When a hash distributed table have a foreign key to a reference
table, there are few restrictions we have to apply in order to
prevent distributed deadlocks or reading wrong results.
The necessity to apply the restrictions arise from cascading
nature of foreign keys. When a foreign key on a reference table
cascades to a distributed table, a single operation over a single
connection can acquire locks on multiple shards of the distributed
table. Thus, any parallel operation on that distributed table, in the
same transaction should not open parallel connections to the shards.
Otherwise, we'd either end-up with a self-distributed deadlock or
read wrong results.
As briefly described above, the restrictions that we apply is done
by tracking the distributed/reference relation accesses inside
transaction blocks, and act accordingly when necessary.
The two main rules are as follows:
- Whenever a parallel distributed relation access conflicts
with a consecutive reference relation access, Citus errors
out
- Whenever a reference relation access is followed by a
conflicting parallel relation access, the execution mode
is switched to sequential mode.
There are also some other notes to mention:
- If the user does SET LOCAL citus.multi_shard_modify_mode
TO 'sequential';, all the queries should simply work with
using one connection per worker and sequentially executing
the commands. That's obviously a slower approach than Citus'
usual parallel execution. However, we've at least have a way
to run all commands successfully.
- If an unrelated parallel query executed on any distributed
table, we cannot switch to sequential mode. Because, the essense
of sequential mode is using one connection per worker. However,
in the presence of a parallel connection, the connection manager
picks those connections to execute the commands. That contradicts
with our purpose, thus we error out.
- COPY to a distributed table cannot be executed in sequential mode.
Thus, if we switch to sequential mode and COPY is executed, the
operation fails and there is currently no way of implementing that.
Note that, when the local table is not empty and create_distributed_table
is used, citus uses COPY internally. Thus, in those cases,
create_distributed_table() will also fail.
- There is a GUC called citus.enforce_foreign_key_restrictions
to disable all the checks. We added that GUC since the restrictions
we apply is sometimes a bit more restrictive than its necessary.
The user might want to relax those. Similarly, if you don't have
CASCADEing reference tables, you might consider disabling all the
checks.
This table will be used by Citus Enterprise to populate authentication-
related fields in outbound connections; Citus Community lacks support
for this functionality.
After this commit DDL commands honour `citus.multi_shard_modify_mode`.
We preferred using the code-path that executes single task router
queries (e.g., ExecuteSingleModifyTask()) in order not to invent
a new executor that is only applicable for DDL commands that require
sequential execution.
* Change worker_hash_partition_table() such that the
divergence between Citus planner's hashing and
worker_hash_partition_table() becomes the same.
* Rename single partitioning to single range partitioning.
* Add single hash repartitioning. Basically, logical planner
treats single hash and range partitioning almost equally.
Physical planner, on the other hand, treats single hash and
dual hash repartitioning almost equally (except for JoinPruning).
* Add a new GUC to enable this feature
- changes in ruleutils_11.c is reflected
- vacuum statement api change is handled. We now allow
multi-table vacuum commands.
- some other function header changes are reflected
- api conflicts between PG11 and earlier versions
are handled by adding shims in version_compat.h
- various regression tests are fixed due output and
functionality in PG1
- no change is made to support new features in PG11
they need to be handled by new commit
After this change all the logic related to shard data fetch logic
will be removed. Planner won't plan any ShardFetchTask anymore.
Shard fetch related steps in real time executor and task-tracker
executor have been removed.
This is the first of series of window function work.
We can now support window functions that can be pushed down to workers.
Window function must have distribution column in the partition clause
to be pushed down.
We push down order by to worker query when limit is specified
(with some other additional checks). If the query has an expression
on an aggregate or avg aggregate by itself, and there is an order
by on this particular target we may send wrong order by to worker
query with potential to affect query result.
The fix creates a auxilary target entry in the worker query and
uses that target entry for sorting.
With #1804 (and related PRs), Citus gained the ability to
plan subqueries that are not safe to pushdown.
There are two high-level requirements for pushing down subqueries:
* Individual subqueries that require a merge step (i.e., GROUP BY
on non-distribution key, or LIMIT in the subquery etc). We've
handled such subqueries via #1876.
* Combination of subqueries that are not joined on distribution keys.
This commit aims to recursively plan some of such subqueries to make
the whole query safe to pushdown.
The main logic behind non colocated subquery joins is that we pick
an anchor range table entry and check for distribution key equality
of any other subqueries in the given query. If for a given subquery,
we cannot find distribution key equality with the anchor rte, we
recursively plan that subquery.
We also used a hacky solution for picking relations as the anchor range
table entries. The hack is that we wrap them into a subquery. This is only
necessary since some of the attribute equivalance checks are based on
queries rather than range table entries.
This commit introduces a new GUC to limit the intermediate
result size which we handle when we use read_intermediate_result
function for CTEs and complex subqueries.
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.
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 .
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 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.
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.
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.
Onder Kalaci
velioglu
mehmet furkan şahin
Jason Petersen
Marco Slot
Murat Tuncer
Murat Tuncer
Markus Sintonen
Hadi Moshayedi
Brian Cloutier
Burak Yucesoy
Andres Freund
Jason Petersen