While going over this piece of code (a long time ago) it was bothering
to me we keep a bool array with the size of shardcount to iterate only
over shards present in the list of non-pruned shards. Especially since
we keep min/max of the set shards to optimize iteration.
Postgres has the bitmapset datastructure which a) takes significantly
less space, b) has iterator functions to only iterate over set bits, c)
can efficiently skip long sequences of unset bits and d) stops quickly
once the last set bit has been reached.
I have been contemplating if it is worth to keep the minShardOffset
because of readability and the efficient skipping of unset bits,
however, I have decided to keep it -although less readable-, as there
are known usecases where 100k+ shards are pruned to single digit shards.
If these would end up at the end of `shardcount` a hotloop of zero
checks on the first iteration _could_ cause a theoretical performance
regression.
All in all, this code is using less memory in all cases where it
matters, and less cpu in most cases, while using more idiomatic
datastructures for the task at hand.
PG16beta1 added some sanity checks for GUCS, find the Relevant PG
commits below:
1- Add check on initial and boot values when loading GUCs
a73952b795
2- Extend check_GUC_init() with checks on flag combinations when loading
GUCs
009f8d1714
I fixed our currently problematic GUCS, we can merge this directly into
main as these make sense for any PG version.
There was a particular NodeConninfo issue:
Previously we would rely on the fact that NodeConninfo initial value
is an empty string. However, with PG16 enforcing same initial and boot
values, we can't use an empty initial value for NodeConninfo anymore.
Therefore we add a new flag to indicate whether we are at boot check.
With this PR, we allow creating distributed tables with without
specifying a shard key via create_distributed_table(). Here are the
the important details about those tables:
* Specifying `shard_count` is not allowed because it is assumed to be 1.
* We mostly call such tables as "null shard-key" table in code /
comments.
* To avoid doing a breaking layout change in create_distributed_table();
instead of throwing an error, it will inform the user that
`distribution_type`
param is ignored unless it's explicitly set to NULL or 'h'.
* `colocate_with` param allows colocating such null shard-key tables to
each other.
* We define this table type, i.e., NULL_SHARD_KEY_TABLE, as a subclass
of
DISTRIBUTED_TABLE because we mostly want to treat them as distributed
tables in terms of SQL / DDL / operation support.
* Metadata for such tables look like:
- distribution method => DISTRIBUTE_BY_NONE
- replication model => REPLICATION_MODEL_STREAMING
- colocation id => **!=** INVALID_COLOCATION_ID (distinguishes from
Citus local tables)
* We assign colocation groups for such tables to different nodes in a
round-robin fashion based on the modulo of "colocation id".
Note that this PR doesn't care about DDL (except CREATE TABLE) / SQL /
operation (i.e., Citus UDFs) support for such tables but adds a
preliminary
API.
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)
Now that we will soon add another table type having DISTRIBUTE_BY_NONE
as distribution method and that we want the code to interpret such
tables mostly as distributed tables, let's make the definition of those
other two table types more strict by removing
CITUS_TABLE_WITH_NO_DIST_KEY
macro.
And instead, use HasDistributionKey() check in the places where the
logic applies to all table types that have / don't have a distribution
key. In future PRs, we might want to convert some of those
HasDistributionKey() checks if logic only applies to Citus local /
reference tables, not the others.
And adding HasDistributionKey() also allows us to consider having
DISTRIBUTE_BY_NONE as the distribution method as a "table attribute"
that can apply to distributed tables too, rather something that
determines the table type.
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.
Removes unused job boundary tag `SUBQUERY_MAP_MERGE_JOB`.
Only usage is at `BuildMapMergeJob`, which is only called when the
boundary = `JOIN_MAP_MERGE_JOB`. Hence, it should be safe to remove.
* 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
We've had custom versions of Postgres its `foreach` macro which with a
hidden ListCell for quite some time now. People like these custom
macros, because they are easier to use and require less boilerplate.
This adds similar custom versions of Postgres its `forboth` macro. Now
you don't need ListCells anymore when looping over two lists at the same
time.
We re-define the meaning of active shard placement. It used
to only be defined via shardstate == SHARD_STATE_ACTIVE.
Now, we also add one more check. The worker node that the
placement is on should be active as well.
This is a preparation for supporting citus_disable_node()
for MX with multiple failures at the same time.
With this change, the maintanince daemon only needs to
sync the "node metadata" (e.g., pg_dist_node), not the
shard metadata.
Before this commit, we always synced the metadata with superuser.
However, that creates various edge cases such as visibility errors
or self distributed deadlocks or complicates user access checks.
Instead, with this commit, we use the current user to sync the metadata.
Note that, `start_metadata_sync_to_node` still requires super user
because accessing certain metadata (like pg_dist_node) always require
superuser (e.g., the current user should be a superuser).
However, metadata syncing operations regarding the distributed
tables can now be done with regular users, as long as the user
is the owner of the table. A table owner can still insert non-sense
metadata, however it'd only affect its own table. So, we cannot do
anything about that.
Ignore orphaned shards in more places
Only use active shard placements in RouterInsertTaskList
Use IncludingOrphanedPlacements in some more places
Fix comment
Add tests
/*
* The physical planner assumes that all worker queries would have
* target list entries based on the fact that at least the column
* on the JOINs have to be on the target list. However, there is
* an exception to that if there is a cartesian product join and
* there is no additional target list entries belong to one side
* of the JOIN. Once we support cartesian product join, we should
* remove this error.
*/
* Fix partition column index issue
We send column names to worker_hash/range_partition_table methods, and
in these methods we check the column name index from tuple descriptor.
Then this index is used to decide the bucket that the current row will
be sent for the repartition.
This becomes a problem when there are the same column names in the
tupleDescriptor. Then we can choose the wrong index. Hence the
partitioned data will be put to wrong workers. Then the result could
miss some data because workers might contain different range of data.
An example:
TupleDescriptor contains "trip_id", "car_id", "car_id" for one table.
It contains only "car_id" for the other table. And assuming that the
tables will be partitioned by car_id, it is not certain what should be
used for deciding the bucket number for the first table. Assuming value
2 goes to bucket 2 and value 3 goes to bucket 3, it is not certain which
bucket "1 2 3" (trip_id, car_id, car_id) row will go to.
As a solution we send the index of partition column in targetList
instead of the column name.
The old API is kept so that if workers upgrade work, it still works
(though it will have the same bug)
* Use the same method so that backporting is easier
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.
We should not recursively plan an already routable plannable query. An
example of this is (SELECT * FROM local JOIN (SELECT * FROM dist) d1
USING(a));
So we let the recursive planner do all of its work and at the end we
convert the final query to to handle unsupported joins. While doing each
conversion, we check if it is router plannable, if so we stop.
Only consider range table entries that are in jointree
If a range table is not in jointree then there is no point in
considering that because we are trying to convert range table entries to
subqueries for join use case.
Check equality in quals
We want to recursively plan distributed tables only if they have an
equality filter on a unique column. So '>' and '<' operators will not
trigger recursive planning of distributed tables in local-distributed
table joins.
Recursively plan distributed table only if the filter is constant
If the filter is not a constant then the join might return multiple rows
and there is a chance that the distributed table will return huge data.
Hence if the filter is not constant we choose to recursively plan the
local table.
* Fix incorrect join related fields
Ruleutils expect to give the original index of join columns hence we
should consider the dropped columns while setting the fields in
SetJoinRelatedFieldsCompat.
* add some more tests for joins
* Move tests to join.sql and create a utility function
Onur Tirtir
Nils Dijk
Naisila Puka
Teja Mupparti
Marco Slot
aykut-bozkurt
Jeff Davis
Marco Slot
Jelte Fennema
Onder Kalaci
Sait Talha Nisanci
Ahmet Gedemenli
Önder Kalacı
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