PG16 compatibility - Part 3
Check out part 1 42d956888d
and part 2 0d503dd5ac
This commit is in the series of PG compatibility. It makes some changes
to our tests in order to be compatible with the following in PG16:
Use debug_parallel_query in PG16+, force_parallel_mode otherwise
Relevant PG commit
5352ca22e0
5352ca22e0012d48055453ca9992a9515d811291
HINT changed to DETAIL in PG16
Relevant PG commit:
56d0ed3b75
56d0ed3b756b2e3799a7bbc0ac89bc7657ca2c33
Fix removed read-only server setting lc_collate
Relevant PG commit:
b0f6c43716
b0f6c437160db640d4ea3e49398ebc3ba39d1982
Fix unsupported join alias expression in sqlancer_failures
Relevant PG commit:
2489d76c49
2489d76c4906f4461a364ca8ad7e0751ead8aa0d
More PG16 compatibility commits are coming soon ...
DESCRIPTION: Correctly report shard size in citus_shards view
When looking at citus_shards, people are interested in the actual size
that all the data related to the shard takes up on disk.
`pg_total_relation_size` is the function to use for that purpose. The
previously used `pg_relation_size` does not include indexes or TOAST.
Especially the missing toast can have enormous impact on the size of the
shown data.
CREATE FUNCTION command together with it's dependencies.
If the function depends on any nondistributable object,
function will be created only locally. Parameterless
version of create_distributed_function becomes obsolete
with this change, it will deprecated from the code with a subsequent PR.
Before that PR we were updating citus.pg_dist_object metadata, which keeps
the metadata related to objects on Citus, only on the coordinator node. In
order to allow using those object from worker nodes (or erroring out with
proper error message) we've started to propagate that metedata to worker
nodes as well.
* Sort results in citus_shards and give raw size
Sort results so that it is consistent and also similar to citus_tables.
Use raw size in the output so that doing operations on the size is
easier.
* Change column ordering
With citus shard helper view, we can easily see:
- where each shard is, which node, which port
- what kind of table it belongs to
- its size
With such a view, we can see shards that have a size bigger than some
value, which could be useful. Also debugging can be easier in production
as well with this view.
Fetch shards in one go per node
The previous implementation was slow because it would do a lot of round
trips, one per shard to be exact. Hence it is improved so that we fetch
all the shard_name, shard-size pairs per node in one go.
Construct shards_names, sizes query on coordinator
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.
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.
Previously, we prevented creation of partitioned tables on Citus MX.
We decided to not focus on this feature until there is a need. Since
now there are requests for this feature, we are implementing support
for partitioned tables on Citus MX.
- Force all platforms to use the same collation
- Force all platforms to use the same locale
- Use /dev/null or NUL, depending on platform
- Use /tmp or %TEMP%, dpeending on platform
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