Use partition column's collation for range distributed tables
Don't allow non deterministic collations for hash distributed tables
CoPartitionedTables: don't compare unequal types
In plain words, each distributed plan pulls the necessary intermediate
results to the worker nodes that the plan hits. This is primarily useful
in three ways.
(i) If the distributed plan that uses intermediate
result(s) is a router query, then the intermediate results are only
broadcasted to a single node.
(ii) If a distributed plan consists of only intermediate results, which
is not uncommon, the intermediate results are broadcasted to a single
node only.
(iii) If a distributed query hits a sub-set of the shards in multiple
workers, the intermediate results will be broadcasted to the relevant
node(s).
The final item (iii) becomes crucial for append/range distributed
tables where typically the distributed queries hit a small subset of
shards/workers.
To do this, for each query that Citus creates a distributed plan, we keep
track of the subPlans used in the queryTree, and save it in the distributed
plan. Just before Citus executes each subPlan, Citus first keeps track of
every worker node that the distributed plan hits, and marks every subPlan
should be broadcasted to these nodes. Later, for each subPlan which is a
distributed plan, Citus does this operation recursively since these
distributed plans may access to different subPlans, and those have to be
recorded as well.
Prevent Citus extension being distributed
Because that could prevent doing rolling upgrades, where users may
prefer to upgrade the version on the coordinator but not the workers.
There could be some other edge cases, so I'd prefer to keep Citus
extension outside the picture for now.
DESCRIPTION: Expression in reference join
Fixed: #2582
This patch allows arbitrary expressions in the join clause when joining to a reference table. An example of such joins could be found in CHbenCHmark queries 7, 8, 9 and 11; `mod((s_w_id * s_i_id),10000) = su_suppkey` and `ascii(substr(c_state,1,1)) = n2.n_nationkey`. Since the join is on a reference table these queries are able to be pushed down to the workers.
To implement these queries we will widen the `IsJoinClause` predicate to not check if the expressions are a type `Var` after stripping the implicit coerciens. Instead we define a join clause when the `Var`'s in a clause come from more than 1 table.
This allows more clauses to pass into the logical planner's `MultiNodeTree(...)` planning function. To compensate for this we tighten down the `LocalJoin`, `SinglePartitionJoin` and `DualPartitionJoin` to check for direct column references when planning. This allows the planner to work with arbitrary join expressions on reference tables.
When the user picks "round-robin" policy, the aim is that the load
is distributed across nodes. However, for reference tables on the
coordinator, since local execution kicks in immediately, round-robin
is ignored.
With this change, we're excluding the placement on the coordinator.
Although the approach seems a little bit invasive because of
modifications in the placement list, that sounds acceptable.
We could have done this in some other ways such as:
1) Add a field to "Task->roundRobinPlacement" (or such), which is
updated as the first element after RoundRobinPolicy is applied.
During the execution, if that placement is local to the coordinator,
skip it and try the other remote placements.
2) On TaskAccessesLocalNode()@local_execution.c, check
task_assignment_policy, if round-robin selected and there is local
placement on the coordinator, skip it. However, task assignment is done
on planning, but this decision is happening on the execution, which
could create weird edge cases.
Phase 1 seeks to implement minimal infrastructure, so does not include:
- dynamic generation of support aggregates to handle multiple arguments
- configuration methods to direct aggregation strategy,
or mark an aggregate's serialize/deserialize as safe to operate across nodes
Aggregates can be distributed when:
- they have a single argument
- they have a combinefunc
- their transition type is not a pseudotype
This is necassery to support Q20 of the CHbenCHmark: #2582.
To summarize the fix: The subquery is converted into an INNER JOIN on a
table. This fixes the issue, since an INNER JOIN on a table is already
supported by the repartion planner.
The way this replacement is happening.:
1. Postgres replaces `col in (subquery)` with a SEMI JOIN (subquery) on col = subquery_result
2. If this subquery is simple enough Postgres will replace it with a
regular read from a table
3. If the subquery returns unique results (e.g. a primary key) Postgres
will convert the SEMI JOIN into an INNER JOIN during the planning. It
will not change this in the rewritten query though.
4. We check if Postgres sends us any SEMI JOINs during its join order
planning, if it doesn't we replace all SEMI JOINs in the rewritten
query with INNER JOIN (which we already support).
Postgres doesn't require you to add all columns that are in the target list to
the GROUP BY when you group by a unique column (or columns). It even actively
removes these group by clauses when you do.
This is normally fine, but for repartition joins it is not. The reason for this
is that the temporary tables don't have these primary key columns. So when the
worker executes the query it will complain that it is missing columns in the
group by.
This PR fixes that by adding an ANY_VALUE aggregate around each variable in
the target list that does is not contained in the group by or in an aggregate.
This is done only for repartition joins.
The ANY_VALUE aggregate chooses the value from an undefined row in the
group.
It looks like the logic to prevent RETURNING in reference tables to
have duplicate entries that comes from local and remote executions
leads to missing some tuples for distributed tables.
With this PR, we're ensuring to kick in the logic for reference tables
only.
* Remove unused executor codes
All of the codes of real-time executor. Some functions
in router executor still remains there because there
are common functions. We'll move them to accurate places
in the follow-up commits.
* Move GUCs to transaction mngnt and remove unused struct
* Update test output
* Get rid of references of real-time executor from code
* Warn if real-time executor is picked
* Remove lots of unused connection codes
* Removed unused code for connection restrictions
Real-time and router executors cannot handle re-using of the existing
connections within a transaction block.
Adaptive executor and COPY can re-use the connections. So, there is no
reason to keep the code around for applying the restrictions in the
placement connection logic.
We've changed the logic for pulling RTE_RELATIONs in #3109 and
non-colocated subquery joins and partitioned tables.
@onurctirtir found this steps where I traced back and found the issues.
While looking into it in more detail, we decided to expand the list in a
way that the callers get all the relevant RTE_RELATIONs RELKIND_RELATION,
RELKIND_PARTITIONED_TABLE, RELKIND_FOREIGN_TABLE and RELKIND_MATVIEW.
These are all relation kinds that Citus planner is aware of.
This completely hides `ListCell` to the user of the loop
Example usage:
```c
WorkerNode *workerNode = NULL;
foreach_ptr(workerNode, workerNodeList) {
// Do stuff with workerNode
}
```
Instead of:
```c
ListCell *workerNodeCell = NULL;
foreach(cell, workerNodeList) {
WorkerNode *workerNode = lfirst(workerNodeCell);
// Do stuff with workerNode
}
```
This is an improvement over #2512.
This adds the boolean shouldhaveshards column to pg_dist_node. When it's false, create_distributed_table for new collocation groups will not create shards on that node. Reference tables will still be created on nodes where it is false.
Areas for further optimization:
- Don't save subquery results to a local file on the coordinator when the subquery is not in the having clause
- Push the the HAVING with subquery to the workers if there's a group by on the distribution column
- Don't push down the results to the workers when we don't push down the HAVING clause, only the coordinator needs it
Fixes#520Fixes#756Closes#2047
Objectives:
(a) both super user and regular user should have the correct owner for the function on the worker
(b) The transactional semantics would work fine for both super user and regular user
(c) non-super-user and non-function owner would get a reasonable error message if tries to distribute the function
Co-authored-by: @serprex
DESCRIPTION: Disallow distributed functions for functions depending on an extension
Functions depending on an extension cannot (yet) be distributed by citus. If we would allow this it would cause issues with our dependency following mechanism as we stop following objects depending on an extension.
By not allowing functions to be distributed when they depend on an extension as well as not allowing to make distributed functions depend on an extension we won't break the ability to add new nodes. Allowing functions depending on extensions to be distributed at the moment could cause problems in that area.
DESCRIPTION: Propagate CREATE OR REPLACE FUNCTION
Distributed functions could be replaced, which should be propagated to the workers to keep the function in sync between all nodes.
Due to the complexity of deparsing the `CreateFunctionStmt` we actually produce the plan during the processing phase of our utilityhook. Since the changes have already been made in the catalog tables we can reuse `pg_get_functiondef` to get us the generated `CREATE OR REPLACE` sql.
DESCRIPTION: Propagate ALTER FUNCTION statements for distributed functions
Using the implemented deparser for function statements to propagate changes to both functions and procedures that are previously distributed.
This PR aims to add all the necessary logic to qualify and deparse all possible `{ALTER|DROP} .. {FUNCTION|PROCEDURE}` queries.
As Procedures are introduced in PG11, the code contains many PG version checks. I tried my best to make it easy to clean up once we drop PG10 support.
Here are some caveats:
- I assumed that the parse tree is a valid one. There are some queries that are not allowed, but still are parsed successfully by postgres planner. Such queries will result in errors in execution time. (e.g. `ALTER PROCEDURE p STRICT` -> `STRICT` action is valid for functions but not procedures. Postgres decides to parse them nevertheless.)
When a function is marked as colocated with a distributed table,
we try delegating queries of kind "SELECT func(...)" to workers.
We currently only support this simple form, and don't delegate
forms like "SELECT f1(...), f2(...)", "SELECT f1(...) FROM ...",
or function calls inside transactions.
As a side effect, we also fix the transactional semantics of DO blocks.
Previously we didn't consider a DO block a multi-statement transaction.
Now we do.
Co-authored-by: Marco Slot <marco@citusdata.com>
Co-authored-by: serprex <serprex@users.noreply.github.com>
Co-authored-by: pykello <hadi.moshayedi@microsoft.com>
Since the distributed functions are useful when the workers have
metadata, we automatically sync it.
Also, after master_add_node(). We do it lazily and let the deamon
sync it. That's mainly because the metadata syncing cannot be done
in transaction blocks, and we don't want to add lots of transactional
limitations to master_add_node() and create_distributed_function().
DESCRIPTION: Provide a GUC to turn of the new dependency propagation functionality
In the case the dependency propagation functionality introduced in 9.0 causes issues to a cluster of a user they can turn it off almost completely. The only dependency that will still be propagated and kept track of is the schema to emulate the old behaviour.
GUC to change is `citus.enable_object_propagation`. When set to `false` the functionality will be mostly turned off. Be aware that objects marked as distributed in `pg_dist_object` will still be kept in the catalog as a distributed object. Alter statements to these objects will not be propagated to workers and may cause desynchronisation.
DESCRIPTION: Rename remote types during type propagation
To prevent data to be destructed when a remote type differs from the type on the coordinator during type propagation we wanted to rename the type instead of `DROP CASCADE`.
This patch removes the `DROP` logic and adds the creation of a rename statement to a free name.
DESCRIPTION: Add feature flag to turn off create type propagation
When `citus.enable_create_type_propagation` is set to `false` citus will not propagate `CREATE TYPE` statements to the workers. Types are still distributed when tables that depend on these types are distributed.
This PR simply adds the columns to pg_dist_object and
implements the necessary metadata changes to keep track of
distribution argument of the functions/procedures.
DESCRIPTION: Distribute Types to worker nodes
When to propagate
==============
There are two logical moments that types could be distributed to the worker nodes
- When they get used ( just in time distribution )
- When they get created ( proactive distribution )
The just in time distribution follows the model used by how schema's get created right before we are going to create a table in that schema, for types this would be when the table uses a type as its column.
The proactive distribution is suitable for situations where it is benificial to have the type on the worker nodes directly. They can later on be used in queries where an intermediate result gets created with a cast to this type.
Just in time creation is always the last resort, you cannot create a distributed table before the type gets created. A good example use case is; you have an existing postgres server that needs to scale out. By adding the citus extension, add some nodes to the cluster, and distribute the table. The type got created before citus existed. There was no moment where citus could have propagated the creation of a type.
Proactive is almost always a good option. Types are not resource intensive objects, there is no performance overhead of having 100's of types. If you want to use them in a query to represent an intermediate result (which happens in our test suite) they just work.
There is however a moment when proactive type distribution is not beneficial; in transactions where the type is used in a distributed table.
Lets assume the following transaction:
```sql
BEGIN;
CREATE TYPE tt1 AS (a int, b int);
CREATE TABLE t1 AS (a int PRIMARY KEY, b tt1);
SELECT create_distributed_table('t1', 'a');
\copy t1 FROM bigdata.csv
```
Types are node scoped objects; meaning the type exists once per worker. Shards however have best performance when they are created over their own connection. For the type to be visible on all connections it needs to be created and committed before we try to create the shards. Here the just in time situation is most beneficial and follows how we create schema's on the workers. Outside of a transaction block we will just use 1 connection to propagate the creation.
How propagation works
=================
Just in time
-----------
Just in time propagation hooks into the infrastructure introduced in #2882. It adds types as a supported object in `SupportedDependencyByCitus`. This will make sure that any object being distributed by citus that depends on types will now cascade into types. When types are depending them self on other objects they will get created first.
Creation later works by getting the ddl commands to create the object by its `ObjectAddress` in `GetDependencyCreateDDLCommands` which will dispatch types to `CreateTypeDDLCommandsIdempotent`.
For the correct walking of the graph we follow array types, when later asked for the ddl commands for array types we return `NIL` (empty list) which makes that the object will not be recorded as distributed, (its an internal type, dependant on the user type).
Proactive distribution
---------------------
When the user creates a type (composite or enum) we will have a hook running in `multi_ProcessUtility` after the command has been applied locally. Running after running locally makes that we already have an `ObjectAddress` for the type. This is required to mark the type as being distributed.
Keeping the type up to date
====================
For types that are recorded in `pg_dist_object` (eg. `IsObjectDistributed` returns true for the `ObjectAddress`) we will intercept the utility commands that alter the type.
- `AlterTableStmt` with `relkind` set to `OBJECT_TYPE` encapsulate changes to the fields of a composite type.
- `DropStmt` with removeType set to `OBJECT_TYPE` encapsulate `DROP TYPE`.
- `AlterEnumStmt` encapsulates changes to enum values.
Enum types can not be changed transactionally. When the execution on a worker fails a warning will be shown to the user the propagation was incomplete due to worker communication failure. An idempotent command is shown for the user to re-execute when the worker communication is fixed.
Keeping types up to date is done via the executor. Before the statement is executed locally we create a plan on how to apply it on the workers. This plan is executed after we have applied the statement locally.
All changes to types need to be done in the same transaction for types that have already been distributed and will fail with an error if parallel queries have already been executed in the same transaction. Much like foreign keys to reference tables.
/*
* local_executor.c
*
* The scope of the local execution is locally executing the queries on the
* shards. In other words, local execution does not deal with any local tables
* that are not shards on the node that the query is being executed. In that sense,
* the local executor is only triggered if the node has both the metadata and the
* shards (e.g., only Citus MX worker nodes).
*
* The goal of the local execution is to skip the unnecessary network round-trip
* happening on the node itself. Instead, identify the locally executable tasks and
* simply call PostgreSQL's planner and executor.
*
* The local executor is an extension of the adaptive executor. So, the executor uses
* adaptive executor's custom scan nodes.
*
* One thing to note that Citus MX is only supported with replication factor = 1, so
* keep that in mind while continuing the comments below.
*
* On the high level, there are 3 slightly different ways of utilizing local execution:
*
* (1) Execution of local single shard queries of a distributed table
*
* This is the simplest case. The executor kicks at the start of the adaptive
* executor, and since the query is only a single task the execution finishes
* without going to the network at all.
*
* Even if there is a transaction block (or recursively planned CTEs), as long
* as the queries hit the shards on the same, the local execution will kick in.
*
* (2) Execution of local single queries and remote multi-shard queries
*
* The rule is simple. If a transaction block starts with a local query execution,
* all the other queries in the same transaction block that touch any local shard
* have to use the local execution. Although this sounds restrictive, we prefer to
* implement in this way, otherwise we'd end-up with as complex scenarious as we
* have in the connection managements due to foreign keys.
*
* See the following example:
* BEGIN;
* -- assume that the query is executed locally
* SELECT count(*) FROM test WHERE key = 1;
*
* -- at this point, all the shards that reside on the
* -- node is executed locally one-by-one. After those finishes
* -- the remaining tasks are handled by adaptive executor
* SELECT count(*) FROM test;
*
*
* (3) Modifications of reference tables
*
* Modifications to reference tables have to be executed on all nodes. So, after the
* local execution, the adaptive executor keeps continuing the execution on the other
* nodes.
*
* Note that for read-only queries, after the local execution, there is no need to
* kick in adaptive executor.
*
* There are also few limitations/trade-offs that is worth mentioning. First, the
* local execution on multiple shards might be slow because the execution has to
* happen one task at a time (e.g., no parallelism). Second, if a transaction
* block/CTE starts with a multi-shard command, we do not use local query execution
* since local execution is sequential. Basically, we do not want to lose parallelism
* across local tasks by switching to local execution. Third, the local execution
* currently only supports queries. In other words, any utility commands like TRUNCATE,
* fails if the command is executed after a local execution inside a transaction block.
* Forth, the local execution cannot be mixed with the executors other than adaptive,
* namely task-tracker, real-time and router executors. Finally, related with the
* previous item, COPY command cannot be mixed with local execution in a transaction.
* The implication of that any part of INSERT..SELECT via coordinator cannot happen
* via the local execution.
*/
DESCRIPTION: Refactor ensure schema exists to dependency exists
Historically we only supported schema's as table dependencies to be created on the workers before a table gets distributed. This PR puts infrastructure in place to walk pg_depend to figure out which dependencies to create on the workers. Currently only schema's are supported as objects to create before creating a table.
We also keep track of dependencies that have been created in the cluster. When we add a new node to the cluster we use this catalog to know which objects need to be created on the worker.
Side effect of knowing which objects are already distributed is that we don't have debug messages anymore when creating schema's that are already created on the workers.
* Add tuplestore helpers
* More detailed error messages in tuplestore
* Add CreateTupleDescCopy to SetupTuplestore
* Use new SetupTuplestore helper function
* Remove unnecessary copy
* Remove comment about undefined behaviour
See a9c35cf85c
clang raises a warning due to FunctionCall2InfoData technically being variable sized
This is fine, as the struct is the size we want it to be. So silence the warning
master_deactivate_node is updated to decrement the replication factor
Otherwise deactivation could have create_reference_table produce a second record
UpdateColocationGroupReplicationFactor is renamed UpdateColocationGroupReplicationFactorForReferenceTables
& the implementation looks up the record based on distributioncolumntype == InvalidOid, rather than by id
Otherwise the record's replication factor fails to be maintained when there are no reference tables
Before this commit, we've recorded the relation accesses in 3 different
places
- FindPlacementListConnection -- applies all executor in tx block
- StartPlacementExecutionOnSession() -- adaptive executor only
- StartPlacementListConnection() -- router/real-time only
This is different than Citus 8.2, and could lead to query execution times
increase considerably on multi-shard commands in transaction block
that are on partitioned tables.
Benchmarks:
```
1+8 c5.4xlarge cluster
Empty distributed partitioned table with 365 partitions: https://gist.github.com/onderkalaci/1edace4ed6bd6f061c8a15594865bb51#file-partitions_365-sql
./pgbench -f /tmp/multi_shard.sql -c10 -j10 -P 1 -T 120 postgres://citus:w3r6KLJpv3mxe9E-NIUeJw@c.fy5fkjcv45vcepaogqcaskmmkee.db.citusdata.com:5432/citus?sslmode=require
cat /tmp/multi_shard.sql
BEGIN;
DELETE FROM collections_list;
DELETE FROM collections_list;
DELETE FROM collections_list;
COMMIT;
cat /tmp/single_shard.sql
BEGIN;
DELETE FROM collections_list WHERE key = :aid;
DELETE FROM collections_list WHERE key = :aid;
DELETE FROM collections_list WHERE key = :aid;
COMMIT;
cat /tmp/mix.sql
BEGIN;
DELETE FROM collections_list WHERE key = :aid;
DELETE FROM collections_list WHERE key = :aid;
DELETE FROM collections_list WHERE key = :aid;
DELETE FROM collections_list;
DELETE FROM collections_list;
DELETE FROM collections_list;
COMMIT;
```
The table shows `latency average` of pgbench runs explained above, so we have a pretty solid improvement even over 8.2.2.
| Test | Citus 8.2.2 | Citus 8.3.1 | Citus 8.3.2 (this branch) | Citus 8.3.1 (FKEYs disabled via GUC) |
| ------------- | ------------- | ------------- |------------- | ------------- |
|multi_shard | 2370.083 ms |3605.040 ms |1324.094 ms |1247.255 ms |
| single_shard | 85.338 ms |120.934 ms |73.216 ms | 78.765 ms |
| mix | 2434.459 ms | 3727.080 ms |1306.456 ms | 1280.326 ms |
This causes no behaviorial changes, only organizes better to implement modifying CTEs
Also rename ExtactInsertRangeTableEntry to ExtractResultRelationRTE,
as the source of this function didn't match the documentation
Remove Task's upsertQuery in favor of ROW_MODIFY_NONCOMMUTATIVE
Split up AcquireExecutorShardLock into more internal functions
Tests: Normalize multi_reference_table multi_create_table_constraints
Marco Slot
SaitTalhaNisanci
Philip Dubé
Hadi Moshayedi
Hadi Moshayedi
Önder Kalacı
Hanefi Onaldi
Onur TIRTIR
Halil Ozan Akgul
Nils Dijk
Jelte Fennema
Marco Slot