If replication factor eqauls to 2 and there are two worker nodes,
even if two modifications hit different shards, Citus doesn't use
2PC. The reason is that it doesn't fit into the definition of
"expanding participating worker nodes".
Thus, we're simply fixing the test to fit in the comment
on top of it.
The feature is only intended for getting consistent outputs for the regression tests.
RETURNING does not have any ordering gurantees and with unified executor, the ordering
of query executions on the shards are also becoming unpredictable. Thus, we're enforcing
ordering when a GUC is set.
We implicitly add an `ORDER BY` something equivalent of
`
RETURNING expr1, expr2, .. ,exprN
ORDER BY expr1, expr2, .. ,exprN
`
As described in the code comments as well, this is probably not the most
performant approach we could implement. However, since we're only
targeting regression tests, I don't see any issues with that. If we
decide to expand this to a feature to users, we should revisit the
implementation and improve the performance.
We used to rely on PG function flatten_join_alias_vars
to resolve actual columns referenced in target entry list.
The function goes deep and finds the actual relation. This logic
usually works fine. However, when joins are given an alias, inner
relation names are not visible to target entry entry. Thus relation
resolving should stop when we the target entry column refers an
rte of an aliased join.
We stopped using PG function and provided our own flatten function.
The rule for infinite recursion is the following:
- If the query contains a subquery which is recursively planned, and
no other subqueries can be recursively planned due to correlation
(e.g., LATERAL joins), the planner keeps recursing again and again.
One interesting thing here is that even if a subquery contains only intermediate
result(s), we re-recursively plan that. In the end, the logic in the code does the following:
- Try recursive planning any of the subqueries in the query tree
- If any subquery is recursively planned, call the planner again
where the subquery is replaced with the intermediate result.
- Try recursively planning any of the queries
- If any subquery is recursively planned, call the planner again
where the subquery (in this case it is already intermediate result)
is replaced with the intermediate result.
- Try recursively planning any of the queries
- If any subquery is recursively planned, call the planner again
where the subquery (in this case it is already intermediate result)
is replaced with the intermediate result.
- Try recursively planning any of the queries
- If any subquery is recursively planned, call the planner again
where the subquery (in this case it is already intermediate result)
is replaced with the intermediate result.
......
Following scenario resulted in distributed deadlock before this commit:
CREATE TABLE partitioning_test(id int, time date) PARTITION BY RANGE (time);
CREATE TABLE partitioning_test_2009 (LIKE partitioning_test);
CREATE TABLE partitioning_test_reference(id int PRIMARY KEY, subid int);
SELECT create_distributed_table('partitioning_test_2009', 'id'),
create_distributed_table('partitioning_test', 'id'),
create_reference_table('partitioning_test_reference');
ALTER TABLE partitioning_test ADD CONSTRAINT partitioning_reference_fkey FOREIGN KEY (id) REFERENCES partitioning_test_reference(id) ON DELETE CASCADE;
ALTER TABLE partitioning_test_2009 ADD CONSTRAINT partitioning_reference_fkey_2009 FOREIGN KEY (id) REFERENCES partitioning_test_reference(id) ON DELETE CASCADE;
ALTER TABLE partitioning_test ATTACH PARTITION partitioning_test_2009 FOR VALUES FROM ('2009-01-01') TO ('2010-01-01');
Since flattening query may flatten outer joins' columns into coalesce expr that is
in the USING part, and that was not expected before this commit, these queries were
erroring out. It is fixed by this commit with considering coalesce expression as well.
The configuration for the build is in the YAML file; the changes to the
regression runner are backward-compatible with Travis and just add the
logic to detect whether our custom (isolation- and vanilla-enabled) pkg
is present.
Before this commit, round-robin task assignment policy was relying
on the taskId. Thus, even inside a transaction, the tasks were
assigned to different nodes. This was especially problematic
while reading from reference tables within transaction blocks.
Because, we had to expand the distributed transaction to many
nodes that are not necessarily already in the distributed transaction.
In this context, we define "Fast Path Planning for SELECT" as trivial
queries where Citus can skip relying on the standard_planner() and
handle all the planning.
For router planner, standard_planner() is mostly important to generate
the necessary restriction information. Later, the restriction information
generated by the standard_planner is used to decide whether all the shards
that a distributed query touches reside on a single worker node. However,
standard_planner() does a lot of extra things such as cost estimation and
execution path generations which are completely unnecessary in the context
of distributed planning.
There are certain types of queries where Citus could skip relying on
standard_planner() to generate the restriction information. For queries
in the following format, Citus does not need any information that the
standard_planner() generates:
SELECT ... FROM single_table WHERE distribution_key = X; or
DELETE FROM single_table WHERE distribution_key = X; or
UPDATE single_table SET value_1 = value_2 + 1 WHERE distribution_key = X;
Note that the queries might not be as simple as the above such that
GROUP BY, WINDOW FUNCIONS, ORDER BY or HAVING etc. are all acceptable. The
only rule is that the query is on a single distributed (or reference) table
and there is a "distribution_key = X;" in the WHERE clause. With that, we
could use to decide the shard that a distributed query touches reside on
a worker node.
Failure&Cancellation tests for initial start_metadata_sync() calls
to worker and DDL queries that send metadata syncing messages to an MX node
Also adds message type definitions for messages that are exchanged
during metadata syncing
-
We used to error out if there is a reference table
in the query participating a union. This has caused
pushdownable queries to be evaluated in coordinator.
Now we let reference tables inside union queries as long
as there is a distributed table in from clause.
Existing join checks (reference table on the outer part)
sufficient enought that we do not need check the join relation
of reference tables.
Previously we allowed task assignment policy to have affect on router queries
with only intermediate results. However, that is erroneous since the code-path
that assigns placements relies on shardIds and placements, which doesn't exists
for intermediate results.
With this commit, we do not apply task assignment policies when a router query
hits only intermediate results.
We disable bunch of planning options on the workers. This might be
risky if any concurrent test relies on EXPLAIN OUTPUT as well. Still,
we want to keep this test, so we should try to not parallelize this
test with such test.
Before this commit, Citus supported INSERT...SELECT queries with
ON CONFLICT or RETURNING clauses only for pushdownable ones, since
queries supported via coordinator were utilizing COPY infrastructure
of PG to send selected tuples to the target worker nodes.
After this PR, INSERT...SELECT queries with ON CONFLICT or RETURNING
clauses will be performed in two phases via coordinator. In the first
phase selected tuples will be saved to the intermediate table which
is colocated with target table of the INSERT...SELECT query. Note that,
a utility function to save results to the colocated intermediate result
also implemented as a part of this commit. In the second phase, INSERT..
SELECT query is directly run on the worker node using the intermediate
table as the source table.
Description: Support round-robin `task_assignment_policy` for queries to reference tables.
This PR allows users to query multiple placements of shards in a round robin fashion. When `citus.task_assignment_policy` is set to `'round-robin'` the planner will use a round robin scheduling feature when multiple shard placements are available.
The primary use-case is spreading the load of reference table queries to all the nodes in the cluster instead of hammering only the first placement of the reference table. Since reference tables share the same path for selecting the shards with single shard queries that have multiple placements (`citus.shard_replication_factor > 1`) this setting also allows users to spread the query load on these shards.
For modifying queries we do not apply a round-robin strategy. This would be negated by an extra reordering step in the executor for such queries where a `first-replica` strategy is enforced.
In recent postgres builds you cannot set client_min_messages to
values higher then ERROR, if will silently set it to ERROR if so.
During some tests we would set it to fatal to hide random values
(eg. pid's of processes) from the test output. This patch will use
different tactics for hiding these values.
After Fast ALTER TABLE ADD COLUMN with a non-NULL default in PG11, physical heaps might not contain all attributes after a ALTER TABLE ADD COLUMN happens. heap_getattr() returns NULL when the physical tuple doesn't contain an attribute. So we should use heap_deform_tuple() in these cases, which fills in the missing attributes.
Our catalog tables evolve over time, and an upgrade might involve some ALTER TABLE ADD COLUMN commands.
Note that we don't need to worry about postgres catalog tables and we can use heap_getattr() for them, because they only change between major versions.
This also fixes#2453.
Assign the distributed transaction id before trying to acquire the
executor advisory locks. This is useful to show this backend in citus
lock graphs (e.g., dump_global_wait_edges() and citus_lock_waits).
I'm pretty sure a lot of this test functionality may be covered in some
of our existing regression tests, but I've included them to ensure we
put all failure-based tests under our new testing method for that kind
of test.
Didn't include lower replication factor, as (for a single-shard mod.),
it's indistinguishable from modifying a reference table. So these all
test modifications which hit a single, replicated shard.
We made PG11 builds optional when we had an issue
with mx isolation test that we could not solve back then.
This commit solves the issue with a workaround by running
start_metadata_sync_to_node outside the transaction block.
Fairly straightforward; verified that modifications fail atomically if
a worker is down or fails mid-transaction (i.e. all workers need to ack
modifications to reference tables in order to persist changes).
Including several examples from #1926. I couldn't understand why the
recover_prepared_transactions "should be an error", and EXPLAIN has
changed since the original bug (so that it runs EXPLAINs in txns, I
think for EXPLAIN ANALYZE to not have side effects); other than that,
most of the reported bugs now error out rather than crash or return
an empty result set.
VACUUM runs outside of a transaction, so the failure modes for it are
somewhat straightforward, though ANALYZE runs in a 1pc transaction and
multi-table VACUUM can fail between statements (PG 11 and higher).
Tests various failure points during a multi-shard modification within
a transaction with multiple statements. Verifies three cases:
* Reference tables (single shard, many placements)
* Normal table with replication factor two
* Multi-shard table with no replication
In the replication-factor case, we expect shard health to be affected
in some transactions; most others fail the transaction entirely and
all we need verify is that no effects of the transaction are visible.
Had trouble testing the final PREPARE/COMMIT/ROLLBACK phase of the 2pc,
in particular because the error message produced includes the PID of
the backend, which is unpredictable.
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.
PG now allows foreign keys on partitioned tables.
Each foreign key constraint on partitioned table
is propagated down to partitions.
We used to create all constraints on shards when we are creating
a new shard, or when just simply moving a shard from one worker
to another. We also used the same logic when creating a copy of
coordinator table in mx node.
With this change we create the constraint on worker node only if
it is not an inherited constraint.
We used to set the execution mode in the truncate trigger. However,
when multiple tables are truncated with a single command, we could
set the execution mode very late. Instead, now set the execution mode
on the utility hook.
By setting the CPU tuple cost so high, we were triggering JIT. Instead,
we should use parallel_tuple_cost.
See: rhaas.blogspot.com/2018/06/using-forceparallelmode-correctly.html
This reverts commit a2fb5a84f1.
JIT wasn't actually interfering with the operation of Citus, a test was
just written in a way which caused JIT to run for a function on every
row in a 150k-row table.
With this commit, we all partitioned distributed tables with
replication factor > 1. However, we also have many restrictions.
In summary, we disallow all kinds of modifications (including DDLs)
on the partition tables. Instead, the user is allowed to run the
modifications over the parent table.
The necessity for such a restriction have two aspects:
- We need to acquire shard resource locks appropriately
- We need to handle marking partitions INVALID in case
of any failures. Note that, in theory, the parent table
should also become INVALID, which is too aggressive.
Reason for the failure is that PG11 introduced a new relation kind
RELKIND_PARTITIONED_INDEX to be used for partitioned indices.
We expanded our check to cover that case.
This commit uses *_walker instead of *_mutator for performance reasons.
Given that we're only updating a functionId in the tree, the approach
seems fine.
PG11 introduced PROCEDURE concept similar to FUNCTION
Procedure's allow committing/rolling back behavior.
This commmit adds regression tests for procedure calls.
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 test's output changes depending on which worker is
picked for explain (e.g., worker port in the output changes).
Given that the test is only aiming to ensure that CTEs inside
CTEs work fine in DML queries, it should be fine to get rid of
the EXPLAIN. The output is verified to be correct as well.
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 enables support for TRUNCATE on both
distributed table and reference tables.
The basic idea is to acquire lock on the relation by sending
the TRUNCATE command to all metedata worker nodes. We only
skip sending the TRUNCATE command to the node that actually
executus the command to prevent a self-distributed-deadlock.
This commit should be reverted once a new PostgreSQL 11 beta is
available: it's due to a bug in the partitioning code which has been
fixed in REL_11_STABLE but (not yet) a released tag.
Make sure that the coordinator sends the commands when the search
path synchronised with the coordinator's search_path. This is only
important when Citus sends the commands that are directly relayed
to the worker nodes. For example, the deparsed DLL commands or
queries always adds schema qualifications to the queries. So, they
do not require this change.
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.
- mitmdump now listens on port 9060
- Add some logging to fluent.py, making issues like this easier to debug in the future
- Fail the tests if something is already running on the port mitmProxy tries to use
- check-failure now works with VPATH builds
This commit adds an extensive failure testing, which covers quite
a bit of things and their combinations:
- 1PC vs 2PC
- Replication factor 1 and Replication factor 2
- Network failures and query cancellations
- Sequential vs Parallel query execution mode
- Lots of detail is in src/test/regress/mitmscripts/README
- Create a new target, make check-failure, which runs tests
- Tells travis how to install everything and run the tests
We can now support more complex count distinct operations by
pulling necessary columns to coordinator and evalutating the
aggreage at coordinator.
It supports broad range of expression with the restriction that
the expression must contain a column.
When a hash distributed table have a foreign key to a reference
table, there are few restrictions we have to apply in order to
prevent distributed deadlocks or reading wrong results.
The necessity to apply the restrictions arise from cascading
nature of foreign keys. When a foreign key on a reference table
cascades to a distributed table, a single operation over a single
connection can acquire locks on multiple shards of the distributed
table. Thus, any parallel operation on that distributed table, in the
same transaction should not open parallel connections to the shards.
Otherwise, we'd either end-up with a self-distributed deadlock or
read wrong results.
As briefly described above, the restrictions that we apply is done
by tracking the distributed/reference relation accesses inside
transaction blocks, and act accordingly when necessary.
The two main rules are as follows:
- Whenever a parallel distributed relation access conflicts
with a consecutive reference relation access, Citus errors
out
- Whenever a reference relation access is followed by a
conflicting parallel relation access, the execution mode
is switched to sequential mode.
There are also some other notes to mention:
- If the user does SET LOCAL citus.multi_shard_modify_mode
TO 'sequential';, all the queries should simply work with
using one connection per worker and sequentially executing
the commands. That's obviously a slower approach than Citus'
usual parallel execution. However, we've at least have a way
to run all commands successfully.
- If an unrelated parallel query executed on any distributed
table, we cannot switch to sequential mode. Because, the essense
of sequential mode is using one connection per worker. However,
in the presence of a parallel connection, the connection manager
picks those connections to execute the commands. That contradicts
with our purpose, thus we error out.
- COPY to a distributed table cannot be executed in sequential mode.
Thus, if we switch to sequential mode and COPY is executed, the
operation fails and there is currently no way of implementing that.
Note that, when the local table is not empty and create_distributed_table
is used, citus uses COPY internally. Thus, in those cases,
create_distributed_table() will also fail.
- There is a GUC called citus.enforce_foreign_key_restrictions
to disable all the checks. We added that GUC since the restrictions
we apply is sometimes a bit more restrictive than its necessary.
The user might want to relax those. Similarly, if you don't have
CASCADEing reference tables, you might consider disabling all the
checks.
-[x] drop constraint
-[x] drop column
-[x] alter column type
-[x] truncate
are sequentialized if there is a foreign constraint from
a distributed table to a reference table on the affected relations
by the above commands.
Make sure that intermediate results use a connection that is
not associated with any placement. That is useful in two ways:
- More complex queries can be executed with CTEs
- Safely use the same connections when there is a foreign key
to reference table from a distributed table, which needs to
use the same connection for modifications since the reference
table might cascade to the distributed table.
This table will be used by Citus Enterprise to populate authentication-
related fields in outbound connections; Citus Community lacks support
for this functionality.
We're relying on multi_shard_modify_mode GUC for real-time SELECTs.
The name of the GUC is unfortunate, but, adding one more GUC
(or renaming the GUC) would make the UX even worse. Given that this
mode is mostly important for transaction blocks that involve modification
/DDL queries along with real-time SELECTs, we can live with the confusion.
After this commit DDL commands honour `citus.multi_shard_modify_mode`.
We preferred using the code-path that executes single task router
queries (e.g., ExecuteSingleModifyTask()) in order not to invent
a new executor that is only applicable for DDL commands that require
sequential execution.
Previously we checked if an operator is in pg_catalog, and if it wasn't we prefixed it with namespace in worker queries. This can have a huge impact on performance of physical planner when using custom data types.
This happened regardless of current search_path config, because Citus overrides the search path in get_query_def_extended(). When we do so, the check for existence of the operator in current search path in generate_operator_name() fails for any operators outside pg_catalog. This means that nothing gets cached, and in the following calls we will again recheck the system tables for existence of the operators, which took an additional 40-50ms for some of the usecases we were seeing.
In this change we skip the pg_catalog check, and always prefix the operator with its namespace.
* 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
utilityStmt sometimes (such as when it's inside of a plpgsql function)
comes from a cached plan, which is kept in a child of the
CacheMemoryContext. When we naively call copyObject we're copying it into
a statement-local context, which corrupts the cached plan when it's
thrown away.
- 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
- Add install.pl to instal .sql files on Windows
- Remove a hack to PGDLLIMPORT some variables
- Add citus_version.o to the Makefile
- Fix pg_regress_multi's PATH generation on Windows
- Output regression.diffs when the tests fail
- Fix permissions in data directory, make sure postgres can play with it
PostgreSQL might remove some of the subqueries when they do not
contribute to the query result at all. Citus should not try to
access such subqueries during planning.
This PR adds support for multiple AND expressions in Having
for pushdown planner. We simply make a call to make_ands_explicit
from MultiLogicalPlanOptimize for the having qual in
workerExtendedOpNode.
After this commit large_table_shard_count wont be used to
check whether broadcast join, which is renamed as reference
join, can be applied. Reference join can only be applied over
reference tables.
We recently added partitionin support to Citus MX. We should not execute
DROP table commands from MX workers but at the moment we try to execute
such commands for partitioned tables. This PR fixes that problem by
adding check.
Previously, we prevented creation of partitioned tables on Citus MX.
We decided to not focus on this feature until there is a need. Since
now there are requests for this feature, we are implementing support
for partitioned tables on Citus MX.
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.
- Force all platforms to use the same collation
- Force all platforms to use the same locale
- Use /dev/null or NUL, depending on platform
- Use /tmp or %TEMP%, dpeending on platform
- don't hardcode path names
- replace system calls for rm/mkdir/rm -rf with perl equivalents
- force utf-8 encoding
- the Windows shell uses different quoting and escape rules
Pushing down limit and order by into workers may produce
wrong output when distinct on() clause has expressions,
aggregates, or window functions.
This checking allows pushing down of limits only if
distinct clause is a superset of group by clause. i.e. it contains all clauses in group by.
This commit checks the connection status right after any IO happens
on the socket.
This is necessary since before this commit we didn't pass any information
to the higher level functions whether we're done with the connection
(e.g., no IO required anymore) or an errors happened during the IO.
This is the first of series of window function work.
We can now support window functions that can be pushed down to workers.
Window function must have distribution column in the partition clause
to be pushed down.
We push down order by to worker query when limit is specified
(with some other additional checks). If the query has an expression
on an aggregate or avg aggregate by itself, and there is an order
by on this particular target we may send wrong order by to worker
query with potential to affect query result.
The fix creates a auxilary target entry in the worker query and
uses that target entry for sorting.
Before this PR, we were trusting on the columns of group by about
guaranteeing the uniqueness of the results. However, this assumption
is correct only if the columns in the group by is subset of columns
in the distinct clause. It can be wrong if we have part of group by
columns and some aggregation columns in the distinct clause. With
this PR, we add distinct plan on top of aggregate plan when necessary.
With #1804 (and related PRs), Citus gained the ability to
plan subqueries that are not safe to pushdown.
There are two high-level requirements for pushing down subqueries:
* Individual subqueries that require a merge step (i.e., GROUP BY
on non-distribution key, or LIMIT in the subquery etc). We've
handled such subqueries via #1876.
* Combination of subqueries that are not joined on distribution keys.
This commit aims to recursively plan some of such subqueries to make
the whole query safe to pushdown.
The main logic behind non colocated subquery joins is that we pick
an anchor range table entry and check for distribution key equality
of any other subqueries in the given query. If for a given subquery,
we cannot find distribution key equality with the anchor rte, we
recursively plan that subquery.
We also used a hacky solution for picking relations as the anchor range
table entries. The hack is that we wrap them into a subquery. This is only
necessary since some of the attribute equivalance checks are based on
queries rather than range table entries.
We were allowing count distict queries even if they were
not directly on columns if the query is grouped on
distribution column.
When performing these checks we were skipping subqueries
because they also perform this check in a more concise manner.
We relied on oid SUBQUERY_RELATION_ID (10000) to decide if
a given RTE relation id denotes a subquery, however, we also
use SUBQUERY_PUSHDOWN_RELATION_ID (10001) for some subqueries.
We skip both type of subqueries with this change.
With this fix, we traverse the graph with DFS which was originally
intended. Note that, before the fix, we traverse the graph with BFS
which might lead to killing some unrelated backend that is not
involved in the distributed deadlock.
By sharing the implementation of the function AppendOptionListToString on
three call sites, we would expand an extra OPTIONS keyword in a create index
statement, and omit other bits of the specific syntax here.
This patch introduces an AppendStorageParametersToString() function that is
very similar to AppendOptionListToString() but handles WITH(a="foo",...)
syntax that is used in reloptions (aka Storage Parameters).
Fixes#1747.
PostgreSQL implements support for several relation kinds in a single
statement, such as in the AlterTableStmt case, which supports both tables
and indexes and more (see ATExecSetRelOptions in PostgreSQL source code file
src/backend/commands/tablecmds.c for an example of that).
As a consequence, this patch implements support for setting and resetting
storage parameters on both relation kinds.
The command is now distributed among the shards when the table is
distributed. To that effect, we fill in the DDLJob's targetRelationId with
the OID of the table for which the index is defined, rather than the OID of
the index itself.
Citus sometimes have regressions around non-default schema support, meaning
not public and not in the search_path, per @marcocitus. This patch changes
some regression tests to use a non-default schema in order to cover more
cases.
The implementation was already mostly in place, but the code was protected
by a principled check against the operation. Turns out there's a nasty
concurrency bug though with long identifier names, much as in #1664.
To prevent deadlocks from happening, we could either review the DDL
transaction management in shards and placements, or we can simply reject
names with (NAMEDATALEN - 1) chars or more — that's because of the
PostgreSQL array types being created with a one-char prefix: '_'.
clause is not supported
This change allows unsupported clauses to go through query pushdown
planner instead of erroring out as we already do for non-outer joins.
We used to error out if the join clause includes filters like
t1.a < t2.a even if other filter like t1.key = t2.key exists.
Recently we lifted that restriction in subquery planning by
not lifting that restriction and focusing on equivalance classes
provided by postgres.
This checkin forwards previously erroring out real-time queries
due to join clauses to subquery planner and let it handle the
join even if the query does not have a subquery.
We are now pushing down queries that do not have any
subqueries in it. Error message looked misleading, changed to a more descriptive one.
We were creating intermediate query result's target
names from subquery target list. Now we also check
if cte re-defines its column name aliases, and create
intermediate result query accordingly.
This commit introduces a new GUC to limit the intermediate
result size which we handle when we use read_intermediate_result
function for CTEs and complex subqueries.
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
Subquery pushdown planning is based on relation restriction
equivalnce. This brings us the opportuneatly to allow any
other joins as long as there is an already equi join between
the distributed tables.
We already allow that for joins with reference tables and
this commit allows that for joins among distributed tables.
With this commit, we allow pushing down subqueries with only
reference tables where GROUP BY or DISTINCT clause or Window
functions include only columns from reference tables.
While attaching a partition to a distributed table in schema, we mistakenly
used unqualified name to find partitioned table's oid. This caused problems
while using partitioned tables with schemas. We are fixing this issue in
this PR.
It's possible to build INSERT SELECT queries which include implicit
casts, currently we attempt to support these by adding explicit casts to
the SELECT query, but this sometimes crashes because we don't update all
nodes with the new types. (SortClauses, for instance)
This commit removes those explicit casts and passes an unmodified SELECT
query to the COPY executor (how we implement INSERT SELECT under the
scenes). In lieu of those cases, COPY has been given some extra logic to
inspect queries, notice that the types don't line up with the table it's
supposed to be inserting into, and "manually" casting every tuple before
sending them to workers.
This patch adds --with-reports-host configure option, which sets the
REPORTS_BASE_URL constant. The default is reports.citusdata.com.
It also enables stats collection in tests.
This commit makes a change in relay_event_utility.c to check if the
Alter Table command adds a constraint using index. If this is the
case, it appends the shard id to the index name.
By this commit, citus minds the replica identity of the table when
we distribute the table. So the shards of the distributed table
have the same replica identity with the local table.
Expands count distinct coverage by allowing more cases. We used to support
count distinct only if we can push down distinct aggregate to worker query
i.e. the count distinct clause was on the partition column of the table,
or there was a grouping on the partition column.
Now we can support
- non-partition columns, with or without grouping on partition column
- partition, and non partition column in the same query
- having clause
- single table subqueries
- insert into select queries
- join queries where count distinct is on partition, or non-partition column
- filters on count distinct clauses (extends existing support)
We first try to push down aggregate to worker query (original case), if we
can't then we modify worker query to return distinct columns to coordinator
node. We do that by adding distinct column targets to group by clauses. Then
we perform count distinct operation on the coordinator node.
This work should reduce the cases where HLL is used as it can address anything
that HLL can. However, if we start having performance issues due to very large
number rows, then we can recommend hll use.
Adds ```citus.enable_statistics_collection``` GUC variable, which ```true``` by default, unless built without libcurl. If statistics collection is enabled, sends basic usage data to Citus servers every 24 hours.
The data that is collected consists of:
- Citus version
- OS name & release
- Hardware Id
- Number of tables, rounded to next power of 2
- Size of data, rounded to next power of 2
- Number of workers
This commit provides the support for window functions in subquery and insert
into select queries. Note that our support for window functions is still limited
because it must have a partition by clause on the distribution key. This commit
makes changes in the files insert_select_planner and multi_logical_planner. The
required tests are also added with files multi_subquery_window_functions.out
and multi_insert_select_window.out.
We sent multiple commands to worker when starting a transaction.
Previously we only checked the result of the first command that
is transaction 'BEGIN' which always succeeds. Any failure on
following commands were not checked.
With this commit, we make sure all command results are checked.
If there is any error we report the first error found.
Basically we just care whether the running version is before or after
PostgreSQL 10, so testing the major version against 9 and printing a
boolean is sufficient.
Citus can handle INSERT INTO ... SELECT queries if the query inserts
into local table by reading data from distributed table. The opposite
way is not correct. With this commit we warn the user if the latter
option is used.
When a NULL connection is provided to PQerrorMessage(), the
returned error message is a static text. Modifying that static
text, which doesn't necessarly be in a writeable memory, is
dangreous and might cause a segfault.
With this commit, we relax the restrictions put on the reference
tables with subquery pushdown.
We did three notable improvements:
1) Relax equi-join restrictions
Previously, we always expected that the non-reference tables are
equi joined with reference tables on the partition key of the
non-reference table.
With this commit, we allow any column of non-reference tables
joined using non-equi joins as well.
2) Relax OUTER JOIN restrictions
Previously Citus errored out if any reference table exists at
any point of the outer part of an outer join. For instance,
See the below sketch where (h) denotes a hash distributed relation,
(r) denotes a reference table, (L) denotes LEFT JOIN and
(I) denotes INNER JOIN.
(L)
/ \
(I) h
/ \
r h
Before this commit Citus would error out since a reference table
appears on the left most part of an left join. However, that was
too restrictive so that we only error out if the reference table
is directly below and in the outer part of an outer join.
3) Bug fixes
We've done some minor bugfixes in the existing implementation.
With this PR we add isolation tests for
COPY to reference table vs. other operations
COPY to partitioned table vs. other operations
Multi row INSERTs vs other operations
INSERT/SELECT vs. other operations
UPSERT vs. other operations
DELETE vs. other operations
TRUNCATE vs. other operations
DROP vs. other operations
DDL vs. other operations
other operations consist of basic SQL operations (like SELECT,
INSERT, DELETE, UPSERT, COPY TRUNCATE, CREATE INDEX) as well
as some Citus functionalities (like master_modify_multiple_shards,
master_apply_delete_command, citus_total_relation_size etc.)
This is necessary for multi-row INSERTs for the same reasons we use it
in e.g. UPSERTs: if the range table list has more than one entry, then
PostgreSQL's deparse logic requires that vars be prefixed by the name
of their corresponding range table entry. This of course doesn't affect
single-row INSERTs, but since multi-row INSERTs have a VALUE RTE, they
were affected.
The piece of ruleutils which builds range table names wasn't modified
to handle shard extension; instead UPSERT/INSERT INTO ... SELECT added
an alias to the RTE. When present, this alias is favored. Doing the
same in the multi-row INSERT case fixes RETURNING for such commands.
This change adds support for SAVEPOINT, ROLLBACK TO SAVEPOINT, and RELEASE SAVEPOINT.
When transaction connections are not established yet, savepoints are kept in a stack and sent to the worker when the connection is later established. After establishing connections, savepoint commands are sent as they arrive.
This change fixes#1493 .
Now that we already have the necessary infrastructure for detecting
distributed deadlocks. Thus, we don't need enable_deadlock_prevention
which is purely intended for preventing some forms of distributed
deadlocks.
This commit adds all the necessary pieces to do the distributed
deadlock detection.
Each distributed transaction is already assigned with distributed
transaction ids introduced with
3369f3486f. The dependency among the
distributed transactions are gathered with
80ea233ec1.
With this commit, we implement a DFS (depth first seach) on the
dependency graph and search for cycles. Finding a cycle reveals
a distributed deadlock.
Once we find the deadlock, we examine the path that the cycle exists
and cancel the youngest distributed transaction.
Note that, we're not yet enabling the deadlock detection by default
with this commit.
This GUC has two settings, 'always' and 'never'. When it's set to
'never' all behavior stays exactly as it was prior to this commit. When
it's set to 'always' only SELECT queries are allowed to run, and only
secondary nodes are used when processing those queries.
Add some helper functions:
- WorkerNodeIsSecondary(), checks the noderole of the worker node
- WorkerNodeIsReadable(), returns whether we're currently allowed to
read from this node
- ActiveReadableNodeList(), some functions (namely, the ones on the
SELECT path) don't require working with Primary Nodes. They should call
this function instead of ActivePrimaryNodeList(), because the latter
will error out in contexts where we're not allowed to write to nodes.
- ActiveReadableNodeCount(), like the above, replaces
ActivePrimaryNodeCount().
- EnsureModificationsCanRun(), error out if we're not currently allowed
to run queries which modify data. (Either we're in read-only mode or
use_secondary_nodes is set)
Some parts of the code were switched over to use readable nodes instead
of primary nodes:
- Deadlock detection
- DistributedTableSize,
- the router, real-time, and task tracker executors
- ShardPlacement resolution
This change declares two new functions:
`master_update_table_statistics` updates the statistics of shards belong
to the given table as well as its colocated tables.
`get_colocated_shard_array` returns the ids of colocated shards of a
given shard.
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.
With this PR, Citus starts to support all possible ways to create
distributed partitioned tables. These are;
- Distributing already created partitioning hierarchy
- CREATE TABLE ... PARTITION OF a distributed_table
- ALTER TABLE distributed_table ATTACH PARTITION non_distributed_table
- ALTER TABLE distributed_table ATTACH PARTITION distributed_table
We also support DETACHing partitions from partitioned tables and propogating
TRUNCATE and DDL commands to distributed partitioned tables.
This PR also refactors some parts of distributed table creation logic.
- master_activate_node and master_disable_node correctly toggle
isActive, without crashing
- master_add_node rejects duplicate nodes, even if they're in different
clusters
- master_remove_node allows removing nodes in different clusters
This change removes distributed tables' dependency on distribution key columns. We already check that we cannot drop distribution key columns in ErrorIfUnsupportedAlterTableStmt() at multi_utility.c, so we don't need to have distributed table to distribution key column dependency to avoid dropping of distribution key column.
Furthermore, having this dependency causes some warnings in pg_dump --schema-only (See #866), which are not desirable.
This change also adds check to disallow drop of distribution keys when citus.enable_ddl_propagation is set to false. Regression tests are updated accordingly.
We try to run our isolation tests paralles as much as possible. In
some of those isolation tests we used same table name which causes
problem while running them in paralles. This commit changes table
names in those tests to ensure tests can run in parallel.
This commit is preperation for introducing distributed partitioned
table support. We want to clean and refactor some code in distributed
table creation logic so that we can handle partitioned tables in more
robust way.
In this commit, we add ability to convert global wait edges
into adjacency list with the following format:
[transactionId] = [transactionNode->waitsFor {list of waiting transaction nodes}]
This change adds a general purpose infrastructure to log and monitor
process about long running progresses. It uses
`pg_stat_get_progress_info` infrastructure, introduced with PostgreSQL
9.6 and used for tracking `VACUUM` commands.
This patch only handles the creation of a memory space in dynamic shared
memory, putting its info in `pg_stat_get_progress_info`, fetching the
progress monitors on demand and finalizing the progress tracking.
- Never release locks
- AddNodeMetadata takes ShareRowExclusiveLock so it'll conflict with the
trigger which prevents multiple primary nodes.
- ActivateNode and SetNodeState used to take AccessShareLock, but they
modify the table so they should take RowExclusiveLock.
- DeleteNodeRow and InsertNodeRow used to take AccessExclusiveLock but
only need RowExclusiveLock.
- master_add_node enforces that there is only one primary per group
- there's also a trigger on pg_dist_node to prevent multiple primaries
per group
- functions in metadata cache only return primary nodes
- Rename ActiveWorkerNodeList -> ActivePrimaryNodeList
- Rename WorkerGetLive{Node->Group}Count()
- Refactor WorkerGetRandomCandidateNode
- master_remove_node only complains about active shard placements if the
node being removed is a primary.
- master_remove_node only deletes all reference table placements in the
group if the node being removed is the primary.
- Rename {Node->NodeGroup}HasShardPlacements, this reflects the behavior it
already had.
- Rename DeleteAllReferenceTablePlacementsFrom{Node->NodeGroup}. This also
reflects the behavior it already had, but the new signature forces the
caller to pass in a groupId
- Rename {WorkerGetLiveGroup->ActivePrimaryNode}Count
This commit adds distributed transaction id infrastructure in
the scope of distributed deadlock detection.
In general, the distributed transaction id consists of a tuple
in the form of: `(databaseId, initiatorNodeIdentifier, transactionId,
timestamp)`.
Briefly, we add a shared memory block on each node, which holds some
information per backend (i.e., an array `BackendData backends[MaxBackends]`).
Later, on each coordinated transaction, Citus sends
`SELECT assign_distributed_transaction_id()` right after `BEGIN`.
For that backend on the worker, the distributed transaction id is set to
the values assigned via the function call.
The aim of the above is to correlate the transactions on the coordinator
to the transactions on the worker nodes.
Comes with a few changes:
- Change the signature of some functions to accept groupid
- InsertShardPlacementRow
- DeleteShardPlacementRow
- UpdateShardPlacementState
- NodeHasActiveShardPlacements returns true if the group the node is a
part of has any active shard placements
- TupleToShardPlacement now returns ShardPlacements which have NULL
nodeName and nodePort.
- Populate (nodeName, nodePort) when creating ShardPlacements
- Disallow removing a node if it contains any shard placements
- DeleteAllReferenceTablePlacementsFromNode matches based on group. This
doesn't change behavior for now (while there is only one node per
group), but means in the future callers should be careful about
calling it on a secondary node, it'll delete placements on the primary.
- Create concept of a GroupShardPlacement, which represents an actual
tuple in pg_dist_placement and is distinct from a ShardPlacement,
which has been resolved to a specific node. In the future
ShardPlacement should be renamed to NodeShardPlacement.
- Create some triggers which allow existing code to continue to insert
into and update pg_dist_shard_placement as if it still existed.
These functions are holdovers from pg_shard and were created for unit
testing c-level functions (like InsertShardPlacementRow) which our
regression tests already test quite effectively. Removing because it
makes refactoring the signatures of those c-level functions
unnecessarily difficult.
- create_healthy_local_shard_placement_row
- update_shard_placement_row_state
- delete_shard_placement_row
This commit is intended to be a base for supporting declarative partitioning
on distributed tables. Here we add the following utility functions and their
unit tests:
* Very basic functions including differnentiating partitioned tables and
partitions, listing the partitions
* Generating the PARTITION BY (expr) and adding this to the DDL events
of partitioned tables
* Ability to generate text representations of the ranges for partitions
* Ability to generate the `ALTER TABLE parent_table ATTACH PARTITION
partition_table FOR VALUES value_range`
* Ability to apply add shard ids to the above command using
`worker_apply_inter_shard_ddl_command()`
* Ability to generate `ALTER TABLE parent_table DETACH PARTITION`
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.
- Use native postgres function for composite key btree functions
- Move explain tests to multi_explain.sql (get rid of .out _0.out files)
- Get rid of input/output files for multi_subquery.sql by moving table creations
- Update some comments
With this commit, we start to use custom compiled PostgreSQL builds in
Travis for merge commits. This allows us to run isolation tests and
PostgreSQL's own regression tests along with our regression tests in
Travis.
Since manually compiling PostgreSQL takes more time and we also add new
tests, we only enable running these tests on merge commits.
* Accept invalidation messages before accessing the metadata cache
This commit is crucial to prevent stale metadata reads from the
cache. Without this commit, some of the operations may use stale
metadata which could end up with various bugs such as crashes,
inconsistent/lost data etc.
As an example, consider that a COPY operation is blocked on shard
metadata lock. Another concurrent session updates the metadata and
invalidates the cache. However, since Citus doesn't accept invalidations,
COPY continues with the stale metadata once it acquires the lock.
With this commit, we make sure that invalidation messages are accepted
just before accessing the metadata cache and preventing any operation to
use stale metadata.
* Add isolation tests for placement changes and conccurrent operations
- add node with reference table vs COPY/insert/update/DDL
- repair shard vs COPY/insert/update/DDL
- repair shard vs repair shard
Distributed query planning for subquery pushdown is done on the original
query. This prevents the usage of external parameters on the execution.
To overcome this, we manually replace the parameters on the original
query.
* Support for subqueries in WHERE clause
This commit enables subqueries in WHERE clause to be pushed down
by the subquery pushdown logic.
The support covers:
- Correlated subqueries with IN, NOT IN, EXISTS, NOT EXISTS,
operator expressions such as (>, <, =, ALL, ANY etc.)
- Non-correlated subqueries with (partition_key) IN (SELECT partition_key ..)
(partition_key) =ANY (SELECT partition_key ...)
Note that this commit heavily utilizes the attribute equivalence logic introduced
in the 1cb6a34ba8. In general, this commit mostly
adjusts the logical planner not to error out on the subqueries in WHERE clause.
* Improve error checks for subquery pushdown and INSERT ... SELECT
Since we allow subqueries in WHERE clause with the previous commit,
we should apply the same limitations to those subqueries.
With this commit, we do not iterate on each subquery one by one.
Instead, we extract all the subqueries and apply the checks directly
on those subqueries. The aim of this change is to (i) Simplify the
code (ii) Make it close to the checks on INSERT .. SELECT code base.
* Extend checks for unresolved paramaters to include SubLinks
With the presence of subqueries in where clause (i.e., SubPlans on the
query) the existing way for checking unresolved parameters fail. The
reason is that the parameters for SubPlans are kept on the parent plan not
on the query itself (see primnodes.h for the details).
With this commit, instead of checking SubPlans on the modified plans
we start to use originalQuery, where SubLinks represent the subqueries
in where clause. The unresolved parameters can be found on the SubLinks.
* Apply code-review feedback
* Remove unnecessary copying of shard interval list
This commit removes unnecessary copying of shard interval list. Note
that there are no copyObject function implemented for shard intervals.
- There was a crash when the table a shardid belonged to changed during
a session. Instead of crashing (a failed assert) we now throw an error
- Update the isolation test which was crashing to no longer exercise
that code path
- Add a regression test to check that the error is thrown
* Enabling physical planner for subquery pushdown changes
This commit applies the logic that exists in INSERT .. SELECT
planning to the subquery pushdown changes.
The main algorithm is followed as :
- pick an anchor relation (i.e., target relation)
- per each target shard interval
- add the target shard interval's shard range
as a restriction to the relations (if all relations
joined on the partition keys)
- Check whether the query is router plannable per
target shard interval.
- If router plannable, create a task
* Add union support within the JOINS
This commit adds support for UNION/UNION ALL subqueries that are
in the following form:
.... (Q1 UNION Q2 UNION ...) as union_query JOIN (QN) ...
In other words, we currently do NOT support the queries that are
in the following form where union query is not JOINed with
other relations/subqueries :
.... (Q1 UNION Q2 UNION ...) as union_query ....
* Subquery pushdown planner uses original query
With this commit, we change the input to the logical planner for
subquery pushdown. Before this commit, the planner was relying
on the query tree that is transformed by the postgresql planner.
After this commit, the planner uses the original query. The main
motivation behind this change is the simplify deparsing of
subqueries.
* Enable top level subquery join queries
This work enables
- Top level subquery joins
- Joins between subqueries and relations
- Joins involving more than 2 range table entries
A new regression test file is added to reflect enabled test cases
* Add top level union support
This commit adds support for UNION/UNION ALL subqueries that are
in the following form:
.... (Q1 UNION Q2 UNION ...) as union_query ....
In other words, Citus supports allow top level
unions being wrapped into aggregations queries
and/or simple projection queries that only selects
some fields from the lower level queries.
* Disallow subqueries without a relation in the range table list for subquery pushdown
This commit disallows subqueries without relation in the range table
list. This commit is only applied for subquery pushdown. In other words,
we do not add this limitation for single table re-partition subqueries.
The reasoning behind this limitation is that if we allow pushing down
such queries, the result would include (shardCount * expectedResults)
where in a non distributed world the result would be (expectedResult)
only.
* Disallow subqueries without a relation in the range table list for INSERT .. SELECT
This commit disallows subqueries without relation in the range table
list. This commit is only applied for INSERT.. SELECT queries.
The reasoning behind this limitation is that if we allow pushing down
such queries, the result would include (shardCount * expectedResults)
where in a non distributed world the result would be (expectedResult)
only.
* Change behaviour of subquery pushdown flag (#1315)
This commit changes the behaviour of the citus.subquery_pushdown flag.
Before this commit, the flag is used to enable subquery pushdown logic. But,
with this commit, that behaviour is enabled by default. In other words, the
flag is now useless. We prefer to keep the flag since we don't want to break
the backward compatibility. Also, we may consider using that flag for other
purposes in the next commits.
* Require subquery_pushdown when limit is used in subquery
Using limit in subqueries may cause returning incorrect
results. Therefore we allow limits in subqueries only
if user explicitly set subquery_pushdown flag.
* Evaluate expressions on the LIMIT clause (#1333)
Subquery pushdown uses orignal query, the LIMIT and OFFSET clauses
are not evaluated. However, logical optimizer expects these expressions
are already evaluated by the standard planner. This commit manually
evaluates the functions on the logical planner for subquery pushdown.
* Better format subquery regression tests (#1340)
* Style fix for subquery pushdown regression tests
With this commit we intented a more consistent style for the
regression tests we've added in the
- multi_subquery_union.sql
- multi_subquery_complex_queries.sql
- multi_subquery_behavioral_analytics.sql
* Enable the tests that are temporarily commented
This commit enables some of the regression tests that were commented
out until all the development is done.
* Fix merge conflicts (#1347)
- Update regression tests to meet the changes in the regression
test output.
- Replace Ifs with Asserts given that the check is already done
- Update shard pruning outputs
* Add view regression tests for increased subquery coverage (#1348)
- joins between views and tables
- joins between views
- union/union all queries involving views
- views with limit
- explain queries with view
* Improve btree operators for the subquery tests
This commit adds the missing comprasion for subquery composite key
btree comparator.
So far citus used postgres' predicate proofing logic for shard
pruning, except for INSERT and COPY which were already optimized for
speed. That turns out to be too slow:
* Shard pruning for SELECTs is currently O(#shards), because
PruneShardList calls predicate_refuted_by() for every
shard. Obviously using an O(N) type algorithm for general pruning
isn't good.
* predicate_refuted_by() is quite expensive on its own right. That's
primarily because it's optimized for doing a single refutation
proof, rather than performing the same proof over and over.
* predicate_refuted_by() does not keep persistent state (see 2.) for
function calls, which means that a lot of syscache lookups will be
performed. That's particularly bad if the partitioning key is a
composite key, because without a persistent FunctionCallInfo
record_cmp() has to repeatedly look-up the type definition of the
composite key. That's quite expensive.
Thus replace this with custom-code that works in two phases:
1) Search restrictions for constraints that can be pruned upon
2) Use those restrictions to search for matching shards in the most
efficient manner available:
a) Binary search / Hash Lookup in case of hash partitioned tables
b) Binary search for equal clauses in case of range or append
tables without overlapping shards.
c) Binary search for inequality clauses, searching for both lower
and upper boundaries, again in case of range or append
tables without overlapping shards.
d) exhaustive search testing each ShardInterval
My measurements suggest that we are considerably, often orders of
magnitude, faster than the previous solution, even if we have to fall
back to exhaustive pruning.
This determines whether it's possible to perform binary search on
sortedShardIntervalArray or not. If e.g. two shards have overlapping
ranges, that'd be prohibitive.
That'll be useful in later commit introducing faster shard pruning.
That's useful when comparing values a hash-partitioned table is
filtered by. The existing shardIntervalCompareFunction is about
comparing hashed values, not unhashed ones.
The added btree opclass function is so we can get a comparator
back. This should be changed much more widely, but is not necessary so
far.
Marco Slot
Onder Kalaci
Philip Dubé
Hanefi Onaldi
Hadi Moshayedi
Hadi Moshayedi
Jason Petersen
Murat Tuncer
Nils Dijk
velioglu
Jason Petersen
Hanefi Onaldi
Jason Petersen
Nils Dijk
Hadi Moshayedi
Brian Cloutier
mehmet furkan şahin
Brian Cloutier
Dimitri Fontaine
Brian Cloutier
Burak Yucesoy
Murat Tuncer
Metin Doslu
Markus Sintonen
Marco Slot
Eren Başak
Andres Freund
jmunsch