This commit adds support for long partition names for distributed tables:
- ALTER TABLE dist_table ATTACH PARTITION ..
- CREATE TABLE .. PARTITION OF dist_table ..
Note: create_distributed_table UDF does not support long table and
partition names, and is not covered in this commit
* Introduce 3 partitioned size udfs
* Add tests for new partition size udfs
* Fix type incompatibilities
* Convert UDFs into pure sql functions
* Fix function comment
ConnParams(AuthInfo and PoolInfo) gets a snapshot, which will block the
remote connectinos to localhost. And the release of snapshot will be
blocked by the snapshot. This leads to a deadlock.
We warm up the conn params hash before starting a new transaction so
that the entries will already be there when we start a new transaction.
Hence GetConnParams will not get a snapshot.
With https://github.com/citusdata/citus/pull/4806 we enabled
2PC for any non-read-only local task. However, if the execution
is a single task, enabling 2PC (CoordinatedTransactionShouldUse2PC)
hits an assertion as we are not in a coordinated transaction.
There is no downside of using a coordinated transaction for single
task local queries.
Because setting the flag doesn't necessarily mean that we'll
use 2PC. If connections are read-only, we will not use 2PC.
In other words, we'll use 2PC only for connections that modified
any placements.
Before this commit, Citus used 2PC no matter what kind of
local query execution happens.
For example, if the coordinator has shards (and the workers as well),
even a simple SELECT query could start 2PC:
```SQL
WITH cte_1 AS (SELECT * FROM test LIMIT 10) SELECT count(*) FROM cte_1;
```
In this query, the local execution of the shards (and also intermediate
result reads) triggers the 2PC.
To prevent that, Citus now distinguishes local reads and local writes.
And, Citus switches to 2PC only if a modification happens. This may
still lead to unnecessary 2PCs when there is a local modification
and remote SELECTs only. Though, we handle that separately
via #4587.
Postgres keeps AFTER trigger state for each transaction, because we can have deferred AFTER triggers which will be fired at the end of a transaction. Postgres cleans up this state at the end of transaction.
Postgres processes ON COMMIT triggers after cleaning-up the AFTER trigger states. So if we fire any triggers in ON COMMIT, the AFTER trigger state won't be cleaned-up properly and the transaction state will be left in an inconsistent state, which might result in assertion failure.
So with this commit, we remove foreign keys between columnar metadata tables and enforce constraints between them manually when dropping columnar tables.
* Skip 2PC for readonly connections in a transaction
* Use ConnectionModifiedPlacement() function
* Remove the second check of ConnectionModifiedPlacement()
* Add order by to prevent flaky output
* Test using pg_dist_transaction
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.
* Reimplement citus_update_table_statistics
* Update stats for the given table not colocation group
* Add tests for reimplemented citus_update_table_statistics
* Use coordinated transaction, merge with citus_shard_sizes functions
* Update the old master_update_table_statistics as well
* 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
/*
* 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.
*/
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.
When executing alter_table / undistribute_table udf's, we should not try
to change sequence dependencies on MX workers if new table wouldn't
require syncing metadata.
Previously, we were checking that for input table. But in some cases, the
fact that input table requires syncing metadata doesn't imply the same
for resulting table (e.g when undistributing a Citus table).
Even more, doing that was giving an unexpected error when undistributing
a Citus table so this commit actually fixes that.
It seems that we need to consider only pseudo constants while doing some
shortcuts in planning. For example there could be a false clause but it
can contribute to the result in which case it will not be a pseudo
constant.
We would exclude tables without relationRestriction from conversion
candidates in local-distributed table joins. This could leave a leftover
local table which should have been converted to a subquery.
Ideally I would expect that in each call to CreateDistributedPlan we
would pass a new plan id, but that seems like a bigger change.
/*
* 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.
*/
We do not include dummy column if original task didn't return any
columns.
Otherwise, number of columns that original task returned wouldn't
match number of columns returned by worker_save_query_explain_analyze.
When COPY is used for copying into co-located files, it was
not allowed to use local execution. The primary reason was
Citus treating co-located intermediate results as co-located
shards, and COPY into the distributed table was done via
"format result". And, local execution of such COPY commands
was not implemented.
With this change, we implement support for local execution with
"format result". To do that, we use the buffer for every file
on shardState->copyOutState, similar to how local copy on
shards are implemented. In fact, the logic is similar to
local copy on shards, but instead of writing to the shards,
Citus writes the results to a file.
The logic relies on LOCAL_COPY_FLUSH_THRESHOLD, and flushes
only when the size exceeds the threshold. But, unlike local
copy on shards, in this case we write the headers and footers
just once.
* Sort results in citus_shards and give raw size
Sort results so that it is consistent and also similar to citus_tables.
Use raw size in the output so that doing operations on the size is
easier.
* Change column ordering
With #4338, the executor is smart enough to failover to
local node if there is not enough space in max_connections
for remote connections.
For COPY, the logic is different. With #4034, we made COPY
work with the adaptive connection management slightly
differently. The cause of the difference is that COPY doesn't
know which placements are going to be accessed hence requires
to get connections up-front.
Similarly, COPY decides to use local execution up-front.
With this commit, we change the logic for COPY on local nodes:
Try to reserve a connection to local host. This logic follows
the same logic (e.g., citus.local_shared_pool_size) as the
executor because COPY also relies on TryToIncrementSharedConnectionCounter().
If reservation to local node fails, switch to local execution
Apart from this, if local execution is disabled, we follow the
exact same logic for multi-node Citus. It means that if we are
out of the connection, we'd give an error.
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.
pg_get_tableschemadef_string doesn't know how to deparse identity
columns so we cannot reflect those columns when creating table
from scratch. For this reason, we don't allow using alter_table udfs
with tables having any identity cols.
pg_get_tableschemadef_string doesn't know how to deparse identity
columns so we cannot reflect those columns when creating shell
relation.
For this reason, we don't allow adding local tables -having identity cols-
to metadata.
Postgres doesn't allow inserting into columns having GENERATED ALWAYS
AS (...) STORED expressions.
For this reason, when executing undistribute_table or an alter_* udf,
we should skip copying such columns.
This is not bad since Postgres would already generate such columns.
When finding columns owning sequences, we shouldn't rely on atthasdef
since it might be true when column has GENERATED ALWAYS AS (...)
STORED expression.
Since create_citus_local_table doesn't specify cascadeViaForeignKeys
option, we can't directly call citus_add_local_table_to_metadata
from create_citus_local_table.
Instead, implement an internal method and call it from deprecated udf
too.
* 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
* Make undistribute_table() and citus_create_local_table() work with columnar
* Rename and use LocallyExecuteUtilityTask for UDF check
* Remove 'local' references in ExecuteUtilityCommand
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
Currently we choose an arbitrary colocation id from all the matches for
a colocation id. This could mean that 2 distributed tables, which have
the same scheme could go into different colocation groups. This fix
makes sure that the same match will go to the same colocation group.
/*
* Creating Citus local tables relies on functions that accesses
* shards locally (e.g., ExecuteAndLogDDLCommand()). As long as
* we don't teach those functions to access shards remotely, we
* cannot relax this check.
*/
The reason behind skipping postgres tables is that we support
foreign keys between postgres tables and reference tables
(without converting postgres tables to citus local tables)
when enable_local_reference_table_foreign_keys is false or
when coordinator is not added to metadata.
For certaion purposes, we drop and recreate the foreign
keys. As we acquire exclusive locks on the tables in between
drop and re-create, we can safely skip validation phase of
the foreign keys. The reason is purely being performance as
foreign key validation could take a long value.
When enabled any foreign keys between local tables and reference
tables supported by converting the local table to a citus local
table.
When the coordinator is not in the metadata, the logic is disabled
as foreign keys are not allowed in this configuration.
Because master_add_node(or others) might acquire ExclusiveLock
and their initiated sessions may call CoordinatorAddedAsWorkerNode().
With this we prevent potential deadlocks.
If relation is not involved in any foreign key relationships,
foreign key graph would not return any relations for given
relationId as expected.
But even if it's the case, we should still undistribute the table
itself.
With citus shard helper view, we can easily see:
- where each shard is, which node, which port
- what kind of table it belongs to
- its size
With such a view, we can see shards that have a size bigger than some
value, which could be useful. Also debugging can be easier in production
as well with this view.
Fetch shards in one go per node
The previous implementation was slow because it would do a lot of round
trips, one per shard to be exact. Hence it is improved so that we fetch
all the shard_name, shard-size pairs per node in one go.
Construct shards_names, sizes query on coordinator
* Replace master_add_node with citus_add_node
* Replace master_activate_node with citus_activate_node
* Replace master_add_inactive_node with citus_add_inactive_node
* Use master udfs in old scripts
* Replace master_add_secondary_node with citus_add_secondary_node
* Replace master_disable_node with citus_disable_node
* Replace master_drain_node with citus_drain_node
* Replace master_remove_node with citus_remove_node
* Replace master_set_node_property with citus_set_node_property
* Replace master_unmark_object_distributed with citus_unmark_object_distributed
* Replace master_update_node with citus_update_node
* Replace master_update_shard_statistics with citus_update_shard_statistics
* Replace master_update_table_statistics with citus_update_table_statistics
* Rename master_conninfo_cache_invalidate to citus_conninfo_cache_invalidate
Rename master_dist_local_group_cache_invalidate to citus_dist_local_group_cache_invalidate
* Replace master_copy_shard_placement with citus_copy_shard_placement
* Replace master_move_shard_placement with citus_move_shard_placement
* Rename master_dist_node_cache_invalidate to citus_dist_node_cache_invalidate
* Rename master_dist_object_cache_invalidate to citus_dist_object_cache_invalidate
* Rename master_dist_partition_cache_invalidate to citus_dist_partition_cache_invalidate
* Rename master_dist_placement_cache_invalidate to citus_dist_placement_cache_invalidate
* Rename master_dist_shard_cache_invalidate to citus_dist_shard_cache_invalidate
* Drop master_modify_multiple_shards
* Rename master_drop_all_shards to citus_drop_all_shards
* Drop master_create_distributed_table
* Drop master_create_worker_shards
* Revert old function definitions
* Add missing revoke statement for citus_disable_node
We used to need WarnAboutLeakedPreparedTransaction()
as we didn't have auto 2PC recovery. But, we long have
2PC recovery by https://github.com/citusdata/citus/pull/1574
So, we don't need anymore.
* Rethrow original concurrent index creation failure message
* Alter test outputs for concurrent index creation
* Detect duplicate table failure in concurrent index creation
* Add test for conc. index creation w/out duplicates
* Prevent deadlock for long named partitioned index creation on single node
* Create IsSingleNodeCluster function
* Use both local and sequential execution
On top of our foreign key graph, implement the infrastructure to get
list of relations that are connected to input relation via a foreign key
graph.
We need this to support cascading create_citus_local_table &
undistribute_table operations.
Also add regression tests to see what our foreign key graph is able to
capture currently.
With this commit, we remove visited flags from ForeignConstraintRelationshipNode
struct since keeping local state in global object is both dangerous and
meaningless.
Also to improve readability, this commit also converts needless recursion to
iterative DFS to avoid passing local hash-map as another parameter to
GetConnectedListHelper function.
Attribute number in a subquery RTE and relation RTE means different
things. In a relation attribute number will point to the column number
in the table definition including the dropped columns as well however in
subquery, it means the index in the target list. When we convert a
relation RTE to subquery RTE we should either correct all the relevant
attribute numbers or we can just add a dummy column for the dropped
columns. We choose the latter in this commit because it is practically
too vulnerable to update all the vars in a query.
Another thing this commit fixes is that in case a join restriction
clause list contains a false clause, we should just returns a false
clause instead of the whole list, because the whole list will contain
restrictions from other RTEs as well and this breaks the query, which
can be seen from the output changes, now it is much simpler.
Also instead of adding single tests for dropped columns, we choose to
run the whole mixed queries with tables with dropped columns, this
revealed some bugs already, which are fixed in this commit.
It seems that there are only very few cases where that is useful, and
for now we prefer not having that check. This means that we might
perform some unnecessary checks, but that should be rare and not
performance critical.
Instead of sending NULL's over a network, we now convert the subqueries
in the form of:
SELECT t.a, NULL, NULL FROM (SELECT a FROM table)t;
And we recursively plan the inner part so that we don't send the NULL's
over network. We still need the NULLs in the outer subquery because we
currently don't have an easy way of updating all the necessary places in
the query.
Add some documentation for how the conversion is done
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.
It seems that most of the updates were broken, we weren't aware of it
because there wasn't any data in the tables. They are broken mostly
because local tables do not have a shard id and some code paths should
be updated with that information, currently when there is an invalid
shard id, it is assumed to be pruned.
Consider local tables in router planner
In case there is a local table, the shard id will not be valid and there
are some checks that rely on shard id, we should skip these in case of
local tables, which is handled with a dummy placement.
Add citus local table dist table join tests
add local-dist table mixed joins tests
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 we wrap an RTE to subquery we are updating the variables varno's as
1, however we should also update the varno's of vars in quals.
Also some other small code quality improvements are done.
The previous algorithm was not consistent and it could convert different
RTEs based on the table orders in the query. Now we convert local tables
if there is a distributed table which doesn't have a unique index. So if
there are 4 tables, local1, local2, dist1, dist2_with_pkey then we will
convert local1 and local2 in `auto` mode. Converting a distributed table
is not that logical because as there is a distributed table without a
unique index, we will need to convert the local tables anyway. So
converting the distributed table with pkey is redundant.
Remove FillLocalAndDistributedRTECandidates and use
ShouldConvertLocalTableJoinsToSubqueries, which simplifies things as we
rely on a single function to decide whether we should continue
converting RTE to subquery.
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.
When doing local-distributed table joins we convert one of them to
subquery. The current policy is that we convert distributed tables to
subquery if it has a unique index on a column that has unique
index(primary key also has a unique index).
The logical planner cannot handle joins between local and distributed table.
Instead, we can recursively plan one side of the join and let the logical
planner handle the rest.
Our algorithm is a little smart, trying not to recursively plan distributed
tables, but favors local tables.
A utility function is added so that each caller can implement a handler
for each index on a given table. This means that the caller doesn't need
to worry about how to access each index, the only thing that it needs to
do each to implement a function to which each index on the table is
passed iteratively.
When Citus needs to parallelize queries on the local node (e.g., the node
executing the distributed query and the shards are the same), we need to
be mindful about the connection management. The reason is that the client
backends that are running distributed queries are competing with the client
backends that Citus initiates to parallelize the queries in order to get
a slot on the max_connections.
In that regard, we implemented a "failover" mechanism where if the distributed
queries cannot get a connection, the execution failovers the tasks to the local
execution.
The failover logic is follows:
- As the connection manager if it is OK to get a connection
- If yes, we are good.
- If no, we fail the workerPool and the failure triggers
the failover of the tasks to local execution queue
The decision of getting a connection is follows:
/*
* For local nodes, solely relying on citus.max_shared_pool_size or
* max_connections might not be sufficient. The former gives us
* a preview of the future (e.g., we let the new connections to establish,
* but they are not established yet). The latter gives us the close to
* precise view of the past (e.g., the active number of client backends).
*
* Overall, we want to limit both of the metrics. The former limit typically
* kics in under regular loads, where the load of the database increases in
* a reasonable pace. The latter limit typically kicks in when the database
* is issued lots of concurrent sessions at the same time, such as benchmarks.
*/
When distributing a columnar table, as well as changing options on a distributed columnar table, this patch will forward the settings from the coordinator to the workers.
For propagating options changes on an already distributed table this change is pretty straight forward. Before applying the change in options locally we will create a `DDLJob` that contains a call to `alter_columnar_table_set(...)` for every shard placement with all settings of the current table. This goes both for setting an option as well as resetting. This will reset the values to the defaults configured on the coordinator. Having the effect that the coordinator is authoritative on the settings and makes sure the shards have the same settings set as the table on the coordinator.
When a columnar table is distributed it is using the `TableDDLCommand` infra structure to create a new kind of `TableDDLCommand`. This new type, called a `TableDDLCommandFunction` contains a context and 2 function pointers to execute. One function returns the command as applied on the table, the second function will return the sql command to apply to a shard with a given shard id. The schema name is ignored as it will use the fully qualified name of the shard in the same schema as the base table.
Multi-row execution already uses sequential execution. When shards
are local, using local execution is profitable as it avoids
an extra connection establishment to the local node.
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.
The name of the function is different than the implemantation. Because
the function is designed to only consider SELECT queries. Also this
changes the assert with an error.
Refactor internals on how Citus creates the SQL commands it sends to recreate shards.
Before Citus collected solely ddl commands as `char *`'s to recreate a table. If they were used to create a shard they were wrapped with `worker_apply_shard_ddl_command` and send to the workers. On the workers the UDF wrapping the ddl command would rewrite the parsetree to replace tables names with their shard name equivalent.
This worked well, but poses an issue when adding columnar. Due to limitations in Postgres on creating custom options on table access methods we need to fall back on a UDF to set columnar specific options. Now, to recreate the table, we can not longer rely on having solely DDL statements to recreate a table.
A prototype was made to run this UDF wrapped in `worker_apply_shard_ddl_command`. This became pretty messy, hard to understand and subsequently hard to maintain.
This PR proposes a refactor of the internal representation of table ddl commands into a `TableDDLCommand` structure. The current implementation only supports a `char *` as its contents. Based on the use of the DDL statement (eg. creating the table -mx- or creating a shard) one of two different functions can be called to get the statement to send to the worker:
- `GetTableDDLCommand(TableDDLCommand *command)`: This function returns that ddl command to create the table. In this implementation it will just return the `char *`. This has the same functionality as getting the old list and not wrapping it.
- `GetShardedTableDDLCommand(TableDDLCommand *command, uint64 shardId, char *schemaName)`: This function returns the ddl command wrapped in `worker_apply_shard_ddl_command` with the `shardId` as an argument. Due to backwards compatibility it also accepts a. `schemaName`. The exact purpose is not directly clear. Ideally new implementations would work with fully qualified statements and ignore the `schemaName`.
A future implementation could accept 2.function pointers and a `void *` for context to let the two pointers work on. This gives greater flexibility in controlling what commands get send in which situations. Also, in a future, we could implement the intermediate step of creating the `parsetree` datastructure of statements based on the contents in the catalog with a corresponding deparser. For sharded queries a mutator could be ran over the parsetree to rewrite the tablenames to the names with the shard identifier. This will completely omit the requirement for `worker_apply_shard_ddl_command`.
Considering the adaptive connection management
improvements that we plan to roll soon, it makes it
very helpful to know the number of active client
backends.
We are doing this addition to simplify yhe adaptive connection
management for single node Citus. In single node Citus, both the
client backends and Citus parallel queries would compete to get
slots on Postgres' `max_connections` on the same Citus database.
With adaptive connection management, we have the counters for
Citus parallel queries. That helps us to adaptively decide
on the remote executions pool size (e.g., throttle connections
if necessary).
However, we do not have any counters for the total number of
client backends on the database. For single node Citus, we
should consider all the client backends, not only the remote
connections that Citus does.
Of course Postgres internally knows how many client
backends are active. However, to get that number Postgres
iterates over all the backends. For examaple, see [pg_stat_get_db_numbackends](8e90ec5580/src/backend/utils/adt/pgstatfuncs.c (L1240))
where Postgres iterates over all the backends.
For our purpuses, we need this information on every connection
establishment. That's why we cannot affort to do this kind of
iterattion.
Hanefi Onaldi
Ahmet Gedemenli
Onur Tirtir
SaitTalhaNisanci
Halil Ozan Akgul
Önder Kalacı
Marco Slot
Onder Kalaci
Naisila Puka
Philip Dubé
Hadi Moshayedi
Brian Bergeron
Nils Dijk
Jeff Davis
naisila
Marco Slot
Jeff Davis