We keep accumulating more and more scripts to flag issues in CI. This is
good, but we are currently missing consistent documentation for them.
This commit moves all these scripts to the `ci` directory and adds some
documentation for all of them in the README. It also makes sure that the
last line of output of a failed script points to this documentation.
This is needed to automatically generate .bc (bitcode) files when
postgres is compiled with llvmjit support.
It also has the advantage that cmake is not required for the build
anymore.
On some distros (e.g. Redhat 7) there is cmake version 2 and cmake version 3,
safestringlib requires cmake version 3. On those distros the binary is called
cmake3, so try to use that one before falling back to regular cmake binary.
DESCRIPTION: add gitref to the output of citus_version
During debugging of custom builds it is hard to know the exact version of the citus build you are using. This patch will add a human readable/understandable git reference to the build of citus which can be retrieved by calling `citus_version();`.
A better fix for #2975. Apparently for OSX cpp -MF and -MT shouldn't have a
space in between the flag and their value. Without the space it still works for
gcc as well.
@thanodnl told me it was a bit of a problem that it's impossible to see
the history of a UDF in git. The only way to do so is by reading all the
sql migration files from new to old. Another problem is that it's also
hard to review the changed UDF during code review, because to find out
what changed you have to do the same. I thought of a IMHO better (but
not perfect) way to handle this.
We keep the definition of a UDF in sql/udfs/{name_of_udf}/latest.sql.
That file we change whenever we need to make a change to the the UDF. On
top of that you also make a snapshot of the file in
sql/udfs/{name_of_udf}/{migration-version}.sql (e.g. 9.0-1.sql) by
copying the contents. This way you can easily view what the actual
changes were by looking at the latest.sql file.
There's still the question on how to use these files then. Sadly
postgres doesn't allow inclusion of other sql files in the migration sql
file (it does in psql using \i). So instead I used the C preprocessor+
make to compile a sql/xxx.sql to a build/sql/xxx.sql file. This final
build/sql/xxx.sql file has every occurence of #include "somefile.sql" in
sql/xxx.sql replaced by the contents of somefile.sql.
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.
Drop schema command fails in mx mode if there
is a partitioned table with active partitions.
This is due to fact that sql drop trigger receives
all the dropped objects including partitions. When
we call drop table on parent partition, it also drops
the partitions on the mx node. This causes the drop
table command on partitions to fail on mx node because
they are already dropped when the partition parent was
dropped.
With this work we did not require the table to exist on
worker_drop_distributed_table.
With this commit, we implement two views that are very similar
to pg_stat_activity, but showing queries that are involved in
distributed queries:
- citus_dist_stat_activity: Shows all the distributed queries
- citus_worker_stat_activity: Shows all the queries on the shards
that are initiated by distributed queries.
Both views have the same columns in the outputs. In very basic terms, both of the views
are meant to provide some useful insights about the distributed
transactions within the cluster. As the names reveal, both views are similar to pg_stat_activity.
Also note that these views can be pretty useful on Citus MX clusters.
Note that when the views are queried from the worker nodes, they'd not show the distributed
transactions that are initiated from the coordinator node. The reason is that the worker
nodes do not know the host/port of the coordinator. Thus, it is advisable to query the
views from the coordinator.
If we bucket the columns that the views returns, we'd end up with the following:
- Hostnames and ports:
- query_hostname, query_hostport: The node that the query is running
- master_query_host_name, master_query_host_port: The node in the cluster
initiated the query.
Note that for citus_dist_stat_activity view, the query_hostname-query_hostport
is always the same with master_query_host_name-master_query_host_port. The
distinction is mostly relevant for citus_worker_stat_activity. For example,
on Citus MX, a users starts a transaction on Node-A, which starts worker
transactions on Node-B and Node-C. In that case, the query hostnames would be
Node-B and Node-C whereas the master_query_host_name would Node-A.
- Distributed transaction related things:
This is mostly the process_id, distributed transactionId and distributed transaction
number.
- pg_stat_activity columns:
These two views get all the columns from pg_stat_activity. We're basically joining
pg_stat_activity with get_all_active_transactions on process_id.
This commit fixes a bug where a concurrent DROP TABLE deadlocks
with SELECT (or DML) when the SELECT is executed from the workers.
The problem was that Citus used to remove the metadata before
droping the table on the workers. That creates a time window
where the SELECT starts running on some of the nodes and DROP
table on some of the other nodes.
This commit by default enables hiding shard names on MX workers
by simple replacing `pg_table_is_visible()` calls with
`citus_table_is_visible()` calls on the MX worker nodes. The latter
function filters out tables that are known to be shards.
The main motivation of this change is a better UX. The functionality
can be opted out via a GUC.
We also added two views, namely citus_shards_on_worker and
citus_shard_indexes_on_worker such that users can query
them to see the shards and their corresponding indexes.
We also added debug messages such that the filtered tables can
be interactively seen by setting the level to DEBUG1.
This table will be used by Citus Enterprise to populate authentication-
related fields in outbound connections; Citus Community lacks support
for this functionality.
* 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
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 change introduces the `pg_dist_node_metadata` which has a single jsonb value. When creating
the extension, a random server id is generated and stored in there. Everything in the metadata table
is added as a nested objected to the json payload that is sent to the reports server.
This will provide the full project name (i.e. Citus/Citus Enterprise),
and the host system, compiler, and architecture word size.
I wanted to limit the number of copied files in 'config', so I added
only config.guess and call it manually, rather than using the macro
AC_CANONICAL_HOST, which requires several other files.
Jelte Fennema
Marco Slot
Nils Dijk
Önder Kalacı
Hanefi Onaldi
Murat Tuncer
velioglu
mehmet furkan şahin
Jason Petersen
Markus Sintonen
Hadi Moshayedi
Brian Cloutier