/CAApHDvoH4ASzsAOyHcxkuY01Qf++8JJ0paw+03dk+W25tQEcNQ@mail.gmail.com
This replaces the bool flag for randomAccess. An upcoming patch requires
adding another option, so instead of breaking the API for that, then
breaking it again one day if we add more options, let's just break it
once. Any boolean options we add in the future will just make use of an
unused bit in the flags.
Any extensions making use of tuplesorts will need to update their code
to pass TUPLESORT_RANDOMACCESS instead of true for randomAccess.
TUPLESORT_NONE can be used for a set of empty options.
Author: David Rowley
Reviewed-by: Justin Pryzby
Discussion: https://postgr.es/m/CAApHDvoH4ASzsAOyHcxkuY01Qf%2B%2B8JJ0paw%2B03dk%2BW25tQEcNQ%40mail.gmail.com
This Patch introduces three SQL standard JSON functions:
JSON() (incorrectly mentioned in my commit message for f4fb45d15c)
JSON_SCALAR()
JSON_SERIALIZE()
JSON() produces json values from text, bytea, json or jsonb values, and
has facilitites for handling duplicate keys.
JSON_SCALAR() produces a json value from any scalar sql value, including
json and jsonb.
JSON_SERIALIZE() produces text or bytea from input which containis or
represents json or jsonb;
For the most part these functions don't add any significant new
capabilities, but they will be of use to users wanting standard
compliant JSON handling.
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
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
This patch intrdocuces the SQL standard IS JSON predicate. It operates
on text and bytea values representing JSON as well as on the json and
jsonb types. Each test has an IS and IS NOT variant. The tests are:
IS JSON [VALUE]
IS JSON ARRAY
IS JSON OBJECT
IS JSON SCALAR
IS JSON WITH | WITHOUT UNIQUE KEYS
These are mostly self-explanatory, but note that IS JSON WITHOUT UNIQUE
KEYS is true whenever IS JSON is true, and IS JSON WITH UNIQUE KEYS is
true whenever IS JSON is true except it IS JSON OBJECT is true and there
are duplicate keys (which is never the case when applied to jsonb values).
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
This introduces some of the building blocks used by the SQL/JSON
constructor and query functions. Specifically, it provides node
executor and grammar support for the FORMAT JSON [ENCODING foo]
clause, and values decorated with it, and for the RETURNING clause.
The following SQL/JSON patches will leverage these.
Nikita Glukhov (who probably deserves an award for perseverance).
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
This allows specifying an optional column list when adding a table to
logical replication. The column list may be specified after the table
name, enclosed in parentheses. Columns not included in this list are not
sent to the subscriber, allowing the schema on the subscriber to be a
subset of the publisher schema.
For UPDATE/DELETE publications, the column list needs to cover all
REPLICA IDENTITY columns. For INSERT publications, the column list is
arbitrary and may omit some REPLICA IDENTITY columns. Furthermore, if
the table uses REPLICA IDENTITY FULL, column list is not allowed.
The column list can contain only simple column references. Complex
expressions, function calls etc. are not allowed. This restriction could
be relaxed in the future.
During the initial table synchronization, only columns included in the
column list are copied to the subscriber. If the subscription has
several publications, containing the same table with different column
lists, columns specified in any of the lists will be copied.
This means all columns are replicated if the table has no column list
at all (which is treated as column list with all columns), or when of
the publications is defined as FOR ALL TABLES (possibly IN SCHEMA that
matches the schema of the table).
For partitioned tables, publish_via_partition_root determines whether
the column list for the root or the leaf relation will be used. If the
parameter is 'false' (the default), the list defined for the leaf
relation is used. Otherwise, the column list for the root partition
will be used.
Psql commands \dRp+ and \d <table-name> now display any column lists.
Author: Tomas Vondra, Alvaro Herrera, Rahila Syed
Reviewed-by: Peter Eisentraut, Alvaro Herrera, Vignesh C, Ibrar Ahmed,
Amit Kapila, Hou zj, Peter Smith, Wang wei, Tang, Shi yu
Discussion: https://postgr.es/m/CAH2L28vddB_NFdRVpuyRBJEBWjz4BSyTB=_ektNRH8NJ1jf95g@mail.gmail.com
This commit adds support for decoding of sequences to the built-in
replication (the infrastructure was added by commit 0da92dc530).
The syntax and behavior mostly mimics handling of tables, i.e. a
publication may be defined as FOR ALL SEQUENCES (replicating all
sequences in a database), FOR ALL SEQUENCES IN SCHEMA (replicating
all sequences in a particular schema) or individual sequences.
To publish sequence modifications, the publication has to include
'sequence' action. The protocol is extended with a new message,
describing sequence increments.
A new system view pg_publication_sequences lists all the sequences
added to a publication, both directly and indirectly. Various psql
commands (\d and \dRp) are improved to also display publications
including a given sequence, or sequences included in a publication.
Author: Tomas Vondra, Cary Huang
Reviewed-by: Peter Eisentraut, Amit Kapila, Hannu Krosing, Andres
Freund, Petr Jelinek
Discussion: https://postgr.es/m/d045f3c2-6cfb-06d3-5540-e63c320df8bc@enterprisedb.com
Discussion: https://postgr.es/m/1710ed7e13b.cd7177461430746.3372264562543607781@highgo.ca
This introduces some of the building blocks used by the SQL/JSON
constructor and query functions. Specifically, it provides node
executor and grammar support for the FORMAT JSON [ENCODING foo]
clause, and values decorated with it, and for the RETURNING clause.
The following SQL/JSON patches will leverage these.
Nikita Glukhov (who probably deserves an award for perseverance).
Reviewers have included (in no particular order) Andres Freund, Alexander
Korotkov, Pavel Stehule, Andrew Alsup. Erik Rijkers, Zihong Yu and
Himanshu Upadhyaya.
Discussion: https://postgr.es/m/cd0bb935-0158-78a7-08b5-904886deac4b@postgrespro.ru
Valgrind animal skink shows a crash in this new code. I couldn't
reproduce the problem locally, but going by blind code inspection,
initializing insert_destrel should be sufficient to fix the problem.
When an update on a partitioned table referenced in foreign key
constraints causes a row to move from one partition to another,
the fact that the move is implemented as a delete followed by an insert
on the target partition causes the foreign key triggers to have
surprising behavior. For example, a given foreign key's delete trigger
which implements the ON DELETE CASCADE clause of that key will delete
any referencing rows when triggered for that internal DELETE, although
it should not, because the referenced row is simply being moved from one
partition of the referenced root partitioned table into another, not
being deleted from it.
This commit teaches trigger.c to skip queuing such delete trigger events
on the leaf partitions in favor of an UPDATE event fired on the root
target relation. Doing so is sensible because both the old and the new
tuple "logically" belong to the root relation.
The after trigger event queuing interface now allows passing the source
and the target partitions of a particular cross-partition update when
registering the update event for the root partitioned table. Along with
the two ctids of the old and the new tuple, the after trigger event now
also stores the OIDs of those partitions. The tuples fetched from the
source and the target partitions are converted into the root table
format, if necessary, before they are passed to the trigger function.
The implementation currently has a limitation that only the foreign keys
pointing into the query's target relation are considered, not those of
its sub-partitioned partitions. That seems like a reasonable
limitation, because it sounds rare to have distinct foreign keys
pointing to sub-partitioned partitions instead of to the root table.
This misbehavior stems from commit f56f8f8da6 (which added support for
foreign keys to reference partitioned tables) not paying sufficient
attention to commit 2f17844104 (which had introduced cross-partition
updates a year earlier). Even though the former commit goes back to
Postgres 12, we're not backpatching this fix at this time for fear of
destabilizing things too much, and because there are a few ABI breaks in
it that we'd have to work around in older branches. It also depends on
commit f4566345cf, which had its own share of backpatchability issues
as well.
Author: Amit Langote <amitlangote09@gmail.com>
Reviewed-by: Masahiko Sawada <sawada.mshk@gmail.com>
Reviewed-by: Álvaro Herrera <alvherre@alvh.no-ip.org>
Reported-by: Eduard Català <eduard.catala@gmail.com>
Discussion: https://postgr.es/m/CA+HiwqFvkBCmfwkQX_yBqv2Wz8ugUGiBDxum8=WvVbfU1TXaNg@mail.gmail.com
Discussion: https://postgr.es/m/CAL54xNZsLwEM1XCk5yW9EqaRzsZYHuWsHQkA2L5MOSKXAwviCQ@mail.gmail.com
In commit bf7ca1587, I had the bright idea that we could make the
result of a whole-row Var (that is, foo.*) track any column aliases
that had been applied to the FROM entry the Var refers to. However,
that's not terribly logically consistent, because now the output of
the Var is no longer of the named composite type that the Var claims
to emit. bf7ca1587 tried to handle that by changing the output
tuple values to be labeled with a blessed RECORD type, but that's
really pretty disastrous: we can wind up storing such tuples onto
disk, whereupon they're not readable by other sessions.
The only practical fix I can see is to give up on what bf7ca1587
tried to do, and say that the column names of tuples produced by
a whole-row Var are always those of the underlying named composite
type, query aliases or no. While this introduces some inconsistencies,
it removes others, so it's not that awful in the abstract. What *is*
kind of awful is to make such a behavioral change in a back-patched
bug fix. But corrupt data is worse, so back-patched it will be.
(A workaround available to anyone who's unhappy about this is to
introduce an extra level of sub-SELECT, so that the whole-row Var is
referring to the sub-SELECT's output and not to a named table type.
Then the Var is of type RECORD to begin with and there's no issue.)
Per report from Miles Delahunty. The faulty commit dates to 9.5,
so back-patch to all supported branches.
Discussion: https://postgr.es/m/2950001.1638729947@sss.pgh.pa.us
Create subroutines ExecUpdatePrologue / ExecUpdateAct /
ExecUpdateEpilogue, and similar for ExecDelete.
Introduce a new struct to be used internally in nodeModifyTable.c,
dubbed ModifyTableContext, which contains all context information needed
to perform these operations, as well as ExecInsert and others.
This allows using a different schedule and a different way of evaluating
the results of these operations, which can be exploited by a later
commit introducing support for MERGE. It also makes ExecUpdate and
ExecDelete proper shorter and (hopefully) simpler.
Author: Álvaro Herrera <alvherre@alvh.no-ip.org>
Reviewed-by: Amit Langote <amitlangote09@gmail.com>
Reviewed-by: Japin Li <japinli@hotmail.com>
Reviewed-by: Zhihong Yu <zyu@yugabyte.com>
Discussion: https://postgr.es/m/202202271724.4z7xv3cf46kv@alvherre.pgsql
There are three parallel ways to call parse/analyze: with fixed
parameters, with variable parameters, and by supplying your own parser
callback. Some of the involved functions were confusingly named and
made this API structure more confusing. This patch renames some
functions to make this clearer:
parse_analyze() -> parse_analyze_fixedparams()
pg_analyze_and_rewrite() -> pg_analyze_and_rewrite_fixedparams()
(Otherwise one might think this variant doesn't accept parameters, but
in fact all three ways accept parameters.)
pg_analyze_and_rewrite_params() -> pg_analyze_and_rewrite_withcb()
(Before, and also when considering pg_analyze_and_rewrite(), one might
think this is the only way to pass parameters. Moreover, the parser
callback doesn't necessarily need to parse only parameters, it's just
one of the things it could do.)
parse_fixed_parameters() -> setup_parse_fixed_parameters()
parse_variable_parameters() -> setup_parse_variable_parameters()
(These functions don't actually do any parsing, they just set up
callbacks to use during parsing later.)
This patch also adds some const decorations to the fixed-parameters
API, so the distinction from the variable-parameters API is more
clear.
Reviewed-by: Nathan Bossart <bossartn@amazon.com>
Discussion: https://www.postgresql.org/message-id/flat/c67ce276-52b4-0239-dc0e-39875bf81840@enterprisedb.com
This is pretty queasy-making on general principles, and the more so
once you notice that CommitTransactionCommand() is actually stomping
on the values saved by _SPI_commit(). It's okay as long as the
active values didn't change during HoldPinnedPortals(); but that's
a larger assumption than I think we want to make, especially since
the fix is so simple.
Discussion: https://postgr.es/m/1533956.1645731245@sss.pgh.pa.us
SPI_commit previously left it up to the caller to recover from any error
occurring during commit. Since that's complicated and requires use of
low-level xact.c facilities, it's not too surprising that no caller got
it right. Let's move the responsibility for cleanup into spi.c. Doing
that requires redefining SPI_commit as starting a new transaction, so
that it becomes equivalent to SPI_commit_and_chain except that you get
default transaction characteristics instead of preserving the prior
transaction's characteristics. We can make this pretty transparent
API-wise by redefining SPI_start_transaction() as a no-op. Callers
that expect to do something in between might be surprised, but
available evidence is that no callers do so.
Having made that API redefinition, we can fix this mess by having
SPI_commit[_and_chain] trap errors and start a new, clean transaction
before re-throwing the error. Likewise for SPI_rollback[_and_chain].
Some cleanup is also needed in AtEOXact_SPI, which was nowhere near
smart enough to deal with SPI contexts nested inside a committing
context.
While plperl and pltcl need no changes beyond removing their now-useless
SPI_start_transaction() calls, plpython needs some more work because it
hadn't gotten the memo about catching commit/rollback errors in the
first place. Such an error resulted in longjmp'ing out of the Python
interpreter, which leaks Python stack entries at present and is reported
to crash Python 3.11 altogether. Add the missing logic to catch such
errors and convert them into Python exceptions.
We are probably going to have to back-patch this once Python 3.11 ships,
but it's a sufficiently basic change that I'm a bit nervous about doing
so immediately. Let's let it bake awhile in HEAD first.
Peter Eisentraut and Tom Lane
Discussion: https://postgr.es/m/3375ffd8-d71c-2565-e348-a597d6e739e3@enterprisedb.com
Discussion: https://postgr.es/m/17416-ed8fe5d7213d6c25@postgresql.org
David Rowley
Peter Eisentraut
Tom Lane
Andres Freund
Alvaro Herrera
Michael Paquier
Tomas Vondra
Andrew Dunstan