This is a pretty substantial refactoring of the existing modify path
within the router executor and planner. In particular, we now hunt for
all VALUES range table entries in INSERT statements and group the rows
contained therein by shard identifier. These rows are stashed away for
later in "ModifyRoute" elements. During deparse, the appropriate RTE
is extracted from the Query and its values list is replaced by these
rows before any SQL is generated.
In this way, we can create multiple Tasks, but only one per shard, to
piecemeal execute a multi-row INSERT. The execution of jobs containing
such tasks now exclusively go through the "multi-router executor" which
was previously used for e.g. INSERT INTO ... SELECT.
By piggybacking onto that executor, we participate in ongoing trans-
actions, get rollback-ability, etc. In short order, the only remaining
use of the "single modify" router executor will be for bare single-
row INSERT statements (i.e. those not in a transaction).
This change appropriately handles deferred pruning as well as master-
evaluated functions.
Add a second implementation of INSERT INTO distributed_table SELECT ... that is used if
the query cannot be pushed down. The basic idea is to execute the SELECT query separately
and pass the results into the distributed table using a CopyDestReceiver, which is also
used for COPY and create_distributed_table. When planning the SELECT, we go through
planner hooks again, which means the SELECT can also be a distributed query.
EXPLAIN is supported, but EXPLAIN ANALYZE is not because preventing double execution was
a lot more complicated in this case.
Custom Scan is a node in the planned statement which helps external providers
to abstract data scan not just for foreign data wrappers but also for regular
relations so you can benefit your version of caching or hardware optimizations.
This sounds like only an abstraction on the data scan layer, but we can use it
as an abstraction for our distributed queries. The only thing we need to do is
to find distributable parts of the query, plan for them and replace them with
a Citus Custom Scan. Then, whenever PostgreSQL hits this custom scan node in
its Vulcano style execution, it will call our callback functions which run
distributed plan and provides tuples to the upper node as it scans a regular
relation. This means fewer code changes, fewer bugs and more supported features
for us!
First, in the distributed query planner phase, we create a Custom Scan which
wraps the distributed plan. For real-time and task-tracker executors, we add
this custom plan under the master query plan. For router executor, we directly
pass the custom plan because there is not any master query. Then, we simply let
the PostgreSQL executor run this plan. When it hits the custom scan node, we
call the related executor parts for distributed plan, fill the tuple store in
the custom scan and return results to PostgreSQL executor in Vulcano style,
a tuple per XXX_ExecScan() call.
* Modify planner to utilize Custom Scan node.
* Create different scan methods for different executors.
* Use native PostgreSQL Explain for master part of queries.
Adds support for PostgreSQL 9.6 by copying in the requisite ruleutils
file and refactoring the out/readfuncs code to flexibly support the
old-style copy/pasted out/readfuncs (prior to 9.6) or use extensible
node APIs (in 9.6 and higher).
Most version-specific code within this change is only needed to set new
fields in the AggRef nodes we build for aggregations. Version-specific
test output files were added in certain cases, though in most they were
not necessary. Each such file begins by e.g. printing the major version
in order to clarify its purpose.
The comment atop citus_nodes.h details how to add support for new nodes
for when that becomes necessary.
All citusdb references in
- extension, binary names
- file headers
- all configuration name prefixes
- error/warning messages
- some functions names
- regression tests
are changed to be citus.