Commit 5262f7a4fc added similar support
for parallel index scans; this extends that work to index-only scans.
As with parallel index scans, this requires support from the index AM,
so currently parallel index-only scans will only be possible for btree
indexes.
Rafia Sabih, reviewed and tested by Rahila Syed, Tushar Ahuja,
and Amit Kapila
Discussion: http://postgr.es/m/CAOGQiiPEAs4C=TBp0XShxBvnWXuzGL2u++Hm1=qnCpd6_Mf8Fw@mail.gmail.com
In combination with 569174f1be, which
taught the btree AM how to perform parallel index scans, this allows
parallel index scan plans on btree indexes. This infrastructure
should be general enough to support parallel index scans for other
index AMs as well, if someone updates them to support parallel
scans.
Amit Kapila, reviewed and tested by Anastasia Lubennikova, Tushar
Ahuja, and Haribabu Kommi, and me.
It's always been possible for index AMs to cache data across successive
amgettuple calls within a single SQL command: the IndexScanDesc.opaque
field is meant for precisely that. However, no comparable facility
exists for amortizing setup work across successive aminsert calls.
This patch adds such a feature and teaches GIN, GIST, and BRIN to use it
to amortize catalog lookups they'd previously been doing on every call.
(The other standard index AMs keep everything they need in the relcache,
so there's little to improve there.)
For GIN, the overall improvement in a statement that inserts many rows
can be as much as 10%, though it seems a bit less for the other two.
In addition, this makes a really significant difference in runtime
for CLOBBER_CACHE_ALWAYS tests, since in those builds the repeated
catalog lookups are vastly more expensive.
The reason this has been hard up to now is that the aminsert function is
not passed any useful place to cache per-statement data. What I chose to
do is to add suitable fields to struct IndexInfo and pass that to aminsert.
That's not widening the index AM API very much because IndexInfo is already
within the ken of ambuild; in fact, by passing the same info to aminsert
as to ambuild, this is really removing an inconsistency in the AM API.
Discussion: https://postgr.es/m/27568.1486508680@sss.pgh.pa.us
Since 69f4b9c plain expression evaluation (and thus normal projection)
can't return sets of tuples anymore. Thus remove code dealing with
that possibility.
This will require adjustments in external code using
ExecEvalExpr()/ExecProject() - that should neither be hard nor very
common.
Author: Andres Freund and Tom Lane
Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de
Evaluation of set returning functions (SRFs_ in the targetlist (like SELECT
generate_series(1,5)) so far was done in the expression evaluation (i.e.
ExecEvalExpr()) and projection (i.e. ExecProject/ExecTargetList) code.
This meant that most executor nodes performing projection, and most
expression evaluation functions, had to deal with the possibility that an
evaluated expression could return a set of return values.
That's bad because it leads to repeated code in a lot of places. It also,
and that's my (Andres's) motivation, made it a lot harder to implement a
more efficient way of doing expression evaluation.
To fix this, introduce a new executor node (ProjectSet) that can evaluate
targetlists containing one or more SRFs. To avoid the complexity of the old
way of handling nested expressions returning sets (e.g. having to pass up
ExprDoneCond, and dealing with arguments to functions returning sets etc.),
those SRFs can only be at the top level of the node's targetlist. The
planner makes sure (via split_pathtarget_at_srfs()) that SRF evaluation is
only necessary in ProjectSet nodes and that SRFs are only present at the
top level of the node's targetlist. If there are nested SRFs the planner
creates multiple stacked ProjectSet nodes. The ProjectSet nodes always get
input from an underlying node.
We also discussed and prototyped evaluating targetlist SRFs using ROWS
FROM(), but that turned out to be more complicated than we'd hoped.
While moving SRF evaluation to ProjectSet would allow to retain the old
"least common multiple" behavior when multiple SRFs are present in one
targetlist (i.e. continue returning rows until all SRFs are at the end of
their input at the same time), we decided to instead only return rows till
all SRFs are exhausted, returning NULL for already exhausted ones. We
deemed the previous behavior to be too confusing, unexpected and actually
not particularly useful.
As a side effect, the previously prohibited case of multiple set returning
arguments to a function, is now allowed. Not because it's particularly
desirable, but because it ends up working and there seems to be no argument
for adding code to prohibit it.
Currently the behavior for COALESCE and CASE containing SRFs has changed,
returning multiple rows from the expression, even when the SRF containing
"arm" of the expression is not evaluated. That's because the SRFs are
evaluated in a separate ProjectSet node. As that's quite confusing, we're
likely to instead prohibit SRFs in those places. But that's still being
discussed, and the code would reside in places not touched here, so that's
a task for later.
There's a lot of, now superfluous, code dealing with set return expressions
around. But as the changes to get rid of those are verbose largely boring,
it seems better for readability to keep the cleanup as a separate commit.
Author: Tom Lane and Andres Freund
Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de
After a tuple is routed to a partition, it has been converted from the
root table's row type to the partition's row type. ExecConstraints
needs to report the failure using the original tuple and the parent's
tuple descriptor rather than the ones for the selected partition.
Amit Langote
Commit 2ac3ef7a01 added a TupleTapleSlot
for partition tuple slot to EState (es_partition_tuple_slot) but it's
more logical to have it as part of ModifyTableState
(mt_partition_tuple_slot) and CopyState (partition_tuple_slot).
Discussion: http://postgr.es/m/1bd459d9-4c0c-197a-346e-e5e59e217d97@lab.ntt.co.jp
Amit Langote, per a gripe from me
The previous coding failed to work correctly when we have a
multi-level partitioned hierarchy where tables at successive levels
have different attribute numbers for the partition key attributes. To
fix, have each PartitionDispatch object store a standalone
TupleTableSlot initialized with the TupleDesc of the corresponding
partitioned table, along with a TupleConversionMap to map tuples from
the its parent's rowtype to own rowtype. After tuple routing chooses
a leaf partition, we must use the leaf partition's tuple descriptor,
not the root table's. To that end, a dedicated TupleTableSlot for
tuple routing is now allocated in EState.
Amit Langote
When the input value to a CoerceToDomain expression node is a read-write
expanded datum, we should pass a read-only pointer to any domain CHECK
expressions and then return the original read-write pointer as the
expression result. Previously we were blindly passing the same pointer to
all the consumers of the value, making it possible for a function in CHECK
to modify or even delete the expanded value. (Since a plpgsql function
will absorb a passed-in read-write expanded array as a local variable
value, it will in fact delete the value on exit.)
A similar hazard of passing the same read-write pointer to multiple
consumers exists in domain_check() and in ExecEvalCase, so fix those too.
The fix requires adding MakeExpandedObjectReadOnly calls at the appropriate
places, which is simple enough except that we need to get the data type's
typlen from somewhere. For the domain cases, solve this by redefining
DomainConstraintRef.tcache as okay for callers to access; there wasn't any
reason for the original convention against that, other than not wanting the
API of typcache.c to be any wider than it had to be. For CASE, there's
no good solution except to add a syscache lookup during executor start.
Per bug #14472 from Marcos Castedo. Back-patch to 9.5 where expanded
values were introduced.
Discussion: https://postgr.es/m/15225.1482431619@sss.pgh.pa.us
Commit 5dfc198146 introduced the use
of a new type of hash table with linear reprobing for hash aggregates.
Such a hash table behaves very poorly if keys are inserted in hash
order, which does in fact happen in the case where a query use a
Finalize HashAggregate node fed (via Gather) by a Partial
HashAggregate node. In fact, queries with this type of plan tend
to run effectively forever.
Fix that by seeding the hash value differently in each worker
(and in the leader, if it participates).
Andres Freund and Robert Haas
Table partitioning is like table inheritance and reuses much of the
existing infrastructure, but there are some important differences.
The parent is called a partitioned table and is always empty; it may
not have indexes or non-inherited constraints, since those make no
sense for a relation with no data of its own. The children are called
partitions and contain all of the actual data. Each partition has an
implicit partitioning constraint. Multiple inheritance is not
allowed, and partitioning and inheritance can't be mixed. Partitions
can't have extra columns and may not allow nulls unless the parent
does. Tuples inserted into the parent are automatically routed to the
correct partition, so tuple-routing ON INSERT triggers are not needed.
Tuple routing isn't yet supported for partitions which are foreign
tables, and it doesn't handle updates that cross partition boundaries.
Currently, tables can be range-partitioned or list-partitioned. List
partitioning is limited to a single column, but range partitioning can
involve multiple columns. A partitioning "column" can be an
expression.
Because table partitioning is less general than table inheritance, it
is hoped that it will be easier to reason about properties of
partitions, and therefore that this will serve as a better foundation
for a variety of possible optimizations, including query planner
optimizations. The tuple routing based which this patch does based on
the implicit partitioning constraints is an example of this, but it
seems likely that many other useful optimizations are also possible.
Amit Langote, reviewed and tested by Robert Haas, Ashutosh Bapat,
Amit Kapila, Rajkumar Raghuwanshi, Corey Huinker, Jaime Casanova,
Rushabh Lathia, Erik Rijkers, among others. Minor revisions by me.
So far the hashtable stored representative tuples in the form of its
input slot, with all columns in the hashtable that are not
needed (i.e. not grouped upon or functionally dependent) set to NULL.
Thats good for saving memory, but it turns out that having tuples full
of NULL isn't free. slot_deform_tuple is faster if there's no NULL
bitmap even if no NULLs are encountered, and skipping over leading NULLs
isn't free.
So compute a separate tuple descriptor that only contains the needed
columns. As columns have already been moved in/out the slot for the
hashtable that does not imply additional per-row overhead.
Author: Andres Freund
Reviewed-By: Heikki Linnakangas
Discussion: https://postgr.es/m/20161103110721.h5i5t5saxfk5eeik@alap3.anarazel.de
Previously we did a ExecProject() for each individual aggregate
argument. That turned out to be a performance bottleneck in queries with
multiple aggregates.
Doing all the argument computations in one ExecProject() is quite a bit
cheaper because ExecProject's fastpath can do the work at once in a
relatively tight loop, and because it can get all the required columns
with a single slot_getsomeattr and save some other redundant setup
costs.
Author: Andres Freund
Reviewed-By: Heikki Linnakangas
Discussion: https://postgr.es/m/20161103110721.h5i5t5saxfk5eeik@alap3.anarazel.de
The more efficient hashtable speeds up hash-aggregations with more than
a few hundred groups significantly. Improvements of over 120% have been
measured.
Due to the the different hash table queries that not fully
determined (e.g. GROUP BY without ORDER BY) may change their result
order.
The conversion is largely straight-forward, except that, due to the
static element types of simplehash.h type hashes, the additional data
some users store in elements (e.g. the per-group working data for hash
aggregaters) is now stored in TupleHashEntryData->additional. The
meaning of BuildTupleHashTable's entrysize (renamed to additionalsize)
has been changed to only be about the additionally stored size. That
size is only used for the initial sizing of the hash-table.
Reviewed-By: Tomas Vondra
Discussion: <20160727004333.r3e2k2y6fvk2ntup@alap3.anarazel.de>
lockdefs.h was only split from lock.h relatively recently, and
represents a minimal subset of the old lock.h. heapam.h only needs
that smaller subset, so adjust it to include only that. This requires
some corresponding adjustments elsewhere.
Peter Geoghegan
The original coding had three separate booleans representing partial
aggregation behavior, which was confusing, unreadable, and error-prone,
not least because the booleans weren't always listed in the same order.
It was also inadequate for the allegedly-desirable future extension to
support intermediate partial aggregation, because we'd need separate
markers for serialization and deserialization in such a case.
Merge these bools into an enum "AggSplit" to provide symbolic names for
the supported operating modes (and document what those are). By assigning
the values of the enum constants carefully, we can treat AggSplit values
as options bitmasks so that tests of what to do aren't noticeably more
expensive than before.
While at it, get rid of Aggref.aggoutputtype. That's not needed since
commit 59a3795c2 got rid of setrefs.c's special-purpose Aggref comparison
code, and it likewise seemed more confusing than helpful.
Assorted comment cleanup as well (there's still more that I want to do
in that line).
catversion bump for change in Aggref node contents. Should be the last
one for partial-aggregation changes.
Discussion: <29309.1466699160@sss.pgh.pa.us>
The previous display was sort of confusing, because it didn't
distinguish between the number of workers that we planned to launch
and the number that actually got launched. This has already confused
several people, so display both numbers and label them clearly.
Julien Rouhaud, reviewed by me.
Now indexes (but only B-tree for now) can contain "extra" column(s) which
doesn't participate in index structure, they are just stored in leaf
tuples. It allows to use index only scan by using single index instead
of two or more indexes.
Author: Anastasia Lubennikova with minor editorializing by me
Reviewers: David Rowley, Peter Geoghegan, Jeff Janes
This is necessary infrastructure for supporting parallel aggregation
for aggregates whose transition type is "internal". Such values
can't be passed between cooperating processes, because they are
just pointers.
David Rowley, reviewed by Tomas Vondra and by me.
Per discussion, the new extensible node framework is thought to be
better designed than the custom path/scan/scanstate stuff we added
in PostgreSQL 9.5. Rework the latter to be more like the former.
This is not backward-compatible, but we generally don't promise that
for C APIs, and there probably aren't many people using this yet
anyway.
KaiGai Kohei, reviewed by Petr Jelinek and me. Some further
cosmetic changes by me.
postgres_fdw can now sent an UPDATE or DELETE statement directly to
the foreign server in simple cases, rather than sending a SELECT FOR
UPDATE statement and then updating or deleting rows one-by-one.
Etsuro Fujita, reviewed by Rushabh Lathia, Shigeru Hanada, Kyotaro
Horiguchi, Albe Laurenz, Thom Brown, and me.
This patch widens SPI_processed, EState's es_processed field, PortalData's
portalPos field, FuncCallContext's call_cntr and max_calls fields,
ExecutorRun's count argument, PortalRunFetch's result, and the max number
of rows in a SPITupleTable to uint64, and deals with (I hope) all the
ensuing fallout. Some of these values were declared uint32 before, and
others "long".
I also removed PortalData's posOverflow field, since that logic seems
pretty useless given that portalPos is now always 64 bits.
The user-visible results are that command tags for SELECT etc will
correctly report tuple counts larger than 4G, as will plpgsql's GET
GET DIAGNOSTICS ... ROW_COUNT command. Queries processing more tuples
than that are still not exactly the norm, but they're becoming more
common.
Most values associated with FETCH/MOVE distances, such as PortalRun's count
argument and the count argument of most SPI functions that have one, remain
declared as "long". It's not clear whether it would be worth promoting
those to int64; but it would definitely be a large dollop of additional
API churn on top of this, and it would only help 32-bit platforms which
seem relatively less likely to see any benefit.
Andreas Scherbaum, reviewed by Christian Ullrich, additional hacking by me
This patch doesn't put the new infrastructure to use anywhere, and
indeed it's not clear how it could ever be used for something like
postgres_fdw which has to send an SQL query and wait for a reply,
but there might be FDWs or custom scan providers that are CPU-bound,
so let's give them a way to join club parallel.
KaiGai Kohei, reviewed by me.
Aggregate nodes now have two new modes: a "partial" mode where they
output the unfinalized transition state, and a "finalize" mode where
they accept unfinalized transition states rather than individual
values as input.
These new modes are not used anywhere yet, but they will be necessary
for parallel aggregation. The infrastructure also figures to be
useful for cases where we want to aggregate local data and remote
data via the FDW interface, and want to bring back partial aggregates
from the remote side that can then be combined with locally generated
partial aggregates to produce the final value. It may also be useful
even when neither FDWs nor parallelism are in play, as explained in
the comments in nodeAgg.c.
David Rowley and Simon Riggs, reviewed by KaiGai Kohei, Heikki
Linnakangas, Haribabu Kommi, and me.
The original parallel sequential scan commit included only very limited
changes to the EXPLAIN output. Aggregated totals from all workers were
displayed, but there was no way to see what each individual worker did
or to distinguish the effort made by the workers from the effort made by
the leader.
Per a gripe by Thom Brown (and maybe others). Patch by me, reviewed
by Amit Kapila.
In addition, this path fills in a number of missing bits and pieces in
the parallel infrastructure. Paths and plans now have a parallel_aware
flag indicating whether whatever parallel-aware logic they have should
be engaged. It is believed that we will need this flag for a number of
path/plan types, not just sequential scans, which is why the flag is
generic rather than part of the SeqScan structures specifically.
Also, execParallel.c now gives parallel nodes a chance to initialize
their PlanState nodes from the DSM during parallel worker startup.
Amit Kapila, with a fair amount of adjustment by me. Review of previous
patch versions by Haribabu Kommi and others.
Commit 4a4e6893aa, which introduced this
mechanism, failed to account for the fact that the RECORD pseudo-type
uses transient typmods that are only meaningful within a single
backend. Transferring such tuples without modification between two
cooperating backends does not work. This commit installs a system
for passing the tuple descriptors over the same shm_mq being used to
send the tuples themselves. The two sides might not assign the same
transient typmod to any given tuple descriptor, so we must also
substitute the appropriate receiver-side typmod for the one used by
the sender. That adds some CPU overhead, but still seems better than
being unable to pass records between cooperating parallel processes.
Along the way, move the logic for handling multiple tuple queues from
tqueue.c to nodeGather.c; tqueue.c now provides a TupleQueueReader,
which reads from a single queue, rather than a TupleQueueFunnel, which
potentially reads from multiple queues. This change was suggested
previously as a way to make sure that nodeGather.c rather than tqueue.c
had policy control over the order in which to read from queues, but
it wasn't clear to me until now how good an idea it was. typmod
mapping needs to be performed separately for each queue, and it is
much simpler if the tqueue.c code handles that and leaves multiplexing
multiple queues to higher layers of the stack.
The original Gather code failed to mark a Gather node as not able to
do projection, but it couldn't, even though it did call initialize its
projection info via ExecAssignProjectionInfo. There doesn't seem to
be any good reason for this node not to have projection capability,
so clean things up so that it does. Without this, plans using Gather
nodes might need to carry extra Result nodes to do projection.
In the previous coding, before returning from ExecutorRun, we'd shut
down all parallel workers. This was dead wrong if ExecutorRun was
called with a non-zero tuple count; it had the effect of truncating
the query output. To fix, give ExecutePlan control over whether to
enter parallel mode, and have it refuse to do so if the tuple count
is non-zero. Rewrite the Gather logic so that it can cope with being
called outside parallel mode.
Commit 7aea8e4f2d is largely to blame
for this problem, though this patch modifies some subsequently-committed
code which relied on the guarantees it purported to make.
This fixes a long-standing bug which was discovered while investigating
the interaction between the new join pushdown code and the EvalPlanQual
machinery: if a ForeignScan appears on the inner side of a paramaterized
nestloop, an EPQ recheck would re-return the original tuple even if
it no longer satisfied the pushed-down quals due to changed parameter
values.
This fix adds a new member to ForeignScan and ForeignScanState and a
new argument to make_foreignscan, and requires changes to FDWs which
push down quals to populate that new argument with a list of quals they
have chosen to push down. Therefore, I'm only back-patching to 9.5,
even though the bug is not new in 9.5.
Etsuro Fujita, reviewed by me and by Kyotaro Horiguchi.
A Gather executor node runs any number of copies of a plan in an equal
number of workers and merges all of the results into a single tuple
stream. It can also run the plan itself, if the workers are
unavailable or haven't started up yet. It is intended to work with
the Partial Seq Scan node which will be added in future commits.
It could also be used to implement parallel query of a different sort
by itself, without help from Partial Seq Scan, if the single_copy mode
is used. In that mode, a worker executes the plan, and the parallel
leader does not, merely collecting the worker's results. So, a Gather
node could be inserted into a plan to split the execution of that plan
across two processes. Nested Gather nodes aren't currently supported,
but we might want to add support for that in the future.
There's nothing in the planner to actually generate Gather nodes yet,
so it's not quite time to break out the champagne. But we're getting
close.
Amit Kapila. Some designs suggestions were provided by me, and I also
reviewed the patch. Single-copy mode, documentation, and other minor
changes also by me.
Per discussion, nowadays it is possible to have tablespaces that have
wildly different I/O characteristics from others. Setting different
effective_io_concurrency parameters for those has been measured to
improve performance.
Author: Julien Rouhaud
Reviewed by: Andres Freund
If there are two different aggregates in the query with same inputs, and
the aggregates have the same initial condition and transition function,
only calculate the state value once, and only call the final functions
separately. For example, AVG(x) and SUM(x) aggregates have the same
transition function, which accumulates the sum and number of input tuples.
For a query like "SELECT AVG(x), SUM(x) FROM x", we can therefore
accumulate the state function only once, which gives a nice speedup.
David Rowley, reviewed and edited by me.
The original implementation of TABLESAMPLE modeled the tablesample method
API on index access methods, which wasn't a good choice because, without
specialized DDL commands, there's no way to build an extension that can
implement a TSM. (Raw inserts into system catalogs are not an acceptable
thing to do, because we can't undo them during DROP EXTENSION, nor will
pg_upgrade behave sanely.) Instead adopt an API more like procedural
language handlers or foreign data wrappers, wherein the only SQL-level
support object needed is a single handler function identified by having
a special return type. This lets us get rid of the supporting catalog
altogether, so that no custom DDL support is needed for the feature.
Adjust the API so that it can support non-constant tablesample arguments
(the original coding assumed we could evaluate the argument expressions at
ExecInitSampleScan time, which is undesirable even if it weren't outright
unsafe), and discourage sampling methods from looking at invisible tuples.
Make sure that the BERNOULLI and SYSTEM methods are genuinely repeatable
within and across queries, as required by the SQL standard, and deal more
honestly with methods that can't support that requirement.
Make a full code-review pass over the tablesample additions, and fix
assorted bugs, omissions, infelicities, and cosmetic issues (such as
failure to put the added code stanzas in a consistent ordering).
Improve EXPLAIN's output of tablesample plans, too.
Back-patch to 9.5 so that we don't have to support the original API
in production.
Allow CustomPath to have a list of paths, CustomPlan a list of plans,
and CustomPlanState a list of planstates known to the core system, so
that custom path/plan providers can more reasonably use this
infrastructure for nodes with multiple children.
KaiGai Kohei, per a design suggestion from Tom Lane, with some
further kibitzing by me.
This SQL standard functionality allows to aggregate data by different
GROUP BY clauses at once. Each grouping set returns rows with columns
grouped by in other sets set to NULL.
This could previously be achieved by doing each grouping as a separate
query, conjoined by UNION ALLs. Besides being considerably more concise,
grouping sets will in many cases be faster, requiring only one scan over
the underlying data.
The current implementation of grouping sets only supports using sorting
for input. Individual sets that share a sort order are computed in one
pass. If there are sets that don't share a sort order, additional sort &
aggregation steps are performed. These additional passes are sourced by
the previous sort step; thus avoiding repeated scans of the source data.
The code is structured in a way that adding support for purely using
hash aggregation or a mix of hashing and sorting is possible. Sorting
was chosen to be supported first, as it is the most generic method of
implementation.
Instead of, as in an earlier versions of the patch, representing the
chain of sort and aggregation steps as full blown planner and executor
nodes, all but the first sort are performed inside the aggregation node
itself. This avoids the need to do some unusual gymnastics to handle
having to return aggregated and non-aggregated tuples from underlying
nodes, as well as having to shut down underlying nodes early to limit
memory usage. The optimizer still builds Sort/Agg node to describe each
phase, but they're not part of the plan tree, but instead additional
data for the aggregation node. They're a convenient and preexisting way
to describe aggregation and sorting. The first (and possibly only) sort
step is still performed as a separate execution step. That retains
similarity with existing group by plans, makes rescans fairly simple,
avoids very deep plans (leading to slow explains) and easily allows to
avoid the sorting step if the underlying data is sorted by other means.
A somewhat ugly side of this patch is having to deal with a grammar
ambiguity between the new CUBE keyword and the cube extension/functions
named cube (and rollup). To avoid breaking existing deployments of the
cube extension it has not been renamed, neither has cube been made a
reserved keyword. Instead precedence hacking is used to make GROUP BY
cube(..) refer to the CUBE grouping sets feature, and not the function
cube(). To actually group by a function cube(), unlikely as that might
be, the function name has to be quoted.
Needs a catversion bump because stored rules may change.
Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund
Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas
Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule
Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
Add a TABLESAMPLE clause to SELECT statements that allows
user to specify random BERNOULLI sampling or block level
SYSTEM sampling. Implementation allows for extensible
sampling functions to be written, using a standard API.
Basic version follows SQLStandard exactly. Usable
concrete use cases for the sampling API follow in later
commits.
Petr Jelinek
Reviewed by Michael Paquier and Simon Riggs
The distance function can now set *recheck = false, like index quals. The
executor will then re-check the ORDER BY expressions, and use a queue to
reorder the results on the fly.
This makes it possible to do kNN-searches on polygons and circles, which
don't store the exact value in the index, but just a bounding box.
Alexander Korotkov and me
Previously, FDWs could only do "early row locking", that is lock a row as
soon as it's fetched, even though local restriction/join conditions might
discard the row later. This patch adds callbacks that allow FDWs to do
late locking in the same way that it's done for regular tables.
To make use of this feature, an FDW must support the "ctid" column as a
unique row identifier. Currently, since ctid has to be of type TID,
the feature is of limited use, though in principle it could be used by
postgres_fdw. We may eventually allow FDWs to specify another data type
for ctid, which would make it possible for more FDWs to use this feature.
This commit does not modify postgres_fdw to use late locking. We've
tested some prototype code for that, but it's not in committable shape,
and besides it's quite unclear whether it actually makes sense to do late
locking against a remote server. The extra round trips required are likely
to outweigh any benefit from improved concurrency.
Etsuro Fujita, reviewed by Ashutosh Bapat, and hacked up a lot by me
The newly added ON CONFLICT clause allows to specify an alternative to
raising a unique or exclusion constraint violation error when inserting.
ON CONFLICT refers to constraints that can either be specified using a
inference clause (by specifying the columns of a unique constraint) or
by naming a unique or exclusion constraint. DO NOTHING avoids the
constraint violation, without touching the pre-existing row. DO UPDATE
SET ... [WHERE ...] updates the pre-existing tuple, and has access to
both the tuple proposed for insertion and the existing tuple; the
optional WHERE clause can be used to prevent an update from being
executed. The UPDATE SET and WHERE clauses have access to the tuple
proposed for insertion using the "magic" EXCLUDED alias, and to the
pre-existing tuple using the table name or its alias.
This feature is often referred to as upsert.
This is implemented using a new infrastructure called "speculative
insertion". It is an optimistic variant of regular insertion that first
does a pre-check for existing tuples and then attempts an insert. If a
violating tuple was inserted concurrently, the speculatively inserted
tuple is deleted and a new attempt is made. If the pre-check finds a
matching tuple the alternative DO NOTHING or DO UPDATE action is taken.
If the insertion succeeds without detecting a conflict, the tuple is
deemed inserted.
To handle the possible ambiguity between the excluded alias and a table
named excluded, and for convenience with long relation names, INSERT
INTO now can alias its target table.
Bumps catversion as stored rules change.
Author: Peter Geoghegan, with significant contributions from Heikki
Linnakangas and Andres Freund. Testing infrastructure by Jeff Janes.
Reviewed-By: Heikki Linnakangas, Andres Freund, Robert Haas, Simon Riggs,
Dean Rasheed, Stephen Frost and many others.
The RLS capability is built on top of the WITH CHECK OPTION
system which was added for auto-updatable views, however, unlike
WCOs on views (which are mandated by the SQL spec to not fire until
after all other constraints and checks are done), it makes much more
sense for RLS checks to happen earlier than constraint and uniqueness
checks.
This patch reworks the structure which holds the WCOs a bit to be
explicitly either VIEW or RLS checks and the RLS-related checks are
done prior to the constraint and uniqueness checks. This also allows
better error reporting as we are now reporting when a violation is due
to a WITH CHECK OPTION and when it's due to an RLS policy violation,
which was independently noted by Craig Ringer as being confusing.
The documentation is also updated to include a paragraph about when RLS
WITH CHECK handling is performed, as there have been a number of
questions regarding that and the documentation was previously silent on
the matter.
Author: Dean Rasheed, with some kabitzing and comment changes by me.
Robert Haas