This would be useful for testing #6773. This is because, given that
#6773
only adds support for router / fast-path queries, theoretically almost
all
the tests that we have in that test file should work for null-shard-key
tables too (and they indeed do).
I deliberately did not replace multi_router_planner_fast_path.sql with
the one that I'm adding into arbitrary configs because we might still
want to see when we're able to go through fast-path planning for the
usual distributed tables (the ones that have a shard key).
Fixes#6672
2) Move all MERGE related routines to a new file merge_planner.c
3) Make ConjunctionContainsColumnFilter() static again, and rearrange the code in MergeQuerySupported()
4) Restore the original format in the comments section.
5) Add big serial test. Implement latest set of comments
DESCRIPTION: Support ALTER TABLE .. ADD PRIMARY KEY ... command
Before processing
> **ALTER TABLE ... ADD PRIMARY KEY ...**
command
1. Create a primary key name to use as the constraint name.
2. Change the **ALTER TABLE ... ADD PRIMARY KEY ...** command to into
**ALTER TABLE ... ADD CONSTRAINT \<constraint name> PRIMARY KEY ...**
form.
This is the only form we can specify a name for a primary key. If we run
ALTER TABLE .. ADD PRIMARY KEY, postgres
would create a constraint name internally in its own scheme. But the
problem is that we need to create constraint names
for shards in our own scheme which is \<constraint name>_\<shardid>.
Hence we need to create a name and send it to workers so that the
workers can append the shardid.
4. Run the changed command on the coordinator to make sure we are using
the same constraint name across the board.
5. Send the changed command to workers such that it is executed for the
main table as well as for the shards.
Fixes#6515.
This UDF coordinates connectivity checks accross the whole cluster.
This UDF gets the list of active readable nodes in the cluster, and
coordinates all connectivity checks in sequential order.
The algorithm is:
for sourceNode in activeReadableWorkerList:
c = connectToNode(sourceNode)
for targetNode in activeReadableWorkerList:
result = c.execute(
"SELECT citus_check_connection_to_node(targetNode.name,
targetNode.port")
emit sourceNode.name,
sourceNode.port,
targetNode.name,
targetNode.port,
result
- result -> true -> connection attempt from source to target succeeded
- result -> false -> connection attempt from source to target failed
- result -> NULL -> connection attempt from the current node to source node failed
I suggest you use the following query to get an overview on the connectivity:
SELECT bool_and(COALESCE(result, false))
FROM citus_check_cluster_node_health();
Whenever this query returns false, there is a connectivity issue, check in detail.
To run tests in parallel use:
```bash
make check-arbitrary-configs parallel=4
```
To run tests sequentially use:
```bash
make check-arbitrary-configs parallel=1
```
To run only some configs:
```bash
make check-arbitrary-base CONFIGS=CitusSingleNodeClusterConfig,CitusSmallSharedPoolSizeConfig
```
To run only some test files with some config:
```bash
make check-arbitrary-base CONFIGS=CitusSingleNodeClusterConfig EXTRA_TESTS=dropped_columns_1
```
To get a deterministic run, you can give the random's seed:
```bash
make check-arbitrary-configs parallel=4 seed=12312
```
The `seed` will be in the output of the run.
In our regular regression tests, we can see all the details about either planning or execution but this means
we need to run the same query under different configs/cluster setups again and again, which is not really maintanable.
When we don't care about the internals of how planning/execution is done but the correctness, especially with different configs
this infrastructure can be used.
With `check-arbitrary-configs` target, the following happens:
- a bunch of configs are loaded, which are defined in `config.py`. These configs have different settings such as different shard count, different citus settings, postgres settings, worker amount, or different metadata.
- For each config, a separate data directory is created for tests in `tmp_citus_test` with the config's name.
- For each config, `create_schedule` is run on the coordinator to setup the necessary tables.
- For each config, `sql_schedule` is run. `sql_schedule` is run on the coordinator if it is a non-mx cluster. And if it is mx, it is either run on the coordinator or a random worker.
- Tests results are checked if they match with the expected.
When tests results don't match, you can see the regression diffs in a config's datadir, such as `tmp_citus_tests/dataCitusSingleNodeClusterConfig`.
We also have a PostgresConfig which runs all the test suite with Postgres.
By default configs use regular user, but we have a config to run as a superuser as well.
So the infrastructure tests:
- Postgres vs Citus
- Mx vs Non-Mx
- Superuser vs regular user
- Arbitrary Citus configs
When you want to add a new test, you can add the create statements to `create_schedule` and add the sql queries to `sql_schedule`.
If you are adding Citus UDFs that should be a NO-OP for Postgres, make sure to override the UDFs in `postgres.sql`.
You can add your new config to `config.py`. Make sure to extend either `CitusDefaultClusterConfig` or `CitusMXBaseClusterConfig`.
On the CI, upon a failure, all logfiles will be uploaded as artifacts, so you can check the artifacts tab.
All the regressions will be shown as part of the job on CI.
In your local, you can check the regression diffs in config's datadirs as in `tmp_citus_tests/dataCitusSingleNodeClusterConfig`.
Onur Tirtir
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Marco Slot
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