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.
UPDATEs on partitioned tables that affect only row partitions should
succeed, the rest should fail.
Also rename CStoreScan to ColumnarScan to make the error message more
relevant.
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.
Columnar options were by accident linked to the relfilenode instead of the regclass/relation oid. This PR moves everything related to columnar options to their own catalog table.
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.
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.
* Move local execution after the remote execution
Before this commit, when both local and remote tasks
exist, the executor was starting the execution with
local execution. There is no strict requirements on
this.
Especially considering the adaptive connection management
improvements that we plan to roll soon, moving the local
execution after to the remote execution makes more sense.
The adaptive connection management for single node Citus
would look roughly as follows:
- Try to connect back to the coordinator for running
parallel queries.
- If succeeds, go on and execute tasks in parallel
- If fails, fallback to the local execution
So, we'll use local execution as a fallback mechanism. And,
moving it after to the remote execution allows us to implement
such further scenarios.
The adaptive executor emulates the TCP's slow start algorithm.
Whenever the executor needs new connections, it doubles the number
of connections established in the previous iteration.
This approach is powerful. When the remote queries are very short
(like index lookup with < 1ms), even a single connection is sufficent
most of the time. When the remote queries are long, the executor
can quickly establish necessary number of connections.
One missing piece on our implementation seems that the executor
keeps doubling the number of connections even if the previous
connection attempts have been finalized. Instead, we should
wait until all the attempts are finalized. This is how TCP's
slow-start works. Plus, it decreases the unnecessary pressure
on the remote nodes.
I got this warning when compiling citus:
```
../columnar/write_state_management.c: In function ‘PendingWritesInUpperTransactions’:
../columnar/write_state_management.c:364:20: warning: ‘entry’ may be used uninitialized in this function [-Wmaybe-uninitialized]
if (found && entry->writeStateStack != NULL)
~~~~~^~~~~~~~~~~~~~~~
```
I fixed this by checking by always initializing entry, by using an early
return if `WriteStateMap` didn't exist. Instead of using the `found`
variable to check for existence of the key, I now simply check the
`entry` variable itself.
To quote the postgres comment on the hash_enter function:
> If foundPtr isn't NULL, then *foundPtr is set true if we found an
> existing entry in the table, false otherwise. This is needed in the
> HASH_ENTER case, but is redundant with the return value otherwise.
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.
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.
TableAM API doesn't allow us to pass around a state variable along all of the tuple inserts belonging to the same command. We require this in columnar store, since we batch them, and when we have enough rows we flush them as stripes.
To do that, we keep a (relfilenode) -> stack of (subxact id, TableWriteState) global mapping.
**Inserts**
Whenever we want to insert a tuple, we look up for the relation's relfilenode in this mapping. If top of the stack matches current subtransaction, we us the existing TableWriteState. Otherwise, we allocate a new TableWriteState and push it on top of stack.
**(Sub)Transaction Commit/Aborts**
When the subtransaction or transaction is committed, we flush and pop all entries matching current SubTransactionId.
When the subtransaction or transaction is committed, we pop all entries matching current SubTransactionId and discard them without flushing.
**Reads**
Since we might have unwritten rows which needs to be read by a table scan, we flush write states on SELECTs. Since flushing the write state of upper transactions in a subtransaction will cause metadata being written in wrong subtransaction, we ERROR out if any of the upper subtransactions have unflushed rows.
**Table Drops**
We record in which subtransaction the table was dropped. When committing a subtransaction in which table was dropped, we propagate the drop to upper transaction. When aborting a subtransaction in which table was dropped, we mark table as not deleted.
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.
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.
* 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
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.
It seems like Postgres could call set_rel_pathlist() for
the same relation multiple times. This breaks the logic
where we assume relationCount eqauls to the number of
entries in relationRestrictionList.
In summary, relationRestrictionList may contain duplicate
entries.
With this commit, we make sure that local execution adds the
intermediate result size as the distributed execution adds. Plus,
it enforces the citus.max_intermediate_result_size value.
We should not access CurrentLocalExecutionStatus directly because that
would mean that we could also set it directly, which we shouldn't
because we have checks to see if the new state is possible, otherwise we
error.
Before this commit, the logic was:
- As long as the outer side of the JOIN is not a JOIN (e.g., relation
or subquery etc.), we check for the existence of any recurring
tuples. There were two implications of this decision.
First, even if a subquery which is on the outer side contains
distributed table JOIN reference table, Citus would unnecessarily throw
an error. Note that, the JOIN inside the subquery would already
be going to be tested recursively. But, as long as that check
passes, there is no reason for the upper JOIN to fail. An example, which
used to fail and now works:
SELECT * FROM (SELECT * FROM dist JOIN ref) as foo LEFT JOIN dist;
Second, certain JOINs, especially with ON (true) conditions were not
represented as Citus expects the JOINs to be in the format
DeferredErrorIfUnsupportedRecurringTuplesJoin().
Use short lived per-tuple context in citus_evaluate_expr like
(pg) evaluate_expr does.
We should not use planState->ExprContext when evaluating expressions
as it might lead to freeing the same executor twice (first one happens
in citus_evaluate_expr itself and the other one happens when postgres
doing clean-up for the top level executor state), which in turn might
cause seg.faults.
However, now as we don't have necessary planState info to evaluate
prepared statements, we also add planState->es_param_list_info to
per-tuple ExprContext.
With postgres 13, there is a global lock that prevents multiple VACUUMs
happening in the current database. This global lock is taken for a short
time but this creates a problem because of the following:
- We execute the VACUUM for the shell table through the standard process
utility. In this step the global lock is taken for the current database.
- If the current node has shard placements then it tries to execute
VACUUM over a connection to localhost with ExecuteUtilityTaskList.
- the VACUUM on shard placements cannot proceed because it is waiting
for the global lock for the current database to be released.
- The acquired lock from the VACUUM for shell table will not be released
until the transaction is committed.
- So there is a deadlock.
As a solution, we commit the current transaction in case of VACUUM after
the VACUUM is executed for the shell table. Executing the VACUUM on a
shell table is not important because the data there will probably be
truncated. PostprocessVacuumStmt takes the necessary locks on the shell
table so we don't need to take any extra locks after we commit the
current transaction.
The prepare for upgrade script creates the `'public.pg_dist_rebalance_strategy` table which is not dropped when the upgrade is finished. This may block future upgrades.
Multi-row & router INSERT's were crashing with local execution if at
least one of the DEFAULT columns were not specified in VALUES list.
This was because, the changes we make on query->values_lists and
query->targetList was sufficient for deparsing given INSERT for remote
execution but not sufficient for local execution.
With this commit, DEFAULT value normalization for multi-row & router
INSERT's is fixed by adding dummy column references for unspecified
DEFAULT columns.
Citus has the logic to truncate the long shard names to prevent
various issues, including self-deadlocks. However, for partitioned
tables, when index is created on the parent table, the index names
on the partitions are auto-generated by Postgres. We use the same
Postgres function to generate the index names on the shards of the
partitions. If the length exceeds the limit, we switch to sequential
execution mode.
In postmasters execution of _PG_init, IsUnderPostmaster will be false and
we want to do the cleanup at that time only, otherwise there is a chance that
there will be parallel queries and we might do a cleanup for things that are
already in use.
After the connection timeout, we fail the session/pool. However, the
underlying connection can still be trying to connect. That is dangerous
because the new placement executions have already been in place. The
executor cannot handle the situation where multiple of
EXECUTION_ORDER_ANY task executions succeeds.
Adding a regression test doesn't seem easily doable. To reproduce the issue
- Add 2 worker nodes
- create a reference table
- set citus.node_connection_timeout to 1ms (requires code change)
- Continiously execute `SELECT count(*) FROM ref_table`
- Sometime later, you hit an out-of-array access in
`ScheduleNextPlacementExecution()` hence crashing.
- The reason for that is sometimes the first connection
successfully established while the executor is already
trying to execute the query on the second node.
We currently do not support volatile functions in update/delete statements
because the function evaluation logic does not know how to distinguish
volatile functions (that need to be evaluated per row) from stable functions
(that need to be evaluated per query), and it is also not safe to push the
volatile functions down on replicated tables.
Add sort method parameter for regression tests
Fix check-style
Change sorting method parameters to enum
Polish
Add task fields to OutTask
Add test into multi_explain
Fix isolation test
As the previous versions of Citus don't know how to handle citus local
tables, we should prevent downgrading from 9.5 to older versions if any
citus local tables exists.
Pushing down the CALLs to the node that the CALL is executed is
dangerous and could lead to infinite recursion.
When the coordinator added as worker, Citus was by chance preventing
this. The coordinator was marked as "not metadatasynced" node
in pg_dist_node, which prevented CALL/function delegation to happen.
With this commit, we do the following:
- Fix metadatasynced column for the coordinator on pg_dist_node
- Prevent pushdown of function/procedure to the same node that
the function/procedure is being executed. Today, we do not sync
pg_dist_object (e.g., distributed functions metadata) to the
worker nodes. But, even if we do it now, the function call delegation
would prevent the infinite recursion.
* Not take ShareUpdateExlusiveLock on pg_dist_transaction
We were taking ShareUpdateExlusiveLock on pg_dist_transaction during
recovery to prevent multiple recoveries happening concurrenly. VACUUM(
not FULL) also takes ShareUpdateExclusiveLock, and they can conflict. It
seems that VACUUM will skip the table if there is a conflicting lock
already taken unless it is doing the vacuum to prevent id wraparound, in
which case there can be a deadlock. I guess the deadlock happens if:
- VACUUM takes a lock on pg_dist_transaction and is done for id
wraparound problem
- The transaction in the maintenance tries to take a lock but
cannot as that conflicts with the lock acquired by VACUUM
- The transaction in the maintenance daemon has a very old xid hence
VACUUM cannot proceed.
If we take a row exclusive lock in transaction recovery then it wouldn't
conflict with VACUUM hence it could proceed so the deadlock would be
resolved. To prevent concurrent transaction recoveries happening, an
advisory lock is taken with ShareUpdateExlusiveLock as before.
* Use CITUS_OPERATIONS tag
* Not allow removing a single node with ref tables
We should not allow removing a node if it is the only node in the
cluster and there is a data on it. We have this check for distributed
tables but we didn't have it for reference tables.
* Update src/test/regress/expected/single_node.out
Co-authored-by: Onur Tirtir <onurcantirtir@gmail.com>
* Update src/test/regress/sql/single_node.sql
Co-authored-by: Onur Tirtir <onurcantirtir@gmail.com>
This commit brings following features:
Foreign key support from citus local tables to reference tables
* Foreign key support from reference tables to citus local tables
(only with RESTRICT & NO ACTION behavior)
* ALTER TABLE ENABLE/DISABLE trigger command support
* CREATE/DROP/ALTER trigger command support
and disallows:
* ALTER TABLE ATTACH/DETACH PARTITION commands
* CREATE TABLE <postgres table> ATTACH PARTITION <citus local table>
commands
* Foreign keys from postgres tables to citus local tables
(the other way was already disallowed)
for citus local tables.
Introduce table entry utility functions
Citus table cache entry utilities are introduced so that we can easily
extend existing functionality with minimum changes, specifically changes
to these functions. For example IsNonDistributedTableCacheEntry can be
extended for citus local tables without the need to scan the whole
codebase and update each relevant part.
* Introduce utility functions to find the type of tables
A table type can be a reference table, a hash/range/append distributed
table. Utility methods are created so that we don't have to worry about
how a table is considered as a reference table etc. This also makes it
easy to extend the table types.
* Add IsCitusTableType utilities
* Rename IsCacheEntryCitusTableType -> IsCitusTableTypeCacheEntry
* Change citus table types in some checks
create_distributed_function(function_name,
distribution_arg_name,
colocate_with text)
This UDF did not allow colocate_with parameters when there were no
disttribution_arg_name supplied. This commit changes the behaviour to
allow missing distribution_arg_name parameters when the function should
be colocated with a reference table.
* Hide citus.subquery_pushdown flag
This flag is dangerous and could likely to let queries
return wrong results.
The flag has a very specific purpose for a very specific
data distribution and query structure. In those cases, when
the flag is set, the user can skip recursive planning altogether
*at their own risk*.
The meaning of the flag is that "I know what I'm doing such that
the query structure/data distribution is on my control, so Citus
can skip many correctness checks".
For regular users, enabling this flag is discouraged. We have to
keep the support only for backward compatibility for some users.
In addition to that, give a NOTICE to discourage new users to
use it.
RemoveCoordinatorPlacement does not do what it says. It removes the
coordinator placement only if there are other placements, so it is not a
single node, and only if the coordinator has a placement.
AllTargetExpressionsAreColumnReferences would return false if a query
had an entry that is referencing the outer query. It seems safe to not
have this for non-distributed tables, such as reference tables. We
already have separate checks for other cases such as having limits.
FindNodeCheck is not clear about what the function is doing. They are
renamed to FindNodeMatchingCheckFunctionXXX. Also for choosing elements in these
functions, CheckNodeFunc type is introduced.
It seems that currently we process even postgres tables in explain
commands. This is because we register a hook for explain and we don't
have any check to see if the query has any citus table.
With this commit, we now send the buffer usage as well to the relevant
API. There is some duplicate in the code but it is because of the
existing structure, we can refactor this separately.
The codebase is updated to use varattnosync and varnosyn and we defined
the macros for older versions. This way we can just remove the macros
when we drop an older version.
CMDTAG_SELECT exists in PG12 hence defining a MACRO such as
CMDTAG_SELECT -> "SELECT" is not possible. I chose CMDTAG_SELECT_COMPAT
because with the COMPAT suffix it is explicit that it maps to different
things in different versions and also has a less chance of mapping
something irrevelant. For example if we used SELECT as a macro, then it
would map every SELECT to whatever it is mapping to, which might have
unexpected/undesired behaviour.
The error message when index has opclassopts is improved and the commit
from postgres side is also included for future reference.
Also some minor style related changes are applied.
Error out if index has opclassopts.
Changelog entry on PG13:
Allow CREATE INDEX to specify the GiST signature length and maximum number of integer ranges (Nikita Glukhov)
Postgres 13 added a new VACUUM option, PARALLEL. It is now supported
in our code as well.
Relevant changelog message on postgres:
Allow VACUUM to process indexes in parallel (Masahiko Sawada, Amit Kapila)
This commit mostly adds pg_get_triggerdef_command to our ruleutils_13.
This doesn't add anything extra for ruleutils 13 so it is basically a copy
of the change on ruleutils_12
When there is a join alias, var->varnosync will point to the alias and
var->varno will point to the table itself, but we need to use the alias
when deparsing the query. Hence a workaround is introduced to solve this
problem in ruleutils. Normally this case can be understood with
dpns->plan == NULL check but in our case, dpns->plan is always NULL. We
should sync our ruleutils at some point with postgres ruleutils. This
could be a wrong solution as well but the tests pass.
Rte index is increased by range table index offset in pg >= 13. The
offset is removed with the pg >= 13.
Currently pushdown for union all is disabled because translatedVars is
set to nil on postgres side, and we were using translatedVars to
figure out if partition key has the same index in both sides of union
all. This should be fixed.
Commit on postgres side:
6ef77cf46e81f45716ec981cb08781d426181378
fix union all pushdown logic for pg13
Before pg 13, there was a field, translatedVars, and we were using that
to understand if the partition key has the same index on both sides of
the union all. With pg13 there is a parent_colnos field in appendRelInfo
and we can use that to get the attribute numbers(varattnos) in union all
vars. We make use of parent_colnos instead of translatedVars in pg >=13.
For joins 3 new fields are added, joinleftcols, joinrightcols, and
joinmergedcols. We are not interested in joinmergedcols because we
always expand the column used in joins. There joinmergedcols is always 0
in our case.
For filling joinleftcols and joinrightcols we basically construct the
lists with sequences so either list is of the form: [1 2 3 4 .... n]
Ruleutils is not completed synced with postgres ruleutils and the most
important part is identify_join_columns function change, which now uses
joinleftcols and joinrightcols.
Commit on postgres side:
9ce77d75c5ab094637cc4a446296dc3be6e3c221
A useful email thread:
https://www.postgresql.org/message-id/flat/7115.1577986646%40sss.pgh.pa.us#0ae1d66feeb400013fbaa67a7cccd6ca
PG13 uses joinmergedcols, joinleftcols and joinrightcols for finding
join order now. There relevant fields are set on citus side.
Postgres side commit:
9ce77d75c5ab094637cc4a446296dc3be6e3c221
Postgres changed some join related fields and therefore they also
changed ruleutils, this commit applies those changes to our copy of
ruleutils.
Related commit on postgres side:
9ce77d75c5ab094637cc4a446296dc3be6e3c221
Postgres introduced QueryCompletion struct. Hence a compat utility is
added to finish query completion for older versions and pg >= 13.
The commit on Postgres side:
2f9661311b83dc481fc19f6e3bda015392010a40
addRangeTableEntryXXX methods return a ParseNamespaceItem with pg >= 13.
RangeTableEntryFromNSItem macro is added so that we return the range
table entry from the ParseNamespaceItem in pg>=13 and for pg < 13 rte
would already be returned with addRangeTableEntryXXX methods.
Commit on Postgres side:
5815696bc66b3092f6361f53e0394909647042c8
Since PG13 changed the list, a listcell doesn't contain data anymore.
Therefore Set_ptr_value macro is created, so that depending on the
version it will either use cell->data.ptr_value or cell->ptr_value.
Commit on Postgres side:
1cff1b95ab6ddae32faa3efe0d95a820dbfdc164
Since ExplainOnePlan expects BufferUsage as well with PG >= 13,
ExplainOnePlanCompat is added.
Commit on Postgres side:
ed7a5095716ee498ecc406e1b8d5ab92c7662d10
Commit on postgres side:
05d8449e73694585b59f8b03aaa087f04cc4679a
Command on postgres side:
git log --all --grep="hashutils"
include common/hashfn.h for pg >= 13
tag_hash was moved from hsearch.h to hashutils.h then to hashfn.h
Commits on Postgres side:
9341c783cc42ffae5860c86bdc713bd47d734ffd
PortalDefineQuery doesn't accept char* for command tag anymore with PG
>= 13. We are currently only using it with Select, therefore a Portal
define query compat for select is created.
Commit on PG side:
2f9661311b83dc481fc19f6e3bda015392010a40
As the new planner and pg_plan_query_compat methods expect the query
string as well, macros are defined to be compatible in different
versions of postgres.
Relevant commit on Postgres:
6aba63ef3e606db71beb596210dd95fa73c44ce2
Command on Postgres:
git log --all --grep="pg_plan_query"
With PG13 heap_* (heap_open, heap_close etc) are replaced with table_*
(table_open, table_close etc).
It is better to use the new table access methods in the codebase and
define the macros for the previous versions as we can easily remove the
macro without having to change the codebase when we drop the support for
the old version.
Commits that introduced this change on Postgres:
f25968c49697db673f6cd2a07b3f7626779f1827
e0c4ec07284db817e1f8d9adfb3fffc952252db0
4b21acf522d751ba5b6679df391d5121b6c4a35f
Command to see relevant commits on Postgres side:
git log --all --grep="heap_open"
Pass the list to lnext API
lnext API now expects the list as well.
The commit on Postgres that introduced the change: 1cff1b95ab6ddae32faa3efe0d95a820dbfdc164
lnext_compat and list_delete_cell_compat macros are introduced so that
we can use these macros in the codebase without having to use #if
directives in the codebase.
Related commit on postgres:
1cff1b95ab6ddae32faa3efe0d95a820dbfdc164
Command to search in postgres:
git log --all --grep="list_delete_cell"
add ListCellAndListWrapper
When iterating a list in separate function calls, we need both the list
and the current cell starting from PG13, therefore
ListCellAndListWrapper is added to store both as a wrapper.
Use ListCellAndListWrapper in foreign key test udfs
As we iterate a list in these udfs using a functionContext, we need to
use the wrapper to be able to access both the list and the current cell.
With this patch, we introduce `locally_reserved_shared_connections.c/h` files
which are responsible for reserving some space in shared memory counters
upfront.
We sometimes need to reserve connections, but not necessarily
establish them. For example:
- COPY command should reserve connections as it cannot know which
connections it needs in which order. COPY establishes connections
as any input data hits the workers. For example, for router COPY
command, it only establishes 1 connection.
As discussed here (https://github.com/citusdata/citus/pull/3849#pullrequestreview-431792473),
COPY needs to reserve connections up-front, otherwise we can end
up with resource starvation/un-detected deadlocks.
* ensure propagation of CHECK statements to workers with parantheses & adjust regression test outputs
* add tests for distributing tables with simple CHECK constraints
* added test for CHECK on bool variable
Enable custom aggregates with multiple parameters to be executed on workers.
#2921 introduces distributed execution of custom aggregates. One of the limitations of this feature is that only aggregate functions with a single aggregation parameter can be pushed to worker nodes. Aim of this change is to remove that limitation and support handling of multi-parameter aggregates.
Resolves: #3997
See also: #2921
* Use CalculateUniformHashRangeIndex in HashPartitionId
INT32_MIN definition can change among different platforms hence it is
possible to get overflow, we would see crashes because of this in debian
distros. We have already solved a similar problem with introducing
CalculateUniformHashRangeIndex method, hence to solve it we can use the
same method, this also removes some duplication and has a single place
to decide that.
* Use PG_INT32_XX instead of INT32_XX to be safer
With adaptive connection management, we might have some connections
which are not fully initialized. Those connections should not be
qualified as available.
1) Rename CONNECTION_PER_PLACEMENT to REQUIRE_CLEAN_CONNECTION. This is
mostly to make things clear as the new name reveals more.
2) We also make sure that mark all the copy connections critical,
even if they are accessed earlier in the transction
The executor relies on WorkerPool, and many other places rely on WorkerNode.
With this commit, we make sure that they are sorted via the same function/logic.
Some GUCs support a list of values which is indicated by GUC_LIST_INPUT flag.
When an ALTER ROLE .. SET statement is executed, the new configuration
default for affected users and databases are stored in the
setconfig(text[]) column in a pg_db_role_setting record.
If a GUC that supports a list of values is used in an ALTER ROLE .. SET
statement, we need to split the text into items delimited by commas.
* use adaptive executor even if task-tracker is set
* Update check-multi-mx tests for adaptive executor
Basically repartition joins are enabled where necessary. For parallel
tests max adaptive executor pool size is decresed to 2, otherwise we
would get too many clients error.
* Update limit_intermediate_size test
It seems that when we use adaptive executor instead of task tracker, we
exceed the intermediate result size less in the test. Therefore updated
the tests accordingly.
* Update multi_router_planner
It seems that there is one problem with multi_router_planner when we use
adaptive executor, we should fix the following error:
+ERROR: relation "authors_range_840010" does not exist
+CONTEXT: while executing command on localhost:57637
* update repartition join tests for check-multi
* update isolation tests for repartitioning
* Error out if shard_replication_factor > 1 with repartitioning
As we are removing the task tracker, we cannot switch to it if
shard_replication_factor > 1. In that case, we simply error out.
* Remove MULTI_EXECUTOR_TASK_TRACKER
* Remove multi_task_tracker_executor
Some utility methods are moved to task_execution_utils.c.
* Remove task tracker protocol methods
* Remove task_tracker.c methods
* remove unused methods from multi_server_executor
* fix style
* remove task tracker specific tests from worker_schedule
* comment out task tracker udf calls in tests
We were using task tracker udfs to test permissions in
multi_multiuser.sql. We should find some other way to test them, then we
should remove the commented out task tracker calls.
* remove task tracker test from follower schedule
* remove task tracker tests from multi mx schedule
* Remove task-tracker specific functions from worker functions
* remove multi task tracker extra schedule
* Remove unused methods from multi physical planner
* remove task_executor_type related things in tests
* remove LoadTuplesIntoTupleStore
* Do initial cleanup for repartition leftovers
During startup, task tracker would call TrackerCleanupJobDirectories and
TrackerCleanupJobSchemas to clean up leftover directories and job
schemas. With adaptive executor, while doing repartitions it is possible
to leak these things as well. We don't retry cleanups, so it is possible
to have leftover in case of errors.
TrackerCleanupJobDirectories is renamed as
RepartitionCleanupJobDirectories since it is repartition specific now,
however TrackerCleanupJobSchemas cannot be used currently because it is
task tracker specific. The thing is that this function is a no-op
currently.
We should add cleaning up intermediate schemas to DoInitialCleanup
method when that problem is solved(We might want to solve it in this PR
as well)
* Revert "remove task tracker tests from multi mx schedule"
This reverts commit 03ecc0a681.
* update multi mx repartition parallel tests
* not error with task_tracker_conninfo_cache_invalidate
* not run 4 repartition queries in parallel
It seems that when we run 4 repartition queries in parallel we get too
many clients error on CI even though we don't get it locally. Our guess
is that, it is because we open/close many connections without doing some
work and postgres has some delay to close the connections. Hence even
though connections are removed from the pg_stat_activity, they might
still not be closed. If the above assumption is correct, it is unlikely
for it to happen in practice because:
- There is some network latency in clusters, so this leaves some times
for connections to be able to close
- Repartition joins return some data and that also leaves some time for
connections to be fully closed.
As we don't get this error in our local, we currently assume that it is
not a bug. Ideally this wouldn't happen when we get rid of the
task-tracker repartition methods because they don't do any pruning and
might be opening more connections than necessary.
If this still gives us "too many clients" error, we can try to increase
the max_connections in our test suite(which is 100 by default).
Also there are different places where this error is given in postgres,
but adding some backtrace it seems that we get this from
ProcessStartupPacket. The backtraces can be found in this link:
https://circleci.com/gh/citusdata/citus/138702
* Set distributePlan->relationIdList when it is needed
It seems that we were setting the distributedPlan->relationIdList after
JobExecutorType is called, which would choose task-tracker if
replication factor > 1 and there is a repartition query. However, it
uses relationIdList to decide if the query has a repartition query, and
since it was not set yet, it would always think it is not a repartition
query and would choose adaptive executor when it should choose
task-tracker.
* use adaptive executor even with shard_replication_factor > 1
It seems that we were already using adaptive executor when
replication_factor > 1. So this commit removes the check.
* remove multi_resowner.c and deprecate some settings
* remove TaskExecution related leftovers
* change deprecated API error message
* not recursively plan single relatition repartition subquery
* recursively plan single relation repartition subquery
* test depreceated task tracker functions
* fix overlapping shard intervals in range-distributed test
* fix error message for citus_metadata_container
* drop task-tracker deprecated functions
* put the implemantation back to worker_cleanup_job_schema_cachesince citus cloud uses it
* drop some functions, add downgrade script
Some deprecated functions are dropped.
Downgrade script is added.
Some gucs are deprecated.
A new guc for repartition joins bucket size is added.
* order by a test to fix flappiness
Here are the updated make targets:
- install: install everything except downgrade scripts.
- install-downgrades: build and install only the downgrade migration scripts.
- install-all: install everything along with the downgrade migration scripts.
The reason we should use ActiveReadableNodeList instead of ActiveReadableNonCoordinatorNodeList is that if coordinator is added to cluster as a worker, it should be counted as well. Otherwise if there is only coordinator in the cluster, the count will be 0, hence we get a warning.
In MultiTaskTrackerExecute, we should connect to coordinator if it is
added to the cluster because it will also be assigned tasks.
ActiveReadableWorkerNodeList doesn't include coordinator, however if
coordinator is added as a worker, we should also include that while
planning. The current methods are very easily misusable and this
requires a refactoring to make the distinction between methods that
include coordinator and that don't very explicit as they can introduce
subtle/major bugs pretty easily.
We were using ALL_WORKERS TargetWorkerSet while sending temporary schema
creation and cleanup. We(well mostly I) thought that ALL_WORKERS would also include coordinator when it is added as a worker. It turns out that it was FILTERING OUT the coordinator even if it is added as a worker to the cluster.
So to have some context here, in repartitions, for each jobId we create
(at least we were supposed to) a schema in each worker node in the cluster. Then we partition each shard table into some intermediate files, which is called the PARTITION step. So after this partition step each node has some intermediate files having tuples in those nodes. Then we fetch the partition files to necessary worker nodes, which is called the FETCH step. Then from the files we create intermediate tables in the temporarily created schemas, which is called a MERGE step. Then after evaluating the result, we remove the temporary schemas(one for each job ID in each node) and files.
If node 1 has file1, and node 2 has file2 after PARTITION step, it is
enough to either move file1 from node1 to node2 or vice versa. So we
prune one of them.
In the MERGE step, if the schema for a given jobID doesn't exist, the
node tries to use the `public` schema if it is a superuser, which is
actually added for testing in the past.
So when we were not sending schema creation comands for each job ID to
the coordinator(because we were using ALL_WORKERS flag, and it doesn't
include the coordinator), we would basically not have any schemas for
repartitions in the coordinator. The PARTITION step would be executed on
the coordinator (because the tasks are generated in the planner part)
and it wouldn't give us any error because it doesn't have anything to do
with the temporary schemas(that we didn't create). But later two things
would happen:
- If by chance the fetch is pruned on the coordinator side, we the other
nodes would fetch the partitioned files from the coordinator and execute
the query as expected, because it has all the information.
- If the fetch tasks are not pruned in the coordinator, in the MERGE
step, the coordinator would either error out saying that the necessary
schema doesn't exist, or it would try to create the temporary tables
under public schema ( if it is a superuser). But then if we had the same
task ID with different jobID it would fail saying that the table already
exists, which is an error we were getting.
In the first case, the query would work okay, but it would still not do
the cleanup, hence we would leave the partitioned files from the
PARTITION step there. Hence ensure_no_intermediate_data_leak would fail.
To make things more explicit and prevent such bugs in the future,
ALL_WORKERS is named as ALL_NON_COORD_WORKERS. And a new flag to return
all the active nodes is added as ALL_DATA_NODES. For repartition case,
we don't use the only-reference table nodes but this version makes the
code simpler and there shouldn't be any significant performance issue
with that.
DESCRIPTION: Force aliases in deparsing for queries with anonymous column references
Fixes: #3985
The root cause has todo with discrepancies in the query tree we create. I think in the future we should spend some time on categorising all changes we made to ruleutils and see if we can change the data structure `query` we pass to the deparser to have an actual valid postgres query for the deparser to render.
For now the fix is to keep track, besides changing the names of the entries in the target list, also if we have a reference to an anonymous columns. If there are anonymous columns we set the `printaliases` flag to true which forces the deparser to add the aliases.
Rename TargetWorkerSet enums to make them more explicit about what they
mean. Ideally it would be good to treat everything as a node without the
'worker' concept because it makes things complicated. Another
improvement could be to rename TargetWorkerSet as TargetNodeSet but it
goes to renaming many occurrences of Worker, which is probably too big
for this PR.
Static analysis found some issues where we used the result from
ExtractResultRelationRTE, without checking that it wasn't NULL. It seems
like in all these cases it can never actually be NULL, since we have checked
before that it isn't a SELECT query. So, this PR is mostly to make static
analysis happy (and protect a bit against future changes of the code).
Static analysis found an issue where we could dereference `NULL`, because
`CreateDummyPlacement` could return `NULL` when there were no workers. This
PR changes it so that it never returns `NULL`, which was intended by
@marcocitus when doing this change: https://github.com/citusdata/citus/pull/3887/files#r438136433
While adding tests for citus on a single node I also added some more basic
tests and it turns out we error out on repartition joins. This has been
present since `shouldhaveshards` was introduced and is not trivial to fix.
So I created a separate issue for this: https://github.com/citusdata/citus/issues/3996
We keep accumulating more and more scripts to flag issues in CI. This is
good, but we are currently missing consistent documentation for them.
This commit moves all these scripts to the `ci` directory and adds some
documentation for all of them in the README. It also makes sure that the
last line of output of a failed script points to this documentation.
Sait Talha Nisanci
Onder Kalaci
Onur Tirtir
Marco Slot
Hadi Moshayedi
jeff-davis
Jeff Davis
Ahmet Gedemenli
Hadi Moshayedi
Nils Dijk
Önder Kalacı
Jelte Fennema
Hanefi Onaldi
Halil Ozan Akgul
Simon Kelly
nukoyluoglu
Benjamin Satzger
Philip Dubé
citus bot