When we use PROCESS_UTILITY_TOPLEVEL it causes some problems when
combined with other extensions such as pg_audit. With this commit we use
PROCESS_UTILITY_QUERY in the codebase to fix those problems.
(cherry picked from commit dcf54eaf2a)
Conflicts:
src/backend/distributed/commands/alter_table.c
src/backend/distributed/commands/cascade_table_operation_for_connected_relations.c
src/backend/distributed/executor/local_executor.c
src/backend/distributed/utils/role.c
src/backend/distributed/worker/worker_create_or_replace.c
src/backend/distributed/worker/worker_data_fetch_protocol.c
/*
* Colocated intermediate results are just files and not required to use
* the same connections with their co-located shards. So, we are free to
* use any connection we can get.
*
* Also, the current connection re-use logic does not know how to handle
* intermediate results as the intermediate results always truncates the
* existing files. That's why, we use one connection per intermediate
* result.
*/
(cherry picked from commit 5d5a357487)
It looks like we forgot to set the placement accesses, and
this could lead to self-deadlocks on complex transaction blocks.
(cherry picked from commit 36bdeef1bb)
As described in the comment, we have observed crashes in production
due to a segfault caused by the dereference of a NULL pointer in our
connection statemachine.
As a mitigation, preventing system crashes, we provide an error with
a small explanation of the issue. Unfortunately the case is not
reliably reproduced yet, hence the inability to add tests.
DESCRIPTION: Prevent segfaults when SAVEPOINT handling cannot recover from connection failures
(cherry picked from commit d127516dc8)
Logical replication status can take wal_receiver_status_interval
seconds to get updated. Default is 10s, which means tests in
which logical replication is used can take a long time to finish.
We reduce it to 1 second to speed these tests up.
Logical replication apply launcher launches workers every
wal_retrieve_retry_interval, so if we have many shard moves with
logical replication consecutively, they will be throttled by this
parameter. Default is 5s, we reduce it to 1s so we finish tests
faster.
(cherry picked from commit 0e0fd6599a)
If MemoryContextAlloc errors out -e.g. during an OOM-, ConnectionHashEntry->connections
stays as NULL.
With this commit, we add isValid flag to ConnectionHashEntry that should be set to true
right after we allocate & initialize ConnectionHashEntry->connections list properly, and we
check it before accesing to ConnectionHashEntry->connections.
(cherry picked from commit 7f3d1182ed)
It seems that we forgot to pass the revelant
flag to enable Postgres' parallel query
capabilities on the shards when user does
EXPLAIN ANALYZE on a distributed table.
(cherry picked from commit b0ddbbd33a)
Before this commit, we let AdaptiveExecutorPreExecutorRun()
to be effective multiple times on every FETCH on cursors.
That does not affect the correctness of the query results,
but adds significant overhead.
(cherry picked from commit c433c66f2b)
CitusTableTypeIdList() function iterates on all the entries of pg_dist_partition
and loads all the metadata in to the cache. This can be quite memory intensive
especially when there are lots of distributed tables.
When partitioned tables are used, it is common to have many distributed tables
given that each partition also becomes a distributed table.
CitusTableTypeIdList() is used on every CREATE TABLE .. PARTITION OF.. command
as well. It means that, anytime a partition is created, Citus loads all the
metadata to the cache. Note that Citus typically only loads the accessed table's
metadata to the cache.
(cherry picked from commit 7accbff3f6)
Conflicts:
src/test/regress/bin/normalize.sed
If one wishes to iterate through a List and insert list elements in
PG13, it is not safe to use for_each_ptr as the List representation
in PostgreSQL no longer linked lists, but arrays, and it is possible
that the whole array is repalloc'ed if ther is not sufficient space
available.
See postgres commit 1cff1b95ab6ddae32faa3efe0d95a820dbfdc164 for more
information
When a relation is used on an OUTER JOIN with FALSE filters,
set_rel_pathlist_hook may not be called for the table.
There might be other cases as well, so do not rely on the hook
for classification of the tables.
Aliases that postgres choose for partitioned tables in explain output
might change in different pg versions, so normalize them and remove
the alternative test output
RemoveDuplicateJoinRestrictions() function was introduced with the aim of decrasing the overall planning times by eliminating the duplicate JOIN restriction entries (#1989). However, it turns out that the function itself is so CPU intensive with a very high algorithmic complexity, it hurts a lot more than it helps. The function is a clear example of premature optimization.
The table below shows the difference clearly:
"distributed query planning
time master" RemoveDuplicateJoinRestrictions() execution time on master "Remove the function RemoveDuplicateJoinRestrictions()
this PR"
5 table INNER JOIN 9 msec 2msec 7 msec
10 table INNER JOIN 227 msec 194 msec 29 msec
20 table INNER JOIN 1 sec 235 msec 1 sec 139 msec 90 msecs
50 table INNER JOIN 24 seconds 21 seconds 1.5 seconds
100 table INNER JOIN 2 minutes 16 secods 1 minute 53 seconds 23 seconds
250 table INNER JOIN Bottleneck on JoinClauseList 18 minutes 52 seconds Bottleneck on JoinClauseList
5 table INNER JOIN in subquery 9 msec 0 msec 6 msec
10 table INNER JOIN subquery 33 msec 10 msec 32 msec
20 table INNER JOIN subquery 132 msec 67 msec 123 msec
50 table INNER JOIN subquery 1.2 seconds 900 msec 500 msec
100 table INNER JOIN subquery 6 seconds 5 seconds 2 seconds
250 table INNER JOIN subquery 54 seconds 37 seconds 20 seconds
5 table LEFT JOIN 5 msec 0 msec 5 msec
10 table LEFT JOIN 11 msec 0 msec 13 msec
20 table LEFT JOIN 26 msec 2 msec 30 msec
50 table LEFT JOIN 150 msec 15 msec 193 msec
100 table LEFT JOIN 757 msec 71 msec 722 msec
250 table LEFT JOIN 8 seconds 600 msec 8 seconds
5 JOINs among 2 table JOINs 37 msec 11 msec 25 msec
10 JOINs among 2 table JOINs 536 msec 306 msec 352 msec
20 JOINs among 2 table JOINs 794 msec 181 msec 640 msec
50 JOINs among 2 table JOINs 25 seconds 2 seconds 22 seconds
100 JOINs among 2 table JOINs Bottleneck on JoinClauseList 9 seconds Bottleneck on JoinClauseList
150 JOINs among 2 table JOINs Bottleneck on JoinClauseList 46 seconds Bottleneck on JoinClauseList
On top of the performance penalty, the function had a critical bug #4255, and with #4254 we hit one more important bug. It should be fixed by adding the followig check to the ContextCoversJoinRestriction():
```
static bool
JoinRelIdsSame(JoinRestriction *leftRestriction, JoinRestriction *rightRestriction)
{
Relids leftInnerRelIds = leftRestriction->innerrel->relids;
Relids rightInnerRelIds = rightRestriction->innerrel->relids;
if (!bms_equal(leftInnerRelIds, rightInnerRelIds))
{
return false;
}
Relids leftOuterRelIds = leftRestriction->outerrel->relids;
Relids rightOuterRelIds = rightRestriction->outerrel->relids;
if (!bms_equal(leftOuterRelIds, rightOuterRelIds))
{
return false;
}
return true;
}
```
However, adding this eliminates all the benefits tha RemoveDuplicateJoinRestrictions() brings.
I've used the commands here to generate the JOINs mentioned in the PR: https://gist.github.com/onderkalaci/fe8654f9df5916c7af4c7c5eb892561e#file-gistfile1-txt
Inner and outer JOINs behave roughly the same, to simplify the table only added INNER joins.
Hanefi Onaldi
Onur Tirtir
SaitTalhaNisanci
Ahmet Gedemenli
Onder Kalaci
Nils Dijk
Hadi Moshayedi
Önder Kalacı
Halil Ozan Akgül