DESCRIPTION: Adds citus_stat_counters view that can be used to query
stat counters that Citus collects while the feature is enabled, which is
controlled by citus.enable_stat_counters. citus_stat_counters() can be
used to query the stat counters for the provided database oid and
citus_stat_counters_reset() can be used to reset them for the provided
database oid or for the current database if nothing or 0 is provided.
Today we don't persist stat counters on server shutdown. In other words,
stat counters are automatically reset in case of a server restart.
Details on the underlying design can be found in header comment of
stat_counters.c and in the technical readme.
-------
Here are the details about what we track as of this PR:
For connection management, we have three statistics about the inter-node
connections initiated by the node itself:
* **connection_establishment_succeeded**
* **connection_establishment_failed**
* **connection_reused**
While the first two are relatively easier to understand, the third one
covers the case where a connection is reused. This can happen when a
connection was already established to the desired node, Citus decided to
cache it for some time (see citus.max_cached_conns_per_worker &
citus.max_cached_connection_lifetime), and then reused it for a new
remote operation. Here are the other important details about these
connection statistics:
1. connection_establishment_failed doesn't care about the connections
that we could establish but are lost later in the transaction. Plus, we
cannot guarantee that the connections that are counted in
connection_establishment_succeeded were not lost later.
2. connection_establishment_failed doesn't care about the optional
connections (see OPTIONAL_CONNECTION flag) that we gave up establishing
because of the connection throttling rules we follow (see
citus.max_shared_pool_size & citus.local_shared_pool_size). The reaason
for this is that we didn't even try to establish these connections.
3. For the rest of the cases where a connection failed for some reason,
we always increment connection_establishment_failed even if the caller
was okay with the failure and know how to recover from it (e.g., the
adaptive executor knows how to fall back local execution when the target
node is the local node and if it cannot establish a connection to the
local node). The reason is that even if it's likely that we can still
serve the operation, we still failed to establish the connection and we
want to track this.
4. Finally, the connection failures that we count in
connection_establishment_failed might be caused by any of the following
reasons and for now we prefer to _not_ further distinguish them for
simplicity:
a. remote node is down or cannot accept any more connections, or
overloaded such that citus.node_connection_timeout is not enough to
establish a connection
b. any internal Citus error that might result in preparing a bad
connection string so that libpq fails when parsing the connection string
even before actually trying to establish a connection via connect() call
c. broken citus.node_conninfo or such Citus configuration that was
incorrectly set by the user can also result in similar outcomes as in b
d. internal waitevent set / poll errors or OOM in local node
We also track two more statistics for query execution:
* **query_execution_single_shard**
* **query_execution_multi_shard**
And more importantly, both query_execution_single_shard and
query_execution_multi_shard are not only tracked for the top-level
queries but also for the subplans etc. The reason is that for some
queries, e.g., the ones that go through recursive planning, after Citus
performs the heavy work as part of subplans, the work that needs to be
done for the top-level query becomes quite straightforward. And for such
query types, it would be deceiving if we only incremented the query stat
counters for the top-level query. Similarly, for non-pushable INSERT ..
SELECT and MERGE queries, we perform separate counter increments for the
SELECT / source part of the query besides the final INSERT / MERGE
query.
DESCRIPTION: Ensure that a MERGE command on a distributed table with a
`WHEN NOT MATCHED BY SOURCE` clause runs against all shards of the
distributed table.
The Postgres MERGE command updates a table using a table or a query as a
data source. It provides three ways to match the target table with the
source: `WHEN MATCHED` means that there is a row in both the target and
source; `WHEN NOT MATCHED` means that there is a row in the source that
has no match (is not present) in the target; and, as of PG17, `WHEN NOT
MATCHED BY SOURCE` means that there is a row in the target that has no
match in the source.
In Citus, when a MERGE command updates a distributed table using a
local/reference table or a distributed query as source, that source is
repartitioned, and for each repartitioned shard that has data (i.e. 1 or
more rows) the MERGE is run against the corresponding distributed table
shard. Suppose the distributed table has 32 shards, and the source
repartitions into 4 shards that have data, with the remaining 28 shards
being empty; then the MERGE command is performed on the 4 corresponding
shards of the distributed table. However, the semantics of `WHEN NOT
MATCHED BY SOURCE` are that the specified action must be performed on
the target for each row in the target that is not in the source; so if
the source is empty, all target rows should be updated. To see this,
consider the following MERGE command:
```
MERGE INTO target AS t
USING source AS s ON t.id = s.id
WHEN NOT MATCHED BY SOURCE THEN UPDATE t SET t.col1 = 100
```
If the source has zero rows then every row in the target is updated s.t.
its col1 value is 100. Currently in Citus a MERGE on a distributed table
with a local/reference table or a distributed query as source ignores
shards of the distributed table when the corresponding shard of the
repartitioned source has zero rows. However, if the MERGE command
specifies a `WHEN NOT MATCHED BY SOURCE` clause, then the MERGE should
be performed on all shards of the distributed table, to ensure that the
specified action is performed on the target for each row in the target
that is not in the source. This PR enhances Citus MERGE execution so
that when a repartitioned source shard has zero rows, and the MERGE
command specifies a `WHEN NOT MATCHED BY SOURCE` clause, the MERGE is
performed against the corresponding shard of the distributed table using
an empty (zero row) relation as source, by generating a query of the
form:
```
MERGE INTO target_shard_0002 AS t
USING (SELECT id FROM (VALUES (NULL) ) source_0002(id) WHERE FALSE) AS s ON t.id = s.id
WHEN NOT MATCHED BY SOURCE THEN UPDATE t set t.col1 = 100
```
This works because each row in the target shard will be updated, and
`WHEN MATCHED` and `WHEN NOT MATCHED`, if specified, will be no-ops
because the source has zero rows.
To implement this when the source is a local or reference table involves
teaching function `ExcuteSourceAtCoordAndRedistribution()` in
`merge_executor.c` to not prune tasks when the query has `WHEN NOT
MATCHED BY SOURCE` but to instead replace the task's query to one that
uses an empty relation as source. And when the source is a distributed
query, function
`ExecuteMergeSourcePlanIntoColocatedIntermediateResults()` (also in
`merge_executor.c`) instead of skipping empty tasks now generates a
query that uses an empty relation as source for the corresponding target
shard of the distributed table, but again only when the query has `WHEN
NOT MATCHED BY SOURCE`. A new function `BuildEmptyResultQuery()` is
added to `recursive_planning.c` and it is used by both the
aforementioned functions in `merge_executor.c` to build an empty
relation to use as the source. It applies the appropriate type to each
column of the empty relation so the join with the target makes sense to
the query compiler.
This is prep work for successful compilation with PG17
PG17added foreach_ptr, foreach_int and foreach_oid macros
Relevant PG commit
14dd0f27d7cd56ffae9ecdbe324965073d01a9ff
14dd0f27d7
We already have these macros, but they are different with the
PG17 ones because our macros take a DECLARED variable, whereas
the PG16 macros declare a locally-scoped loop variable themselves.
Hence I am renaming our macros to foreach_declared_
I am separating this into its own PR since it touches many files. The
main compilation PR is https://github.com/citusdata/citus/pull/7699
This change adds a script to programatically group all includes in a
specific order. The script was used as a one time invocation to group
and sort all includes throught our formatted code. The grouping is as
follows:
- System includes (eg. `#include<...>`)
- Postgres.h (eg. `#include "postgres.h"`)
- Toplevel imports from postgres, not contained in a directory (eg.
`#include "miscadmin.h"`)
- General postgres includes (eg . `#include "nodes/..."`)
- Toplevel citus includes, not contained in a directory (eg. `#include
"citus_verion.h"`)
- Columnar includes (eg. `#include "columnar/..."`)
- Distributed includes (eg. `#include "distributed/..."`)
Because it is quite hard to understand the difference between toplevel
citus includes and toplevel postgres includes it hardcodes the list of
toplevel citus includes. In the same manner it assumes anything not
prefixed with `columnar/` or `distributed/` as a postgres include.
The sorting/grouping is enforced by CI. Since we do so with our own
script there are not changes required in our uncrustify configuration.
Similar to https://github.com/citusdata/citus/pull/7077.
As PG 16+ has changed the join restriction information for certain outer
joins, MERGE is also impacted given that is is also underlying an outer
join.
See #7077 for the details.
1) For distributed tables that are not colocated.
2) When joining on a non-distribution column for colocated tables.
3) When merging into a distributed table using reference or citus-local tables as the data source.
This is accomplished primarily through the implementation of the following two strategies.
Repartition: Plan the source query independently,
execute the results into intermediate files, and repartition the files to
co-locate them with the merge-target table. Subsequently, compile a final
merge query on the target table using the intermediate results as the data
source.
Pull-to-coordinator: Execute the plan that requires evaluation at the coordinator,
run the query on the coordinator, and redistribute the resulting rows to ensure
colocation with the target shards. Direct the MERGE SQL operation to the worker
nodes' target shards, using the intermediate files colocated with the data as the
data source.
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