In preparation of sorting and grouping all includes we wanted to move
this file to the toplevel includes for good grouping/sorting.
(cherry picked from commit 0dac63afc0)
PG16 compatibility - Part 6
Check out part 1 42d956888d
part 2 0d503dd5ac
part 3 907d72e60d
part 4 7c6b4ce103
part 5 6056cb2c29
This commit is in the series of PG16 compatibility commits.
It handles the Permission Info changes in PG16. See below:
The main issue lies in the following entries of PlannedStmt: {
rtable
permInfos
}
Each rtable has an int perminfoindex, and its actual permission info is
obtained through the following:
permInfos[perminfoindex]
We had crashes because perminfoindexes were not updated in the finalized
planned statement after distributed planner hook.
So, basically, everywhere we set a query's or planned statement's rtable
entry, we need to set the rteperminfos/permInfos accordingly.
Relevant PG commits:
a61b1f7482
a61b1f74823c9c4f79c95226a461f1e7a367764b
b803b7d132
b803b7d132e3505ab77c29acf91f3d1caa298f95
More PG16 compatibility commits are coming soon ...
PG16 compatibility - Part 5
Check out part 1 42d956888d
part 2 0d503dd5ac
part 3 907d72e60d
part 4 7c6b4ce103
This commit is in the series of PG16 compatibility commits. Find the explanation below:
If we allow to adjust partitioning, we get a crash when accessing
amcostestimate of partitioned indexes, because amcostestimate is NULL
for them. The following PG commit is the culprit:
3c569049b7
3c569049b7b502bb4952483d19ce622ff0af5fd6
Previously, partitioned indexes would just be ignored.
Now, they are added in the list. However get_relation_info expects the
tables which have partitioned indexes to have the inh flag set properly.
AdjustPartitioningForDistributedPlanning plays with that flag, hence we
don't get the desired behaviour.
The hook is simply removing all partitioned indexes from the list.
More PG16 compatibility commits are coming soon ...
1) For distributed tables that are not colocated.
2) When joining on a non-distribution column for colocated tables.
3) When merging into a distributed table using reference or citus-local tables as the data source.
This is accomplished primarily through the implementation of the following two strategies.
Repartition: Plan the source query independently,
execute the results into intermediate files, and repartition the files to
co-locate them with the merge-target table. Subsequently, compile a final
merge query on the target table using the intermediate results as the data
source.
Pull-to-coordinator: Execute the plan that requires evaluation at the coordinator,
run the query on the coordinator, and redistribute the resulting rows to ensure
colocation with the target shards. Direct the MERGE SQL operation to the worker
nodes' target shards, using the intermediate files colocated with the data as the
data source.
Enable router planner and a limited version of INSERT .. SELECT planner
for the queries that reference colocated null shard key tables.
* SELECT / UPDATE / DELETE / MERGE is supported as long as it's a router
query.
* INSERT .. SELECT is supported as long as it only references colocated
null shard key tables.
Note that this is not only limited to distributed INSERT .. SELECT but
also
covers a limited set of query types that require pull-to-coordinator,
e.g.,
due to LIMIT clause, generate_series() etc. ...
(Ideally distributed INSERT .. SELECT could handle such queries too,
e.g.,
when we're only referencing tables that don't have a shard key, but
today
this is not the case. See
https://github.com/citusdata/citus/pull/6773#discussion_r1140130562.
DESCRIPTION: Adds views that monitor statistics on tenant usages
This PR adds `citus_stats_tenants` view that monitors the tenants on the
cluster.
`citus_stats_tenants` shows the node id, colocation id, tenant
attribute, read count in this period and last period, and query count in
this period and last period of the tenant.
Tenant attribute currently is the tenant's distribution column value,
later when schema based sharding is introduced, this meaning might
change.
A period is a time bucket the queries are counted by. Read and query
counts for this period can increase until the current period ends. After
that those counts are moved to last period's counts, which cannot
change. The period length can be set using 'citus.stats_tenants_period'.
`SELECT` queries are counted as _read_ queries, `INSERT`, `UPDATE` and
`DELETE` queries are counted as _write_ queries. So in the view read
counts are `SELECT` counts and query counts are `SELECT`, `INSERT`,
`UPDATE` and `DELETE` count.
The data is stored in shared memory, in a struct named
`MultiTenantMonitor`.
`citus_stats_tenants` shows the data from local tenants.
`citus_stats_tenants` show up to `citus.stats_tenant_limit` number of
tenants.
The tenants are scored based on the number of queries they run and the
recency of those queries. Every query ran increases the score of tenant
by `ONE_QUERY_SCORE`, and after every period ends the scores are halved.
Halving is done lazily.
To retain information a longer the monitor keeps up to 3 times
`citus.stats_tenant_limit` tenants. When the tenant count hits `3 *
citus.stats_tenant_limit`, last `citus.stats_tenant_limit` tenants are
removed. To see all stored tenants you can use
`citus_stats_tenants(return_all_tenants := true)`
- [x] Create collector view that gets data from all nodes. #6761
- [x] Add monitoring log #6762
- [x] Create enable/disable GUC #6769
- [x] Parse the annotation string correctly #6796
- [x] Add local queries and prepared statements #6797
- [x] Rename to citus_stat_statements #6821
- [x] Run pgbench
- [x] Fix role permissions #6812
---------
Co-authored-by: Gokhan Gulbiz <ggulbiz@gmail.com>
Co-authored-by: Jelte Fennema <github-tech@jeltef.nl>
Fixes#6672
2) Move all MERGE related routines to a new file merge_planner.c
3) Make ConjunctionContainsColumnFilter() static again, and rearrange the code in MergeQuerySupported()
4) Restore the original format in the comments section.
5) Add big serial test. Implement latest set of comments
This implements the phase - II of MERGE sql support
Support routable query where all the tables in the merge-sql are distributed, co-located, and both the source and
target relations are joined on the distribution column with a constant qual. This should be a Citus single-task
query. Below is an example.
SELECT create_distributed_table('t1', 'id');
SELECT create_distributed_table('s1', 'id', colocate_with => ‘t1’);
MERGE INTO t1
USING s1 ON t1.id = s1.id AND t1.id = 100
WHEN MATCHED THEN
UPDATE SET val = s1.val + 10
WHEN MATCHED THEN
DELETE
WHEN NOT MATCHED THEN
INSERT (id, val, src) VALUES (s1.id, s1.val, s1.src)
Basically, MERGE checks to see if
There are a minimum of two distributed tables (source and a target).
All the distributed tables are indeed colocated.
MERGE relations are joined on the distribution column
MERGE .. USING .. ON target.dist_key = source.dist_key
The query should touch only a single shard i.e. JOIN AND with a constant qual
MERGE .. USING .. ON target.dist_key = source.dist_key AND target.dist_key = <>
If any of the conditions are not met, it raises an exception.
(cherry picked from commit 44c387b978)
This implements MERGE phase3
Support pushdown query where all the tables in the merge-sql are Citus-distributed, co-located, and both
the source and target relations are joined on the distribution column. This will generate multiple tasks
which execute independently after pushdown.
SELECT create_distributed_table('t1', 'id');
SELECT create_distributed_table('s1', 'id', colocate_with => ‘t1’);
MERGE INTO t1
USING s1
ON t1.id = s1.id
WHEN MATCHED THEN
UPDATE SET val = s1.val + 10
WHEN MATCHED THEN
DELETE
WHEN NOT MATCHED THEN
INSERT (id, val, src) VALUES (s1.id, s1.val, s1.src)
*The only exception for both the phases II and III is, UPDATEs and INSERTs must be done on the same shard-group
as the joined key; for example, below scenarios are NOT supported as the key-value to be inserted/updated is not
guaranteed to be on the same node as the id distribution-column.
MERGE INTO target t
USING source s ON (t.customer_id = s.customer_id)
WHEN NOT MATCHED THEN - -
INSERT(customer_id, …) VALUES (<non-local-constant-key-value>, ……);
OR this scenario where we update the distribution column itself
MERGE INTO target t
USING source s On (t.customer_id = s.customer_id)
WHEN MATCHED THEN
UPDATE SET customer_id = 100;
(cherry picked from commit fa7b8949a8)
Recursive planner should handle all the tree from bottom to top at
single pass. i.e. It should have already recursively planned all
required parts in its first pass. Otherwise, this means we have bug at
recursive planner, which needs to be handled. We add a check here and
return error.
DESCRIPTION: Fixes wrong results by throwing error in case recursive
planner multipass the query.
We found 3 different cases which causes recursive planner passes the
query multiple times.
1. Sublink in WHERE clause is planned at second pass after we
recursively planned a distributed table at the first pass. Fixed by PR
#6657.
2. Local-distributed joins are recursively planned at both the first and
the second pass. Issue #6659.
3. Some parts of the query is considered to be noncolocated at the
second pass as we do not generate attribute equivalances between
nondistributed and distributed tables. Issue #6653
This implements the phase - II of MERGE sql support
Support routable query where all the tables in the merge-sql are distributed, co-located, and both the source and
target relations are joined on the distribution column with a constant qual. This should be a Citus single-task
query. Below is an example.
SELECT create_distributed_table('t1', 'id');
SELECT create_distributed_table('s1', 'id', colocate_with => ‘t1’);
MERGE INTO t1
USING s1 ON t1.id = s1.id AND t1.id = 100
WHEN MATCHED THEN
UPDATE SET val = s1.val + 10
WHEN MATCHED THEN
DELETE
WHEN NOT MATCHED THEN
INSERT (id, val, src) VALUES (s1.id, s1.val, s1.src)
Basically, MERGE checks to see if
There are a minimum of two distributed tables (source and a target).
All the distributed tables are indeed colocated.
MERGE relations are joined on the distribution column
MERGE .. USING .. ON target.dist_key = source.dist_key
The query should touch only a single shard i.e. JOIN AND with a constant qual
MERGE .. USING .. ON target.dist_key = source.dist_key AND target.dist_key = <>
If any of the conditions are not met, it raises an exception.
All the tables (target, source or any CTE present) in the SQL statement are local i.e. a merge-sql with a combination of Citus local and
Non-Citus tables (regular Postgres tables) should work and give the same result as Postgres MERGE on regular tables. Catch and throw an
exception (not-yet-supported) for all other scenarios during Citus-planning phase.
Comment from the code is clear on this:
/*
* The statistics objects of the distributed table are not relevant
* for the distributed planning, so we can override it.
*
* Normally, we should not need this. However, the combination of
* Postgres commit 269b532aef55a579ae02a3e8e8df14101570dfd9 and
* Citus function AdjustPartitioningForDistributedPlanning()
* forces us to do this. The commit expects statistics objects
* of partitions to have "inh" flag set properly. Whereas, the
* function overrides "inh" flag. To avoid Postgres to throw error,
* we override statlist such that Postgres does not try to process
* any statistics objects during the standard_planner() on the
* coordinator. In the end, we do not need the standard_planner()
* on the coordinator to generate an optimized plan. We call
* into standard_planner() for other purposes, such as generating the
* relationRestrictionContext here.
*
* AdjustPartitioningForDistributedPlanning() is a hack that we use
* to prevent Postgres' standard_planner() to expand all the partitions
* for the distributed planning when a distributed partitioned table
* is queried. It is required for both correctness and performance
* reasons. Although we can eliminate the use of the function for
* the correctness (e.g., make sure that rest of the planner can handle
* partitions), it's performance implication is hard to avoid. Certain
* planning logic of Citus (such as router or query pushdown) relies
* heavily on the relationRestrictionList. If
* AdjustPartitioningForDistributedPlanning() is removed, all the
* partitions show up in the, causing high planning times for
* such queries.
*/
* Fix issue : 6109 Segfault or (assertion failure) is possible when using a SQL function
* DESCRIPTION: Ensures disallowing the usage of SQL functions referencing to a distributed table and prevents a segfault.
Using a SQL function may result in segmentation fault in some cases.
This change fixes the issue by throwing an error message when a SQL function cannot be handled.
Fixes#6109.
* DESCRIPTION: Ensures disallowing the usage of SQL functions referencing to a distributed table and prevents a segfault.
Using a SQL function may result in segmentation fault in some cases. This change fixes the issue by throwing an error message when a SQL function cannot be handled.
Fixes#6109.
Co-authored-by: Emel Simsek <emel.simsek@microsoft.com>
Before, this was the default mode for CustomScan providers.
Now, the default is to assume that they can't project.
This causes performance penalties due to adding unnecessary
Result nodes.
Hence we use the newly added flag, CUSTOMPATH_SUPPORT_PROJECTION
to get it back to how it was.
In PG15 support branch we created explain functions to ignore
the new Result nodes, so we undo that in this commit.
Relevant PG commit:
955b3e0f9269639fb916cee3dea37aee50b82df0
use RecurseObjectDependencies api to find if an object is citus depended
make vanilla tests runnable to see if citus_depended function is working correctly
* Remove if conditions with PG_VERSION_NUM < 13
* Remove server_above_twelve(&eleven) checks from tests
* Fix tests
* Remove pg12 and pg11 alternative test output files
* Remove pg12 specific normalization rules
* Some more if conditions in the code
* Change RemoteCollationIdExpression and some pg12/pg13 comments
* Remove some more normalization rules
With Citus 9.0, we introduced `citus.single_shard_commit_protocol` which
defaults to 2PC.
With this commit, we prevent any user to set it to 1PC and drop support
for `citus.single_shard_commit_protocol`.
Although this might add some overhead for users, it is already the default
behaviour (so less likely) and marking placements as INVALID is much
worse.
With this commit, we make sure to prevent infinite recursion for queries
in the format: [subquery with a UNION ALL] JOIN [table or subquery]
Also, fixes a bug where we pushdown UNION ALL below a JOIN even if the
UNION ALL is not safe to pushdown.
* Use translated vars in postgres 13 as well
Postgres 13 removed translated vars with pg 13 so we had a special logic
for pg 13. However it had some bug, so now we copy the translated vars
before postgres deletes it. This also simplifies the logic.
* fix rtoffset with pg >= 13
It seems that we were not considering the case where coordinator was
added to the cluster as a worker in the optimization of intermediate
results.
This could lead to errors when coordinator was added as a worker.
Baseinfo also has pushed down filters etc, so it makes more sense to use
BaseRestrictInfo to determine what columns have constant equality
filters.
Also RteIdentity is used for removing conversion candidates instead of
rteIndex.
AllDataLocallyAccessible and ContainsLocalTableSubqueryJoin are removed.
We can possibly remove ModifiesLocalTableWithRemoteCitusLocalTable as
well. Though this removal has a side effect that now when all the data
is locally available, we could still wrap a relation into a subquery, I
guess that should be resolved in the router planner itself.
Add more tests
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.
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.
Disallow `ON TRUE` outer joins with reference & distributed tables
when reference table is outer relation by fixing the logic bug made
when calling `LeftListIsSubset` function.
Also, be more defensive when removing duplicate join restrictions
when join clause is empty for non-inner joins as they might still
contain useful information for non-inner joins.
Maksim Melnikov
Jelte Fennema-Nio
Nils Dijk
Naisila Puka
Teja Mupparti
Onur Tirtir
Halil Ozan Akgül
Marco Slot
aykut-bozkurt
Önder Kalacı
Philip Dubé
Emel Şimşek
Burak Velioglu
Jeff Davis
Marco Slot
Ahmet Gedemenli
Önder Kalacı
SaitTalhaNisanci