Subquery pushdown planning is based on relation restriction
equivalnce. This brings us the opportuneatly to allow any
other joins as long as there is an already equi join between
the distributed tables.
We already allow that for joins with reference tables and
this commit allows that for joins among distributed tables.
With this commit, we allow pushing down subqueries with only
reference tables where GROUP BY or DISTINCT clause or Window
functions include only columns from reference tables.
While attaching a partition to a distributed table in schema, we mistakenly
used unqualified name to find partitioned table's oid. This caused problems
while using partitioned tables with schemas. We are fixing this issue in
this PR.
It's possible to build INSERT SELECT queries which include implicit
casts, currently we attempt to support these by adding explicit casts to
the SELECT query, but this sometimes crashes because we don't update all
nodes with the new types. (SortClauses, for instance)
This commit removes those explicit casts and passes an unmodified SELECT
query to the COPY executor (how we implement INSERT SELECT under the
scenes). In lieu of those cases, COPY has been given some extra logic to
inspect queries, notice that the types don't line up with the table it's
supposed to be inserting into, and "manually" casting every tuple before
sending them to workers.
This commit makes a change in relay_event_utility.c to check if the
Alter Table command adds a constraint using index. If this is the
case, it appends the shard id to the index name.
By this commit, citus minds the replica identity of the table when
we distribute the table. So the shards of the distributed table
have the same replica identity with the local table.
Expands count distinct coverage by allowing more cases. We used to support
count distinct only if we can push down distinct aggregate to worker query
i.e. the count distinct clause was on the partition column of the table,
or there was a grouping on the partition column.
Now we can support
- non-partition columns, with or without grouping on partition column
- partition, and non partition column in the same query
- having clause
- single table subqueries
- insert into select queries
- join queries where count distinct is on partition, or non-partition column
- filters on count distinct clauses (extends existing support)
We first try to push down aggregate to worker query (original case), if we
can't then we modify worker query to return distinct columns to coordinator
node. We do that by adding distinct column targets to group by clauses. Then
we perform count distinct operation on the coordinator node.
This work should reduce the cases where HLL is used as it can address anything
that HLL can. However, if we start having performance issues due to very large
number rows, then we can recommend hll use.
This commit provides the support for window functions in subquery and insert
into select queries. Note that our support for window functions is still limited
because it must have a partition by clause on the distribution key. This commit
makes changes in the files insert_select_planner and multi_logical_planner. The
required tests are also added with files multi_subquery_window_functions.out
and multi_insert_select_window.out.
We sent multiple commands to worker when starting a transaction.
Previously we only checked the result of the first command that
is transaction 'BEGIN' which always succeeds. Any failure on
following commands were not checked.
With this commit, we make sure all command results are checked.
If there is any error we report the first error found.
Basically we just care whether the running version is before or after
PostgreSQL 10, so testing the major version against 9 and printing a
boolean is sufficient.
Citus can handle INSERT INTO ... SELECT queries if the query inserts
into local table by reading data from distributed table. The opposite
way is not correct. With this commit we warn the user if the latter
option is used.
With this commit, we relax the restrictions put on the reference
tables with subquery pushdown.
We did three notable improvements:
1) Relax equi-join restrictions
Previously, we always expected that the non-reference tables are
equi joined with reference tables on the partition key of the
non-reference table.
With this commit, we allow any column of non-reference tables
joined using non-equi joins as well.
2) Relax OUTER JOIN restrictions
Previously Citus errored out if any reference table exists at
any point of the outer part of an outer join. For instance,
See the below sketch where (h) denotes a hash distributed relation,
(r) denotes a reference table, (L) denotes LEFT JOIN and
(I) denotes INNER JOIN.
(L)
/ \
(I) h
/ \
r h
Before this commit Citus would error out since a reference table
appears on the left most part of an left join. However, that was
too restrictive so that we only error out if the reference table
is directly below and in the outer part of an outer join.
3) Bug fixes
We've done some minor bugfixes in the existing implementation.
This is necessary for multi-row INSERTs for the same reasons we use it
in e.g. UPSERTs: if the range table list has more than one entry, then
PostgreSQL's deparse logic requires that vars be prefixed by the name
of their corresponding range table entry. This of course doesn't affect
single-row INSERTs, but since multi-row INSERTs have a VALUE RTE, they
were affected.
The piece of ruleutils which builds range table names wasn't modified
to handle shard extension; instead UPSERT/INSERT INTO ... SELECT added
an alias to the RTE. When present, this alias is favored. Doing the
same in the multi-row INSERT case fixes RETURNING for such commands.
This change adds support for SAVEPOINT, ROLLBACK TO SAVEPOINT, and RELEASE SAVEPOINT.
When transaction connections are not established yet, savepoints are kept in a stack and sent to the worker when the connection is later established. After establishing connections, savepoint commands are sent as they arrive.
This change fixes#1493 .
Now that we already have the necessary infrastructure for detecting
distributed deadlocks. Thus, we don't need enable_deadlock_prevention
which is purely intended for preventing some forms of distributed
deadlocks.
This commit adds all the necessary pieces to do the distributed
deadlock detection.
Each distributed transaction is already assigned with distributed
transaction ids introduced with
3369f3486f. The dependency among the
distributed transactions are gathered with
80ea233ec1.
With this commit, we implement a DFS (depth first seach) on the
dependency graph and search for cycles. Finding a cycle reveals
a distributed deadlock.
Once we find the deadlock, we examine the path that the cycle exists
and cancel the youngest distributed transaction.
Note that, we're not yet enabling the deadlock detection by default
with this commit.
This GUC has two settings, 'always' and 'never'. When it's set to
'never' all behavior stays exactly as it was prior to this commit. When
it's set to 'always' only SELECT queries are allowed to run, and only
secondary nodes are used when processing those queries.
Add some helper functions:
- WorkerNodeIsSecondary(), checks the noderole of the worker node
- WorkerNodeIsReadable(), returns whether we're currently allowed to
read from this node
- ActiveReadableNodeList(), some functions (namely, the ones on the
SELECT path) don't require working with Primary Nodes. They should call
this function instead of ActivePrimaryNodeList(), because the latter
will error out in contexts where we're not allowed to write to nodes.
- ActiveReadableNodeCount(), like the above, replaces
ActivePrimaryNodeCount().
- EnsureModificationsCanRun(), error out if we're not currently allowed
to run queries which modify data. (Either we're in read-only mode or
use_secondary_nodes is set)
Some parts of the code were switched over to use readable nodes instead
of primary nodes:
- Deadlock detection
- DistributedTableSize,
- the router, real-time, and task tracker executors
- ShardPlacement resolution
This change declares two new functions:
`master_update_table_statistics` updates the statistics of shards belong
to the given table as well as its colocated tables.
`get_colocated_shard_array` returns the ids of colocated shards of a
given shard.
This is a pretty substantial refactoring of the existing modify path
within the router executor and planner. In particular, we now hunt for
all VALUES range table entries in INSERT statements and group the rows
contained therein by shard identifier. These rows are stashed away for
later in "ModifyRoute" elements. During deparse, the appropriate RTE
is extracted from the Query and its values list is replaced by these
rows before any SQL is generated.
In this way, we can create multiple Tasks, but only one per shard, to
piecemeal execute a multi-row INSERT. The execution of jobs containing
such tasks now exclusively go through the "multi-router executor" which
was previously used for e.g. INSERT INTO ... SELECT.
By piggybacking onto that executor, we participate in ongoing trans-
actions, get rollback-ability, etc. In short order, the only remaining
use of the "single modify" router executor will be for bare single-
row INSERT statements (i.e. those not in a transaction).
This change appropriately handles deferred pruning as well as master-
evaluated functions.
- master_activate_node and master_disable_node correctly toggle
isActive, without crashing
- master_add_node rejects duplicate nodes, even if they're in different
clusters
- master_remove_node allows removing nodes in different clusters
- master_add_node enforces that there is only one primary per group
- there's also a trigger on pg_dist_node to prevent multiple primaries
per group
- functions in metadata cache only return primary nodes
- Rename ActiveWorkerNodeList -> ActivePrimaryNodeList
- Rename WorkerGetLive{Node->Group}Count()
- Refactor WorkerGetRandomCandidateNode
- master_remove_node only complains about active shard placements if the
node being removed is a primary.
- master_remove_node only deletes all reference table placements in the
group if the node being removed is the primary.
- Rename {Node->NodeGroup}HasShardPlacements, this reflects the behavior it
already had.
- Rename DeleteAllReferenceTablePlacementsFrom{Node->NodeGroup}. This also
reflects the behavior it already had, but the new signature forces the
caller to pass in a groupId
- Rename {WorkerGetLiveGroup->ActivePrimaryNode}Count
This commit adds distributed transaction id infrastructure in
the scope of distributed deadlock detection.
In general, the distributed transaction id consists of a tuple
in the form of: `(databaseId, initiatorNodeIdentifier, transactionId,
timestamp)`.
Briefly, we add a shared memory block on each node, which holds some
information per backend (i.e., an array `BackendData backends[MaxBackends]`).
Later, on each coordinated transaction, Citus sends
`SELECT assign_distributed_transaction_id()` right after `BEGIN`.
For that backend on the worker, the distributed transaction id is set to
the values assigned via the function call.
The aim of the above is to correlate the transactions on the coordinator
to the transactions on the worker nodes.
Comes with a few changes:
- Change the signature of some functions to accept groupid
- InsertShardPlacementRow
- DeleteShardPlacementRow
- UpdateShardPlacementState
- NodeHasActiveShardPlacements returns true if the group the node is a
part of has any active shard placements
- TupleToShardPlacement now returns ShardPlacements which have NULL
nodeName and nodePort.
- Populate (nodeName, nodePort) when creating ShardPlacements
- Disallow removing a node if it contains any shard placements
- DeleteAllReferenceTablePlacementsFromNode matches based on group. This
doesn't change behavior for now (while there is only one node per
group), but means in the future callers should be careful about
calling it on a secondary node, it'll delete placements on the primary.
- Create concept of a GroupShardPlacement, which represents an actual
tuple in pg_dist_placement and is distinct from a ShardPlacement,
which has been resolved to a specific node. In the future
ShardPlacement should be renamed to NodeShardPlacement.
- Create some triggers which allow existing code to continue to insert
into and update pg_dist_shard_placement as if it still existed.
These functions are holdovers from pg_shard and were created for unit
testing c-level functions (like InsertShardPlacementRow) which our
regression tests already test quite effectively. Removing because it
makes refactoring the signatures of those c-level functions
unnecessarily difficult.
- create_healthy_local_shard_placement_row
- update_shard_placement_row_state
- delete_shard_placement_row
This commit is intended to be a base for supporting declarative partitioning
on distributed tables. Here we add the following utility functions and their
unit tests:
* Very basic functions including differnentiating partitioned tables and
partitions, listing the partitions
* Generating the PARTITION BY (expr) and adding this to the DDL events
of partitioned tables
* Ability to generate text representations of the ranges for partitions
* Ability to generate the `ALTER TABLE parent_table ATTACH PARTITION
partition_table FOR VALUES value_range`
* Ability to apply add shard ids to the above command using
`worker_apply_inter_shard_ddl_command()`
* Ability to generate `ALTER TABLE parent_table DETACH PARTITION`
Adds support for PostgreSQL 10 by copying in the requisite ruleutils
and updating all API usages to conform with changes in PostgreSQL 10.
Most changes are fairly minor but they are numerous. One particular
obstacle was the change in \d behavior in PostgreSQL 10's psql; I had
to add SQL implementations (views, mostly) to mimic the pre-10 output.
Add a second implementation of INSERT INTO distributed_table SELECT ... that is used if
the query cannot be pushed down. The basic idea is to execute the SELECT query separately
and pass the results into the distributed table using a CopyDestReceiver, which is also
used for COPY and create_distributed_table. When planning the SELECT, we go through
planner hooks again, which means the SELECT can also be a distributed query.
EXPLAIN is supported, but EXPLAIN ANALYZE is not because preventing double execution was
a lot more complicated in this case.
- Use native postgres function for composite key btree functions
- Move explain tests to multi_explain.sql (get rid of .out _0.out files)
- Get rid of input/output files for multi_subquery.sql by moving table creations
- Update some comments
Distributed query planning for subquery pushdown is done on the original
query. This prevents the usage of external parameters on the execution.
To overcome this, we manually replace the parameters on the original
query.
* Support for subqueries in WHERE clause
This commit enables subqueries in WHERE clause to be pushed down
by the subquery pushdown logic.
The support covers:
- Correlated subqueries with IN, NOT IN, EXISTS, NOT EXISTS,
operator expressions such as (>, <, =, ALL, ANY etc.)
- Non-correlated subqueries with (partition_key) IN (SELECT partition_key ..)
(partition_key) =ANY (SELECT partition_key ...)
Note that this commit heavily utilizes the attribute equivalence logic introduced
in the 1cb6a34ba8. In general, this commit mostly
adjusts the logical planner not to error out on the subqueries in WHERE clause.
* Improve error checks for subquery pushdown and INSERT ... SELECT
Since we allow subqueries in WHERE clause with the previous commit,
we should apply the same limitations to those subqueries.
With this commit, we do not iterate on each subquery one by one.
Instead, we extract all the subqueries and apply the checks directly
on those subqueries. The aim of this change is to (i) Simplify the
code (ii) Make it close to the checks on INSERT .. SELECT code base.
* Extend checks for unresolved paramaters to include SubLinks
With the presence of subqueries in where clause (i.e., SubPlans on the
query) the existing way for checking unresolved parameters fail. The
reason is that the parameters for SubPlans are kept on the parent plan not
on the query itself (see primnodes.h for the details).
With this commit, instead of checking SubPlans on the modified plans
we start to use originalQuery, where SubLinks represent the subqueries
in where clause. The unresolved parameters can be found on the SubLinks.
* Apply code-review feedback
* Remove unnecessary copying of shard interval list
This commit removes unnecessary copying of shard interval list. Note
that there are no copyObject function implemented for shard intervals.
- There was a crash when the table a shardid belonged to changed during
a session. Instead of crashing (a failed assert) we now throw an error
- Update the isolation test which was crashing to no longer exercise
that code path
- Add a regression test to check that the error is thrown
* Enabling physical planner for subquery pushdown changes
This commit applies the logic that exists in INSERT .. SELECT
planning to the subquery pushdown changes.
The main algorithm is followed as :
- pick an anchor relation (i.e., target relation)
- per each target shard interval
- add the target shard interval's shard range
as a restriction to the relations (if all relations
joined on the partition keys)
- Check whether the query is router plannable per
target shard interval.
- If router plannable, create a task
* Add union support within the JOINS
This commit adds support for UNION/UNION ALL subqueries that are
in the following form:
.... (Q1 UNION Q2 UNION ...) as union_query JOIN (QN) ...
In other words, we currently do NOT support the queries that are
in the following form where union query is not JOINed with
other relations/subqueries :
.... (Q1 UNION Q2 UNION ...) as union_query ....
* Subquery pushdown planner uses original query
With this commit, we change the input to the logical planner for
subquery pushdown. Before this commit, the planner was relying
on the query tree that is transformed by the postgresql planner.
After this commit, the planner uses the original query. The main
motivation behind this change is the simplify deparsing of
subqueries.
* Enable top level subquery join queries
This work enables
- Top level subquery joins
- Joins between subqueries and relations
- Joins involving more than 2 range table entries
A new regression test file is added to reflect enabled test cases
* Add top level union support
This commit adds support for UNION/UNION ALL subqueries that are
in the following form:
.... (Q1 UNION Q2 UNION ...) as union_query ....
In other words, Citus supports allow top level
unions being wrapped into aggregations queries
and/or simple projection queries that only selects
some fields from the lower level queries.
* Disallow subqueries without a relation in the range table list for subquery pushdown
This commit disallows subqueries without relation in the range table
list. This commit is only applied for subquery pushdown. In other words,
we do not add this limitation for single table re-partition subqueries.
The reasoning behind this limitation is that if we allow pushing down
such queries, the result would include (shardCount * expectedResults)
where in a non distributed world the result would be (expectedResult)
only.
* Disallow subqueries without a relation in the range table list for INSERT .. SELECT
This commit disallows subqueries without relation in the range table
list. This commit is only applied for INSERT.. SELECT queries.
The reasoning behind this limitation is that if we allow pushing down
such queries, the result would include (shardCount * expectedResults)
where in a non distributed world the result would be (expectedResult)
only.
* Change behaviour of subquery pushdown flag (#1315)
This commit changes the behaviour of the citus.subquery_pushdown flag.
Before this commit, the flag is used to enable subquery pushdown logic. But,
with this commit, that behaviour is enabled by default. In other words, the
flag is now useless. We prefer to keep the flag since we don't want to break
the backward compatibility. Also, we may consider using that flag for other
purposes in the next commits.
* Require subquery_pushdown when limit is used in subquery
Using limit in subqueries may cause returning incorrect
results. Therefore we allow limits in subqueries only
if user explicitly set subquery_pushdown flag.
* Evaluate expressions on the LIMIT clause (#1333)
Subquery pushdown uses orignal query, the LIMIT and OFFSET clauses
are not evaluated. However, logical optimizer expects these expressions
are already evaluated by the standard planner. This commit manually
evaluates the functions on the logical planner for subquery pushdown.
* Better format subquery regression tests (#1340)
* Style fix for subquery pushdown regression tests
With this commit we intented a more consistent style for the
regression tests we've added in the
- multi_subquery_union.sql
- multi_subquery_complex_queries.sql
- multi_subquery_behavioral_analytics.sql
* Enable the tests that are temporarily commented
This commit enables some of the regression tests that were commented
out until all the development is done.
* Fix merge conflicts (#1347)
- Update regression tests to meet the changes in the regression
test output.
- Replace Ifs with Asserts given that the check is already done
- Update shard pruning outputs
* Add view regression tests for increased subquery coverage (#1348)
- joins between views and tables
- joins between views
- union/union all queries involving views
- views with limit
- explain queries with view
* Improve btree operators for the subquery tests
This commit adds the missing comprasion for subquery composite key
btree comparator.
So far citus used postgres' predicate proofing logic for shard
pruning, except for INSERT and COPY which were already optimized for
speed. That turns out to be too slow:
* Shard pruning for SELECTs is currently O(#shards), because
PruneShardList calls predicate_refuted_by() for every
shard. Obviously using an O(N) type algorithm for general pruning
isn't good.
* predicate_refuted_by() is quite expensive on its own right. That's
primarily because it's optimized for doing a single refutation
proof, rather than performing the same proof over and over.
* predicate_refuted_by() does not keep persistent state (see 2.) for
function calls, which means that a lot of syscache lookups will be
performed. That's particularly bad if the partitioning key is a
composite key, because without a persistent FunctionCallInfo
record_cmp() has to repeatedly look-up the type definition of the
composite key. That's quite expensive.
Thus replace this with custom-code that works in two phases:
1) Search restrictions for constraints that can be pruned upon
2) Use those restrictions to search for matching shards in the most
efficient manner available:
a) Binary search / Hash Lookup in case of hash partitioned tables
b) Binary search for equal clauses in case of range or append
tables without overlapping shards.
c) Binary search for inequality clauses, searching for both lower
and upper boundaries, again in case of range or append
tables without overlapping shards.
d) exhaustive search testing each ShardInterval
My measurements suggest that we are considerably, often orders of
magnitude, faster than the previous solution, even if we have to fall
back to exhaustive pruning.
That's useful when comparing values a hash-partitioned table is
filtered by. The existing shardIntervalCompareFunction is about
comparing hashed values, not unhashed ones.
The added btree opclass function is so we can get a comparator
back. This should be changed much more widely, but is not necessary so
far.
With this commit, we started to send explain queries within a savepoint. After
running explain query, we rollback to savepoint. This saves us from side effects
of EXPLAIN ANALYZE on DML queries.
With this change we add an option to add a node without replicating all reference
tables to that node. If a node is added with this option, we mark the node as
inactive and no queries will sent to that node.
We also added two new UDFs;
- master_activate_node(host, port):
- marks node as active and replicates all reference tables to that node
- master_add_inactive_node(host, port):
- only adds node to pg_dist_node
Before this commit, we were erroring out for queries containing parameterized SQL functions
like 'SELECT parameterized_sql_query(value)' as we should, however we were returning wrong
results for queries like 'SELECT * FROM parameterized_sql_query(value)'. With this commit
we started to error out on such queries too.
In this PR, we aim to deduce whether each of the RTE_RELATION
is joined with at least on another RTE_RELATION on their partition keys. If each
RTE_RELATION follows the above rule, we can conclude that all RTE_RELATIONs are
joined on their partition keys.
In order to do that, we invented a new equivalence class namely:
AttributeEquivalenceClass. In very simple words, a AttributeEquivalenceClass is
identified by an unique id and consists of a list of AttributeEquivalenceMembers.
Each AttributeEquivalenceMember is designed to identify attributes uniquely within the
whole query. The necessity of this arise since varno attributes are defined within
a single level of a query. Instead, here we want to identify each RTE_RELATION uniquely
and try to find equality among each RTE_RELATION's partition key.
Whenever we find an equality clause A = B, where both A and B originates from
relation attributes (i.e., not random expressions), we create an
AttributeEquivalenceClass to record this knowledge. If we later find another
equivalence B = C, we create another AttributeEquivalenceClass. Finally, we can
apply transitity rules and generate a new AttributeEquivalenceClass which includes
A, B and C.
Note that equality among the members are identified by the varattno and rteIdentity.
Each equality among RTE_RELATION is saved using an AttributeEquivalenceClass where
each member attribute is identified by a AttributeEquivalenceMember. In the final
step, we try generate a common attribute equivalence class that holds as much as
AttributeEquivalenceMembers whose attributes are a partition keys.
With this change, we start to error out if loaded citus binaries does not match
the available major version or installed citus extension version. In this case
we force user to restart the server or run ALTER EXTENSION depending on the
situation
Coordinator code marks index as invalid as a base, set it as valid in a
transactional layer atop that base, then proceeds with worker commands.
If a worker command has problems, the rollback results in an index with
isvalid = false. If everything succeeds, the user sees a valid index.
Before this commit, in certain cases router planner allowed pushing
down JOINs that are not on the partition keys.
With @anarazel's suggestion, we change the logic to use uninstantiated
parameter. Previously, the planner was traversing on the restriction
information and once it finds the parameter, it was replacing it with
the shard range. With this commit, instead of traversing the restrict
infos, the planner explicitly checks for the equivalence of the relation
partition key with the uninstantiated parameter. If finds an equivalence,
it adds the restrictions. In this way, we have more control over the
queries that are pushed down.
Some tests relied on worker errors though local commands were invalid.
Fixed those by ensuring preconditions were met to have command work
correctly. Otherwise most test changes are related to slight changes
in local/remote error ordering.
When running under Enterprise, some of the GRANT commands and whatnot
are propagated. Guarding that section with a call to disable DDL prop.
fixes everything.
Custom Scan is a node in the planned statement which helps external providers
to abstract data scan not just for foreign data wrappers but also for regular
relations so you can benefit your version of caching or hardware optimizations.
This sounds like only an abstraction on the data scan layer, but we can use it
as an abstraction for our distributed queries. The only thing we need to do is
to find distributable parts of the query, plan for them and replace them with
a Citus Custom Scan. Then, whenever PostgreSQL hits this custom scan node in
its Vulcano style execution, it will call our callback functions which run
distributed plan and provides tuples to the upper node as it scans a regular
relation. This means fewer code changes, fewer bugs and more supported features
for us!
First, in the distributed query planner phase, we create a Custom Scan which
wraps the distributed plan. For real-time and task-tracker executors, we add
this custom plan under the master query plan. For router executor, we directly
pass the custom plan because there is not any master query. Then, we simply let
the PostgreSQL executor run this plan. When it hits the custom scan node, we
call the related executor parts for distributed plan, fill the tuple store in
the custom scan and return results to PostgreSQL executor in Vulcano style,
a tuple per XXX_ExecScan() call.
* Modify planner to utilize Custom Scan node.
* Create different scan methods for different executors.
* Use native PostgreSQL Explain for master part of queries.
During later work the transaction debug output will change (as it will
in postgres 10), which makes it hard to see actual changes in the
INSERT ... SELECT ... test. Reduce to DEBUG2 after changing a debug
message to that log level.
This change ignores `citus.replication_model` setting and uses the
statement based replication in
- Tables distributed via the old `master_create_distributed_table` function
- Append and range partitioned tables, even if created via
`create_distributed_table` function
This seems like the easiest solution to #1191, without changing the existing
behavior and harming existing users with custom scripts.
This change also prevents RF>1 on streaming replicated tables on `master_create_worker_shards`
Prior to this change, `master_create_worker_shards` command was not checking
the replication model of the target table, thus allowing RF>1 with streaming
replicated tables. With this change, `master_create_worker_shards` errors
out on the case.
This change allows users to drop sequences on MX workers. Previously, Citus didn't allow dropping
sequences on MX workers because it could cause shards to be dropped if `DROP SEQUENCE ... CASCADE`
is used. We now allow that since allowing sequence creation but not dropping hurts user experience
and also may cause problems with custom Citus solutions.
- Break CheckShardPlacements into multiple functions (The most important
is MarkFailedShardPlacements), so that we can get rid of the global
CoordinatedTransactionUses2PC.
- Call MarkFailedShardPlacements in the router executor, so we mark
shards as invalid and stop using them while inside transaction blocks.
With this change DropShards function started to use new connection API. DropShards
function is used by DROP TABLE, master_drop_all_shards and master_apply_delete_command,
therefore all of these functions now support transactional operations. In DropShards
function, if we cannot reach a node, we mark shard state of related placements as
FILE_TO_DELETE and continue to drop remaining shards; however if any error occurs after
establishing the connection, we ROLLBACK whole operation.
All router, real-time, task-tracker plannable queries should now have
full prepared statement support (and even use router when possible),
unless they don't go through the custom plan interface (which
basically just affects LANGUAGE SQL (not plpgsql) functions).
This is achieved by forcing postgres' planner to always choose a
custom plan, by assigning very low costs to plans with bound
parameters (i.e. ones were the postgres planner replanned the query
upon EXECUTE with all parameter values provided), instead of the
generic one.
This requires some trickery, because for custom plans to work the
costs for a non-custom plan have to be known, which means we can't
error out when planning the generic plan. Instead we have to return a
"faux" plan, that'd trigger an error message if executed. But due to
the custom plan logic that plan will likely (unless called by an SQL
function, or because we can't support that query for some reason) not
be executed; instead the custom plan will be chosen.
This adds a replication_model GUC which is used as the replication
model for any new distributed table that is not a reference table.
With this change, tables with replication factor 1 are no longer
implicitly MX tables.
The GUC is similarly respected during empty shard creation for e.g.
existing append-partitioned tables. If the model is set to streaming
while replication factor is greater than one, table and shard creation
routines will error until this invalid combination is corrected.
Changing this parameter requires superuser permissions.
This enables proper transactional behaviour for copy and relaxes some
restrictions like combining COPY with single-row modifications. It
also provides the basis for relaxing restrictions further, and for
optionally allowing connection caching.
Marco Slot
mehmet furkan şahin
Onder Kalaci
metdos
Burak Yucesoy
Brian Cloutier
Murat Tuncer
velioglu
Murat Tuncer
Marco Slot
Hadi Moshayedi
Jason Petersen
Eren Başak
Andres Freund
Brian Cloutier
Jason Petersen