This is so we don't need to calculate it twice in
insert_select_executor.c and multi_explain.c, which can
cause discrepancy if an update in one of them is not
reflected in the other site.
In #3901 the "Data received from worker(s)" sections were added to EXPLAIN
ANALYZE. After merging @pykello posted some review comments. This addresses
those comments as well as fixing a other issues that I found while addressing
them. The things this does:
1. Fix `EXPLAIN ANALYZE EXECUTE p1` to not increase received data on every
execution
2. Fix `EXPLAIN ANALYZE EXECUTE p1(1)` to not return 0 bytes as received data
allways.
3. Move `EXPLAIN ANALYZE` specific logic to `multi_explain.c` from
`adaptive_executor.c`
4. Change naming of new explain sections to `Tuple data received from node(s)`.
Firstly because a task can reference the coordinator too, so "worker(s)" was
incorrect. Secondly to indicate that this is tuple data and not all network
traffic that was performed.
5. Rename `totalReceivedData` in our codebase to `totalReceivedTupleData` to
make it clearer that it's a tuple data counter, not all network traffic.
6. Actually add `binary_protocol` test to `multi_schedule` (woops)
7. Fix a randomly failing test in `local_shard_execution.sql`.
Sadly this does not actually work yet for binary protocol data, because
when doing EXPLAIN ANALYZE we send two commands at the same time. This
means we cannot use `SendRemoteCommandParams`, and thus cannot use the
binary protocol. This can still be useful though when using the text
protocol, to find out that a lot of data is being sent.
We wrap worker tasks in worker_save_query_explain_analyze() so we can fetch
their explain output later by a call worker_last_saved_explain_analyze().
Fixes#3519Fixes#2347Fixes#2613Fixes#621
Implements worker_save_query_explain_analyze and worker_last_saved_explain_analyze.
worker_save_query_explain_analyze executes and returns results of query while
saving its EXPLAIN ANALYZE to be fetched later.
worker_last_saved_explain_analyze returns the saved EXPLAIN ANALYZE result.
We had many fields in task related to query strings. It was kind of
complex, and only of them could be set at a time. Therefore it makes
more sense to abstract this and use a union so that it is clear that
only of them should be set.
We have three fields that could have query related strings:
- queryForLocation
- queryStringLazy
- perPlacementQueryStrings
Relatively, they can be set with:
- SetTaskQueryString
- SetTaskQueryIfShouldLazyDeparse
- SetTaskPerPlacementQueryStrings
The direct usage of the query related fields are also removed.
Rename queryForLocalExecution
Currently queryForLocalExecution is only used for deparsing purposes,
therefore it makes sense to rename it to what it is doing.
TaskQueryStringForPlacement simplifies how the executor gets the query
string for a given placement. Task will use the necessary fields to
return the correct query placement string. Executor doesn't need to know
the details for this.
rename TaskQueryString as TaskQueryStringAllPlacements
TaskQueryString returns the query string that will be the same for all
the placements. In INSERT..SELECT the query string can be different for
each placement. Adaptive executor uses TaskQueryStringForPlacement,
which returns the query string for a placement. It makes sense to rename
TaskQueryString as TaskQueryStringAllPlacements as it is returning the
query string for all placements.
rename SetTaskQuery as SetTaskQueryIfShouldLazyDeparse
SetTaskQuery does not always sets the task query. It can set the query
string as well. So it is more clear to name it
SetTaskQueryIfShouldLazyDeparse, since it will set the query not query
string only when we should deparse the query in a lazy way.
Deparsing and parsing a query can be heavy on CPU. When locally executing
the query we don't need to do this in theory most of the time.
This PR is the first step in allowing to skip deparsing and parsing
the query in these cases, by lazily creating the query string and
storing the query in the task. Future commits will make use of this and
not deparse and parse the query anymore, but use the one from the task
directly.
- changes in ruleutils_11.c is reflected
- vacuum statement api change is handled. We now allow
multi-table vacuum commands.
- some other function header changes are reflected
- api conflicts between PG11 and earlier versions
are handled by adding shims in version_compat.h
- various regression tests are fixed due output and
functionality in PG1
- no change is made to support new features in PG11
they need to be handled by new commit
With this commit, Citus recursively plans subqueries that
are not safe to pushdown, in other words, requires a merge
step.
The algorithm is simple: Recursively traverse the query from bottom
up (i.e., bottom meaning the leaf queries). On each level, check
whether the query is safe to pushdown (or a single repartition
subquery). If the answer is yes, do not touch that subquery. If the
answer is no, plan the subquery seperately (i.e., create a subPlan
for it) and replace the subquery with a call to
`read_intermediate_results(planId, subPlanId)`. During the the
execution, run the subPlans first, and make them avaliable to the
next query executions.
Some of the queries hat this change allows us:
* Subqueries with LIMIT
* Subqueries with GROUP BY/DISTINCT on non-partition keys
* Subqueries involving re-partition joins, router queries
* Mixed usage of subqueries and CTEs (i.e., use CTEs in
subqueries as well). Nested subqueries as long as we
support the subquery inside the nested subquery.
* Subqueries with local tables (i.e., those subqueries
has the limitation that they have to be leaf subqueries)
* VIEWs on the distributed tables just works (i.e., the
limitations mentioned below still applies to views)
Some of the queries that is still NOT supported:
* Corrolated subqueries that are not safe to pushdown
* Window function on non-partition keys
* Recursively planned subqueries or CTEs on the outer
side of an outer join
* Only recursively planned subqueries and CTEs in the FROM
(i.e., not any distributed tables in the FROM) and subqueries
in WHERE clause
* Subquery joins that are not on the partition columns (i.e., each
subquery is individually joined on partition keys but not the upper
level subquery.)
* Any limitation that logical planner applies such as aggregate
distincts (except for count) when GROUP BY is on non-partition key,
or array_agg with ORDER BY
Uncrustify 0.65 appears to have changed some defaults, resulting in
breakages for those of us who have already upgraded; Travis still uses
Uncrustify 0.64, but these changes work with both versions (assuming
appropriately updated config), so this should permit use of either
version for the time being.
Adds support for PostgreSQL 10 by copying in the requisite ruleutils
and updating all API usages to conform with changes in PostgreSQL 10.
Most changes are fairly minor but they are numerous. One particular
obstacle was the change in \d behavior in PostgreSQL 10's psql; I had
to add SQL implementations (views, mostly) to mimic the pre-10 output.
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.
With this commit, we started to send explain queries within a savepoint. After
running explain query, we rollback to savepoint. This saves us from side effects
of EXPLAIN ANALYZE on DML queries.
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.
All router, real-time, task-tracker plannable queries should now have
full prepared statement support (and even use router when possible),
unless they don't go through the custom plan interface (which
basically just affects LANGUAGE SQL (not plpgsql) functions).
This is achieved by forcing postgres' planner to always choose a
custom plan, by assigning very low costs to plans with bound
parameters (i.e. ones were the postgres planner replanned the query
upon EXECUTE with all parameter values provided), instead of the
generic one.
This requires some trickery, because for custom plans to work the
costs for a non-custom plan have to be known, which means we can't
error out when planning the generic plan. Instead we have to return a
"faux" plan, that'd trigger an error message if executed. But due to
the custom plan logic that plan will likely (unless called by an SQL
function, or because we can't support that query for some reason) not
be executed; instead the custom plan will be chosen.
Instead use pg_plan_query() like the normal explain does, and use that
to explain the query. That's important because it allows to remove
the duplicated planner logic from multi_explain - and that logic is
about to get more complicated.
They make fixing explain for prepared statement harder, and they don't
really fit into EXPLAIN in the first place. Additionally they're
currently not exercised in any tests.
This commit adds INSERT INTO ... SELECT feature for distributed tables.
We implement INSERT INTO ... SELECT by pushing down the SELECT to
each shard. To compute that we use the router planner, by adding
an "uninstantiated" constraint that the partition column be equal to a
certain value. standard_planner() distributes that constraint to all
the tables where it knows how to push the restriction safely. An example
is that the tables that are connected via equi joins.
The router planner then iterates over the target table's shards,
for each we replace the "uninstantiated" restriction, with one that
PruneShardList() handles. Do so by replacing the partitioning qual
parameter added in multi_planner() with the current shard's
actual boundary values. Also, add the current shard's boundary values to the
top level subquery to ensure that even if the partitioning qual is
not distributed to all the tables, we never run the queries on the shards
that don't match with the current shard boundaries. Finally, perform the
normal shard pruning to decide on whether to push the query to the
current shard or not.
We do not support certain SQLs on the subquery, which are described/commented
on ErrorIfInsertSelectQueryNotSupported().
We also added some locking on the router executor. When an INSERT/SELECT command
runs on a distributed table with replication factor >1, we need to ensure that
it sees the same result on each placement of a shard. So we added the ability
such that router executor takes exclusive locks on shards from which the SELECT
in an INSERT/SELECT reads in order to prevent concurrent changes. This is not a
very optimal solution, but it's simple and correct. The
citus.all_modifications_commutative can be used to avoid aggressive locking.
An INSERT/SELECT whose filters are known to exclude any ongoing writes can be
marked as commutative. See RequiresConsistentSnapshot() for the details.
We also moved the decison of whether the multiPlan should be executed on
the router executor or not to the planning phase. This allowed us to
integrate multi task router executor tasks to the router executor smoothly.
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.
We can now support richer set of queries in router planner.
This allow us to support CTEs, joins, window function, subqueries
if they are known to be executed at a single worker with a single
task (all tables are filtered down to a single shard and a single
worker contains all table shards referenced in the query).
Fixes : #501
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.
Hadi Moshayedi
citus bot
Marco Slot
Jelte Fennema
SaitTalhaNisanci
Philip Dubé
Jason Petersen
Onder Kalaci
Murat Tuncer
Jason Petersen
Andres Freund
Metin Doslu
Lukas Fittl