Having to maintain two lists of regression test scripts has proven
annoyingly error-prone.  We can achieve the effect of the
serial_schedule by running the parallel_schedule with
"--max_connections=1"; so do that and remove serial_schedule.

This causes cosmetic differences in the progress output, but it
doesn't seem worth restructuring pg_regress to avoid that.

Discussion: https://postgr.es/m/899209.1620759506@sss.pgh.pa.us

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